global strategy to improve agricultural and rural...

Global Strategy to improve Agricultural and Rural Statistics

Global Strategy to im

prove Agricultural and Rural Statistics

TRAINING IN AGRICULTURAL STATISTICS (Manual)

Global strateGy to improve aGricultural and rural statisticstraininG in aGricultural statistics (manual)

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Global Strategy to improve Agricultural and Rural Statistics

TRAINING IN AGRICULTURAL STATISTICS (Manual)

Title of training Agricultural statistics

Duration 10 days

Training type Face-to-face

Training level Degree or Master’s in statistics - agronomists and economists with previous training in statistics

Requirements Statistics, sampling, general economics


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Contents

LIST of TAbLeS ivLIST of boxeS ivLIST of fIGUReS vACRoNyMS viiiACkNowLeDGeMeNTS ixINTRoDUCTIoN x

MoDULe 0: STATISTICAL ReVIew 1 0.1. DefINITIoNS 1

0.1.1. Population and unit types 1

0.1.2. Data, statistics and information 2

0.1.3. Collection period and reference period 3

0.2. STePS of A STATISTICAL SURVey 4

0.3. SAMPLING MeTHoD 6

0.3.1. Sample for an integrated survey or a repeated survey 7

0.3.2. Sampling technique 8

0.3.3. Sample size 11

0.4. DATA CoLLeCTIoN 14

0.5. DATA PRoCeSSING 15

0.5.1. Recording questionnaires 15

0.5.2. Data entry and editing 15

0.5.3. Verification process (data editing) 15

0.5.4. Imputation of missing data and processing of partial and total non-responses 16

0.5.5. Extrapolation 17

0.6. DATA ANALySIS 18

0.6.1. Defining and calculating indicators: differences in the agricultural statistics produced 18

0.6.2. Tabulation 19

0.6.3. Statistical software 20

0.7. DATA DISSeMINATIoN 20

0.8. DATA QUALITy MANAGeMeNT 21

MoDULe 1: AN oVeRVIew of THe GeNeRAL fRAMewoRk of AGRICULTURAL STATISTICS 25

1.1. SCoPe of THe CoURSe 25

1.1.1. The ISIC approach (Rev 4.) 26

1.1.2. The approach of the Global strategy to improve agricultural and rural statistics 27

1.2. CoNCePTUAL fRAMewoRk of THe GLobAL STRATeGy To IMPRoVe AGRICULTURAL

AND RURAL STATISTICS AND ITS eCoNoMIC, SoCIAL AND eNVIRoNMeNTAL ASPeCTS 28

1.3. STRATeGIC PLANS foR AGRICULTURAL AND RURAL STATISTICS (SPARS)

AND NATIoNAL STRATeGIeS foR THe DeVeLoPMeNT of STATISTICS (NSDS) 31

1.4. USeRS AND USeS of AGRICULTURAL STATISTICS 32


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MoDULe 2: STATISTICS To be PRoDUCeD, PRoDUCeRS, DATA SoURCeS, STATISTICAL UNITS AND DATA CoLLeCTIoN MeTHoDS 33

2.1. STATISTICS To be PRoDUCeD 34

2.1.1. Crop production statistics 34

2.1.2. Livestock statistics 40

2.1.3. Aquaculture statistics 42

2.1.4. Fishery statistics 43

2.1.5. Silviculture and agroforestry statistics 44

2.1.6. Environment statistics 45

2.1.7. Rural statistics 49

2.1.8. Price statistics 50

2.2. DATA PRoDUCeRS: CeNTRALIZeD AND DeCeNTRALIZeD STATISTICAL SySTeMS 50

2.3. SoURCeS of AGRICULTURAL STATISTICS 52

2.3.1. Agricultural censuses 52

2.3.2. Agricultural sample surveys 58

2.3.3. Administrative sources 73

2.3.4. Remote sensing and Geographic information system (GIS) in agriculture 73

2.3.5. Monitoring systems / observatories 74

2.4. STATISTICAL UNITS 76

2.4.1. Agricultural holding 77

2.4.2. Household 80

2.4.3. Aquacultural holding 80

2.4.4. Establishment 80

2.4.5. Community or locality 81

2.4.6. Natural unit and management unit 81

2.5. DATA CoLLeCTIoN 81

2.5.1. Survey period and crop calendar 81

2.5.2. Questionnaires 82

2.5.3. Interviewing methods 83

2.5.4. Use of new collection technologies 84

2.5.5. Typical holding 87

MoDULe 3: DATA PRoCeSSING, ANALySIS AND DISSeMINATIoN 91 3.1. GeNeRAL oVeRVIew of CURReNT PRoCeSSING PRACTICeS AND LIMITATIoNS obSeRVeD 91

3.1.1. Lack of qualified personnel 92

3.1.2. Inadequacy of statistical methods 92

3.1.3. Inconsistencies in the production of core indicators 92

3.1.4. Lack of modern equipment 92

3.1.5. Poor data quality 93

3.2. AReAS AND yIeLDS of PURe AND MIxeD CRoPS 93

3.2.1. Data necessary for estimating yield or area 93

3.2.2. Calculating areas from field data (extrapolation) 98

3.2.3. Calculating yields from field data 100

3.3. PRoDUCTIoN 103

3.4. CRoP foReCASTING 103

3.4.1. Forecasting from cropping areas 103

3.4.2. Forecasting by interview 105


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3.5. ANALySIS AND DISSeMINATIoN 107

3.5.1. Analysis techniques 107

3.5.2. Metadata 109

3.5.3. Archiving 110

3.5.4. Database and CountryStat 112

3.5.5. Safeguarding data – Dissemination systems 113

MoDULe 4: ANALyTICAL fRAMewoRkS AND DeRIVeD STATISTICS 115 4.1. eCoNoMIC ACCoUNTS foR AGRICULTURe AND eNVIRoNMeNTAL-eCoNoMIC ACCoUNTS 115

4.1.1. Economic accounts for agriculture 115

4.1.2. Environmental-Economic accounts 117

4.2. CoSTS of PRoDUCTIoN 118

4.2.1. Use and importance of costs of production statistics 118

4.2.2. Units used 119

4.2.3. Indicators 120

4.3. PoST-HARVeST LoSSeS 121

4.3.1. Types of post-harvest losses 122

4.3.2. Methods of estimating post-harvest losses 122

4.3.3. Extent and estimation of losses 123

4.3.4. Factors influencing losses 123

4.3.5. The consequences of post-harvest losses 124

4.4. AGRICULTURAL PRICeS AND PRICe INDexeS 126

4.4.1. The various price types 126

4.4.2. Price indexes 126

4.5. fooD SeCURITy AND fooD bALANCe SHeeT 136

4.5.1. Food security 136

4.5.2. Food balance sheet 136

ReCoMMeNDATIoNS 143

bIbLIoGRAPHy 144

ANNex 1: LIST of INDICAToRS foR AGRICULTURAL STATISTICS 147

ANNex 2: MINIMUM SeT of CoRe DATA 153

ANNex 3: TARGeT PoPULATIoN AND SAMPLeD PoPULATIoN 156

ANNex 4: fIeLDS CoVeReD by eNVIRoNMeNT STATISTICS 157


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List of illustrations

LIST of TAbLeSTable 1: Classification of land use 36

Table 2: Problems with sampling frames and their solutions 67

Table 3: Characteristics of sampling frames 69

Table 4: Survey calendar (Tanzania) 71

Table 5: Example of a timetable for conducting AGRIS modules 72

Table 6: Statistical units by theme 86

Table 7: Dimensions and components of costs of production 119

Table 8: Components of the production chain for estimating post-harvest losses 121

Table 9: Change in prices and quantities required of rice and maize 127

Table 10 : Change in prices and quantities required of rice and maize 131

Table 11: Potential products in a food balance sheet 137

Table 12: Type of chart for estimating internal availability and uses 139

Table 13: Type of chart for estimating availability and total uses 139

LIST of boxeSbox 1: Renewing the sample 8

box 2: Stratification 10

box 3: Types of missing data 16

box 4: Hot-deck and cold-deck 17

box 5: Skills for data processing 18

box 6: Differences in data 19

box 7: Data Quality Assessment Framework (DQAF) 21

box 8: Nine classes for land under crops 37

box 9: Livestock systems 41

box 10: Importance of the sampling frame 66

box 11: Master sample 70

box 12: The agricultural market observatory (OMA) 75

box 13: Definition of an agricultural holding 77

box 14: Definition of a household 77

box 15: Correspondence between agricultural holding and household 78

box 16: Components of the area of a holding 79

box 17: Use of GPS-equipped PDA (Brazil) 87

box 18: Current methods of dividing the area of a parcel containing a main crop and a secondary crop 99

box 19: Spatial analysis 108

box 20: Methodology for the environmental-economic accounts 118

box 21: Post-harvest loss equivalent 125


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LIST of fIGUReSfigure 1: Different steps in a survey 13

figure 2: Data quality assessment frameworks 23

figure 3: Conceptual framework for agricultural statistics 30

figure 4: The Integrated Survey framework 55

figure 5: Agricultural production chain 121

figure 6: Composition of a chain 126


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Acronyms

AGRIS: Agricultural Integrated Survey

NASA: National agricultural statistics agencies

MSb: Main sampling base

eAA: Economic accounts for agriculture

eAe: Economic accounts for the environment

DQAf: Data quality assessment framework

NQAf: National quality assurance framework

CoICoP: Classification of Individual Consumption by Purpose

UNSD: United Nations Statistics Division

fAo: Food and Agriculture Organization of the United Nations

fDeS: Framework for the Development of Environment Statistics

IMf: International Monetary Fund

UNCCf: United Nations Climate Change Fund

GPS: Global positioning system

Ha: Hectare

INSee: National Institute of Statistics and Economic Studies

SCbI: Statistical capacity building indicators

ISA: Integrated Surveys on Agriculture

LSMS: Living Standards Measurement Study

SDDS: Special data dissemination standard

PDA: Personal digital assistant

SPARS: Strategic plans for agricultural and rural statistics

GDDS: General data dissemination system

NSDS: National strategies for the development of statistics

UNeCe: United Nations Economic Commission for Europe

wCA: World Programme for the Census of Agriculture


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Acknowledgements

This Training manual on Agricultural Statistics was produced by Tiral Sidi, statistician. Substantial contributions were made by Ankouvi Nayo, consultant in Agricultural Statistics to the Global Office of the Global Strategy to improve Agricultural and Rural Statistics (GSARS). It was reviewed by a team from the Global Office. This team made very significant contributions to the completion of the document. The team comprised Christophe Duhamel, Valérie Bizier, Carola Fabi, Dramane Bako, Neli Georgieva and Franck Cachia.

Harouna Soumare, consultant to the FAO, also made a substantial contribution to improving the training material.

This work was carried out under the coordination, supervision and guidance of Christophe Duhamel, coordinator of the Global Office, and Valérie Bizier, Technical Assistance and Training Coordinator in the same institution.

The support documents accompanying the manual are as follows:• An exercise booklet;• A syllabus and the training manual;• A set of Powerpoint presentations.

The exercise manual was produced by Ankouvi Nayo with the help of Valérie Bizier. Some of the exercises were taken from the exercise collection for agricultural statistics training of the National School of Statistics and Applied Economics (ENSEA) of Abidjan.

This manual was prepared with the support of the Global Strategy trust fund, financed by the Bill & Melinda Gates Foundation and the UK Department for International Development (DFID).

It was translated from French by Helen Wormald and formatted by Laura Monopoli, under the supervision of Norah DeFalco.


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Introduction

The objective of this manual is to support agricultural statistics trainers responsible for training National Agricultural Statistics Agency (NASA) officers who are statisticians, but not specialized in agricultural statistics.

The course is divided into four modules. Each of these modules is subdivided into topics or submodules which should be covered as theoretical sessions by the trainer. These sessions should be illustrated and supported by examples and exercises to enhance understanding.

The manual itself consists of an introduction comprising a review of statistical considerations and four modules:

Review of statistical considerationsThis statistical review was not designed to be included in the training itself as trainees are assumed to have the required background in statistics. This brief review will cover:• Some concepts and definitions;• A brief summary of sampling methods;• Recommendations for data collection;• Important aspects of data processing, analysis and dissemination;• Aspects of data quality management.

For further details on these various aspects, trainees can refer to more specialized documents (see bibliography).

Module 1: Conceptual frameworkAn overview of the general framework of agricultural statistics in national statistical systems is presented in this module. The topics covered are:• Scope of the course;• Conceptual framework of the Global Strategy to improve Agricultural and Rural Statistics and its economic,

social and environmental dimensions;• Strategic plans for agricultural and rural statistics (SPARS) and National strategies for the development of

statistics (NSDS); • Evaluation of the National Agricultural Statistics System (NASS) and the resulting agricultural statistical

capacity indicators;• Users and uses of agricultural statistics.


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Module 2: Data sources, statistical units and collection methods. This module reviews data collection methods. It is the core of the agricultural statistics training. Its aim is to allow participants to have an overview of agricultural statistics producers, the main data sources and basic methodological issues in the design of censuses and agricultural sample surveys.

The following topics will be covered:• Statistics to be produced;• Data producers: centralized and decentralized statistical systems;• The sources of agricultural statistics (direct and indirect);• Statistical units;• Collection methods.

Module 3: Data processing and analysisThis module describes the processing and analysis operations to be performed on a data set in order to obtain the desired information from a source such as a census, sample survey or administrative record. Special emphasis will be placed on elements specific to agricultural statistics. In particular:• current practices in data processing and limits observed;• measuring areas and yield, in particular for mixed crops;• estimating agricultural production;• crop forecasting;• analysis and dissemination.

Module 4: Analytical frameworks and statistics derivedThis module will review the following analytical frameworks and derived statistics:• Economic accounts for agriculture and economic accounts for the environment (at a subnational and national

level);• Costs of production statistics;• Post-harvest losses;• Agricultural producer prices and price indexes (monthly and annual);• Food security and food balance sheets.


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11

0Module 0: Statistical review

0.1. DefINITIoNS

0.1.1. Population and unit types

The population consists of all the individuals to which the survey results will apply.

The target population or scope of a survey is the entire group of observation units you want to study. It should be carefully defined for each study. It is not always obvious.

The statistical unit (or statistical individual) is any component, all of which together form the population or universe. In other words, it is any component of the population (target). It can be of any nature (village, household1, hamlet, cultivated plot, business, physical person, etc.), for example:

Population Statistical unit

Farm labour Any farm employee or any farm operation

Farm assets, equipment and machinery A tractor

The sampled population is a list of all the observation units which could be selected to form a sample. It does not always coincide with the target population (see Annex 3).

The sampling unit consists of each “member” of the sampling frame. The sampling frame is, moreover, a comprehensive list of the statistical units of a given population. The sampling unit refers to the level at which sampling is done. These are the units directly subject to a selection operation. The sampling unit can be an agricultural holding2, a household, a farm plot, a child, a housing, a school, a health training, etc.

1 Refer to section 2.4.1 for a definition of household.2 Refer to section 2.4.1 for a definition of agricultural holding.


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The analytical unit is the level to which the analysis relates. The agricultural holding can be, for example, the sampling unit (agricultural holdings are then selected), but the analysis can relate to plots which are then the analytical units.

The reporting unit is a unit which supplies information on each statistical unit (e.g. a farm manager interviewed about the holding’s plots, a mother asked about her children, or a head teacher asked about the school).

The observation unit or unit of interest is the unit on which information is requested. For example, agricultural holdings, plots for which the farm manager has provided information, children for whom the mother has provided information or a state primary school for which the head teacher has provided information. It is therefore the object that has been measured. It is the basic unit observed. For human populations, it is an individual.

0.1.2. Data, statistics and informationThe terms “data”, “statistic” and “information” are often used interchangeably, but there are important distinctions between them.

An item of data is the basic component of a wider information system. When statisticians produce data, they try to measure or count phenomena (individuals or activities) which are part of the real world.

Examples of data obtained by counting: number of cows on a farm, surface area of a field, number of people in a household, or number of children in a family.

Another example of a measure: If the question was: “How much did you spend last year on improved seeds?”, the reply should be given by a respondent referring to a document or from memory.

Data are not very useful by themselves. They must be organized into statistics to make them understandable and usable.

Statistics are compilations of numerical facts and figures. These facts and figures are created from data and are organized so they can be used. They appear in tables, diagrams, graphs or maps.

Statistics is also a mathematical science which focuses on the collection, analysis, interpretation or explanation, and presentation of data. Data can come from:• Surveys (censuses or by sampling);• Opinion polls;• Administrative data (for example, imports and exports).

A distinction should be made between official statistics (produced by government bodies) and unofficial statistics (privately produced or with no official recognition).

Information is very specific. In other words, it cannot be used generally, but instead relates to a specific issue. In this respect, information can be used to support decisions in a variety of situations. This is data processed and communicated (i.e. made available to the public in some form) - with the corollary that data that have not been disseminated are not considered information.


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0.1.3. Collection period and reference periodOperationally it is advisable to decide on these two periods well in advance.

The collection period is the period during which data are collected in the field. It should guarantee good control of sample identification processes (neutralization of seasonal effects in particular).

For example, agricultural surveys are generally conducted during the period covering the crop cycle. When organizing the schedule for interviewers, the crop calendar should be taken into consideration, along with the crop growth cycle and differences between cereals, tubers and roots, vegetables, cash crops, fruit production and other crops.

Reference period: This is the period to which the data relate. It depends on the survey objectives. Depending on the case, it is an interval of time (week, month, year, agricultural season, etc.) or a specific date. It should be noted that variables can have different reference periods in the same survey.

For example, the reference period of a crop production survey is the agricultural season. The reference period for births, purchases and natural deaths of livestock depends on the species. It is generally one year for cattle, six months for small ruminants and pigs and one month for poultry.


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0.2. STePS of A STATISTICAL SURVey

Surveys can be conducted on different topics. Even if there is a specific methodology for each topic, the design of all surveys generally follows the same steps. A survey, either statistical, agricultural or other, is the result of team work performed by several partners including the statistician. It consists in a chain of complex activities starting from administrative and technical preparation to the publication of the results, including methodological design, data collection in the field, and data processing, analysis and dissemination. The main steps in conducting a survey are:• Identifying information needs / Defining the objectives of the survey and resources;• Determining the collection period and reference period;• Preparing a work plan and budget;• Choosing the sampling frame and units;• Defining the sample design;• Deciding on the data collection method;• Designing technical documents (questionnaires, instruction manuals, etc.);• Recruiting and training staff;• Testing and/or pilot surveys;• Organizing and monitoring field activities;• Data processing (input, tabulation, processing and analysis);• Preparing the report and disseminating the results.

As an illustration, the following figure (Generic Statistical Business Process Model V, UNECE3) shows the various steps in a survey and their interactions.

3 United Nations Economic Commission for Europe (UNECE)


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0.3. SAMPLING MeTHoD

The choice of an optimal sampling design is crucial. The following are determined at this stage:• sample size;• selection procedures;• derived estimators, and their theoretical accuracy.

There are various possible sampling designs for any given survey. However, the desired accuracy of the data and the available resources should always be taken into account. In this context, always remember the optimization principle, i.e. look for:i. a given level of accuracy for the lowest cost, or ii. maximum accuracy for a given cost fixed in advance.

There are three types of sample design plan connected to the three types of sampling frame:• List sample design;• Area sample design;• Multiple-frame sample design;

In these sample design types, a random sample of survey units is selected from the sampling frame (lists, area or multiple). But if there are no reliable lists of units, a design with at least two stages is generally adopted:• at the first stage, a sample of the primary sampling units (PSU) (village, data production section, counting zone,

etc.) is set. Preferably, the distribution of PSU per stratum is related to the variables to be measured (e.g. agro-ecological area, high-, medium- or low-agricultural area density, etc.) and containing the survey units. A listing of all the survey units is undertaken within each PSU;

• at the second stage, a sample of the secondary sampling units (SSU) is set from the survey units previously listed within the PSU.

The principle of the selection methodology adopted is to have a random sample, i.e. a sample which gives all survey units in the survey’s scope a non-zero chance (probability) of being included in the sample. However, to avoid biased estimates by favouring some survey units over others as regards the probabilities of belonging to the sample, it is sometimes preferable, in addition to the non-zero criterion of probabilities for all the statistical units concerned, to consider equality of probabilities: all operations of a similar type (same stratum) having the same (or almost the same) chance of belonging to the sample will be the principle to be adopted in the two-stage sampling design.

In a two-stage sampling design, the secondary units can have the same chances of belonging to the overall sample in two different ways:i. The primary units have an equal probability of being selected and the complete listing of secondary units is

established within each primary unit; the same proportion of secondary units is selected from each of these lists (the same probability of being selected is applied). We then have an equiprobable sample of secondary units whose size is only known afterwards; the size cannot therefore be fixed in advance. To fix the sample size in advance, the primary units can first be assigned to a stratum based on their size, before applying the method. In each of the strata which are merely size categories, the number of primary units sampled and the number of secondary units to be selected within each primary unit sampled are fixed. The number of secondary units to be selected from the primary units sampled is larger, the larger the size of the units in the stratum;

ii. At both the first and second stage, unequal probability sampling is carried out so that the two inequalities “neutralize one another”: if at the first stage the probability of selecting a primary unit is directly proportional to a dimension relating to this unit (size for example), it will be inversely proportional at the second stage. So if a primary unit has more chance of being selected because of its large size in terms of secondary units, at the


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second stage the proportion of secondary units to be selected from this unit must be smaller. Mathematically this is reflected by the probability of selecting primary units being proportional to size, and the probability of selecting secondary units being inversely proportional to the size of the primary unit containing them.

0.3.1. Sample for an integrated survey or a repeated survey Agricultural surveys are periodic in nature. In relation to this periodicity, once a sample of holdings has been selected, should it be kept indefinitely or renewed periodically? Ideally the same holdings should be surveyed regularly. It has been shown that the accuracy of estimating the variability of a variable is far better for panels in which the same sample is kept over time. This has the advantage of better estimating the variability of the variables of interest.

Let:

y1 the estimate of a variable y from a sample E1 at time t1;

y2 the estimate of a variable y from a sample E2 at time t2;

The estimate variance of the variability of y between dates t1 and t2 is:

a) If samples E1 and E2 are independent, then:

So:

b) In the case of panels where it is the same sample (E1=E2) at times t1 and t2, it is shown that COV (y1, y2) is positive, so:

But in practice, monitoring the same sample of holdings has the following drawbacks, among others:• The survey may represent a constraint for the units surveyed (e.g. holdings) due to response burden and

eventually result in a refusal to cooperate;• The enumerator could be tempted to produce fake data for an agricultural year using data for previous seasons;• The structure of the units surveyed (e.g. households) can change over time.

2 1 1 2 1 2V(y -y )=V(y ) + V(y ) - 2COV(y ,y )

1 2COV(y ,y )=0

2 1 1 2V(y -y )=V(y ) + V(y )

2 1 1 2V(y -y )<V(y ) + V(y )


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0.3.2. Sampling techniqueTo illustrate sampling techniques, we will use the following notations:

a. Selection of primary unitsAs shown above, PSU are most often selected with unequal probabilities. The probability of selection is related in this case to a variable which varies from one PSU to another. Selecting with equal probability is extensively described in the section relating to secondary units.

The sampling frame provides the size of the population for each PSU at the census date. The proportion representing the population of the PSU in the universe (the ratio of its population to that of the universe) represents its weight and therefore its probability of selection.

If the number of PSU sampled is n, the probability for selecting a primary unit will be n times the ratio of its population to the total population. The selection probability of primary units is therefore proportional to size.

Generally, probability will be related to numerical information present in the sampling frame in the form of a population size P which allows each PSU to have a relative size in the universe and corresponds to the concept of size. According to the sampling frame used, this size can be the number of inhabitants, the number of households, the number of holdings, etc.

i. Sampling with replacementSampling with replacement allows a sampling unit to be selected more than once. The selection procedure basically consists in selecting sampling units successively from a target population, while considering the population as a whole each time before making the next selection. So any unit previously selected is not put aside before undertaking the next selection. Practically speaking, systematic sampling methods from cumulative totals or systematic sampling based on cumulative probability can be used.

box 1: ReNewING THe SAMPLe

To lessen bias caused by distortion of units (e.g. holdings) over time, it is advisable to renew the sample

of holdings. To reduce the cost of such an operation, the sample can be renewed progressively, for

example by changing one third, one quarter or one fifth of the sample annually. The whole sample will

then be renewed after 3, 4 or 5 years.

Progressive renewal has the additional advantage of allowing longitudinal and cross-sectional analyses.

Longitudinal analysis is useful for evaluating the impact of interventions or shocks.

Agriculturalstatisticstrainingmanual Page16

• The structure of the units surveyed (e.g. households) can change over time.

Box 1: Renewing the sample

To lessen bias caused by distortion of units (e.g. holdings) over time, it is advisable to renew the sample of holdings. To reduce the cost of such an operation, the sample can be renewed progressively, for example by changing one third, one quarter or one fifth of the sample annually. The whole sample will then be renewed after 3, 4 or 5 years.

Progressive renewal has the additional advantage of allowing longitudinal and cross-sectional analyses. Longitudinal analysis is useful for evaluating the impact of interventions or shocks.

0.3.2. SamplingtechniqueTo illustrate sampling techniques, we will use the following notations:

selected be tounits SamplingPrimary ofnumber =n

iunit of size relative =PPi

=p

)N1,2,...., =(i i PSU theof size =PPSUs of size totalP

(PSU) units SamplingPrimary ofnumber total

i

i

N=

=

a. Selection of primary units

As shown above, PSU are most often selected with unequal probabilities. The probability of selection is related in this case to a variable which varies from one PSU to another. Selecting with equal probability is extensively described in the section relating to secondary units.

The sampling frame provides the size of the population for each PSU at the census date. The proportion representing the population of the PSU in the universe (the ratio of its population to that of the universe) represents its weight and therefore its probability of selection.

If the number of PSU sampled is n, the probability for selecting a primary unit will be n times the ratio of its population to the total population. The selection probability of primary units is therefore proportional to size.

Generally, probability will be related to numerical information present in the sampling frame in the form of a population size P which allows each PSU to have a relative size in the universe and corresponds to the concept of size. According to the sampling frame used, this size can be the number of inhabitants, the number of households, the number of holdings, etc.

i. Sampling with replacement

Sampling with replacement allows a sampling unit to be selected more than once. The selection procedure basically consists in selecting sampling units successively from a target population, while considering the population as a whole each time before making the next selection. So any unit previously selected is not put aside before undertaking the next selection. Practically speaking, systematic sampling methods from cumulative totals or systematic sampling based on cumulative probability can be used.


9

ii. Sampling without replacementBy contrast with sampling with replacement, where the probability for a unit of being selected was constant over the process, here each selection alters the conditions of subsequent selection. So, in addition to probabilities p1

i of being chosen at the first selection, you need to know the p2i/j of being selected of Ui at the

second selection, given that unit Uj was selected at the first selection, and so on.

We can see that the problem rapidly becomes intractable if the number of selections is large, as is the case with primary units where the sample is sometimes large. Numerous studies have been conducted on sampling with unequal probabilities without replacement, but they generally result in very complex procedures, difficult to apply in surveys. When the sampling fraction is small, as is generally the case in practice for most surveys, sampling with unequal probabilities without replacement is shown to differ very little from sampling with replacement. So in this case sampling without replacement is often similar to sampling with replacement.

b. Selection of secondary unitsHoldings are selected from a complete listing of SSU undertaken within the sampled PSU. The listing of holdings within the PSU is an onerous task, but it is decisive in the extrapolation of data and the correct sampling procedure of holdings to be surveyed. It is then important to conduct the listing of SSU in all the PSU (when appropriate), even for a period where the variation in the number of holdings is considered to be small.

The sample of secondary units (SSU), assumed to represent all the SSU, should not be affected by any bias in the sampling procedure. To avoid bias, the SSU can be stratified and the selection can be done taking account of these strata. If there is insufficient information available to carry out such stratification, empirical knowledge can be applied, such as the link between the agricultural production of a holding and the number of people working there which can depend on the total population of the holding. So, for selecting holdings to be surveyed per village, it is sufficient to divide the holdings into three categories (large, medium and small) and to represent each category in proportion to its population.

The small number of holdings to be selected per PSU requires a selection without replacement which has more satisfactory accuracy in this case.

Let us assume that we want to select m units with an equal probability and without replacement from a list frame of M units.

To set a sample of m holdings, one of the following two options can be used:

i. Simple random samplingIn this selection approach, “all holdings are equal”. The same weight is assigned to all holdings within the same PSU, and in probabilistic terms we could say that the M holdings in the primary sampling unit have the same probability of being in the sample, or that they are selected with the same probability, . The simple arithmetic mean of values obtained from the m holdings sampled can be used to estimate the average value of any measured variable for the holdings contained in the primary unit.

ii. Systematic samplingIn addition to its relative simplicity, a systematic sampling can implicitly carry out proportional allocation between the strata if the draw units are classified in order of the stratification criterion. In a systematic sampling, two situations may arise:• Selection with equal probability: all the units are equivalent and are considered to have the same weight;• Selection with unequal probability: The link between production and the population can be considered in

selecting the sample of holdings: in this case, the weight of the production of each holding in the primary

mM


10

unit will depend on what its population represents in the total population. The probability of selecting each holding will be proportional to its size.

StratificationThe selection procedures described above can lead to any type of sample: from samples that are as representative as possible of the population to the least representative samples. As the selection is random, it is left to chance and the larger the sample, the greater the chance of best representing the population.As the best representation of the population is that where all the varieties are represented, it is therefore important to know some characteristics about the sampling units so they can be classified into homogeneous groups (called strata) in relation to the survey variables. Units can then just be selected at random, in each group (strata) of units, for all the groups to be represented. It may also be necessary to obtain sufficiently precise information for some population groups (region, department, etc.). With a random sample where the population groups targeted are not controlled in any way, the number of statistical units in each population group is completely random and, in some cases, it may be insufficient to provide reliable results. It is then preferable to carry out the sampling independently in each group (stratum), which is not possible unless the characteristics defining the groups are known for each sample unit.

The stratification most used in agricultural surveys is that corresponding to the various administrative divisions, not simply to satisfy users as regards their development policy needs, but also in line with the collection systems generally adopted by administrative divisions.However, in countries with agroclimatic zones (or agro-ecological zones), much will be gained by including this information in the stratification. The strata will then be the overlap between these zones and the administrative divisions. In the absence of an agroclimatic division, latitude (north/south) can be used as zones are less agricultural if they are located in certain parts of the country. Then, for selecting holdings within the sampled PSU, the size of holdings in terms of agricultural workers can be considered. This stratification of holdings according to the number of agricultural workers stems from the realistic assumption of a close link, in a rural area, between the number of workers and agricultural production. For this, when listing holdings in the sampled PSU, elements that can allow this stratification need to be collected from each one.

box 2: STRATIfICATIoN

Stratification therefore responds to concerns relating firstly to a better representation of the population

for better accuracy of estimators, and secondly to a better representation of the fields for which precise

information is required.


11

0.3.3. Sample sizeThe size of the sample in a two-stage survey consists of the size of the sample of PSU (e.g. village, counting area, etc.) and the number of SSU (e.g. holdings) to be selected per sampled PSU. This sampling plan links the number e of SSU sampled and the number n of PSU sampled by the relation e = n x m where m represents the fixed number of SSU to be surveyed per PSU sampled. Knowing any two of the three factors in the equation results in the third. The number of villages or holdings sampled can then be referred to equally, but the elements allowing the determination of m will be given beforehand.

a. Number of SSU per PSU sampledThe number of SSU sampled per PSU depends firstly on the degree of dispersion of SSU in the PSU with regard to the variable of interest, and secondly on the contribution of the second stage to the precision of the estimates. The number of SSUs will be greater, the higher these parameters.

• The dispersion in PSU (e.g. village) is low if SSUs (e.g. holdings) in the same PSU are similar to one another in terms of the variable of interest (e.g. production). Statistically, this dispersion is measured by the variance or the standard deviation of the studied variable (or of any other correlated variable) between holdings in the PSUs. In the absence of elements for calculating this dispersion, the evident dispersion of holdings by size (small, medium and large holdings) will initially be sufficient. This size may be related to the population or to agricultural inputs. A minimum number of three holdings to be surveyed can then be fixed in each PSU in the hope of having one large, one medium and one small. This minimum number can be increased to four or five to take into account the large number of medium holdings in an allocation (the number of medium holdings is clearly larger than the number of large or small holdings);

• The contribution of the second stage in the precision of estimators is measured by the variability of estimates when the sample is changed within the same PSU. It depends on several factors:i. Variability between holdings in the PSU;ii. PSU size which influences the probability of selecting holdings, but above all the probability of

selection of PSUs; it then contributes to increasing far more the contribution in precision of the first stage of selection at the expense of the second stage;

iii. Variability between PSUs to which the contribution of the first stage is proportional.


12

b. Number of PSU sampledIn practice, the number of observation units e is determined first, by answering one of the following two questions, as preferred. The answer gives a number of holdings to be surveyed from which the number of PSU (e.g. villages) is determined (knowing m).i. How many holdings can be surveyed? The answer to this question relates to the resources available to conduct

the survey (number of interviewers, number of controllers, logistics, and means of travel); the quality of the estimators is determined afterwards, and is therefore dictated by resources.

ii. How many holdings should be surveyed? The answer to this question is more technical: it involves defining the sample required to make good estimates. The resources are then dictated by the quality you want to give the estimators. This quality is generally measured by the coefficient of variation of the estimator of the mean defined in the case of a simple random sampling with replacement by:4

It emerges that the sample size, i.e. the minimum number of holdings that can represent all the holdings together, depends on the degree of homogeneity of the holdings: the more the holdings resemble one another as regards agricultural production, the less need there is for a large number to represent them.

The overall size of the sample of holdings will be used to determine the overall sample of villages. The overall sizes can be determined nationally and the sample will then be allocated between the various strata. The sample size can also be determined independently, stratum by stratum. In the first case, we start with the desired precision on a national level and precision at stratum level is calculated afterwards, whereas the procedure is the reverse in the second case.

In general, the sample size of surveys in different countries is mainly determined based on the desired representativeness of the administrative entities for which results must be produced or the level of a technical-administrative unit that can lead the field operations.

The sample can be allocated between administrative entities according to the aims and features of the administrative divisions in an equal or proportional manner. If it is done in a proportional manner, the reference variables (the most widely used for holdings) are the number of holdings, the number of households and the number of inhabitants.

If the aim is overall precision, the allocation methods are described below.

4 Cet indicateur est le rapport de deux variances théoriques pour un estimateur. La variance au numérateur est la variance réelle pour un plan de sondage donné. Au dénominateur est utilisée la variance en supposant la même taille d'échantillon pour un échantillonnage aléatoire simple sans remise.


resources are then dictated by the quality you want to give the estimators. This quality is generally measured by the coefficient of variation of the estimator of the mean defined in the case of a simple random sampling with replacement by:

CV yyy( ) =

σ( y is the estimator of the mean and σ y the standard deviation of y )

As σσ

yy

n= where σ y is the standard deviation of y, this will give

CV yn y n

CV yy( ) ( )= × = ×1 1σ

(CV y( ) is the coefficient of variation of y)

n CV yCV y

CV yd

= =( )( )

( )2

2

2

2 (d represents the relative precision desired for the mean)

This size n should be adjusted according to the design effect (Deff)4 and the non-response rate (r), let:

)1(' rDeffnn +××= .

It emerges that the sample size, i.e. the minimum number of holdings that can represent all the holdings together, depends on the degree of homogeneity of the holdings: the more the holdings resemble one another as regards agricultural production, the less need there is for a large number to represent them.

The overall size of the sample of holdings will be used to determine the overall sample of villages. The overall sizes can be determined nationally and the sample will then be allocated between the various strata. The sample size can also be determined independently, stratum by stratum. In the first case, we start with the desired precision on a national level and precision at stratum level is calculated afterwards, whereas the procedure is the reverse in the second case.

In general, the sample size of surveys in different countries is mainly determined based on the desired representativeness of the administrative entities for which results must be produced or the level of a technical-administrative unit that can lead the field operations.

The sample can be allocated between administrative entities according to the aims and features of the administrative divisions in an equal or proportional manner. If it is done in a proportional manner, the reference variables (the most widely used for holdings) are the number of holdings, the number of households and the number of inhabitants.

If the aim is overall precision, the allocation methods are described below.

e. Allocation of the sample between strata

If the aim of stratification is local precision, the problem of allocation of the sample clearly does not arise. According to the precision fixed, the necessary size is determined separately for each individual subpopulation. The total size will be the sum of the various sizes.

4 This indicator is the ratio of two theoretical variances for an estimator. The numerator variance is the real variance for a given sample design. For the denominator, variance is used assuming the same sample size for simple random sampling without replacement.


13

c. Allocation of the sample between strataIf the aim of stratification is local precision, the problem of allocation of the sample clearly does not arise. According to the precision fixed, the necessary size is determined separately for each individual subpopulation. The total size will be the sum of the various sizes.

If stratification is used to improve overall precision, one question logically springs to mind: what is the best allocation between strata of a total sample size n? This question is a simplified form of the following general problem: for a given sample structure, what is the optimal composition (size and allocation) of the sample?

For a total size n, we shall assume here that the best allocation between strata is that which provides the best precision with the minimum resources possible. In other words, the allocation criterion is to minimize the survey budget for a given variance or to minimize variance for a given budget, the objective being the best use of resources.

In a stratified sample targeting an overall precision, there are three main types of allocation to determine how to allocate the sample between strata:i. Equal allocation;ii. Proportional allocation;iii. Neyman allocation.

The examination of the usual estimators of simple random sampling with or without replacement and stratified sampling considering the various types of allocation, namely proportional allocation and optimal allocation, shows that optimal allocation yields better precision than proportional allocation which is, in turn, more precise than simple random sampling


14

0.4. DATA CoLLeCTIoN

Data collection is the operational phase, also called the field phase. It takes place after the upstream finalization of the office work (developing the methodology, finalizing tools, finalizing administrative procedures, recruitment and training of field agents (or enumerators), etc.).

It should be noted that one of the key phases, prior to a collection operation, is awareness raising. Awareness raising is important for the success of any survey or field work. Awareness raising usually targets two population categories: firstly the local and customary administrative authorities, opinion leaders and organized social groups, and secondly the target population.

Awareness raising in the first group aims to seek their participation in conveying the information related to the study objectives to populations under their tutelage and sphere of influence (or action).

Awareness raising in the target population aims to seek their cooperation and willingness to provide the information sought to the enumerators and to inform them about the schedule of the field work.

Once the preliminary activities have been completed, the field work can start.

During this phase, visits are made to all the zones selected for the survey. These visits are made by enumerators generally organized into small groups (2 to 5 people) under the supervision of a team leader, a supervisor or a control officer. The target populations are then identified in the selected zones and observations are made (direct interview, observation, etc.). The information is collected using purpose-designed tools.

Enumerators are deployed in these zones according to a clearly defined calendar.

For the field work to be successful, emphasis should be placed on the following in particular:• the chronology of the various phases which should necessarily be carried out in succession. A phase cannot be

implemented until the previous one has been completed;• the identification of sampling units;• the identification of survey units (observation units);• the training of field work staff (interviewers, controllers);• the assurance of statistical quality.

The data collected will undergo appropriate processing to obtain audited data available for subsequent analyses.


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0.5. DATA PRoCeSSING

Data processing is carried out in four main steps:• The recording process involves checking that the source document (questionnaire) is complete for coding,

capturing and editing;• Processing involves cleaning and editing the data capture and the imputation of missing data as necessary;• The production process involves calculating weight for extrapolation and producing tables or ratios from the

data after processing and conversion into useful information.• Creating a final microdata file which can be used for analysis and archiving.

Some of these steps are described in greater detail below.

0.5.1. Recording questionnairesThe completed questionnaires should be correctly organized, then forwarded to the units responsible for data coding, data entry, editing and cleaning.

0.5.2. Data entry and editingData can be captured in a computer system by a data entry clerk using a keyboard or by an optical reader. With new technologies, collection can be done directly on tablets or smartphones and data transferred to a server without the need to capture them again after collection. In this case, validation is partly done at the time of the survey (while in the field).

When agricultural data are captured, it is important to check their accuracy. There are two methods of doing this, used alone or in combination:• Validation, which involves making computer checks to determine whether the data entered meet certain

requirements, for example data captured, ranges, field length, data type, figures, etc.;• Verification or Editing, which involves trying to determine whether the data entered are correct, by double entry,

for example. This second method requires additional costs which must be budgeted for in advance.

0.5.3. Verification process (data editing)Data editing systems can be very simple or very sophisticated depending on the scale of the survey. Here are some examples of editing:• Making sure that a question is associated with only one response;• Making sure that a response is consistent with previous items;• Making sure that coded values are valid;• Making sure of completeness overall and in the various subgroups.


16

0.5.4. Imputation of missing data and processing of partial and total non-responses

The problem of missing data is common when analysing data from a sample survey or census. Missing data can generate biased results or affect the representativeness of the results if they are not processed appropriately. It is important, when deciding how to process missing data, to know why they are missing.

box 3: TyPeS of MISSING DATA

Missing data should be divided into three types:

1. Missing Completely at Random: MCAR

2. Missing at Random: MAR

3. Missing Not at Random: MNAR

1. Missing Completely at Random: MCAR

A non-response is an MCAR if there is no link between the missing information and the intrinsic value

or between the missing information and the whole data set. The missing item is entirely random and

cannot be explained.

2. Missing at Random: MAR

A non-response is an MAR if there is a link between the missing information and any variable of the

data set. This situation occurs in the example below.

• Women are less likely to give their age and weight than men.

This case is also described as “missing conditionally at random”: the absence of the information

is conditional upon observable information. In the previous example, even if it has been noted that

women have a greater tendency not to respond, it is nevertheless difficult to predict the value which

should have been recorded, hence the uncertainty.

3. Missing Not at Random: MNAR

In this case, there is a link between the absence of information and its value. This non-response is also

called a “non-ignorable non-response”. This could occur in the following situations:

• People with a high income are less likely to declare it;

• Less educated people are less likely to declare their level of education.

MNAR is described as “non-ignorable” because the missing data mechanism can be modelled to find

out why data are missing and what the probable values are.


17

After categorizing missing data, the following mechanisms can be used to process them:• Analysis of all complete cases (which involves excluding all units with missing data or results). This applies to

MCAR and MAR;• Analysis of available cases (which involves excluding a variable or a series of variables because of their high

rate of missing data). This applies to MCAR and MAR; • Weighting according to the rate of missing data (which involves finding a means of reweighting the sample to

re-establish its representativeness). This applies to MCAR and MAR;• Imputation of missing values. Imputation is a process which involves replacing missing data with plausible

values. A few available methods are:�� Mean imputation. In this case, you should make sure that the distribution does not contain extreme

values (likely to affect the means);�� Last observation carried forward (historical imputation);�� Regression imputation;�� The use of information inferred from connected observations (deterministic or deductive imputation).

This applies to MNAR;�� Imputation from a donor has similar characteristics (nearest neighbour imputation). This applies to

MAR and MNAR;�� Hot-deck and cold-deck imputation.

0.5.5. extrapolationThe application of a weight which respects the structure of the sample design is necessary to obtain unbiased (representative) results. The survey weight W is the inverse of the inclusion probability of sampled units. In multistage sampling, the survey weight W is the product of the survey weights at each stage.

Example of a two-stage samplen: Number of units in the samplepi: probability of inclusion of the unit iWi: survey weight of unit iW: total survey weight of the sample designWe have:

box 4: HoT-DeCk AND CoLD-DeCk

• Hot-deck: The missing value is replaced by the value of the previous unit in the file, or the value of the

last unit found, preferably in the same geographical entity, and believed to be sufficiently similar to the

unit for which information is missing. The advantage of this method is that it can be readily automated.

• Cold-deck: Information “external” to the survey relating to the unit for which information is missing is

used, for example the value of the variable at a previous date. This approach is particularly applicable

in panel surveys.


- Regression imputation; - The use of information inferred from connected observations (deterministic or

deductive imputation). This applies to MNAR; - Imputation from a donor has similar characteristics (nearest neighbour

imputation). This applies to MAR and MNAR; - Hot-deck and cold-deck imputation.

Box 4: Hot-deck and cold-deck

• Hot-deck: The missing value is replaced by the value of the previous unit in the file, or the value of the last unit found, preferably in the same geographical entity, and believed to be sufficiently similar to the unit for which information is missing. The advantage of this method is that it can be readily automated.

• Cold-deck: Information “external” to the survey relating to the unit for which information is missing is used, for example the value of the variable at a previous date. This approach is particularly applicable in panel surveys.

0.5.5. Extrapolation

The application of a weight which respects the structure of the sample design is necessary to obtain unbiased (representative) results. The survey weight W is the inverse of the inclusion probability of sampled units. In multistage sampling, the survey weight W is the product of the survey weights at each stage.

Example of a two-stage sample n: Number of units in the sample

pi: probability of inclusion of the unit i Wi: survey weight of unit i

W: total survey weight of the sample design We have:

ipiW 1= and ∏∏==

==n

ip

n

ii i

WW1

1

1

0.6. Dataanalysis

Data analysis can be defined as the process of converting raw data into relevant information by analytical and logical reasoning. Data analysis generally results in observations or conclusions. Various statistical methods (descriptive, exploratory, etc.) are used in data analysis.

Box 5: Skills for data processing

The professionals responsible for processing and analysing agricultural data must be familiar with monitoring and supervising survey questionnaires, data input and audit, missing data imputation methods, tabulation techniques, archiving, calculating sampling error, and statistical software packages.


18

0.6. DATA ANALySIS

Data analysis can be defined as the process of converting raw data into relevant information by analytical and logical reasoning. Data analysis generally results in observations or conclusions. Various statistical methods (descriptive, exploratory, etc.) are used in data analysis.

0.6.1. Defining and calculating indicators: differences in the agricultural statistics produced

Differences are likely in estimates, and there are various reasons for this. Some of the reasons for these differences are described below (ONU-CEA-CAS5).

a. Difference in design and definitionThe way in which an indicator is defined or designed can vary with the country, its traditions, culture, statistical practices, etc.

b. Difference in data sourcesSample surveys and censuses are the data sources most widely used to calculate indicators relating to the development of agriculture in developing countries. If different sources are used to calculate the same indicator, either separately or together, the results could be different. This situation generally occurs when the populations targeted by these sources are different (lack of a master sampling frame, different definitions and concepts according to studies, coverage issues, different methodological approaches, etc.). This has a significant adverse effect on the degree of comparability of data between countries and sometimes within regions of the same country.

c. Differences in demographic estimates and denominatorsTo calculate indicators for evaluating the development of agriculture, disaggregated demographic estimates should be available during intercensal periods. These estimates are calculated from the previous census on the basis of various hypotheses (growth rate, mortality and migration trends).Furthermore, projection methods can be single-component or multiple-component, in which case they allow projections in different scenarios, as recommended by the UN Population Division.The differences in projection methods or the series of hypotheses used will very probably cause discrepancies in demographic estimates and therefore differences in development indicators

5 ONU-CEA-CAS (2011). A Handbook on Data Collection, Compilation, Analysis and Use of Disaggregated Data Including Those from Administrative Sources.

box 5: SkILLS foR DATA PRoCeSSING

The professionals responsible for processing and analysing agricultural data must be familiar with

monitoring and supervising survey questionnaires, data input and audit, missing data imputation

methods, tabulation techniques, archiving, calculating sampling error, and statistical software packages.


19

d. Inadequate institutional coordinationBetween national, regional and international stakeholdersIn developing countries, the activities of various bodies working in the field of data collection, processing, analysis and dissemination of agricultural statistics are not always well coordinated. National stakeholders may collect, process, analyse and disseminate data for calculating indicators without consultation with regional and international stakeholders involved in similar activities. This lack of consultation and coordination between these bodies usually leads to non-comparability of data due, for example, to a lack of consistency in the concepts and methods used, and a mismatch between international standards and local statistical needs. This may also be noted between regional and international stakeholders who do not usually have the same priorities, policies and approaches to countries.

Between national stakeholdersSeveral bodies produce agricultural statistics in each country. The Ministry for Agriculture generally has a statistics agency or unit which carries out statistical studies, in addition to those performed by the National Statistical Office (NSO). In the absence of appropriate coordination by a strong central body such as the NSO as part of a National Strategy for the Development of Statistics (NSDS), the Ministry’s work can result in unnecessary duplication and a waste of limited resources and, ultimately, differences in development indicators.

e. Incomplete metadataMetadata are data about the contents of data; they describe a set of data by providing information on topics such as the data source, the definitions of the variables collected, the estimation methods used and other relevant technical and methodological aspects. The partial or complete absence of metadata is the cause of discrepancies when analysing results. A series of data without comprehensive information cannot be correctly analysed and interpreted.

0.6.2. TabulationData compilation and tabulation are processes that involve determining numbers of individuals or cases corresponding to specified combinations of characteristics from records in a dataset.

Tabulations generated from a dataset can be precisely defined using domain of interests (in that case, the tables summarize the characteristics of interest, e.g. agricultural production, number of livestock, etc.) by important domains (e.g. administrative regions, agroclimatic regions, farm size, etc.).

The specifications of tabulations must be understandable both for specialists and staff responsible for data processing, and be sufficiently detailed so that the staff responsible for data processing do not take decisions concerning tabulation contents.

box 6: DIffeReNCeS IN DATA

The production of increasingly consistent indicators is a key stage in the process of agricultural

development evaluation. Appropriate tools must be designed for this purpose to allow the national,

regional and international bodies concerned to progressively reduce differences in data. Professional

statisticians who process, analyse and disseminate agricultural statistics are now expected to be able to

deal with the problems of differences in data.


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0.6.3. Statistical softwareStatistical software packages are specialized computer programs designed to carry out statistical analysis. The staff responsible for processing, analysis and dissemination of agricultural data must be proficient in the use of the relevant statistical softwares. Softwares are generally divided into four groups:• Open-source software packages, including programs such as R (a free application of language S) and DAP (the

free version of the program SAS);• Public domain programs, including CSPro (Census and Survey Processing System, mainly used to capture,

tabulate, map and disseminate survey and census data), Survey Solutions (SuSo) and Epi Info (specialized in epidemiology);

• Free software, such as GeoDa (free software for spatial data analysis, geovisualization, spatial autocorrelation and spatial modelling), QGis (an open-source geographical information system) and WinBUGS [Bayesian analysis software using Markov chain Monte-Carlo (MCMC) methods];

• Commercial software, including programs such as EViews (econometric analysis software), Stata (general statistical software), SAS (general statistical software), S-PLUS (general statistical software) and SPSS (general statistical software).

0.7. DATA DISSeMINATIoN

Data dissemination refers to all means used to make data public, including:• Publication of documents, in particular press releases, periodicals and special issues;• Electronic dissemination of statistics, for example on CD-ROM, USB stick or via the internet;• Sending statistics in a printed or electronic version in response to direct requests;• Setting up automated systems to provide access to statistics on request by telephone or internet.

To ensure that data are used to their full potential, it is important to consult users to determine the dissemination method most suited to their needs.


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0.8. DATA QUALITy MANAGeMeNT

There are several international references for data quality management. Several international organizations mention data quality:• International Monetary Fund (IMF) with:

�� General Data Dissemination System (GDDS) �� Special Data Dissemination Standard (SDDN)�� Data Quality Assessment Framework (DQAF)

• The United Nations Statistics Division: National Quality Assurance Framework (NQAF)• Paris21: Questionnaire on Statistical Capacity Building Indicators (SCBI) • Statistical Institutes: Statistics Canada, Eurostat, INSEE, etc. • The FAO, which shows that the quality of agricultural statistics can be substantially improved by:

�� Setting up an integrated surveys system;�� An integrated database which allows more analyses to be performed over time;�� The emergence of new technologies (PDA, GPS, remote sensing).

The use of nomenclatures derived from international standards (COICOP, CITI, CPC, SCN68, SCN93, SCN2008, etc.) also helps to have standardized concepts and indicators to guarantee data comparability in time and space.The majority of international data quality frameworks are based on the framework developed by the IMF, the Data Quality Assessment Framework (DQAF).

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6 Data Quality Assessment Framework (DQAF) for public finance statistics (IMF, DQAF July 2003)

box 7: DATA QUALITy ASSeSSMeNT fRAMewoRk (DQAf)

The DQAF distinguishes six dimensions of quality6:

• Prerequisites of quality: the elements and indicators grouped under this heading have a key role as

institutional parameters essential to statistical quality;

• Integrity: statistical systems must respect the principle of objectivity in the collection, compilation

and dissemination of statistics; this dimension encompasses institutional arrangements that ensure

professionalism in statistical policies and practices, transparency, and ethical standards;

• Methodological soundness: statistics should be produced on a sound methodological basis, which can

be guaranteed by following internationally accepted standards, guidelines or good practices;

• Accuracy and reliability: this dimension covers the idea that statistical outputs sufficiently portray the

reality of the economy; data sources must be appropriate for compiling statistics; statistical techniques

must be sound and core data, intermediate data and statistical outputs must be regularly checked and

validated, including in revision studies;

• Serviceability: statistics must contain relevant information related to the field in question, be

disseminated with appropriate periodicity in a timely fashion, be consistent internally and with other

datasets, and finally follow a regular revision policy;

• Accessibility: this dimension relates to the need for data and metadata to be presented in a clear and

understandable manner on an easily available and impartial basis; in short it is expected that metadata

should be up-to-date and relevant and that rapid, competent support services are available.


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International organizations identify the following six aspects to define the quality of official statistics:i. The relevance of the information expresses how the information meets the real needs of users;ii. The accuracy and reliability of the statistical information expresses the extent to which the information

correctly describes the phenomenon it should assess;iii. Statistical information is timely and punctual, taking into account its publication data in relation to its reference

data;iv. Accessibility and clarity refer to the ease with which it can be obtained from the data producer;v. Interpretability or metadata are characterized by the availability of additional information necessary for their

interpretation (metadata);vi. The consistency and comparability of the statistical information is guaranteed when this information can be

reconciled with other statistical information in a general analytical framework.

The prerequisites of data quality are:

• Favourable legal and institutional framework;• Appropriate human, financial and material resources to compile agricultural statistics programmes;• Acknowledging that:

�� Statistics contain information relevant to the area of specialization;�� Quality is a condition that governs all statistical compilation work.

Quality should be sought in the following:• The data production system (institutional aspects, human, material and financial resources);• The outputs (tools, methodology, operations).

The quality approach should be an integral part of statistical activities:• Internal and external checks should be carried out at all steps in the programming and statistical compilation

process;• The quest for quality is a requirement for international comparability, but it has a cost, hence the importance of

assessing the cost-benefit of quality control and assurance activities and of planning them carefully as part of on-going official statistics compilation and management activities.

Figure 2 below compares the quality components of data determined by international organizations7.

7 Global Strategy, Technical Report on the Integrated Survey Framework (June 2014)


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fIGURe 2: DATA QUALITy ASSeSSMeNT fRAMewoRkSfA

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1Module 1: An overview of the general framework of agricultural statistics

1.1. SCoPe of THe CoURSe

The scope of agricultural statistics is defined by:• The System of National Accounts (SNA) which defines international standards as regards concepts, definitions

and classifications of economic activity. System of environmental-economic accounting (SEEA), which is a satellite account of the SNA, is the starting point for environmental statistics;

• Socioeconomic variables relating to agricultural holdings come from the national accounts.

LeARNING objeCTIVeS of THe MoDULe

Training participants must:

• Have an overview of the conceptual framework of the global strategy to improve agricultural and rural

statistics;

• Identify links between the economic, social and environmental aspects of this conceptual framework;

• Understand the importance of Strategic Plans for Agricultural and Rural Statistics (SPARS) and their

integration into National Strategies for the Development of Statistics (NSDS);

• List the main users and main uses of agricultural statistics


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The nomenclature of the various groups in the International standard industrial classification of all economic activities (ISIC Rev.4) constituting the scope of the agricultural census will be covered:• Group 011: Non-perennial (Temporary) crops;• Group 012: Perennial (Permanent) crops;• Group 013: Plant propagation;• Group 014: Animal (Livestock) production;• Group 015: Mixed farming.

The FAO uses this classification to determine the scope of the agricultural census described in the World Programme for the Census of Agriculture 2020.

In addition to the ISIC (Rev. 4), the Central Products Classification (CPC) provides an additional international standard. Its latest revision CPC (version 2.1 (UN, 2015a)) contains numerous amendments and details concerning agriculture, forestry, fisheries and food. Items such as crops, livestock products, machines and equipment, fertilizers and pesticides, which are mentioned in the World Programme for the Census of Agriculture (WCA 2020), are also classified in the CPC. The ISIC and the CPC provide important tools for integrating agricultural statistics into national statistical systems.

For the full scope of agricultural statistics, two additional approaches should be considered:

1.1.1. The ISIC1 approach (Rev 4.) In this approach, agricultural activities comprise the following: a) Crop production which covers non-perennial crops and perennial crops:

• Non-perennial crops: Plants that do not last for more than two growing seasons. Growing plants for seed production is included in this group. This group consists of seven ISIC classes:i. ISIC class 0111: Growing of cereals (except rice), leguminous crops and oil seeds;ii. ISIC class 0112: Growing of rice;iii. ISIC class 0113: Growing of vegetables and melons, roots and tubers;iv. ISIC class 0114: Growing of sugar cane;v. ISIC class 0115: Growing of tobacco;vi. ISIC class 0116: Growing of fibre crops;vii. ISIC class 0119: Growing of other non-perennial crops.

• Perennial crops (growing cycle of more than one year), namely the growing of plants which last for more than two seasons of crop growth, either they die after each season or they grow continually. This group also covers seed production for these plants. This group consists of nine classes:i. ISIC class 0121: Growing of grapes;ii. ISIC class 0122: Growing of tropical and subtropical fruits;iii. ISIC class 0123: Growing of citrus fruits;iv. ISIC class 0124: Growing of pome fruits and stone fruits;v. ISIC class 0125: Growing of other tree and bush fruits and nuts;vi. ISIC class 0126: Growing of oleaginous fruits;vii. ISIC class 0127: Growing of beverage crops;viii. ISIC class 0128: Growing of spices, aromatic, drug and pharmaceutical crops;ix. ISIC class 0129: Growing of other perennial crops.

1 International standard industrial classification of all economic activities


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b) Animal production which includes raising livestock and the selection of all livestock except aquatic animals. It consists of seven ISIC classes:

i. ISIC class 0141: Raising of cattle and buffaloes;ii. ISIC class 0142: Raising of horses and other equines;iii. ISIC class 0143: Raising of camels and other camelids;iv. ISIC class 0144: Raising of sheep and goats;v. ISIC class 0145: Raising of swine/pigs;vi. ISIC class 0146: Raising of poultry;vii. ISIC class 0149: Raising of other animals.

1.1.2. The approach of the Global strategy to improve agricultural and rural statistics

This approach is more complete and covers, in addition to plant production and animal production, the forestry, fisheries and aquaculture sectors and other fields such as geospatial aspects of land, the environment, and non-agricultural rural activities:a. Forestry and agroforestry are related to the production of forest products and to the interface between forestry and

agriculture as an environmental impact domain. The collection and dissemination of data needed for forestry and forested land other than for agriculture should be included in the national statistical system;

b. Aquaculture and fisheries are important components of the food supply and food security, as well as household income;

c. The geospatial aspects of land should be considered in agricultural statistics. The geospatial scope of agricultural statistics requires special attention to be paid to land use for agriculture and forestry, and is positioned in the wider setting of national land use statistics;

d. Agricultural statistics should also cover the use of water for agricultural purposes for irrigation and other uses, sources of irrigation water, irrigated land, irrigation methods, and resulting production.

And cross-disciplinary activities, i.e.:e. The environment: the impact of all agricultural activities on the environment (land use intensity, energy, biodiversity,

greenhouse gases, etc.);f. Non-agricultural rural activities practised by agricultural households making up the majority of the population in

a rural setting.


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1.2. CoNCePTUAL fRAMewoRk of THe GLobAL STRATeGy To IMPRoVe AGRICULTURAL AND RURAL STATISTICS AND ITS eCoNoMIC, SoCIAL AND eNVIRoNMeNTAL ASPeCTS

A Global strategy to improve agricultural and rural statistics was developed and adopted in February 2010 by the United Nations Statistics Commission (UNSC) to try to address the numerous challenges involved in meeting the needs of agricultural statistics users in developing countries. Its aim is to provide a framework and a methodology to improve the availability and quality of national and international food and agricultural statistics, to guide policy analysis and decision making.

It is founded on three pillars: i. Creating a minimum set of core data that countries will provide to meet their current and emerging needs

These indicators cover the agricultural sector and rural development as well as subsectors such as plant and animal production; indicators specific to climate change, land, the environment and the rural economy are also included.

The basic information list and the associated data form a framework for implementing the agricultural and rural components of National Strategies for the Development of Statistics (NSDS). They will constitute a basis for establishing methodologies and integrating agricultural and rural statistics in the national statistical system.

Determining the elements in the minimum set of core data begins with production statistics for the main crops, animal production, aquaculture, fisheries and forestry, and extends to agricultural inputs, socioeconomic data, land cover and public expenditure. These elements are covered in the next section, followed by the conceptual framework allowing countries to add their specific national needs to the basic list, and also to determine the frequency with which basic data and national interest data should be produced.

ii. Integrating agriculture in their National Statistical System (NSS) to guarantee data comparability between countries and over time

The process of improving agricultural statistics begins with integrating agriculture in the national statistical system. This will be achieved by setting up a master sample specific to agriculture to ensure it is pertinent and comprehensive and is used to implement (i) a coordinated data collection programme aiming to produce data that are appropriate and accurate, as well as consistent and comparable, and (ii) a data dissemination strategy ensuring data accessibility. The integration of agriculture in the national statistical system is necessary for several reasons.

Integrated statistical systems can provide solutions to the majority of these problems by avoiding unnecessary duplication of work, preventing the publication of contradictory statistics and ensuring the best use of resources.

Agriculture will be integrated in the national statistical system based on methodological approaches that establish a close link between the results obtained by the various statistical processes and the various statistical units. This requires the development of a master sample and the adoption of sample designs that allow subsample replications, and the synchronization of the design of survey and census questionnaires.


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iii. Guaranteeing the viability of the National Agricultural Statistics System (NASS) by strengthening governance and statistical capacity building

Governance at a national level implies the organization of a national statistical system which includes sector ministries and other organizations that produce data. In the case of agricultural statistics, this will include ministries responsible for agriculture, forestry, fisheries and any other institution that collects agricultural data.

Capacity building should take into account the quality of agricultural statistics in terms of whether they are accurate, pertinent, up-to-date, comparable, available and accessible. These various aspects of statistical quality should be taken into account when designing agricultural statistical systems, in particular in capacity building work for data analysis and collection. Implementing the Strategy requires a level of expertise which can be difficult to find (or maintain) in many developing countries. The use of remote sensing technologies, the design of an integrated survey framework and the use of a data management system require experienced technical staff.

Many users express the need to have new improved indicators on the following2 :• trade;• water;• land;• soil;• household consumption;• food security;• socioeconomic data;• economic accounts;• managing natural disasters.

Data on new domains, in addition to the above, are needed, for example:• fisheries (capture fishery activities carried out by households);• the environment / greenhouse gas (GHG) emissions (essential agro-environmental data on GG and ammonia

emissions);• energy and biofuels;• environmental aspects of agriculture;• climate change;• biodiversity.

We also need to have and use geospatial and remote sensing data, and to achieve better integration, accessibility and user-friendliness of databases.

The conceptual framework places the emphasis on the cause and effect relationships that connect the economic, environmental and social aspects of agriculture.

2 Global strategy to improve agricultural and rural statistics (FAO Report, September 2010)


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fIGURe 3: CoNCePTUAL fRAMewoRk foR AGRICULTURAL STATISTICS3

Links between agriculture and environmental and social aspects are increasingly important. These links should be considered in a global context where agriculture covers three inter-dependent dimensions which constitute the three pillars or components underpinning sustainable development:• The economic dimension of agriculture is based on land, the work force and capital introduced into the

production process and the resulting products. The economic aspect covers the following: �� agricultural production;�� markets; �� agricultural and non-agricultural income.

• Information relating to the social dimension of agriculture and to rural development primarily concerns households and members of households, both agricultural and non-agricultural. The social aspect covers the following:

�� the need to reduce risks and vulnerability;�� food security; �� gender issues;�� rural poverty;�� rural employment, particularly of women, but also of children;�� youth unemployment.

• The environmental dimension of agriculture can be divided between two roles:�� Theroleofthesectorasauserofnaturalresources−mainlylandandwater–andasaproviderof

environmental services. In addition to the direct use of natural resources in production, its impacts also relate to waste and to the emission of byproducts generated by production;

�� The environmental aspect of agriculture generally touches on how climate change relates to agriculture, sector sustainable development and environmental services.

The geospatial aspect of land is an important part of the environmental dimension. It should be considered in agricultural statistics, paying special attention to land use for agriculture and forestry.



• socioeconomic data;

• economic accounts;

• managing natural disasters. Data on new domains, in addition to the above, are needed, for example:

• fisheries (capture fishery activities carried out by households);

• the environment / greenhouse gas (GHG) emissions (essential agro-environmental data on GG and ammonia emissions);

• energy and biofuels;

• environmental aspects of agriculture;

• climate change;

• biodiversity. We also need to have and use geospatial and remote sensing data, and to achieve better integration, accessibility and user-friendliness of databases.

The conceptual framework places the emphasis on the cause and effect relationships that connect the economic, environmental and social aspects of agriculture.

Figure 3: Conceptual framework for agricultural statistics10

Links between agriculture and environmental and social aspects are increasingly important. These links should be considered in a global context where agriculture covers three inter-dependent dimensions which constitute the three pillars or components underpinning sustainable development:

• The economic dimension of agriculture is based on land, the work force and capital introduced into the production process and the resulting products. The economic aspect covers the following:



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1.3. STRATeGIC PLANS foR AGRICULTURAL AND RURAL STATISTICS (SPARS) AND NATIoNAL STRATeGIeS foR THe DeVeLoPMeNT of STATISTICS (NSDS)

The global action plan of the Global strategy to improve agricultural and rural statistics is founded on three pillars (see section 1.2.). The integration of agriculture in the National Statistical System (NSS) is the second pillar; it requires countries to develop and implement Strategic plans for agricultural and rural statistics (SPARS) in the framework of National strategies for the development of statistics (NSDS) to facilitate the integration of agriculture into NSS.

This integration is an essential component of the Global Strategy which recognizes that the first stage in improving agricultural statistics is incorporating SPARS in the NSS, beginning with its integration in the NSDS.

Where they exist, NSDS should be reviewed and, if necessary, revised to reflect the integration of agriculture into the national statistical system and also to take into account the implementation of the master sample, the integrated survey framework (administrative data, information systems on agroprocessing and the market, community surveys, remote sensing and important information collected by experts) and the data management system. The complete integrated survey framework comprises the sampling design concept, questionnaires, collection methods, and data analysis and estimation.

SPARS are recommended as guides in the sector implementation, paving the way for compilation and the consistent use of agricultural and rural statistics in developing countries. Developing them within countries should do the following:• help to resolve coordination problems within the various elements of the agricultural statistics system;• serve as a statistical coordination framework between agricultural systems, subsystems and the NSO, and

between the government and technical and financial partners as regards funding agricultural and rural statistical activities;

• finally help to integrate new components (see section 1.2.), such as those recommended by the Global strategy to improve agricultural and rural statistics.

To integrate agricultural statistics in the NSDS, we need to ensure that the NSDS process takes into account the aims and operating methods of a food and agricultural statistical system as a subsystem of the national statistical system.

To achieve this, it is important:• to assess all the constraints on the production of agricultural statistics and resources available for production and

to propose actions aiming to overcome these constraints and provide the resources necessary for the effective production of statistics to meet the needs expressed;

• to ensure that all users and their agricultural and rural statistics needs are assessed and taken into consideration and that priorities are defined in the development and implementation of data collection, analysis and dissemination programmes;

• to carry out effective production that continuously meets the needs of all users.


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The development process of a Strategic plans for agricultural and rural statistics should comprise a series of steps4 in four phases:i. the launch phase, which will produce a road map approved by the national authorities;ii. the assessment phase which will consist of an in-depth evaluation of the national agricultural statistical system; iii. the planning phase which will result in defining a mission, a vision, strategic priorities and a costed action plan

comprising a communication and advocacy plan, a funding plan and a monitoring and evaluation framework;iv. The actual implementation phase of the strategic plan.

1.4. USeRS AND USeS of AGRICULTURAL STATISTICS

Agricultural data are used by various user categories:• public services;• training and research institutions;• NGO;• international organizations;• the private sector;• producer trade organizations;• civil society.

In developing countries in particular, public services use them to:• formulate development programmes and monitor their implementation;• formulate food security policies;• formulate foreign trade policies;• formulate poverty reduction policies;• formulate appropriate land reform policies;• develop national accounts;• develop economic accounts for agriculture and the environment;• monitor indicators of sustainable development goals (SDG).

They are also useful for the private sector to position itself best on the market (production forecasts and estimates, agricultural product prices, use of equipment and agricultural machinery, fertilizer, pesticides, livestock feed, etc.).

The statistics production process should be aimed at the use of information. Users’ needs should be met through consultations and discussions and by drawing up questionnaires and surveys to produce the desired results.

4 Global Strategy, SPARS (Strategic plans for agricultural and rural statistics, June 2014)


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Module 2: Statistics to be produced, producers, data sources, statistical units and data collection methods

This module, which is the core of the manual, will answer the following questions:• What are the main agricultural statistics to be produced?• How is the statistical system organized?• What are the sources of agricultural statistics?• What are the statistical units containing agricultural data?• What are the collection methods?

MoDULe TRAINING objeCTIVeS

• To identify the demand for agricultural statistics (what people want to know);

• To describe the organization of the statistical system to better understand how to organize the

production of agricultural data;

• To explore the main sources of agricultural statistics;

• To describe statistical units (where the information is);

• To describe data sources and statistical methods for obtaining information (how to obtain information

and what the collection methods are).

2


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2.1. STATISTICS To be PRoDUCeD

The agricultural statistics to be produced in each country depend on users’ needs and are generally defined in the Strategic Plans for Agricultural and Rural Statistics. The Global Strategy has established a Minimum Set of Core Data necessary for decision-making in three dimensions (economic, social and environmental) related to agriculture (see Annex 2). This minimum set is regularly reviewed to include emerging global statistics needs, in particular those related to monitoring and evaluation of Sustainable Development Goals. Annex 1 also includes commonly produced statistics used to meet users’ needs.

The following statistics are generally useful:

Holdings:• geographical distribution;• gender of holder;• age of holder;• agricultural activities.

Holder population:• geographical distribution;• structure by age;• structure by gender;• level of training.

More specific data to be provided from:• crop production;• livestock production;• aquaculture;• fishery;• silviculture and agroforestry;• environment;• rural areas;• prices.

These statistics are also used as inputs when developing derived analytical and statistical frameworks such as economic accounts for agriculture and the environment, costs of production, post-harvest losses, agricultural prices and price indexes as well as food security and food balance sheets. These will be covered in more detail in Module 4.

2.1.1. Crop production statisticsProduction is the core agricultural activity. Production is cyclical in nature (as it corresponds to the result of an agricultural season’s work), but it is also structural as the level of production characterizes production zones.

Production is understood to be the actual quantity of products, after drying and processing, ready to be sold or consumed, having deducted any losses sustained before, during and after harvest.

Production statistics are used to assess wealth creation (farm income, GDP), and to monitor food security.


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Crop production covers temporary and permanent crops: • Temporary crops: cereals, leguminous crops, oil seeds, vegetables, roots and tubers; growing of sugar cane;

growing of tobacco; fibre crops; etc.• Permanent crops: growing of grapes; growing of tropical and subtropical fruits; growing of citrus fruits; growing

of pome fruits and stone fruits; growing of other tree and bush fruits and nuts; oleaginous fruits; growing of beverage crops; growing of spices, aromatic crops and drug and pharmaceutical crops, etc.

Agricultural production data should be essentially presented according to the tenure system, the crop, the production factor and the production system.

Tenure systemA holding can have one or more tenure systems corresponding to each block of land. • Self-management or owner-like possession recognized in law are related to land rights which confer the

security of legal tenure. • Self-management or owner-like possession not recognized in law describes various informal arrangements

which do not confer security of tenure, and which are such that, under certain circumstances, the farmer can be dispossessed of land.

• Indirect tenure under which land is leased or rented by the holding, generally for a limited period. Payment can take several forms. Land can be leased against the payment of a fixed fee in cash and/or in kind, against a share in production or against services. Land can also be occupied free of charge.

But each country can define its own tenure systems according to national circumstances.

Production resources• Land is the main production asset for crops or plant production. Agriculture is practised on land belonging to

the holding, in order to obtain products. Table 1 shows the two main sources of land classification (Annex 8, WCA 2020). An area of land can, moreover, have more than one use, and its multipurpose agricultural use (high-potential crops) depends on the concepts of parcel, field and plot, as well as the tenure system (these concepts and their definitions are covered in section 2.4.1).


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TAbLe 1: CLASSIfICATIoN of LAND USe

Classes in the SeeA classification of land use Land use classes recommended by wCA 2020

Agriculture

Land under temporary crops Land under temporary crops

Land under temporary meadows and pastures Land under temporary meadows and pastures

Land with temporary fallow Land temporarily fallow

Land under permanent crops Land under permanent crops

Land under permanent meadows and pastures

Cultivated

Land under permanent meadows and pasturesNaturally grown

Land under protective cover Land under farm buildings and farmyards

ForestryForest land

Forest and other wooded landOther wooded land

Land used for aquaculture Area used for aquaculture (including inland and coastal waters if part of the holding)

Inland waters used for aquaculture or holding facilities

Coastal waters used for aquaculture or holding facilities

Use of built-up and related areas

Other area, not elsewhere classified

Land used for maintenance and restoration of environmental functions

Inland waters used for maintenance and restoration of environmental functions

Coastal waters used for maintenance and restoration of environmental functions

Other uses of land, not elsewhere classified

Other uses of inland waters, not elsewhere classified

Other uses of coastal waters, not elsewhere classified

Land not in use

Inland waters not in use

Coastal waters not in use

Land use is distinct from “land cover”, which describes the physical characteristics of the land, such as meadow or forest. Areas in a holding are classified according to main land use.

Nine classes are distinguished for land under crops (WCA 2020):• land under temporary crops;• land under temporary meadows and pastures;• land temporarily fallow;• land under permanent crops;• land under permanent meadows and pastures;• land under farm buildings and farmyards;• forests and other wooded land;• area used for aquaculture (including inland and coastal waters if part of the holding);• other area, not elsewhere classified.


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box 8: NINe CLASSeS foR LAND UNDeR CRoPS1

• Land under temporary crops includes all land used for crops with a less than one-year growing cycle;

that is, they must be newly sown or planted for further production after the harvest. Some crops that

remain in the field for more than one year may also be considered temporary crops. For example,

strawberries, pineapples and bananas are considered to be annual crops in some areas. Such crops

could be classified as temporary or permanent according to the practice in the country. Land under

temporary crops also includes land used for growing temporary crops under protective cover.

• Land under temporary meadows and pastures includes land temporarily cultivated with herbaceous

forage crops for mowing or pasture. A period of less than five years is used to differentiate between

temporary and permanent meadows and pastures. If country practice differs from this, the country

definition should be clearly indicated in census reports.

• Land temporarily fallow refers to arable land at prolonged rest before re-cultivation. This may be part

of the holding’s crop rotation system or because the normal crop cannot be planted because of flood

damage, lack of water, unavailability of inputs or other reasons.

• Land under permanent crops refers to land cultivated with long-term crops which do not have to be

replanted for several years; land under trees and shrubs producing flowers, such as roses and jasmine;

and nurseries (except those for forest trees, which should be classified under “forest and other wooded

land”). Land under permanent crops also includes land used for growing permanent crops under

protective cover. Land under permanent meadows and pastures is excluded from this category.

• Land under permanent meadows and pastures includes land used permanently (for five years or more)

to grow herbaceous forage crops, through cultivation or naturally (as wild prairie or grazing land).

Whether land under permanent meadows and pastures is cultivated or naturally grown has important

environmental implications; therefore countries are encouraged to further subdivide it according to this

characteristic.

• Land under farm buildings and farmyards refers to surfaces occupied by operating farm buildings

(hangars, barns, cellars, silos), buildings for animal production (stables, cow sheds, sheep pens, poultry

yards) and farmyards. The area of the holder’s house (including the yard around it) is also classified

here if it makes up part of the agricultural holding.

• Forest and other wooded land is land not classified as mainly “agricultural land” that satisfies either

of the following definitions: i) Forest land is land spanning more than 0.5 ha with trees higher than 5

metres (m) and a canopy cover of more than 10%, or trees that are able to reach these thresholds in

situ; ii) Other wooded land is land spanning more than 0.5 ha² with: (i) trees higher than 5 m and a

canopy cover of 5 to 10%, or trees able to reach these thresholds in situ; or (ii) trees not able to reach

a height of 5 m in situ, but with a canopy cover of more than 10 % (for example, some alpine tree

vegetation types, arid zone mangroves, etc.); or (iii) combined cover of shrubs, bushes and trees of

more than 10%.

• Area used for aquaculture includes area (land, inland waters or coastal waters) used for aquaculture

facilities, including supporting facilities. Aquaculture refers to farming of aquatic organisms such

as fish, molluscs, crustaceans, plants, crocodiles, alligators and amphibians. Farming implies some

form of intervention in the rearing process to enhance production, such as regular stocking, feeding,

protection from predators, etc.

��


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An area of land can have more than one use, and its multipurpose agricultural use (high-potential crops) depends on the concepts of parcel, field and plot, as well as the tenure system. For definitions of these concepts, see section 2.4.1.

The statistical data relating to areas of land are as follows:�� Areas of land under temporary crops;�� Areas of land temporarily fallow;�� Areas of land under permanent crops;�� Distribution of areas by crops;�� Distribution of areas by cultivation methods;�� Areas of land by tenure system;�� Area of managed crops by crop group.

• Machinery and equipment: production assets other than land includes machinery and equipment used for agricultural purposes on the holding. The item machinery and equipment is understood in its broad sense and covers all machinery, equipment and tools used as means of agricultural production.

• Work force: the work force of agricultural holdings includes: �� the work force provided by the household or members of households on the holding;�� paid external workers.

• Inputs: these are seeds, fertilizers and pesticides. Fertilizers are substances applied to give the plants the nutrients they need or to enhance their growth.

The seed types are:�� Certified seed of modern variety;�� Uncertified seed of modern variety;�� Uncertified seed of farmers’ variety.

Fertilizers are divided into the following:• Mineral fertilizers, also called chemical fertilizers, artificial fertilizers and inorganic fertilizers;• Organo-mineral fertilizers obtained through blending or processing organic materials with mineral fertilizers;• Organic fertilizers are from processed vegetable or animal material and/or unprocessed mineral materials

(such as lime, rock or phosphate, for example);• Biofertilizers are products containing living or dormant micro-organisms, such as bacteria and fungi, which

provide nutrients to enhance plant growth;• Manure is fertilizer prepared from organic material;• Other organic materials to enhance plant growth refers to any plant, animal or unprocessed mineral other than

fertilizers, which are applied to the soil to correct low nutrient content or any other problem. This includes green manure, compost and sewage sludge, lime, gypsum, sawdust, crop residue and synthetic soil conditioners.

• other area not elsewhere classified includes all other areas on the holding that are not elsewhere classified.

It includes uncultivated land producing some kind of utilizable vegetable product, such as reeds or rushes

for matting and bedding for livestock, wild berries, or plants and fruit. It also includes land which could be

brought into crop production with a little more effort than that required for common cultivation practices.

Also included under this category: land used for aquaculture, land occupied by non-farm buildings, parks

and ornamental gardens, roads or lanes (except forest roads, which are included in forest), open spaces

needed for storing equipment and products, wasteland, land under water not used for aquaculture, and any

other area not reported under previous classes (such as marshlands, wetlands, etc.).


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Pesticides are substances intended to repel, kill or control pests (including human or animal disease vectors) and undesirable plant or animal species, or to control the behaviour or physiology of pests or plants during production or storage. The various pesticide types are:

�� Insecticides are substances used to kill or repel insects;�� Herbicides are substances used to destroy or inhibit the growth of plants, such as weeds;�� Fungicides are substances that destroy or inhibit the growth of fungi;�� Rodenticides are substances that kill, repel or control rodents.

Statistics on input use:• Area fertilized for each type of fertilizer and major crop type• Sources of seed inputs for each major crop type

�� self-production�� exchanges within community�� local market�� seed company�� donation

• Type of seed used for each major crop type �� certified seed of modern variety�� uncertified seed of modern variety�� uncertified seed of farmers’ variety�� other

• Use of pesticides by holders�� insecticides�� herbicides�� fungicides�� other pesticides

Agricultural credit It consists in seasonal loan, equipment loan or marketing credit sometimes used to purchase production inputs; the different types of credit are important for agricultural activities.

Agricultural credit covers several aspects:• Access to credit (convenience, presence of credit facilities, distance, etc.);• Credit sources (financial institutions, input suppliers, government, etc.);• Guarantees given to obtain credit (holder’s land, other assets);• Credit period (seasonal loan, equipment loan, marketing credit);• The reasons for seeking credit and credit allocation.

Production systems• Rainfed: the rainfed production system corresponds to temporary crops whose mode of water supply is

exclusively by rain. • Irrigated: irrigation refers to purposely providing land with water for improving pasture or crop production.

Irrigation usually implies the existence of infrastructure and equipment for applying water to crops, such as irrigation channels, pumps, sprinklers or localized watering systems.

• Cultivated wetlands and inland valley bottoms: these are lowland areas subject to seasonal flooding that are used for cropping when covered with water. Water control structures, such as canals, may be constructed to help in crop cultivation.

• Flood recession: this refers to areas along the edges of rivers or other areas under water where cultivation occurs, making use of water from receding floods. Floating rice is included as a flood recession crop. Structures may be built to retain the receding water.


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As production can be used differently, including for bioenergy, the choice of products to be grown can have major implications for the food supply. Consequently, production statistics must often be combined with other basic information to give a more global understanding of decisions concerning crop production. For example, it will therefore be useful to establish links between the following variables:• Areas planted and harvested, yield and production;• Quantities in stock at the start of harvest;• Area of cultivated land under irrigation;• Farm gate price and consumer price;• Own consumption quantities for food, forage, seeds, textiles, food oil production, bioenergy, and net foreign

trade balance or imports and exports;• Early warning information, such as precipitation, rapid appraisal of crop conditions, and vegetation indices

provided by satellite observations.

2.1.2. Livestock statistics Livestock breeding includes all the agricultural activities involved in the multiplication of often domestic and sometimes wild animals for human use.

The population includes all the animals, birds and insects kept or reared in captivity, mainly for agricultural purposes. It includes livestock (cattle, buffalo, sheep, goats, camelids and pigs), but also poultry, bees and silkworms, except aquatic animals. Domestic animals, such as cats and dogs, are excluded unless they are being raised for food or other agricultural purposes.

A vast amount of livestock data is required for various users to design policies and effective investment projects in the sector.

Production resources• Numbers of animals: the number of animals is the animal population on the holding at a specific point in time.

The animal population refers to the number of animals being raised by the holding on the date chosen as the reference date, regardless of ownership. Animals raised include those present on the holding, as well as those being grazed temporarily on communal grazing land or in transit at the time of enumeration. A holding is said to be raising an animal if it has primary responsibility for looking after the animal on a long-term basis and making day-to-day decisions about its use.

• Grazing: the study of grazing is first of all an inventory of plant species, an assessment of their potential production of forage crops and their reaction to livestock grazing, trampling, etc. It should result mainly in an estimate of its carrying capacity: how many animals can it support without deteriorating? Grazing should therefore be regarded as a machine with a long service life, capable of supplying meat, milk or work without deteriorating. We therefore need to understand its nature and mechanism, its potential and its weaknesses. How to use it and maintain it should therefore be set down in an instruction manual, available for any potential user.

One of the first steps is to characterize the grazing, i.e. describe it, estimate its production and forecast its vitality. So a good knowledge of plants and vegetation types in the area is necessary:

�� What are the species and what is their relative number (abundance)?�� What are the typical species?�� To what physiognomic vegetation type does the pasture belong (desert, subdesert steppe, grass steppe

with scrub, bushland, savanna woodland with open forest, etc?


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• Watering points: these are for sedentary, transhumant or nomadic herds. They consist of boreholes, wells and surface water. They are essential for animals belonging to both rearing systems.

Livestock systemsThe livestock system indicates the characteristics and general livestock practices of the holding.

Statistics on numbers and production will be produced according to livestock systems. The livestock systems identified by the World Programme for the Census of Agriculture 2020 are given in Box 9: Livestock systems.

box 9: LIVeSToCk SySTeMS

• The grazing system is characterized by ruminants (for example cattle, sheep, goats and camelids)

grazing mainly on grasses and other herbaceous plants, often on communal or open-access areas and

often in a mobile fashion. In this system, more than 90% of the dry matter fed to animals comes from

grazed grasses and other herbaceous plants. The following categories can be considered:

� Nomadic or totally pastoral refers to livestock raised in a situation where the agricultural holder has

no permanent place of residence and does not practise regular cultivation. Livestock moves from

place to place with the agricultural holder and his/her household, depending on the season and the

availability of feed or water. Watering points are essential for this type of stock.

� Semi-nomadic, semi-pastoral or transhumant refers to livestock raised by holders who live a

semi-nomadic life. Typically, the holder has a permanent residence to which he/she returns for

several months of the year according to seasonal factors. For semi-nomadic and semi-pastoral

systems, the holder establishes a semi-permanent home for several months or years and may

cultivate crops as a supplementary food source. Herds are moved on transhumance to assure

forage and water.

� Sedentary pastoral refers to livestock raised by holders who have a permanent residence. Livestock

raising is generally practised in combination with agriculture.

• Ranching refers to large-scale livestock activities carried out on large areas of land set aside for

extensive grazing, where livestock graze mainly on grasses and other herbaceous plants. In recent

years, the numbers of nomadic and semi-nomadic holdings are declining and the majority of holdings

within the grazing system are sedentary pastoral. Often, ranching is limited to a small number of

holdings in the non-household sector (corporations or government holdings).

• Mixed system describes the largest and the most heterogeneous livestock systems in which cropping

and livestock-rearing are linked activities. It is defined as a system in which grazing may be largely

practised but more than 10% of the dry matter fed to animals comes from crop or crop by-products or

stubble; and less than 90% of the dry matter of the animal feed is off-farm produced.

• Industrial system refers to intensive livestock-raising methods in which at least 90% of the dry matter

of the animal feed is off-farm produced. It often consists of a single species (beef cattle, pigs or poultry)

fed in feedlots or other in-house systems of feeding.


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Reference is sometimes also made to intensive and extensive systems:• Intensive livestock raising aims to increase yield by increasing the density of animals on the holding or by

using the surrounding area as little as possible (confinement). This livestock system is characterized by the use of small areas with a high population density, and the animals can be housed in buildings;

• Extensive livestock raising or extensive grazing (or ranching) is practised over vast areas, but with a low yield. It is a livestock method characterized by a low stocking density of animals per hectare.

Livestock productionLivestock products include:• meat;• milk;• wool;• skins and leather;• chickens;• eggs.

Statistics are produced on these products to help estimate revenue created by livestock rearing and are used to monitor food security.

2.1.3. Aquaculture statisticsAquaculture is the farming of aquatic organisms such as fish, crustaceans, molluscs, plants, crocodiles, alligators and amphibians according to ISIC (Rev. 4). In this context, farming refers to some intervention in the rearing process to enhance production, such as regular stocking, feeding and protection from predators. Aquaculture normally involves rearing of organisms from fry, spat or juveniles. Aquaculture may be carried out in ponds, paddy fields, lagoons, estuaries, irrigation canals or the sea, using structures such as cages or tanks. It may take place in freshwater or saltwater1.

Aquaculture (or aquiculture) is the generic term that covers all animal or plant production activities in an aquatic environment.

Aquaculture activities can be integrated with agricultural production, such as rice-cum-fish culture, or aquaculture and agriculture can use the same production resources, such as machinery and labour. For a complete picture of aquaculture activities in a country, the frame must include all aquaculture holdings at both the household and non-household levels, and not just those associated with an agricultural holding.

Aquaculture data can be collected by means of a specific survey or in the agricultural census in different ways:• by including a few sections on aquaculture production for agricultural holdings which also practise aquaculture; • or in an integrated aquaculture and agricultural census, both for agricultural holdings and aquaculture holdings,

so that data are also collected from aquaculture units which are not associated with agriculture.

1 WCA 2020 (FAO)


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The main sections2 concerning aquaculture statistics in a census are:• Presence of aquaculture on the holding;• Area of aquaculture according to type of site (land-based sites, inland waters, coastal sites);• Area of aquaculture according to type of production facility (rice-cum-fish culture, ponds, pens, cages, tanks,

floating rafts, lines, ropes, bags and stakes);• Type of water (freshwater, brackish water, saltwater);• Sources of water for aquaculture (rainfed, groundwater, rivers/canals, lakes/reservoirs, dams, estuaries/lagoons,

coves/bays/sea);• Type of aquaculture organism cultivated (freshwater fish, diadromous fish, marine fish, crustaceans, molluscs,

other aquatic animals, aquatic plants).• A distinction should be made between aquaculture and other forms of aquatic exploitation such as capture

fishing.

2.1.4. fishery statisticsFishing is the activity consisting of capturing aquatic animals (fish, but also crustaceans, cephalopods, etc.) in their natural environment (oceans, seas, water courses, ponds, lakes, pools) or collecting “wild” aquatic plants. An important characteristic of capture fishery is that the aquatic organisms exploited are a common resource, while they belong to the holding in the case of aquaculture.

A distinction should be drawn between two activities according to the sampling frames (or statistical units) used:• small-scale fishing at household level that are not agricultural holdings and from which additional data can be

collected. A module on fisheries introduced to WCA 2020 proposes items which will collect data on small-scale fishing at household level. The items covered by this module are outside the scope of agriculture as defined under ISIC (Rev. 4) and the statistical units to be taken into consideration are rural households engaged in fishery activities. The main items to be entered are:

�� Engagement of household members in fishing activity (within the household in other economic units);�� Number of household members engaged in fishing activity, by gender (engaged in fishing activity

within the household, engaged in fishing activity in other economic units);�� Number of fishers by gender employed by the household;�� Access arrangements for fishing (marine fishing, freshwater fishing, no access arrangement required for

marine fishing, no access arrangement required for freshwater fishing). Access arrangements include formal tenure (such as licences) and informal tenure, given either to individuals or to communities;

�� Main purpose of household fishing activity (own consumption, sale);�� Type of fishing vessel used by source (motorized vessel/ non-motorized vessel owned solely/jointly

with other households/loaned, no vessel used);�� Type of fishing gear used (surrounding nets, seine nets, trawls, dredges, lift nets, falling gear, gillnets

and entangling nets, traps, hooks and lines, grappling and wounding gear, harvesting machines, miscellaneous gear (including gathering by hand with simple hand implements)).

• large-scale commercial fishing, which uses fishing boats. The following are distinguished in commercial fishing:�� offshore fishing which involves mainly trawlers on the majority of continental shelves and marine fronts.

The fish is frequently packed on board; �� deep-sea fishing which is carried out on the high seas for fishing expeditions that can last several months,

on large boats; in the case of factory-type fishing vessels, the fish is processed on board and production is well known.

2 WCA 2020 (FAO)


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Production systemsIf production systems need to be defined in fishing and aquaculture, begin by determining:• aquaculture (see previous section);• traditional fishing, which can be subdivided according to fishing type: marine and continental on the one hand,

and according to the various fishing equipment used (net, cast net, dugout, etc.) on the other;• commercial or industrial fishing.

NB: Aquacultural (or aquaculture) production is the reproduction and growth in captivity of species captured in their natural environment (i.e. the difference in weight between captured fish and fish sold after growing in cages). While fish production corresponds to the quantities fished.

2.1.5. Silviculture and agroforestry statisticsSilviculture is all the techniques involved in the creation and logical use of forests while ensuring their conservation and regeneration. These activities can be undertaken on land that may or may not be part of the holding.

It relates to both natural and planted forests and forest roads, firebreaks and other small open areas. Windbreaks, live fencing and forest tree nurseries should be included.

Silviculture production refers to the production and extraction of forest goods, both wood and non-wood forest products, such as oils, leaves and bark.

Agroforestry refers to silvicultural practices that complement agricultural activities, such as by improving soil fertility, reducing soil erosion, improving watershed management or providing shade and food for livestock. Agroforestry is characterized by the existence of both ecological and economic interactions between the different components. Agroforestry includes agrosilvicultural (trees and crops), silvopastoral (trees and livestock) and agrosilvopastoral (trees, crops and livestock) systems.

Silviculture, forest utilization and agroforestry are activities practised by agricultural holdings in the household sector. While agroforestry is a sustainable agricultural management system involving purposely cultivating forest tree species and other wood-producing plants on land used for agriculture and/or livestock rearing, silviculture and forest utilization are economic activities.

The data to be collected on silviculture and forest utilization3 are the following:• Areas of woodland;• Purpose of woodland (production, soil and water conservation, improving agricultural production, social and

cultural values, etc.);• Whether agroforestry is practised;• Agroforestry system;• Agroforestry species.

3 WCA 2020 (FAO)


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2.1.6. environment statisticsThe objective of environment statistics is to provide quality information to improve knowledge of the environment, support policies and decision-making and provide information for the various users.

The System of environmental-economic accounting (SEEA) has redefined relevant environment statistics according to stocks and fluctuations in and between the environment and the economy, based on the principles of the System of national accounts (SNA). It has thus created links between environment statistics and the SNA and facilitates the analysis of relationships between the economy and the environment.

Environment statistics summarize data from different source types:• statistical surveys (for example, censuses or sample surveys of the population, housing, agriculture, enterprises,

households, employment, and various aspects of environmental management);• administrative reports by governmental and non-governmental organizations responsible for natural resources,

and by other ministries and authorities;• remote sensing and thematic mapping (for example, satellite imagery and mapping of land use and land cover,

water or forest cover);• monitoring systems (for example, field monitoring stations for water quality, air or climate pollution);• scientific research and special projects to meet national or international demand.

These different source types are generally used in combination. For example, when estimating some types of air emissions, statistical surveys are used in combination with scientific research. Whereas statistical surveys and administrative reports are currently used in all fields of statistics (economic, social and environmental) and the use of remote sensing data has become very widespread, as has the use of data from monitoring networks, scientific research and special projects.

Statistical classifications, along with less formal categorizations which relate to specific subcategories of environment statistics, have been developed by international organizations, specialist institutions, and intergovernmental organizations or NGOs such as FAO (see Land cover classification system).

The majority of the above classifications have been revised, adapted and used in the System of environmental-economic accounting (SEEA) which covers categories of activities considered to be environmental resource protection and management activities. The SEEA is used primarily to produce statistics on environmental protection and resource management expenditure.

There is a strong interaction between agricultural activities and the environment in the sense that they can transform ecosystems and physical conditions by means of irrigation, drainage and deforestation. Moreover, intensive agriculture needs the increasing use of infrastructure and machinery, chemical fertilizers, pesticides and genetically modified organisms (GMOs) and can lead to changes in physical conditions such as temperature, humidity and precipitation.

A further aspect of this interaction is that environmental conditions and quality largely determine the agricultural potential of a country. Agricultural production uses environmental resources such as land, soil, water and energy resources, whereas these resources are modified both qualitatively and quantitatively. For example, water may become polluted. Furthermore, manufactured inputs such as fertilizers, pesticides and other agrochemical products (for crops), antibiotics and hormones (for livestock) are also used in agricultural production and discarded in the environment.


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Agriculture therefore both contributes to and is profoundly influenced by climate change. It creates greenhouse gas (GHG) emissions, contributes to methane emissions (by cultivating rice and raising ruminants), releases nitrous oxide via nitrogen-based fertilizers, and emits CO2 via machinery and transport. In its turn, as a result of climate change, Agriculture faces changes in water availability, increased exposure to thermal stress, greater soil erosion by stronger winds and precipitation, and increased frequency of forest fires and droughts.

Environmental phenomena have a temporal dimension (droughts, floods, etc.) which influences measures. Variations can occur daily and, at other times, be seasonal depending on what is measured. Taking into account these temporal aspects, statistics often emphasize the maximum and minimum and are not limited to a sum or the average over a longer period.

The presence and impacts of environmental phenomena are also distributed according to a spatial dimension. The most significant spatial units for environment statistics are natural units, such as catchment areas, ecosystems, ecoareas, landscape or land cover units, or management and planning units, such as protected areas, coastal areas or hydrographic districts.

Economic and social statistics are traditionally aggregated according to administrative units. This difference can complicate the collection and analysis of environment statistics, particularly when they need to be combined with data from economic and social statistics. There is a tendency, however, to produce more georeferenced data, which would help to solve this problem.

Pertinent environment statistics are necessary for information on issues related to agriculture and the environment. The agro-environmental indicators (AEI) currently used concern greenhouse gas (GHG) emissions, wood resources, aquatic resources, crops, livestock and other uncultivated biological resources.

a. Greenhouse gasesAgro-environmental data on greenhouse gases (GHG) and ammonia emissions can help countries to assess their greenhouse gas emissions with a view to improving their national GHG inventories, thus enabling planning for effective climate change responses and facilitating access to international funding.

The following are the main data on greenhouse gases (GHG): • Agricultural practices (enteric fermentation, manure management [application to soil, deposit on pastures],

combustion [biomass, savanna, crop residue]);• Land use intensity;• Intensity of input use and input types used (organic/inorganic);• Production practices in the broad sense (tillage method, type of energy/fuel used, etc.);• Land use (forest, cultivated, grassland);• Quantity of water used on the holding for irrigation;• Agro-ecological area to which the holding belongs.

b. Wood resourcesWood resources can be natural or cultivated and are important resources for the environment in many countries. Forest activities can include the application of fertilizer and parasite control. Statistics on fertilizer and the use of pesticides in forestry are very important for assessing their impact on the environment.


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The following are the main statistics to be produced:• Wood resources

�� Wood resource stocks�� Natural growth�� Relocation �� Residue/felling �� Natural losses�� Catastrophic losses

• Amount used of: �� Fertilizers�� Pesticides

• Forest production• Heating wood production• Forest product imports• Forest product exports

c. Aquatic resourcesAquatic resources include fish, crustaceans, molluscs, aquatic mammals and other aquatic organisms. The most important economic activity related to the extraction, harvest and management of aquatic resources is fishery and aquaculture (ISIC Rev. 4).

The following are the main statistics to be produced:• Capture fish production • Aquaculture production • Fishery product imports• Fishery product exports• Aquaculture inputs• Amount used of:

�� Hormones�� Colouring agents�� Antibiotics�� Fungicides

• Aquatic resources�� Aquatic resource stocks�� Reduction in aquatic resources

d. CropsCrops refer to plants or agricultural products cultivated for consumption or other economic purposes, such as clothing or forage for livestock (ISIC Rev. 4).

The area used for crops and the yields cultivated are both important in terms of environment statistics. The area harvested is particularly important and natural fertilizers, chemical fertilizers and pesticides have a key role in the yield and quantity of crops produced, as well as in the environmental effects of agriculture.


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The following are the main statistics to be produced:• For key crops:

�� Area planted �� Area harvested �� Total production�� Organic production�� Production of genetically modified crops

• Amount used of:�� Natural fertilizers (for example manure, compost or lime) �� Chemical fertilizers�� Pesticides �� Genetically modified seeds

• Monoculture/resources - intensive agricultural systems�� Area used for production �� Total production�� Production of genetically modified crops

• Crop imports• Crop exports

e. LivestockThe rearing of livestock is associated with many environmental effects. It contributes to GHG emissions and occupies a large percentage of land (pasture and feedstock production). Pasture land and forage crops have led to widespread deforestation and the loss of biodiversity, and overgrazing results in erosion.

Despite these current environmental implications, livestock rearing nevertheless contributes to the livelihood of millions of poor around the world, providing a source of income and sometimes the only source for many. Measuring the impacts of livestock is consequently important.

The following are pertinent environment statistics on livestock rearing:• For livestock:

�� Number of live animals �� Number of animals slaughtered

• Amount used of: �� Antibiotics�� Hormones

• Livestock imports• Livestock exports


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f. Other uncultivated biological resourcesUncultivated biological natural resources include fungi, bacteria, fruit, sap and other plant resources that are harvested, as well as wild animals trapped or killed for human consumption and commercialization (ISIC Rev. 4).

The following are the pertinent environment statistics for this item:• Licence for regulated hunting and trapping of wild animals:

�� Number of permits issued annually �� Number of animals authorized per licence

• Imports of rare species• Export of rare species• Wild animals declared killed or trapped for consumption or sale• Trade in wild species raised in captivity• Non-wood forest products and other plants

2.1.7. Rural statisticsThe socioeconomic characteristics of agricultural and rural households are, in particular, the household income according to sources of income as a key measure of the well-being of rural households, necessary for strategic decision-making concerning development work to reduce poverty. The periodic data required also concern the number of households, employment, population, age, sex and educational attainment.

Rural households fall within the scope of agricultural statistics. Agricultural development is a means of reducing the poverty and hunger affecting the rural poor, in particular by improving the income of agricultural small-holders by waged employment in agriculture and rural activities off-farm or by migration. The need for rural development statistics has led to the production of the Wye Group Handbook on Rural Households’ Livelihood and Well-Being (United Nations, 2007). The data necessary to produce most of the indicators necessary for following rural development and economic growth resulting in the reduction of poverty and hunger are based on the rural household as the statistical unit.

All other rural household activities will also be taken into account:• Home crafts;• Trade;• Gold panning;• Etc.

Indicators relating to the existence of basic infrastructure are also useful for monitoring community development projects and activities. The construction, maintenance and improvement of infrastructure have been identified as important means of achieving the Millennium Development Goals (MDG) (Antonio E., 2007). The main aspects considered are:

• Access to basic infrastructure (water supply sources, lighting source, access to communications and telecommunications, etc.);

• Road types and access to transport, access to markets;• Access to basic public services (health centres, schools, etc.);• Etc.


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2.1.8. Price statisticsA distinction will be made between producer prices and consumer prices of the following:• main crops;• main livestock populations and products;• main forest products;• main fishery and aquaculture products.

There are also derived statistics such as product price indexes calculated from market price reports per product and per location and consumer price indexes estimated from monthly or seasonal prices paid by the consumer.

The prices of inputs (fertilizers, seeds, and phytosanitary products) are also very important as they have an impact on agricultural productivity.

2.2. DATA PRoDUCeRS: CeNTRALIZeD AND DeCeNTRALIZeD STATISTICAL SySTeMS

The production of agricultural statistical data is carried out by various stakeholders according to the type of national statistical system. There are generally two types of statistical systems: (i) the centralized system (in this case, statistics are produced mainly in the National Statistical Office [NSO]); and (ii) the decentralized system (in this case statistics are produced by numerous bodies such as the NSO and statistical departments of the ministries for agriculture, the environment, livestock, etc.).

Centralized systemIn the centralized system, the central statistical body is responsible for all (or a very large majority) of the areas of statistical production.

The advantages of the centralized system are:• Economies of scale;• Homogeneity of statistician units;• Easier overall management of the system;• De facto harmonization of concepts, methods and nomenclatures.

The risks of a centralized system are:• Understanding users’ needs is more difficult owing to the greater autonomy of producers in relation to users and

the creation of parallel data production systems if needs are not taken into account;• Data sharing is more difficult. Decentralized systemIt should be noted that the decentralization process includes, depending on the situation, the concepts of horizontal decentralization and vertical decentralization. For further information on these concepts, see the statistical organization manual, available at the following link: (http://unstats.un.org/unsd/publication/SeriesF/SeriesF_88f.pdf, p.12, p.148).


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Vertical decentralization involves the delegation of decision-making powers from the strategic summit to the base, within the reporting hierarchy. In this system, each of the areas of statistical production comes under the control of the central office. The role of regional or sector offices is ordinarily limited exclusively to data collection according to the requirements, norms and standards of the central office.

The advantages of a vertical decentralized system are:• Saving in time spent on data processing at a central level owing to standards defined upstream;• More appropriate relationship with respondents and users; • Improvement in cooperation between sector departments and the statistical system based on cooperative

agreements.

The risks of the vertical decentralized system are:• More difficult to manage the system if the reporting line is not well coordinated;• Risk of cumbersome administration and red tape if not coordinated;• Sometimes poor decision-making autonomy in decentralized offices;• Insufficient resources for recruiting and training all regional stakeholders uniformly. This can lead to

inconsistencies in concepts, methods and processes;• Data quality more difficult to verify if the data arrive at the central office already aggregated.

Horizontal decentralization involves delegation of decision-making powers to bodies outside the reporting hierarchy. This type of decentralization is also known as a non-hierarchical system. In this system, each area of statistical production comes under the control of the ministry or a specialist body responsible for the area.

The advantages of a horizontal decentralized system are:• Easy access to administrative sources of data;• Convergence between statisticians and decision-makers for the subsystem in question;• Better use of the data produced in the subsystem in question.

The risks of a horizontal decentralized system are:• More difficult to manage the system;• Unharmonized working programme;• Inconsistency of concepts, methods, and nomenclatures.


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2.3. SoURCeS of AGRICULTURAL STATISTICS

Agricultural statistics are produced from several sources depending on the data required and according to appropriate collection methods and tools. These are primarily:• agricultural censuses;• thematic sample surveys;• administrative sources;• remote sensing and geographic information systems (GIS) in agriculture; and,• monitoring and observation systems.

2.3.1. Agricultural censuses a. What is an agricultural census?An agricultural census is a statistical operation covering the whole or a significant part of the country, which involves collecting, processing and disseminating structural data on the agricultural sector. These data relate mainly to the size of agricultural holdings, land tenure, land use, cultivated area harvested, irrigation, livestock numbers, work force and other production inputs. In an agricultural census, the data are collected directly from agricultural holdings, but some information may also be collected from communities. An agricultural census is normally based on the collection of key structural data, i.e.:• by the comprehensive enumeration of all agricultural holdings, sometimes combined with the collection of more

detailed data, by sampling;• or entirely by sampling of a very large sample.

As regards fields covered by agriculture, the agricultural census covers not only plant and animal production activities (in some countries, livestock censuses are separate from plant censuses), but also other activities related to food and agriculture. Fisheries and forestry lie outside the scope of agricultural censuses, but the World Programme for the Census of Agriculture (WCA 2020) takes into account that some countries collect data at the same time on agricultural holdings in the sector of households that practise these activities. Generally, the field to be covered should have the following census objectives:• understanding the structure of agriculture;• having a sampling frame for specific surveys on agricultural activities.

The scope of an agricultural census as recommended by FAO can be defined as follows, based on the ISIC (Rev.4):• Group 011: Growing of non-perennial crops;• Group 012: Growing of perennial crops;• Group 013: Plant propagation;• Group 014: Animal production; • Group 015: Mixed farming.

FAO uses this classification to determine the scope of the agricultural census described in the World Programme for the Census of Agriculture 2020.

An agricultural census should theoretically cover all the agricultural activities of a country, according to the above ISIC groups. In the past, many countries have applied a minimum size criterion for inclusion in census units, or excluded some areas, such as urban centres.

An agricultural census may not cover all agricultural activities for various reasons. In planning the agricultural


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census, countries should be realistic about what can be done within available budgets and staff resources, and then do it well.

It should be recognized that, in an integrated agricultural statistics system, any exclusions from the agricultural census affect not only the results of the census, but also the agricultural surveys that are conducted based on the census. Thus, an agricultural production survey based on an agricultural census frame will not cover the census out-of-coverage units, and agricultural production estimates from the survey will be affected accordingly. This is particularly true if socioeconomic data have been obtained from marginal groups for developing policies concerning them and also concerning rare crops.

b. History of the World Programme for the Census of AgricultureThe World Programme for the Census of Agriculture 2020, which covers agricultural censuses to be carried out by countries between 2016 and 2025, is the 10th in a series started in 1930.

The 1930 and 1940 programmes were sponsored by the International Institute of Agriculture (IIA) and the seven subsequentprogrammes–in1950,1960,1970,1980,1990,2000and2010–werepromotedbyFAO,whichtookover from the IIA after its dissolution in 1946.

The world programme aimed to obtain international data relating to the same period.

The first two programmes sought to provide comprehensive, detailed agricultural statistics, particularly on production.

The 1950 Programme had a more restricted content, concentrating on the structural aspects of agriculture, such as farm size, land use and livestock numbers. Later programmes retained this focus on structural data, but gradually expanded the census content to reflect current areas of concern.

The2000Programme,whichcoveredagriculturalcensusesconductedduringtheperiod1996–2005,gavespecialemphasis to aquaculture, employment and the environment. The requirement to undertake censuses in all countries in the same year was also relaxed. For conducting their agricultural censuses, the governments of all countries have been increasingly required to make compromises and to tailor their statistical information needs to the resources available.

In developing the series of agricultural census programmes, FAO recognizes that countries are at different stages of economic and statistical development, and has encouraged countries to develop and implement an agricultural census that suits their specific situation, while reminding them of the need to collect a minimum data set for international comparison purposes.

The agricultural census should also take into account the main recommendations of the World Programme for the Census of Agriculture 2020.


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There are four methodological approaches4 :i. The classical approach is a census carried out in a single operation during which all the census information

is recorded. It includes the short-long questionnaire concept. In this approach, the short questionnaire is administered to obtain the target population, while the long questionnaire is administered only to holdings identified as being above the established threshold or to a sample of such holdings. The short questionnaire collects basic information on all households and identifies holdings by using thresholds. The long questionnaire collects more detailed information from holdings. This approach is appropriate for countries having an integrated census and survey programme or wishing to collect some additional items from small administrative units.

ii. The modular approach (introduced into the 2010 Programme) which has a good cost-effectiveness ratio for collecting a wide range of additional items according to the needs of the country and the resources available. This approach has a clearly identified core module and one or more additional modules. The information collected in the core module is used as the sampling frame for the additional module(s). A core module with a single additional module, the combination of which covers all “essential” items, is considered to be the short-long questionnaire concept and therefore corresponds to a classical approach.

iii. The use of registers and administrative records as sources of data for the census. The production of census-type statistics is faster, cheaper and more complete when large quantities of information can be obtained from administrative sources. When registers cannot provide all the essential items, a combined approach using data from administrative records and surveys/censuses is a possible option.

iv. The integrated census and survey programme aims to reinforce the integration of the agricultural census in a multi-year census/survey programme using the Agriculture and Rural Integrated Survey concept (AGRIS). This approach produces a wide range of data on various dimensions of agricultural holdings with a better cost-effectiveness ratio. It proposes to reduce the burden of conducting censuses by implementing a census with a fairly light core module while scheduling the collection of thematic data over a ten-year period. This will contribute to a more regular flow of data, which would be more in line with the limited capacities currently in place for the production and use of statistics in many countries. It should also facilitate the funding of the census and survey programme by spreading the total cost over ten years.

4 WCA 2020 (FAO)


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fIGURe 4: THe INTeGRATeD SURVey fRAMewoRk5

In addition to these options for conducting agricultural censuses, the World Programme 2020 recommends:

• using recent progress in technology and emphasizing archiving, dissemination and publication in a machine-readable format. There is an increased use of information technologies in data collection and processing, but also in data dissemination. Technological progress must be used in census operations such as computer-assisted personal interviewing (CAPI), which helps to improve data quality and reduces the time lag between data collection and data analysis. Similarly, the use of interactive products and on-line data (tables, graphs, maps), and access to anonymized microdata has brought new opportunities for census dissemination. User-friendly dissemination tools support informed decision-making, release the analytical creativity of users and elevate the value of census data for agricultural policy purposes for research and enterprises, in addition to the usual statistical uses.

• integration with general population censuses. This recommendation by the 2010 Programme is retained in the 2020 Programme. It is relevant above all for countries where there is a close relationship between households and agricultural holdings as the majority of agricultural activities come within the household sector, as is the case in many developing countries. However, as the population census covers only households and not enterprises, integration of the agricultural census and the population and housing census can only apply to agricultural holdings in the household sector. Agricultural holdings in the non-household sector should be treated separately.

5 Global strategy (FAO, World Bank)-Sept. 2010


integration of the agricultural census in a multi-year census/survey programme using the Agriculture and Rural Integrated Survey concept (AGRIS). This approach produces a wide range of data on various dimensions of agricultural holdings with a better cost-effectiveness ratio. It proposes to reduce the burden of conducting censuses by implementing a census with a fairly light core module while scheduling the collection of thematic data over a ten-year period. This will contribute to a more regular flow of data, which would be more in line with the limited capacities currently in place for the production and use of statistics in many countries. It should also facilitate the funding of the census and survey programme by spreading the total cost over ten years.

Figure 4: The Integrated Survey framework17

In addition to these options for conducting agricultural censuses, the World Programme 2020 recommends:

• using recent progress in technology and emphasizing archiving, dissemination and publication in a machine-readable format. There is an increased use of information technologies in data collection and processing, but also in data dissemination. Technological progress must be used in census operations such as computer-assisted personal interviewing (CAPI), which helps to improve data quality and reduces the time lag between data collection and data analysis. Similarly, the use of interactive products and on-line data (tables, graphs, maps), and access to anonymized microdata has brought new opportunities for census dissemination. User-friendly dissemination tools

17 Global strategy (FAO, World Bank)-Sept. 2010


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c. Objectives, scope and contentAgricultural censuses are primarily focused on data on the basic organizational structure of agricultural holdings, such as farm size, land tenure, land use, crop areas, livestock numbers, work force, use of machinery and other production inputs. Data that change from year to year, such as production or agricultural price data, are not generally included.

In brief, the census objective is the study of rural activities, highlighting the following for each one:• Its geographical distribution: in which areas is it practised?• Its practise by populations: who practises it?• The resources available to the population for its practise: how is it practised?

The census will then serve, among other purposes, as a basis for all the studies in these sectors.

The census can extend to a determination of production in kind or in value or even the revenue account of the subsector.

The agricultural census does not provide current statistics on production, except for the reference year of the census. It nevertheless has a key role in compiling national accounts. It provides information for defining the structural components of the national accounts and for economic accounts of agriculture.

The statistical unit, the basic unit for which data are collected, is the agricultural holding.

The information collected in the agricultural census relates to specific items. The main items (see WCA 2020, chapter 7) are divided into three groups:

i. Items relating to the agricultural holding are grouped by topic:�� Theme 1 - Identification and general characteristics�� Theme 2 - Land�� Theme 3 – Irrigation �� Theme 4 – Crops�� Theme 5 – Livestock�� Theme 6 – Agricultural practices�� Theme 7 – Services for agriculture �� Theme 8 – Demographic and social characteristics�� Theme 9 – Work on the holding�� Theme 10 – Intrahousehold distribution of managerial decisions and ownership on the holding�� Theme 11 – Household food security�� Theme 12 – Aquaculture�� Theme 13 – Forestry �� Theme 14 – Fisheries�� Theme 15 – Environment / greenhouse gas (GHG) emissions


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ii. Frame itemsFrame items are directly relevant for constructing the sampling frame for additional modules for countries using the modular approach and for subsequent surveys. There are 15, six of which are also essential items:

�� 0101 Identification and location of agricultural holding�� 0107 Main purpose of production of the holding�� 0108 Other economic activities of the household�� 0201 Total area of holding�� 0301 Use of irrigation on the holding: fully and partially controlled irrigation�� 0401 Types of temporary crops on the holding�� 0405 Types of permanent crops present on the holding and whether in compact plantations�� 0413 Presence of nurseries�� 0415 Presence of cropped land under protective cover�� 0502 Number of animals�� 0602 Use of genetically modified (GM) seeds�� 1201 Presence of aquaculture on the holding�� 1301 Presence of woodland on the holding�� 1304 Whether agroforestry is practised?�� 1401 Engagement of household members in fishing activity6

iii. Supplementary data items to be collected at community levelIn addition to items on the holding, WCA2020 also proposes data items to be collected at community level. These items help in decentralized planning and planning of targeted local area development programmes. They are:

�� spatial distribution;�� socioeconomic conditions;�� infrastructure and community services;�� development programmes.

d. Institutional organizationAs defined, the agricultural census is a huge operation involving data collection from agricultural holdings, covering all agricultural and connected activities.

Several bodies including the National Statistics Office have a key role in it. To take into account the statistical nature of the operation and its scope, coordination within the national statistical system is necessary to ensure it is successful.

Setting up a steering committee, on which the NSO will sit, is generally advised to manage activities.

The steering committee is responsible in particular for the following:1. contacting data users and making sure that the census meets real needs;2. periodically examining the progress of census operations and reporting necessary initiatives and measures where

applicable;3. approving activities to be undertaken during the various phases of the census;4. monitoring the implementation of the census as a whole;5. approving the publication of the census results;6. ensuring that the timetable for implementing the census is strictly complied with.

6 Fishery is outside the scope of the agricultural census, but item 1401 is included in the frame items as it is suitable for countries considering a wider scope.


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2.3.2. Agricultural sample surveysAgricultural surveys can be carried out on different themes covering the agricultural sector in its broadest sense. This type of survey estimates the values of variables in a sample of units selected from the total population of the field studied. The sample can be random (it is then called probability sampling) or non-random (non-probability sampling or empirical sampling). The agricultural census7 is the specific case of a survey in which the values of the variables are obtained for all the units in the population (holdings).

To guarantee that the data produced are good quality, probability sampling should be given preference. To define the probability model on which estimates of the variables are based and to calculate their statistical accuracy, the following should be specified:• Sampling frame (sampled population);• Random selection procedure;• Survey variables;• Estimators for each survey variable;• Variance of the estimators which gives the accuracy of the estimates.

Without going into detail about sampling, the concepts are addressed of sampling frame, master sample and survey programming in the context of agricultural surveys. Some types of current surveys in the agricultural field are also discussed.

To meet the information needs of the agricultural sector, various surveys (thematic surveys) are conducted in conjunction with the statistics to be produced (2.1). The following subsections cover these various surveys.

a. Surveys on crops (or crop production)The main requirement of crops surveys is to obtain annual or seasonal data on the production of key crops, which can be done by a single survey on crop production or, more commonly, by a series of surveys.

For example, a country may decide to carry out a semi-annual survey on rice production, along with annual surveys on cassava and coffee production, planning each survey so that it coincides with the harvest of the crop in question. A specific crop production survey could consist of several elements, for example an interview with producers to gather information on items such as the planted area and harvested area, varieties, work force and inputs used, as well as a component based on crop cutting to estimate yield according to reference parcels.

It may sometimes be necessary to carry out other types of crop surveys on agricultural holdings:• Surveys on post-harvest losses; • Survey on food stocks of holdings;• Survey on commercialization of agricultural production;• Specific survey on a given crop.

7 FAO uses the term agricultural census (or census of agriculture) for a survey which is conducted based on a complete enumeration or sam-pling whose primary objective is to determine the agricultural sturcture of the country


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An agricultural survey of production is a solution to the problem of determining the production of all agricultural holdings with the aim of calculating national production. It involves estimating total production from that of a sample of agricultural holdings.

i. Determination of production To determine the production of a given area (region, district, country, etc.) from a sample of holdings,

either the production of the sampled holdings can be aggregated, or the area and yield of the location can be calculated and its production determined from that.

i. aggregation of the production of the sampled holdings: the total production of each sampled agricultural holding is collected directly;

ii. determination of area and yield: �� area: the area in question will be obtained by aggregation of the areas of the sampled holdings;�� yield: the yield of a location is an average yield obtained from the total cultivated area of the location.

An average yield is determined from yields obtained from crop cutting experiments on plots. Crop cutting experiments are generally carried out in uniform size squares established in parcels on holdings (called yield grids).

ii. Direct determination of the production of sampled holdings: Production is evaluated either by direct measurement or by declaration. The following approaches are

therefore recommended:i. weighing the whole of the holder’s harvest;ii. the holder declares the holding’s production in local units of measurement (LUM) in the absence of units

of weight measurement;iii. the holder declares the holding’s production in units of weight (kg, tonnes, etc.).

iii. Weighing the holder’s harvest The drawback of this method is its impracticality. It is easy to imagine the complexity of a harvest weighing

operation on a random sample of holdings from the point of view of both the amount of resources to be used and the inconvenience that this can cause to respondents. The size of the task may require a drastic reduction in the number of holdings to be surveyed, which will not help improve the quality of estimates.

iv. The holder declares the harvest This method lacks reliability owing to a lack of accuracy in the local units of measurement used for the

harvest (baskets, sacks, bunches, etc). This lack of accuracy is due to the fact there is a difference between local units of measurement and kilograms, both between holdings and on the same holding. Furthermore, the great diversity of such units does not facilitate calibration.

Despite the multiple nature of these measurements and, had it not been for the lack of accuracy in weight characterizing them, the agricultural survey would be sufficiently reduced by simply carrying out an interview to determine production.

v. The holder gives the weight of the harvest This method can only be used in cases where all holdings customarily routinely weigh their harvest. This is

clearly far from the case in rural areas of developing countries.

vi. Determining area There are several collection methods for measuring areas. The agricultural survey generally uses the seed

quantity and rectangular coordinate methods.


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vii. Measuring area by seed quantity: In a given locality (country, region) the average quantity of seed used per hectare is determined initially.

Surveyed holdings are then asked for the total quantity of seed used to determine the area seeded. The main problem with this method is that the holder is not always able to give the weights of seeds used, and will probably use local units. The conversion of local units into weight measurements involves a further distortion (undoubtedly greater) in addition to that of the method itself and the eventual result will be extremely skewed estimates of area. The reliability of the farmer’s declaration is related more to practices in the survey locality.

viii. Measuring the area of parcels Compass method (less commonly used): The record sheet of parcel measurements is used to determine the

area of the parcel using the following data recorded for each side of the parcel:i. the angle with the north-south axis;ii. its length.

This information is entered on a computer using a standard FAO-designed program. The area is then estimated with a closure error which helps to validate the result (if it is less than 5%) or otherwise to restart the measurement.

GPS: The area is calculated from the geographical coordinates of the points in the parcel. In practical terms, start from a fixed point on the circumference of the parcel to be measured and, with the GPS held horizontally in the palm of your hand, go around the parcel keeping as far as possible to its outer limits and avoiding parts covered by foliage until you are back at the fixed starting point. Stop the circumference (by pressing the STOP button) and the area is automatically displayed on the GPS screen with the margin of error. (See a detailed description of this method in section 2.5 Data collection).

N. B. These measurements are used to calculate only areas of land portions. As regards estimating the areas of associated crops, see section 3.2. Calculating areas and yields of associated crops.

ix. Determining yield The factors on which yield depends are related to conditions, which can be divided into two groups:

i. Conditions dictated by nature in relation to the geographical position of the crop;ii. Conditions dictated by the production system (type of association, maintenance, etc.).

In a fairly small geographical area, a village for example, the conditions dictated by the production system are essentially those which determine yield, as conditions dictated by nature are almost identical. The parcel, which is the production system unit, can therefore be validly used as a sampling unit for determining yields. Variations in yield may, however, be observed between different parts of the same parcel. These variations are greater the larger the size of the parcel.

Average yield is determined primary unit by primary unit (e.g. village) from yield grids placed at random on parcels called yield squares for convenience. The number of yield squares per primary unit will depend exclusively on the number of parcels (selection of parcels with equal probability) or the number of parcels and their area (selection of parcels with probability in proportion to size). The ideal number of yield squares to be laid will be obtained by successive analysis of variance (from one season to the next) of previously determined samples.


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x. Drawing of yield parcels Yield parcels are selected by primary unit. It may, however, be decided for some less important crops to

determine a yield not by primary unit, but by region (or any other geographical entity), or even yield at a national level, and in this case selection will be done by region or at national level.

xi. Laying yield squares The laying of yield squares in a parcel must follow a random procedure so that they are not systematically

laid in specific places in the parcel. Several methods can be used to achieve this.

N. B. To estimate the yields of associated crops, refer to section 3.2. Calculating areas and yields of associated crops.

b. Surveys on livestock production To produce quality statistics on livestock and livestock products, periodic surveys of livestock production are generally required. A series of specific surveys is usually necessary.

For example, quarterly surveys of holdings raising cattle can provide data on cow milk production, while annual surveys of holdings raising sheep can provide information on wool production. The data from these surveys are generallysupplementedbyinformationfromothersources–cattlemarketingboards,slaughterhouses,meatprocessingplants,butcheriesordairyestablishments–toobtainafullpictureoflivestockproduction.

Periodic surveys of animal feeding may be necessary to determine the quantity and composition of feed given to different types of livestock, along with seasonal variations in the availability of this feed. Surveys can also be conducted to estimate the production of forage crops, often based on crop cutting to measure the nutrient value of these crops. Information on stocking rates are often also collected to assess forage use.

The other types of detailed surveys of livestock are surveys on herd structure, particularly on specific animal breeds, and surveys on sales value by product type.

The key items for cattle, sheep, pigs, goats and poultry are those which must be compulsorily collected in surveys of livestock production. These species are major sources of the food supply and agricultural revenue. The consumption of products from these species increases as countries develop and revenue increases. The increasing demand for livestock products leads directly to the increasing use of plant products for animal feed, and can cause situations in which livestock production competes with human consumption of food, even though forage is an input of animal feed production. Livestock are also sources of methane emission, water pollution and disease risk. All these factors can be affected by policy decisions. The core data required for livestock include in particular the following: • Annual numbers and animals born;• Production of meat, milk, eggs and wool, etc. along with net trade flows or imports and exports;• Production systems.


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i. Livestock parameters Livestock production is estimated by applying livestock parameters (growth rate, offtake rate, milking rate,

etc.) to cattle populations determined during stock census operations.

To carry out a stock census, the survey methodology of monitoring livestock parameters is based on monitoring a sample of herds from which data are collected over 12 months to calculate parameters such as:• Production parameters (overall mortality rate, mortality rate by category, overall loss and theft rate, loss

and theft rate by category, number of females per breeding male, milking rate);• Holding parameters (growth rate, offtake rate);• Reproduction parameters (fertility rate, fecundity rate, abortion rate, sex-ratio of calves, age at first

calving).

ii. Production of pasture crops To assess the production of pasture crops, a herbage biomass reporting system can be used. The aim of this

operation is to assess the forage resources annually available.

The methodology used consists of the following:• conducting ten-day monitoring of the pastoral countryside;• applying the double sampling method to assess forage production on the ground in a pastoral area;• setting up a plate meter in pastoral enclaves in the agricultural and agropastoral area;• assessing crop residue production;• analysing the correlation between data on the ground and satellite data;• determining the end of season forage balance.

c. Surveys of aquaculture and fisheryThe census of agriculture takes into account only aquaculture activities practised in association with agriculture. If aquaculture has a major role in a country, an aquaculture census should be combined with an agricultural census to provide structural data on the production plant type, water type, origin of the water used, type of organism reared and the machinery used for aquaculture. This census can be used as a basis for subsequent surveys on this sector of activity. Periodic surveys of aquaculturists might be necessary to obtain data on aquaculture production.

Aquaculture and fishery contribute significantly to the food supply. Aquaculture production involves the use of land and water resources. Fishery provides for the livelihood of inland smallholdings. The core data required for this sector are the following:• For aquaculture: Areas used, production, imports and exports;• For fishery: The quantities captured, landed and rejected, number of fishing days, quantities processed for

food and non-food uses, imports and exports. The aim of fishery surveys is to collect data to estimate fishery production, fishing effort, catch by unit effort, and efficiency of fishing gear, and to know the average weight of catch by species and the monetary value of catches.

The frame for conducting surveys on fishery is the census of fishery holdings or fishers and their characteristics. It mainly involves traditional fishery, as the data on industrial fishery are adequately organized.


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The scope of data collection concerns all fisheries and all aquaculture sites. The information sought can also be classified by fishery type, itself classified by ecologically homogeneous areas, for which the following fields can be mentioned:• rivers and their tributaries;• lakes;• ponds and artificial impoundments.

For example, the method used by Burkina Faso in the 2006 general census of agriculture.

d. Surveys on forestry and agroforestryForestry is a major form of land use, it generates revenue and it has a significant role in understanding the forces that affect climate change. Data that can be collected in surveys include the following:• Woodland areas and forests, quantities removed and prices for land connected to agricultural holdings;• Woodland areas and forests, quantities cut and prices of corresponding products from non-agricultural holdings

and their respective uses.

e. Surveys on households and rural areasThe socioeconomic characteristics of agricultural and rural households are in particular the household income according to sources of income as a key measure of the well-being of rural households, necessary for strategic decision-making concerning development work to reduce poverty. The periodic data required also concern the number of households, employment, population, age, gender and educational attainment.

All other activities of rural households, such as home crafts, trade, gold panning and others, will also be taken into account. Access to services and infrastructure may also be measured to give a more general picture of the level of rural development.

Surveys of households also help to collect information on food consumption, experience of food insecurity and food waste, important variables in the study of food security.

THe GeNeRAL PRoCeDURe USeD foUR STePS:

1. Firstly, the “village” approach was primarily used for data collection. This approach has the advantage

of better identifying professional fishers, their age, their nationality, various movements involved

in a fishing expedition, number of employees with whom they work, number of days spent fishing

per week, various types of fishing gear, vessels, other activities carried out besides fishing, and

educational attainment;

2. The surveyors secondly conducted a systematic census in each village of all species most fished from

the water surfaces and courses attached to the village and the prices applied per kg for the various

species;

3. Thirdly, in areas where these sites were observed, a census was conducted of the site and its

characteristics, orientation and annual production recorded;

4. Finally a fourth record was started to count fish traders and fish processors.


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f. Environment-related surveysThe United Nations Statistics Division, via the department in charge of environment statistics, takes part in the coordination of environmental data collection. The most recent edition of the Framework for the Development of Environment Statistics issued by this department was produced in 2013 (FDES 2013).

This framework defines the scope of environment statistics and provides an organizational structure to guide their collection and compilation in order to summarize data from different sources, covering aspects of the environment which are relevant for analysis, policy and decision-making.

Environmental information includes quantitative and qualitative facts describing the state of the environment and changes. Quantitative information on the environment is generally in the form of data, statistics and indicators, and is generally disseminated through databases, spreadsheets and yearbooks. Qualitative environment information consists of descriptions (texts and images) of the environment or its constituent parts which cannot be appropriately represented by precise quantitative descriptors.

Environmental data contain large quantities of observations and measurements of the environment. These data can be collected or compiled in specialized modules from an existing collection operation (sample surveys or censuses) or may come from administrative registers, geographical databases, inventories, sentinel networks, thematic mapping, remote sensing, scientific research and field studies.

Not all environment data are used to produce environment statistics. The FDES provides a framework which identifies environment data considered pertinent. Policy frameworks such as the Millennium Development Goals (MDG), Sustainable Development Goals (SDG), and the Pressure-State-Response-Impact model are generally used to identify and structure indicators.

The main UN organizations that supervise data collection on the environment are the United Nations Statistics Division (UNSD), the Food and Agriculture Organization of the United Nations (FAO) and the United Nations Climate Change Fund (UNCCF).

To avoid duplication of roles, the United Nations Division has focused since 2006 on the collection of data relating to specific fields such as water and waste. Data relating to “air quality” and “land use” are collected exclusively by the UNCCF and the UNSD and FAO. A memorandum of understanding relating to data sharing and transfer between the UNSD and these two institutions has been developed for this purpose.

The fields covered by environment statistics are covered in Annex 4. There is also a focus on the minimum set of core statistics on the environment, comprising thirty indicators (see FDES 2013, p 200).

g. Surveys on holding management, agricultural inputs and costs of productionSurveys of holding management provide detailed data on all aspects of decision-making on holdings. Collected data generally concern investments, assets, and organization and allocation of resources. Holding management surveys are often combined with costs of production surveys. Costs of production surveys collect information on agricultural production inputs and analyse the cost structure of activities.


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The main agricultural production inputs are work force, fertilizers, seeds by type, insecticides and pesticides, water, energy and capital stocks. Inputs are considered essential items as, combined with production data, they provide key measures of agricultural productivity for monitoring and assessing steps to reduce poverty and hunger. The necessary core data are the following:• Quantities of fertilizers, seeds, insecticides and pesticides used and their costs;• Consumption and costs of water and energy use;• Capital stocks such as equipment according to its use (tillage, harvest, etc.) and market value;• Number of persons old enough to work by gender;• Number of workers employed by agricultural holders and salaries paid;• Employment and work schedule of household members on the agricultural holding.

h. Price surveysPrice surveys concern:• Prices of crop products;• Prices of inputs for crop production;• Prices of livestock products;• Prices of inputs for animal production;• Prices of fishery products;• Prices of fishery inputs;• Prices of gathered goods.

For collecting farmgate prices, the collection sites are the primary markets or rural markets. These prices can be regarded as the prices paid to producers. Producers offer their produce on the rural market (usually weekly or twice-weekly). Transport and storage costs are consequently not included in the price paid to the producer.

As regards the collection method, the first stage sampling frame is a comprehensive list of the country’s rural markets. The number of rural markets to be included depends mainly on budget constraints. Furthermore, selection is by judgement sampling and the criteria for choosing the markets in the sample are the following:• Market accessibility;• Products present on the market;• Market frequency (weekly, twice weekly);• Market duration.

NB: Only the major crops of countries are included. The number of products to be monitored usually ranges from 10 to 15.

Once the market has been chosen, the prices for each target crop are recorded from three sellers at a distance from one another. They are collected when the market is in full swing. The data gathered are: prices and quantities or volumes for liquids in conventional units (kilograms or litres). If products are sold in local units of measurement (ear for maize and millet-sorghum, bundle for rice, millet, etc.), it is advisable to weigh them or convert them to conventional units.

To facilitate analysis after data collection, the statistician should use a conversion table from local units to conventional units. The conversion table should be produced from a previous survey to establish equivalence.


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i. Work schedule surveyWork schedule surveys have full national cover in principle, but a work schedule survey specific to agricultural holdings may be useful in countries where it is difficult to measure the contribution of household members to work on the holding. A work schedule survey could help to combine data on the time that each household member spends on activities such as land preparation, seedlings, crop maintenance or harvesting, post-harvest activities, feeding livestock and providing support services to agricultural workers. These surveys are particularly useful for measuring the role of women in agriculture.

j. Agricultural sampling frames and master sample

i. Agricultural sampling frames and master sampleThe sampling frame is an essential item for carrying out sampling. It is a list of all the units that can be surveyed because they belong to the population about which inferences will be made.

A major innovation introduced by WCA 2010 recommends a complete enumeration of a very limited number of data in a core module, with the use of sampling to collect more detailed data in specific, pertinent modules. Sampling is a key component of this new approach, in which the availability of an effective sampling frame becomes crucial.

WCA 2010 recommends including questions to identify agricultural households in population and housing censuses. The household frame resulting from the population and housing census can always be used as a starting point for the list frame of components of households in the agricultural census.

Nepal: Nepal is a good example of constructing a sampling frame for agricultural censuses: “The sample design offers two frames: a list frame for large private holdings and state-run farms and an area frame for all other holdings related to households. The list frame, which was only a part of all the private holdings and state-run farms, will be completely enumerated. For smallholder farms, the sampling procedure will choose districts as strata. A sample of enumeration areas (EA) is selected in each stratum as primary sampling units (PSU). A sample of agricultural households is then selected from each EA sampled as secondary sampling units (SSU).”

box 10: IMPoRTANCe of THe SAMPLING fRAMe

Carrying out field work in the process of a survey requires access to the statistical units. The sampling

frame is the means of access to the target population. Constructing a reliable sampling frame is therefore

one of the major problems to be solved when deciding to conduct a sample survey, as it is on this that the

sampling system, i.e. the procedures involved in sample selection and estimating population parameters,

will depend.

So be prepared, if necessary, to arrange financial resources to develop or update it.


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In relation to the sample design to be used, the sampling frame or set of primary sampling units (PSU) must have the following characteristics:• the units as a whole cover the study area completely without overlapping;• the units are well defined;• the PSU are small so as to limit the listing phase and allow the selection of secondary sampling units, but at the

same time large enough so that the number of secondary units to be surveyed can always be found;• the units are fairly homogeneous in size;• the list constituting the sampling frame must contain data on each unit to allow stratification of the sampling

frame;• for the same statistical unit, the size of the units and of the study area are known, to allow extrapolations.

The qualities of a good sampling frame are the following:• Quality 1: Units that are unambiguously identifiable• Quality 2: Completeness• Quality 3: Containing no repetitions (no duplicates)• Quality 4: Up-to-date (or at least not too old)

A sampling frame can therefore come only from a comprehensive listing of units in the study area (comprehensive enumeration of holdings, population, households, dwellings, land registers, satellite images, etc.).

TAbLe 2: PRobLeMS wITH SAMPLING fRAMeS AND THeIR SoLUTIoNS

Problem Solution

Presence of non-target individuals (not belonging to the target population or the population to be sampled)

Do not take problems into account and disregard any impact

Duplicate or repeated units in the list Correct the list by deleting repeated items

Missing unitsIdentify missing units via any sources in which they have been recorded and update the sampling frame

There are three types of sampling frame:• List sampling frames (holdings, households, villages or localities, etc.):

A list sampling frame is defined as a physical list containing all the population units from which information is to be collected. In the context of agricultural statistics, the list sampling frames currently used are those of holdings or agricultural holdings, rural households, enumeration area or small administrative areas such as villages and associated land. They are generally created from information from recent agricultural or population censuses or from administrative sources.

• Area sampling frames (enumeration areas of the population census, other portions of land, etc.): an area sampling frame is defined based on the division of the country’s terrestrial physical space into sampling units. The requirements of the Global Strategy encourage the increased use of methodologies using area sampling frames. Satellite imagery helps in the stratification of land for area sampling frames. In multistage sampling, the primary sampling units are often areas (enumeration area, enumeration section, etc.) or administrative units (villages). The list of these units is incorrectly called an area sampling frame as it represents portions of land. Area frames normally derive from segmentation of land in the area concerned (country, region, etc.).In the area sample, the sampling unit and the reference unit are different. The sampling unit is generally a small area (it could be a point) called a segment. The reference unit can be a holding or a parcel in agricultural surveys and a household in household surveys. Several procedures can be considered for data collection. Some survey


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variables, such as the cropping area, crop yields, soil degradation or water pollution, can be observed directly on the ground using appropriate instruments. For example, a portable GPS or digital computerized instruments along with maps and satellite imagery can help measure the cropping area.The area sampling frame is used to collect a wide range of agricultural data:

�� Crop areas;�� Livestock herds (e.g. transect counts of cattle);�� Technical and economic data (great geographical and economic diversity).

• Multistage sampling frames: If a single sampling frame (list or area type) is not sufficient to cover the whole survey field separately, a combination of two or more sample designs can be used to cover the whole field and exploit the strengths of each of the different frames used. The Global Strategy (2015)8 provides an overview of multistage sampling frames which can be used where there is a great variation in size and types of agricultural holding with a subset of large commercial holdings. The list of these holdings can be stratified by size and type, and the area sampling frame guarantees comprehensive cover of the study field, the representativeness of smallholdings and subsistence farms.

8 http://gsars.org/wp-content/uploads/2016/06/Handbook-on-MSF-FR-WEBFILE-280616.pdf


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TAbLe 3: CHARACTeRISTICS of SAMPLING fRAMeSTy

pes

of

sam

plin

g f

ram

e

frame Comprehensive Reliable Up-to-dateAdditional

information

List

sam

plin

g fr

ame

Cens

us

Popu

latio

n ce

nsus List of enumeration

areas (EA)Yes Yes Yes

Information sometimes available on characteristics of EA

Comprehensive list of enumerated households

A few households may be missed due to non-response or not being updated

NoCan rapidly become obsolete

Unspecified information on agricultural sector

Agric

ultu

ral c

ensu

s List of enumeration areas (EA)

Yes Yes YesInformation sometimes available on characteristics of EA

Agricultural holdings

A few holdings may be missed due to non-responses or not being updated

No owing to its rapid obsolescence

Can rapidly become obsolete

Information available for a survey in the agricultural sector

Administrative records

Rarely complete as individuals or holdings are not obliged to register. Potentially incomplete depending on legislation

May contain significant omissions and duplicates

Can rapidly become obsolete

Information available in the case of agricultural records which can be useful when designing agricultural surveys

Are

a fr

ame

Cens

uses Po

pula

tion

cens

us The comprehensive set of enumeration areas (EA) with information on their form of maps (digitization of EA, geolocation, etc.)

Yes Yes Yes

Remote sensing can be a source of additional data

If necessary, information relating to EA, their location, etc.

Agric

ultu

ral c

ensu

s The comprehensive set of enumeration areas (EA) with information on their form (digitization of EA, geolocation, etc.) with, if necessary, information relating to EA, their location, etc.

Yes Yes Yes

Remote sensing can be a source of additional data

If necessary, information relating to EA, their location, etc.

Area frame Yes Yes Yes

Little informationRemote sensing can be a source of additional data

Mul

tiple

fr

ame Combined use of two or more sampling

frames Combination of the advantages of several types of frame to exploit the strengths and weaknesses of each

Source: Authors, based on information from “Handbook on MSF” (Global Strategy, 2015).


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The above table shows that sampling frames based on land distribution or units (frames derived from censuses or area frames) are comprehensive.

ii. Master sampleA final approach involves using multiple sampling frames (Global Strategy, 2015) to create a master sample that combines the advantages of area surveys and those based on lists.

During specific master sample training, the development methodology can be described, analysed and reinforced with practical examples in conjunction with area sample designs and lists of holdings and using information from remote sensing integrated in a geographic information system (GIS).9

Several tools can be used to construct master samples:• Remote sensing can be used to construct master samples. Firstly this helps to obtain conventional maps and

thematic maps (land use) to help the initial stratification of the territory; secondly it helps to provide geographic information to aid the delineation of sampling units and to improve estimates. Satellite imagery can also be particularly useful for preparing the equipment needed for field work;

• GPS/GLONASS

k. Planning agricultural surveysPlanning agricultural surveys at country level over a 10-year cycle within the framework of a Strategic plan for agricultural and rural statistics (SPARS) results in the development and production of reliable agricultural and rural statistics that are up to date, consistent and integrated. It is a major asset for all countries that have an SPARS.

A large-scale agricultural survey programme (with the agricultural holding as the basic statistical unit) comprises periodic surveys of agricultural production and detailed surveys of elements such as costs of production and use of time. Surveys of food and agriculture for which the basic statistical unit is not the agricultural holding (surveys of food consumption, income and expenditure, the active rural population, household food security, etc.) are a major source of agricultural data. They generally cover all rural households. Finally, some surveys related to agriculture cover completely different types of units (for example a survey on agricultural service providers).

A long-term programme of censuses and surveys is strongly recommended. “Carrying out a series of surveys on a continual basis has several advantages for the development of a global agricultural statistics system. For example it guarantees a continuous work flow for field collection teams, which will help them improve their skills. These activities must be planned in advance as the organization of a specific survey depends on the availability of funds. There are arguments in favour of conducting modular surveys with a core module and a set of additional

9 Global strategy for improving agricultural and rural statistics (FAO Report, September 2010)

box 11: MASTeR SAMPLe9

The underlying principle is that the master sample becomes the source for all survey samples on

agricultural holdings, agricultural households and non-agricultural rural households. This means that the

samples must be designed so that they can allow a cross-sectional analysis of data across surveys. Once

the master sample has been set up, the various institutions of the national statistical system will be able

to access it for survey purposes with guiding principles stating that the results are available for analysis

through the other data collections conducted.


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modules on a rotating basis (for example on expenditure, employment and agriculture to meet national accounts needs). It is consequently essential to draw up a precise list of census and surveys taking priorities into account and proposing a calendar in line with national policy needs to create a platform which is accepted by all stakeholders in the agricultural statistics system” (Global Strategy, 2014).

Three examples:

TAbLe 4: SURVey CALeNDAR (TANZANIA)10

Survey name frequencyyear

2014/15 2015/16 2016/17 2017/18 2018/19

Population and housing survey

Every 10 years

Agricultural sample census

Every 10 years X

Annual sample survey of agriculture

Annual Pilot Deployment XExpanded module

National panel – LSMS-ISA survey

Every 2 years X X X

Household budget survey

Every 5 years X

Questionnaire on quarterly production NBS

Quarterly or annual X X X X X

Crop forecasting and early warning

Twice weekly X X X X X

Routine data collection for price monitoring

Wholesale (3 times weekly)-Retail sale (monthly)-Livestock (weekly)

X X X X X

Routine collection for commercial data

X X X X X

Routine data collection on fishery

X X X X X

10 STRATEGIC PLANS FOR AGRICULTURAL AND RURAL STATISTICS (Global Strategy, June 2014)


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• Réunion: The agricultural statistics service of Réunion periodically conducts major statistical operations such as the census of agriculture (2000; 2010). This service carries out more regular surveys of:

�� farmers, on the structure of holdings, plant and livestock production, cropping practices, accounting results, the environment, etc.;

�� agricultural supply enterprises (input prices); �� locations for the sale of agricultural products: wholesale markets, farmers’ markets, supermarkets

(prices of agricultural foodstuffs);�� local communities, of the use of local products in mass catering;�� use of space by field surveys throughout the island, to support a GIS.

• Agriculture Integrated Survey (AGRIS)

The Agriculture Integrated Survey (AGRIS) proposes a multi-year programme of modular surveys of agricultural holdings. The AGRIS methodology has been developed by FAO within the framework of the Global strategy for improving agricultural and rural statistics. AGRIS aims to complement other initiatives such as the World Bank’s LSMS-ISA. The implementation of the AGRIS has just started with partner countries.

With the census of agriculture which it complements, a multipurpose information system on agricultural markets and the appropriate use of remote sensing and administrative data, AGRIS is a cornerstone of the implementation of a rural information system.

AGRIS is synchronized with the census of agriculture and operates over a 10-year cycle. AGRIS aims to reduce the burden of conducting censuses by planning thematic data collection during this period.

The following table summarizes a possible module sequence for the four recommended modules: “economy”, “labour force”, “machinery, equipment, other assets and decisions” and “production methods and environment”. The proposed modules can furthermore be revised and planned to improve their national relevance and cost-effectiveness.

TAbLe 5: exAMPLe of A TIMeTAbLe foR CoNDUCTING AGRIS MoDULeS

year 0 1 2 3 4 5 6 7 8 9 10

Censusofagriculture(•)andinter-censussurvey(o) • o •

Core module

EA list • • • • • • • • •

Agricultural production • • • • • • • • •

Other key variables • • • • • • • • •

Rotating module 1 Economy • • • •

Rotating module 2 Work force • •

Rotating module 3Machinery, equipment, other assets and decisions

•

Rotating module 4 Production methods and environment • •


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2.3.3. Administrative sourcesAdministrative data are those which have been obtained for administrative purposes (e.g. for administering, regulating or collecting taxes from enterprises or individuals), not for statistical purposes (to study groups of individuals, enterprises, agricultural holdings or farms, etc.). They come mainly from technical services, decentralized departments of ministries (region, district, constituency, and village) and trustee organizations. They are capitalized in the form of a report (monthly and annual) relating, for example, to the situation of staff, infrastructure, use of inputs, livestock numbers, health aspects, livestock production and industries, commercialization, etc. Countries’ reporting systems are generally the main sources used in developing countries, but there are also registers and additional data conventionally treated as administrative data (e.g. rainfall data).

Government interventions such as subsidies, regulations and legislation often therefore require agricultural holders to report production information. Surveys of landed property and the land registry also provide useful information for creating registers. Control and inspection of foodstuffs, animal health inspections and commercial data provide information for product use accounts.

The following are the main constraints:• a delay in sending reports (monthly and/or annual);• under-utilization of existing primary data;• inadequacy of human or material resources in decentralized government departments;• sometimes problematic access to administrative data in a form that can be used for statistical purposes;• absence of methodological standards and norms, concepts, definition and fields covered by these data.

2.3.4. Remote sensing and Geographic information system (GIS) in agricultureRemote sensing and thematic mapping: remote sensing is the science of obtaining information on remote objects or areas, generally from aircraft or satellites. Sensors are capable of detecting and classifying objects on or under the earth’s surface. Remote sensing can gather data on areas that are dangerous (e.g. areas of conflict) or inaccessible or replace slow, costly data collection in the field. The use of images from satellites, aircraft, spacecraft, ships, balloons and helicopters helps to create data to analyse and compare, for example, the impact of natural disasters, changes in the field of soil erosion, the extent of pollution, changes in land cover or animal species population estimates. These aspects can be mapped, imaged, monitored and observed. Combined with thematic mapping data and adequate validation using field measurements, remote sensing generally provides consistent, high-quality data for agricultural and environment statistics.

Satellite remote sensing data can be analysed in a Geographic Information System (GIS) to produce land cover and land use maps. Organized with other data types, such data become a powerful tool for aiding decision-making in anything concerning crops and cropping practices. The GIS can help to establish links between the various data types and layers by providing powerful tools for storing and analysing spatial data and by integrating databases from different sectors in the same format and the same structure.

In agricultural statistics, remote sensing images can, in conjunction with field data, help in the following:• estimating areas;• estimating yield by remote sensing remains experimental, mainly by estimating the vegetation index;• mapping agricultural parcels (identifying crops, distinguishing vegetation types);• developing master sample frames.


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Satellite navigation systems (GPS, GLONASS, etc.) are used to obtain geospatial information which describes the site and characteristics of the various attributes of the climate and land. This information is used to describe, present and analyse data such as land use, water resources and natural disasters. The possibility of superimposing several data series using software, for example on population, environmental quality and environmental health, allows a more detailed analysis of the relationship between these phenomena.

In agricultural statistics, these systems are necessary to geolocate and digitize the items that make up the sampling frame. They are, moreover, particularly useful for data collection as these navigation systems can guide interviewers to find the relevant units for collection.

Data geo-referencing is a fundamental means of assessing the effects of agriculture on the environment and monitoring changes in land cover and use. Changes that affect land cover are slow; related data do not therefore need to be collected annually. Nevertheless, cartographic or digital data from remote sensing must cover the whole of a specific area or the territory of a country with the following classifications:• Cropping area;• Forest area;• Grassland;• Wetlands;• Housing;• Other land use;• Water.

2.3.5. Monitoring systems / observatoriesA monitoring system or observatory is a structure or organization collecting and centralizing data of interest in the form of indicators. The information collected is intended to describe specific phenomena, give a warning in an emergency and assess the change in key indicators.

The data observed can come from various sources:• Administrative (civil status, health insurance)• Sentinel network by direct observation (epidemiological surveillance, child labour monitoring system, etc.);• Objective measurement based on recordings (rainfall, temperature, water quality, etc.);• A one-time collection operation or by collection or capture points.

Owing to the numerous uncertainties (climatic variations, epizootic diseases, parasitic attacks, etc.) encountered in the agricultural sector, warning systems have been set up in some countries. Information collected through the system helps to better account for these uncertainties in decision-making:• To control the spread and ensure the eradication of epizootic diseases (rinderpest, avian influenza, etc.) and

parasitic attacks (swollen shoot, etc.);• To initiate actions to reduce the impacts of climatic variations (drought, tsunami, flooding, etc.), or locust plague; • To guarantee a minimum income for holders through the price information system.


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Below are a few examples of warning systems or observatories:• Locust monitoring and control centre;• National permanent commission for monitoring avian influenza (Mauritania);• Mauritanian epidemiological network for early warning of animal diseases;• Sahara and Sahel observatory: environmental monitoring system;• Agricultural markets information systems in sub-Saharan Africa;• Centre food security and nutritional observatory (OSANC) (Haïti);• Agricultural market observatory (OMA).11

•

11 http://www.hubrural.org/IMG/pdf/mali-pres-oma.pdf

box 12: THe AGRICULTURAL MARkeT obSeRVAToRy (oMA)

“The agricultural market observatory is a Malian institution created in 1998. Its missions are to:

• Produce and disseminate commercial statistical information to users;

• Analyse trends in prices and other indicators to allow an assessment of the agricultural market

situation and its changes in the short, medium and long term;

• Conduct studies, research and planning concerning the factors that influence price formation on its

own initiative or on request;

• Encourage exchanges between producers, traders, processors and decision-makers within and outside

the country11. »

To achieve its aims and fulfil its mission, the OMA collects data on the prices and quantities of all

agricultural products including agricultural inputs and equipment and fishery products. In addition to

quantitative information, it makes qualitative observations about the general state of markets (whether

foreign operators are present at weekly markets, the direction of cross-border flows, etc.). Data are

also collected on a weekly basis from 66 sites (markets) distributed throughout the national territory.

Information on agricultural inputs and equipment is collected from 11 markets on a monthly basis.


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2.4. STATISTICAL UNITS

The statistical unit to be surveyed must be specified for each study. The definition of this unit determines the methodology–thesampledesign,collectionandprocessing–tobeadoptedtoobtaintherequiredinformation.Table 6 below gives the corresponding unit or units for each theme:

TAbLe 6: STATISTICAL UNITS by THeMe

Theme Statistical unit

Census of agricultureSurvey of agricultural production

Agricultural holding or agricultural household

Aquacultural census Aquacultural holding

Population census Household

Fishing activity Household

Fishing survey Fishing site or body of water

Silviculture-agroforestry Household

Census of agriculture and population Household engaged in agricultural activities

Rural activities Rural household

Economic census Establishment

Community data Community, locality

Yield (area and production)Parcels (although the observation of other units could help to determine production and areas, taking information on parcels into account is sometimes useful for better measuring precision)

Price data Agricultural products

Food security, nutrition and resilience Households/household members

Environmental dataNatural units (catchment areas, ecosystems, ecoareas, landscape; land cover units; management and planning units (protected areas, coastal areas or hydrographic districts).

Variables are measured for each statistical unit and estimated in appropriate specific units of measurement.

For example:• On an agricultural holding, the cropping area can be measured in hectares (ha), crop yields in kg/ha, number

of cattle, etc.;• In a rural household: the annual income (in USD); the proportion of children under the age of five with

malnutrition, etc.


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2.4.1. Agricultural holdingAgricultural activity is the result of technical and economic units (including land and livestock, used for agricultural purposes, under single management) called agricultural holdings. The basic unit is generally the agricultural holding.

The statistical unit, the basic unit for which data are collected, is the agricultural holding, and this unit has been kept in the 2010 and 2020 programmes.

12

There are two types of agricultural holdings: i. holdings in the household sector which are operated by household members;

ii. holdings in the non-household sector, such as corporations and government institutions.

In the majority of developing countries, agricultural production mainly concerns the household sector. The concept of “agricultural holding” is therefore closely linked with the concept of “household”.13

12 World programme for the census of agriculture 2010 (FAO)13 World programme for the census of agriculture 2010 (FAO)

box 13: DefINITIoN of AN AGRICULTURAL HoLDING

An agricultural holding is an economic unit of agricultural production under single management

comprising all livestock kept and all land used wholly or partly for agricultural production purposes,

without regard to title, legal form or size. Single management may be exercised by an individual or

household, jointly by two or more individuals or households, by a clan or tribe, or by a juridical person

such as a corporation, cooperative or government agency. The holding’s land may consist of one or more

parcels, located in one or more separate areas or in one or more territorial or administrative divisions,

providing the parcels share the same production means, such as labour, farm buildings, machinery or

draft animals12.

box 14: DefINITIoN of A HoUSeHoLD

The concept of household is based on the arrangements made by persons, individually or in groups, for

providing themselves with food or other essentials for living. A household may be either

• a one person household, that is to say, a person who makes provision for his or her own food or

other essentials for living without combining with any other person to form part of a multi-person

household; or

• a multi-person household, that is to say, a group of two or more persons living together who make

common provision for food or other essentials for living. The persons in the group may pool their

resources and may have a common budget; they may be related or unrelated persons, or constitute a

combination of persons both related and unrelated13.


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14

Agricultural holdings in the non-household sector comprise the following:• Corporation• Cooperative• Government agency.

The following should be identified:• the main characteristics of the holding which may influence its activity or its production;• the key parameters for the production of monitoring indicators.

The characteristics of agricultural holdings in the non-household sector are those of a conventional enterprise.

Agricultural holdings in the household sector are characterized by:• Activities: agricultural holdings are characterized by the agricultural activities they carry out.• Location of the holding: The place of residence of the holder is normally identified by means of a geographic

coding system based on the country’s administrative structure. Holdings can be located using the Global positioning system (GPS). This system can geo-reference holdings, households and land in the appropriate administrative areas.

• Size: The size of the holding is understood to be the total area of the holding, i.e. the area of all the land comprising the household agricultural holding.

14 WCA 2020 (FAO)

box 15: CoRReSPoNDeNCe beTweeN AGRICULTURAL HoLDING AND HoUSeHoLD

In the household sector, there is usually a one-to-one correspondence between an agricultural holding

and a household with own-account agricultural production (either for sale or for own use); in other

words, all the own-account agricultural production activities by members of a given household are

usually undertaken under single management. Managing agricultural production activities usually goes

hand-in-hand with making common arrangements for food and other essentials, pooling incomes, and

having a common budget. Even if there is a degree of independence in the agricultural activities of

individual household members, the income or produce generated by different household members is

usually pooled. Often, different members of the same household own land, but usually the agricultural

operations in the household are carried out as a single unit.

There are two special cases where the agricultural holding and household concepts may diverge:

• If there are two or more units making up a household, such as where a married couple lives in the

same dwelling as their parents, the two units may operate land independently but, as members of the

same household, they make common arrangements for food and pool incomes.

• In addition to an individual household’s agricultural production activities, a household may operate

land or keep livestock jointly with another household or group of households. In this case, there

are two agricultural production units associated with the household and two sets of activities: (i)

the agricultural production activities of the individual household itself, and (ii) the joint agricultural

operations with the other household(s)14.


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15 Estimating areas uses the concepts of parcel, field and plot: A distinction should be made between these terms. The agricultural holding is divided into parcels.

�� A parcel is any piece of land of one land tenure type entirely surrounded by other land, water, roads, forest or other features not forming part of the holding, or forming part of the holding under a different land tenure type. A parcel may consist of one or more fields or plots adjacent to each other.

�� A field is a piece of land in a parcel, separated from the rest of the parcel by easily recognizable demarcation lines, such as cadastral boundaries, fences, waterways or hedges. A field may consist of one or more plots.

�� A plot is a piece of land with a single tenant, corresponding to a field or part of a field on which a specific crop or crop mixture is cultivated, or which is fallow or waiting to be planted.

• Household size: Household size refers to the number of members of the holder’s household.• Sex: This is important for analysing the gender aspects of agricultural production and, in particular, for examining

the role of women in managing agricultural holdings. This item could also be useful as the basis for a sampling frame for special gender surveys.

• Age: The section on the age of the holder and members of the holding is important for examining the relationship between age and the characteristics of agricultural holdings and, in particular, for making comparisons between young and old farmers. It is also useful for analysing gender issues. Able-bodied workers are production assets for holdings.

• Educational attainment: Educational attainment data are useful for examining the effects of education on characteristics such as cropping systems, agricultural practices and household food security. Educational attainment refers to the highest grade of formal education completed or the last year of studies attended by a person in the official educational system. Educational attainment data should be collected for both the agricultural holder and the other members of the holding, as the educational level of other members can be important factors in agricultural and family activities.

15 WCA 2020 (FAO)

box 16: CoMPoNeNTS of THe AReA of A HoLDING

The area of a holding includes the following15

• land used for growing crops (temporary and permanent, including cropped land under protective

cover), meadows and pastures, and fallow land;

• unutilized agricultural land;

• forest and other wooded land;

• bodies of water;

• farmyards and land occupied by farm buildings;

• land for which a holding does not have any rights to agricultural use, except for the products of the

trees grown on it.


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2.4.2. HouseholdAs regards the household statistical unit, the following three types could be distinguished, although these units are not mutually exclusive:• Household engaged in agricultural activities;• Non-agricultural production household which generally concentrates on rural production activities (rural work

force, fishing activities, use of aquaculture or forestry, data which are covered by agriculture-related sectors). Data collection from non-agricultural production households aims more particularly to gather information to create the sampling frame for more detailed rural surveys or for additional modules when the modular approach to the census of agriculture is used;

• Rural household: defined as a household living in areas designated as rural, usually defined by the population census. Note that a census of rural households does not itself cover all agricultural holdings, as some households living in urban areas are engaged in crop and livestock production activities.

2.4.3. Aquacultural holdingAn aquacultural holding is an economic unit of aquacultural production under single management comprising all aquaculture facilities without regard to title, legal form or size. Single management may be exercised by an individual or household, jointly by two or more individuals or households, by a clan or tribe, or by a juridical person such as a corporation, cooperative or government agency. The aquacultural holding’s facilities can be located in one or more separate areas or in one or more territorial or administrative divisions, providing the facilities share the same production means, such as labour, buildings and machinery16.

It may also refer to households engaged in own-account aquacultural production activity.

2.4.4. establishmentA community can be defined as a self-contained unit of social and economic activities (FAO, 1983). Population and housing census use the similar concept of locality, which is “a distinct population cluster (also designated as inhabited place, populated centre, settlement, etc.) in which the inhabitants live in neighbouring sets of living quarters and that has a name or a locally recognized status” (UN, 2015). Under these definitions, the community or locality may not be the same as the lowest administrative unit.

Other statistical units are used in population census: enumeration area (EA), enumeration section (ES). These purely statistical units are designed to facilitate field work. They are sometimes used to conduct various surveys in a country.

16 WCA 2020


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2.4.5. Community or localityA community can be defined as a self-contained unit of social and economic activities (FAO, 1983). Population and housing census use the similar concept of locality, which is “a distinct population cluster (also designated as inhabited place, populated centre, settlement, etc.) in which the inhabitants live in neighbouring sets of living quarters and that has a name or a locally recognized status” (UN, 2015). Under these definitions, the community or locality may not be the same as the lowest administrative unit.

Other statistical units are used in population census: enumeration area (EA), enumeration section (ES). These purely statistical units are designed to facilitate field work. They are sometimes used to conduct various surveys in a country.

2.4.6. Natural unit and management unitThe most significant spatial units for environment statistics are: natural units, such as catchment areas (watersheds), ecosystems, ecoareas, landscape or land cover units; or management and planning units based on natural units, such as protected areas, coastal areas or hydrographic districts.

2.5. DATA CoLLeCTIoN

Owing to the unique features of the agricultural sector (seasonality, agro-ecological dependence, etc.), data collection relating to this sector should meet both methodological and organizational requirements. The quality of the data can be affected if any of these requirements are disregarded. The points covered in this section are: • Survey period and crop calendar• Comprehensive enumeration and sample-based listing• Questionnaire design• Conducting interviews (PAPI, CAPI, CASI, CATI, etc.)• Use of new data collection technologies• Value of defining a typical holding

2.5.1. Survey period and crop calendarOperationally it is advisable to decide on these two periods well in advance.

Crop calendar and survey period: The crop cycle of plants (seedlings, crop growth and harvest) generally comprises key times.

Establishing a crop calendar to make national information available should reconcile regional differences in terms of cropping practices and the crop cycle. The participation of local authorities, as well as of agricultural technicians, is necessary in order to establish this calendar, which should be followed at all collection levels.

The survey period is the period during which data are collected in the field. It should guarantee good control of sample identification processes (neutralization of seasonal effects in particular).


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For example, agricultural surveys are generally conducted during the period covering the crop cycle, and visits by enumerators are organized according to the progress of the agricultural season. The crop calendar, the crop growth cycle and differences between cereals, tubers and roots, vegetables and fruit production should be taken into account.

Reference period: This is the period to which the data refer. It depends on the survey aims. Depending on the case, it can be an interval of time (week, month, year, agricultural season, etc.) or a specific date. It should be noted that variables can have different reference periods in the same survey. The reference period of a crop production survey is the agricultural season. The reference period for births, acquisitions and natural deaths of livestock depends on the species. It is generally one year for cattle, six months for small ruminants and pigs and one month for poultry.

The use of an annual reference period in census data collection is especially important for national accounts estimates and to assess the relationship of work statistics with other economic and social statistics that also use a long reference period, such as statistics on household income, poverty, social exclusion and education. Good coordination between the census of agriculture and the economic census helps to determine the reference year for national accounts. The agricultural census is one of the major sources for the development of the input-output matrix and for compilation of supply-use sheets17.

2.5.2. Questionnaires

Questionnaires and manualsSpecial attention needs to be given to the design of questionnaires so that they can collect the required data. The questionnaire for the census or any other sample survey should be developed with reference to a tabulation plan previously designed to ensure that all the relevant census or survey data are included in the questionnaire and thus to avoid the surprise discovery, once the data have been collected, that they do not correspond to the needs of the tabulation programme.

The questionnaire is designed bearing in mind field procedures for data collection according to methods appropriate to local conditions, based on recommended concepts and definitions. It serves as a guide for interviews at holdings with a view to collecting the primary data necessary to produce statistics (see section 2.2.).

Several questions are often necessary to cover an item in the census of agriculture or other survey. Some items imply abstract concepts, and cannot be covered by posing a direct question to those concerned. For example, individuals should not be directly asked if they are unemployed; they should be asked a series of questions about their activity as part of their work, to determine whether they meet the conditions so classify them as unemployed.

Questionnaires can be used as an aid for interviews and measurements. They are supported by manuals which will serve as a reference for field agents in order to complete the questionnaires better.

Paper questionnaires and manuals are increasingly being replaced by electronic questionnaires which use new collection technologies (tablet, smartphones, etc.) (see sections 2.4.3. and 2.4.4.).

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2.5.3. Interviewing methodsVarious methods are used to gather information. Face-to-face interviewing methods (personal interview) are still commonly practised in census. However, the traditional paper and pen approach is increasingly being replaced by the use of mobile devices18:

• Paper and Pen Interview (PAPI) - The PAPI is a traditional method in which enumerators interview respondents and data are collected by enumerators using paper questionnaires. The method is useful when respondents need assistance to complete the questionnaires and requires little technical knowledge for implementation. However, the method requires complex logistics for other areas, such as preparation and printing of questionnaires, distribution, centralization and storage of materials, as well as hiring of data-entry operators and supervisors which are additional operational costs;

• Methods using advanced technologies–Recentdevelopmentsinnewtechnologies,particularlyinformationand communication technologies and geo-referencing devices, provide new opportunities to improve timeliness and also to reduce the potential for enumerator and data processing errors and to improve quality checks, thus improving the overall quality of data. The main methods using new technologies are: i. Computer Assisted Personal Interview (CAPI)–IntheCAPImethodtheenumeratorconductsan

interview with the respondent using an electronic questionnaire on a mobile device, such as a personal digital assistant, tablet, laptop or smartphone, which the enumerator uses to record responses. The devices can also be preloaded with addresses or maps of the enumeration area for use during field work. The devices can also be programmed to provide real-time sample selection, which can be particularly useful for countries adopting the modular approach. For the census of agriculture, the CAPI method is often used with GPS, either directly through the device or by linking to external GPS devices. This allows identification of the geographic coordinates for the holding or parcels and is used in some cases to measure areas. The CAPI method also allows for improvements to management of data collection by supervisors at regional and central levels;

ii. Computer-Assisted Telephone Interview (CATI). This method collects data for holdings by telephone, with the operator located centrally reading and completing the questionnaire on a computer. In many countries, the CATI method may not be feasible for data collection from the majority of holdings but could be used for follow-up or quality checking, particularly for certain populations such as commercial farms or government farms.

• Other methods, such as Computer-Assisted Self-Interviewing (CASI) and Mail-out/Mail-back and Drop-off/Pick-up. These methods cannot, however, be used in some regions such as Africa where the population is largely illiterate and computers still remain out of reach for reasons of cost and education.

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2.5.4. Use of new collection technologiesNew technologies allow the use of satellite imaging to monitor land use, estimate cropped areas and provide early warning systems as regards changes in growth conditions of crops. Global positioning systems (GPS) allow geo-referencing of observations and collection of land use data provided by satellite imaging. The emergence of the internet and other technologies, such as the use of personal digital assistants (PDA), tablets or smartphones equipped with GPS for data collection and their direct connectivity with databases, offers huge potential for reducing the time between data collection and dissemination, allowing an improvement in data quality at the same time.

When conducting census and various sample surveys, the effects of modernizing data collection practices are increasingly being felt.

GPS (Global Positioning System)The GPS (Global Positioning System) is a satellite navigation, positioning and location system, which has been developed by the USA over the last 20 years. It works by means of 24 satellites around the earth in 6 different orbits, at a distance of approximately 22 000 km. With a GPS receiver, satellite signals can be received anywhere, free of charge and at any time, to determine a position.

GPS is based on triangulation. Signals transmitted by at least four satellites are received and used to determine the location (of the GPS receiver) in space in three dimensions (longitude, latitude and altitude).

Global positioning system (GPS) and Geographic information system (SIG) - GPS makes it possible to find the geographic position of a point on the earth’s surface by longitude and latitude. Collection of GPS coordinates has several advantages for preparation of the census frame or master sample frame. It can allow geo-referencing of holdings, households and land in the appropriate administrative areas. It can also enable linking to satellite imaging to establish area frames for agricultural surveys. The GPS system is suitable for face-to-face interview methods of enumeration. During enumeration, GPS-enabled devices, together with customized location software, can be used to assist enumerators to locate the route and holdings to be enumerated or to assist with managing census operations.

Geolocation data collected with GPS devices are useful both for census preparatory activities and during the field work. Location data can be used to assist with census cartography, for instance to adjust enumeration areas. GPS can also be combined with the Geographic information system (GIS) to monitor enumerators when the location of the holding is collected during enumeration. When data transmission from the field is timely, the location data can be used to provide near real-time monitoring by overlaying the locations covered over maps of enumeration areas and maps of holdings in the enumerator’s workload.

The GPS programme has been developed to collect geographic coordinates of households, infrastructure and parcels to calculate their respective areas.

GPS allows measurements of all holdings during a comprehensive enumeration using GPS. This is possible with a tape measure, stakes and a compass only on a subsample in a smaller survey, for example in the modular approach, and with a clearly greater collection time.


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Electronic collection devicesThe development of ICT has contributed to the emergence of electronic data collection. There are various devices for aiding data collection electronically, for example:• Personal digital assistants (PDA)• Tablets/smartphones• Notebooks/laptops

These types of equipment are portable digital devices. They allow the enumerator to carry out interviews using a questionnaire (data entry mask) loaded on the tool.

The new technologies help to reduce problems associated with the listing of households in the primary units (e.g. villages) sampled. These devices, depending on the characteristics chosen, are usually equipped with a GPS receiver enabling the longitude, latitude and altitude of the housing of households to be visited to be obtained and the primary unit accordingly delineated. Files can be transferred between devices (via a local wireless network, by wifi, bluetooth or infrared), and map files can subsequently be combined to create a complete map of the entity in question.

Once the households in the village have been geo-referenced, the number of households is known and the households can be selected in any way, creating a simple random sample or a statistically valid cluster sample (random segment of households).

Thorough training is necessary before taking part in a survey. The enumerator must be capable in particular of strictly observing the question sequence. With electronic devices (PDA, tablet, smartphone, etc.), the software manages interview flow automatically, so that the enumerator only asks the questions that are relevant to the interviewee. The system automatically ignores questions that do not apply. Although these devices manage the interview flow, training must be done with as many precautions as for PAPI. Furthermore, it is recommended that training begins with paper questionnaires so that enumerators first become familiar with the subject and the survey questionnaire before proceeding to technical training.

Unnecessary functions are removed or locked to facilitate the use of the device. You can access most of the mapping, navigation or interview functions by pressing on a button or using a stylus. Enumerators are very enthusiastic about the idea of using this new technology and are even proud of it, but a minimum level of training and aptitude for ICT are required.

Data collection by this new approach allows detailed checking of data at the collection site. The team leader can therefore manage the enumerator’s work more easily and carry out tabulations as necessary to assess data quality. Data collected by enumerators in the field can be combined into a single database, generally in a few hours. A preliminary report can therefore be generated just a few days after carrying out the survey. The report can be accompanied by a map of the various entities sampled representing all their households and their inclusion status in the sample. The use of standardized questionnaires helps to develop a standardized report template using shared software (EpiInfo2002, for example). In this way preliminary reports can be generated faster.

Geographic information from the mapping program can be readily exported to the majority of Geographic information systems (GIS). The mapping database includes latitude, longitude, altitude, the identifier of the entity (e.g. village), and the household identifier. This information can be exported from collection devices (PDA, tablet, smartphone) to a PC in a given database format (e.g. Access). It can then be easily used with ArcView (ESRI), Microsoft Access or MapInfo. Combined with other GIS information, it offers numerous spatial analysis options for survey data, which are also exported in Access format. The program can also map other useful elements not included in the sampling frame (such as storage establishments), thus expanding the spatial analysis field.


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Additional tools• The Geographic information system (GIS) can also be useful for census management, as location data can be

mapped as part of enumerator management. GIS also has advantages for analysis and dissemination of census data. It allows the incorporation of detailed geo-referenced data in the analysis of the census and sampling surveys.

• Short Message Service (SMS) - SMS may be used in various stages of the survey project (census, sampling) to share information with field personnel and respondents. This service may be used to send passwords, guidelines, alerts, reminders, etc. If the SMS gateway is integrated in the central database of the census, various alerts can be sent to manage the census when various census-critical events and violations occur - for example when the monitoring system detects that “coverage is lower than expected”.

Advantages and drawbacks of using GPS and electronic devices (PDA, tablets, smartphones)Ease of collection and collection time will be important points to emphasize. GPS devices can provide objective measurements of holding areas or, in some cases, allow respondents’ data to be checked against more accurate data. GPS devices can measure areas far more quickly than traditional objective methods. As objective measurements take longer to be collected, they would only be collected from a subset of holdings.

The use of preprogrammed GPS-equipped collection devices provides opportunities via the following operations:• Quickly mapping households and other useful components of an entity (e.g. village). Several users can share

the work and subsequently combine the cartography files;• Obtaining a statistically valid sample (single random sample or cluster sample);• Exporting sites and identification information relating to selected households to a GPS navigation program,

which will then allow enumerators to find the various households in the sample;• Guiding the enumerator throughout the interview and ignoring questions that do not apply to the interviewee

(preprogrammed question sequences) and entering data (the data are checked during entry);• Modifying data and establishing quality procedures when the enumerator enters collected data in the collection

device to optimize the quality of the collected information and reduce data checking in the field;• Sending information from various enumerators to a laptop or desktop to carry out pooling and rapid preliminary

analyses facilitated by the use of standard questionnaires and standard analysis programs;• Exporting tabular data to any GIS to create spatial analyses or maps. Existing cartographic data can also be used

for these analyses;• Storing data securely.

However, as with CAPI methods (see section 2.5.3), consideration needs to be given to the cost and use of devices, while ensuring that enumerators have sufficient computer literacy and training to operate the devices. Furthermore, the necessary energy to power the devices may not be available in some rural areas of some countries.

NB: The advantages of using new technologies, which are a testament to progress and the future, do not need to be further demonstrated. It should, however, be noted that the old methods are still used to varying degrees in many countries. It can nevertheless be observed that the transition to the use of new technologies is constantly evolving. For example, GPS is taking over from the compass and the PDA from the paper questionnaire.


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2.5.5. Typical holdingA further collection method used increasingly by the Brazil NSO and regional and global networks is to design data specific to a region on costs of production and other variables based on an expert opinion and structural data for a fictional holding, called a typical holding. The method supplements sample surveys.

The data refer to a holding which, in the case of Brazil, is defined and chosen by a panel of experts as the model holding in the region of interest. Once the model holding has been defined, the technical coefficients are determined by the panel for all fixed and variable inputs combined with information on production and the agricultural unit prices of inputs.

The main stages in the typical holding approach use available statistics to: i) identify the relevant region and ii) identify the relevant agricultural characteristics, such as holding size, production schedule, combination of enterprises or land ownership.

a. Selection of regions and sitesFor a given product, the regions to be included in the data construction process are determined based on their importance in the country’s total production. The number of selected regions and the cut-off threshold depend on the spatial distribution of production and on the final uses of the data (regional and/or national information) and on the budget allocated to the programme.

b. Determining the typical holdingOne or more typical holdings are determined in each of the regions selected for the programme. The typical holding can be defined in several ways, but it is generally constructed to represent the commonest characteristics of holdings in the region, namely the model holding. The following are some of the characteristics used in constructing the typical holding:• Production type (conventional, organic, etc.);• Technology used (use of chemical inputs, work, mechanization rate, etc.);• Combination of enterprises (for example, specialized arable crop holdings, mixed holdings);• Holding size (in ha, production value, etc.);• Topographic and agroclimate conditions;• Land tenure type (land owned or rented);• Production destination (mainly for own consumption, for sale on the domestic market and/or international

markets);• And any other aspect that may reflect local production conditions.

box 17: USe of GPS-eQUIPPeD PDA (bRAZIL)

The integration of census of agriculture statistics in the population enumeration carried out by the

brazilian Institute of Geography was facilitated by the use of GPS-equipped PDA for data collection. The

list of 5.2 million agricultural holdings is thus referenced to households in the population census. Each

agricultural holding can be visualized by means of Google Earth images combined with the enumeration

area grid of the agricultural census. The list frame of agricultural holdings with their respective

coordinates and all the enumeration areas surveyed by the census of agriculture form the area frame and

constitute the master sample frame.


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The definition of these characteristics will depend on the available statistics. Advisers in the selected region are subsequently questioned in order to define other characteristics of the typical holding. For example, if the majority of agricultural land in the region is occupied by conventional producers (i.e. not organized), the typical holding will also be conventional. If the majority of farmers rent their land, the typical holding will also take into account the agricultural practices of rented cultivated land, etc.

If different homogeneous groups of holdings can be distinguished, each representing a substantial proportion of the region’s production, the selection of several typical holdings to reflect this diversity may help to ensure the minimum representativeness of the derived statistics. This is clearly at the expense of an increase in programme costs.

c. Determining the expert groupThe model or typical holding and its economic characteristics, including costs of production, are determined by a panel of experts consisting of a wide range of stakeholders in the food and agriculture sector.

La composition des panels peut varier, mais ils comprennent généralement :• selected farmers;• cooperatives and associations;• technical services and other assistance agencies;• government bodies and non-governmental organizations related to agriculture;• producers of agricultural inputs, machinery and equipment;• agricultural research agencies.

The main advantage of inviting producers onto the panel is that they have their own holding in mind when speaking of a typical holding, but are not required to disclose any individual information which could be considered confidential and/or strategic.

The number of participants at a round table is generally limited (3-5 in the agrifood reference network, 10-15 in the case of Brazil) to ensure effective discussions and that a consensus is reached. The organization responsible for the programme is generally responsible for coordinating and facilitating discussions, and for making the required information (data, publications, events, etc.) available to the experts before, during and after discussions. It is also responsible for collating the results and ensuring they are consistent in time, space and raw materials.


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d. Data determination processThe basic parameters and technical coefficients used to construct costs of production data are determined by an expert group consensus. If structural data are available on the holding, they should be used as a starting point for discussions. These parameters are then combined by the organization responsible for the programme with data on prices and production levels to construct cost of production statistics. The result of a cost calculation programme is presented to the commission for cross-checking, which can lead to a revision of the underlying parameters and a new series of calculations. Several repeats may be necessary until a consensus is reached on the final results.

The advantages and drawbacks of the typical holding approach are described briefly below.

e. Advantages and drawbacksAs all the major technical parameters are documented, all types of analyses concerning environmental problems can be performed (such as GHG emissions and nutrient balances). For the same reason, all types of productivity figures (such as labour, capital and nitrogen) can also be analysed. The options for stimulating production and/or productivity can also be identified because it is known, for example, to what extent operations are mechanized and to what level labour or inputs are used.

The main advantage of these approaches from a global point of view is that the results are comparable as data collection and cost distribution are carried out uniformly and systematically. This means that the results can be used to understand the economic performance of specific production systems compared with competitors in other parts of the world.

Of course, this level of detail makes the whole process relatively complex and time-consuming. Consequently, unless government funding in each country becomes available, the number of typical holdings to be considered is normally small (about three).

Data constructed on the basis of these approaches do not take into account the diversity of systems and production conditions in which holdings operate. By their nature, the results of these approaches cannot be interpreted as national or even regional averages without a significant loss of precision, except in specific cases where production is strongly dominated by holdings of a single type. This warning can be mitigated to some extent by increasing the number of typical holdings. But this leads to an increase in data collection costs (diminishing one of the main advantages of this approach).

Furthermore, the determination of typical farms is in itself a complicated exercise, given the multiple characteristics to be considered and the requirements of the data on which this determination should be based.


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Conclusion: Given their level of detail and potential reliability, data obtained from typical holdings may be useful for managers of local agriculture and decision-makers who want to understand how and to what extent costs of production depend on the characteristics of the holding, its practices and its environment.

These approaches can also complement standard survey approaches. For countries with little or no statistical infrastructure, they are a means of estimating costs of production, which should be improved and supplemented by sample surveys where applicable. They can also be a source of useful information for less important crops for which the use of surveys is not economically justified.

However, institutions involved in developing these approaches emphasize the fact that one or two typical holdings cannot normally be used to create a national average. Nevertheless, these institutions also recognize that these approaches can be a source of pertinent regional or national information in cases where production is highly concentrated in holdings of a similar type or where several typical holdings are selected to better reflect the diversity of agricultural practices and conditions.


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Module 3: Data processing, analysis and dissemination

3.1. GeNeRAL oVeRVIew of CURReNT PRoCeSSING PRACTICeS AND LIMITATIoNS obSeRVeD

It is generally observed that countries’ current practices involve two main institutions in the processing, analysis and dissemination of agricultural data obtained through sample surveys and agricultural censuses. These are the National Statistical Office and the Ministry for Agriculture. Training centres or university statistics departments are responsible for training.

The problems and limitations lie mainly in the shortage of qualified personnel, inadequacy of statistical methods, inconsistency in the production of core indicators to measure the development of agriculture, lack of modern equipment and poor data quality.

MoDULe LeARNING objeCTIVeS

• To understand the problems which arise during processing, analysis and dissemination of agricultural

data;

• To become familiar with the concepts of areas and yields, placing special emphasis on the necessary

data and calculation methods;

• To become familiar with production estimation methods;

• To understand the methods used for crop forecasting;

• To become familiar with agricultural data processing steps and with analysis and dissemination

methods.

3


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3.1.1. Lack of qualified staffMany developing countries suffer in particular from a lack of qualified staff in the processing, analysis and dissemination of agricultural data, as confirmed by the following:• Vacant positions at various levels;• High turnover of qualified staff;• Lack of professional training.

3.1.2. Inadequacy of statistical methodsDespite the importance of the agricultural sector for the development of the economy, poverty reduction and improvement in food security, critical problems persist in how the development of agriculture is assessed. Problems range from survey design to dissemination of results. For example there are relatively few data collection standards; the level of cover and comprehensiveness varies; there are fewer disaggregated data available on small administrative units and target groups; there is an inability to provide reliable statistics on the increasingly large range of agricultural activities that generate income for rural households; and the methods vary for imputation of missing values and processing of outliers.

3.1.3. Inconsistencies in the production of core indicatorsThe issue of the frequency of censuses and surveys deserves particular attention. Many sample surveys have been conducted in recent years in several countries. The relatively small number of analysts capable of processing and analysing these major data sets, as well as backlogs and inconsistencies, has prevented the regular production of a series of core indicators for agriculture. Some key agricultural sector indicators can change radically from one year to the next due to the effect of weather or policy initiatives, which requires annual or seasonal data collection. For example, data should probably be collected each year or season on cropping areas or harvested volumes. Similar gaps have been observed in censuses.

3.1.4. Lack of modern equipmentCensus or sample survey data are traditionally captured manually in data processing software, and the accuracy of the data is checked by double entry. New digitization technologies now help to save time and can greatly improve data accuracy. These technologies also make it possible to access the results more quickly.

The Geographic information system (GIS), a computerized system that facilitates data processing, analysis and dissemination, has improved the scope and quality of geo-referenced agricultural data.

Note that some countries still do not have modern equipment (e.g. GIS) for collecting data in agricultural censuses and surveys and for producing agriculture statistics.


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3.1.5. Poor data qualityHigh-quality agricultural data are required to design better agricultural and rural policies and to measure progress in terms of development. Countries should therefore produce statistics to allow individuals to assess the state of the nationandtojudgethegovernment’sperformance–statisticsthataretrustworthyforusersandsociety.

OutlookThe quality of agricultural data has admittedly improved considerably over the last three decades, but documents reveal problems in data quality, mainly concerning relevance, accuracy, timeliness and accessibility. All these problems are related to the data processing, analysis and dissemination methods.

The key to solving these problems is to consider them when developing Strategic plans for agricultural and rural statistics (SPARS). These plans should pay particular attention to modernizing the production process and to capacity building for statistics staff, particularly those assigned to data collection, processing and analysis, two conditions necessary for a rapid improvement in data quality.

3.2. AReAS AND yIeLDS of PURe AND MIxeD CRoPS

3.2.1. Data necessary for estimating yield or area

a. Estimating areas from land register information

i. Availability of a parcel register with crops present by seasonIn this particular case, the recommended approach is to carry out stratified sampling taking the regions or districts as the stratum. A fixed number of villages is sampled per stratum. However, the type of villages sampled in terms of land development and type of parcel use (homogeneous, dispersed and by patch) is known only a posteriori (post-stratification). Areas are then estimated in terms of parcel use using the domain estimation approach (Särndal et al., 1992). The parcels (statistical units) in each sampled village are enumerated comprehensively and the areas collected according to the information recorded in the administrative register. This is the case in India, for example.

ii. Register of available parcels, but absence of information relating to land useIn this configuration, the available land register does not provide information on parcel use. A two-stage survey is therefore recommended. Villages or EA constitute the first stage, while the list of parcels constitutes a second-stage sampling frame. For the sampled parcels, the information collected relates to areas as well as additional data to confirm declarations (quantity of inputs used per crop type, farm costs, etc.). This declared information can be supplemented by real measurements made on a subsample of parcels. Parcel use for each crop can be determined by a quick eye estimation.

b. Sample design with an area sampling frame for measuring areas In cases where the sampling frame is an area frame (EA, district, village, etc.), a two-stage sample design will be used with the sampling frame elements as PSU (EA, district, village, etc.) and segments as SSU. Information relating to the parcels identified in the sampled segments is then collected on a declarative basis from the holders concerned.

Area collection operations are then carried out on a subsample of parcels with the appropriate equipment (GPS, compass, tape measure, etc.).


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c. Sample design to estimate areas with mixed crops for a household surveyData collection through household surveys is necessary when parcels and type of land use cannot be identified from any source of information. In this case, the appropriate sample design is the two-stage design (with EA/villages as primary sampling unit (PSU) and households as secondary sampling unit (SSU)).

Information on parcel use and to assess areas (quantity of inputs used, etc.) per crop is collected by declaration.

The area collection operation will, furthermore, be carried out on a subsample of households, taken from a subsample of PSU. The information will be collected for all eligible parcels. There are different types (homogeneous, dispersed and by patch). Area collection is carried out by direct measurement using appropriate equipment (GPS, compass, tape measure, etc.). The areas associated with each crop will be allocated in proportion to parcel use. Parcel use is determined for each crop in the following cases:• Quick eye estimation;• Estimation from additional information;• Objective estimation by laying a density grid and counting the seedlings (mixed, with homogeneous density);• Estimation based on counting seedlings (dispersed or by patch).

The enumerators must collect all the information required for this evaluation (the data collection tools have been developed for this purpose).

d. Useful information for measuring yieldThe approach described below is valid regardless of the specific cases mentioned above. The statistical unit is the parcel. A list of parcels is selected from parcels identified from registers, or from the various sampled segments or from households. Two stages should be considered. The first involves obtaining information by declaration based on the holder’s estimation/perception (by declaration). Information obtained in this way relates to production and area.

The second stage involves laying yield grids on a sample of parcels initially used for area measurement.


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e. Calculating the level of parcel useSeveral different cases should be considered to determine the level of parcel use:• Non-homogeneous arrangement of mixed crops;• Homogeneous cover:

�� Laying a density grid;�� Spacing between seedlings;�� Direct observation.

i. Rate of seedingIf crops have not been planted homogeneously, the rate of seeding could be used to assess the area of the various crops. Planted seeds might not all have germinated. However, this information is a proxy for having the information.

Consider crops A and B. Let a and b be the quantity of seed planted for crops A and B, respectively. To avoid confusion, consider A and B to be the recommended quantities of seed for crops A and B, respectively.

Let S be the parcel area under a crop mix and Si the area associated with crop i. So:

ii. Crop densityWhere mixed crops are sown with no homogeneous configuration, areas under the various mixed crops can be assessed based on seedling density. Seedling density is determined objectively by laying a density grid at selected points at random. The density is the number of seedlings per unit of area for each crop in the mixture. The area of each crop in the mixture can be estimated by calculating the density ratio of the seedlings.

Consider three mixed crops on an area estimated to be 0.8 ha. Suppose that a density grid contains the following for each of the crops in the mixture:• 10 seedlings of A instead of 60 in a pure crop;• 18 seedlings of B instead of 25 in a pure crop;• 20 seedlings of C instead of 50 in a pure crop.


i. Rate of seeding

If crops have not been planted homogeneously, the rate of seeding could be used to assess the area of the various crops. Planted seeds might not all have germinated. However, this information is a proxy for having the information.

Consider crops A and B. Let a and b be the quantity of seed planted for crops A and B, respectively. To avoid confusion, consider A and B to be the recommended quantities of seed for crops A and B, respectively.

Let S be the parcel area under a crop mix and Si the area associated with crop i. So:

!" =$/&

$/& + (/)∗ !

!+ =(/)

$/& + (/)∗ !

As an illustration, consider the following parameters: S= 0.4 ha, a=50 kg, A = 120 kg/ha, b=1 kg and B = 5 kg/ha. The rate of seeding is: (50/120) for A and (1/5) for B.

!" = (0.42/(0.42 + 0.2))x0.4 = 0.27ha !+ = (0.2/(0.42 + 0.2))x0.4 = 0.13ha

ii. Crop density

Where mixed crops are sown with no homogeneous configuration, areas under the various mixed crops can be assessed based on seedling density. Seedling density is determined objectively by laying a density grid at selected points at random. The density is the number of seedlings per unit of area for each crop in the mixture. The area of each crop in the mixture can be estimated by calculating the density ratio of the seedlings. Consider three mixed crops on an area estimated to be 0.8 ha. Suppose that a density grid contains the following for each of the crops in the mixture:

• 10 seedlings of A instead of 60 in a pure crop;

• 18 seedlings of B instead of 25 in a pure crop;

• 20 seedlings of C instead of 50 in a pure crop.

The density ratio is therefore:


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The density ratio is therefore:

iii. Interplanted crops: seedling line numbersWhere mixed crops are sown in lines (interplanted crop), but in separate rows, the area under each constituent crop can be divided based on the number of lines of each constituent crop. The number of lines on a row of interplanted crops is counted at three places at random in the selected field to determine the average number of lines.

Consider two interplanted crops A and B, sown over 0.5 ha. Suppose that the number of lines of crop A where the crops are mixed is 25, whereas the recommended density in a pure crop is 40. For crop B, the number of mixed lines is 2 whereas the density is 5 in a pure crop.

The area ratio for A and B is therefore:


9!" =1060

= 0.167

9!+ =1825

= 0.72

9!= =2050

= 0.4

The areas thus allocated to crops A, B and C are:

!" =9!"

9!" + 9!+ + 9!=∗ S =

0.1670.167 + 0.72 + 0.4

∗ 0.8ha = 0.13 ∗ 0.8ha = 0.10ha

!+ =9!+

9!" + 9!+ + 9!=∗ S =

0.720.167 + 0.72 + 0.4

∗ 0.8ha = 0.56 ∗ 0.8ha = 0.45ha

!= =9!=

9!" + 9!+ + 9!=∗ S =

0.40.167 + 0.72 + 0.4

∗ 0.8ha = 0.31 ∗ 0.8ha = 0.25ha

iii. Interplanted crops: seedling line numbers

Where mixed crops are sown in lines (interplanted crop), but in separate rows, the area under each constituent crop can be divided based on the number of lines of each constituent crop. The number of lines on a row of interplanted crops is counted at three places at random in the selected field to determine the average number of lines.



9!" =2540

= 0.625

9!+ =25

= 0.4 The areas thus allocated to crops A and B are:

!" =9!"

9!" + 9!+∗ S =

0.6250.625 + 0.4

∗ 0.5ha = 0.61 ∗ 0.7ha = 0.43ha

!+ =9!+

9!" + 9!+∗ S =

0.40.625 + 0.4

∗ 0.5ha = 0.39 ∗ 0.7ha = 0.27ha


9!" =1060

= 0.167

9!+ =1825

= 0.72

9!= =2050

= 0.4

The areas thus allocated to crops A, B and C are:

!" =9!"

9!" + 9!+ + 9!=∗ S =

0.1670.167 + 0.72 + 0.4

∗ 0.8ha = 0.13 ∗ 0.8ha = 0.10ha

!+ =9!+

9!" + 9!+ + 9!=∗ S =

0.720.167 + 0.72 + 0.4

∗ 0.8ha = 0.56 ∗ 0.8ha = 0.45ha

!= =9!=

9!" + 9!+ + 9!=∗ S =

0.40.167 + 0.72 + 0.4

∗ 0.8ha = 0.31 ∗ 0.8ha = 0.25ha

iii. Interplanted crops: seedling line numbers

Where mixed crops are sown in lines (interplanted crop), but in separate rows, the area under each constituent crop can be divided based on the number of lines of each constituent crop. The number of lines on a row of interplanted crops is counted at three places at random in the selected field to determine the average number of lines.



9!" =2540

= 0.625

9!+ =25

= 0.4 The areas thus allocated to crops A and B are:

!" =9!"

9!" + 9!+∗ S =

0.6250.625 + 0.4

∗ 0.5ha = 0.61 ∗ 0.7ha = 0.43ha

!+ =9!+

9!" + 9!+∗ S =

0.40.625 + 0.4

∗ 0.5ha = 0.39 ∗ 0.7ha = 0.27ha


97

iv. Interplanted crops: distance between seedlingsWhere mixed crops are sown in lines (interplanted crop), but in separate rows, the area under each constituent crop can be divided based on the distance between seedlings.

Consider two interplanted crops A and B, sown over 1 ha. Suppose that the average distances observed (information obtained at, at least, three points chosen at random on the parcel) is 2 m between seedlings of A and 3 m between seedlings of B.

The areas allocated to each crop are as follows:

Note:The cases mentioned below and the appropriate approach should be noted:• In the case of a non-homogeneous, fairly complex mixture, the recommended approach is to determine the

level of crop cover by subjective estimates (observation, declaration by the enumerator);• If crops cover a relatively marginal area (less than 10%), they should be ignored;• A simple estimate consists in dividing the total parcel area by the number of mixed crops. This is a simple

method, but it will give overestimates of plant production.• A rough estimate involves allocating the total seeded area to each crop in the mixture: the method is very

rough and can therefore lead to overestimated areas.• If temporary crops (seasonal and annual crops) are sown at the same time in a crop mixture and harvested

at different times, the parcel is treated as double harvested. The total area is recorded under each crop in the mixture for seasons when they are harvested.

v. Allocation of area when temporary crops are sown with permanent cropsMixtures of permanent and temporary crops are frequently observed. The area under permanent crops is the area under the canopy. “The canopy is the upper layer of the forest, directly influenced by solar radiation” .

Theestimatedareacanbecalculatedbasedontheaverageareaofthecanopy(πr2),assessedunderthreeorfivetrees selected at random, where r is the radius of the canopy. The area is estimated by simply multiplying the average area of the canopy by the number of permanent crop seedlings.

This area is then inferred from the total area of the parcel. The resulting area is allocated to temporary crops (generally sown as interplanted crops).

Manueldeformationenstatistiquesagricoles Page110

iv. Cas des cultures intercalaires : écartement entre les plants

Lorsque les cultures en association sont semées en ligne (culture intercalaire), mais dans des rangées séparées, la superficie sous chaque culture constitutive peut être répartie sur la base de l’écartement entre les plants

Considérons deux cultures en intercalaires A et B, emblavée sur 1 ha. Supposons que les écartements moyens observés (information recueillie en au moins trois points pris de manière aléatoire sur la parcelle) sont de 2 m entre les plants de A et de 3 m entre les plants de B :

Les superficies allouées à chaque culture sont les suivantes:

!# =B#

B# + B,∗ S =

22 + 3

∗ 1ha = 0,4 ∗ 1ha = 0,4ha

!, =B,

B# + B,∗ S =

32 + 3

∗ 1ha = 0,6 ∗ 1ha = 0,6ha

Remarque :

Il convient de relever les cas énoncés ci-dessous et le traitement approprié

• Dans le cas d’une association non homogène et assez complexe, il est recommandé de déterminer le taux d’occupation des cultures par des estimations subjectives (observation, déclaration de l’enquêteur);

• Si des cultures occupent une aire relativement marginale (moins de 10 %), il est recommandé de les ignorer;

• Une estimation simple consiste à diviser la superficie totale de la parcelle par le nombre de cultures en association. C’est une méthode simple, mais elle donnera des surestimations de la production végétale.

• Une estimation grossière consiste à allouer la superficie totale semée à chaque culture de l’association : la méthode est très grossière et peut donc conduire à des superficies surestimées.

• Si des cultures temporaires (cultures saisonnières et annuelles) sont semées à la même période dans une association de culture, et sont récoltées à des périodes différentes, la parcelle est traitée comme doublement récolté. La superficie totale est enregistrée sous chaque culture dans l’association pour les saisons respectives où la récolte a été effectuée.

v. Affectation de la superficie lorsque les cultures temporaires sont semées avec des cultures permanentes :

Il est fréquent d’observer des associations entre cultures permanentes et cultures temporelles. La superficie sous culture permanente est la superficie sous la canopée. « La canopée est l’étage


98

3.2.2. Calculating areas from field data (extrapolation)The total area of a holding is the sum of the areas of the parcels on the holding, and it is estimated at stratum level from the total where the variable represents the area.

Consider two-stage sampling with the stages defined as follows:• Stage 1: Systematic sampling in each stratum (province, district, etc.) of primary sampling units (PSU) (village,

EA, etc.) with probability proportional to their sizes in secondary sampling units (SSU) (household, holding);• Stage 2: Simple random sampling without replacement of SSU in each PSU sampled.

Annotation:S : total area of a given crop (maize, rice, etc.) in a given stratumi : PSU indexj : SSU indexM : number of PSU in the stratum consideredm : number of PSU sampled in the stratum consideredNi : Total number of SSU in PSU ini : Number of SSU sampled in PSU iN : total number of SSU in the stratum consideredyij : area of the crop considered in SSU j of PSU i of the stratum consideredπi : probability of inclusion of PSU iπij : probability of inclusion of SSU j of PSU i

Estimation of the area of crop c

If for a unit i, πi ≥ 1, this unit is automatically chosen to be part of the sample with probability 1 and m-1 other PSU will then have to be selected from the remaining M–1 units.

The total area S is given by:

The difficulty in applying this formula lies in determining yij (the areas per agricultural holding for a given crop) from the collected data.

In cases where the crop concerned is mixed on some plots with other crops in the holding, the area allocated to the crop cannot in fact be clearly determined. There are several methods of doing this and the free application of either of these methods makes any area comparison inappropriate.


supérieur de la forêt, directement influencée par le rayonnement solaire »31.

La superficie estimée peut être calculée sur la base de la superficie moyenne du couvert (πr2), évaluée sous trois ou cinq arbres sélectionnés au hasard, où r est le rayon de la canopée. La superficie est estimée multipliant simplement la superficie moyenne de la canopée par le nombre de plants de culture permanente.

Cette superficie est donc déduite de la superficie totale de la parcelle. La superficie résultante de cette déduction est allouée aux cultures temporelles (généralement semées en cultures intercalaires).

3.2.2. Lecalculdessuperficiesàpartirdesdonnéesdeterrain(extrapolation)La superficie totale d’une exploitation est la somme des superficies des parcelles de l’exploitation, son estimation au niveau de la strate se ramène à celle du total où la variable y représente la superficie.

Considérons un sondage à deux degrés définis comme suit :

- Degré 1 : Tirage systématique dans chaque strate (province, département…) d’unités primaires (UP) (village, ZD,…) à probabilités proportionnelles à leurs tailles en unités secondaires (US) (ménage, exploitation);

- Degré 2 : Tirage aléatoire simple sans remise d’US dans chaque UP échantillonnée

Notation :

S : superficie totale d’une culture donnée (maïs, riz…) dans une strate donnée

i : indice de l’UP j : indice de l’US

M : nombre d’UP de la strate considérée m : nombre d’UP échantillonnées dans la strate considérée

Ni : Nombre total d’US dans l’UP i ni : Nombre d’US échantillonnées dans l’UP i

N : nombre totale d’US dans la strate considérée yij : superficie de la culture considérée dans l’US j de l’UP i de la strate considérée

πi : probabilité d’inclusion de l’UP i πij : probabilité d’inclusion de l’US j de l’UP i

Estimation de la superficie de la culture c

iim NN

π =

Si pour une unité i, πi ≥ 1, cette unité est désignée d’office pour faire partie de l’échantillon avec la probabilité 1 et on aura alors à tirer m-1 autres UP parmi les M–1 unités restantes.

31 https://fr.wikipedia.org/wiki/Canopée


jij

i

nN

π =

La superficie totale S est donnée par :

1 1

1 jnm

iji jj

NS ym n= =

= ∑ ∑

La difficulté de l’application de cette formule réside dans la détermination des yij (les superficies par exploitation agricole pour une culture donnée) à partir des données collectées.

En effet, dans le cas où dans l’exploitation la culture concernée est en association sur certaines parcelles avec d’autres cultures, la détermination de la superficie allouée à la culture n’est pas évidente. On y relève plusieurs méthodes et, l’application libre de l’une ou l’autre de ces méthodes rend inappropriée toute comparaison de superficie.

Encadré 18 : Méthodes actuellement pratiquées pour la répartition de la superficie d’une parcelle comportant une culture principale et une culture secondaire

i) La superficie de la parcelle est comptée deux fois : elle est simultanément octroyée à la culture principale et à la culture secondaire, on parle alors de superficies développées : la somme des superficies cultivées par culture est supérieure à la superficie totale cultivée, mais la somme des superficies par culture est égale à la superficie totale cultivée,

ii) La superficie de la parcelle n’est comptée que pour la seule culture principale, on parle alors de superficies physiques : les superficies par culture sont plutôt sous-estimées :

iii) La superficie de la parcelle est répartie entre les deux cultures suivant une proportion fixée systématiquement à 1 pour les cultures principales et 0,5 pour les cultures secondaires : la superficie totale cultivée est surestimée,

iv) La superficie de la parcelle est répartie équitablement entre les deux cultures, la somme de la superficie des cultures est égale à la superficie totale cultivée,

v) Méthode des densités : cette méthode se propose de faire l’allocation de la superficie d’une parcelle entre ses différentes cultures en utilisant pour chaque culture le rapport qu’il y a entre sa densité en culture pure et sa densité en cultures associées. Malheureusement, dans la pratique les rapports calculés ne se prêtent pas bien à cette allocation.

Dans le calcul de la production, il suffit soit d’appliquer un rendement moyen de la culture (toutes associations confondues) au total des superficies où elle est présente, soit appliquer à la superficie de chaque le rendement correspondant : le rendement de la culture en pure sera appliqué à la superficie en pure, le rendement de la culture en double association aux superficies double association, etc. Dans ce cas, on prendra le soin d’estimer d’une part la superficie totale cultivée en pure, d’autre part la superficie totale cultivée en association en distinguant les types d’association non pas selon les cultures qui s’ajoutent à la culture donnée, mais selon l’importance de la culture.

Ainsi, pour la culture on estimera la superficie de toutes les associations :

Si Ci est le coefficient d’extrapolation associé à l’unité primaire i issu du plan de sondage adopté


jij

i

nN

π =

La superficie totale S est donnée par :

1 1

1 jnm

iji jj

NS ym n= =

= ∑ ∑

La difficulté de l’application de cette formule réside dans la détermination des yij (les superficies par exploitation agricole pour une culture donnée) à partir des données collectées.

En effet, dans le cas où dans l’exploitation la culture concernée est en association sur certaines parcelles avec d’autres cultures, la détermination de la superficie allouée à la culture n’est pas évidente. On y relève plusieurs méthodes et, l’application libre de l’une ou l’autre de ces méthodes rend inappropriée toute comparaison de superficie.

Encadré 18 : Méthodes actuellement pratiquées pour la répartition de la superficie d’une parcelle comportant une culture principale et une culture secondaire

i) La superficie de la parcelle est comptée deux fois : elle est simultanément octroyée à la culture principale et à la culture secondaire, on parle alors de superficies développées : la somme des superficies cultivées par culture est supérieure à la superficie totale cultivée, mais la somme des superficies par culture est égale à la superficie totale cultivée,

ii) La superficie de la parcelle n’est comptée que pour la seule culture principale, on parle alors de superficies physiques : les superficies par culture sont plutôt sous-estimées :

iii) La superficie de la parcelle est répartie entre les deux cultures suivant une proportion fixée systématiquement à 1 pour les cultures principales et 0,5 pour les cultures secondaires : la superficie totale cultivée est surestimée,

iv) La superficie de la parcelle est répartie équitablement entre les deux cultures, la somme de la superficie des cultures est égale à la superficie totale cultivée,

v) Méthode des densités : cette méthode se propose de faire l’allocation de la superficie d’une parcelle entre ses différentes cultures en utilisant pour chaque culture le rapport qu’il y a entre sa densité en culture pure et sa densité en cultures associées. Malheureusement, dans la pratique les rapports calculés ne se prêtent pas bien à cette allocation.

Dans le calcul de la production, il suffit soit d’appliquer un rendement moyen de la culture (toutes associations confondues) au total des superficies où elle est présente, soit appliquer à la superficie de chaque le rendement correspondant : le rendement de la culture en pure sera appliqué à la superficie en pure, le rendement de la culture en double association aux superficies double association, etc. Dans ce cas, on prendra le soin d’estimer d’une part la superficie totale cultivée en pure, d’autre part la superficie totale cultivée en association en distinguant les types d’association non pas selon les cultures qui s’ajoutent à la culture donnée, mais selon l’importance de la culture.

Ainsi, pour la culture on estimera la superficie de toutes les associations :

Si Ci est le coefficient d’extrapolation associé à l’unité primaire i issu du plan de sondage adopté


99

When calculating production, it is sufficient either to apply an average crop yield (all mixtures together) to all areas where the crop is present, or to apply to the area of each one the corresponding yield: the pure crop yield will be applied to the pure area, the mixed cropping yield to mixed cropping areas, etc. In this case, care should be taken to estimate first the total pure cropped area, and second the total mixed cropped area by distinguishing the types of mixed crops not according to the crops added to a given crop, but according to crop weight (in terms of main or secondary crop in the mixture).

The area of all the mixed crops will thus be estimated for the crop:

If Ci is the weight associated with primary sampling unit i resulting from the sample design adopted and a, a given crop, we have:

• Pure area:

• Mixed area where crop a is the main crop:

box 18: CURReNT MeTHoDS of DIVIDING THe AReA of A PARCeL CoNTAINING A MAIN CRoP

AND A SeCoNDARy CRoP

i) The area of the parcel is counted twice: it is allocated at the same time to the main crop and to the

secondary crop, and these are then called developed areas: the sum of cropped areas per crop is

greater than the total cropped area, but the sum of areas per crop is equal to the total cropped area;

ii) The area of the parcel is counted only for the main crop, and is then referred to as physical areas: the

areas per crop tend to be underestimated;

iii) The area of the parcel is divided between the two crops in a proportion routinely fixed at 1 for main

crops and 0.5 for secondary crops: the total cropped area is overestimated;

iv) The area of the parcel is evenly divided between the two crops, the sum of the area under crops is

equal to the total cropped area;

v) Densities method: this method proposes to allocate the area of a parcel between its various crops

using the ratio between its pure crop density and its mixed crop density for each crop. Unfortunately,

calculated ratios do not lend themselves well to this method in practice.


distinguishing the types of mixed crops not according to the crops added to a given crop, but according to crop weight (in terms of main or secondary crop in the mixture).



- Pure area: !S1 = C yi i∑ 1́ where ʹy i1 is the estimated total area of parcels in

primary sampling unit i containing only crop a

- Mixed area where crop a is the main crop:

!S2 = C yi i∑ 2́ where ʹy i2 is the estimated total area of parcels of

primary sampling unit i containing a mixture in which crop a is the main crop

- Mixed area where crop a is a secondary crop:

!S3 = C yi i∑ 3́ where ʹy i3 is the estimated total area of parcels in

primary sampling unit i containing a mixture in which crop a is a secondary crop.

3.2.3. CalculatingyieldsfromfielddataYield in primary sampling units Calculating average yield is related to the selection method used to obtain yield plots. There are two methods of determining yield in primary sampling units (villages, enumeration areas or enumeration sections):

a) Yield plots are selected with equal probability

In a primary sampling unit where Na parcels of crop “a” have been listed, the selection of n yield plots with equal probability is equivalent to probability pi for all the parcels given by:

p nN

ia

= i = 1, 2, 3, ..., Na

In a yield plot i of area Si , the probability for a yield grid j of area sij is:

p sS

ijij

i= , or, more generally, if parcel i has to have im yield grids,

p m sS

iji ij

i= .

Overall, the probability of a yield grid being selected is:
















p nN

ia

= i = 1, 2, 3, ..., Na


p sS

ijij


p m sS

iji ij

i= .



100

• Mixed area where crop a is a secondary crop:

3.2.3. Calculating yields from field dataYield in primary sampling unitsCalculating average yield is related to the selection method used to obtain yield plots. There are two methods of determining yield in primary sampling units (villages, enumeration areas or enumeration sections):

a. Yield plots are selected with equal probabilityIn a primary sampling unit where parcels of crop “a” have been listed, the selection of n yield plots with equal probability is equivalent to probability for all the parcels given by:

In a yield plot i of area Si, the probability for a yield grid j of areaSij is:


The weight to be applied to production and to the area of grid ij to determine the parcel production (Pr odi) is therefore:

To apply this coefficient, follow these steps:

For an estimate of the production of the primary sampling unit:i. Estimate the production of each yield plot using the yield of the grids it contains;ii. Determine the average production of a parcel from that of the yield plots;iii. Infer the production for all the parcels from the primary sampling unit.
















p nN

ia

= i = 1, 2, 3, ..., Na


p sS

ijij


p m sS

iji ij

i= .



et a une culture donnée, on a :

- Superficie en pure : !S1 = C yi i∑ 1́ où ʹy i1 est l’estimation de la superficie totale des parcelles de

l’unité primaire i ne portant que la culture a

- Superficie en association où la culture a est la culture principale :

!S2 = C yi i∑ 2́ où ʹy i2 est l’estimation de la superficie totale des parcelles de

l’unité primaire i portant une association où la culture a est la culture principale

- Superficie en association où la culture a est une culture secondaire :


l’unité primaire i portant une association où la culture a est une culture secondaire.

3.2.3. LecalculdesrendementsàpartirdesdonnéesdeterrainLe rendement au niveau des unités primaires Le calcul du rendement moyen est lié à la méthode de tirage utilisée pour avoir les parcelles à rendement. On relève deux méthodes pour la détermination des rendements au niveau des unités primaires (villages, zones de dénombrement ou sections d’énumération) :

a) Les parcelles à rendement sont tirées avec probabilités égales

Dans une unité primaire où on a recensé Na parcelles d’une culture « a », le tirage de n parcelles à rendement avec probabilités égales équivaut à une probabilité pi pour toutes les parcelles donnée par :

p nN

ia

= i = 1, 2, 3,....... Na

Dans une parcelle i à rendement dont la superficie est Si , la probabilité pour un carré j de superficie sij est :

p sS

ijij

i= , ou, plus généralement, si la parcelle i doit porter mi carrés de rendement

p msS

iji ij

i= .

Au total la probabilité d’un carré de rendement d’être sélectionné est :

i

iji

aijiij

Ssm

NnppP ==

Ainsi le coefficient d’extrapolation à appliquer à la production et à la superficie du carré ij pour la détermination de la production de la parcelle ( Pr odi ) est :
















p nN

ia

= i = 1, 2, 3, ..., Na


p sS

ijij


p m sS

iji ij

i= .



et a une culture donnée, on a :

- Superficie en pure : !S1 = C yi i∑ 1́ où ʹy i1 est l’estimation de la superficie totale des parcelles de

l’unité primaire i ne portant que la culture a

- Superficie en association où la culture a est la culture principale :


l’unité primaire i portant une association où la culture a est la culture principale

- Superficie en association où la culture a est une culture secondaire :


l’unité primaire i portant une association où la culture a est une culture secondaire.

3.2.3. LecalculdesrendementsàpartirdesdonnéesdeterrainLe rendement au niveau des unités primaires Le calcul du rendement moyen est lié à la méthode de tirage utilisée pour avoir les parcelles à rendement. On relève deux méthodes pour la détermination des rendements au niveau des unités primaires (villages, zones de dénombrement ou sections d’énumération) :

a) Les parcelles à rendement sont tirées avec probabilités égales

Dans une unité primaire où on a recensé Na parcelles d’une culture « a », le tirage de n parcelles à rendement avec probabilités égales équivaut à une probabilité pi pour toutes les parcelles donnée par :

p nN

ia

= i = 1, 2, 3,....... Na

Dans une parcelle i à rendement dont la superficie est Si , la probabilité pour un carré j de superficie sij est :

p sS

ijij

i= , ou, plus généralement, si la parcelle i doit porter mi carrés de rendement

p msS

iji ij

i= .

Au total la probabilité d’un carré de rendement d’être sélectionné est :

i

iji

aijiij

Ssm

NnppP ==

Ainsi le coefficient d’extrapolation à appliquer à la production et à la superficie du carré ij pour la détermination de la production de la parcelle ( Pr odi ) est :


i

iji

aijiij

Ssm

NnppP ==

The weight to be applied to production and to the area of grid ij to determine the parcel production (ABCDE) is therefore:

CP

Nn

Sm s

ijij

a i

i ij= =1

ABCDE = FEG×IEGJEEKL where IEG is the production of the yield grid ij

It therefore follows9MNDE = OPQRSTS

(average yield of a given crop in parcel i).


For an estimate of the production of the primary sampling unit:

i) Estimate the production of each yield plot using the yield of the grids it contains;

ii) Determine the average production of a parcel from that of the yield plots;

iii) Infer the production for all the parcels from the primary sampling unit.

Similarly, to estimate the area of the primary sampling unit:

i) Estimate the average area of a parcel from that of the plots selected for the crop cutting experiment,

ii) Infer the area for all the parcels from the primary sampling unit. The yield of the primary sampling unit is then obtained from the ratio of its production to its area.

b) Yield plots are selected with probability proportional to size

The probability of selecting a parcel i of area Si is then:

unit samplingprimary thefrom selected be toplots yield ofnumber theis where nSnSipi =

In yield plot i , the probability of selecting yield grid sij is psS

ijij

i=

Overall, the probability of selecting yield grid sij is:

P nSSsS

nsS

iji ij

i

ij= =

The weight of the grid is then: C Sns

ijij

=

The production Prod and area Sup of the primary sampling unit are then estimated to infer its yield called Rend:


101

Similarly, to estimate the area of the primary sampling unit:i. Estimate the average area of a parcel from that of the plots selected for the crop cutting experiment,ii. Infer the area for all the parcels from the primary sampling unit.

The yield of the primary sampling unit is then obtained from the ratio of its production to its area.

b. Yield plots are selected with probability proportional to sizeThe probability of selecting a parcel i of area Si is then:


The average yield of crop selected in a primary sampling unit is therefore the simple arithmetic average of yields obtained from yield grids in the primary sampling unit.

Stratum yieldAn average yield (pure, mixed, pure and mixed together) is therefore linked to each sampling unit in the first-stage sample and for each crop; an average yield should now be inferred from this at the stratum level.

Yield plots do not constitute a third stage of selection in the sample design adopted above. If this was the case, the yield at stratum level would be obtained by calculating an average yield from those of the sample villages, all weighted by their proportion in the sample of respective villages, in other words.


i

iji

aijiij

Ssm

NnppP ==

The weight to be applied to production and to the area of grid ij to determine the parcel production (ABCDE) is therefore:

CP

Nn

Sm s

ijij

a i

i ij= =1

ABCDE = FEG×IEGJEEKL where IEG is the production of the yield grid ij

It therefore follows9MNDE = OPQRSTS

(average yield of a given crop in parcel i).


For an estimate of the production of the primary sampling unit:

i) Estimate the production of each yield plot using the yield of the grids it contains;

ii) Determine the average production of a parcel from that of the yield plots;

iii) Infer the production for all the parcels from the primary sampling unit.

Similarly, to estimate the area of the primary sampling unit:

i) Estimate the average area of a parcel from that of the plots selected for the crop cutting experiment,

ii) Infer the area for all the parcels from the primary sampling unit. The yield of the primary sampling unit is then obtained from the ratio of its production to its area.

b) Yield plots are selected with probability proportional to size

The probability of selecting a parcel i of area Si is then:

unit samplingprimary thefrom selected be toplots yield ofnumber theis where nSnSipi =

In yield plot i , the probability of selecting yield grid sij is psS

ijij

i=

Overall, the probability of selecting yield grid sij is:

P nSSsS

nsS

iji ij

i

ij= =

The weight of the grid is then: C Sns

ijij

=



Prod ijijiji j ij

syynsS

grid of production theis where∑∑=

Sup Snss S

nijji

ij

ji

= =∑∑ ∑∑

It then follows that

9MND =ABCD!UV ∑∑

∑∑

∑∑==

i j ij

iji jij

sy

nnS

ynsijS

1

The average yield of crop selected in a primary sampling unit is therefore the simple arithmetic average of yields obtained from yield grids in the primary sampling unit.

Stratum yield An average yield (pure, mixed, pure and mixed together) is therefore linked to each sampling unit in the first-stage sample and for each crop; an average yield should now be inferred from this at the stratum level.

Yield plots do not constitute a third stage of selection in the sample design adopted above. If this was the case, the yield at stratum level would be obtained by calculating an average yield from those of the sample villages, all weighted by their proportion in the sample of respective villages, in other words

r

PPr

PP

Pr

P

ii

i

i n

i

i

i n

i ii

i n

ii

i n= ==

=

=

==

=

=

=

∑

∑

∑

∑1

1

1

1

There is clearly a bias in this calculation due to the weightPPi of yield ri : the weight

of the yield of a PSU is as great as the PSU is large. So, in a situation where the stratum has two sample PSU:

- one which is small, P1 , where the whole population grows a crop a and the average yield of which in the village is r1 ,

- the other which is highly populated (with population P2 ) where crop a is virtually non-existent; just one grid has been placed by chance on one of the rare, small parcels in the village and a yield of r2 has been observed on this grid.

The yield of the stratum (according to the following formula) rP r P rP P

=+

+1 1 2 2

1 2 tends far

more towards r2 than towards r1 , which is inconsistent and unrealistic.

We are left with the same inconsistency as if we had used a simple arithmetic average.

To better understand how to calculate stratum yield, the survey of yield plots should be


102

There is clearly a bias in this calculation due to the weight of yield ri: the weight of the yield of a PSU is as great as the PSU is large. So, in a situation where the stratum has two sample PSU:• one which is small Pi where the whole population grows a crop a and the average yield of which in the village

is ri,• the other which is highly populated (with population P2) where crop a is virtually non-existent; just one grid

has been placed by chance on one of the rare, small parcels in the village and a yield of r2 ahas been observed on this grid.

The yield of the stratum (according to the following formula) tends far more towards r2 than towards r1, which is inconsistent and unrealistic.

We are left with the same inconsistency as if we had used a simple arithmetic average.

To better understand how to calculate stratum yield, the survey of yield plots should be considered a separate light survey, performed independently in each sample primary unit with the objective of determining yield at primary sampling unit level. Knowing the yield in each sample primary unit, the aim will then be to estimate the stratum yield.

The yield ri of primary sampling unit i is assumed to represent the ratio of production Pi to area Si of the stratum :


Le rendement ri de l’unité primaire i est supposé représenter le rapport de la production Pi à

la superficie Si de la strate : rPSii

i

= . Pour une strate ayant nunités primaires échantillons, on

est donc dans une situation où on a n rendements rPSii

i

= i n= 1 2 3, , ,....., et on doit déterminer

un rendement r pour l’ensemble.

On a n

nn

n

n

nni

ii

ni

ii

SSS

SSPS

SPS

SP

SSSSPPPP

S

Pr

+⋅⋅⋅⋅⋅++

+⋅⋅⋅⋅⋅++

=+⋅⋅⋅⋅⋅⋅+++

+⋅⋅⋅⋅⋅⋅+++==

∑

∑=

=

=

=

21

22

21

1

1

321

321

1

1

=+ + ⋅ ⋅ ⋅ ⋅ ⋅+

+ + ⋅ ⋅ ⋅ ⋅ ⋅+=∑∑

r S r S r SS S S

r SS

n n

n

i i

i

1 1 2 2

1 2

La superficie Si étant estimée par iiy

nNi où iy est la somme des superficies des exploitations

échantillon de l’unité primaire i on aboutit alors à :

1

1

i i

i ni

i ii n

ii

i i

N y rnrN yn

=

==

=

=∑

∑

Il existe aussi des estimations par télédétection avec des résultats qu’il faut valider par les données de terrain.

3.3. ProductionLa production comme produit de la superficie par le rendement : Connaissant la superficie et le rendement d’une culture, la production de la strate s’obtient par le produit des deux grandeurs exprimées dans les compatibles. Si dans le calcul du rendement il a été tenu compte du type d’association (selon que la culture soit pure, principale ou secondaire ou selon le nombre de cultures associées), le rendement de chaque type d’association sera appliqué à la superficie correspondante. Si au contraire il n’a pas été tenu compte des types d’association, le rendement moyen obtenu sera appliqué à la superficie totale.

La production comme donnée extrapolée : Si la production est connue pour chaque exploitation, la détermination de la production de la strate se ramène à l’estimation d’un total où y représente la production. Cette méthode d’estimation n’est encore utilisée que dans les pays en développement du fait de l’impossibilité d’obtenir la production de chaque exploitation.

À partir des méthodes d’estimation de la production agricole ci-dessus décrites, on peut déduire deux types de technique de prévision de récoltes : celles utilisant les superficies cultivées et les rendements prévisionnels (prévision par les carrés de densité) et celles utilisant la déclaration du paysan (prévision par interview).

3.4. Prévisiondesrécoltes

La méthode suivante qui s’applique aux céréales n’est pas adaptée aux autres cultures.

. For a stratum having n sample primary units, we are therefore in a situation where we have n

yields




i



i



On a n

nn

n

n

nni

ii

ni

ii

SSS

SSPS

SPS

SP

SSSSPPPP

S

Pr

+⋅⋅⋅⋅⋅++

+⋅⋅⋅⋅⋅++

=+⋅⋅⋅⋅⋅⋅+++

+⋅⋅⋅⋅⋅⋅+++==

∑

∑=

=

=

=

21

22

21

1

1

321

321

1

1

=+ + ⋅ ⋅ ⋅ ⋅ ⋅+

+ + ⋅ ⋅ ⋅ ⋅ ⋅+=∑∑

r S r S r SS S S

r SS

n n

n

i i

i

1 1 2 2

1 2




1

1

i i

i ni

i ii n

ii

i i

N y rnrN yn

=

==

=

=∑

∑







and a yield must be determined for the whole.

Area Si being estimated by




i



i



On a n

nn

n

n

nni

ii

ni

ii

SSS

SSPS

SPS

SP

SSSSPPPP

S

Pr

+⋅⋅⋅⋅⋅++

+⋅⋅⋅⋅⋅++

=+⋅⋅⋅⋅⋅⋅+++

+⋅⋅⋅⋅⋅⋅+++==

∑

∑=

=

=

=

21

22

21

1

1

321

321

1

1

=+ + ⋅ ⋅ ⋅ ⋅ ⋅+

+ + ⋅ ⋅ ⋅ ⋅ ⋅+=∑∑

r S r S r SS S S

r SS

n n

n

i i

i

1 1 2 2

1 2




1

1

i i

i ni

i ii n

ii

i i

N y rnrN yn

=

==

=

=∑

∑







where yi is the sum of the areas of the sample holdings in primary sampling unit i the following then results:

There are also remote sensing estimates with results that need to be validated by field data.


Sup Snss S

nijjiij

ji= =∑∑ ∑∑

Il vient alors Re Prnd

odSup

Snsij

y

Sn

nys

ij

ji ij

ijji

= = =∑∑

∑∑∑∑

1

Le rendement moyen d’une culture de l’unité primaire est donc la moyenne arithmétique simple des rendements obtenus sur les carrés de rendement de l’unité primaire.

Le rendement de la strate À chaque unité du premier degré échantillon et pour chaque culture est donc rattaché un rendement moyen (pure, association, pure et association confondue), il s’agit maintenant d’en déduire un rendement moyen au niveau de la strate.

Les parcelles à rendement ne constituent pas un 3e degré de tirage du plan de sondage adopté ci-dessus. En effet, si c’était le cas, le rendement de la strate s’obtiendrait en calculant un rendement moyen à partir de ceux des villages échantillons, tous pondérés par leur poids dans l’échantillon des villages respectifs c’est-à-dire,

r

PPr

PP

Pr

P

ii

i

i n

i

i

i n

i ii

i n

ii

i n= ==

=

=

==

=

=

=

∑

∑

∑

∑1

1

1

1

Il y a manifestement un biais dans ce calcul dû au poidsPPi du rendement ri : le poids du

rendement d’une UP est d’autant plus important que cette UP est grande. Ainsi, dans une situation où la strate à deux UP échantillons :

- l’une de petite taille P1 où toute la population pratique une culture a et dont le rendement moyen au niveau du village est r1 ,

- l’autre très peuplé (avec une population de P2 ) où la culture a est presque inexistante, seulement, par hasard, un carré a pu être placé sur l’une des rares et petites parcelles existantes dans le village, un rendement de r2 a été observé sur ce carré.

Le rendement de la strate (d’après la formule ci-dessus) rP r P rP P

=+

+1 1 2 2

1 2

tend beaucoup plus

vers r2 que vers r1 , ce qui est incohérent et loin de la réalité.

On reste dans la même incohérence si l’on se contente d’une moyenne arithmétique simple.

Pour mieux comprendre comment calculer le rendement de la strate, il faut considérer l’enquête des parcelles à rendement comme une enquête légère à part, exécutée indépendamment dans chaque unité primaire échantillon et ayant comme objectif la détermination du rendement au niveau unité primaire. Connaissant le rendement dans chaque unité primaire échantillon, on se proposera alors d’estimer le rendement de la strate.


Sup Snss S

nijjiij

ji= =∑∑ ∑∑


odSup

Snsij

y

Sn

nys

ij

ji ij

ijji

= = =∑∑

∑∑∑∑

1




r

PPr

PP

Pr

P

ii

i

i n

i

i

i n

i ii

i n

ii

i n= ==

=

=

==

=

=

=

∑

∑

∑

∑1

1

1

1






=+

+1 1 2 2

1 2

tend beaucoup plus





Sup Snss S

nijjiij

ji= =∑∑ ∑∑


odSup

Snsij

y

Sn

nys

ij

ji ij

ijji

= = =∑∑

∑∑∑∑

1




r

PPr

PP

Pr

P

ii

i

i n

i

i

i n

i ii

i n

ii

i n= ==

=

=

==

=

=

=

∑

∑

∑

∑1

1

1

1






=+

+1 1 2 2

1 2

tend beaucoup plus





considered a separate light survey, performed independently in each sample primary unit with the objective of determining yield at primary sampling unit level. Knowing the yield in each sample primary unit, the aim will then be to estimate the stratum yield.

The yield ri of primary sampling unit i is assumed to represent the ratio of production

Pi to area Si of the stratum: rPSii

i

= . For a stratum having n sample primary units,

we are therefore in a situation where we have n yields rPSii

i

= i n= 1 2 3, , ,....., and a

yield r must be determined for the whole.

We have n

nn

n

n

nni

ii

ni

ii

SSS

SSPS

SPS

SP

SSSSPPPP

S

Pr

+⋅⋅⋅⋅⋅++

+⋅⋅⋅⋅⋅++

=+⋅⋅⋅⋅⋅⋅+++

+⋅⋅⋅⋅⋅⋅+++==

∑

∑=

=

=

=

21

22

21

1

1

321

321

1

1

=+ + ⋅ ⋅ ⋅ ⋅ ⋅+

+ + ⋅ ⋅ ⋅ ⋅ ⋅+=∑∑

r S r S r SS S S

r SS

n n

n

i i

i

1 1 2 2

1 2

Area Si being estimated by iiy

nNi , where iy is the sum of the areas of the sample

holdings in primary sampling unit i , the following then results:

1

1

i i

i ni

i ii n

ii

i i

N y rnrN yn

=

==

=

=∑

∑

There are also remote sensing estimates with results that need to be validated by field data.

3.3. ProductionProduction as a product of area and yield: Knowing the area and yield of a crop, the production of the stratum is obtained by multiplying the two values expressed in compatible units. If the yield calculation has taken into account the type of mixture (whether the crop is pure, main or secondary or according to the number of mixed crops), the yield of each type of mixture will be applied to the corresponding area. If, however, the types of mixture have not been taken into account, the average yield obtained will be applied to the total area.

Production as extrapolated data: If production is known for each holding, determining the production of the stratum involves estimating a total where y represents production. This method of estimation is still used only in developing countries as it is impossible to obtain the production of each holding.




i



i



On a n

nn

n

n

nni

ii

ni

ii

SSS

SSPS

SPS

SP

SSSSPPPP

S

Pr

+⋅⋅⋅⋅⋅++

+⋅⋅⋅⋅⋅++

=+⋅⋅⋅⋅⋅⋅+++

+⋅⋅⋅⋅⋅⋅+++==

∑

∑=

=

=

=

21

22

21

1

1

321

321

1

1

=+ + ⋅ ⋅ ⋅ ⋅ ⋅+

+ + ⋅ ⋅ ⋅ ⋅ ⋅+=∑∑

r S r S r SS S S

r SS

n n

n

i i

i

1 1 2 2

1 2




1

1

i i

i ni

i ii n

ii

i i

N y rnrN yn

=

==

=

=∑

∑








103

3.3. PRoDUCTIoN

Production as a product of area and yield:Knowing the area and yield of a crop, the production of the stratum is obtained by multiplying the two values expressed in compatible units. If the yield calculation has taken into account the type of mixture (whether the crop is pure, main or secondary or according to the number of mixed crops), the yield of each type of mixture will be applied to the corresponding area. If, however, the types of mixture have not been taken into account, the average yield obtained will be applied to the total area.

Production as extrapolated data:If production is known for each holding, determining the production of the stratum involves estimating a total where represents production. This method of estimation is still used only in developing countries as it is impossible to obtain the production of each holding.

The methods of estimating agricultural production described above can result in two types of crop forecasting techniques: those using cropping areas and yield forecasting (forecasting by density grids) and those using declaration by the farmer (forecasting by interview).

3.4. CRoP foReCASTING

The following method which applies to cereals is not suited to other crops.

3.4.1. forecasting from cropping areasThe principle of this method is the same as that of estimating final production, i.e. determining an average yield to be applied to the various cropping areas. Whereas yield for final production is obtained using yield grids, yield forecasting uses density grids.

The yield grids used to estimate final yields are also used in yield forecast estimates when they are known as density grids. The density grid is in some cases a part of the yield grid with the aim of facilitating the field work.

Remember that the method of estimating yield from yield grids involves placing a grid at random in a subsample or in all of the parcels of sample holdings to determine an average yield after harvest. The method of estimating yield forecasts consists in counting the number of potential cobs in the yield grids before harvest, when crop forecasting takes place.

Forecast production is calculated in the same way as final production, as described above, with yield grids being replaced by their yield forecasts.

The yield forecast of the grid is obtained by multiplying the number of potential cobs by mean cob weight.


104

Determination of mean cob weightMean cob weight is a parameter recorded in the field and entered in survey forms. It is determined for a given crop from the random collection of a sample of cobs, where possible from the previous year, from each sample holding.

The purpose of determining mean cob weight is to apply it to the number of potential cobs present in the density grids to determine the yield forecast. To do this, given all the factors that influence cob weight, the mean weights corresponding to cobs produced under the same conditions as the environment of the grid would ideally be applied to the number of cobs. Three cases can thus be envisaged in a given stratum and the application of these depends on how the variation in mean cob weight is assessed: i. Using the mean cob weight of the holding to which the parcel belongs where the grid is placed. A sample is

then collected of the previous year’s harvest from all the holdings where a density grid is placed, and the mean weight of the cobs collected is calculated by crop variety;

ii. Applying a single mean weight, that of the village, to all the density grids in the same village. A sample of cobs by variety will be collected from the previous year’s harvest from sampled holdings to calculate mean cob weight in the PSU in question.

iii. Finally, applying the same mean weight, that of the stratum, to potential cobs in the same stratum. The sample of cobs to be collected from the previous year’s harvest from sampled holdings to calculate mean cob weight in a PSU will be taken from the whole of the stratum in question.

Yield forecast estimateThe purpose of the method is to forecast production for each yield grid. To do this, the number of potential cobs is multiplied by the mean cob weight. The difference between the forecast and the real production of the grid thus depends on two elements: first the difference between the number of potential cobs and the number of cobs at harvest, and second the difference between the mean weight applied and the real weights of the cobs harvested.i. Number of potential cobs: This number is at least equal to the number of cobs actually collected; not all potential

cobs in the yield grid will necessarily reach maturity for various reasons. However, because of the collection period of data for forecasts (e.g. September for Sahelian countries), the majority of potential cobs found must be able to reach maturity. The number of potential cobs is closer to the number of cobs harvested, the nearer the forecasting period is to the harvest. Keeping to the scheduled periods for counting potential cobs is therefore vital in this forecasting method;

ii. The mean cob weight to be applied to cobs in the density grid can create great differences between the forecast production of the grid and its final production. The difference between the forecast production and the final production of the grid depends on the differences between the mean cob weight (to be applied) and the real weights of the various cobs.


105

3.4.2. forecasting by interviewThe method is based on producers’ ability to assess the forecast production of their cereal parcels. Each sampled producer declares the previous year’s production and the forecast production for each cereal crop. The forecast growth rate of production is determined from these two parameters. This forecast growth rate is applied to the final production of the previous year obtained by the survey for forecast production. As the results of the previous year are provided by an administrative or other division (region, province, district, etc.), the rates will be calculated according to the same bodies. Two collection methods1 are distinguished in the interview with the farmer for a given crop:i. the case where the farmer is asked for the production of each of the farm’s parcels for the previous year and

their forecast production;ii. the case where the farmer is asked for the farm’s total production (all parcels together) for the previous year and

the forecast production of each parcel.

To illustrate these two cases, we can adopt the following notations for a region (province, district) containing m strata:y : forecast production of a given crop according to the farmer’s declarationy' : previous year’s production of a given crop according to the producer’s declaration

i : identification number of strata,


i : numéro d’identification des strates, i m= 1 2 3, , ,.....,

ni : nombre d’unités primaires de la strate i

Yi' : la production définitive de la strate i selon l’enquête agricole

Pi : la taille de la strate i (habitants, ménages, concessions ou exploitations)

j numéro d’identification des unités primaires

Pij : la taille de l’unité primaire j de la strate i

ijm : nombre d’unités secondaires de l’unité primaire j (village, SE ou ZD) de la strate i

ijM : le nombre d’exploitations de l’unité primaire j de la strate i

k : numéro d’identification des unités secondaires (exploitation)

pk : nombre de parcelles de l’exploitation k portant la culture étudiée au cours de la présente campagne

pk' : nombre de parcelles de l’exploitation k ayant porté la culture étudiée au cours de la

campagne précédente 1er cas : L’exploitant déclare pour chaque parcelle (y compris les parcelles abandonnées), la production de la campagne précédente et la production prévisionnelle :

La production prévisionnelle de l’exploitation k de l’unité primaire (village, SE ou ZD) j de la strate i est:

y yijk ll

l pk=

=

=

∑1

, sa production de la campagne précédente est :

y yijk ll

l pk' '

'

==

=

∑1

La production prévisionnelle de strate i est :

j

mk

kijknj

j ij

ij

i

ii m

y

PM

nPy

j

i ∑∑

=

==

=

= 1

1

= ijk

ni

i

mj

jij

j

ijk

ij

ijni

i

mj

j i

i yCmy

PM

nP i ji j

∑∑∑∑=

=

=

=

=

=

=

=

=1 11 1

où Cij est le coefficient

d’extrapolation de l’unité primaire (village, SE ou ZD) j

De même, la production de la campagne précédente est :

'

1 1

'

1 1

1

'

1

'ijk

ni

i

mj

jij

ij

ijk

ij

ijni

i

mj

j i

i

ij

mk

kijknj

j ij

ij

i

ii yC

my

PM

nP

m

y

PM

nPy

i ji j

j

i

∑∑∑∑∑

∑=

=

=

=

=

=

=

=

=

==

=

===

où Cij est le coefficient d’extrapolation de la strate j .

ni : number of primary sampling units in stratum iyi' : final production of stratum according to the agricultural surveyPi : size of stratum i (inhabitants, households, compounds or holdings) j : identification number of primary sampling unitsPij : size of primary sampling unit j of stratum imij : number of secondary units of primary sampling unit j (village, ES or EA) of stratum iMij : number of holdings in primary sampling unit j of stratum ik : identification number of secondary units (holding)pk : number of parcels of holding k growing the crop studied in the current yearPk' : number of parcels of holding k having grown the crop studied in the previous year

1st case: The holder declares the production of the previous year and the forecast production for each parcel (including abandoned parcels):The forecast production of holding k of primary sampling unit (village, ES or EA) j of stratum i is:

1 Each of these two methods has drawbacks that cannot be ignored: - 1st method: harvesting practice means that the farmer is often more able to give the total production for the previous year than parcel by

parcel production; - 2nd method: there is a risk, in asking farmers for the total production of the holding in the previous year, that they will limit themselves

to the production of common parcels only, ignoring that of individual parcels; however, as forecasting by interview is based on the deter-mination of a growth rate, the method can be corrected, using only the common parcels on the holding to carry out the exercise.


To illustrate these two cases, we can adopt the following notations for a region (province, district) containing m strata:

y : forecast production of a given crop according to the farmer’s declaration

y ' : previous year’s production of a given crop according to the producer’s declaration

i : identification number of strata, i m= 1 2 3, , ,.....,

ni : number of primary sampling units in stratum i

Yi' : final production of stratum i according to the agricultural survey

Pi : size of stratum i (inhabitants, households, compounds or holdings)

j : identification number of primary sampling units

Pij : size of primary sampling unit j of stratum i

ijm : number of secondary units of primary sampling unit j (village, ES or EA) of stratum i

ijM : number of holdings in primary sampling unit j of stratum

k : identification number of secondary units (holding)

pk : number of parcels of holding k growing the crop studied in the current year

pk' : number of parcels of holding k having grown the crop studied in the

previous year 1st case: The holder declares the production of the previous year and the forecast production for each parcel (including abandoned parcels):

The forecast production of holding k of primary sampling unit (village, ES or EA) j of stratum i is:

y yijk ll

l pk=

=

=

∑1

, its production for the previous year is:

y yijk ll

l pk' '

'

==

=

∑1

i


106

The forecast production of stratum i is:

The forecast growth rate


Le calcul du taux d’accroissement prévisionnel Tyyii

i

= ' de la production de la strate i entre la

campagne précédente et la campagne s’en suit et, la production prévisionnelle de la strate sera

!Y T Yi i i= × '

2e cas : L’exploitation donne le total de sa production lors de la dernière campagne puis pour chaque parcelle exploitée, elle donne une production prévisionnelle.

Les calculs se ramènent à ceux du 1er cas, la seule différence est qu’ici les yijk ne sont pas calculés comme ci-dessus, mais donnés directement par l’exploitation.

Cette méthode a l’avantage d’être simple au niveau de la collecte des données (interview), cependant, elle est beaucoup plus apte à donner une tendance (plutôt grossière) qu’un chiffre de prévision très proche de la réalisation, surtout quand la campagne n’est pas uniforme dans son évolution par rapport à l’année de référence. En outre, l’imperfection des unités de mesure locale comme unité de mesure de poids, et le manque de précision dans la déclaration de prévision de l’exploitant font que le taux d’accroissement à appliquer à la production de la campagne précédente est souvent indicatif.

of the production of stratum i between the previous year and the following year is calculated and the forecast production of the stratum will be

2nd case: The holding gives the whole of its production in the previous year, then for each parcel farmed, it gives a forecast production.

The calculations are similar to those in the 1st case, the only difference being that in this case yijk are not calculated as above, but given directly by the holding.

This method has the advantage of being simple as regards data collection (interview), however it is far more likely to give a (rather rough) trend than a forecast figure very close to the real figure, particularly when the new crop year is not uniform in comparison with the reference year. Furthermore, the shortcomings of local units of measurement as units of measuring weight, and inaccuracies in forecast declarations by the holder mean that the growth rate to be applied to the previous year’s production is often just a guide.


The forecast production of stratum i is:

j

mk

kijknj

j ij

ij

i

ii m

y

PM

nPy

j

i ∑∑

=

==

=

= 1

1

= ijk

ni

i

mj

jij

j

ijk

ij

ijni

i

mj

j i

i yCmy

PM

nP i ji j

∑∑∑ ∑=

=

=

=

=

=

=

=

=1 11 1

where Cij is the weight of

primary sampling unit (village, ES or EA) j

Similarly, the production for the previous year is:

'

1 1

'

1 1

1

'

1

'ijk

ni

i

mj

jij

ij

ijk

ij

ijni

i

mj

j i

i

ij

mk

kijknj

j ij

ij

i

ii yC

my

PM

nP

m

y

PM

nPy

i ji j

j

i

∑ ∑∑ ∑∑

∑=

=

=

=

=

=

=

=

=

==

=

===

where Cij is the weight of stratum j .

The forecast growth rate T yyii

i

= ' of the production of stratum i between the previous

year and the following year is calculated and the forecast production of the stratum will be

!Y T Yi i i= × '

2nd case: The holding gives the whole of its production in the previous year, then for each parcel farmed, it gives a forecast production.

The calculations are similar to those in the 1st case, the only difference being that in this case yijk are not calculated as above, but given directly by the holding.

This method has the advantage of being simple as regards data collection (interview), however it is far more likely to give a (rather rough) trend than a forecast figure very close to the real figure, particularly when the new crop year is not uniform in comparison with the reference year. Furthermore, the shortcomings of local units of measurement as units of measuring weight, and inaccuracies in forecast declarations by the holder mean that the growth rate to be applied to the previous year’s production is often just a guide.


Le calcul du taux d’accroissement prévisionnel Tyyii

i

= ' de la production de la strate i entre la

campagne précédente et la campagne s’en suit et, la production prévisionnelle de la strate sera

!Y T Yi i i= × '

2e cas : L’exploitation donne le total de sa production lors de la dernière campagne puis pour chaque parcelle exploitée, elle donne une production prévisionnelle.

Les calculs se ramènent à ceux du 1er cas, la seule différence est qu’ici les yijk ne sont pas calculés comme ci-dessus, mais donnés directement par l’exploitation.

Cette méthode a l’avantage d’être simple au niveau de la collecte des données (interview), cependant, elle est beaucoup plus apte à donner une tendance (plutôt grossière) qu’un chiffre de prévision très proche de la réalisation, surtout quand la campagne n’est pas uniforme dans son évolution par rapport à l’année de référence. En outre, l’imperfection des unités de mesure locale comme unité de mesure de poids, et le manque de précision dans la déclaration de prévision de l’exploitant font que le taux d’accroissement à appliquer à la production de la campagne précédente est souvent indicatif.


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3.5. ANALySIS AND DISSeMINATIoN

Analysis and dissemination are key stages in a survey process. They are the means of checking that the aims of the survey have been fully met. They require a cooperative approach with users. There are several production methods and means of making them available to users.

3.5.1. Analysis techniques The socioeconomic situation generally varies between regions and subregions in a given country. This is particularly the case in developing countries where the equal distribution of scarce resources is a major problem. In the majority of cases, resources are distributed unequally at the central level, with the result that poverty is rarely uniform.

a. Census and sample survey integration (small domain estimation)As poverty reduction measures carried out locally, in districts and subdistricts for example, become more effective, understanding poverty and levels of inequality in different places is a major step forwards in designing appropriate development programmes to improve socioeconomic status. It is therefore necessary to have available reliable data on socioeconomic status or, more precisely, on quality of life, which are disaggregated geographically to prepare development plans and monitor progress. Indicators disaggregated by geographic sector provide information on the geographical distribution of poverty and inequality in a country.

Census and surveys of households are the two main sources of data on quality of life. Census data concern all households and can be used to calculate reliable estimates according to very detailed disaggregation, at the level of towns and small villages. However, census do not collect the required information on income or consumption to produce reliable indicators on quality of life levels and their variations, for example poverty levels of indicators of inequality.

Household surveys often include a detailed section on income or consumption expenditure. But as the sample size is relatively small, the collected information is generally representative only of geographic sectors or larger regions of the country.

The disaggregation of poverty indicators by small geographic sector is often called a “poverty map”. The method most widely used to map poverty is the small area estimation technique (Ghosh and Rao, 1994; Hentschel et al., 1998; Hentschel et al., 2000; Elbers and Lanjouw, 2000 and 2003; World Bank, 2000 and 2003; Rogers et al, 2006; other poverty mapping approaches have been proposed by Davis, 2003; Robinson et al., 2007; Birungi et al., 2005).

The small area technique involves combining sample survey and census data, given that household surveys provide poverty indicators (generally quality of life indicators derived from consumption or expenditure) that are valid only for a sample of households, while census cover all households but cannot produce direct indicators of poverty.

To apply this technique, data from at least two sources are needed. The first data source is a detailed household survey which includes a quality of life indicator, generally consumption per inhabitant. The second data source is a national census or, if unavailable, a large-scale national survey targeting a significant part of the country’s population.


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The basic principle of small area estimation is “to borrow strength” from related domains by means of indirect estimators based on implicit or explicit matching models, with the aid of additional information. By combining the two data sources, this technique makes use of detailed data derived from sample surveys of households and the comprehensive cover of census (ONU-CEA-CAS)2.

N. B. Professionals responsible for data processing, analysis and dissemination must be capable of applying small area estimation methods.

b. Spatial analysisIn statistics, spatial analysis or spatial statistics covers all formal techniques that allow the study of entities based on their topological, geometric or geographical properties (Wikipedia; Cressie, N., 19933).

Spatial analysis involves examining places and attributes and studying the relations of characteristics in spatial data and using other analytical techniques to answer a question or produce useful knowledge. Spatial analysis creates or extracts knowledge from spatial data, which broadly speaking amounts to answering the question “what is happening where?”.

Spatial statistics covers numerous disciplines and uses methods which vary according to the specific questions studied, whether it involves making forecasts or processing large volumes of data generated by GPS and remote sensing. Spatial statistical analysis techniques can, among other advantages, offer the possibility of summarizing complex spatial trends, making it possible to assimilate them by eye and by the human mind; orientations and interventions can thus be chosen on a more informed basis.

Agriculture, geology, soil, hydrology, the environment, ecology, mineral extraction, oceanography, forestry, air quality, remote sensing, epidemiology and disease mapping are all fields where spatial statistics can be applied. In agriculture, establishing a correlation between yield and topography can, for example, be vitally important.

Spatial data are unique in that geographic location is a key aspect that is common, either exactly or approximately, to data from different sources. Geographic information systems and the related science which is causing these technologies to evolve have a significant effect on spatial analysis.


3 Cressie, N. (1993). Statistics for Spatial Data. Revised edition. New York: Wiley.

box 19: SPATIAL ANALySIS

As the evaluation of the development of agriculture is related to numerous issues of spatial distribution,

spatial data analysis techniques are important for processing and analysing agricultural data.


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c. Geographic information system (GIS)The GIS can be defined as a system designed to capture, record, handle, analyse, manage and present all types of geographic data.

For example it allows analysis of agricultural yield data in combination with data on experimental parcels or fields. The GIS can be considered a system which combines mapping, statistical analysis and data processing. Owing to its increased reliability, the GIS can process large quantities of data obtained by remote sensing.

Remote sensing, GPS and GIS technologies now allow the agricultural sector to obtain data rapidly and to observe cyclical changes. As the applications of remote sensing, GPS and GIS technologies are becoming basic tools for evaluating agricultural development, countries are increasingly inclined to adopt GIS methods. These methods are useful for mapping agricultural yields, crops, harmful organisms, etc. and for carrying out relevant analyses. GIS can, for example, detect differences, for example in areas with a higher or lower yield.

Remote sensing can obtain information on the state of crops and forests. Rapid take-up of the use of remote sensing in nature conservation and protection, for example, has coincided with numerous reports suggesting a change in ecosystems and the destruction of the habitat of fauna on a vast scale. The concern created by the increase in unfavourable environmental conditions has led remote sensing specialists and users to keep up with technological developments (Lillesand et al., 2008).

As the agricultural sector is one of those that generates very large quantities of spatial data in all countries, national bodies should ensure that they take full advantage of it and take the necessary measures to have the required capacity to assess agricultural development appropriately.

N. B. Professionals responsible for processing agricultural data should be capable of using GIS technologies.

3.5.2. MetadataStatistical data without at least some types of metadata would be virtually unusable.

Metadata can be defined simply as any information which helps users to find, understand and use data and information. Metadata help to judge the quality of a survey and determine whether it meets expected needs. “Statistics without metadata would be like packs without labels on a supermarket shelf”.

Metadata are, so to speak, data about the content of data; they describe a set of data by providing information on subjects such as the source of the data, a definition of variables, calculation methods and other relevant technical and methodological aspects. The partial or total lack of metadata causes discrepancies in the analysis of results.

Metadata cover the following main items:• Concepts and definitions;• Objective measured;• Reference period of the data;• Target population;• Calculation method;• Sources of raw data;• Relevant level of disaggregation;• Any adjustments made to statistical sources and estimation procedures;• Shortcomings and proposed improvements;


110

• Contact person for updating the indicator;• All useful documents concerning the preparation and conduct of the collection operation (enumerator’s manual,

study protocol, etc.).

There is no single better standard for all types of metadata, but DDI4 is ideal for survey data.

The Statistical Data and Metadata eXchange (SDMX)5, which is a global initiative for establishing and improving statistical information exchange standards, should also be mentioned. This project started in 2002 with the support of seven institutions6. In 2005 it was recognized as an ISO standard with technical specification status (ISO/TS 17369). The revision of this standard was registered in 2013. This standard describes and formalizes how statistical data are exchanged. It also provides standard data and metadata formats, content directives, and IT architecture for data and metadata exchange.

Finally, metadata are important for long-term dissemination and storage (archived data) and archiving is important for supporting analysis and research.

3.5.3. ArchivingData archiving involves storing data in the long term. Archives are essential for official reference purposes. The organization generating the data must have a repository of official copies of all information made public, as archives of agricultural survey and census records. Furthermore, archives are an institutional memory, a systematic, reliable history of the experience of the organization which can be consulted for planning and evaluation purposes. They also serve to store data for future use.

The rapid fall in the cost of data processing and storage in digital form now means that all or almost all the information relating to any data collection operation can be archived cheaply, including planning files, operational documents, questionnaires, monitoring forms, data sets, final results and evaluation files. Efficient archiving involves organization of data production work; files must be named, indexed, protected and backed up appropriately, and must be stored in appropriate digital archives at the end of the whole process. The following points deserve particular attention.

a. ContentsAll information made public must be archived. Individual records containing all the data from census and sample surveys must be archived with their metadata to process them.

Given the unlimited capacity of digital media, the main constraints on the volume to be archived lie in the ability of the organization in question to store documents as they are produced and to index them so that the information can be readily retrieved in the future.

4 The Data Documentation Initiative is an international effort to establish a standard for technical documentation describing social science data. A membership-based Alliance is developing the DDI specification, which is written in XML (see http://www.ddialliance.org/).

5 http://ec.europa.eu/eurostat/fr/web/sdmx-web-services/sdmx, 20/02/20176 Bank for International Settlements (BIS), European Central Bank, Statistical Office of the European Union (Eurostat), International Mon-

etary Fund (IMF), Organisation for Economic Co-operation and Development (OECD), United Nations (Statistics Division) and World Bank.


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Numerous field operations involve the preparation of sketch maps of sampled regions or, in the case of a population census, of all the populated regions of the country. It may be desirable to archive these sketch maps, but it is not always possible. The maps used are, however, increasingly generated by geographic information software. In this case, the maps are created in digital format and they should be archived with the other documents.

b. Protection and maintenanceData protection involves protecting them against loss, corruption and unauthorized access. The rapid development of information technologies has created a new risk, the loss of archives stored on an obsolete computer medium. Computer archives must be protected against this risk by setting up a regular program to copy them to a different medium. Technologies evolve so rapidly that a medium may become obsolete in a few years.

Digital archives need hardware support which must be protected against risks of deterioration, loss and unauthorized access, in the same way as traditional paper archives. Owing to the relative ease with which digital data can be copied and moved, copies of digital archives can be stored in several places. Digital archives thus offer the possibility of reducing archiving costs and greatly facilitating access to the data.

However, digital media involve risks in terms of data security that do not exist with traditional paper media. The ease with which digital data can be changed creates risks of alteration, inadvertent corruption, and even total loss owing to unintentional deletion, which do not exist with traditional media. Measures are available to reduce these risks and render them negligible, but it is essential to understand the risks and to implement these measures.

c. Storing data securely It is important to pay attention to how data will be managed. When choosing the type of data dissemination, security issues and privacy should be considered.

If personal information is collected about individuals during data collection - for example information on their age, sex or address which could be used to identify them - it must be stored in a secure place. If there are data lists on paper, information that could enable identification must be locked away when not in use and access to it must be limited to a small number of people. For data stored on computer, security measures include creating individual password-protected user accounts and setting automatic exit from the screen or the connection (ONU-CEA-CAS7).



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3.5.4. Database and CountryStatAgriculturalcensusandsurveysgeneratealargenumberofdatasoadatabase–astructureddatasetorganizedforeasy, quick storage and recovery of data - generally needs to be created.

Numerous tools have been developed by international institutions to encourage countries to make data available to users. The main tools in current use over the previous two decades are the following:• The World Bank’s 2gLDB system;• DevInfo, developed by UNICEF jointly with the United Nations system;• FAO’s CountrySTAT;• StatBase, the ECA statistical database;• CensusInfo, developed by the United Nations Statistics Division, in partnership with UNICEF and UNFPA;• IMIS-Redatam, developed by ECLAC (Economic Commission for Latin America and the Caribbean);• NADA (National Data Archive), released by IHSN, the international household survey network, with the

operational support of the World Bank, PARIS21 and the OECD;• Data Portal, proposed by the ADB.

For example, Cameroon, Gabon, Mauritania, Niger and Senegal have adopted some of these tools to facilitate the dissemination of their data and metadata and provide easy access for users.

FAO promotes CountrySTAT. With this platform, FAO aims to harmonize data from different sources according to international standards and, at the same time, to guarantee the quality, reliability and comparability of the data (FAO, 20138).

CountrySTAT is a web-based information system for food and agriculture statistics at a national and subregional level and allows decision-makers to access statistics across thematic areas such as production, prices, trade and consumption. FAO has signed partnerships with central statistical offices and Ministries responsible for agriculture, fisheries and forestry to install the system and build national capacity to use it. Please refer to the following link to access the site: http://www.fao.org/economic/ess/ess-capacity/countrystathome/fr/

When creating a database, the issue of data security must be examined, the key point being to provide reliable, accurate data while preventing the disclosure of personal information on the individuals interviewed.

During production, the data which are processed and converted into useful information are presented in the form of tables in the required tabulation or report formats. The metadata that describe the data and provide essential information about them for interpreting the data and using them wisely must be included in the database.

8 FAO (2013). Statistical reference manual on data dissemination for the CountrySTAT system. Revision 3. Available at the following address: www.fao.org.fileadmin/templates/ess/documents/Statistical_Reference_Manual_2013.pdf.


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3.5.5. Safeguarding data – Dissemination systemsMethods of managing data dissemination mechanisms include publications (reports, summaries, brochures, etc.) in printed or electronic versions, web sites and workshops.

Persons responsible for analysing agricultural data must be familiar with the following: • database management methods;• data security issues;• existing mechanisms for disseminating agricultural information.

The data management system performs three functions:The data management system performs three functions: i) access to official statistics for dissemination purposes; ii) storage and extraction of survey results; and iii) access to holding and household data and geo-referenced data for research work.

The data management system must cover the various data sources which are necessary for preparing supply/utilization accounts, food balance sheets and various economic and environmental accounts, and all derived statistics. This system also enables agriculture statistics to be incorporated into the national statistical system.


114


115115

Module 4: Analytical frameworks and derived statistics

4.1. eCoNoMIC ACCoUNTS foR AGRICULTURe AND eNVIRoNMeNTAL-eCoNoMIC ACCoUNTS

4.1.1. economic accounts for agriculture

Brief description of economic accounts for agricultureAgricultural sector production consists of the sum of the production of agricultural products and goods and services produced as part of secondary, non-agricultural activities. Agricultural activities have a unique feature compared with other sectors of activity. Agricultural production takes place as part of a process (production process) which generally takes time (ongoing production which can span two successive years) before resulting in the final product. For production, agricultural holdings use inputs (intermediate consumption), labour (work force), and equipment (investment). Agricultural holders can also use non-produced assets (in particular land leasing and loans) belonging to third parties. In return for this use, they pay rent and interest.

MoDULe LeARNING objeCTIVeS

To review the following analytical frameworks and derived statistics:

• Economic accounts for agriculture and Environmental-economic accounts;

• Costs of production statistics;

• Post-harvest losses;

• Agricultural prices and price indexes;

• Food security and food balance sheets.

4


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This process is described by a sequence of accounts (production account and operating account). To set up these accounts, production and intermediate consumption must be evaluated using methods appropriate to the sector (production account). Production activities create worth for living and investing. This worth is broken down according to production factors in the operating account.

It is important to have tools and instruments for monitoring, analysing and evaluating the agricultural economy as a whole and in relation to other sectors (suppliers of agricultural inputs or users of agricultural products in the natural or processed state). In this case the economic account for agriculture (EAA) is a statistical summary. It uses the recording and accounting rules of national accounts while offering the necessary flexibility for adaptation to different national agricultural contexts.

Owing to the specific nature of the agricultural sector, EAAs also recommend setting up income accounts for the enterprise (agricultural holder), taking into consideration operating income, property income, and capital account income. The latter account measures investments which consist essentially of gross fixed capital formation and capital transfers.

To support production activities and guarantee food security for citizens, the state can subsidize products, inputs, activities or investments.

Economic accounts for agriculture (EAA) do the following:• really offer an integrated framework to describe the economic operation of the sector;• also enable agricultural sector statistical data to be organized and structured according to rules and standards

drawn up by the United Nations to produce indicators for the analysis and evaluation of the sector’s economic performance and its relations with the remainder of the economy through accounts recommended by the current system of national accounts (SNA);

• enable statisticians to benefit from the overall consistency of statistical data and additional information sources. Statistical activities and methods then benefit from being integrated.

EAAs aim to describe the economic operations deriving from the performance of an agricultural activity, i.e. the agricultural production process and the primary income resulting from it. Their aim is not therefore to analyse all the income of units engaged in agricultural production (particularly agricultural households) as these units can have forms of income or expenditure other than those described in the accounts for agriculture. They measure total agricultural production, which comprises the following :• sales (including exchange of agricultural goods and services between agricultural units);• changes in stocks;• production for own consumption or own account gross fixed capital formation;• goods produced to then be processed by other agricultural producers; and • consumption of feed.

The methods of drawing up EAAs refer to the system in force in the United Nations framework (SNA 2008). To draw up EAAs, it is necessary: i) to address the concept of production and economic units in agriculture; ii) to describe the production process, investments, and recording and evaluation methods, as well as the statistical aspect of drawing up economic accounts (statistical sources, etc.).


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Statistical sources of EAAsProduction: Agricultural sector production is evaluated, for almost all products, using statistics of quantities and prices. The only noteworthy exception is the production of agricultural services for which value data are available.

Intermediate consumption: This is the consumption of fertilizer and dressing, crop protection products, oil products, veterinary expenses, equipment maintenance, building maintenance and other goods and services (except insurance services). Furthermore, purchases of seed by agricultural units are evaluated by quantity and value, as is feed consumption.

Subsidies: Data relating to subsidies (product subsidies and operating subsidies) come from administrative sources.

The other items in the operating and income accounts are the following:• Wage level;• Property tax amounts;• Interest paid by agricultural sector units;• Net rental costs.

Gross fixed capital formation in non-agricultural products comprises the following:• Evaluation of gross fixed capital formation (GFCF) in agricultural sector buildings and equipment;• Property transfer costs.

A specific module on economic accounts for agriculture should cover the following:1. Essential concepts and items for drawing up the economic account for agriculture (EAA);2. The basic unit of the concept of production in agriculture;3. Evaluation of production and the recommended price system;4. Composition and evaluation of intermediate consumption in the agricultural sector;5. Determination of items in the operating account and management of subsidies in the EAA;6. Composition and evaluation of gross fixed capital formation (GFCF) in the agricultural sector;7. The income account of the enterprise and the capital account;8. Values, volumes, prices and measurement of economic growth in agriculture;9. Sources and statistical data used in drawing up EAAs.

4.1.2. environmental-economic accountsThe Environmental-Economic Accounts contain detailed statistics describing the following:• the size of natural resource stocks and their contribution to national wealth;• the extraction of these resources and their distribution between enterprises, households, governments and the

rest of the world;• the production of waste (liquid, solid and gases) by industries, households and government services, as well as

the management of this waste;• expenditure by enterprises, households and government services on environmental protection.

The environmental-economic accounts are compatible as far as possible with the System of National Accounts. They meet the need to follow the link between economic activity and the environment closely. They analyse this link by organizing environment data in a manner consistent with the accounting principles of the national accounts.


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The environmental-economic accounts can be used, among other things, for:• identifying the most polluting activities or those that deplete natural resources the most;• determining the role of government services and households;• evaluating expenditure related to environmental protection and determining who should fund it;• assessing the size of the environmental economy in the economy as a whole;• determining the scope of production and the consumption of natural resources and energy.

4.2. CoSTS of PRoDUCTIoN1

4.2.1. Use and importance of costs of production statisticsCosts of production statistics are used at several levels:

• At government level, costs of production are used as follows:�� as a basis for economic analysis;�� as a basis for effective policies;�� for the effective allocation of resources;�� for better farmer targeting programs.

• At economic operator level, as follows:�� for more effective markets;�� for better decision-making by farmers (use of inputs, specialization in commodities, etc.);�� for better decision-making by input producers.

A few examples of uses of costs of production statistics:• Taxation: Morocco has introduced a tax on agricultural activities. Costs of production statistics are essential for

setting the taxation rates, estimating the effects of distribution (winners and losers) and determining the costs and advantages (for the treasury and the economy);

• Price support schemes: India uses annual estimates of costs of production as a basis for defining the level of the price support mechanism of various crops;

• Subsidies: Costs of production estimates are one of the main sources of information for establishing direct subsidies from the European Union (EU) under the common agricultural policy;

• Total factor productivity index (TFPI): The EU has started to compile a TFPI as one of the main indicators for evaluating the new common agricultural policy (CAP). Data on consumption and the costs of intermediate inputs are essential for an indicator of this type.

1 Handbook on Agricultural Cost of Production Statistics, Global Strategy (August 2014)

box 20: MeTHoDoLoGy foR THe eNVIRoNMeNTAL-eCoNoMIC ACCoUNTS

The methodology applied for accounts for the environment is based on the System of environmental-eco-

nomic accounting (SEEA 2012) published by the United Nations, European Commission, International

Monetary Fund, OECD and World Bank.

European accounts for the environment include six modules, namely: i) air emissions accounts; ii)

environmental taxes; iii) material flow accounts; iv) energy flow accounts; v) environmental protection

expenditure; vi) environmental goods and services sector.


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To increase the relevance for various stakeholders, different dimensions of the costs of production and profitability of holdings should be covered. Table 7 shows how costs of production can be divided into components and useful dimensions to meet some of these needs.

TAbLe 7: DIMeNSIoNS AND CoMPoNeNTS of CoSTS of PRoDUCTIoN

Total cost = Variable costs + fixed costs

Monetary costs Capital cost

Seed, fertilizersDepreciation and opportunity cost of capital on machinery, buildings and

equipmentPaid work

Machinery

Non-monetary costs

Cost of the holding

Non-allocated fixed costs

Taxes, permits for the holding

Family labour Cost of land

Livestock and machinery Land taxes and leasing

Combined with information on yield and production, a series of indicators can be defined and compiled to measure the profitability of the holding in its various dimensions.

Estimating the cost of each of the main agricultural activities needs detailed data on the use of inputs and costs for each activity. These technical coefficients can be used in their turn to construct input-output frameworks, which are a powerful analytical tool for better understanding the links between the various agricultural activities and between agricultural activities and the rest of the economy.

The type of indicators of costs of production and the outputs that can be produced depend on a series of factors, such as their intended use and target groups. The data collection method used and the underlying quality and level of detail available from agricultural holding data also shape the analytical framework. For example, data drawn from representative agricultural surveys can be used to calculate regional or national averages.

4.2.2. Units usedThe unit in which products and indicators are presented will depend first on the type of agricultural activity. The standardization unit should also have an economic sense, be compatible with the unit used to value production, and be easy to understand and use by farmers, analysts and other stakeholders affected by the agricultural economy. For example, the usual units (number of bags of a certain weight or volume, etc.) can be chosen in addition to other measures if it is the unit generally used in markets. For example:

Land areaThis unit is used for crop growing. The seeded area harvested by region or the total land area can be chosen, depending on each country’s context. Costs, which can be expressed per hectare, are likely to be more stable in the short term as technologies and production techniques vary less from year to year than crop yields, which are affected by growth conditions and meteorological events.


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Quantities producedThese can be used equally for crop growing and stockbreeding. Whereas standardization by units in the field better reflects differences in production technologies, costs expressed on a quantity produced provide a more direct measure of the profitability of the holding, for example maize (50 kg bag), cattle (number of head, weight of an animal), milk (cost of 100 litres of fresh milk), eggs (cost of production of 500 eggs), etc.

Production valuesExpressed in this unit, costs provide a direct indication of the profitability and relative competitivity of agricultural holdings. This ratio measures the proportion of costs in gross incomes. This indicator must be compatible with the unit chosen for the quantities produced.

4.2.3. IndicatorsMain indicators• Total costs per ha= [monetary costs + non-monetary costs + cost of land + investment costs (depreciation and

opportunity cost of capital) + general agricultural expenditure] / Total land area in ha;• Netyieldpertonneofproduction=[Valueofproduction–Totalcosts]/Totalproduction(intonnes);• Price per production unit = Total costs / total production.

Additional indicators• Use of energy per hectare = [fuel & lubricants used + electricity use] / Land area;• Use of fertilizer per hectare = [fertilizer use] / land area;• Use of pesticides per hectare = [pesticide use] / land area;• Environmental pressure = Index [use x emission factor] / Land area;• Productivity = [Production value] / use of inputs;• Total productivity of growth factors = [Changes in production value - Changes in input value].


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4.3. PoST-HARVeST LoSSeS

Post-harvest losses include all losses of agricultural products, essentially food, along the agricultural value chain from production in the fields to final product distribution. Post-harvest losses can occur at all stages in the production chain. This production chain comprises, among other things (where these stages are applicable), the harvest itself, stacking, threshing, cleaning/winnowing, drying, storage, processing, packaging, transport and marketing.

TAbLe 8: CoMPoNeNTS of THe PRoDUCTIoN CHAIN foR eSTIMATING PoST-HARVeST LoSSeS

Harvest Storage

Stacking Transport

Threshing Processing

Cleaning/winnowing Packaging

Drying Marketing

It is, moreover, important to distinguish between post-harvest losses and waste. Losses cover everything that is unintentional on the part of production chain stakeholders, and waste is related to losses caused intentionally by stakeholders. For example, losses concerning household consumption are usually considered to be waste as households control the quantity of food they need. However, losses sustained by a farmer during winnowing are considered post-harvest losses as they are unintentional.

fIGURe 5: AGRICULTURAL PRoDUCTIoN CHAIN

Agricultural product losses are a major problem in the post-harvest chain. They can be caused by various factors, ranging from production practices (harvest and post-harvest activities) to transport at the retail level. Not only are losses clearly a waste of food, but they are also a parallel waste of human effort, agricultural inputs, livelihood, investment and scarce resources such as water.

1

Loss istheresultofunintended actions,decisionsorsituations.Waste resultsfromsomeelementsofadiscretionaryprocess.

AgricultureProduction/SupplyChain

Loss &Waste Waste&Loss

Pre-harvest/Pre-slaughter

AgricultureProduction

Post-harvest/Post-slaughterHandlingandStorage

ProcessingandPacking

Distributiontoretail Retail

Publicandhouseholdconsumption

Concept of “Loss” in FBS


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4.3.1. Types of post-harvest lossesThe food supply chains of vegetable products have been divided into five levels corresponding to the different types of food loss and waste for which estimates have been made (FAO, 2012). The following should be distinguished:• Agricultural production losses are due to mechanical damage and/or spillage during harvest operations (for

example threshing or fruit picking), post-harvest sorting, etc.• Losses in post-harvest operations and storage are due to spillage and degradation during handling, storage

and transport between farm and distribution centres.• Losses in processing are due to spillage and degradation during industrial or domestic processing, such as juice

production, canning and bread baking. Losses may occur when crops are rejected if unsuitable for processing, or during washing, peeling, slicing or boiling or following process interruptions or accidental spillage.

• Losses and waste in distribution occur during marketing, e.g. at wholesale markets, supermarkets, retailers and fresh products markets.

• Losses and waste in consumption occur during household consumption.

4.3.2. Methods of estimating post-harvest lossesPost-harvest losses can be measured according to probability surveys or case studies, or based on multivariate linear models or equations.

Probability surveysA representative sample of the study population is selected and interviews are conducted to gather information on post-harvest losses. In this case, two approaches can be used: the objective approach and the declarative approach. The objective approach involves using scientific tools to estimate post-harvest losses. Physical measurements form part of the category of objective approaches; they involve reproducing the farmer’s agricultural practices (as practised by the farmer) for all the operations in the production chain and measuring losses at each stage. Another type of objective approach is to use visual scales. This involves showing farmers images of various stages of deterioration of an agricultural product so that they can choose the image that matches their situation best.

The subjective approach is to ask farmers directly about the losses they have recorded at different stages in the production chain. It is described as subjective because farmers give an assessment based on their perceptions, rather than on measured losses.

Case studies or field testsField tests are also used to measure post-harvest losses. Storage simulation tests can be performed at research stations with a high degree of control of the experimental conditions.

Multivariate linear models or equationsMultivariate linear models can be used to estimate post-harvest losses. These models can consider various variables (temperature, humidity, insect attacks, etc.) of different types (quantitative and/or qualitative). The interactions between the different variables are sometimes taken into account.

Other organizations such as APHLIS (African Postharvest Losses Information System) have developed algorithms to measure post-harvest losses from available data sent by various stakeholders. However, it is very difficult to guarantee the statistical quality of estimates by calculating variance or bias.


123

4.3.3. extent and estimation of lossesAccurate estimates of the extent of food loss and waste are still inadequate, particularly in developing countries. There is, nevertheless, no doubt that levels of food loss and waste remain too high (FAO, 2012).

Recent studies commissioned by FAO have estimated global annual food loss and waste to be approximately 30 percent for cereals, 40-50 percent for tubers and fruit and vegetables, 20 percent for oil crops, meat and dairy products, and 30 percent for fish. Approximately one-third worldwide of edible food intended for human consumption is lost or wasted, equivalent to approximately 1.3 billion tonnes annually on average. Food is wasted throughout the food chain, from initial production to final consumption by households.

FAO and its research and development partners are investing in setting up databases on post-harvest losses in southern countries.

Estimates are generally made for basic foods, particularly cereals, and far less for perishable foods (such as fruit and vegetables, roots and tubers), even though these are subject to huge losses, and even less so for livestock products (fish, milk or meat).

4.3.4. factors influencing lossesa. Internal factorsThese are loss factors occurring at all stages in the food supply chain, from harvest to handling, storage, processing and marketing. The main factors are:• Harvest: the best time for harvesting is determined by how ripe crops are and by meteorological conditions.

The main causes of losses during harvesting are the following:�� Lack of an established ripeness indicator for some products;�� Poor meteorological conditions affecting harvesting operations;�� Use of inappropriate harvesting methods.

• Transport: the main transport challenges in the supply chain are related to inadequate infrastructure (roads, bridges, etc.), lack of appropriate transport systems and lack of refrigerated transport.

• Storage: infrastructure, hygiene and monitoring facilities should be sufficient to ensure effective storage over time. For closed facilities (granaries, warehouses, airtight silos or hoppers), controlling cleanliness, temperature and humidity is crucial. It is equally important to manage damage caused by pests (insects or rodents) and mould, which can lead to the deterioration of facilities (for example mites in wooden posts) and also cause losses in nutrient quality and value.

• Primary processing: the causes of post-harvest losses in this phase include the limited availability of varieties suited to processing, lack of appropriate processing technologies, unsuitability of new marketing techniques, lack of basic infrastructure, unsuitability of equipment and infrastructure, and poor promotion of processed products.


124

b. External factorsThese are factors that have an influence outside the food supply chain. These factors can be divided into environmental and socioeconomic factors.• Biological, microbiological and chemical factors

�� The biological causes of losses, which are strongly dependent on environmental factors, are related among other things to the transpiration rate of products, the action and production of ethylene, the rate of change of product composition (associated with colour, texture, flavour and nutrient value), mechanical damage, physiological imbalance, etc.

�� Micro-organisms are also responsible for post-harvest losses. Toxic substances produced by moulds (called mycotoxins) cause food losses in terms of quality and nutrient value.

�� Chemical factors are related to the natural presence of chemicals in stored foods, which can react spontaneously and cause loss of colour, flavour, texture and nutritional value. An example is the ‘Maillard reaction’ which causes browning and discoloration of dried fruit and other products. Some chemical products such as pesticides can also cause losses.

• Environmental factors: climatic conditions such as wind, humidity, precipitation and temperature can influence harvest quantity and quality;

• Socioeconomic factors: Socioeconomic factors are related in particular to urbanization, which creates a high demand for food products in urban centres; this requires food supply chains to be more efficient and sustained;

• Other factors are related to the importation of products which can introduce new pest species and constitute a major socioeconomic problem (Bocal 2001 in ACF, 2014).

4.3.5. The consequences of post-harvest lossesa. Socioeconomic consequencesFood losses and waste have socioeconomic repercussions on poverty and hunger reduction, on nutrition, on income generation and on economic growth. According to FAO (2012), food losses are a sign of food chain dysfunction and poor performance and they therefore constitute an economic loss for operators working in these chains. Food chains are increasingly globalized today. Some foods are produced, processed and consumed in different regions of the world. The presence of foods on international markets and waste in some regions of the world can therefore have consequences for the availability and price of other foods.

Food losses contribute to an increase in food prices, as part of the world supply is withdrawn from the market. According to FAO (2012), high losses due to poor management and lack of suitable infrastructure have reduced the potential economic benefits of increased performance, particularly for smallholders, the majority of whom are women.


125

b. Consequences for food and nutrition securityThe consequences of post-harvest losses for food and nutrition security are related to a reduction in the quantity of food available for the producer, which increases food insecurity. According to USAID (2013), post-harvest losses affect the food security of a country through food availability, price levels and quality, as well as its malnutrition and poverty levels. USAID estimates that about one-third of the food produced in the world is lost.

Furthermore, losses in quality can cause a reduction in nutrients. Poor quality foods can, in turn, have unhealthy, adverse effects on the health, wellbeing and productivity of consumers.

c. Consequences for the environment and climateFood losses are involved in environmental degradation and climate change, as precious water, land, labour or fertilizer and fuel resources are used to produce, process and transport more food to offset what has been lost (FAO, 2012).

The energy, biodiversity, greenhouse gases, water, land and all the other resources involved in the production of food that no one consumes mean that food losses and waste have an adverse impact on the environment.

box 21: PoST-HARVeST LoSS eQUIVALeNT

In its July 2013 report “Reducing post-harvest losses to save lives”, USAID estimates that the monetary

value of food lost on a global scale exceeds 14 billion dollars annually, i.e. the equivalent of the minimum

annual food requirements of at least 48 million people. While only 5 percent of agricultural research

expenditure is dedicated to the study of post-harvest losses.


126

4.4. AGRICULTURAL PRICeS AND PRICe INDexeS

4.4.1. The various price typesA chain theoretically consists of five categories of economic agents, the activity of which depends on the number of agents present per category. There is a succession of prices along the supply chain. Farmers, for example, sell a raw product (supply curve) and consumers buy a processed product (demand curve). Intermediaries in the chain act as an interface between producers and consumers agreeing, for a return covering their working time, capital and risks, to pay the producer and to transport, store and process the product to sell it to the consumer at the time, in the place and in the form desired.

fIGURe 6: CoMPoSITIoN of A CHAIN

According to Colman and Young (1989, p. 187).

A different price corresponds to each level in the chain in line with processing (added value) and/or transport or storage costs, etc. These are:• the producer price between the producer and the trader (or farm price if direct sale is possible);• the wholesale price between the trader and the processor or wholesaler;• the wholesale price between the processor or wholesaler and the retailer;• the retail price between the retailer and the consumer.

4.4.2. Price indexesAn index is a statistical tool generally used to describe the change in an economic variable in time or space2 It can be elementary or composite.

2 This variable can also be followed according to other criteria such as socioprofessional categories, sectors of activity, etc.


127

a. Elementary indexConsider the change over time of a variable G, and let, G0, G1, G2,……, Gt, ...... , etc. be values of G on successive dates: 0, 1, 2, ……, t,….

The following ratio is called the elementary index of variable G on date t in relation to date 0:

Date 0 is called base year or reference year. This is the comparison date. Date t, the date which is compared with it, is called the current year. The elementary index measures relative changes in a variable between two periods.

As defined, an elementary index is a dimensionless number which allows the change in this variable to be compared in time (or in space).

Example 1: the elementary index of rice prices between 2017 (current year) and 2016 (reference year). The average price of a kilogram of rice in your country has risen from 2.95 USD in 2016 to 3.39 USD on average in 2017.

What is the change in the price of rice over this period?

This corresponds to:


a. Indice élémentaire Considérons l’évolution temporelle d’une grandeur G, soient, G0, G1, G2,……, Gt, ...... , les valeurs de G aux dates successives : 0, 1, 2, ……, t,….

On appelle indice élémentaire de la grandeur G à la date t par rapport à la date 0, le rapport :

La date 0 est appelée année de base, année de référence. C’est la date de comparaison. La date t, la date qui lui est comparée est appelée année courante. L’indice élémentaire mesure les variations relatives d’une grandeur entre deux périodes.

Tel que défini, l’indice élémentaire est un nombre sans dimension qui permet de comparer l’évolution de cette grandeur dans le temps (ou dans l’espace).

Exemple 1 : l’indice élémentaire des prix du riz entre 2017 (année courante) et 2016 (année de base). Le prix moyen d’un kilogramme de riz dans votre pays est passé de 2,95 USD en 2016 à 3,39 USD en moyenne en 2017.

Quelle est la variation du prix du riz sur cette période ?

Cela correspond à :GHIJK/HIJL =M,MNH,NO= 1,15

Par ailleurs, l’indice élémentaire est le plus souvent exprimé en pourcentage. Dans ce cas, il est noté PHIJK/HIJL = GHIJK/HIJL ∗ 100 =

M,MNH,NO∗ 100 = 115%.

Ce résultat traduit à une augmentation du prix du riz de 15% entre 2016 et 2017

Exemple 2 : le Ministre de l’agriculture de votre pays voudrait connaitre l’évolution des grandeurs (prix, quantités) figurant dans le tableau ci-après :

Tableau : évolution des prix et quantités demandées des riz et de maïs

Année 2015 2016 2017

Produit vivriers

Prix (USD) Quantité Prix (USD) Quantité Prix (USD) Quantité

Riz 2,8 170 2,95 140 3,39 270

Maïs 2 140 1,15 200 1,9 230

Source : Données fictives

A partir de ce tableau, il est possible de calculer les indices élémentaires suivants : NB : pour simplifier l’écriture, 2017 sera assimilé à 17, de même que pour les autres années.

- Indice-prix du riz de 2016 par rapport à 2017 : PRSRT(U:) = M,MN

H,NO∗ 100 = 115% (PR pour

prix du riz)

- Indice-prix du Maïs de 2017 par rapport à 2015 : P(UV)JK/JO =J,NH∗ 100 = 95%. Soit une

baisse de 5% (=100-95) du prix du maïs entre 2015 et 2017. (PM pour prix du maïs)

Furthermore, the elementary index is usually expressed as a percentage. In this case, it is written:










M,MNH,NO∗ 100 = 115%.




Année 2015 2016 2017

Produit vivriers


Riz 2,8 170 2,95 140 3,39 270

Maïs 2 140 1,15 200 1,9 230




H,NO∗ 100 = 115% (PR pour

prix du riz)



This result indicates a 15 percent increase in the price of rice between 2016 and 2017.

Example 2: The Minister for Agriculture of your country would like to know the change in the variables (prices, quantities) shown in the following table:

TAbLe 9: CHANGe IN PRICeS AND QUANTITIeS ReQUIReD of RICe AND MAIZe

year 2015 2016 2017

food product Price (USD) Quantity Price (USD) Quantity Price (USD) Quantity

Rice 2,8 170 2,95 140 3,39 270

Maize 2,0 140 1,15 200 1,90 230

Source: Fictitious data

The following elementary indexes can be calculated from this table:N.B.: to simplify matters, 2017 will be equated to 17, and so on for the other years.

• Price index of rice for 2016 compared with 2017:










M,MNH,NO∗ 100 = 115%.




Année 2015 2016 2017

Produit vivriers


Riz 2,8 170 2,95 140 3,39 270

Maïs 2 140 1,15 200 1,9 230




H,NO∗ 100 = 115% (PR pour

prix du riz)



(PR for price of rice)

• Price index of maize for 2017 compared with 2015:










M,MNH,NO∗ 100 = 115%.




Année 2015 2016 2017

Produit vivriers


Riz 2,8 170 2,95 140 3,39 270

Maïs 2 140 1,15 200 1,9 230




H,NO∗ 100 = 115% (PR pour

prix du riz)



i.e. a 5 percent reduction (=100-95) in the price of maize between 2015 and 2017. (PM for price of maize)










M,MNH,NO∗ 100 = 115%.




Année 2015 2016 2017

Produit vivriers


Riz 2,8 170 2,95 140 3,39 270

Maïs 2 140 1,15 200 1,9 230




H,NO∗ 100 = 115% (PR pour

prix du riz)




128

• Quantity index of maize for 2016 compared with 2015:


- Indice-quantité des Maïs de 2016 par rapport à 2015 : P(WV)JL/JO =HIIJXI∗ 100 = 143%.

Soit une augmentation de 43% de la demande de maïs entre 2015 et 2016.

Interprétation : La grandeur pour laquelle l’on calcul l’indice s’apprécie par rapport à la période de base si l’indice est supérieur à 100. Elle se déprécie dans le cas contraire (ex. : P(UV)JK/JO).

i. Propriétés d’un indice élémentaire

Circularité : C’est une propriété fondamentale qui permet de comparer non seulement les dates 0 et t, mais aussi 0 et t’ (une date intermédiaire). Réversibilité :

Cette propriété est intéressante surtout lorsqu’on se réfère à un autre critère autre que le temps. Exemple : L’indice prix du maïs entre la capitale économique et une autre ville du pays. Supposons que l’indice prix du maïs dans la capitale économique (CE) par rapport à une autre ville (AV) du pays soit :

P(UV)?Y/#Z = 105% Dans ce cas, l’indice prix de ladite ville par rapport à la capitale économique est :

P(UV)#Z/?Y =10000P UV ?Y

#Z

= 95%

Enchaînement :

L’on obtient ainsi l’indice de la date t par rapport à la date 0 en faisant le produit des indices intermédiaires d’une date par rapport à la date précédente.

ii. Taux de variation

On appelle taux de variation de la grandeur X sur la période t1-t2 la valeur.

[\]\R^ =

^\]^\R− 1 ∗ 100 = P\]

\R^ − 1

On peut calculer le taux moyen sur p-périodes

Taux moyen de variation sur p-périodes : [ = ([ `a`abR

^ + 1)c"dJ

e -1

b. Indice synthétique

Considérons une grandeur complexe G, c'est-à-dire une grandeur constituée de plusieurs

éléments G i ou par exemple plusieurs produits agricoles. C’est l’exemple du niveau général

i.e. a 43 percent increase in the demand for maize between 2015 and 2016.

Interpretation: The variable for which the index is calculated has increased in relation to the reference period if the index is greater than 100. It has decreased in the opposite case (e.g. I(PM)17/15).

i. Properties of an elementary indexCircularity:









P(UV)#Z/?Y =10000P UV ?Y

#Z

= 95%

Enchaînement :




[\]\R^ =

^\]^\R− 1 ∗ 100 = P\]

\R^ − 1



^ + 1)c"dJ

e -1




This is a basic property which allows not only dates 0 and t to be compared, but also 0 and t’ (an intermediate date).Reversibility:









P(UV)#Z/?Y =10000P UV ?Y

#Z

= 95%

Enchaînement :




[\]\R^ =

^\]^\R− 1 ∗ 100 = P\]

\R^ − 1



^ + 1)c"dJ

e -1




This property is useful, particularly if a criterion other than time is being referred to.Example: The price index of maize between the economic capital and another city in the country. Assume that the price index of maize in the economic capital (CE) compared with another city (AV) in the country is:

In this case, the price index for the said city compared with the economic capital is as follows:

Chained calculations:

The index on date t compared with date 0 is then obtained by finding the product of the intermediate indexes of a date compared with the previous date.

ii. Rate of changeThe following value is called the rate of change of variable X over period t1-t2.

The average rate can be calculated over p-periods.

Average rate of change over p-periods:









P(UV)#Z/?Y =10000P UV ?Y

#Z

= 95%

Enchaînement :




[\]\R^ =

^\]^\R− 1 ∗ 100 = P\]

\R^ − 1



^ + 1)c"dJ

e -1












P(UV)#Z/?Y =10000P UV ?Y

#Z

= 95%

Enchaînement :




[\]\R^ =

^\]^\R− 1 ∗ 100 = P\]

\R^ − 1



^ + 1)c"dJ

e -1












P(UV)#Z/?Y =10000P UV ?Y

#Z

= 95%

Enchaînement :




[\]\R^ =

^\]^\R− 1 ∗ 100 = P\]

\R^ − 1



^ + 1)c"dJ

e -1












P(UV)#Z/?Y =10000P UV ?Y

#Z

= 95%

Enchaînement :




[\]\R^ =

^\]^\R− 1 ∗ 100 = P\]

\R^ − 1



^ + 1)c"dJ

e -1












P(UV)#Z/?Y =10000P UV ?Y

#Z

= 95%

Enchaînement :




[\]\R^ =

^\]^\R− 1 ∗ 100 = P\]

\R^ − 1



^ + 1)c"dJ

e -1





129

b. Composite indexConsider a complex variable G, i.e. a variable consisting of several elements Gi or, for example, several agricultural products. An example of this is the general level of retail prices of agricultural products; the constituent items are the prices of the various agricultural products on the retail market. The elementary indexes of each item can be calculated. Furthermore, to understand the interactive dynamic of the course of the prices of the various products, interpreting elementary indexes is not very useful and complicates the exercise. It is therefore useful to refer to composite indexes: this type of index makes it possible to capture the combined change in aggregates of the same type (the price of different agricultural products, for example) in a single indicator.

i. From the concept of aggregate value to the construction of a composite indexEconomics is mainly interested in changes in prices or quantities, or in the value (product of price and quantity) of a variable between two dates or two spaces. Three types of indexes can therefore be measured: price index, quantity index and aggregate value index. The aggregate value index is economically less significant than the other two as changes in it depend on changes in prices and in quantities, and no distinction can be made between the contribution of price or quantity in the aggregate change observed. The elementary index of value is expressed as follows:

NB: If this value increases between 0 and t, it is not known to which variable (price or quantity) this change should be attributed. So, either fixed quantities or fixed prices can be considered artificially while the other variable changes. This amounts to the calculation of a price or quantity index of a basket of goods. The three most widely used composite indexes feature in the literature. These composite indexes are described below.

ii. Laspeyres, Paasche and Fisher indexesOn date 0, let


des prix de détail des produits agricoles : les constituants sont les prix des différents produits

agricoles sur le marché de détail. Il est possible de calculer les indices élémentaires de chaque

constituant. Par ailleurs, pour comprendre la dynamique conjointe de l’évolution des prix des

différents produits, l’interprétation des indices élémentaires n’a que peu d’utilité et rendra

l’exercice plus complexe. Il devient donc utile de se référer aux indices synthétiques : ce type

d’indice permet de capter en un seul indicateur l’évolution conjointe des agrégats du même type

(le prix des différents produits agricoles par exemple).

i. De la notion de valeur globale à la construction d’indice synthétique

En économie l’on s’intéresse essentiellement aux variations des prix, ou des quantités ou encore

de la valeur (produit du prix et de la quantité) d’une grandeur entre deux dates ou deux espaces.

Ainsi, trois types indices sont mesurables : indice des prix, des quantités, des valeurs globales.

Économiquement l’indice des valeurs globales est moins significatif que les deux autres dans

la mesure où son évolution dépend de celle des prix et de celle des quantités, sans que l’on

puisse différencier la contribution du prix ou de la quantité dans l’évolution globale observée.

L’indice simple de valeur s’exprime sous la forme :

Remarque : Si cette valeur augmente entre 0 et t l’on ne sait à quelle variable (prix ou quantité)

attribuer cette variation. Ainsi l’on peut considérer artificiellement soit les quantités fixes soit

les prix fixes pendant que l’autre variable évolue. Cela revient au calcul d’un indice prix ou

quantité d’un panier de bien. Trois indices synthétiques les plus couramment utilisés sont

proposés dans la littérature. Ces indices synthétiques sont présentés dans la sous-section ci-

dessous.

ii. Indices de Laspeyres, de Paasche et Fisher

Soit à la date 0, l’importance relative du constituant i dans la grandeur complexe G et la quantité analogue à la date t. Les indices proposés par les économistes Laspeyres et Paasche sont des moyennes des indices élémentaires, pondérées par les coefficients f" .

be the relative size of constituent i in complex variable G and






















dessous.



the similar quantity on date t. The indexes proposed by the economists Laspeyres and Paasche are averages of the elementary indexes, weighted by coefficients






















dessous.



.

Consider a basket of goods K. The relative weight in period “0” of good “i” is expressed as:


Considérons un panier de K biens. Le poids relatif à la période « 0 » du bien « i » se met sous la forme :

fI" =gha∗ih

a

ghj∗ih

jkjlR

est aussi appelé coefficient budgétaire.

a) Indice synthétique de Laspeyres

Ainsi, l’indice de Laspeyres de G, noté m`hn , est la moyenne arithmétique des indices

élémentaires, pondérée par les coefficients de la date de référence fI" :

m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100

L’indice de Laspeyres est le plus utilisé.

b) L’indice synthétique de Paasche L’indice de Paasche de G, noté U`

hn , est la moyenne harmonique des indices élémentaires,

pondérée par les coefficients de l’année courante :

U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100

On privilégie dans cet indice l’année courante. L’indice de Paasche est peu utilisé car son calcul exige de mettre constamment à jour les coefficients budgétaires. L’indice de Fisher de G, noté , est la moyenne géométrique simple des indices de Laspeyres et de Paasche :

Propriétés Circularité : Aucun des trois indices ne possède la propriété de circularité. Réversibilité : Aucun des deux indices de Laspeyres et de Paasche ne possède la propriété de réversibilité. L’indice de Fisher est réversible.

c. Agrégation des constituants L’indice de Laspeyres d’ensemble est égal à l’indice de Laspeyres des indices de Laspeyres de chaque groupe de constituants. . En d’autres termes, si l’on dispose d’un panier de produits agricoles, comprenant des céréales (maïs, riz, mil…), des tubercules et des protéines animales, l’indice de Lapeyres de l’ensemble peut s’exprimer sous cette forme : c’est la moyenne arithmétique des indices de Laspeyres des différentes groupes (céréales, tubercules et protéine animale), pondérée par les poids relatifs de chaque groupe dans le panier de biens. Il en est de même pour l’indice de Paasche.

L’indice de Fisher ne possède pas cette propriété d’agrégation.

i. Indices de quantité

On appelle indice quantité de Laspeyres, de Paasche et de Fischer ; les quantités :

is also called the budgetary coefficient.

a) Laspeyres composite indexThe Laspeyres index of G, written



fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100







, is the arithmetic average of the elementary indexes, weighted by coefficients for reference date



fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100







:

The Laspeyres index is the most widely used.






















dessous.





fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100








130

b) Paasche composite indexThe Paasche index of G, written



fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100







, is the harmonic average of the elementary indexes, weighted by coefficients for the current year



fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100







Preference is given to the current year in this index. The Paasche index is not widely used because it requires budgetary coefficients to be constantly updated.The Fisher index of G, written



fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100







, is the simple geometric average of the Laspeyres and Paasche indexes:

PropertiesCircularity: None of these three indexes possesses the property of circularity. Reversibility: Neither of the Laspeyres and Paasche indexes possesses the property of reversibility. The Fisher index is reversible.

c. Aggregation of constituentsThe overall Laspeyres index is equal to the Laspeyres index of the Laspeyres indexes of each group of constituents



fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100







. In other words, for a basket of agricultural products including cereals (maize, rice, millet, etc.), tubers and animal proteins, the overall Laspeyres index can be expressed as follows: it is the arithmetic average of the Laspeyres indexes of the various groups (cereals, tubers and animal protein), weighted by the relative weights of each group in the basket of goods.

The same applies to the Paasche index



fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100







.The Fisher index does not possess this aggregation property.

i. Quantity indexesThe following quantities are called the Laspeyres, Paasche or Fischer quantity index:

These indexes measure the change in quantity at a fixed price.

ii. Value indexesThe following quantity is called the value index:



fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100









fI" =gha∗ih

a

ghj∗ih

jkjlR





m\In = fI" ∗

n\"

nI"

o

pdJ

∗ 100





U\In =

f\"

P\I(n)

o

pdJ

∗ 100 = fI" ∗nI"

n\"

o

pdJ

∗ 100








m\IW =

UIp ∗ W\

popdJ

UIp ∗ WI

popdJ

∗ 100

U\IW =

U\p ∗ W\

popdJ

U\p ∗ WI

popdJ

∗ 100

q\IW = m\

IW ∗ U\

I(W)

Ces indices mesurent l’évolution de la quantité à prix fixé.

ii. Indices de valeurs On appelle indice de valeurs, la quantité :

P\I(r) =

U\p ∗ W\

popdJ

UIp ∗ WI

popdJ

Cet indice mesure le double effet prix-quantité : P\Ir = m\

IW ∗ U\

IU

P`hr = m`

hU ∗ U`

hW

Exemple : Tableau : évolution des prix et quantités demandées de riz et de maïs.

Année 2015 2016 2017 Produit vivriers

Prix (USD) Quantité Prix

(USD) Quantité Prix (USD) Quantité

Riz 2,8 170 2,95 140 3,39 270 Maïs 2 140 1,15 200 1,9 230

Source : Calcul des indices de Laspeyres, de Paasche, de Fisher prix et quantité et celui de valeur.

mJKJLU =

3,39 ∗ 140 + 1,9 ∗ 2002,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 132,9%

mJKJLW =

2,95 ∗ 270 + 1,15 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 165%

UJKJLU =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 270 + 1,15 ∗ 230

∗ 100 = 127,4%

UJKJLW =

3,39 ∗ 270 + 1,9 ∗ 2303,39 ∗ 140 + 1,9 ∗ 200

∗ 100 = 158%

qJKJLU = mJK

JLU ∗ UJK

JLU = 130%

qJKJLW = mJK

JLW ∗ UJK

JLW = 161,5%

PJKJLr =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 210,3%


m\IW =

UIp ∗ W\

popdJ

UIp ∗ WI

popdJ

∗ 100

U\IW =

U\p ∗ W\

popdJ

U\p ∗ WI

popdJ

∗ 100

q\IW = m\

IW ∗ U\

I(W)



P\I(r) =

U\p ∗ W\

popdJ

UIp ∗ WI

popdJ


IW ∗ U\

IU

P`hr = m`

hU ∗ U`

hW





Riz 2,8 170 2,95 140 3,39 270 Maïs 2 140 1,15 200 1,9 230


mJKJLU =

3,39 ∗ 140 + 1,9 ∗ 2002,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 132,9%

mJKJLW =

2,95 ∗ 270 + 1,15 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 165%

UJKJLU =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 270 + 1,15 ∗ 230

∗ 100 = 127,4%

UJKJLW =

3,39 ∗ 270 + 1,9 ∗ 2303,39 ∗ 140 + 1,9 ∗ 200

∗ 100 = 158%

qJKJLU = mJK

JLU ∗ UJK

JLU = 130%

qJKJLW = mJK

JLW ∗ UJK

JLW = 161,5%

PJKJLr =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 210,3%


131

This index measures the dual price-quantity effect:

Example:

TAbLe 10 : CHANGe IN PRICeS AND QUANTITIeS ReQUIReD of RICe AND MAIZe

year 2015 2016 2017

food product Price (USD) Quantity Price (USD) Quantity Price (USD) Quantity

Rice 2,8 170 2,95 140 3,39 270

Maize 2,0 140 1,15 200 1,90 230

Source: Calculation of the Laspeyres, Paasche, Fisher, price, quantity and value indexes.

NB:1. The Laspeyres index overestimates a rise. Consumers actually tend to buy fewer high-cost goods and more

low-cost goods (law of supply and demand for elastic goods, microeconomic theory). However, the Laspeyres formula (for prices) assumes that the structure of the basket of goods has not changed despite the shock affecting food commodity prices.

2. The Paasche index tends to underestimate a rise.3. The value index can also be written:


Remarque :

1. L’indice de Laspeyres surévalue une hausse. En effet les consommateurs ont tendances à acheter moins de bien de prix élevé et d’avantage de bien de prix bas (loi de l’offre et de la demande pour les biens élastiques, théorie micro économique). Or la formule de Laspeyres (pour les prix) suppose que la structure du panier des biens n’a pas changée, malgré le choc sur les prix.

2. L’indice de Paasche à tendance à sous-évaluer une hausse. 3. L’indice des valeurs peut encore s’écrire : P`

hr = q`

hW ∗ q`

hU

4. L’indice de Fisher est compris entre l’indice de Paasche et l’indice de Laspeyres. On a : P<F<L.

d. Choix des critères de construction

i. Le choix des composantes d’un indice synthétique

Il est recommandé de choisir les constituants les plus représentatifs parmi tous les éléments susceptibles de faire partie de la grandeur complexe. Par exemple, pour le calcul de l’indice des prix à la consommation (IPC), l’on retient un échantillon de biens de consommation de première nécessité qu’on appelle parfois, panier de la ménagère. C’est un vaste échantillon de produits de consommation courante, mis à jour chaque année, correspondant à plus de 10% de l’ensemble de la consommation des ménages. En règle générale, le choix du nombre de composantes est le résultat d’un arbitrage entre les possibilités techniques et financières d’observation et le gain marginal de précision obtenu. Le nombre de composantes à retenir est donc dépendant du but que l’on se fixe dans la construction d’un indice particulier.

Une autre préoccupation à prendre en compte est la pondération des différentes composantes de l’indice.

ii. Le choix de la base

Dans le cadre spatial, la base porte sur un ensemble territorial géographiquement délimité. L’on peut être parfois conduit à soustraire les grandes agglomérations afin d’éviter les biais dans le calcul de l’indice.

En matière d’indices temporels, le choix de la base est délicat. Il faut éviter que la base soit une date exceptionnelle de boom ou de récession. Il est conseillé de choisir une période de base assez large (annuel au lieu de infra-annuelle par exemple) afin d’éviter l’influence des variations saisonnières et accidentelles. Par ailleurs, la base est de moins en moins pertinente au fur et à mesure que l’on s’éloigne dans le temps, du fait des changements de comportement et de structure des paniers de biens considérés. Il faut donc changer de base à des périodes assez régulières. La date de changement s’appelle date de raccordement.

e. Difficultés d’utilisation des indices synthétiques

i. Durée de vie d’un indice.

La durée de vie d’un indice synthétique est limitée. Elle dépend de l’évolution plus ou moins rapide des structures de l’économie (consommation, production, répartition, etc.) et des modes de comportement des agents économiques.

4. The Fisher index is between the Paasche index and the Laspeyres index (P<F<L).


m\IW =

UIp ∗ W\

popdJ

UIp ∗ WI

popdJ

∗ 100

U\IW =

U\p ∗ W\

popdJ

U\p ∗ WI

popdJ

∗ 100

q\IW = m\

IW ∗ U\

I(W)



P\I(r) =

U\p ∗ W\

popdJ

UIp ∗ WI

popdJ


IW ∗ U\

IU

P`hr = m`

hU ∗ U`

hW





Riz 2,8 170 2,95 140 3,39 270 Maïs 2 140 1,15 200 1,9 230


mJKJLU =

3,39 ∗ 140 + 1,9 ∗ 2002,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 132,9%

mJKJLW =

2,95 ∗ 270 + 1,15 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 165%

UJKJLU =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 270 + 1,15 ∗ 230

∗ 100 = 127,4%

UJKJLW =

3,39 ∗ 270 + 1,9 ∗ 2303,39 ∗ 140 + 1,9 ∗ 200

∗ 100 = 158%

qJKJLU = mJK

JLU ∗ UJK

JLU = 130%

qJKJLW = mJK

JLW ∗ UJK

JLW = 161,5%

PJKJLr =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 210,3%


m\IW =

UIp ∗ W\

popdJ

UIp ∗ WI

popdJ

∗ 100

U\IW =

U\p ∗ W\

popdJ

U\p ∗ WI

popdJ

∗ 100

q\IW = m\

IW ∗ U\

I(W)



P\I(r) =

U\p ∗ W\

popdJ

UIp ∗ WI

popdJ


IW ∗ U\

IU

P`hr = m`

hU ∗ U`

hW





Riz 2,8 170 2,95 140 3,39 270 Maïs 2 140 1,15 200 1,9 230


mJKJLU =

3,39 ∗ 140 + 1,9 ∗ 2002,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 132,9%

mJKJLW =

2,95 ∗ 270 + 1,15 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 165%

UJKJLU =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 270 + 1,15 ∗ 230

∗ 100 = 127,4%

UJKJLW =

3,39 ∗ 270 + 1,9 ∗ 2303,39 ∗ 140 + 1,9 ∗ 200

∗ 100 = 158%

qJKJLU = mJK

JLU ∗ UJK

JLU = 130%

qJKJLW = mJK

JLW ∗ UJK

JLW = 161,5%

PJKJLr =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 210,3%


m\IW =

UIp ∗ W\

popdJ

UIp ∗ WI

popdJ

∗ 100

U\IW =

U\p ∗ W\

popdJ

U\p ∗ WI

popdJ

∗ 100

q\IW = m\

IW ∗ U\

I(W)



P\I(r) =

U\p ∗ W\

popdJ

UIp ∗ WI

popdJ


IW ∗ U\

IU

P`hr = m`

hU ∗ U`

hW





Riz 2,8 170 2,95 140 3,39 270 Maïs 2 140 1,15 200 1,9 230


mJKJLU =

3,39 ∗ 140 + 1,9 ∗ 2002,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 132,9%

mJKJLW =

2,95 ∗ 270 + 1,15 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 165%

UJKJLU =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 270 + 1,15 ∗ 230

∗ 100 = 127,4%

UJKJLW =

3,39 ∗ 270 + 1,9 ∗ 2303,39 ∗ 140 + 1,9 ∗ 200

∗ 100 = 158%

qJKJLU = mJK

JLU ∗ UJK

JLU = 130%

qJKJLW = mJK

JLW ∗ UJK

JLW = 161,5%

PJKJLr =

3,39 ∗ 270 + 1,9 ∗ 2302,95 ∗ 140 + 1,15 ∗ 200

∗ 100 = 210,3%


132

d. Choice of constituent criteria

i. The choice of constituents of a composite indexIt is advisable to choose the most representative constituents of all the elements that can be part of the complex variable. For example, to calculate the consumer price index (CPI), a sample is taken of basic consumer goods sometimes called the ‘household shopping basket’. This is a wide sample of current consumer products, updated each year, corresponding to more than 10 percent of all household consumption. As a general rule, the choice of the number of constituents is the result of a balance between the technical and financial capabilities of observation and the marginal gain in accuracy obtained. The number of constituents to be included therefore depends on the goal set when constructing a specific index.

A further consideration is the weighting of the various components of the index.

ii. The choice of baseFor a spatial index, the base is a geographically defined territorial unit. It is sometimes advisable to remove large conurbations to avoid bias in calculating the index.

The choice of base for temporal indexes is difficult. Dates in unusual boom or recession periods should be avoided. It is advisable to choose a fairly broad period (annual rather than infra-annual, for example) to avoid the influence of seasonal and random variations. Furthermore, the base is less relevant the further away in time it gets, owing to changes in behaviour and in the composition of the baskets of goods considered. The base should therefore be changed fairly regularly. The date it is changed is called the ‘link date’.

e. Problems using composite indexes

i. Useful life of an indexThe useful life of a composite index is limited. It depends on the rate of change in components of the economy (consumption, production, distribution, etc.) and the behaviour of economic agents.The method of calculating indexes may change for methodological reasons and the following practical problem then arises: how can changes in an index be monitored over a period during which its definition has partly altered? It might then be necessary to use spliced indexes.The method involves considering the new index to be an exact extension of the old one, from the link date. On this date, the two indexes are calculated simultaneously. Let b be the link date. Note Ib,0 as the value of the old index on the link date. On this date, the value of the new index is hypothetically equal to 100. A splicing coefficient is calculated on date b equal to:

Any value in the new spliced index can therefore be compared with values in the old index: let I’t/b be the new index value on a given date t. The base of this index is b, the link date. The spliced value of the new index on date t can be expressed, at base 0, as follows:


i. Problems using composite indexes

i. Useful life of an index

The useful life of a composite index is limited. It depends on the rate of change in components of the economy (consumption, production, distribution, etc.) and the behaviour of economic agents. The method of calculating indexes may change for methodological reasons and the following practical problem then arises: how can changes in an index be monitored over a period during which its definition has partly altered? It might then be necessary to use spliced indexes. The method involves considering the new index to be an exact extension of the old one, from the link date. On this date, the two indexes are calculated simultaneously. Let b be the link date. Note Ib,0 as the value of the old index on the link date. On this date, the value of the new index is hypothetically equal to 100. A splicing coefficient is calculated on date b equal to:

F9 = _Ä,Y100

=�$ÇUMCÉÑℎMCÇDWNDMÜ�$ÇUMCÉÑℎMNMáWNDMÜ

Any value in the new spliced index can therefore be compared with values in the old index: let I’

t/b be the new index value on a given date t. The base of this index is b, the link date. The spliced value of the new index on date t can be expressed, at base 0, as follows:

ii. Adjustment to quality changes

It is sometimes difficult to monitor a composite index owing to the following three situations:

• The appearance of new elementary constituents (new goods and therefore new consumer habits);

• The disappearance of old constituents; • A change in the quality of the proposed elements.

Various methods have been proposed for quality adjustment in the absence of matched products. Some are more suited to certain product categories. To make satisfactory quality adjustments, it is necessary to have a good understanding of how the consumer market works, be familiar with the technological characteristics of production activities and have access to various data sources. Particular attention should also be paid to categories of products with relatively high weightings and for which product substitutions are frequent. Some of the methods are relatively complex and require detailed knowledge. To make quality adjustments, proceed gradually, product by product. These caveats should not, however, serve as excuses for not estimating price adjustments due to quality differences. The way in which statistics offices handle missing products, even if this consists in not taking them into consideration, gives rise


Il arrive que, pour des raisons méthodologiques, le mode de calcul des indices changent et il se pose alors le problème pratique suivant : comment suivre l’évolution d’un indice sur une période durant laquelle sa définition s’est partiellement modifiée ? L’on est alors conduit à utiliser des raccords d’indices. La méthode consiste à considérer le nouvel indice comme prolongeant exactement l’ancien, à partir de la date de raccordement. A cette date, les deux indices sont calculés simultanément. Soit b la date de raccordement. Notons Ib,0, la valeur de l’ancien indice à la date de raccordement. A cette date, la valeur du nouvel indice est par hypothèse égale à 100. On calcule un coefficient de raccordement à la date b égal à :

Toute valeur du nouvel indice raccordé peut être donc comparée aux valeurs de l’indice ancien : soit I’

t/b la valeur du nouvel indice à une date quelconque t. La base de cet indice est b, date de raccordement. La valeur raccordée du nouvel indice à la date t peut s’exprimer, en base 0, de la façon suivante :

ii. Ajustement aux changements de qualité.

Le suivi d’un indice synthétique est rendu parfois difficile du fait des trois situations ci-dessous :

• L’apparition de nouveaux constituants élémentaires (nouveaux biens et donc nouvelles habitudes de consommation) ;

• La disparition de constituants anciens ; • L’évolution de la qualité des éléments proposés.

Diverses méthodes sont proposées pour l’ajustement de qualité en l’absence de produits appariés. Certaines d’entre elles sont mieux adaptées à certaines catégories de produits. Pour effectuer des ajustements satisfaisants de qualité, il faut bien comprendre le fonctionnement du marché de la consommation, connaître les caractéristiques technologiques des activités de production et avoir accès à diverses sources de données. Il faut aussi accorder une attention particulière aux catégories de produits dont les pondérations sont relativement élevées et pour lesquels les substitutions de produits sont fréquentes. Certaines des méthodes sont relativement complexes et exigent des connaissances approfondies. Pour obtenir des ajustements de qualité, on doit procéder de manière graduelle et produit par produit. Ces mises en gardent ne doivent, cependant, pas servir d’excuses pour ne pas avoir à estimer les ajustements de prix dus aux différences de qualité. La façon dont les offices de statistique traitent les produits manquants, même si cela consiste à ne pas en tenir compte, donne lieu à certains ajustements implicites de la qualité. Cette méthode implicite n’est pas nécessairement la meilleure et peut même introduire des biais. L’ampleur des changements de qualité et la rapidité de l’évolution des technologies exigent des méthodes appropriées. Les méthodes d’ajustement au titre des changements de qualité relèvent généralement de deux catégories : les méthodes d’ajustement implicite/imputé (ou indirect)41 et les méthodes d’ajustement explicite (ou direct)42.

41 L’imputation (dissemblable corrigé), le Recouvrement et la comparaison directe (remplacement en équivalence) 42 L’avis d’un expert, L’ajustement de la quantité, La méthode des différences des coûts de production ou d’option et La méthode hédonique


133

ii. Adjustment to quality changesIt is sometimes difficult to monitor a composite index owing to the following three situations:• The appearance of new elementary constituents (new goods and therefore new consumer habits);• The disappearance of old constituents;• A change in the quality of the proposed elements.

Various methods have been proposed for quality adjustment in the absence of matched products. Some are more suited to certain product categories. To make satisfactory quality adjustments, it is necessary to have a good understanding of how the consumer market works, be familiar with the technological characteristics of production activities and have access to various data sources. Particular attention should also be paid to categories of products with relatively high weightings and for which product substitutions are frequent. Some of the methods are relatively complex and require detailed knowledge. To make quality adjustments, proceed gradually, product by product. These caveats should not, however, serve as excuses for not estimating price adjustments due to quality differences. The way in which statistics offices handle missing products, even if this consists in not taking them into consideration, gives rise to certain implicit quality adjustments. This implicit method is not necessarily the best and can even introduce biases. The scope of quality changes and the speed of technological development require appropriate methods. The methods of adjustment to changes in quality generally fall into two categories: implicit/imputed (or indirect) adjustment methods3 and explicit (or direct) adjustment methods4.

f. A few examples of useful composite indexes i. Consumer price index

a) DefinitionThe consumer price index (CPI) is a tool for measuring inflation. It explicitly measures changes in the average prices of goods and services consumed by households, weighted according to their share in average household consumption (budgetary coefficient). The CPI covers all marketable goods and services consumed on the territory by resident and non-resident households (such as tourists).

Furthermore, the consumer price index is not a cost of living index. It measures the effects of price changes on the acquisition cost of products consumed by households. The cost of living index, by contrast, measures changes in acquisition costs to maintain the standard of living of households at a specific level.

Use: it estimates the average change, between two data periods, in the prices of products consumed by households. It is a composite measure of changes in product prices, at constant quality. However, it is not a cost of living indicator as it does not take into account change in the apportionment of expenditure (see Maurice Halbwachs).

National price indexes have been harmonized in some communities (Europe, EMUVA, etc.) to allow comparisons between member states: the European Union Harmonized Index of Consumer Prices (HICP) and the WAEMU Harmonized Index of Consumer Prices (HICP).

3 Imputation (corrected dissimilar), Overlap pricing and Direct comparison (replacement with equivalence)4 Expert judgement, Quantity adjustment, Differences in costs of production or option method and The hedonic method


134

b) Construction de l’IPCAs it is technically impossible to monitor changes in all prices, statistical institutes construct a basket of final representative goods and services, weighted according to their share in consumption. Prices are determined by continuous sampling and surveys.

Calculating the index is difficult, mainly due to innovation which is reflected in the appearance of new products or services or a change in an existing product.

The construction of the index also takes into account changes in the structure of the household basket. The weighting assigned to goods and services changes in parallel, but sometimes up to a year later (the weighting used for inflation in year N is based on consumption in year N-1).

As regards the collection of data required for calculating the CPI, the sample design is generally stratified according to three types of criteria: geographic, product type, point of sale type. The data are, moreover, collected by enumerators throughout the month. Records are monthly.

Its theoretical scope is defined as that of household actual final monetary consumption.

The level of coverage of the CPI was 97 percent in 2016 (2015 base).

The main gaps in coverage are still private hospital services and life insurance.

c) LimitsConstructing the index is difficult, primarily due to innovation which is reflected in the appearance of a new product or service or a novel feature in an old product.

It is secondly difficult owing to a change in the structure of the basket of goods. If the price of an item increases one year more quickly than the prices of other items and if its share in consumption decreases (by substitution of the item in question by other goods), it is difficult to link it to the previous year; if the previous year’s sales only are considered, this does not take into account the fact that the relative price increase may have resulted in deferred consumption.

One limit of using the price index as a tool is that it is based on the average consumer basket. The price index can therefore show a price increase if a person with a marginal profile perceives a fall in prices. Constructing the CPI comes up against other problems, for example how to account for changes in product quality, consumer taste changes, or exchange rate fluctuation.

ii. Producer price indexes or Agriculture producer price index (APPI)5 a) Definitions and information sources

The producer price index measures the average annual change in selling prices received by agricultural holders (farmgate prices). Three categories of producer price indexes are produced and available in FAOSTAT. These are the elementary producer price index for a single commodity6, composite index for a commodity group (cereals, fruit and vegetables, etc.) and agriculture producer price indexes (all agricultural commodities and livestock products produced in a given country). Annual data are available for more than 80 countries in FAOSTAT.

5 http://fenixservices.fao.org/faostat/static/documents/PI/PI_f.pdf6 For example, the wheat producer price index, the rice producer price index, or the maize producer price index


135

This index measures changes in income from products sold by farmers, i.e. income received excluding subsidies (farmgate price). Marketing is, however, carried out increasingly by cooperatives or producer, industry or trader groups and in this case, it is difficult to determine the farmgate price.

These indexes are drawn up using data on prices expressed in standardized local currency (SLC).

b) Special featuresCountries covered: Indexes are available for all the countries for which price and production data are published on FAOSTAT.

Reference year: The reference period for the indexes is 2004-2006. The weighting coefficient used for aggregated indexes (the Agriculture producer price index and the Commodity group indexes) is the average production value for the period 2004-2006.

The APPI is an aggregate index using the fixed-base Laspeyres index, with reference 2010. Weightings are taken from the accounts for agriculture. A specific APPI nomenclature is used.

iii. Agricultural production index7

a) Index type and reference periodAgricultural production indexes show the relative global agricultural production volume for each year in comparison with the reference period. They are a composite Laspeyres index of quantities of the various agricultural commodities after the deduction of quantities used for seed and animal feed. The quantities are therefore weighted using the average international prices of commodities for the reference period. For example, FAO takes 2004-2006 as the reference period. It is, in fact, recommended that periods not subject to specific shocks (poor rainfall, storms, etc.) be considered. Moreover, to obtain the index, the aggregate calculated for the numerator is divided by the average aggregate for the reference period 2004-2006 to lessen the specific effects of each year in the reference period.

b) Scope of the index and international pricesFor FAO the indexes are calculated on the assumption that agriculture is regarded as a single enterprise, and seed and animal feed quantities are subtracted from the production data so that they are not counted twice, once in production and once as intermediate consumption for new productions (crop or livestock). The deduction for seed (in the case of eggs, for hatching) and for rearing and feeding poultry also applies to both imported and exported products. They only cover primary agricultural commodities intended for animal feed (e.g. maize, potatoes, milk, etc.).

These "international prices", expressed in "international dollars", are obtained by using a Geary-Khamis8 formula for the agricultural sector. This method assigns a single "price" to each product. For example, one tonne of wheat has the same price regardless of the country where it was produced. The monetary unit in which prices are expressed has no influence on the published indexes.

7 This section has been taken from the following document: "http://fenixservices.fao.org/faostat/static/documents/QI/QI_f.pdf"(accessed on 08/05/2017)

8 https://fr.wikipedia.org/wiki/Dollar_Geary-Khamis


136

c) Products included in the indexThe products covered by the calculation of agricultural production indexes are all the products of crops and livestock in each country. Virtually all products are covered, except forage. The category of food production includes products considered edible which contain nutrients. Coffee and tea are consequently excluded, like other inedible products, because though edible they have virtually no nutrient value.

Meat production indexes are calculated from livestock production data, which takes into account the meat equivalent of live animals exported, but excludes the meat equivalent of live animals imported. Annual changes in the number of animals and poultry or their average live weight are not taken into account in calculating indexes. The indexes are calculated from agricultural production data for a given calendar year. FAO indexes may differ from those produced by countries themselves owing to differences in the concepts (production, etc) of cover, weighting, reference period considered and calculation methods.

4.5. fooD SeCURITy AND fooD bALANCe SHeeT

4.5.1. food securityFood security is defined by a situation in which all members of a household at all times are consuming enough safe and nutritious food for normal growth and development, and for an active and healthy life.

This definition has five dimensions which are availability, accessibility, stability, quality and the right to food.

The concept of food security is complex as it has four interacting (4) dimensions. These are: i. availability, defined as all the food resources produced, stored or imported for a given period;ii. accessibility, understood as the ways and means whereby households can obtain the food products they need;iii. stability, which implies regularity of food availability in terms of both time and space;iv. food use, which assumes that all individuals have a food intake which meets their needs in quantity and quality.

Here this concept reflects nutritional quality which, if it is too low, can result in malnutrition, “an abnormal physical condition caused by an imbalance between food intake and the body’s requirements”.

4.5.2. food balance sheetThe food balance sheet system aims primarily to provide a framework for recording measurable variables and figures concerning the food situation in a country. Food balance sheets are an objective method of assessing food availability, estimating food deficits and surpluses and determining whether extra imports and/or aid are necessary to bridge the gaps or dispose of harvest surpluses.

The food balance sheet needs to take into account all potentially edible products, regardless of whether they are consumed by humans or used for non-food purposes (FAO).

Any balance sheet reflects variations between uses and resources. In the case of a food balance sheet, the extremes of these variations are the availability of products and the needs of populations during a reference period, all combined in the same unit. Drawing up a food balance sheet therefore requires first of all a list of the products to be considered and sources, stating the origins (resources) and utilizations (uses) of these products.


137

This clearly assumes that statistics are available on the various products and items to be used in drawing up the food balance sheet in its two main sections: availability and consumption (needs).

All edible products consumed by humans or used for other purposes should theoretically be taken into consideration in the food balance sheet. They will generally be basic commodities or, equally, processed products such as sugar, fats, beverages, etc.

The primary data to be collected are the following:• Rainfed crops;• Irrigated (horticultural) crops;• Sugar crops;• Fruit production;• Livestock, poultry and fishery production

Industrial production dataOther data:• external trade data;• data on stocks.

Data on consumption• Consumption standard for various products

Table 11 summarizes the potential products which could be used to draw up a food balance sheet.

TAbLe 11: PoTeNTIAL PRoDUCTS IN A fooD bALANCe SHeeT

Category Product

Cereals Millet, sorghum, maize, fonio, rice, wheat

Pulses Cowpea, bambara nut

Oilcrops Soybean, groundnut, cottonseed, sesame seed

Tubers and roots Yam, sweet potato, manioc, potato

Kitchen garden products Onion, cabbage, locale aubergine, purple aubergine

Fruit Banana, mango, citrus fruit

Sugar crops Sugar cane

Vegetable oils Peanut oil, cottonseed oil, soybean oil, sesame seed oil

Meat Bovine meat, sheep meat, goat meat, pig meat, poultry, other meat and offal

Aquaculture/fishery products

Fish, crustaceans, etc.

Sweetener Sugar

Animal fats Butter

Milk and dairy products

Eggs

Alcoholic and non-alcoholic beverages

N. B. Needless to say, each country will adapt these categories and products as appropriate.


138

Internal availability comprises:Food availability can be defined by taking into account production, imports, exports and changes in stocks according to the following formula:

Availability = production + imports – exports – stock change(stock change = final stock – initial stock)

• Crop production which takes into account all food products derived from a plant (cereals, pulses, tubers, vegetables, fruit, etc.);

• Livestock production which includes all food products derived from animals (meat, fish, eggs, milk and dairy products, honey, etc.). As regards fish in particular, i.e. fishery products, it is the actual ex-water weight of the catch at the time of capture that is considered. Statistics concerning meat are expressed in terms of carcass weight;

• Imports concern all foods coming from outside the country intended for human consumption. Imports of processed products are expressed in terms of their basic commodity equivalent;

• Exports concern all movements of foods outside the country. Exports of processed products are expressed in terms of their basic commodity equivalent;

• Stock change concern stocks that are visible at the beginning and end of the reference period.

Internal consumption (or needs) comprises:Uses or consumption take into account the components described below:

Use = Food + Processing + Feed + Seed + Food for tourists + Industrial use + Loss and Residual and Other uses.

• Food;• Food for tourists;• Processing for food (e.g. sugar, fats);• Feed;• Seed;• Industrial use (e.g. oil to make soap);• Losses;• Residual and other uses.

The availability per inhabitant will then be calculated as the ratio of total availability for consumption by the total population.


139

TAbLe 12: TyPe of CHART foR eSTIMATING INTeRNAL AVAILAbILITy AND USeS

ITeMS VALUe

Population

Internal availability

• Gross production

• Changes in stocks (closing stock – opening stock)

• Trade balance (Imports (including food aid) - Exports)

Internal consumption

• Food

• Processing for food

• Food for tourists

• Feed

• Seed

• Losses

• Industrial use

• Residual and Other uses

Availability/inhabitant

The supply-utilization account refers to double counting, analysing resources on one hand and their use on the other, for the main food products.

TAbLe 13: TyPe of CHART foR eSTIMATING AVAILAbILITy AND ToTAL USeS

As resources, record: As uses, record:

• Net agricultural production (plant and livestock);

• Stocks at the beginning of the financial year;

• Imports;

• Food aid.

• Food consumption;

• Processing for food;

• Food for tourists;

• Feed;

• Seed;

• Losses;

• Industrial use;

• Stocks at the end of the financial year;

• Exports;

• Residual and other uses


140

The items shown in the balance sheet are aggregate items.

Reference period: The food balance sheet covers the calendar year (from 1 January to 31 December of the year in question).

Production: In principle, production figures concern total internal production, carried out inside or outside the agricultural sector, i.e. including non-commercial production and kitchen garden production. Unless stated otherwise, production is measured on the holding for plant and livestock products (i.e. in the case of crops, not counting harvest losses).

Plant production: Any food product derived from a plant (cereals, pulses, roots and tubers, nuts and seeds, vegetables, fruit, etc.).

Livestock production: Any food product derived from the animal kingdom (meat, eggs, milk and dairy products, butter, honey, etc.). Furthermore, for some products such as fish, the data are not generally available in the supply-utilization account. However, caloric values are added in the final food balance sheet evaluation.

Commercial imports: All foods coming from outside the survey framework and which will be consumed by the reference population. Trade exchanges, food aid granted on specific terms, donations and estimates of unrecorded trade should also be included.

Food aid and donations: All foods coming from outside in the form of aid or donations (emergency aid and planned aid).

Exports: All foods produced within the survey framework and marketed outside it. This food is not included in internal availability, but is included in utilization.

Re-exports: the phenomenon of unofficial re-exports distorts the figures. Methods of analysing the informal economy are used to estimate them.

Changes in stocks: The difference between visible stocks at the beginning and end of the reference period. These are changes in stocks occurring at all levels between production and retail marketing. These changes also include fluctuations in government stocks, government trade services, stocks with manufacturers, milling and processing industries, importers, exporters, other wholesalers and retailers, transport and storage enterprises and stocks held by producers (farmers’ stocks).

Internal availability = Gross production - exports + (imports, aid and donations) + decreases in stocks.

An alternative concept considers exports and stock increases: Production + imports + changes in stocks (decrease or increase) = quantities available for export and internal use

Domestic use comprises the following: • Human consumption: Part of production, whether gross, processed or semi-processed, available for human

consumption during the given reference period. It is the quantity of food available for consumption at the level of retail trade. This quantity therefore includes waste (and/or losses) occurring during retail sale or at the consumer level, given that this food was technically available for human consumption. The quantities considered here also represent goods available for consumption not only in households, but also in restaurants and institutions (hospitals, schools, military bases, prisons, etc.).


141

• Food processing refers to quantities of a given food which undergoes a process to be converted into a different edible food identified in the food balance sheet as a product derived from the basic commodity. These separate products could be included in the same food group or groups (e.g. tomatoes processed into tomato paste), or could be completely separate (for example barley is processed into beer, generally aggregated into an alcoholic beverages category and not into the barley balance). For derived products belonging to the same food group as the basic commodity, the food processing variable should disappear in the final stages of completion of the food balance sheet to avoid double counting. For quantities used in the production of derived products allocated to different groups, the food processing variable should remain in the final account. It should be noted here that quantities devoted to the manufacture of non-edible products (such as soap or biofuels) should be accounted for under industrial utilization and not food processing.

• Feed: part of gross production intended to feed animals.• Seed: this is the part of gross production set aside for sowing. This variable is usually estimated by imputation

because few countries have official estimates. For countries with reliable estimates of the cropping area, the imputation process for missing values is actually relatively simple: the use of seeds is simply the product of an average rate of seeding (the quantity of seeds necessary for a given seeded area) and the area seeded the following year (as the use of seeds in year t is actually set aside in year t to use for seeding in year t + 1).

• Food for tourists: this is the quantity of food available for consumption by visitors. It is estimated by determining the number of incoming visitors in a given country. Consumption by seasonal migrant workers is also included. Similarly, the days that a country’s residents spend abroad should not be counted in the availability of domestic food, as these people are not at home to consume food, and food consumed abroad will be counted in the food for tourists of another country. Tourist food should therefore be estimated in net terms. In other words, net tourist food should be calculated by subtracting food which would otherwise be available for travellers from another country from the quantity of food available for incoming visitors. It is consequently likely that this information will have to be completed in the balance sheet by imputation, using the number of visits, lengths of visit and quantity of calories historically available in the country of origin and the destination country. This data to be entered by imputation can be taken from a mixture of official and semi-official sources, as described below.

• Industrial use: this refers to the use of food products in any non-food sector. For example maize, rapeseed, soybean and sugar cane can be used for the production of biofuel, or shea butter, palm oil and coconut oil, which are used in numerous cosmetic products. According to FAO guidelines, practitioners are encouraged first to find industry and product experts (from both the public and private sector) to determine which products are used for industrial purposes in their respective countries and how their use can be modelled in the event of missing data.

• Post-harvest losses: these include quantities of products lost during the year in question, from harvest to distribution (losses before harvest and after distribution are not included) (see 4.3 Post-harvest losses).

• Stocks are defined as the total quantity allocated for storage of a product for use at a future time (the anticipated future use is not important). Stocks can be held by a variety of stakeholders (governments, manufacturers, importers, exporters, wholesalers, farmers) at any level in the supply chain: from production to retail sale (but excluding the latter). As noted in previous equations, stocks can also be counted in two ways as part of the supply-utilization account:

�� Stock levels at the beginning and end of the period are reported on the left and right of the equation, respectively.

�� Alternatively, they can be counted by estimating changes in stocks from one period to the next as a component of supply. In other words, if closing stocks are smaller than opening stocks, this implies that some have been removed during the period, thus increasing supplies. In the opposite case, extra stocks have been created as production and the trade balance were sufficient to cover needs.


142

• Residual and other uses: this item is calculated according to a country’s needs. Some countries calculate this heading ex post as a balancing item in the supply-utilization account. It is therefore estimated in a similar manner to the “imbalance” in “supply = utilization”, after quantities have been estimated for each of the other variables. However, as explained in FAO food balance manual guidelines, this strategy should be used only if imbalances in the equation are small. Some countries can, moreover, decide not to use this heading by simply removing it from the supply-utilization account.


143

Recommendations

We recommend that the following themes are covered separately by specialists. They should then serve as a training tool lasting 3 to 5 days for agricultural statistics officers:

• The use of new technologies (GPS, tablets, remote sensing, GIS):�� in constructing sampling frames and master sampling frames;�� in data collection; �� in data processing;

• Sampling frames (list, area, multiple-frame) and the master sample;• Economic accounts for agriculture (EAA);• Environmental-Economic accounts (EEA);• Compilation of food balance sheets;• Costs of production statistics and the typical holding method;• Specific survey techniques for crop and livestock statistics.


144

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List of indicators for agricultural statistics

Annex 1


148

TAbLe 1: LIST of INDICAToRS foR AGRICULTURAL STATISTICS

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bre

edin

g s

tock

, are

a o

f se

mi-

per

man

ent

cro

ps

such

as

tree

s an

d

vin

eyar

ds,

nu

mb

er o

f tr

ees

and

vin

es.

Ag

ricu

ltu

ral r

eso

urc

e su

rvey

s o

f h

old

ing

s an

d a

gri

cult

ura

l en

terp

rise

s.

Mac

hin

ery

and

eq

uip

men

t sh

ou

ld b

e b

y p

urp

ose

(t

illag

e, h

arve

stin

g, e

tc.)

an

d s

ize.

6D

emo

gra

ph

ics

of

agri

cult

ura

l an

d r

ura

l po

pu

lati

on

Ru

ral p

op

ula

tio

n a

nd

nu

mb

er o

f ru

ral

ho

use

ho

lds,

nu

mb

er o

f ag

ricu

ltu

ral h

ou

seh

old

s an

d p

op

ula

tio

n li

vin

g in

th

em, a

ge

and

ed

uca

tio

n

leve

ls. A

gri

cult

ure

incl

ud

es f

ore

stry

an

d fi

sher

ies

Cen

sus

of

Pop

ula

tio

n, C

ensu

s o

f A

gri

cult

ure

, Ho

use

ho

ld s

urv

ey,

adm

inis

trat

ive

reco

rds

Ru

ral d

efin

ed u

sin

g n

atio

nal

des

crip

tio

n

7R

ura

l po

or

as a

per

cen

t o

f to

tal p

oo

r p

op

ula

tio

n

Ho

use

ho

ld in

com

e an

d c

on

sum

pti

on

est

imat

es

for

nat

ion

al a

nd

ru

ral p

over

ty li

nes

. Pu

rch

asin

g

Pow

er P

arit

ies

(PP

Ps)

fo

r co

mp

aris

on

s ac

ross

co

un

trie

s

Ho

use

ho

ld s

urv

eys,

In

tern

atio

nal

Co

mp

aris

on

Pr

og

ram

me

(IC

P)

for

com

par

iso

ns

acro

ss c

ou

ntr

ies

Co

un

trie

s sh

ou

ld u

se p

over

ty e

stim

ates

bas

ed o

n

PP

Ps

and

ext

rap

ola

te b

etw

een

ICP

ben

chm

arks

8R

ura

l hu

ng

ry a

s a

per

cen

t o

f to

tal p

oo

r p

op

ula

tio

n

Ho

use

ho

ld in

com

e an

d f

oo

d c

on

sum

pti

on

es

tim

ates

fo

r n

atio

nal

min

imu

m e

ner

gy

req

uir

emen

ts.

Ho

use

ho

ld s

urv

eys,

In

tern

atio

nal

Co

mp

aris

on

Pr

og

ram

me

for

com

par

iso

ns

acro

ss c

ou

ntr

ies

Co

un

trie

s sh

ou

ld u

se h

un

ger

est

imat

es f

or

mo

nit

ori

ng

fo

od

dep

riva

tio

n le

vels


149

IND

ICA

ToR

DA

TA R

eQ

UIR

eM

eN

TS

DA

TA S

oU

RC

eS

Te

CH

NIC

AL

No

Te

S

9Fo

od

pro

du

ctio

n in

dex

Are

a, p

rod

uct

ion

, an

d y

ield

fo

r fo

od

cro

ps,

liv

esto

ck n

um

ber

s, a

nd

pro

du

ctio

n o

f m

eat,

milk

, eg

gs,

fish

cap

ture

d a

nd

cu

ltu

red

, an

d o

ther

fo

od

p

rod

uct

s, n

on

-fo

od

use

of

foo

d p

rod

uct

s, a

nd

fo

od

imp

ort

s an

d e

xpo

rts.

Ag

ricu

ltu

ral c

ensu

s, s

urv

eys

of

agri

cult

ura

l en

terp

rise

s, fi

sh

lan

din

gs,

ad

min

istr

ativ

e d

ata

such

as

imp

ort

s an

d e

xpo

rts.

Fo

od

bal

ance

s a

nd

ho

use

ho

ld

con

sum

pti

on

su

rvey

s

Follo

w F

AO

gu

idel

ines

fo

r in

clu

sio

ns

and

ex

clu

sio

ns

10C

han

ge

in v

alu

e o

f tr

ade

– im

po

rts

and

exp

ort

s

Imp

ort

s an

d e

xpo

rts

– q

uan

titi

es a

nd

val

ues

of

agri

cult

ura

l pro

du

cts

incl

ud

ing

fish

ery

and

fo

rest

p

rod

uct

s.

Cu

sto

ms

insp

ecti

on

s −

in

som

e co

un

trie

s cu

sto

ms

offi

ces

colle

ct t

he

dat

a, w

hic

h

then

are

tu

rned

ove

r to

th

e n

atio

nal

sta

tist

ical

offi

ces

for

com

pila

tio

n

Nat

ion

al s

tati

stic

al o

ffice

s sh

ou

ld c

olla

bo

rate

w

ith

cu

sto

ms

offi

cial

s to

en

sure

co

din

g a

nd

cl

assi

fica

tio

ns

follo

w in

tern

atio

nal

gu

idel

ines

.

IND

ICA

ToR

S f

oR

AG

RIC

ULT

UR

AL

SU

bS

eC

ToR

S A

ND

RU

RA

L A

Re

AS

11Pr

od

uct

ivit

y o

f cr

op

p

rod

uct

ion

as

mea

sure

d b

y cr

op

yie

lds

Qu

anti

ty h

arve

sted

per

un

it o

f ar

ea s

uch

as

hec

tare

, an

d a

rea

har

vest

ed. A

rea

har

vest

ed

dis

tin

gu

ish

ed b

etw

een

irri

gate

d h

arve

sted

cro

ps

and

rai

nfe

d h

arve

sted

cro

ps.

Cen

sus

of

agri

cult

ure

, cro

p-

cutt

ing

su

rvey

s. P

rod

uct

ion

sa

mp

le s

urv

eys,

pro

cess

or

surv

eys,

su

ch a

s o

il se

ed

cru

sher

s an

d c

ott

on

gin

ner

s.

Diffi

cult

to

mea

sure

wit

h m

ult

i-cr

op

pin

g o

r w

ith

cr

op

s th

at c

an b

e h

arve

sted

mo

re t

han

on

ce a

ye

ar. C

rop

cu

ttin

g c

an o

ver

esti

mat

e yi

eld

s.

12C

han

ges

in c

om

po

nen

ts o

f cr

op

bal

ance

s

Are

a h

arve

sted

, qu

anti

ty h

arve

sted

, qu

anti

ties

im

po

rted

or

exp

ort

ed, c

han

ge

in s

tock

s,

qu

anti

ties

by

uti

lizat

ion

su

ch a

s fo

od

, bio

fuel

s,

ow

n c

on

sum

pti

on

fo

r ev

ery

cro

p in

clu

din

g t

ho

se

pro

du

ced

fo

r fi

ber

an

d o

il.

Su

rvey

s o

f ag

ricu

ltu

ral

ente

rpri

ses,

ad

min

istr

ativ

e d

ata

on

tra

de,

pro

cess

ors

by

uti

lizat

ion

, an

d h

ou

seh

old

su

rvey

s o

f o

wn

co

nsu

mp

tio

n

Cro

p b

alan

ces

sho

uld

refl

ect

the

gro

win

g c

ycle

an

d m

arke

tin

g y

ear,

wh

ich

co

uld

be

diff

eren

t,

fro

m t

he

cale

nd

ar y

ear.

13Li

vest

ock

val

ue

add

ed

Est

imat

es o

f q

uan

tity

an

d v

alu

e o

f p

rod

uct

ion

, p

ou

ltry

, milk

, eg

gs,

by-

pro

du

cts

such

as

hid

es

and

ski

ns,

an

d w

oo

l mo

hai

r m

inu

s co

sts

of

inp

uts

su

ch a

s fe

ed a

nd

rep

lace

men

t st

ock

.

Su

rvey

s o

f ag

ricu

ltu

ral

ho

ldin

gs,

en

terp

rise

s su

ch a

s sl

aug

hte

r p

lan

ts, d

airi

es a

nd

p

roce

sso

rs. H

ou

seh

old

su

rvey

s fo

r o

wn

co

nsu

mp

tio

n

Ow

n c

on

sum

pti

on

sh

ou

ld b

e in

clu

ded

, diffi

cult

to

m

easu

re.

14C

han

ges

in c

om

po

nen

ts o

f liv

esto

ck a

nd

po

ult

ry b

alan

ces

by

spec

ies

Nu

mb

er o

f an

imal

s b

orn

, acq

uir

ed, s

lau

gh

tere

d

and

dea

ths

fro

m d

isea

se. N

um

ber

of

anim

als

by

pu

rpo

se s

uch

as

bre

edin

g, m

eat,

milk

, wo

ol a

nd

b

y ag

e b

reak

do

wn

s re

leva

nt

to s

pec

ie (

see

FAO

20

10 C

ensu

s)

Su

rvey

s o

f ag

ricu

ltu

ral h

old

ing

s at

leas

t an

nu

ally

, bu

t m

ore

oft

en

for

spec

ies

wit

h m

ore

fre

qu

ent

bir

ths

du

rin

g a

ref

eren

ce p

erio

d.

Th

is r

ang

es f

rom

an

nu

ally

fo

r ca

ttle

to

mo

nth

ly f

or

egg

p

rod

uct

ion

.

Dat

a co

llect

ion

inte

rval

s sh

ou

ld r

eflec

t th

e re

pro

du

ctiv

e cy

cles

. . T

his

su

gg

ests

an

nu

al f

or

catt

le, s

emi-

ann

ual

fo

r p

ork

, an

d q

uar

terl

y o

r sh

ort

er f

or

po

ult

ry a

nd

milk


150

IND

ICA

ToR

DA

TA R

eQ

UIR

eM

eN

TS

DA

TA S

oU

RC

eS

Te

CH

NIC

AL

No

Te

S

15C

han

ges

in p

rod

uct

ivit

y

of

cap

ture

fish

pro

du

ctio

n

Qu

anti

ty o

f fi

sh t

aken

by

un

it o

f fi

shin

g e

ffo

rt;

scie

nti

fic

esti

mat

es o

f fi

sh s

tock

s an

d e

xplo

itat

ion

ra

tes

Nat

ion

al fi

sh

ery

surv

eys,

su

rvey

s at

lan

din

g s

ites

, o

nb

oar

d o

bse

rver

s, n

atio

nal

, re

gio

nal

, an

d g

lob

al a

sses

smen

t re

sult

s.

16C

han

ges

in p

rod

uct

ivit

y

of

aqu

acu

ltu

re

Est

imat

es o

f q

uan

tity

an

d v

alu

e o

f p

rod

uct

ion

o

f fi

sh

by

spec

ies

min

us

cost

s an

d q

uan

tity

of

inp

uts

su

ch a

s se

ed, f

eed

, an

d f

erti

lizer

s.

Su

rvey

s o

f aq

uac

ult

ure

en

terp

rise

s, a

nd

ho

ldin

gs,

aq

uac

ult

ure

cen

sus,

mar

ket

cert

ifica

tio

ns

17C

han

ges

in c

om

po

nen

ts

of

fish

bal

ance

s

Qu

anti

ties

an

d v

alu

e o

f ca

ptu

res

fro

m c

oas

tal

and

off

sho

re w

ater

s, r

iver

s an

d la

kes

incl

ud

ing

n

on

-lan

ded

cat

ch; q

uan

titi

es a

nd

val

ue

of

pro

du

cts

fro

m a

qu

acu

ltu

re; u

tiliz

atio

ns

incl

ud

ing

o

wn

co

nsu

mp

tio

n a

nd

dis

card

s, im

po

rts

and

ex

po

rts

Nat

ion

al fi

sher

y su

rvey

s,

fish

ery

cen

sus,

aq

uac

ult

ure

ce

nsu

s, s

urv

eys

of

fish

ery

an

d a

qu

acu

ltu

re e

nte

rpri

ses,

p

roce

sso

rs m

arke

t in

form

atio

n

and

ad

min

istr

ativ

e an

d

insp

ecti

on

so

urc

es

See

CW

P H

and

bo

ok

and

FA

O c

od

ing

an

d

clas

sifi

cati

on

.

18C

han

ge

in c

om

po

nen

ts

of

fore

stry

bal

ance

s Q

uan

tity

an

d v

alu

e o

f re

mov

als

of

pro

du

cts

fro

m

fore

sted

are

as a

nd

res

pec

tive

uti

lizat

ion

s

Ap

pro

pri

ate

min

istr

ies,

sat

ellit

e im

ager

y, p

rice

su

rvey

s, o

r p

roce

sso

r d

ata

19C

om

mo

dit

y p

rice

ind

exes

Mar

ket

rep

ort

s o

f p

rice

bei

ng

off

ered

by

com

mo

dit

y an

d lo

cati

on

. Pri

ces

rece

ived

by

the

ente

rpri

se a

t th

e fi

rst

po

int

of

sale

.

Mar

ket

ob

serv

ers,

su

rvey

s o

f en

terp

rise

s, a

gro

-en

terp

rise

s p

urc

has

ing

co

mm

od

itie

s fr

om

ag

ricu

ltu

ral e

nte

rpri

ses

Car

e n

eed

ed t

o e

nsu

re u

nit

s o

f m

easu

re f

or

pri

cin

g a

re c

om

par

able

20C

on

sum

er p

rice

ind

exes

Mo

nth

ly o

r se

aso

nal

pri

ces

pai

d b

y th

e co

nsu

mer

Co

nsu

mer

pri

ce in

dex

Car

e is

nee

ded

to

en

sure

hig

hly

sea

son

al d

o n

ot

dis

tort

th

e p

rice

ser

ies

21E

arly

war

nin

g o

f ch

ang

e

in f

oo

d s

ecu

rity

Mo

nth

ly o

r se

aso

nal

pri

ces

pai

d b

y th

e co

nsu

mer

Win

dsh

ield

su

rvey

s o

f cr

op

co

nd

itio

ns,

am

ou

nt

of

pre

cip

itat

ion

, sat

ellit

e im

ager

y o

f ve

get

ativ

e in

dex

es, c

han

ges

in

tra

de

dat

a, a

nd

an

imal

d

isea

se o

utb

reak

.

Th

ese

do

no

t h

ave

to b

e st

atis

tica

lly r

igo

rou

s,

mai

nly

to

pro

vid

e an

ear

ly w

arn

ing

th

at o

ther

in

terv

enti

on

s ar

e n

eed

ed.


151

IND

ICA

ToR

DA

TA R

eQ

UIR

eM

eN

TS

DA

TA S

oU

RC

eS

Te

CH

NIC

AL

No

Te

S

CLI

MA

Te

CH

AN

Ge

, LA

ND

AN

D T

He

eN

VIR

oN

Me

NT

22C

han

ge

in la

nd

cov

er a

nd

use

Lan

d c

over

cla

ssifi

cati

on

sys

tem

(LC

CS

), a

rea

and

geo

refe

ren

ced

fo

r cu

ltiv

ated

lan

d, g

rass

or

pas

ture

, in

lan

d w

ater

, mar

ine

wat

er, w

etla

nd

s,

shru

bla

nd

, wo

od

lan

d, f

allo

w o

r id

le c

ult

ivat

ed

lan

d, b

arre

n la

nd

, urb

an o

r d

evel

op

ed a

reas

, ar

eas

equ

ipp

ed f

or

irri

gati

on

Lan

d u

se s

urv

eys,

sat

ellit

e im

ager

y, G

eore

fere

nce

d d

ata

on

eco

no

mic

of

agri

cult

ura

l h

old

ing

s n

eed

ed t

o u

nd

erst

and

ef

fect

of

po

licy

dec

isio

ns

on

la

nd

use

Gro

un

d t

ruth

dat

a re

qu

ired

to

pro

vid

e m

ore

d

etai

led

bre

akd

ow

ns

of

cult

ivat

ed la

nd

, es

pec

ially

fo

r cr

op

s in

sm

all p

lots

. Diffi

cult

to

ap

ply

in d

etai

l wh

ere

mu

lti-

cro

pp

ing

is u

sed

23C

han

ges

in p

rop

ort

ion

of

lan

d

area

cov

ered

by

fore

sts,

rat

e o

f d

efo

rest

atio

n

Are

a g

eore

fere

nce

d t

o m

ap m

ater

ials

Min

istr

y re

spo

nsi

ble

fo

r fo

rest

ry, s

atel

lite

imag

ery

Follo

w L

CC

S c

lass

ifica

tio

n.

24Pe

rcen

t o

f la

nd

an

d w

ater

ar

ea f

orm

ally

est

ablis

hed

as

pro

tect

ed a

reas

Lan

d a

nd

wat

er a

rea

and

geo

refe

ren

ced

to

m

app

ing

mat

eria

l. R

esp

on

sib

le m

inis

try—

sate

llite

im

ager

y.

Follo

w L

CC

S c

od

ing

wit

h e

xpan

sio

n c

over

ing

in

lan

d a

nd

mar

ine

wat

er b

od

ies.

25Ir

riga

ted

lan

d a

s p

erce

nt

of

tota

l cro

pla

nd

Pro

du

ctiv

ity

of

irri

gati

on

Tota

l cro

pla

nd

an

d a

rea

irri

gate

d b

y so

urc

e o

f w

ater

fo

r ir

riga

tio

n (

surf

ace

wat

er, g

rou

nd

wat

er,

trea

ted

was

tew

ater

, etc

.) a

nd

by

met

ho

d (

surf

ace,

sp

rin

kler

, lo

caliz

ed ir

riga

tio

n).

Cro

p y

ield

s fr

om

irri

gate

d la

nd

co

mp

ared

to

yi

eld

s fr

om

no

n-i

rrig

ated

are

as.

Ag

ricu

ltu

ral c

ensu

s, o

ther

cro

p

rela

ted

su

rvey

s o

r w

ater

-use

r su

rvey

.

Irri

gati

on

ref

ers

to t

he

arti

fici

al a

pp

licat

ion

o

f w

ater

to

ass

ist

the

gro

win

g o

f cr

op

s (a

nd

p

astu

res)

. Can

be

do

ne

by

lett

ing

wat

er fl

ow

ov

er t

he

lan

d (

“su

rfac

e ir

riga

tio

n”),

by

spra

yin

g

wat

er u

nd

er p

ress

ure

ove

r th

e la

nd

co

nce

rned

(“

spri

nkl

er ir

riga

tio

n”),

or

by

bri

ng

ing

it d

irec

tly

to t

he

pla

nt

(“lo

caliz

ed ir

riga

tio

n”).

26W

ith

dra

wal

of

wat

er f

or

agri

cult

ure

as

a p

erce

nt

of

tota

l wat

er w

ith

dra

wal

Are

a u

nd

er ir

riga

tio

n, n

um

ber

of

irri

gati

on

s sc

hem

es, i

rrig

atio

n in

ten

sity

an

d r

equ

irem

ents

b

y cr

op

, wat

er w

ith

dra

wal

an

d t

urn

over

rat

e fo

r aq

uac

ult

ure

co

nsu

mp

tio

n, a

nd

per

cap

ita

con

sum

pti

on

by

peo

ple

an

d a

nim

als.

Ap

pro

pri

ate

min

istr

ies,

sp

ecia

l st

ud

ies

or

surv

eys

to e

stim

ate

wat

er u

se in

ag

ricu

ltu

re a

nd

aq

uac

ult

ure

, an

d s

urv

eys

of

aqu

acu

ltu

re e

nte

rpri

ses

and

h

old

ing

s.

Sh

ou

ld in

clu

de

bo

th s

urf

ace

and

gro

un

d w

ater

. C

od

ing

an

d c

lass

ifica

tio

ns

sho

uld

be

defi

ned

27C

han

ge

in s

oil

loss

fro

m

wat

ersh

eds

Red

uct

ion

in c

rop

yie

lds,

red

uct

ion

in a

rea

of

cult

ivat

ed la

nd

Ap

pro

pri

ate

min

istr

ies,

g

eore

fere

nce

d d

ata

wit

h

sate

llite

imag

ery.

28C

han

ge

in a

ffec

t o

f in

pu

ts o

n

the

envi

ron

men

t

Fert

ilize

r, p

esti

cid

e an

d o

ther

ch

emic

als

app

lied

to

th

e so

il, w

ater

bo

die

s, a

nd

pla

nts

by

typ

e o

f cr

op

an

d w

ater

shed

are

a, s

tock

ing

.

Ag

ricu

ltu

ral c

ensu

s an

d/o

r fo

llow

-up

su

rvey

s to

mea

sure

fe

rtili

zer

and

ch

emic

al u

se,

tilla

ge

met

ho

ds.

Dat

a sh

ou

ld b

e g

eore

fere

nce

d t

o la

nd

cov

er a

nd

u

se.


152

IND

ICA

ToR

DA

TA R

eQ

UIR

eM

eN

TS

DA

TA S

oU

RC

eS

Te

CH

NIC

AL

No

Te

S

TH

e A

GR

ICU

LTU

RA

L A

ND

RU

RA

L e

Co

No

My

29N

um

ber

of

fam

ily a

nd

hir

ed

wo

rker

s o

n t

he

ho

ldin

g

Incl

ud

e u

np

aid

lab

or

of

the

op

erat

or

of

the

ho

ldin

g a

nd

fam

ily m

emb

ers

plu

s n

um

ber

of

hir

ed w

ork

ers

Lab

or

forc

e su

rvey

s o

f h

old

ing

s.

Nee

d t

o e

stab

lish

sta

nd

ard

s fo

r m

inim

um

ag

es

of

wo

rker

s an

d t

he

nu

mb

er o

f h

ou

rs w

ork

ed p

er

wee

k to

be

con

sid

ered

a w

ork

er. N

eed

to

defi

ne

refe

ren

ce p

erio

d. N

eed

to

en

sure

fem

ale

wo

rker

s ar

e co

un

ted

.

30N

um

ber

of

ho

use

ho

ld

mem

ber

s em

plo

yed

by

farm

an

no

nfa

rm

Th

e em

plo

ymen

t st

atu

s fo

r w

ork

off

th

e ag

ricu

ltu

ral h

old

ing

fo

r ea

ch h

ou

seh

old

mem

ber

Lab

or

forc

e su

rvey

s −

ho

use

ho

ld s

urv

eys

Nee

d t

o d

isti

ng

uis

h d

efin

ed e

mp

loym

ent

fro

m

un

pai

d h

ou

seh

old

ser

vice

wo

rk s

uch

as

do

mes

tic

cho

res

31C

han

ge

in f

arm

an

d r

ura

l n

on

farm

ho

use

ho

ld in

com

e fr

om

all

sou

rces

Inco

me

to t

he

ho

use

ho

ld b

y se

cto

r, c

rop

, liv

esto

ck, e

tc.,

Inco

me

fro

m in

vest

men

ts o

r em

plo

ymen

t o

uts

ide

the

agri

cult

ura

l ho

ldin

g.

Ru

ral h

ou

seh

old

su

rvey

. R

ura

l to

be

defi

ned

usi

ng

nat

ion

al a

nd

nat

ion

al

defi

nit

ion

s

32Pe

rcen

t o

f ru

ral p

op

ula

tio

n

usi

ng

ser

vice

s o

f fo

rmal

b

anki

ng

inst

itu

tio

ns

Tota

l nu

mb

er o

f ru

ral h

ou

seh

old

s, n

um

ber

usi

ng

cr

edit

or

savi

ng

s se

rvic

es

Cen

tral

ban

k o

r co

mm

erci

al

ban

ks, s

pec

ial s

urv

eys,

ag

ricu

ltu

re c

ensu

s.

33C

han

ge

in s

ales

of

agro

-b

usi

nes

ses

Sal

es, n

et p

rofi

ts o

f en

terp

rise

s p

rovi

din

g

serv

ices

to

ag

ricu

ltu

reS

pec

ial s

urv

eys

Use

sta

nd

ard

acc

ou

nti

ng

pri

nci

ple

s

So

urc

es: F

AO

(20

10)


153153

Minimum set of core dataTAbLe 2: MINIMUM SeT of CoRe DATA

GRoUP of VARIAbLeS

key VARIAbLeS CoRe DATA ITeMS fReQUeNCy

eCoNoMIC

Outputs

Production

Core crops (e.g., wheat, rice, etc.) Core livestock (e.g., cattle, sheep, pigs, etc.) Core forestry products Core fishery and aquaculture products

Annual

Areas harvested and planted Core crops (wheat, rice, etc.) Annual

Yield/births/productivityCore crops, core livestock, core forestry, core fishery

Annual

Trade

Exports in quantity and valueCore crops, core livestock, core forestry, core fishery

Annual

Imports by quantity and valueCore crops, core livestock, core forestry, core fishery

Annual

StocksQuantities in storage at beginning of harvest

Core crops Annual

Stocks of resources

Land cover and use Land area

Economically active population Number of people in working age by sex

Livestock Number of live animals

Machinery Number of tractors, harvesters, seeders, etc.

Inputs

WaterQuantity of water withdrawn for agricultural irrigation

Fertilizers in quantity and value Core fertilizers by core crops

Pesticides in quantity and valueCore pesticides (e.g. fungicides herbicides, insecticides, disinfectants) by core crops

Seeds in quantity and value By core crops

Feed in quantity and value By core crops

Annex 2


154

Agro processing

Volume of core crops/ livestock/ fishery used in processing food

By industry

Value of output of processed food

By industry

Other uses (e.g. biofuel)

Prices

Producer priceCore crops, core livestock, core forestry, core fishery

Consumer priceCore crops, core livestock, core forestry, core fishery

Final expenditure

Government expenditure on agriculture and rural development

Government investment, subsidies, etc.

Private investmentsInvestment in machinery, in research and development, in infrastructure

Household consumptionConsumption of core crops/livestock/etc. in quantity and value

Rural infrastructure (capital stock)

Irrigation, roads, railways and communications

Areas equipped for irrigation/roads in km/railways in km/communications

International transfer

ODA1 for agriculture and rural development

1

1 GSD = Government-supported development


155

SoCIAL

Demographics of urban and rural populations

Sex

Age in completed years By sex

Country of birth By sex

Highest level of education completed

One digit ISCED by sex

Labor status Employed, unemployed, inactive by sex

Status in employment Self-employment and employee by sex

Economic sector in employmentInternational standard industrial classification by sex

Occupation in employmentInternational standard classification of occupations by sex

Total income of the household

Household composition By sex

Number of family s / hired workers on the holding

By sex

Housing conditionsType of building, building character, main material, etc.

eNVIRoNMeNTAL

Land Soil degradation Variables will be based on above core items on land cover and use, water use, and other inputs to production

Water Pollution due to agriculture

Air Emissions due to agriculture

GeoGRAPHICAL LoCATIoN

GIS coordinates Location of the statistical unit Parcel, province, region, country

Degree of urbanization

Urban/rural area

Sources: FAO (2010)

The publication frequency of data for unspecified products will be determined by the prepared framework in the Global strategy framework to determine national priorities in terms of content, scope and frequency. Requirements for frequency will be taken into consideration when constructing the integrated survey framework which will define data sources.


156156

Target population and sampled population

The following situations are examples of when the target population and sampled population might not coincide: • Methodological concern: Difficulty directly selecting the desired statistical units.

�� In practice, it is sometimes not feasible to directly select and contact the statistical units.�� Example: Study on the inventory of available agricultural equipment and their state of operation during

a specific crop year.�� In this case, the statistical units are the agricultural equipment. But it is very difficult to have direct

access to them, to carry out sampling and listing and report their state of operation. �� On the other hand, access seems easy to the agricultural households/holdings. So the sampling units

will be the agricultural households/holdings. The available equipment will then be identified and the required information will be reported within the selected agricultural households/holdings.

• Problem of coverage: exclusion of some units. Difficulty accessing reporting units. A few examples are:�� Exclusion of isolated areas (owing to relatively high travel costs) �� Exclusion of agricultural activities carried out by some institutions (prisons, etc.)�� Limitation of the sampling frame due to lack of information.

If the two populations are different, the sampled population should be reasonably consistent in terms of coverage and matching the target population so that the survey results are relevant.

Annex 3


157157

Fields covered by environment statistics

Components fields

Component 1: environmental conditions and quality

Subcomponent 1.1: Physical conditions

Topic 1.1.1 Atmosphere, climate and weather

Topic 1.1.2 Hydrographic characteristics

Topic 1.1.3 Geological and geographical information

Topic 1.1.4 Soil characteristics

Subcomponent 1.2: Land cover, ecosystems and biodiversity

Topic 1.2.1 Land cover

Topic 1.2.2 Ecosystems and biodiversity

Topic 1.2.3 Forests

Subcomponent 1.3: Environmental quality

Topic 1.3.1 Air quality

Topic 1.3.2 Freshwater quality

Topic 1.3.3 Marine water quality

Topic 1.3.4 Soil pollution

Topic 1.3.5 Noise

Component 2: environmental resources and their use

Subcomponent 2.1: Mineral resources

Topic 2.1.1 Stocks and changes of mineral resources

Topic 2.1.2 Production and trade of minerals

Subcomponent 2.2: Energy resources

Topic 2.2.1 Stocks and changes of energy resources

Topic 2.2.2 Production, trade and consumption of energy

Subcomponent 2.3: Land

Topic 2.3.1 Land use

Topic 2.3.2 Use of forested land

Annex 4


158

Subcomponent 2.4: Soil Resources

Topic 2.4.1 Soil resources

Subcomponent 2.5: Biological resources

Topic 2.5.1 Timber resources

Topic 2.5.2 Aquatic resources

Topic 2.5.3 Crops

Topic 2.5.4 Livestock

Topic 2.5.5 Other non-cultivated biological resources

Subcomponent 2.6: Water resources

Topic 2.6.1 Water resources

Topic 2.6.2 Abstraction, use and returns of water

Component 3: Residuals

Subcomponent 3.1: Emissions to air

Topic 3.1.1 Emissions of greenhouse gases (GHG)

Topic 3.1.2 Consumption of ozone-depleting substances (ODS)

Topic 3.1.3 Emissions of other substances

Subcomponent 3.2: Generation and management of wastewater

Topic 3.2.1 Generation and pollutant content of wastewater

Topic 3.2.2 Collection and treatment of wastewater

Topic 3.2.3 Discharge of wastewater to the environment

Subcomponent 3.3: Generation and management of waste

Topic 3.3.1 Generation of waste

Topic 3.3.2 Management of waste

Subcomponent 3.4: Release of Chemical Substances

Topic 3.4.1 Release of chemical substances

Component 4: extreme events and disasters

Subcomponent 4.1: Natural extreme events and disasters

Topic 4.1.1 Occurrence of natural extreme events and disasters

Topic 4.1.2 Impact of natural extreme events and disasters

Subcomponent 4.2: Technological disasters

Topic 4.2.1 Occurrence of technological disasters

Topic 4.2.2 Impact of technological disasters

Component 5: Human settlements and environmental Health

Subcomponent 5.1: Human settlements

Topic 5.1.1 Urban and rural population

Topic 5.1.2 Access to selected basic services

Topic 5.1.3 Housing conditions

Topic 5.1.4 Exposure to ambient pollution

Topic 5.1.5 Environmental concerns specific to urban settlements

Subcomponent 5.2: Environmental Health

Topic 5.2.1 Airborne diseases and conditions

Topic 5.2.2 Water-related diseases and conditions

Topic 5.2.3 Vector-borne diseases

Topic 5.2.4 Health problems associated with excessive UV radiation exposure

Topic 5.2.5 Toxic substance- and nuclear radiation-related diseases and conditions


159

Component 6: environmental Protection, Management and engagement

Subcomponent 6.1: Environmental Protection and Resource Management Expenditure

Topic 6.1.1 Government environmental protection and resource management expenditure

Topic 6.1.2Corporate, non-profit institution and household environmental protection and resource management expenditure

Subcomponent 6.2: Environmental governance and regulation

Topic 6.2.1 Institutional strength

Topic 6.2.2 Environmental regulation and instruments

Topic 6.2.3 Participation in MEAs and environmental conventions

Subcomponent 6.3: Extreme event preparedness and disaster management

Topic 6.3.1 Preparedness for natural extreme events and disasters

Topic 6.3.2 Preparedness for technological disasters

Subcomponent 6.4: Environmental information and awareness

Topic 6.4.1 Environmental information

Topic 6.4.2 Environmental education

Topic 6.4.3 Environmental perception and awareness

Topic 6.4.4 Environmental engagement

Sources: Author, based on information taken from the FDES². 2013


160

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