asia and pacific commission on agricultural statistics...this global effort involves interaction...

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APCAS/12/7

Asia and Pacific Commission on

Agricultural Statistics

Twenty-fourth Session

Da Lat, Viet Nam, 8-12 October 2012

Agenda Item 5

Report on Initial Country Assessments

Introduction

The Global Strategy for Improving Agricultural and Rural Statistics involves the development of a

Regional Implementation Plan that provides guidelines for strengthening the capacity of national

statistical systems to produce information for informed decision-making related to food security,

sustainable agriculture and rural development. This global effort involves interaction between data

producers, data users and partners in development. The Asia and Pacific Region has initiated efforts

to evaluate the capacity of countries in the region and to identify areas where inputs would be helpful.

This paper makes preliminary assessments of the capacity of national statistical systems and

identification of countries where in-depth assessments could be used to develop technical assistance,

training and research strategies.

Background

The agriculture sector has long been forced to adjust to low priorities and limited National Statistics

Office (NSO) resources to obtain current statistics about the sector. The Global Strategy for

Improving Statistics for Food Security, Sustainable Agriculture and Rural Development1 was

developed by the World Bank (WB) and the Food and Agriculture Organization of the United Nations

(FAO) in close consultation with a large number of national and international stakeholders to

emphasize its importance.

At early stages of development of the Global Strategy to Improve Agriculture and Rural Statistics a

need was felt to develop a standard tool to monitor the capacity of National Statistical Systems to

produce agriculture statistics. An international group was formed at Fifth International Conference on

Agricultural Statistics, Kampala in October 2010 to develop the tool. The core group comprising FAO,

USDA and Australia was formed. AfDB, Russia and Brazil were invited to participate to evolve a

globally acceptable approach. The initial thinking of the group was to take advantage of the work

already done by international organizations like the World Bank (WB), International Monetary Fund

(IMF) and the United Nations Educational, Scientific and Cultural Organization (UNESCO). The

group observed that these Data Quality Assessment Frameworks (DQAFs) have a great deal of

October 2012

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convergence but do not address the specificity of agriculture sector in general and agriculture statistics

in particular.

Given that the main objective of the Global Strategy is to enable countries to produce a globally

agreed core set of agriculture statistics, as well as integration of agriculture statistics into the

mainstream of statistical activities at the country level, the emphasis of the work to develop the tool

was shifted towards assessing the “Capacity of the countries to produce core data” and monitoring

this. As a tradition of collecting data required for this exercise was already there in Asia and Africa

through the FAO regional commissions on agriculture statistics (APCAS and AFCAS, respectively), a

Standard Country Assessment Questionnaire (CAQ) was developed through a consultative process

involving the African Development Bank, United Nations Economic Commission for Africa

(UNECA) and other partners. The Global Strategy identifies a list of seventy-six core data items (see

Table 1, Annex 2) that should be available for making decisions about agricultural and rural

development. Flexibility was given to the regions to modify and adapt the standard questionnaire to

more accurately assess the data requirements. Supplementary information about the agriculture sector

for Asia and Pacific Countries is found in Table 2, Annex 2; this information can be used to compare

the role of agriculture in the countries.

Objectives of the questionnaire

By integrating inputs from various international agencies, statistical offices and donors, the basic

country assessment questionnaire (CAQ) used in the Asia and Pacific Region was prepared prior to the

undertaking of Global Strategy initiatives in Africa, the first region to be reviewed and the location of

the first regional implementation plan.

The immediate objective of the country assessment questionnaire is to categorize countries by their

capacity to produce a minimum set of core data and to serve as a basis for preparation of

comprehensive technical assistance, training and research programmes. Detailed requirements for

technical assistance, training and research for implementation of individual country strategies and

action plans will be finalized as a result of country visits and following consultations with political,

business and farm entities.

The CAQ has been designed to identify the types, frequency of reporting and quality of key

agricultural sector variables and to solicit information about the roles of different government

institutions and of private sector agencies/ individuals in the statistical processes.

The questionnaire responses should be the result of cooperation and interaction among the various

agricultural sector data producers. It was emphasized that the heads of the data producing agencies

support and contribute to the completion of the questionnaire.

Preparation for the preliminary assessments

An early assessment of countries had been made based on various types of feedback from

representatives of some countries during participation in regional meetings and workshops and from

available metadata. It was followed up by sending a 10-sheet EXCEL workbook to heads of statistics

in 59 countries and territories in the Asia and Pacific Region for completion following consultation

with the major agricultural sector statistics producers.

These countries / territories were determined based on the scope of work for FAO, UN ESCAP and

Asian Development Bank (ADB), the partners for implementing the Global Strategy in Asia and the

Pacific. It is possible to classify them into East and North-East Asia, South-East Asia, South and

South-West Asia, North and Central Asia and Pacific sub-regions. Most of the countries are

considered to be developing countries, although several have well-established statistics systems that

provide the majority of agricultural sector statistics that have been included in the core data for the

Global Strategy. In some cases the evaluations are carried out by more than one “region”;

consequently it is possible that some questionnaires were not returned to all regions.

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The country assessment questionnaire (an EXCEL workbook containing one worksheet with

instructions plus ten worksheets for responses) was emailed to countries in November 2011; they were

prefilled by ADB based on metadata available from ADB, ESCAP and FAO. Only 45 of the 59

countries responded even after reminders (see Table 1, Annex 1); also, many returned questionnaires

were incomplete necessitating further action.

A digital form was made available on www.surveymonkey.com for those countries that wanted to use

that alternative; only four countries did and one also completed the EXCEL questionnaire. The

responses for the other three countries were sparse, but were transferred to EXCEL format later for

ease of comparison.

Target Indicators for Monitoring of Country Capacity

It is widely recognized that “Quality” is a wider concept that just the “Quality of the Output”. The

CAQ was thus designed to capture the information on the entire production chain, including:

Institutional environment, Inputs (human and financial resources), Throughput (activities) and Outputs

(available data and their quality as seen by their timeliness, reliability and accessibility), besides

gathering information just the data availability. Thus indicators are needed which allow monitoring

progress of the countries on each of these dimensions and their elements separately and as a whole.

An FAO team in Rome developed a draft set of indicators which could be compiled using the key data

available from the questionnaires, while the data on questionnaires was being collected from countries

in Asia. A set of draft Guidelines on Processing the Country Assessment Questionnaires was

circulated as a work in progress, and is open to comments towards perfection as a globally accepted

tool for monitoring the “country capacity to produce agriculture statistics” over the life span of the

Global Strategy and perhaps beyond. The indicators proposed in the guidelines shall be useful for

comparing the level of development of agriculture statistics system of countries not only within a

region but across continents.

In developing the guidelines the team has taken into account the following:

Recent work of UNSD on a national data quality assessment frame work (NQAF) which

builds on earlier international work in this domain. A correspondence table on dimensions and

elements being measured through the proposed capacity indicators and in those included in

other important international works is available for reference.

The WB Statistical Capacity Indicators which are designed for overall capacity of the National

Statistics System but lack the focus on the agriculture statistics. Further, a review of the

literature suggests that these indicators give emphasis to activities and do not measure the

inherent national capacity of the system. It is well known that many of the large-scale

statistical activities in developing countries are largely governed by availability of donor

funding. Though these activities leave behind some built-in capacity, it is often not sustained

for a variety of reasons.

An attempt thus was made to define and compile a set of 20 indicators on Country Capacity to produce

agriculture statistics in a cost-effective and integrated manner. The draft guidelines for compiling these

indicators based on responses to the CAQ is available for reference and comments.

Basically, the country capacity indicators represent the four dimensions of statistical capacity, viz.,

Capacity Indicator I: Institutional Infrastructure (INFRASTRUCTURE DIMENSION)

Capacity Indicator II: Human and Financial Resources (INPUT DIMENSION)

Capacity Indicator III: Statistical Methods and Practices (THROUGHPUT DIMENSION)

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Capacity Indicator IV: Statistical Information and Availability (OUTPUT DIMENSION)

(based on responses to 59 data items broadly in agreement with core list of data items

identified by the Global Strategy)

A breakdown of these aggregate indicators into indicators for each element defining this dimension

may be seen in the Table 7, Annex 1. These indicators show some convergence with WB indicators to

the extent they are measuring the same element of dimension of the country capacity. Some limitations

in the responses were noted to compile these indicators. It is to be noted that based upon the data

available through the CAQ (Initial Assessment), it was not possible to compile the two indicators for

Capacity Indicator II which related to human and financial capacity or the input dimension. This

indicator should be compiled at the in-depth assessment stage as it needs information from various

sources including both users and producers.

It is foreseen that, ultimately, these indicators would be used as the measure of country capacity and

would be updated periodically to monitor the changes in the country capacity. At this stage it is

apparent that the proposed indicators are in line with the international thinking on the subject. Prima

facie it is concluded that the proposed indicators are more robust than those compiled by WB on

regular basis as these are based on a larger set of questions, and they are specifically designed to

measure the aspects relating to improvement in agriculture statistics.

The FAO Global Strategy team plans to validate the proposed indicators in Africa and elsewhere, and

finalize them towards the end of the year 2012 through a consultative process of partners and

international experts. In the mean time, the indicators compiled on the basis of present level of work

and the available data could be used for:

1. Selection of pilot countries in Asia. Even after more accurate data becomes available

the picture is not likely to change substantially as the convergence of proposed set of

indicators and the other available indicators is already visible. Further, it is expected

that the indicators could provide only a short list of potential countries for selection.

Many other selection criteria, including subjective evaluations of government

commitment, identified in the Global Implantation Plan, need to be considered for

taking a decision.

2. Getting an overall assessment of the main issues and weaknesses in the agriculture

statistics systems in the region. The identification of major common deficiencies in

the country capacities could be useful for deciding the research and training agenda of

the implementation plan for Asia.

In-depth assessments at country level

While these initial country assessments are useful for making a selection of countries and for defining

the priorities of the regional work plan, an in-depth assessment will be carried out in each selected

pilot country. These in-depth assessments will focus on identifying the specific weaknesses in the

countries and will result in recommendations on critical areas of interventions for improvements in the

agriculture statistics system. Guidelines for standard in-depth country assessments are being developed

based on pilot work being done in Laos, Tanzania and Peru in partnership with PARIS 21 and USDA.

At this stage, it is expected that these assessments will be carried out through the expert missions and

involvement of all stake holders at the country level. The assessments will lay the foundation for

preparation of Sector Strategic Plan for Improvement of Agriculture Statistics. It is also expected that

the in-depth assessments will fill any of the data-gaps in compilation of indicators through a

consultative process with countries.

Results of Initial Country Assessments in Asia and Pacific

The pilot exercise of initial Country Assessment in Asia and Pacific showed some limitations in

responses received from the countries. Responses to some sections of the EXCEL worksheets for the

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CAQ are blank, indicating the lack of information or “no”, “none” or “not applicable”. For example, it

is known that some countries do collect and publish core data items, but did not report it on the CAQ.

Similarly, the capacity of some statistical systems did not correspond to the perceptions of

organizations working in these countries. These countries were contacted to verify the responses on

the questionnaire and some replied that they misunderstood the meaning of the responses. It is noted

that these responses are preliminary and must be validated when combined with information from

other sources and from an in-depth country assessment.

Perceptions about the content of the questionnaire and the means to obtain the necessary feedback are

dependent on the reader as are the interpretations of the actual questions on the CAQ. There are

several items on the questionnaire where non-native English speakers might have difficulty in

answering the questions as they were intended. Even for English speakers some of the questions in the

worksheets may be misleading and/or incorrect. Also in many cases, the responses related to only the

National Statistics Office or Ministry of Agriculture and do not reflect the totality of the country

situation.

Preliminary assessments of country capacity

Despite the limitations in the responses, the available data could be utilized to draw some meaningful

conclusions about the situation in Asia and the Pacific, and indications on the level of statistical

development in the countries.

Fifteen constraints were listed in the CAQ questionnaire with a request for the NSO and MOA to

evaluate separately each constraint’s impact on agriculture statistics. A difficult constraint would be

classified as “5”; a response of “1” would indicate that this constraint was not a constraint and any

changes would have little or no impact. The average of country reported levels of constraints is found

in Table 1. A summary of significant constraints by country is found in Annex 1, Table 2.

Table 1: Constraints on Agricultural Sector

Statistics and sub-region averages for countries

that responded Southea

st Asia

South and

Southwest

Asia

North/

Central

Asia East

Asia Pacif

ic Develope

d Number of professional staff at headquarters for

statistical activities 3.00 3.33 2.67 3.00 3.43 1.00

Technical skills of the available statistical staff 2.00 2.50 2.17 3.00 3.14 1.00 Turnover of professional staff. 2.17 3.33 2.50 3.00 3.17 1.00 Transport equipment for field activities 2.50 3.50 2.33 3.00 3.00 1.00 Funds for field-oriented statistical activities vis-à-vis

plans. 3.50 2.83 3.00 3.33 3.00 1.00

Up-to-date information technology hardware 3.50 2.50 2.00 2.67 2.57 1.00 Up-to-date information technology software 3.17 2.33 2.17 2.67 2.86 1.00 Number of field workers for statistical activities 2.67 2.83 3.50 3.33 2.86 1.00 Number of professional staff in the field for


Sound methodology implemented for agricultural

surveys 2.50 3.67 3.17 2.33 3.17 1.00

Building space for office 2.67 3.50 3.00 2.67 3.00 1.00 Appreciation at the policy-making level for

importance of statistical activities 2.83 2.00 2.50 2.00 3.67 1.00

Support at political level in the Government for


Number of support staff at headquarters for


Level of demand for statistics 2.83 2.83 2.67 2.67 4.00 1.50 Note :1=No constraint; 2=Little constraint; 3=Relative constraint; 4=Significant constraint;

5=Dominant constraint ; Averages refer to an arithmetic average of 31 reporting countries across

FAO/UNESCAP defined sub-regions

Based on the rating of critical constraints as reported by the countries, the field programme for

agriculture statistics had significant shortcomings; the lack of capable professional staff (especially in

the Pacific Region) and updated information technology were also concerns.

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A summary table with frequency distribution by sub-Region for some criteria is found in the table

below:

TABLE 2: Summary of Critical Characteristics for Countries in Asia and Pacific Region

Category1 Number

of

Countries

Number2

Reportin

g

Stat

Law3

Stat

Law /w

Agric3

NSDS3

Coord.

“Board”

3

Agric

Census

after

2001

Ave

PCT

Core

items

Ave

PCT

Priority

items

Southeast

Asia 10 10 100 80 70 70 60 42 77 South and

Southwest

Asia 10 9 100 78 72 44 67 51 78 North/

Central

Asia 9 7 100 86 100 43 57 48 85 East Asia 7 5 100 60 80 40 60 47 82 Pacific 19 8 100 75 50 13 63 21 38 Developed 3 3 100 100 100 100 100 53 89 Region 58 42 100 79 75 48 64 44 75

1: Sub regions within Asia-Pacific are defined as those observed by FAORAP. Countries which did

not fall under the mandate of FAORAP were categorized according to the sub region definitions by

UNESCAP. The Russian Federation (which did not report) has not been included as part of the

definition for North/ Central Asia sub-region.

2: Some countries reported minimal or partial information, but did respond to the request for a

Country Assessment Questionnaire.

3: Presented as a percent of countries reporting.

The developed countries satisfy the infrastructural characteristics, but did not indicate that they

provide all of the core or priority data items. Western and Central Asia responses did not reflect the

role of agriculture in its economies, but emphasized the structure and strength of the national statistical

offices. Other Asia countries depend heavily on agriculture, but have maintained a national statistics

office focus that did not allow development of line ministries capacities and support their data

requirements. Many Pacific Island Countries and Territories (PICTs) do not survey agricultural

production and obtain many data items in an agricultural census that occurs less frequently than every

ten years. Few of the PICTs reported to have statistics law and NSDS in place. It will be critical to

review data requirements for the PICTs when a sub-regional set of core data is determined.

Classification of country statistical system

Two methods for evaluation of the country questionnaires have been used to classify the country

statistical systems and are presented in this document. As outlined earlier, the main concerns are

institutional structure, staff capacity and data access. It should be noted that not all countries had

responded to the requests for information on the CAQs and some of the information was incomplete.

It was also clear that language was an obstacle to understanding the content of the questions and that

the responses may not be accurate. Note that the objective methods can only evaluate the information

officially provided and may reflect limitations in the statistical systems that do not exist.

Although efforts have been made to quantify the key characteristics and to develop an indicator that

can be used for comparison of the country agricultural statistical systems, many of the critical

responses were missing and/or contradictory. Each of the methods uses a slightly different approach

to classify the countries into five categories – developed, progressive or above average, developing or

average, developing with constraints and least developed. In-depth country assessments will be used

to fill in gaps in information and to identify specific technical assistance, training and research

requirements.

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A third method, proposed in the Global Action Plan1 (Chapter 8), to classify countries according to

five Levels of Statistical Development was also attempted1. The emphasis of this methodology is on

availability of core data items. However, preliminary results showed inconsistencies. It was

determined that in the Asia and Pacific Region, country requirements were much more diverse and

that use of this methodology was meaningless. The corresponding information is included in Table 3,

Annex 1, but it will not be further discussed here.

Method 1 --- Food Security, Legal Infrastructure and User Participation

For making the second preliminary assessment, several key responses have been highlighted in Table

3, below. The most significant are the presence of a statistical law for collection of data in the

country and then inclusion of the agriculture sector as part of the mandate. In addition there should

be a National Strategy for the Development of Statistics (NSDS) that includes the various sectors

of agriculture – crops, livestock, fisheries, forestry, water resources, and rural development. And

interaction between data producers and data users should exist. Conduct of a recent agricultural

census or of a population and housing census that obtained key agricultural sector responses was

considered.

The Global Strategy identifies a list of seventy-six core data items (see Annex 2) that should be

available for making decisions about agricultural and rural development. Realizing that many

countries are not able to collect the entire set of core variables, an FAO team in Rome identified

fifteen priority items among the core data that every country should collect (listed in Annex 1, Tables

4-6). It does not mean that the remaining items are not essential, but that these fifteen items are

considered more crucial for monitoring food security. Although information about all core items was

requested, the tables below highlight the responses received from the countries for the priority items.

It should be noted that the Institutional Environment has not been completely defined – the reason is

that any responsibility for agricultural and rural statistics should carry with it the financial resources

necessary to complete the task. Several possibilities can happen: One -- the legal authority exists to

carry out agricultural sector surveys; it may be in the hands of one or more institutions. Second -- the

authority may be delegated by the agency with the legal authority without further legal action. Third –

further legal action is required to carry out an agricultural census or agricultural sector surveys.

In some statistical laws it is possible for the NSO to "direct" a line ministry to collect appropriate

information about, say, the health, education, or agricultural sector. In other countries it is possible/

necessary to prepare a statistical decree that allows the Ministry to collect information; in both cases

the mechanism exists, but in one case a simple communication is required while in the other, formal

legal steps must be taken. And directing a line ministry to collect information does not always carry

with it the resources to do it or to do it properly.

In some cases the NSO or the MOA completed the CAQ without feedback from the other; in these

cases responses are missing. In other cases both agencies provided responses to the CAQ, but the

responses were not consistent. For many countries it was necessary to request additional information,

especially an evaluation of the quality of the core statistics produced and of the constraints faced by

the agricultural statistics system.

Some sections of the table are blank, indicating the lack of information on the CAQ; it is known that

some countries do collect and publish core data items, but did not report it on the CAQ. Similarly, the

capacity of some statistical systems did not correspond to FAO perceptions. These countries were

contacted to verify the responses on the questionnaire and some replied that they misunderstood the

meaning of the responses.

1 The five levels consider the percent of core data produced on a regular basis, the recent conduct of an

agricultural census or of a population census that has questions about the agricultural sector, the existence of a

National Statistical Development Strategy (NSDS) that has an agricultural component, a functioning

coordination system and elements of a master sample frame or area frame.

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In the CAQ table for availability (see Table 4, Annex 1), many of the cells were prefilled with “1”s

(available). The remaining cells were left blank. Based on the responses from countries, it is apparent

that countries used the blank as a substitute for “2” (not available) or “3” (not applicable), although it

is not always clear which response is meant. Thus, the quantitative assessment is the number of “1”s.

Some countries report the statistics for some commodities more often than for other commodities.

Since the categories /responses do not specify the crop/livestock, then the least frequent timing is used

for the evaluation – an asterisk “*” will indicate this situation (see Table 5, Annex 1). In some cases

the country did not make an evaluation of the quality of its data; consequently there will be blank

columns for these countries (see Table 6, Annex 1).

As part of the CAQ, countries were asked to identify constraints that would need to be overcome in

order to establish a strong agricultural statistics system. Some involve technical assistance --- which

can assume many forms. As implied by the wording, the assistance can involve consultancies with

experts in the technical skills related to survey design, questionnaire design, data collection and

processing and analysis. The starting point for determining the gaps in capacity will be technical

assistance for carrying out the country assessments and providing guidance with the development of

the Sector Strategic Plans to produce the minimum set of core data. Further technical assistance will

support efforts to establish the governance structure to integrate agriculture into the national statistical

system, provide advocacy promoting the national statistical system, determine the methodology to be

used, and provide guidance for the overall implementation.

TABLE 3: Summary of country statistical characteristics and relative capacity

Country Legal Agric NSDS Users AgCens Stat Percent

priority Constr Rating

Afghanistan 1 1 1 4 10-- x 67 1

American Samoa

Armenia 1 1 4 6 12-2.0 80 2.47 3

Australia 1 1 2 3 2011 13-1.2 87 1.00 5

Azerbaijan 1 1 3 4 2005 14-1.9 93 1.60 4

Bangladesh 1 1 0.5 2 2008 13-1.6 87 2.53 3

Bhutan 1 1 3 3 2009 11-3.0 73 2.93 2

Brunei

Cambodia 1 1 1 0 12 –x 80 3.53 2

China

Cook Islands 1 1 0 0 2011 2-2.0 13 3.13 1

DPR Korea* *

Fiji 1 1 1 6 2009 13-2.1 87 3.00 3

French Polynesia

Guam * 2007

Georgia 1 1 1 3 2004 14-2.0 93 2.92 4

Hong Kong, China 1 0 1 0 8-2.0 53 3

India 1 0.5 4 0 2010 14-2.1 93 2.53 3

Indonesia 1 1 2 1 2003 11-2.4 73 3

Iran 1 1 4 0 2003 15-1.4 100 2.00 4

Japan 1 1 4 1 2010 15--x 100 5

Kazakhstan 1 1 1 0 2006 14-1.9 93 4.33 3

Kiribati 1 0 1 0 2-x 13 2.57 2

Republic of Korea 1 1 1 3 2010 13-1.5 87 2.27 4

Kyrgyzstan

Lao PDR 1 1 0.5 5 2011 10-3.0 67 2.47 2

Macao, China 1 0 1 0 2-1.5 13 3

Maldives 1 1 4 1 8-2.3 53 4.07 2

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Marshall Islands

Malaysia 1 1 4 6 2005 15-1.8 100 3.53 4

Micronesia * 0

Mongolia 1 1 4 3 2012 15-1.7 100 2.93 5

Myanmar 1 1 0.5 5 11-x 3

Nauru * 0 0 1

New Caledonia

Northern Marianas

Nepal 1 1 3 3 2001 13-1.9 87 2.73 3

New Zealand 1 1 3 6 2007 12-1.0 80 1.08 5

Niue 1 1 1 4 2009 0 0 4.27 1

Pakistan * 1 1 1 2 2000 0 2

Palau 1 0 0 0 2011 4-2.0 27 3.87 1 Papua New

Guinea * 1 1 1 0 5-x 33 1

Philippines 1 1 4 5 2002 13-1.0 87 2.87 4 Russian

Federation

Samoa 1 1 0.5 4 9-1.1 60 2.00 3

Solomon Islands

Singapore* 1 0 0 0 0 0 1.00*

Sri Lanka 1 0 0.5 0 2002 10--x 67 2

Tajikistan 1 0 2 1 10--x 60 3.07 3

Taiwan, China 1 1 0.5 0 2010 14-1.8 93 3.13 3

Thailand 1 1 2 0 2003 13-1.2 87 2.13 4

Timor Leste 1 0 8 –x 53 1

Tonga

Turkey

Turkmenistan 1 1 4 0 11-1.0 73 1.47 4

Tuvalu

Uzbekistan

Vanuatu 1 1 0 5 2006 2- x 13 3.00 2

Viet Nam 1 1 4 5 2011 12 --x 80 2.33 3

* partial submission and/or providing no information

KEY:

Legal -- 1, if legal or statutory basis for statistical activities; 0, if not.

Agric -- 1, if agriculture is included in Statistical Law; 0, if not.

NSDS – 1 up to 4 agriculture sub-sectors, if exists; 0.5, if, in process; 0 if not.

Users – number of user categories involved in meetings (max =6)

Statistics— number of priority data items available (0-15); average reliability (1) high to (5)

unacceptable; x, if not evaluated);

Percent priority – Percent of 15 priority variables for food security

Constraints– Average of fifteen responses ranging from 1 to 5 (see Table 1). Not all countries

responded to these questions; for most countries the NSO and MOA responses were repeated.

DPRK gave only one response.

Rating – Infrastructure and Food Security Capacity Indicator

Considering the existence of a legal basis for agricultural statistics, the conduct of a recent agricultural

census, the availability of the 15 priority core items and the number of constraints, the country

statistical systems have been classified as:

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Excellent (4 countries):

a legal basis for agricultural statistics is

present, an agricultural census recently

conducted, at least 12 priority core items

available, average constraint less than 3

Australia, Japan, Mongolia, New Zealand,

Above Average (8 countries) Azerbaijan, Georgia, Iran, Republic of Korea, Malaysia,

Philippines, Thailand, Turkmenistan

Average (14 countries) Armenia, Bangladesh, Fiji, Hong Kong (China), India,

Indonesia, Kazakhstan, Macao (China), Myanmar, Nepal,

Samoa, Taiwan (China), Tajikistan, Viet Nam

Below Average (6 countries) Bhutan, Cambodia, Lao PDR, Maldives, Pakistan, Sri Lanka

Limited (11 countries) Afghanistan, Cook Islands, DPRK, Kiribati, Micronesia,

Nauru, Niue, Palau, Papua New Guinea, Timor Leste,

Vanuatu

Unknown (16 countries) Brunei, China, Kyrgyzstan, Russian Federation, Turkey,

Uzbekistan, American Samoa, French Polynesia, Guam,

Marshall Islands, New Caledonia, Northern Marianas,

Singapore, Solomon Islands, Tonga, Tuvalu

It should be noted that because of the absence of information on the CAQ, some of the countries could

have a stronger national statistical system than was observed. This preliminary classification reflects

the coordination, or lack of, between the NSO and MOA. It also assumes that availability of priority

items is timely. In one country the presence of an NSDS was noted, but also mentioned that it was not

current.

Method 2 –Capacity Indicators

The application of the target objective methodology -- using only the responses provided by the

countries --- is found in Annex 1, Table 7. It is based on the capacity indicators an FAO Team has

developed using draft guidelines4 for evaluation of country assessment questionnaires. The evaluation

is based on four types of capacity indicators –INFRASTRUCTURE, INPUT (Human and Financial

Resources), THROUGHPUT (Statistical Methods and Practices) and OUTPUT (Statistical

Information and Availability).

This indicator provides a summary of measurements on the five main elements of the dimension

relating to intuitional infrastructure as defined below. The overall score on this indicator is taken as the

geometric mean of the scores on the five indicators which measure the five elements of this dimension

of the quality of the agriculture statistical system. The geometric mean (GM) has been chosen

specifically to highlight the weaknesses in the system. In calculation of the GM equal weight is

assigned to the score on each of the elements. For responses that are missing or zero, a value of 20 has

been used in the calculation of the geometric mean.

Some of the indicators have not been calculated at this time because of the lack of information about

budgets and staff capacity and uncertainty about the completeness of responses on data core items.

Five categories of countries are presented. They correspond to the criteria outlined in the draft

guidelines4. It is noted that by choosing a classification based on quartiles for each indicator, country

group levels can be forced downward although the quality of the statistical system is similar to those

in higher groups.

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GROUP 5 (Developed) Mongolia, New Zealand, Philippines

GROUP 4 (Progressive) Armenia, Australia, Azerbaijan, Georgia, India, Iran,

Malaysia, Maldives, Republic of Korea

GROUP 3 (Developing) Bangladesh, Bhutan, Fiji, Indonesia, Japan,

Kazakhstan, Lao PDR, Taiwan (China), Thailand,

Turkmenistan, Viet Nam

GROUP 2 (Developing with constraints) Cambodia, Cook Islands, Hong Kong (China),

Kiribati, Macao (China), Niue, Nepal, Samoa,

Vanuatu

GROUP 1 (Least developed) Afghanistan, Myanmar, Nauru, Pakistan, Papua New

Guinea, Sri Lanka, Tajikistan, Timor Leste,

A summary table that shows the results of the two evaluation methods is found in Table 8, Annex 1.

It should be noted that many of the lower groupings are the result of missing or incomplete

information. Other countries did not provide information.

It is anticipated that countries can update assessments with corresponding changes in these indicators.

Recent updates are not included in this table.

CONCLUSIONS

The criteria used for evaluation of countries using the global action plan methodology are biased

toward producing, on a regular basis, a large percentage of core items, many of which have little

significance for countries in the region. An approach that emphasizes monitoring food security has

more impact on assessing the contribution of agricultural as measured by the national statistical

system. However, it is also necessary to determine the integration of agricultural statistics into the

national statistical system and the requirements to achieve collection, processing and dissemination of

the vital core items on a regular basis.

Constraints in funds for data collection and processing and the overall availability of staff, both

professional staff and support staff, and the level of commitment of national resources to sustain an

integrated national statistics system are serious issues to be addressed during the in-country

assessments.

References

1 The Global Strategy for Improving Statistics for Food Security, Sustainable Agriculture and Rural

Development Statistics

2 Framework for Assessing the Quality of Agriculture and Rural Development Statistics

3 Global Action Plan

4 Guidelines on Compiling Country Capacity Indicators to Produce Agricultural Statistics

Annex 1

12

TABLE 1: Summary of country responses to CAQ

Country Agency Date Country Agency Date

Afghanistan FAO Jan-12 Micronesia* FAO Jan-12

American Samoa ESCAP Mongolia FAO Feb-12

Armenia ADB Dec-

11

Myanmar FAO Mar-12

Australia FAO Dec-

11

Nauru* FAO Jan-12

Azerbaijan ADB Jan-12 New Caledonia ESCAP

Bangladesh FAO Dec-

11

Northern Marianas ESCAP

Bhutan FAO Feb-12 Nepal FAO Feb-12

Brunei ADB New Zealand FAO Dec-11

Cambodia FAO Mar-

12

Niue ESCAP Jan-12

China FAO Pakistan FAO Dec-11

Cook Islands FAO Feb-12 Palau FAO Jul-12

DPR Korea* FAO Papua New Guinea

* FAO Apr-12

Fiji FAO Mar-

12

Philippines FAO Jan-12

French Polynesia ESCAP Russian Federation FAO

Guam* ESCAP Apr-

12

Samoa FAO Jan-12

Georgia ADB Dec-

11

Solomon Islands FAO

Hong Kong, China ESCAP Feb-12 Singapore* ADB Jan-12

India FAO Jan-12 Sri Lanka FAO Dec-11

Indonesia FAO Dec-

11

Tajikistan ADB Jan-12

Iran FAO Jan-12 Taiwan, China ADB Mar-12

Japan FAO Dec-

11

Thailand FAO Nov-11

Kazakhstan ADB Jan-12 Timor Leste FAO Mar-12

Kiribati FAO Jun-12 Tonga FAO

Republic of Korea FAO Mar-

12

Turkey ESCAP

Kyrgyzstan ADB Turkmenistan ADB Feb-12

Lao PDR FAO Mar-

12

Tuvalu FAO

Macao, China ESCAP Dec-

11

Uzbekistan ADB

Maldives FAO Dec-

11

Vanuatu FAO Jul-12

Marshall Islands FAO Viet Nam FAO Jan-12

Malaysia FAO Jan-12

* partial submission and/or providing little information

KEY:

Agency – focal point agency for statistics

Date – month of response to questionnaire

Annex 1

13

TABLE 2: Main constraints reported by countries

Con

stra

int

A A A B B C C D F G I I K K K L M M M N N N P P S T T T T V V

r u z g h m o P i e n r a i o a d a n e e i a h a a a h k a n

m s r d u b o R j o d a z r r o v l g p w u l l m i j a m n m

t k K i i n e a a Z e a o

n a a y l u a 1 * ** ** * * ** ** 2 * ** ** ** 3 * ** ** ** ** 4 * * * * ** ** ** ** ** 5 * * * ** ** * * ** 6 * * * * * * * * * 7 * ** ** ** * ** 8 * * * ** ** ** * * * * 9 * * * ** * ** * * *

10 ** * * * ** ** ** ** 11 * * ** ** * ** * * * 12 * * ** ** * * 13 * ** ** * * ** ** * 14 * * ** * * ** ** * * * * 15 ** ** ** ** ** * *

Blank or “ “no constraint; * a significant constraint; ** a dominant constraint;

Afghanistan, Hong Kong (China), Indonesia, Japan, Macao (China), Myanmar, Micronesia, Nauru, Pakistan, Sri Lanka and Timor Leste did not provide responses to these

questions;

DPR Korea only responded to the first constraint.

1 Number of professional staff at headquarters for statistical activities 9 Up-to-date information technology software

2 Number of support staff at headquarters for statistical activities 10 Funds for field-oriented statistical activities vis-à-vis

plans.

3 Number of professional staff in the field for statistical activities 11 Transport equipment for field activities

4 Number of field workers for statistical activities 12 Building space for office

5 Technical skills of the available statistical staff 13 Sound methodology implemented for agricultural

surveys

6 Appreciation at the policy-making level for importance of statistical

activities 14 Level of demand for statistics

Annex 1

14

7 Support at political level in the Government for statistical activities 15 Turnover of professional staff.

8 Up-to-date information technology hardware

Annex 1

16

Table 3: Criterion for Statistical Development based on country responses to CAQ

Country NSDS Agric Censu

s

Pop

w/agric Coord

w/users Percent core

Afghanistan 1 4 31

American Samoa

Armenia 4 2011 6 53

Australia 2 2011 L 3 38

Azerbaijan 3 2005

M 4 61

Bangladesh 0.5 2008 L 2 64

Bhutan 3 2009 L 3 35

Brunei

Cambodia 1 0 49

China

Cook Islands 0 2011 L 0 7

DP Republic

Korea

Fiji 1 2009

M 6 62

French Polynesia

Guam 2007 L

Georgia 1 2004 L 3 47

Hong Kong, China 1 0 19

India 4 2010 L 2011 0 69

Indonesia 2 2003

M 1 34

Iran 4 2003 L 2011 0 81

Japan 4 2010

M 1 77

Kazakhstan 1 2006 4 58

Kiribati 1 0 7

Republic of Korea 0 2010

M 2010 3 47

Kyrgyzstan

Lao PDR 2011 L 5 28

Macao, China 1 0 14

Maldives 4 1 53

Marshall Islands

Malaysia 4 2005 L 2010 6 70

Micronesia

Mongolia 4 2012

M 2010 3 74

Myanmar 0.5 5 32

Nauru

New Caledonia

Northern Marianas

Nepal 3 2001 3 53

New Zealand 3 2007 L 2006 6 45

Niue 1 2009 4 31

Annex 1

17

M

Pakistan 2000 2

Palau 2011 L 0 18

Papua New

Guinea 3

Philippines 4 2002 L 2010 5 58

Russia

Samoa 0.5 2009 M 4 19

Singapore 0 0

Solomon Islands

Sri Lanka 0.5 2002 0 26

Country NSDS Agric Censu

s

Pop

w/agric Coord

w/users Percent core

Tajikistan 2 1 38

Taiwan, China 0.5 2010 L 83

Thailand 2 2003

A 0 47

Timor Leste 1 2010 0 22

Tonga

Turkey

Turkmenistan 4 0 37

Tuvalu

Uzbekistan

Vanuatu 1 2006 L 2009 6 7

Viet Nam 4 2011 L 5 35

KEY: NSDS – 1 up to 4 agriculture sub-sectors, if exists; 0.5, if, in process; 0 if not.

Agric census/frame – date of last agricultural census and frame used (L-List; M-Multiple; A-

Area)

Pop/w agric – date of last population census that included questions on agriculture

Coord w/users - number of user categories involved in meetings (max =6)

Percent core –percent of core items regularly available

TABLE 4: Availability of priority data Availability: 1. Yes; 2. No; 3. Not applicable

PRIORITY CORE DATA ITEMS / 41

COUNTRIES THAT RESPONDED to

SECTION 2

A A A A B B C C F G H I I I J K K K L M M

f r U z g h m o i e k n n r a a i o a a d

g m S r d u b o j o n d d a p z r r o c v

t k i i o n a k e a

n a n n a o

I. PRODUCTION

Crop

Crop production: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1

Crop yield per area 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2

Area planted 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2

Area harvested 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 2

Livestock

Livestock production: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1

Fishery

Fishery and aquaculture production: quantity 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 2 1

Forestry

Forest production of wood: quantity 1 1 1 1 1 1 3 1 1 1 1 1 1 1 1 2 3

II. EXTERNAL TRADE

Import: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

III. STOCK OF CAPITAL /RESOURCES

Livestock Inventories 1 1 1 1 2 1 1 1 1 1 1 2 2 2

Stocks of main crops: quantity 1 1 1 1 1 1 1 2 2 2

Land and use 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1

Water-related:· Irrigated areas 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2

IV. INPUTS

Fertilizer quantity 1 1 1 1 1 1 1 1 1 2 1 1 1 1 2 2 1

VI. PRICES

Producer prices 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 2 1

Consumer prices 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Annex 1

19

TABLE 4 (cont): Availability of priority

data Availability: 1. Yes; 2. No; 3. Not applicable



SECTION 2

M M M M N N N P P P P T S S T T t T V V

a i n m a e i a a n h a a r a h l k a n

l c g r u p u k l g l i m i j a s m n m

a r r a e a o L

y o u l u a k

I. PRODUCTION

Crop

Crop production: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Crop yield per area 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Area planted 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Area harvested 1 1 1 1 1 1 1 1 1 1 1 1 1

Livestock

Livestock production: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Fishery

Fishery and aquaculture production: quantity 1 1 1 1 1 1 1 1 1 1

Forestry

Forest production of wood: quantity 1 1 2 1 1 1 1

II. EXTERNAL TRADE

Import: quantity 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1


Livestock Inventories 1 1 1 1 1 1 1 1 1

Stocks of main crops: quantity 1 1 1 2

Land and use 1 1 1 1 1 1 1 1 1

Water-related:· Irrigated areas 1 1 1 1 1 1 1 1 1

IV. INPUTS

Fertilizer quantity 1 1 1 1 1 1 1 1 3 1

VI. PRICES

Producer prices 1 1 1 1 1 1 1 2 1 1 1 1

Consumer prices 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Annex 1

20

TABLE 5: Frequency of priority data Frequency: 1. Annual; 2. Seasonal (six monthly); 3. Quarterly; 4. Monthly; 5.Weekly; 6. Daily;

7. Ad-hoc; 8. Every 5-10 years ‘*’ indicates that some reports are more frequent



SECTION 2

A A A A B B C C F G H I I I J K K K L M M f r u z g h m o i e k n n r a a i o a a d g m s r d u b o j o n d d a p z r r o c v

t k i i o n a k e a

n a n n a o

I. PRODUCTION

Crop

Crop production: quantity 7 4 1 1* 4 1 3 1 1 3 3 1 1 1 2 4*

Crop yield per area 7 1 1 1* 4 1 3 1 1 3 3 1 1 1 2

Area planted 7 1 1 1* 4 1 3 1 1 3 4 1 1 1 2

Area harvested 7 1 1* 4 1 3 1 3 4 1 1 1 2

Livestock

Livestock production: quantity 7 4 1 1* 7 1 3 3 1 1 1 4 3 1 7

Fishery

Fishery and aquaculture production: quantity 4 1 1 1 1 1 1 3 1 4 4 1 4*

Forestry

Forest production of wood: quantity 3 1 1 1 1 1 7 1 4 1

II. EXTERNAL TRADE

Import: quantity 4 4 4 1 4 4 4 4 4 2 4 1 4 1 4 4


Livestock Inventories 1 1* 1 7:8 6 3 1 1 7

Stocks of main crops: quantity 7:8 1 4 1 1 1

Land and use 7 1 1 1 1 7:8 1 1 1 1 7

Water-related:· Irrigated areas 7 1 1 1 1 7:8 3 1 1 1 7 1

IV. INPUTS

Fertilizer quantity 1 1* 1 1 3 3 6 1 1 1 7

VI. PRICES

Producer prices 4 4 4 1 3 3 3 4 4 1 4 1 3 1

Consumer prices 4 4 4 4 4 3 4 3 3 4 4 4 4 1 4 4 4 4 3 4 4

Annex 1

21

TABLE 5(cont): Frequency of priority

data Frequency: 1. Annual; 2. Seasonal (six monthly); 3. Quarterly; 4. Monthly; 5.Weekly; 6. Daily;

7. Ad-hoc; 8. Every 5-10 years ‘*’ indicates that some reports are more frequent



SECTION 2

M M M M N N N P P P P T S S T T T T V V



a r r a e a o L

y o u l u a k

I. PRODUCTION

Crops

Crop production: quantity 1 1* 1 8 3 2 7 1* 1 1 1* 3

Crop yield per area 1 1 1 3 2 7 1* 1 1 1 3

Area planted 1 1* 1 8 7 3 2 8 1* 1 1 2 3

Area harvested 1 1* 1 8 3 2 8 1 1 6 3

Livestock

Livestock production: quantity 1 1 1 8 7 3 3 8 1* 1 1 1 3

Fishery

Fishery and aquaculture production: quantity 1 1 1 3 1 4 1* 1 4 1

Forestry

Forest production of wood: quantity 1 1 3 1* 1

II. EXTERNAL TRADE

Import: quantity 4 1* 4 1 1 4 4 4 1 4 4 1 4


Livestock Inventories 1 1 1 4 3 1 7 1 1

Stocks of main crops: quantity 1 1 4

Land and use 1 1 8 1 7 1 7

Water-related:· Irrigated areas 1 1 1 3 1 1 1

IV. INPUTS

Fertilizer quantity 1 1 1 4 1 4

VI. PRICES

Producer prices 4 1* 1 3 4 4 7 4 1

Consumer prices 4 1* 4 1 5 4 4 4 4 3 4

Annex 1

22

TABLE 6: Quality of priority data Quality/Reliability of data: 1. High reliable; 2. Reliable; 3. Acceptable; 4. Workable; 5. Unacceptable.



SECTION 2

A A A A B B C C F G H I I I J K K K L M M f r u z g h m o i e k n n r a a i o a a d g m s r d u b o j o n d d a p z r r o c v

t k i i o n a k e a

n a n n a o

I. PRODUCTION

Crops

Crop production: quantity 2 1 2 1 3 3 2 2 2 2 1 1 3 4

Crop yield per area 2 1 2 1 3 3 2 3 2 2 1 1 3

Area planted 2 1 2 1 3 3 2 3 2 2 1 1 3

Area harvested 2 1 2 1 3 3 2 2 2 1 1 3

Livestock

Livestock production: quantity 2 1 2 1 3 3 2 1 2 1 1 3 4

Fishery

Fishery and aquaculture production: quantity 2 3 2 2 3 3 2 3 2 1 3 3

Forestry

Forest production of wood: quantity 2 2 2 2 3 1 2 2 3

II. EXTERNAL TRADE

Import: quantity 2 1 2 2 2 2 1 2 1 1 3 1 1


Livestock Inventories 1 2 2 1 1 2 2 3

Stocks of main crops: quantity 2 1 2 2 2 3

Land and use 2 1 2 2 3 1 2 2 2 3 2

Water-related:· Irrigated areas 2 1 2 2 1 3 3 4 1

IV. INPUTS

Fertilizer quantity 1 2 2 3 2 2 1 2 3 1

VI. PRICES

Producer prices 2 1 1 2 2 3 1 2 2

Consumer prices 2 1 1 1 4 1 2 1 3 1 2 2 1

Annex 1

23

TABLE 6 (cont): Quality of priority data Quality/Reliability of data: 1. High reliable; 2. Reliable; 3. Acceptable; 4. Workable; 5. Unacceptable.



SECTION 2

M M M M N N N P P P P T S S T T T T V V



a r r a e a o L

y o u l u a k

I. PRODUCTION

Crops

Crop production: quantity 2 1 4 2 1 1 2 1 2 1 1

Crop yield per area 2 1 4 2 1 1 2 1 2 1 1

Area planted 2 1 4 2 1 2 1 2 1 2 1 1

Area harvested 2 1 4 2 1 1 2 1 2 1 1

Livestock

Livestock production: quantity 2 1 2 1 2 1 2 1 2 1 1

Fishery

Fishery and aquaculture production: quantity 2 3 2 1 2 2 1 1 1

Forestry

Forest production of wood: quantity 2 2 1 2

II. EXTERNAL TRADE

Import: quantity 1 1 1 3 2 1 1 1 1


Livestock Inventories 2 2 2 2 1 2 1 1

Stocks of main crops: quantity 2 2 1

Land and use 2 2 4 2 2 1 2

Water-related:· Irrigated areas 2 2 4 2 1 1 2 1

IV. INPUTS

Fertilizer quantity 2 3 4 2 3 1 2 1

VI. PRICES

Producer prices 1 2 2 1 2 1

Consumer prices 1 1 2 1 2 1 1 1

24

Table 7. Capacity Indicators Dimensions measured

Elements MAX afg arm aus azr bgd btn cmb cok fij geo hkg idn ind irn jpn kir kor kzk lao mdv mng

Capacity Indicator I & II Institutional Infrastructure, Human and Financial Resources INFRA-STRUCTURE (INPUT)

1.1 Legal framework 5 40 100 100 100 40 40 80 100 80 100 20 80 100 100 80 20 100 100 100 60 100 1.1 1.2 Coordination in

Statistical System 5 20 80 40 60 80 60 60 0 0 80 0 0 100 100 100 0 100 0 80 100 0

1.2 1.3 Strategic Vision and Planning 6

83 100 100 100 33 50 0 0 17 50 100 0 50 100 100 83 0 17 33 33 100 1.3 1.4 Integration of

Agriculture in National Statistical System

10 0 10 30 50 10 10 0 40 20 20 0 40 90 80 60 0 50 0 60 0 70

1.5 1.5 Relevance (user interface)

11 64 91 73 91 64 64 0 73 73 73 0 55 18 9 27 0 27 64 45 45 73 1.6 2.1 Financial Resources

1.7 2.2 Human Resources

Capacity Indicator III Statistical Methods and Practices (THROUGHPUT

DIMENSION)

3.1 Statistical software capability 3 0 100 33 67 100 100 0 33 33 67 67 67 67 100 33 67 67 33 67 67 67 3.2 Data capture technology 3 67 100 67 67 67 67 0 67 100 67 67 67 67 100 67 0 100 0 67 100 67 3.3 IT infrastructure 3 0 100 0 33 67 0 0 33 67 67 33 0 0 67 0 100 33 67 33 33 67 3.4 International Classifications

4 75 100 100 50 75 25 50 75 75 50 75 100 75 100 25 75 100 25 75 75 50

3.5 General Statistical Activities 7

43 71 86 71 86 57 57 57 43 71 43 86 100 43 57 71 71 71 57 100 71 3.6 Agricultural Sector Statistical

Activities 24

Prices 10 20 40 10 10 50 10 20 10 0 20 40 20 80 10 50 10 50 60 10 50 40

Agriculture 14 14 50 50 64 64 36 7 29 14 43 43 43 93 64 64 0 50 79 21 36 57

3.7 Analysis and use of data 7 13 75 38 50 50 38 25 25 50 50 13 63 63 13 13 13 13 25 13 38 50

Capacity Indicator IV Statistical Information and Availability (OUTPUT DIMENSION)

4.1 Core data availability 5 34 61 37 64 71 42 57 8 62 54 24 36 71 83 81 33 48 58 27 56 78

4.2 Timeliness 3 33 100 100 10 33 33 67 67 33 67 67 100 100 33 67 100 33 67 67 100 100

4.3 Quality, reliability and consistency of data 3 20 80 100 80 80 60 0 100 80 80 80 80 80 100 100 0 60 0 60 100 80

4.4 Data Accessibility 3 33 100 100 100

100 100 100 33 33 100 100 100 100 100 100 33 100 33 33 100 100

4.5 Quality Consciousness 3 0 100 100 33 100 100 0 0 0 33 0 33 67 33 67 33 33 33 33 0 100

COMPOSITE SUM OF PERCENT OF MAX FOR

EACH ELEMENT 1800 559 1458 1163 1191 1170 891 523 750 780 1091 771 968 1320 1235 1091 639 1036 732 882 1093 1269

Capacity Indicator I 38.5 59.2 61.4 77.1 36.8 37.7 32.9 41.0 32.9 56.6 27.6 37.1 60.6 59.2 66.6 26.6 48.7 33.5 59.2 44.9 63.3

Capacity Indicator III

27.3 75.6 40.4 45.1 68.0 35.5 23.1 35.0 42.3 50.9 42.3 50.3 64.8 46.8 35.5 34.0 51.5 41.8 33.8 57.0 57.6

Capacity Indicator IV

27.3 86.6 82.0 70.2 71.7 60.9 43.3 32.4 40.5 62.6 48.0 62.6 82.3 62.1 81.5 37.4 50.1 38.6 41.3 64.5 91.0

25

Table 7. Capacity Indicators (cont.)

Dimensions measured

Elements MAX

mo mmr mys nru npl nzl niu pak pal png phl sam sri tai taj tha tkm tls van vnm

Capacity Indicator I & II Institutional Infrastructure, Human and Financial Resources INFRA-STRUCTURE

(INPUT)

1.1 Legal framework 5 20 80 100 0 60 100 100 0 40 40 100 100 20 100 40 60 100 20 80 100 1.2 Coordination in Statistical System

5 0 0 100 0 100 100 80 0 0 0 100 0 0 100 80 20 100 0 0 0

1.3 Strategic Vision and Planning

6 0 0 100 0 50 100 0 0 0 100 100 50 0 33 0 100 100 0 33 100

1.4 Integration of Agriculture in National Statistical System

10 0 10 100 0 60 80 40 0 10 0 90 20 0 10 20 20 50 0 30 90

1.5 Relevance (user interface) 11 0 64 100 0 55 82 73 0 0 0 100 82 0 18 27 18 27 0 55 73 1.6 Financial Resources

1.7 Human Resources

Capacity Indicator III Statistical Methods and Practices (THROUGHPUT

DIMENSION)

2.1 Statistical software capability

3 67 100 67 0 0 100 33 33 33 0 100 33 0 100 0 0 67 100 67 67

2.2 Data capture technology 3 67 67 67 0 0 67 67 67 33 0 67 67 0 67 0 0 100 33 67 67 2.3 IT infrastructure 4 0 0 33 0 0 67 0 0 100 0 67 67 0 0 0 0 33 0 0 33 2.4 International Classifications

4 75 75 100 0 50 75 50 0 75 0 100 75 25 75 50 75 25 50 75 100

2.5 General Statistical Activities

7 57 43 86 0 57 100 43 0 57 0 100 71 71 100 57 86 71 43 57 71

2.6 Agricultural Sector Statistical Activities

24

Prices 10 0 10 10 0 10 30 0 0 10 0 60 10 10 100 10 30 10 10 10 20

Agriculture 14 0 14 50 0 7 71 14 0 36 0 79 14 14 71 29 57 50 21 14 79 2.7 Analysis and use of data 7 13 25 25 0 38 38 13 13 13 0 63 25 13 50 38 38 50 13 38 38

Capacity

Indicator IV Statistical Information and Availability (OUTPUT DIMENSION)

3.1 Core data availability 5 17 32 71 0 56 45 31 0 15 33 63 19 26 82 42 46 18 22 7 35 3.2 Timeliness 3 100 0 67 0 100 67 33 0 67 0 100 33 0 67 67 100 100 0 100 67 3.3 Quality, reliability and consistency of data 3

100 40 80 0 80 100 60 0 80 0 100 80 0 80 100 100 80 0 0 0 3.4 Data Accessibility 3 33 67 100 0 100 100 33 100 33 0 100 33 100 67 33 33 33 33 33 100 3.5 Quality Consciousness 3 0 0 33 0 0 100 67 33 33 0 33 33 0 33 0 67 33 0 0 33

COMPOSITE SUM OF PERCENT OF MAX

FOR EACH ELEMENT 1800 548 626 1288 0 822 1421 736 246 636 173 1521 813 279 1154 592 850 1048 345 665 1072

Capacity Indicator I 20.0 29.0 100.0 20.0 62.9 91.9 54.1 20.0 20.0 31.7 97.9 43.9 20.0 36.0 32.3 33.7 67.1 20.0 38.7 66.6

Capacity Indicator III

34.3 33.4 44.7 20.0 22.3 63.6 27.6 23.4 34.7 20.0 77.6 36.0 20.0 65.9 26.5 36.7 42.1 28.0 34.6 53.0

Capacity Indicator IV

40.8 32.1 66.1 20.0 61.7 78.7 42.4 30.6 20.0 22.1 73.2 35.4 29.1 62.8 45.1 63.4 43.7 22.6 24.8 43.5

26

Table 8: Preliminary Country Groupings for Statistical Capacity

Country Method

1 Method

2

Country Method

1 Method

2 Composit

e WB CI

Afghanistan 1 1 Mongolia 5 5 5 79

Armenia 3 4 New Zealand 5 5 5

Australia 5 4 Australia 5 4 4.5

Azerbaijan 4 4 Philippines 4 5 4.5 87

Bangladesh 3 3 Azerbaijan 4 4 4 78

Bhutan 2 3 Georgia 4 4 4 94

Cambodia 2 2 Iran 4 4 4

Cook Islands 1 2 Japan 5 3 4

Fiji 3 3 Malaysia 4 4 4 76

Georgia 4 4 Republic of Korea 4 4 4

Hong Kong,

China 3 2

Armenia 3 4 3.5 92

India 3 4 India 3 4 3.5 81

Indonesia 3 3 Thailand 4 3 3.5 83

Iran 4 4 Turkmenistan 4 3 3.5 34

Japan 5 3 Bangladesh 3 3 3 73

Kazakhstan 3 3 Fiji 3 3 3 61

Kiribati 2 2 Indonesia 3 3 3 83

Lao PDR 2 3 Kazakhstan 3 3 3 92

Macao, China 3 2 Maldives 2 4 3 66

Malaysia 4 4 Taiwan, China 3 3 3

Maldives 2 4 Viet Nam 3 3 3 70

Micronesia 1 Bhutan 2 3 2.5 78

Mongolia 5 5 Hong Kong, China 3 2 2.5

Myanmar 3 1 Lao PDR 2 3 2.5 61

Nauru 1 1 Macao, China 3 2 2.5

Nepal 3 2 Nepal 3 2 2.5 61

New Zealand 5 5 Samoa 3 2 2.5 40

Niue 1 2 Cambodia 2 2 2 72

Pakistan 2 1 Kiribati 2 2 2 33

Palau 1 1 Myanmar 3 1 2 48

Papua New

Guinea 1 1

Tajikistan 3 1 2 72

Philippines 4 5 Vanuatu 2 2 2 54

Republic of

Korea 4 4

Cook Islands 1 2 1.5

Samoa 3 2 Niue 1 2 1.5

Sri Lanka 2 1 Pakistan 2 1 1.5 74

Taiwan, China 3 3 Sri Lanka 2 1 1.5 78

Tajikistan 3 1 Afghanistan 1 1 1 47

Thailand 4 3 Micronesia 1 1 24

Timor Leste 1 1 Nauru 1 1 1

Turkmenistan 4 3 Palau 1 1 1 28

27

Vanuatu 2 2

Papua New

Guinea 1 1 1 36

Viet Nam 3 3 Timor Leste 1 1 1 52

Annex 2

28

Table 1: Statistical Domains for Core Variables

ECONOMIC V. AGRO-PROCESSING

I. PRODUCTION Main crops

Crop Post harvest losses

Crop production: quantity Main livestock

Crop production: value Fish: quantity

Crop yield per area Fish: value

Area planted VI. PRICES Area harvested Producer prices

Livestock Wholesale prices

Livestock production: quantity Consumer prices

Livestock production: value Agric. Input prices

Fishery Agric. Export prices

Fishery and aquaculture production: quantity Agric. Import prices

Fishery and aquaculture production: value VII .INVESTMENT SUBSIDIES OR TAXES Forestry Public investment in agriculture

Forest production of wood: quantity Agricultural subsidies

Forest production of wood: value Fishery access fees

Forest production of non wood: quantity Public expenditure for fishery management

Forest production of non wood: value Fishery subsidies

II. EXTERNAL TRADE Water pricing

Export: quantity VIII. RURAL INFRASTRUCTURE /SERVICES

Export: value Area equipped for irrigation

Import: quantity Crop markets

Import: value Livestock markets

III. STOCK OF CAPITAL AND RESOURCES Rural roads (Km)

Livestock Inventories Railways (Km)

Agricultural machinery Communication

Stocks of main crops: quantity Banking and insurance

Land and use SOCIAL

Water-related: Population dependent on agriculture

· Irrigated areas Agricultural workforce (by gender)

· Types of irrigation Fishery workforce (by gender)

· Irrigated crops Aquaculture workforce (by gender)

· Quantity of water used Household income

· Water quality ENVIRONMENTAL

IV. INPUTS Soil degradation

Fertilizer quantity Water pollution due to agriculture

Fertilizer value Emissions due to agriculture

Pesticide quantity Water pollution due to aquaculture

Pesticide value Emissions due to aquaculture

Seeds quantity GEOGRAPHIC LOCATION

Seeds value Geo-coordinate of the statistical unit (parcel,

province, region, country) Animal Feed quantity Animal Feed value Forage quantity

Forage value

Animal vaccines and drugs quantity

Animal vaccines and drugs value Aquatic seeds quantity Aquatic seeds value

Annex 2

29

TABLE 2: Summary Table for Additional criteria (2010) used to Select Pilot Countries

Country Value

Added Agriculture

Percent of

World Cereal

Production

Percent of

population in

rural

Afghanistan 35.0 0.2 77.4

American Samoa 7.0

Armenia 19.2 0.0 35.8

Australia 2.5 1.4 10.9

Azerbaijan 5.7 0.1 48.1

Bangladesh 18.6 2.1 71.9

Bhutan 19.0 0.0 65.3

Brunei 0.7 0.0 24.3

Cambodia 36.1 0.4 79.9

China 10.1 20.4 53.0

Cook Islands 5.1 0.0 24.7

DP Republic Korea 21.2 0.2 39.8

Fiji 12.6 0.0 48.1

French Polynesia 2.4 0.0 48.6

Guam 0.0 6.8

Georgia 8.3 0.0 47.3

Hong Kong, China 0.1 0.0 0.0

India 19.0 9.7 70.0

Indonesia 15.3 3.5 55.7

Iran 9.2 0.9 29.2

Japan 1.4 0.5 33.2

Kazakhstan 4.5 0.5 41.5

Kiribati 26.3 0.0 56.1

Republic of Korea 2.6 0.2 17.0

Kyrgyzstan 20.0 0.1 65.5

Lao PDR 32.0 0.2 66.8

Macao, China 0.0 0.0

Maldives 5.0 0.0 59.9

Marshall Islands 10.0 0.0 28.2

Malaysia 10.4 0.1 27.8

Micronesia 26.0 0.0 77.3

Mongolia 16.2 0.0 38.0

Myanmar 36.4 1.4 66.4

Nauru 6.2 0.0 0.0

New Caledonia 1.6 0.0 42.6

Northern Marianas 0.0 8.7

Nepal 35.0 0.3 81.4

New Zealand 5.5 0.0 13.8

Niue 0.0 62.5

Pakistan 21.2 1.4 64.1

Palau 3.2 0.0 16.6

Papua New Guinea 31.9 0.0 87.5

Philippines 12.3 0.9 51.1

Russia 4.0 2.5 26.8

Samoa 9.6 0.0 79.8

Solomon Islands 28.3 0.0 81.4

Singapore 0.0 0.0 0.0

Sri Lanka 14.1 0.2 85.7

Tajikistan 21.7 0.0 73.7

Annex 2

30

Country Value

Added Agriculture

Percent of

World Cereal

Production

Percent of

population in

rural

Taiwan, China 2.6 0.1 5.8

Thailand 12.4 1.5 66.0

Timor Leste 29.6 0.0 71.9

Tonga 19.9 0.0 76.6

Turkey 9.4 1.3 30.4

Turkmenistan 14.5 0.1 50.5

Tuvalu 21.6 0.0 49.6

Uzbekistan 23.0 0.3 63.8

Vanuatu 19.5 0.0 74.4

Viet Nam 20.6 1.8 69.6 NOTE: Value added and percent of population from ESCAP Data Centre (except Taiwan, China);

Percent of World Cereal Production from FAOSTAT

asia and pacific commission on agricultural statistics...this global effort involves interaction...

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