an analysis of factors affecting use of pre-emergence wheat herbicides in uasin gishu

ii

AN ANALYSIS OF FACTORS AFFECTING USE OF PRE-

EMERGENCE WHEAT HERBICIDES IN UASIN GISHU COUNTY,

KENYA

BY

BETT WILLIAM

[email protected],[email protected]

Phone: +254723103932

JANUARY, 2012

i

DECLARATION

This thesis is my original work and to the best of my knowledge has not been presented for

the award of a degree in any other University.

ii

TABLE OF CONTENTS

DECLARATION ----------------------------------------------------------------------------------------- ii

TABLE OF CONTENTS -------------------------------------------------------------------------------- ii

LIST OF TABLES--------------------------------------------------------------------------------------- iv

LIST OF FIGURES -------------------------------------------------------------------------------------- v

ABBREVIATIONS AND ACRONYMS ------------------------------------------------------------ vi

ACKNOWLEDGEMENTS ----------------------------------------------------------------------------vii

ABSTRACT ---------------------------------------------------------------------------------------------viii

CHAPTER ONE: INTRODUCTION------------------------------------------------------------------ 1

1.1 Background Information--------------------------------------------------------------------- 1

1.2 Problem Statement---------------------------------------------------------------------------10

1.3 General Objective----------------------------------------------------------------------------12

1.4 Specific Objectives --------------------------------------------------------------------------12

1.5 Research Hypotheses------------------------------------------------------------------------12

1.6 Justification of the Study.-------------------------------------------------------------------13

1.7 Area of Study---------------------------------------------------------------------------------14

CHAPTER TWO: LITERATURE REVIEW--------------------------------------------------------17

2.1 Introduction-----------------------------------------------------------------------------------17

2.2 Empirical Input Use Studies----------------------------------------------------------------17

2.3 Evaluation of the Literature ----------------------------------------------------------------22

2.4 Conceptual Framework ---------------------------------------------------------------------25

CHAPTER THREE: METHODOLOGY ------------------------------------------------------------27

3.1 Theoretical Framework ---------------------------------------------------------------------27

iii

3.1.1 Theoretical Model ---------------------------------------------------------------------29

3.1.2 Model Specification -------------------------------------------------------------------33

3.2 Data Types and Sources --------------------------------------------------------------------40

3.3 Research Design -----------------------------------------------------------------------------40

3.3.1 Population, Sample Size and Sampling Techniques ------------------------------41

3.4 Data Collection Methods -------------------------------------------------------------------42

3.5 Data Analysis---------------------------------------------------------------------------------42

3.6 Limitations of the Study --------------------------------------------------------------------43

CHAPTER FOUR: RESULTS AND DISCUSSIONS---------------------------------------------45

4.1 Introduction-----------------------------------------------------------------------------------45

4.2 General Characteristics of the Sample----------------------------------------------------45

4.3 Results from Logit Regression-------------------------------------------------------------60

CHAPTER FIVE: SUMMARY, CONCLUSIONS AND RECOMMENDATIONS----------68

5.1 Summary --------------------------------------------------------------------------------------68

5.2 Conclusions-----------------------------------------------------------------------------------69

5.3 Recommendations ---------------------------------------------------------------------------70

5.4 Areas for Further Research-----------------------------------------------------------------71

REFERENCES-------------------------------------------------------------------------------------------73

APPENDICES--------------------------------------------------------------------------------------------79

iv

LIST OF TABLES

Table Page

Table 1.1: Wheat Production and Consumption in Kenya for

the Period 2003-2007 …………………………………….……………………….7

Table 1.2: Wheat Production in Uasin Gishu County from 2004 to 2008…………........ 9

Table 4.1: Summary Statistics for Continuous Variables …………………………….. 46

Table 4.2: Age of Respondents ………………………………………………………. 50

Table 4.3: Influence from other Farmers……………………………………………… 57

Table 4.4: Summary of Logit Regression Results…………………………………….. 60

Table 4.5: Sufficiency of Income to Purchase Agro-Chemicals………………………. 63

v

LIST OF FIGURES

Figure Page

Figure 1.1: Wheat production and importation trend in Kenya for 1990-2003 ………. 6

Figure 2.1: Conceptual Framework………………………………………………….. 25

Figure 4.1: Distribution of Income around Mean……………………………………. 47

Figure 4.2: Land Size Spread around Mean………………………………………….. 48

Figure 4.3: Number of Persons in Household………………………………………… 50

Figure 4.4: Education Level of Respondents………………………………………… 52

Figure 4.5: Land Tenure System……………………………………………………… 53

Figure 4.6: Presence of Extension Services…………………………………………… 54

Figure 4.7: Membership to a Farmers Association……………………………………. 56

Figure 4.8: Access to Wheat Market Information…………………………………….. 58

Figure 4.9: Access to Credit…………………………………………………………… 59

vi

ABBREVIATIONS AND ACRONYMS

AAK Agrochemicals Association of Kenya

CIMMYT International Maize and Wheat Improvement Center

FAOSTAT Food and Agricultural Organization Statistical Database

GDP Gross Domestic Product

GOK Government of Kenya

IFDC International Center for Soil Fertility and Agriculture

KARI Kenya Agricultural Research Institute

KNBS Kenya National Bureau of Statistics

NPBRC National Plant Breeding Research Centre

SPSS Statistical Package for Social Sciences

UK United Kingdom

US$ United States Dollars

USA United States of America

vii

ACKNOWLEDGEMENTS

I am greatly indebted to my supervisors Dr. .Philip Nyangweso and Dr. Samuel Mwakubo

for guiding me tirelessly during the entire period of research and preparation of this

document. All through this time, my yearning to complete and give educative output was as

a result of the guiding spirit of my father, George Kibet Chumoh, who though not around,

has been my inspiration throughout my entire life.

To my classmates Geoffrey Amusala and Eric Muiruri, I say thank you for all the support

you gave during my struggle to write and present this thesis in a timely and correct manner.

Without their support, it would not have been possible for me to proceed effectively. And to

farmers in Uasin Gishu County, who toil very hard to feed the country, I say thank you for

enabling me work with you constructively.

viii

ABSTRACT

The agrochemical industry is a significant part of the Kenyan economy. It plays a great role in remittance of tax revenue to the government, farmer training, and research into better production technologies, environmental protection campaigns, and credit facilities to farmers. Wheat is a major cereal crop that uses agrochemicals in its production. Its output in Uasin Gishu County has however consistently been very low despite efforts to market agrochemical wheat products to boost its production. This study aimed at determining factors influencing use of pre-emergence wheat herbicides in Uasin Gishu County andunderstanding promotional strategies that could influence use of wheat agrochemicals in Uasin Gishu County. Farmers were stratified into Small Scale and large scale farmers, and a total of 164 farmers were then chosen using systematic random sampling. Primary data was mainly used and was collected using structured questionnaires. Descriptive statistics and maximum likelihood method using the Statistical Package for Social Scientists (SPSS) wereused to analyze the data. It was established that socio-economic factors influencing use of wheat pre-emergence herbicides were education level, average annual income, presence of extension services, and availability of wheat market information, land tenure system and accessibility to credit. Promotional strategies identified that would elicit positive impact in the use of pre-emergence herbicides included enhancing extension services to wheat farmers in Uasin Gishu County. Key recommendations made include encouraging wheat farmers to maximize their farm income from wheat by adopting modern technologies, and increase overall farm income, by diversifying their farming rather than relying on a single crop. It is also important for extension agents to increase the frequency of extension visits to wheat farmers since the farmers recognize the fact that the information gained from extension is very helpful.

1

CHAPTER ONE

INTRODUCTION

1.1 Background Information

Agrochemicals, also known as pesticides or crop protection chemicals, are chemicals

manufactured from a wide range of organic and inorganic chemicals and used to control

pests, including weeds, insects and fungi (Utley, 2008). The global market for

agrochemicals was valued at around US$ 50 billion in 2008, with about two thirds (US$ 35

billion) of this coming from crop protection products and one third (US$ 15 billion) from

non-crop uses, such as forestry, public health and industrial uses (Utley, 2008).

Agrochemicals are used principally to control weeds and pests in agriculture. The market for

this major application was valued, at the end-user level, at around US$ 31.25 billion in 1998

(Bryant, 1999). Agrochemicals are also used in a variety of non-crop applications, which are

generally higher value-added products. Agrochemicals’ global market for non-crop

applications, valued at US$ 10.5 billion in 1998, included applications in forestry, leisure

(such as home, garden and golf courses) and industrial pest control (such as control of

vermin and insects, weed control in towns and on railways) (Bryant, 1999). The non-crop

sector is dominated by insecticides (around a half) and non-selective herbicides.

As is the case in most markets for chemicals, the developed economies in the United States

of America (USA), Europe and Japan account for the lion's share of use, with approximately

70 percent by value at the end-user level. The North American market accounts for 30

percent of the total, with Japan and France second and third, respectively. The global market

may also be divided into the three main types of agrochemicals: herbicides (47 percent),

2

insecticides (29 percent) and fungicides (18 percent). Other agrochemical applications,

mainly fumigants and plant growth regulators, account for the remaining 6 percent of sales

(Bryant, 1999). This study endeavored to look at wheat pre-emergence herbicides use in the

Larger Uasin Gishu County of the North Rift of Kenya. The most common pre-emergence

wheat herbicides used in this region are pendimethelins, chlorsulfurons, tribenuron methyls

and flufanecets. Pre-emergence herbicides generally control germination of weed seeds

before they emerge from the soil. This has got a very profound effect because they almost

completely eliminate competition from weeds.

Pesticides and fertilizers are the primary agrochemicals used by farmers in Kenya. The

farmer uses these agrochemicals to control organisms that destroy crops and infest livestock.

In addition, fertilizers are needed to replenish soil nutrients and consequently improve the

agricultural yield. The public health sector uses pesticides to combat insects and vectors that

transmit diseases to humans and animals. Although Kenya's economy is dependent on

agriculture, only a third of the land is arable. Enormous amounts of the agrochemicals are

imported and extensively used every year. For instance, between 1985 and 1987, Kenya

imported agrochemicals worth 1,732.3 million Kenya Shillings (US$ 69.3 million) (Mutuku

and Kimani, 1993).

The agrochemical industry is an important segment of the Kenya economy. According to the

Agrochemicals Association of Kenya (A.A.K, 2006), the industry increased job

opportunities and tax revenues to the government. This is evident in the manner in which

new products are developed. Other benefits associated with the industry include

3

environmental protection campaigns and credit facilities to farmers. Wheat as a cereal crop

is a major user of agrochemicals as compared to other cereal crops. The major chemicals in

wheat production include herbicides, fungicides, and insecticides among others.

Agrochemical applications are essential technological means for growing field crops. The

most critical technological points in production of grain crops are agrochemical

interventions, so that it is essential to reveal and study soundly their impacts (Czovek et al.,

2006). Chemical applications are seen by the public to be both harmful to crops and

environment. However, appropriate treatments secure high yields and help to reduce serious

weed infestations in ecosystems (Hegedos et al., 2002, Szentpetery et al., 2005a and b,

Tanacs et al., 2008). Weeds are in permanent competition with crop plants and hence the

need for their control. Weed control is essential to establish conditions for optimum crop

performance (Kazinczi et al., 2002, Knezevic et al., 2008). Soils as the fundamental habitat

for any plant growth provide optimal, sub-optimal and hyper-optimal conditions for

vegetation in relation with climatic variability (Lawlor, 2002, Jolankai-Birkas, 2007).

Agrochemicals are hence essential in production so as to provide the plants with what lacks

in the soils and also to provide protection. It is therefore important to evaluate the factors

that influence agrochemical applications as well as to study how plant protection treatments

can influence crop yield and weed infestation of wheat crop.

Use of wheat agrochemicals is highly multi-factorial with the weather, crop types, weed

types, farming intensity, soil characteristics, sowing techniques, planting density and

application rates all combining to give enormous regional and year-on-year variation. Other

key use influencers include number of people to feed, growing affluence, bio-fuels and

4

regulatory environment (Uttley, 2008). These underlying drivers have the capacity to result

into high acreages being planted for crops and high crop prices. High prices for crops, in

turn, translate into high farm incomes giving farmers a financial incentive to increase crop

output further. However, there are constraints in many areas and thus farmers seek to

improve yield per acre through optimal application of fertilizers and crop protection

products (agrochemicals). Agrochemical use varies considerably with crop type, seed type

and conditions for use. However, on average, it represents a small percentage of farmers’

total fixed and variable costs. Therefore, increased use of agrochemicals can be a cost-

efficient way of improving overall income of the farmer (Uttley, 2008).

Wheat is the second most important cereal crop after maize in Kenya (KARI, 1989) and is

becoming an important source of food for both humans and livestock. Wheat production

started at the beginning of the 20th century in Kenya, but it was not until 1927 that a formal

wheat breeding research program was initiated at the Kenya Agricultural Research

Institute’s (KARI) National Plant Breeding Research Centre (NPBRC) in Njoro, Kenya

(Gamba, 2002). Since this program began, many wheat varieties have been released.

Demand for wheat and wheat products is growing at 7 percent per annum and only about 50

percent of domestic consumption requirements are being met (Hassan et al., 1993).

Increasing population, rapid urbanization, rising income levels, and changing tastes and

preferences are major factors contributing towards this demand. Various constraints have

contributed to the failure to meet domestic demand and these include erratic and

unpredictable climatic conditions, lack of credit among farmers, failure to adopt proper and

5

improved technologies in production as well as failure to invest in agrochemicals which are

essential in wheat production (Gamba, 2002).

Wheat production in Kenya has not been sufficient (Nyangito et al, 2002). This has

continued to happen despite concerted efforts by agrochemical companies to market

agrochemicals to farmers. Low use of inputs such as agrochemicals by farmers, due to

market constraints that reduce profitability of input use, is one of the factors responsible for

the gap between potential and actual yields. A comparison of agrochemical consumption

trends in Sub-Saharan Africa and developing countries of Asia shows that while average

annual agrochemical consumption increased by 182 percent in Asia between 1980 – 1989

and 1996 – 2000, it increased by only 16 percent in sub-Saharan Africa (FAOSTAT, 2003).

The slow growth in the use of modern agricultural inputs in the farming systems of sub-

Saharan Africa has resulted in missed opportunities to increase Africa’s agricultural

production, productivity, household incomes and welfare. Agrochemical use in Sub-Saharan

Africa is the lowest in the world and is actually less than 10 percent of the global mean

(about 93 kilograms per hectare) (IFDC, 2006).

It was thus a matter of concern to establish determinants of use of wheat agrochemicals. The

main focus was on the use of pre-emergence wheat herbicides, with the hope of cascading

the findings for use in the other sectors of wheat agrochemicals. This is because wheat,

being the second most important cereal grain in Kenya after maize, requires a lot of

attention.

6

The crop is grown largely for commercial purposes on large scale farms (EPZA, 2005).

Wheat production has, however, over the years not been sufficient as Kenya continues to

rely on wheat imports to meet domestic demand for wheat and wheat products.

Increased wheat imports have led to further decline in wheat production because imports

dampen domestic prices and are a disincentive to production. This is happening despite

world trends that have seen increasing costs of purchasing wheat. Figure 1.1, shows the ever

increasing wheat imports from between 1990 and 2003 and declining production trend.

0

100000

200000

300000

400000

500000

600000

700000

800000

900000

1990 1991

Year

Figure 1.1: Wheat production and importation trend in Kenya for 1990-2003

Source: KNBS, 2004

Per

cent

7

A substantial amount of wheat is still being imported into the country despite the potential to

increase production acreage and yield per unit area. The following data (Table 1.1) from

Ministry of Agriculture effectively makes a case for the need, not only for increase and

sustained use of potential wheat growing area, but also for concerted efforts to increase yield

per hectare.

Table 1.1: Wheat Production and Consumption in Kenya for the period 2003-2007

Source: GOK, 2008

From Table 1.1 it is clear that yield per hectare has continuously remained very low, despite

the fact that Kenya continues to import wheat. Rationality should allow an increased yield

per hectare even if area under wheat changes for one or another reason. Agrochemicals have

Year 2003 2004 2005 2006 2007

Area (Ha) 151,135 145,359 159,477 150,488 104,176

Production: 90Kg Bags 4,207,278 4,173,652 4,063,294 3.978,454 3,936,105

Production: Tons 379,034 397,005 365,696 358,061 354,249.1

Unit Price per Bag (Kshs) 1,718 1,995 1,639 1,714 3,000

Average Yield: Bags/Ha 28 29 25 26 28

Consumption (Tons) 883,120 889,020 893,120 903,120 927,956

Imports (Tons) 502,115 404,060 621,839 - -

Total Value:

(Billion Kshs)

7.23 8.33 6.66 6.82 10.028

8

a role in assisting farmers and hence Kenya to increase yield. Their role in increasing yield

of wheat farms need to be investigated, more so because adoption of these products will;

lead to improved yield of wheat farms. Wheat production registered a 1.1 percent decline

from 3.98 million bags in 2006 to 3.94 million bags in 2007 (GOK, 2008). Production is

also shown to have ranged between 25bags / Ha to 29 bags / Ha, which are a paltry

performance because the same amount can be produced from one acre in other parts of the

world.

International price analysis, (Appendix 2, Table A1) shows an upward trend in prices. This

should act as an incentive to all stakeholders including Kenyan farmers and extension agents

like agrochemical companies to maximize returns from wheat farming. This will save the

hard earned foreign exchange that is used for acquiring the increasingly expensive wheat

imports.

Statistics on imported crop protection products show both increasing and decreasing trends

from 2004/2005 to 2006/2007 as manifested by different categories of agrochemicals. This

is attributed to failure by Kenyan farmers to produce enough wheat to meet domestic

demand especially when yield/ha of their farms still remains very low. The significance of

the increase may, however, be very small considering that this serves to highlight

performance of agrochemicals in different crop subsectors including horticulture,

floriculture and cash crops like tea, coffee, cotton, sisal, pyrethrum and others.

Others, (Appendix 2, Table A2) include fumigants, rodenticides, growth regulators, defoliators,

proteins, surfactants and wetting agents.

9

Import of insecticides and acaricides increased from 2361 in 2005/06 to 2,638 tons in

2006/07 by 11.7%, while imports of herbicides increased by 45% over the same period.

Fungicides also registered the same trend with imports rising by 10.5 %, (GOK, 2008).

Despite the glaring vacuum in producing enough wheat to substitute imported wheat, Kenya

is still not showing any improvements.

There is a worrying trend in yields and acreage of wheat in the larger Uasin Gishu County

(Table 1.2).

Table 1.2: Wheat Production in Uasin Gishu County from 2004 to 2008

Year Area (Ha) Production (90Kgs bags Yield/Ha Production (Tons)

2004 42,100 1,250,000 29.7 112500

2005 37,500 1,237,500 33 111375

2006 37,000 1,186,560 32 106790.4

2007 29,500 649,800 22 58482

2008 37,107 1,021,215 27.5 91909.35

Source: DAO, 2008

Wheat agrochemicals use in Kenya has not led to increased yield per unit as compared to

world standards. This scenario is very evident in Uasin Gishu County.

Low wheat yields and continued imports of wheat consistently over the years are problems

that face Kenya as a country and by extension, Uasin-Gishu County. The country continues

to import wheat to meet its domestic needs despite concerted efforts by agrochemical firms

to market wheat agrochemicals to stimulate increased production. According to Nyangito et

10

al (2002), average wheat yields in Kenya are about 1300kg per acre but this can range from

450kg per acre on small-scale farms to 1600kg per acre on large-scale farms. Yield / Ha

went to some extent to 33 bags (DAO, 2007), which represents low yield according to

international standards. Right and proper agrochemicals-use along with other agronomical

practices can lead to higher production. According to (USDA, 2007), yield (Appendix 2, A4)

in several leading countries was higher than that in Kenya;

1.2 Problem Statement

Wheat is the second-most-important cereal crop in Kenya, but local production does not

satisfy demand, and the deficit is always imported. In 1990, local consumption of wheat was

550 metric tonnes. This, however, rose by 63% to 900 metric tonnes in 2007. Within the

same period, local production rose by a small margin of 14. 8%; the deficit being met by

imports from other wheat producing countries. The annual wheat production in the1990s

averaged about 258,207 tons. Production has, however, been very erratic, ranging from

264,457 tons in 1991 to nearly 126,000 tons in 2000.In contrast to production, wheat

consumption in Kenya has been on a general increase, although there have been declines in

some years, particularly in 1992, 1995 and 1997. Due to the rising demand for wheat, caused

by high population growth and increased urbanization, consumption has increased faster

than production. The current domestic wheat requirements are about 765,000 tons. Kenya

has therefore relied on imports to meet domestic needs in wheat (Nyangito et al., 2002).

Gaps between local consumption and production of wheat have consistently increased from

year to year. Because of the fact that market is highly liberalised, imports have highly

11

become a drain on foreign currency which is very crucial for the well being of Kenya’s

economy. According to USDA, (2007), Kenya, in 1960, years just before independence,

produced 109 metric tonnes of wheat which increased by a 106% in the year 2007 to 225

metric tonnes. During the same period, import of wheat rose from 1 metric tonne to 550

metric tonnes representing a high increase of 55000%. Currently, imports account for about

62.4 percent of Kenya’s domestic needs in wheat (Nyangito et al.,2002).

It is therefore vital to place emphasis on production strategies that will increase wheat grain

yield (Amadi et al., 2004). On average yield of wheat per hectare remained at 1 to 2 tonnes

per hectare despite high demand. This is happening yet the country has a very high potential

of producing enough to meet its local consumption and even have surplus. South African

farms produce up to 5 tonnes per hectare while New Zealand farmers manage up to 8 tonnes

per hectare from their wheat farms (USDA, 2007). This clearly shows that there is room for

Kenyans to increase production of their wheat farms 3 to 4 times from the current levels.

In the years preceding 2004, only 2.7 tonnes per hectare of wheat were realised compared to

the potential of 3.96 tonnes per hectare in Uasin Gishu County. Low production per unit area

was attributed to several factors including low rate of adoption of technical

recommendations, (GOK, March 2004). According to Nyangito et al (2002), these high

yields are currently not being achieved because of the low level of technology adoption,

particularly on small farms. They further point out that other constraints facing wheat

producers in Kenya include poor supply of inputs, low producer prices, and pest infestation.

One of the main inputs used in efficient production of wheat is the element of

agrochemicals. FAO data (FAO,1999),indicate that loss of wheat yield globally due to

fungal infection is accounted for by 16% while that attributed to viral and insect infestation

12

accounts for 3% and 9 % respectively. Weeds infestation on the other hand can cause up to

23% wheat loss.

In spite of enhanced promotional campaigns by agrochemical firms and other extension

agents, wheat production has not increased. It is thus important to understand the drivers of

agrochemical use in Uasin Gishu County, with particular emphasis on pre-emergence

herbicides.

1.3 General Objective

The main objective of this study was to determine the factors influencing use of wheat

agrochemicals in Uasin Gishu County.

1.4 Specific Objectives

a) To determine social factors influencing use of pre-emergence wheat herbicides in

Uasin Gishu County.

b) To determine economic factors influencing use of pre-emergence wheat herbicides in

Uasin Gishu County.

c) To identify promotional strategies that elicits positive impact in the use of pre-

emergence wheat herbicides in Uasin Gishu County.

1.5 Research Hypotheses

In order to get more insights into the agrochemical market in Uasin Gishu County, the

following hypotheses were tested:

13

H01: Social factors such as education level do not influence use of pre-emergence

wheat herbicides in Uasin Gishu County.

H02: Economic factors such as annual level of income do not influence use of pre-

emergence wheat herbicides in Uasin Gishu County.

H03: There are no promotional strategies, such as extension services, that would elicit

positive impact in the use of pre-emergence wheat herbicides in Uasin Gishu County.

1.6 Justification of the Study

Wheat is the second most important grain in Kenya after maize (EPZA, 2005). In Uasin

Gishu County, wheat is a major cash crop among farmers. Despite concerted efforts to

improve the production of wheat in Uasin Gishu County, an area sometimes referred to as

‘Kenya’s grain basket’, results have been minimal with the domestic production performing

dismally. Some of the reasons put forth in this regard are the inadequate and insufficient use

of pre-emergence wheat herbicides which are essential in wheat production to boost yields.

The current study is of importance as it will give insights into this crucial aspect of wheat

farming in the study area and thereby give policy implications for the same. Moreover, this

kind of study has not been carried out in the area and thus it makes it extremely important to

carry out the study given the fact that Uasin Gishu County is a major wheat producer in

Kenya. Agrochemical dealers and manufacturers will get an insight into extension dynamics

emanating from use of agrochemicals or lack of use of them.

14

1.7 Area of Study

The scope of this study was limited to the analysis of factors influencing use of pre-

emergence wheat herbicides in Uasin-Gishu County, Kenya. The County is one of the

counties in Rift Valley province. It extends between longitudes 34o 50’ and 37’ east and 0o

03’ and 0o 55’ north. The County shares common borders with Trans Nzoia County to the

north, Elkeiyo-Marakwet and Keiyo Counties to the east, Baringo County to the south east,

Kericho County to the south and Nandi County to the west. The county has a total area of

3,327.8 km2 (GOK, 2004).

Administratively, the County is divided into three districts, namely Uasin Gishu East, Uasin

Gishu North and Uasin Gishu South. There are six divisions namely Kapseret, Ainabkoi,

Kesses, Soy, Turbo and Moiben. It is further divided into fifty-one locations and ninety-six

sub-locations. Moiben is the largest division with an area of 778.2km2 with ten locations and

twenty-three sub-locations while Kapsaret Division, with an area of 297 km2, is the smallest

(GOK, 2004).

Uasin Gishu County is a highland plateau. Altitudes fall gently from 2700m above sea level

at Timboroa in the east to about 1,500m above sea level at Kipkaren in the west. The County

can roughly be divided into two broad physiographic regions, with Eldoret (2085m) forming

the boundary between the regions. The topography is higher in the east and declines towards

the western borders. The plateau terrain in the County allows easier construction of

infrastructure such as roads and use of modern machinery in farming (GOK, 2008).

15

Uasin Gishu County is in the Lake Victoria water catchments zone. All the rivers in the

County drain into Lake Victoria. Major rivers in the County include; Sosiani , Kipkaren ,

Kerita , Kipkuner , Nderugut , Daragwa and Sambul . These rivers provide water for

livestock, domestic and industrial use (GOK, 2008).

Rainfall in the County is high, reliable and evenly distributed. The average rainfall ranges

between 900mm-1200mm. It occurs between the months of March and September with two

distinct peaks in May and August. The wettest areas are found in Ainabkoi, Kapsaret and

Kesses divisions. Turbo, Moiben and Soy divisions receive relatively lower amounts of

rainfall. The dry spells begin in November and end in February. Temperature ranges

between 8.4oc and 26.1oc (GOK, 2004).

An estimated 90 percent of the land area in the County is arable out of which about

2,000km2 is classified as high potential and about 1000km2 is medium potential. There are

four major soil types in the County that are good for agricultural production. These include

red loam, red clay, brown clay and brown loam (GOK, 2004).

Agriculture is the main economic activity of Uasin Gishu County. A total of 126,311.2

hectares are under crop production, while 204,000 of the population work in agriculture

alone. The sector also contributes 35.5 percent of household income. The importance of the

sector cannot be over emphasized because a great proportion of the population earns its

livelihood from the agricultural sector. The improvement of food security and achievement

of better standards of health also depends heavily on this sector. The sector is also an

16

important revenue and foreign exchange earner. It creates jobs and at the same time

promotes better environmental management for sustainable production.

17

CHAPTER TWO

LITERATURE REVIEW

2.1 Introduction

In this chapter, literature is reviewed under two sections. Section 2.2 reviews empirical

studies that are of particular relevance to this study, commenting on their methodologies,

findings and conclusions. The second section, 2.3, is a critical evaluation of the theoretical

and empirical works, pointing out as far as possible their point of departure from the present

study. The missing gaps in the current study, which this study sets out to fill, are also

identified. The conceptual framework for the study is presented in section 2.4.

2.2 Empirical Input Use Studies

Kenyan economy largely depends on the agricultural sectors, which accounts for an average

of 25 percent of gross domestic product (GDP). About 75 percent of Kenyans owe their

livelihoods to agriculture (EPZA, 2005). This just acts to illustrate the importance of

agricultural sector in Kenya’s economy. Wheat as a crop is the second most important cereal

grain in Kenya. The crop is grown largely for commercial purposes on large scale farms.

Wheat growing areas in Kenya include scenic Rift Valley regions of Uasin-Gishu, Narok,

Marakwet, Elkeiyo, Londiani, Molo, Nakuru and Timau. These areas have altitude ranging

from 1200m and 1500m above sea level, with annual rainfall ranging from between 800mm

and 2000mm with up to 2500mm in high grounds. The area under wheat production in

Kenya increased from 144000ha in 2002 to 150000 ha in 2003 (EPZA, 2005). Despite the

conducive conditions to produce wheat, Kenya, and by extension Uasin-Gishu County,

18

continues to lag behind its potential production capacity of wheat. Previous studies on wheat

have dealt mostly on such aspects as credit and marketing. In their report on the

performance of Kenya’s wheat industry and prospects for regional trade in wheat products,

Nyangito et al. (2002) emphasizes high capital costs, lack of credit for production and low

levels of technology adoption in wheat production especially seed variety for wheat as the

main constraints which have led to decline in wheat production over the years. They,

however, fail to recognize the importance of wheat agro-chemicals in boosting output of

wheat. Wheat agro-chemicals are categorized into four main classes. These include

insecticides, herbicides, fungicides, and others. ‘Others’ fall into such crop protection

chemicals like adjuvant and rodenticides.

Nyangito et al. (2002) focused mainly on the wheat industry from the national and the

international levels. They gave such recommendations as funding of research, extensions,

credit and marketing functions to encourage domestic production of wheat. They went

further to state that import taxes and duties on wheat imports should be eliminated to avoid

protecting insufficient producers and that inputs and output marketing needs to be made

competitive through provision of improved infrastructure. They also pointed out that there is

need for better management of policy on wheat import and trade to avoid distortion in the

wheat market.

Clearly these recommendations are proper in addressing the problems in the wheat industry

from the national and international standpoint. They fail to give recommendations that

would give solutions that are particular to a given locality. These are blanket

recommendations that may not work for individual farmers, agrochemical firms and other

players in the wider wheat industry. This study will suggest solutions specific to wheat

19

farmers in Uasin-Gishu County on factors affecting their purchase decisions of

agrochemicals wheat products and thus increasing their yield of wheat.

While studying the factors affecting farmers’ adoption of agro-chemicals in sugarcane

production, in Mumias sugar belt, Nyakundi (2008) pointed out such variables as farmers

income, price of the agro-chemicals, price of labour, literacy level of farmers affecting

decisions in adoption of agro-chemicals in sugarcane farming. His study, however, did not

include any acceptable model of economic analysis. The study by Nyakundi, (2008) was

important in that it pointed out some variables which are significant in wheat agro-chemical

use analysis. The variables of importance in the current study included product related

aspects, economic factors, and farm and farmer related issues. These variables are not

sufficient to study the problem at hand and give workable solutions to alleviate status of

wheat production locally.

Amadi et al. (2004), in their study, proved that there was need for use of agrochemicals in

growing of wheat. They, however, narrowed themselves to the study of technical aspects of

an herbicide called monitor and they did not consider socio-economic factors that affected

use for the same. The study is more technical in nature because no major economic tenets

were incorporated.

In other areas of the world, some studies that are relevant to this study have also been

undertaken. Szentpetery et al., (2005a), in their study mainly pointed to importance of

agrochemicals in growing of wheat. They pointed out that agrochemicals and nutrition are

20

very important, without which we cannot reap maximum benefits. Their study further points

out that herbicides, insecticides and fungicides are very important in boosting wheat yield.

Again, their study did not show socio-economic factors that influenced use of wheat

agrochemicals. Their study was mainly geared towards analyzing agrochemical products’

attributes.

Jolankai et al. (2008) studied the impact of pesticides and plant nutrition on wheat crop, as

well as their interaction in a small plot field trial run at the experimental site on eutric

cambisol type soil, in 2006 and 2007. The results obtained suggested, that treatments applied

– both the increasing rate of fertilizers (N0P0K0, N40P100K100, N80P100K100,

N120P100K100, and N160P100K100) and the increasing intensity of pesticide application

(O, herbicide, herbicide + fungicide, herbicide + fungicide + insecticide) – had significant

effects on the grain yield of wheat. In case of fertilizer application, each N rate resulted in a

further significant yield increase in the average of pesticide applications. In 2006, the

minimum yield was 2.2 tons per hectare, the maximum 5.5 tons per hectare and the average

4.3 tons per hectare. In 2007, the minimum yield was 1.00 ton per hectare and the maximum

4.6 tons per hectare, and the average, 3.2 tons per hectare. In accordance with the increment

of the level of plant nutrition and plant protection applications, a decreasing magnitude of

yield increase was observed. Plant nutrition applications had a more definite effect on yield

figures in comparison with that of plant protection treatments. Meteorological conditions of

the crop years studied were considerably buffered by agronomic applications applied. This

therefore shows that agrochemicals have a role to play in the production of wheat and hence

21

it is imperative to study what influences the use of these agrochemicals among farmers so as

to enhance wheat yields.

Uttley (2008), while studying the pricing of agrochemicals, noted that the demand for

agrochemicals is multi-factorial with factors such as weather, crop types, weed types,

farming intensity, soil characteristics, sowing techniques, planting density and application

rates all combining to give enormous regional and year-on-year variation. The study found

that there were several demand drivers that influenced an individual’s use of agrochemicals.

Uttley (2008) noted one of these demand drivers as being increasing number of mouths to

feed. The study suggested that with the increase in population, more people needed to be

fed, majority of them living in urban areas. The increase in population triggers food

shortages if there is no matching production. Therefore, an increase in population will result

in need for increased food production will lead to an increase in use of agro-chemicals.

Another use driver noted was that of growing affluence. Uttley, (2008) noted that increased

per capita income, as reflected by growing GDPs especially in developing countries, has led

to increases in the amount of food eaten per capita. Diets have also changed with increasing

affluence, for example, a rise in protein intake, especially meat consumption (meat

production requires grain to be fed to animals and so the demand for grain is also increased

thus more agrochemicals) and an increase in demand for fresh fruit and vegetables (which

consume approximately 25 percent of the use for crop agrochemicals).

22

Other agrochemical use influencers noted are bio-fuels and regulatory environment

incentivising increased crop production. Uttley (2008) argued that many countries are trying

to reduce dependence on oil as a fuel and to decrease polluting gas emissions, giving rise to

a trend to produce renewable energy from crops. Bio-fuels produced, consequently, are

mainly ethanol and agro-diesel which are derived from corn, sugarcane, rapeseed oil,

soybean, palm oil and with small amounts from wheat. This therefore means an increase in

demand for these crops, and by extension, an increase in use for respective agrochemicals.

With respect to regulatory environment incentivising increased crop production, Uttley

(2008) noted the act of international markets such as the United Kingdom and the United

States of America providing bio-fuel subsidies raises the demand for crops used to produce

bio-fuels and this in turn leads to an increase in the agrochemicals used.

2.3 Evaluation of the Literature

The literature reviewed in this study examines the various determinants of use of

agrochemicals. It is identified that cost of agrochemicals is one of the major determinants of

how accessible a particular agrochemical, even those for wheat, will be to an individual.

Credit accessibility is also seen to be a major determinant in that a farmer who is not

financially endowed will not manage to invest in agrochemicals. Rate of adoption of new

technologies will also determine the use. In this case low levels of technological uptake with

regard to agrochemicals will influence the demand for agrochemicals negatively and high

levels of technological uptake will influence use of agrochemicals positively.

23

The literature reviewed also shows that there are various measures that have been taken so

as to boost production of wheat in the country. Such measures include funding of research,

extension, credit, marketing functions, subsidies and regulations on import taxes on both

wheat and the inputs used in the wheat industry to encourage domestic production of wheat.

However, the importance of the wheat agrochemicals in wheat production seems to be

overlooked. This translates to a lack of awareness on agrochemicals among the producers of

wheat.

Literature reviewed in Nyakundi (2008) identifies various factors that influence the adoption

of agrochemicals in sugar cane industry. The factors identified were farmers’ income, price

of the agrochemicals, price of labour and literacy level of farmers’ decision makers in

adoption of agrochemicals in sugarcane farming. Some of these factors were considered, in

the analysis of use of pre-emergence herbicides in Uasin Gishu County along with other

factors inherent to wheat farmers.

It was also established that such factors as population density and growth, growing

affluence, bio-fuels and regulatory environment also have an impact on the demand for

agrochemicals (Uttley, 2008). The missing link identified here, as well as in various other

studies, is that such factors as socio-economic characteristics of the farmer have not been

considered by the reviewed authors. The current study endeavored to identify how socio-

economic characteristics of a farmer such as age, income, household size, education level

and type and scale of production affects use of agrochemicals. This study sought to identify

how promotional strategies can influence the use of pre-emergence wheat herbicides in

24

Uasin Gishu County as this has not been done as seen in studies reviewed in the foregoing

literature.

Doss et al. (2001) had similar findings to the study of use of wheat agrochemicals in Uasin

Gishu County. Socio-economic considerations, such as gender necessitate promotion of

gender parity and equality in access to resources and means of production and are important

for future development of farming and farm set ups in Uasin Gishu County, and indeed for

the whole country.

Issues of extension services in all forms were dealt with by Abdulai et al. (2006). The

findings and recommendations were very similar, pointing to a need for enhancement of

aspect of farmer outreach. This study also gave weight to extension services by

agrochemical companies operating in the county. Their activities, in provision of farmer

outreach services were taken into consideration in arriving at conclusive and across the

board recommendations where government and quasi-government agencies were also

considered. Dobson, (2005) dealt with extension services very well; with fundamentals of

farmer outreach vegetable- growing being given weight, with aids such as training manuals

and others being discussed extensively. Apart from addressing a non-cereal crop, it dealt

with problems of agrochemical use from the perspective of the donor. No economic and

social factors were really put forward, nor adoption really dealt with the aim of knowing

dynamics that dictate household decisions by farmers in light of inputs use.

25

The adoption issues that are put forward here are very much similar to the studies that have

been undertaken by Egyir (2008). Both social and economic factors are well researched and

the outcomes are very similar. Economic considerations such as land tenure issues and

accessibility to credit facilities have come out very strongly in both studies. Social factors

that are facing plantain farmers in Ghana such as literacy levels tend to tally with findings

on social dynamics facing farmers in Uasin Gishu County of the Rift Valley, Kenya. The

only aspect that makes this study more aligned to precision is the fact it concentrated on one

type of agrochemicals, pre-emergence wheat herbicides.

2.4 Conceptual Framework

Figure 2.1: Conceptual Framework

Source: Author, 2010

Use of Wheat Agro-Chemicals

Social Factors Age Education Household size Influence from

other farmers

Economic Factors Land size

under wheat Nature of

land holding Income Credit

accessibility

Promotional factors Extension

services Farmers

association Wheat market

information

26

Figure 2.1 shows the conceptual framework that was used in the study. Use of pre-

emergence wheat herbicides, and which was a dichotomous variable, was conceptualized as

being dependent upon three major factors: social, economic and promotional factors. The

three factors comprised of various explanatory variables from which some were picked to

model use of pre-emergence wheat herbicides in Uasin Gishu County. Under the social

factors, the independent variables were age, education, household size and influence from

other farmers.

Under the economic factors, the following independent variables were conceptualized: land

size under wheat, nature of land holding, income of the farmer and credit accessibility. The

four were combined with the previous four social variables to come up with socio-economic

factors.

The last category consisted of promotional strategies that were deemed necessary in the

decision of whether or not to use pre-emergence wheat herbicides in wheat farming. These

factors were; extension services, membership to farmers associations and wheat market

information.

27

CHAPTER THREE

METHODOLOGY

This chapter discusses the methodology used in this study. The chapter begins by describing

the theoretical framework, then the data types and sources, research design, data collection,

data analysis and the study limitations.

3.1 Theoretical Framework

Adoption is seen as the first or minimal level of behavioural utilization and innovation. It is

an idea, practice, or object; perceived as new by an individual or other units of adoption

(Rogers 2003). According to Feder et al, (1993) an innovation is defined as a technological

factor that changes the production function regarding which there exists some uncertainty,

whether perceived or objective (or both). The uncertainty diminishes over time through the

acquisition of experience and information, and the production function itself may change as

adopters become more efficient in the application of the technology. They continue to argue

that technology adoption may also be viewed from two perspectives. At the micro level,

each decision unit must choose whether to adopt the innovation and its intensity of use if

adopted. Many adoption studies, they further noted, therefore, examine the factors

influencing the firm’s or household’s adoption decision and may be viewed from a static or

dynamic (if learning and experience are incorporated in the decision model) perspective. At

the macro level, they noted, the adoption pattern of the whole firm or household population

is examined over time to identify the specific trends in the diffusion cycle. “Diffusion

studies do not consider the innovation process, but begin at a point in time when the

innovation is already in use”.

28

Determinants of adoption are outlined clearly by (Rogers 2003). He outlined them as being

dependent on perceived attributes, of which comparative advantage or the degree to which

an innovation is perceived better than the idea it supersedes is first taken into account. Other

issues of attributes that he outlined are: complexity (the degree to which a practice is

perceived as relatively difficult to understand and to adopt, negatively related to its rate of

adoption), trialability (degree to which an innovation may be experimented at a limited

basis) and compatibility (degree to which sustainable practice is perceived as consistent with

the existing values, past experience and needs of potential adopters. Rogers (2003) further

described innovation process as a process through which an individual passes from;

knowledge to attitude and finally to adopting (indivual or collective, optional or authority).

He further pointed out the importance of communication channels in innovation process

defining them as interpersonal or mass media, originating from specific or diverse sources.

He also defined Social system as norms, network interconnectedness pointing out that these

socio-cultural practices and norms can inhibit or drive adoption. He stated that efforts of

promotion agent in the past and present are important.The current study drew similarity with

this theory to study factors influencing use of pre-emergence herbicides among wheat

farmers in Uasin Gishu County.

Rogers (2003) categorizes adopters into: 1) innovators who are educated and venturesome;

2) early adopters who are popular educated and are normally social leaders; 3) early

majority who are deliberate and have many social contacts; 4) late majority who are very

skeptical; 5) laggards who are traditional and normally of lower social economic class.

These may end up not adopting the technology. The distribution of these groups follows the

familiar bell-shaped curve, when plotted to indicate their features in the relevant population.

29

3.1.1 Theoretical Model

To understand agrochemical use, an understanding of derived demand was found to be of

significance. This called for indication of direct demand which emulates the following

general demand function of perceived variances:

Dw= F (Px, Pm, Pd, Pa,Pf ,Hin, Hsize, A, Pe, T, E)

Where:

Dw, stands for demand for Wheat

Px, price of wheat locally

Pm, price of Wheat Flour

Pd, price of fuel

Pa, price of agrochemicals

Pf, price of Fertilizer

Hin, Household income

Hsize, Household size

A, Promotion of wheat products

Pe, Price expectation of wheat consumers

T, Household taste or preferences

E, all other factors.

30

In the same way, supply of wheat in the local Kenyan market is subject to various variables

which may determine how much can be produced by the Kenyan farmers. These can be

elaborated as follows, in a generalized wheat supply function:

Sw= F (Px,Pi, Pm, Pd, Pa,Pf ,N, Fsize, A, Pe, T,G, E)

Where:

Sw, stands for supply of Wheat

Px, price of wheat locally

Pi, price of imported wheat

Pm, price of wheat flour

Pd, price of fuel

Pa, price of agrochemicals

Pf, price of Fertilizer

N, number of wheat farmers

Fsize, Total area under wheat locally

A, Promotion of wheat products

Pe, Price expectation of wheat consumers

T, technology for use in wheat farming

G,government policy


The above was considered in light of derived demand. However as postulated earlier, use of

pre-emergence herbicides was studied and used a generalized way of understanding the

31

decisions that farmers consider in purchasing all other classes of agrochemicals. Derived

demand as used in Uasin Gishu County was suggested to be studied using the following

simplified demand function:

Da= F (Px, Pl,Cspy, Hin, Fsize, A, Pe, T, E)

Where:

Da, stands for demand for Wheat Agrochemicals

Px, price of Agrochemicals

Pl, price Labour

Cspy, cost of spraying agrochemicals

Hin, Household income

Fsize, Total area under wheat locally

A, Promotional strategies for pre-emergence wheat herbicides

Pe, Price expectation of wheat farmers

T, Household taste or preferences


The behavior of all the above were found to follow the following logic:

a) Use of agrochemicals tends to reduce with an increase in their prices.

b) The labor price positively affects the use for pre-emergence wheat herbicides. As

general wage price increases, pre-emergence herbicides, which are substitutes

gain more acceptances, and therefore their demand goes up.

32

c) When prices of pre-emergence complements go up, notably the cost of spraying,

the use of the agrochemicals is negatively affected.

d) This is a psychological aspect of the wheat farmers. If wheat farmers perceive an

expected price increase in wheat agrochemicals, they will generally buy more.

e) The general income levels of households affect use of wheat

agrochemicals. This includes income from non-farming sources. The more

the total income there is available to wheat farmers, the more their

purchasing power, which will imply that the use of wheat

agrochemicals will be heightened.

f) Promotional strategies, like advertisement, for agrochemicals will

positively influence its use. The more the frequency and amount of

advertising budget the higher the expected use of wheat

agrochemicals.

g) The more the number of farmers the higher the expected use of wheat

agrochemicals.

h) The larger the total area under wheat the higher the expected use of

wheat agrochemicals.

i) Tastes and preferences highly affect the use of wheat agrochemicals.

Some farmers may prefer post emergence herbicides to pre-emergence

herbicides as an example, while some may prefer to spray less of

agrochemicals. These are decisions affected by both individual and

cultural perceptions.

33

3.1.2 Model Specification

To meet the objectives of this study, Logit model was fitted on household data. The major

focus of the study was the likelihood or probability of the outcome, that is, whether the

respondent uses pre-emergence wheat or not. The binary response in this study was whether

the respondent uses wheat pre-emergence herbicides (“Success”) or does not use pre-

emergence herbicides (“Failure”).

An explanation of logistic regression begins with an explanation of the logistic function:

=

Wikipedia (2011)

The input is z and the output is ƒ (z). The logistic function is useful because it can take as an

input any value from negative infinity to positive infinity, whereas the output is confined to

values between 0 and 1. The variable z represents the exposure to some set of independent

variables, while ƒ (z) represents the probability of a particular outcome, given that set of

explanatory variables. The variable z is a measure of the total contribution of all the

independent variables used in the model and is known as the logit, Wikipedia (2011)

The variable z is usually defined as:

34

Z=β0 +β1x1+ β2x2 + β3x3+………………………..+ βnxn

Wikipedia, (2011)

Where β0 is called the "intercept" and β1, β2, β3, and so on, are called the "regression

coefficients" of x1, x2, x3 respectively. The intercept is the value of z when the value of all

independent variables is zeros (e.g. the value of z in someone with no risk factors). Each of

the regression coefficients describes the size of the contribution of that risk factor. A

positive regression coefficient means that the explanatory variable increases the probability

of the outcome, while a negative regression coefficient means that the variable decreases the

probability of that outcome; a large regression coefficient means that the risk factor strongly

influences the probability of that outcome; while a near-zero regression coefficient means

that that risk factor has little influence on the probability of that outcome Wikipedia, (2011)

Logistic regression is a useful way of describing the relationship between one or more

independent variables (e.g., age and sex) and a binary response variable, expressed as a

probability, that has only two possible values, such as death ("dead" or "not dead")

,Wikipedia (2011)

If F(Z)=Y is the random variable (dichotomous), it is then assumed that Yi takes on the

values 0 or 1, where 0 denotes non-occurrence of the event in question and 1 denotes

occurrence of the event in question (Maddala, 1983). If X1, -------------------, Xp are the

characteristics to be related to occurrence of this outcome, then the Logistic model specifies

35

that the conditional probability of event (that is, that Y = 1) given the values of X1, -----------

--------, Xp is as follows.

P(Y) = 1 / [1 + exp – (α - ∑βiXi)]

In order to linearize the right hand side, a Logit transformation is applied by taking

logarithm of both sides. The logarithmic transformation stabilizes the variance if the

standard deviation in the original scales varies directly as the mean. This results into:

Logit P(Y) = α + ∑βiXi + μi

Where:

Yi = 1 if success (respondent uses pre-emergence wheat herbicides)

0 if failure (respondent does not use pre-emergence wheat herbicides)

α = Constant term

βi’s = Logistic coefficients for the independent variables

μi = Error term

Xi’s = Independent variables such that:

X1 = Age of the respondent in years – categorical variable.

X2 = Level of education of the respondent – categorical variable.

X3 = Household size: number of persons in the household of the respondent – continuous

variable.1

X4 = Extension services – categorical variable.

X5 = Farmers association – categorical variable.

X6 = Total land size for wheat – continuous variable.

1 The variables captured are grouped into continuous and categorical variables .Categorical variables will take a discrete value, while the continuous variables will project a continuity within a given interval.. The variables so captured were easier to obtain, and provided no ambiguity as far as data analysis is concerned.

36

X7 = Nature of land holding – categorical variable.

X8 = Wheat market information – categorical variable.

X9 = Income – continuous variable.

X10 = Credit accessibility - Binary variable.

X11 = Influence from other farmers – categorical variable.

Continuous and Categorical Variables

The continuous variables take any numerical value in a real integral, when properly

measured while categorical variables take a numerical of one or zero. They are discussed as

follows:

Age –x1

Age was measured in years and categorized under four categories namely; under 25

years, between 25 and 45 years, between 46 and 60 years and over 60 years. It was

expected that age of a respondent can positively influence one’s use of pre-emergence

wheat herbicides because with age comes experience as well as readiness and

willingness to adopt new production technologies. The expected sign here was positive.

Education Level-x2

This variable was used to establish the level of formal education of a respondent. Four

categories were considered namely: nursery / primary level, secondary level, tertiary

level and those without formal education. Respondents were asked to indicate their

levels of education from the categories given. In the case where a respondent had no

formal education, it was expected that he / she may not adequately use of pre-emergence

wheat herbicides as opposed to those who had formal education. A positive sign was

37

expected for this variable. Education was hypothesized to have a positive influence on

use of pre-emergence herbicides.

Household Size-x3

This variable referred to the number of persons in a household and it was measured

quantitatively. Respondents were asked to indicate the number of persons in their

household. It was expected that a respondent’s household size would influence their use

of pre-emergence wheat herbicides depending on the financial responsibilities that the

household size puts on the respondent. The expected sign was indeterminate and

therefore neutral.

Extension Services-x4

Farmers who access extension services from relevant agents have access to information

on products available to them. This in turn has an impact on use of pre-emergence wheat

herbicides. Activities associated with extension services such as agricultural trainings,

field days and field demonstrations were also expected to have an impact on a farmer’s

use of pre-emergence wheat herbicides. This is a categorical variable which was to take

a value of one if farmer visited by an extension officer or attended trainings, field days

and field demonstrations (Xi=1) and zero otherwise (Xi = 0). The expected sign for this

variable was positive.

38

Farmer’s Association-x5

Membership to a farmer association is a key component that leads a farmer to attain

useful information on farming. Such associations which cater solely for the farmers are

like the Cereal Growers Association (C.G.A). Thus logic infers that membership to such

associations will lead to increased use of pre-emergence wheat herbicides among

farmers in Uasin Gishu. For this variable, the expected sign was positive.

Total land Size for Wheat-x6

This reflects the number of hectares a farmer reserves for wheat. Total land size was

expected to have an effect on individual farmer’s use of pre-emergence wheat

herbicides. The larger the land size the greater the use. The expected sign for land size

was positive.

Nature of Land Holding-x7

The nature of land holding, whether it is leased or owned privately influenced the use of

pre-emergence wheat herbicides. Those who own land, consequently have an enhanced

purchasing power unlike those who lease. Coding for lease took zero (Xi=0) and for

owned farms took one (Xi = 1). In this case, the expected sign was indeterminate and

therefore neutral.

Wheat Market Information-x8

Increased chances of a farmer having suitable information relating to the market

influences use of wheat agrochemicals in that information on the market for wheat will

39

influence a farmer’s decision to plant wheat or not. By extension, that will also influence

one’s use of pre-emergence wheat herbicides. Market information determines whether

farmers have access to information or not. Market distance has a value when purchasing

agrochemicals. The nearer the supply point for agrochemicals to a farmer the cheaper it

will be for a farmer to use the pre-emergence wheat herbicides. Market distance has a

bearing operation cost which in turn influences use of pre-emergence wheat herbicides.

The expected sign for this variable was positive.

Income-x9

The hypothesis was that the more income a farmer has from all sources available for him

the more chance he has for increasing use for wheat agrochemical. This level of income

was coded as a continuous variable. A positive sign was expected for income.

Credit Accessibility-x10

Accessibility to credit boosts one’s financial status and hence a farmer who has access to

credit is more likely to use pre-emergence wheat herbicides as opposed to a farmer

without credit access. Some extension agents offer seasonal credit to farmers. Farmers

who access this type of credit are given products at the beginning of the wheat season

and are expected to repay after harvest. For this variable, the expected sign was positive.

Influence from other Farmers-x11

Often, farmers base their decisions on the success that they have witnessed from other

farmers. This is especially so in the case of agrochemicals. A farmer who has witnessed

40

successful use of a particular agrochemical on another farm is more likely to use that

agrochemical than one who has not witnessed such success. This variable measured

whether a respondent’s decision to use pre-emergence wheat herbicides is influenced by

other farmers or not. The expected sign here was indeterminate and thus neutral.

3.2 Data Types and Sources

Primary data was mainly used in the study. Primary data was gathered from the farmers

through survey. This included data on household socio-economic characteristics such as age,

education level and household size. Other data collected from the field included farmer’s

average annual income, land size, land tenure system, membership to a farmers association,

influence from other farmers, credit accessibility, and availability of wheat market

information and presence of extension services.

3.3 Research Design

The study used a survey design. The survey aimed at collecting data from wheat farmers in

Uasin Gishu County in order to determine their current status with respect to use of wheat

agrochemicals in the County. Survey design was preferred because it is useful in exploring

existing status of two or more variables at a given point in time and the best method

available to social scientists who are interested in collecting original data for purposes of

describing a population that is too large to observe directly. Pre-emergence herbicides active

ingredients, namely; pendimethalin, chlorsulfuron tribenuron methyl and flufanecet, with

different trade names were used. Trade names like Stomp, Glean, Granstar and Tiara,

41

corresponding to the above respectively were used for ease of farmer understanding. These

are herbicides used before weeds emerge in a plantation of wheat.

3.3.1 Population, Sample size and Sampling Techniques

The target population for this study was all wheat farmers in Uasin Gishu County. The

County is one of the several counties in Kenya that grow wheat. A sample size of 164 wheat

farmers was used in the study and was determined as follows.

n = z2 p q

d2

Where:

n = the desired sample size

z = the standard normal deviate at 0.1 confidence level

p = the proportion in the target population estimated to have characteristics being measured

q = 1 – p

d = level of statistical significance set at 0.05.

The z – statistic at 90 percent confidence level is 1.282. Since there was no estimate

available of the proportion in the target population that was assumed to have the

characteristics of interest (p), 50 percent was used as recommended by Fisher et al (1983).

Therefore, p was 0.5. The level of significance, d, was 0.05. Therefore, the sample size, n,

was calculated as:

n = z2 p q

d2

42

n = (1.2822) × 0.5 × 0.5

(0.052)

n = 164

Stratified random sampling was used to pick the sample from the population .Wheat farmers

were first grouped into two strata either as large scale farmers or small scale farmers. A

systematic random sampling procedure was then used to identify farmers to be included in

the sample from each stratum. The first farmer was picked randomly and thereafter a farmer

was picked after an interval of 10 households.

3.4 Data Collection Methods

Data collection was done using a well structured questionnaire administered to the farmers.

Both open ended and closed ended questions were used in the questionnaire. A pre-test of

the questionnaire was done in two districts; Narok and Nakuru. This involved giving 15

questionnaires to wheat farmers in the two districts. This was done to ensure that the

questionnaires were interpreted in the intended manner and any corrections deemed

necessary were observed and adjustments made accordingly.

Both quantitative and qualitative data was collected for this study using well structured

questionnaires. The data was collected by the researcher with the assistance of appointed

enumerators.

3.5 Data Analysis

Data was analyzed both descriptively and inferentially. Data was analyzed descriptively

using mean, mode and standard deviation and results presented using frequency distribution

43

tables, graphs, bar charts and pie charts. This was to help in describing emerging

relationships between variables. Inferential statistics involved the use of binary logistic

regression analysis, particularly the maximum likelihood ratio method. Logistic regression

analysis was used to evaluate causality relationships between variables using the Statistical

Package for Social Scientists (SPSS).

3.6 Limitations of the Study

The limitations that were experienced during the study included the following.

a) Inadequate financial outlay and time constraints prevented the collection of data

from a larger sample and hence limiting the sample size to one hundred and sixty

four wheat farmers. The sample was, however, representative.

b) There was language barrier especially among the elderly people and without formal

education. This called for more explanations to these people on the questionnaire

details and hence spending more time than intended

c) Some of the respondents withheld information on income and household size.

d) Some questions in the questionnaire were answered inaccurately while some were

not returned and these led to elimination of data from such questionnaires from

analysis.

The number of respondents, after elimination of those whose details were not filled was 133.

These were found to be adequate for the study. To reduce these problems, a letter of

authorization from the University was acquired so as to create credibility to enumerators in

eyes of the respondents. Ministry of Agriculture personnel, local Provincial Administration

44

and local Cereals Growers Association were used to further mitigate any arising doubts from

the farmers.

45

CHAPTER FOUR

RESULTS AND DISCUSSION

4.1 Introduction

This chapter consists of empirical results and discussion of the findings. The chapter

presents the major socio-economic constraints and promotional strategies that influence the

use of wheat agrochemical in Uasin Gishu County. The chapter begins by giving the general

socio-economic characteristics of the sample followed by a detailed presentation and

discussion of empirical results.

4.2 General Socio-Economic Characteristics of the Sample

Summary statistics for the analyzed data pertaining continuous variables is given in table

4.1. The table includes summaries of results for average income, land size and household

size. The basis of the data collection was on use of pre-emergence herbicides with an aim of

cascading all findings to relate to dynamics of what happens in use of all classifications of

agrochemicals. An in-depth analysis2 shows that only 36% farmers have received trainings

on pre-emergence herbicides while the rest have not. Only 12% use pre-emergence wheat

herbicides. The summary results are presents in table 4.1 below.

2

Data was collected from all the 164 sampled farmers, but analyzed from 133 farmers from whom

questionnaires were received. Data from 33 questionnaires was not included in the analysis either because the

questionnaires were not returned or the information was inaccurate. Therefore, the response rate was 81

percent.

46

Table 4.1: Summary Statistics for Continuous Variables

__________________________________________________________________________

Variable Minimum Maximum Mean Mode Standard

Deviation

Average Income 15,000 300,000,000 10,000,000 50,000 39,329,815.726

Land Size 0.20 2000 80.938 3.00 267.989

Household Size 2 20 7.42 5 3.941

__________________________________________________________________________

Source: Author’s Survey Data, 2010

According to the results obtained (table 4.1), the average annual income of the farmers was

Kshs. 10,000,000 with a minimum of Kshs. 15,000 and a maximum of Kshs. 300,000,000.

The modal income was Kshs. 50,000 with 9 percent of respondents having a similar annual

average income. Further results on farm income are presented in figure 4.1 which shows

distribution of farm income around the mean.

The larger the amount of disposable income available for use by each farming household,

the greater the ability to purchase pre-emergence wheat herbicides and other inputs. This

will in turn lead to improved management of wheat hazards brought in by pest and diseases;

translating to increased yield per unit area of grown wheat. The figure below is a

presentation of distribution of income among the sampled households.

47

Figure 4.1 shows that 88 percent of the respondents had an annual farm income which was

below the mean annual income and only 12 percent of the respondents had their annual

income being above the mean annual income. The results depict a scenario where farmers

can be grouped into two categories namely small scale and large scale wheat farmers. Most

of the farmers who constituted the sample were small scale farmers and they are the ones

who had an average annual income of between Kshs. 15,000 and Kshs. 10,000,000.

Compared to the large scale farmers, who had average annual incomes exceeding Kshs.

10,000,000, the small scale farmers were observed to operate on smaller pieces of land and

that would have also contributed to their smaller income levels. Egyir (2008) noted that

households in Ghana with higher income from sale of plantain in the various season had a

higher probability of using agrochemicals. Egyir also noted that other studies agreed that a

higher disposable income led to this, noting contributions by (Abdulai et al., 2006

and;Moser et al., 2003).

Below mean income

88%

Above mean income

12%

Figure 4.1: Distribution of Income around Mean


Below mean income

88%

Above mean income

12%

Figure 4.1: Distribution of Income around Mean


48

Data on land size was analyzed and results obtained (table 4.1) showed that the least land

size amongst the sampled farmers was 0.20 hectares while the maximum was 2000 hectares.

The mean land size was 80.938 hectares and the modal land size was 3.0 hectares with 11

percent of respondents having a land size of 3 hectares. Results on land size are further

supplemented by results in figure 4.2 which show spread of land size around the mean.

Figure 4.2 shows that most of the respondents (88 percent) practiced wheat farming on land

not exceeding 80 hectares and only 12 percent of the respondents farmed on land bigger than

80 hectares. The latter group was assumed to be those producing wheat on a large scale.

Generally large tracts of land imply that wheat production involves mechanization leading to

benefits of economies of scale. Mechanization leads to better efficacy and improved

efficiency in use of agrochemicals. Therefore, land size would have an impact on the use of

From 0.2 - 80 hectares88%

From 80 - 2000 hectares

12%

Figure 4.2: Land Size Spread around Mean


49

agro-chemicals depending on the land size. Doss et al. (2001) noted in their study done in

Ghana that land was associated with adoption of new technology, because wealthier farmers

are better able to bear risks and thus are more likely to try new technologies.

With regard to household size, results (table 4.1) indicate that households which had 5

persons were leading with 17 percent. The mean household size was 7 persons. The

minimum number of persons in a household was 2 and the maximum was 20. Further results

indicated that 58 percent of respondents had household sizes of up to 7 persons while 42

percent of respondents had a household size of between 7 and 20 persons as indicated in

figure 4.3.

Generally the smaller the household size, the lower the overall household expenditure, thus

letting households to devote more resources to farming including purchase of pre-emergence

wheat herbicides. Due to lesser pressure of consumption needs per head, smaller families

tend to have more disposable income and are entirely in agreement with Idrisa et al., (2008)

who found that families with many members had little income left for spending in

production investment. The figure below depicts household sizes among sampled farmers in

the study area.

50

Data on age of respondents was analyzed and the results are as shown in table 4.2.

Table 4.2: Age of Respondents

Age bracket Frequency Percent

Under 25 years 12 9

26 – 45 years 82 62

46 – 60 years 30 23

Over 60 years 9 6

Total 133 100


0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

Per

cent

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Household Size

Figure 4.3: Number of Persons in Household


51

Results (table 4.2) show that 62 percent of the respondents were aged between 26 and 45

years. That was followed by the group of 46 – 60 years with 23 percent of the respondents,

and then less than 25 years with 9 percent and the age group that had the least number of

respondents was that of over 60 years with 6 percent of respondents. These results indicate

that wheat farming is mostly carried out by the active section of the population in Uasin

Gishu County. The results further imply that wheat farming is carried out by mature farmers

who are capable of having the basic understanding of information regarding wheat farming

and particularly the use of agro-chemicals that are required in wheat production. Idrisa et al.,

(2008) said that the age of between 30-49 years in a farming population, which was

predominant in their study implied that they are more active in their farms and are more

receptive to agricultural extension programs.

Education level of respondents was hypothesized as a variable that would influence the use

of pre-emergence wheat herbicides amongst wheat farmers. The results obtained on analysis

of data on education level are indicated in figure 4.4.

52

Figure 4.4 shows that 53 percent of respondents had attained up to O’ level, 21 percent had

college / university level of education, 16 percent up to primary school and 8 percent had A’

level education. Only 2 percent of respondents did not have any formal education. It is

therefore seen that the wheat farmers sampled in Uasin Gishu District had formal education.

That implies that the farmers were in a position to know about pre-emergence wheat

herbicides and know how to use them in their wheat farming. This is further corroborated by

16%

53%

8%

21%

2%

0%

10%

20%

30%

40%

50%

60%

Primary O' level A' level College / University

Uneducated

Education Level

Per

cent

16%

53%

8%

21%

2%

0%

10%

20%

30%

40%

50%

60%

Primary O' level A' level College / University

Uneducated

Education Level

Per

cent

Figure 4.4: Education Level of Respondents


53

Azeez et al. (2009) who emphasized that good education enabled farmers to understand the

use of improved technologies and apply it to achieve increased production.

The nature of land ownership would also impact on a farmer’s usage of pre-emergence

wheat herbicides. Data on land tenure system of respondents was analyzed and the results

are indicated in Figure 4.5.

Figure 4.5 indicates that a majority of the respondents (92 percent) owned the land on which

they produced their wheat since they had private ownership. On the other hand, 7 percent of

respondents produced wheat on leased land while 1 percent of respondents produced wheat

on privately owned land and lease land. Therefore, with most of the farmers owning the land

on which they produced wheat, then they would have more disposable income since they are

not under pressure to meet leasehold costs. This study did not however factor in the

Private ownership

92%

Leasehold

7%

Both leasehold and privateOwnership

1%

Figure 4.5: Land Tenure System


54

opportunity cost of land as owned by farmers who did not lease their farms. This is because

for those who own land, this fact does not influence household decisions at the time of

purchasing farm inputs. Besides, while producing on privately owned land, there are no

extra production costs that would be incurred by the farmer as is the case with leased land

and therefore such costs would not constrain use of pre-emergence wheat herbicides.

Extension services would influence use of pre-emergence wheat herbicides amongst wheat

farmers depending on whether such services are available or not. Data on impact of

extension services in Uasin Gishu County was analyzed and the results obtained are as

indicated in Figure 4.6.

No

Yes

4%

96%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Per

cent

Whether receive extension services

Figure 4.6: Presence of Extension Services


55

According to results in Figure 4.6, 96 percent of the respondents reported that extension

services were available in their areas while 4 percent reported otherwise. Extension services

are important in wheat farming since they are an avenue for providing farmers with vital

information on how to maximize profits from their produce. They contribute greatly to

gaining of knowledge on technological advancement and adoption of the same so as to keep

up with the latest development in research. Majority of farmers reported presence of

extension services, which implies that it has an impact on the use of pre-emergence wheat

herbicides. Doss et al., (2001); Idrisa et al., (2009) and Azeez et al., (2009), all noted the

importance of extension services in enhancing more and better use of farm inputs. In their

conclusion, Idrisa et al., (2009) indicated that farmers should be given informal education

through extension services with a view to enhance their understanding of modern

agricultural production techniques and easy access to improved technologies to boost

agricultural production.

There are farmer associations in the wheat sector that provide wheat farmers with agro-

chemicals as well as information on how to use the agro-chemicals. This study sought to

establish whether wheat farmers in Uasin Gishu belong to any such associations and the

results obtained are indicated in Figure 4.7.

56

Results (Figure 4.7) indicated that 79 percent of respondents were members of a farmers

association while 21 percent were not. That implies that any benefits arising from such

associations with respect to pre-emergence wheat herbicides would not trickle down to non-

members and therefore would have an impact on agro-chemicals use amongst such farmers.

However, should non-member farmers interact with those who belong to such associations,

then that would have an impact on agro-chemical use. In this regard, data on whether

79%

21%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Per

cent

No Yes

Membership to a farmers association

Figure 4.7: Membership to a Farmers Association


57

farmers used pre-emergence wheat herbicides as a result of influence from other farmers

was analyzed and the results are presented in Table 4.3.

Table 4.3: Influence from other Farmers

Response Frequency Percent

No 13 9

Yes 120 91

Total 133 100


Table 4.3 shows that 120 respondents (91 percent) had used agro-chemicals as a result of

influence from other farmers while 13 respondents (9 percent) had not. This implies that the

knowledge and experience gained from other farmers has an impact on a farmer’s use of

pre-emergence wheat herbicides.

Access to wheat market information would also influence a farmer’s use of pre-emergence

wheat herbicides. Data concerning accessibility to wheat market information among farmers

in Uasin Gishu County was analyzed and the results are presented in Figure 4.8.

58

According to the results (Figure 4.8), 59 percent of respondents had access to wheat market

information while 41 percent of them had no access. Accessibility to wheat market

information will allow a farmer to gain knowledge on how to improve his / her produce

(wheat) so as to be competitive and reap maximum profits. Such market information would

include information on input use and therefore it would have a positive impact in the use of

pre-emergence wheat herbicides as one of the inputs used in wheat production. Doss et al.,

(2001) also noted that information was paramount towards allowing more use of farm inputs

41%

59%

0%

10%

20%

30%

40%

50%

60%

No Yes

Access to wheat market information

Figure 4.8: Access to Wheat Market Information


59

and further noted that (Abdulai et al., 2006 and Moser et al. (2003) had agreed with this

observation.

Credit to farmers is of vital importance in production in that it helps farmers cushion their

inadequacies of income required to cater for costs incurred during the production process,

for instance cost of pre-emergence wheat herbicides. This study sought to establish whether

wheat farmers in Uasin Gishu County had access to credit from financial institutions. The

results are indicated in figure 4.9.

Results (Figure 4.9) indicate that 71 percent of respondents had access to credit from

financial institutions while 29 percent had no access to credit. That is attributed to the fact

that a majority of the respondents owned the land on which they produced their wheat, as

earlier established, thus making it easier for them to fulfill collateral requirements to access

No

29%

Yes

71%

Figure 4.9: Access to Credit


60

credit. Therefore, accessibility to credit amongst farmers in Uasin Gishu County had

positive impact on the use of pre-emergence wheat herbicides. Expansion of existing credit

programs could have beneficial effects on agricultural production of smallholders, Zeller et

al. (1997) made in their conclusion when they were studying market access by smallholder

farmers in Malawi.

4.3 Results from Logit Regression

This section presents results obtained from Logit regression as well as discussion of the

results. Table 4.4 presents a summary of the regression results.

Table 4.4: Summary of Logit Regression Results

__________________________________________________________________________

Variable Coefficient Standard Error Exp. (β)

Age 4.484 1.346 14.138

Education Level 28.241** 2.274 89.121

Household Size 0.227 0.030 1.011

Land Size 16.213 0.003 51.156

Land Tenure -5.978* 2.308 0.003

Average Income 3.154** 0.462 9.941

Extension Services 5.757** 4.288 18.146

Membership 0.420 0.706 2.104

Market Info. 5.796** 3.529 18.272

Credit Access 2.153* 3.305 7.975

Influence 1.547 3.154 4.866

Constant -6.089 2.548 0.001

-2 Log likelihood → 30.090

Cox & Snell R Square → 0.423

Nagelkerke R Square → 0.823

Omnibus test for model coefficients - Chi-square → 32.958

61

Hosmer & Lemeshow test - Model Chi-square → 3.088

Percentage correct prediction → 81.3

Sample Size → n = 133

The ** and * represent 1% and 5% levels of significance respectively.

__________________________________________________________________________


Logit regression results (table 4.4) indicated a positive and significant relation between use

of pre-emergence wheat herbicides and education level at 1 percent. A unit increase in the

level of education was observed to bring about an increase in the log of odds in favor of use

of pre-emergence chemicals by 28.241 among wheat farmers in Uasin Gishu County. The

positive relationship implies that people who are educated are more likely to use pre-

emergence wheat herbicides in wheat production than those who are not since it is expected

that the educated will know how to use the pre-emergence wheat herbicides as well as know

the benefits of using agro-chemicals and indeed any other technology in wheat production.

Therefore, they would adopt the use of pre-emergence wheat herbicides more easily than

those without formal education.

Land tenure system also influenced the use of pre-emergence wheat herbicides in Uasin

Gishu County. The relationship between use of pre-emergence wheat herbicides and land

tenure system was seen to be negative and significant at 5 percent. The negative relationship

was observed when land tenure system was leasehold. For leasehold, a unit change

decreased the log of odds in favor of use of pre-emergence wheat herbicides by 5.978

among wheat farmers in Uasin Gishu County. That implies that for those who did not own

the land on which they produced their wheat, they would probably not use pre-emergence

wheat herbicides in their production. That is attributed to the fact that farmers who produce

62

on leased land incur extra costs for leases and that curtails their production budget.

Consequently, farmers end up using inadequate portions of inputs such as agro-chemicals or

not using them at all so as to remain within their production budget. This affects the quality

of their output and production and income from their farms is not optimized. It would

therefore be necessary to encourage farmers who own land to intensify production of wheat.

That would be done through extension services campaigns in print and electronic media to

enlighten farmers on the benefits of producing on privately owned land. In the long run, that

would promote the use of pre-emergence wheat herbicides by wheat farmers. It would also

be necessary to ensure that government agencies involved with land registration make the

process of acquiring the documentation necessary for obtaining private ownership of land is

easy, accessible and timely.

Average annual income of respondents influenced the use of wheat pre-emergence wheat

herbicides. The relationship between average income per year and use of pre-emergence

wheat herbicides was positive and significant at 1 percent. A unit increase in the average

annual income brings about an increase in the log of odds in favor of use of pre-emergence

wheat herbicides by 3.154 among wheat farmers in Uasin Gishu County. A positive

relationship here implied that those wheat farmers who were able to have a larger amount of

annual farm income would increase their usage of pre-emergence wheat herbicides due to

larger capital outlays implied by larger incomes. Though the mean annual income for wheat

farmers in Uasin Gishu District was Kshs. 10,000,000, it was observed that most of the

respondents had annual income below the average figure. These were respondents engaged

63

in small scale production. Those in large scale production were able to have higher incomes

than the annual average.

Further analysis with regard to average annual income indicated that most of the income

earned by farmers within a production season was not sufficient to purchase adequate pre-

emergence wheat herbicides. Results are presented in table 4.5.

Table 4.5: Sufficiency of Income to Purchase Pre-emergence Wheat Herbicides

Response Frequency Percent

No 77 58

Yes 56 42

Total 133 100


About 58 percent of the respondents did not have sufficient income to purchase adequate

pre-emergence wheat herbicides within a season. It is therefore necessary for farmers to

maximize their production by adopting modern technologies in their production so as to

increase their income margins. In addition, they should also seek to diversify their farming

and not rely on wheat only as their income earner so as to have more avenues of generating

income. On the other hand, government should curtail wheat imports so as to boost local

production and wheat prices which would in turn ensure that farmers increase their profit

margins and by extension, their income. All these strategies will in the long run enable

64

farmers to use pre-emergence wheat herbicides due to the availability of income to cover the

costs incurred.

The presence of extension services was also seen as having an impact on the use of pre-

emergence wheat herbicides by farmers in Uasin Gishu County. Presence of extension

services and use of pre-emergence wheat herbicides had a positive and significant

association at 1 percent. A unit change in the presence of extension services brought about

an increase in the log of odds in favor of the use of pre-emergence wheat agro chemicals by

5.757 among wheat farmers in the County. This implies that extension services contribute

significantly to the use of wheat pre-emergence wheat herbicides. However, though farmers

said that there was extension services carried out in their areas, the frequency with which

extension was done was not satisfactory. It was reported that extension services were

provided once in a year for most farmers. The extension services were carried out by agents

from the Ministry of Agriculture, agro-chemical companies and non-governmental

organizations. The farmers were appreciative of the extension services noting that the

information acquired through extension was very helpful. Therefore, providers of extension

services should ensure that they increase the number of extension visits to farmers within a

production period so as to keep track of farmers’ progress and ensure that the right

procedures were followed when using agro-chemicals. This is particularly important because

new agro-chemicals keep being developed and one way to make sure that farmers use the

right agro-chemicals in the right way and amount is through extension since it is the closest

link between the farmers and research.

65

There was a positive and significant relationship at one percent between use of pre-

emergence wheat herbicides and availability of wheat market information in Uasin Gishu

County. A unit increase in the access to wheat market information brought about an increase

in the log of odds in favor of use of pre-emergence wheat herbicides by 5.796 among wheat

farmers in the County. That implies that a farmer with access to market information was

more likely to use pre-emergence wheat herbicides than one without such information.

Market information enables one to follow the latest developments in a particular industry

and be able to know what is required in the market. In so doing, farmers are able to involve

themselves in demand driven kind of production as opposed to supply driven production. A

farmer will be in a position to know what varieties of wheat he can produce in his area as

well as the agro-chemicals that best suite that variety. Thus the farmer is able to meet the

market demand and hence consumers are satisfied whereas the farmer gets to sell his

produce thereby earning income. It is therefore necessary to ensure that farmers have access

to market information. This can be effected through extension services by both government

and agro-chemical companies as well as through media and educative forums such as

seminars and workshops so as to keep farmers informed.

The use of agro-chemicals requires a farmer to have adequate finances so as to purchase the

agro-chemicals sufficient for a full production period. Since wheat farming is practiced on

large farms involving mechanized labour, a farmer needs to have sufficient income to

sustain production. Inadequate income will require the farmer to seek extra funding through

credit. This study sought to establish whether wheat farmers in Uasin Gishu County had

access to credit from financial institutions. There was a positive and significant relationship

66

at 5 percent between use of pre-emergence wheat herbicides and access to credit. A unit

increase in access to credit brought about an increase in the log of odds in favor of use of pre

emergent wheat agro-chemicals by 2.153 in the County. The implication is that farmers with

access to credit were more likely to meet the cost of pre-emergence wheat herbicides and

therefore use them in the right quantities. Farmers were of the opinion that the cost of

acquiring wheat agro-chemicals was very high and therefore there is need to enable them

meet these costs by having more accessibility to credit. This can be achieved by ensuring

that financial institutions have less costly credit that can be advanced to wheat farmers.

Further, credit access can be widened by having agro-chemical companies offer credit to

wheat farmers for acquisition of pre-emergence wheat herbicides at fair rates to the farmers.

This ensures that wheat farmers do not fail to use agro-chemicals or use inadequate portions

due to the inability to purchase them. In the long run, that promotes the use of pre-

emergence wheat herbicides.

Further promotional strategies that would elicit positive impact in the usage of wheat agro-

chemicals in Uasin Gishu County were observed in the remedies for constraints faced by

farmers in the use of wheat agro-chemicals. The major constraints identified include lack of

experience in using agro-chemicals, distant supply points, lack of equipment, health risks,

small scale production hindering machine use, extortion by middlemen, costly agro-

chemicals and equipment, poor quality of agro-chemicals, poor timing for application, lack

of knowledge on usage and poor handling of agro-chemicals. The suggested remedies for

these constraints that would enhance use of pre-emergence wheat herbicides include having

more training on the use of agro-chemicals through field-days, demonstrations and more

67

extension, good timing for application of wheat agro-chemicals, have supply points nearer to

farmers, using modern equipment and technology and government to subsidize cost of agro-

chemicals so as to have them more affordable to farmers.

From the logit regression results, six variables were significant in modeling the factors

influencing the use of pre-emergence wheat herbicides in Uasin Gishu District. These

parameters were level of education, average annual income, and presence of extension

services and availability of wheat market information which were significant at 1 percent.

The other two significant parameters were land tenure system and accessibility to credit

which were significant at 5 percent. To test the hypothesis in the study, t-test was run on the

significant parameters and promotional strategies identified. The computed t values

exceeded the tabulated t value of 1.960 for the surveyed sample indicating that the

parameters and promotional strategies identified were statistically significant at 5 percent.

Here promotional strategies are efforts by extension agents to influence behavior of target

farmers. This was best exemplified by various aspects of extension work by the agents.

Therefore, the null hypothesis that socio-economic factors do not influence use of pre-

emergence wheat herbicides and that there are no promotional strategies that would elicit

positive impact in the use of wheat pre-emergence wheat herbicides in Uasin Gishu County

were rejected.

68

CHAPTER FIVE

SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

5.1 Summary

This study was focused on identifying the factors influencing use of pre-emergence wheat

herbicides in Uasin Gishu County. To achieve this, a survey design was used to collect data.

One hundred and sixty four wheat farmers were sampled from the County. Data was

collected by means of questionnaires. However, data was analyzed for 133 respondents

whose questionnaires were returned with accurate information. Eleven variables were

considered for evaluation in determining factors influencing use of pre-emergence wheat

herbicides. Logit regression was used to analyze the data using the Statistical Package for

Social Sciences (SPSS).

The study had three specific objectives. The first and second objectives were aimed at

determining socio-economic factors influencing use of pre-emergence wheat herbicides in

Uasin Gishu County. Logit regression identified six variables as significant in explaining the

factors influencing use of pre-emergence wheat herbicides among the eleven variables

considered for evaluation. The six variables were education level, average annual income,

presence of extension services, and availability of wheat market information, land tenure

system and accessibility to credit. Five variables namely education, average annual income,

presence of extension services, availability of wheat market information and accessibility to

credit had a direct relationship with use of pre-emergence wheat herbicides while land

tenure system had an inverse relationship.

69

The third objective was to identify promotional strategies that would elicit positive impact in

the use of pre-emergence wheat herbicides in Uasin Gishu County. The promotional strategy

mainly identified was increasing the frequency of extension services. Other strategy was

having more training on the usage of pre-emergence herbicides.

5.2 Conclusions

The findings of this study indicate that education level, an example of social factor, favored

use of pre-emergence wheat herbicides. It was observed that a farmer was more likely to use

pre-emergence wheat herbicides if he / she had formal education than if he / she did not have

formal education. Thus the null hypothesis that social factors had no effect on the use of pre-

emergence wheat herbicides in Uasin Gishu County was rejected.

Land tenure system is an economic factor that influenced use pre-emergence wheat

herbicides. The association between land tenure system and use of pre-emergence wheat

herbicides was negative in the case of leasehold land tenure system. Other economic factors

influencing the use of pre-emergence wheat herbicides were average annual farm income

and access to credit. However, most of the respondents were in small scale production and

had their farm incomes below the annual average and therefore their income was not

sufficient to fund a season’s supply of wheat agro-chemicals. Credit access also influenced

use of pre-emergence wheat herbicides positively. Access to credit was particularly

important in assisting the farmers meet the cost of acquiring pre-emergence wheat

herbicides which was reported to be very high. The results led to the conclusion that use of

pre-emergence wheat herbicides in Uasin Gishu County is influenced by economic factors

70

and therefore the null hypothesis that economic factors had no effect on the use of pre-

emergence wheat herbicides in Uasin Gishu County was rejected.

It was also established in the study that there are promotional strategies that would elicit

positive impact in the usage of pre-emergence wheat herbicides in Uasin Gishu District.

Provision of extension services, a promotional factor, influenced use of pre-emergence

wheat herbicides positively. As a result, the null hypothesis that there were no promotional

strategies that would elicit positive impact in the use of pre-emergence wheat herbicides in

Uasin Gishu District was also rejected.

5.3 Recommendations

Wheat agro-chemicals are an important aspect of the inputs that are used in wheat

production. It is therefore important to ensure that the usage of these agro-chemicals is

enhanced amongst wheat farmers. In this regard, the following recommendations were

made. Firstly, farmers in Uasin Gishu County with own-land should be encouraged to do

wheat farming. In that connection also, government should ensure that the process of

acquiring the necessary documentation for private land ownership is easy. Secondly, wheat

farmers should be encouraged to maximize their farm income from wheat by adopting

modern technologies, and increase overall farm income, by diversifying their farming rather

than relying on a single crop. Government should also reduce wheat imports so as to boost

local production and thereby increase profit margins through better wheat prices which

would translate to higher farm incomes for the farmers. Thirdly, it is important for extension

agents to increase the frequency of extension visits to wheat farmers since the farmers

71

recognize the fact that the information gained from extension is very helpful. Fourthly,

awareness should be enhanced through media and educative forums such as seminars and

workshops so as to increase farmer access to wheat market information. Lastly, accessibility

to credit should be increased by having financial institutions offer affordable credit to the

farmers and also having agro-chemical companies offer credit to purchase pre-emergence

wheat herbicides and other agro-chemicals at fair rates to wheat farmers. It is also necessary

to have further research on how extension services can enhance wheat production.

5.4 Areas for Further Research

As stipulated above, there is a necessity of looking into further research on how extension

services can enhance wheat production. Various tenets of extension encompassing types of

extension and extension targeting and segmentation of farmer aspects in light of this, needs

to be looked into. For example advertisement needs to be looked at on its own merits, while

farm visits and farmer trainings need to be given more re-emphasis.

A very glaring disparity in analysis of household income came out clearly during the study.

It was found that 12% of farmers owned land areas above 80 hactares, while the rest were

considered small scale farmers and had less household income. Average income was found

to be Kshs 10,000,000 from farming accruing to households. There is need for further

research in analyzing household dynamics of the small scale farmers alone, more since their

number is expected to increase in future because of the expected population growth in

Kenya. The findings will help predict future dynamics that will affect input purchase

decisions by majority of farmers in future.

72

Seed care and seed quality also affect yield and therefore profitability of any crop. This

needs to be given weight in future studies with an aim of understanding the current status of

Seed care policies in Kenya regarding wheat and a possibility of improving this aspects.

Seed quality policies and areas of improvement need to be given more emphasis in such

studies.

73

REFERENCES

Abdulai, A.C, and C. Binder (2006). “Slash and Burn Cultivation Practice and Agricultural

Input Demand and Output Supply”. Journal of Environment and Development

Economics, 11(2), 201-220.

Agrochemical Association of Kenya, (2006), Newsletter

Amadi D.O.K and others, (2004), Evaluation of the Herbicide Monitor, Alone and in Tank-

Mixes for Weed Control in Wheat (Triticum aestivum L., 12th Regional Wheat

Workshop for Eastern, Central, and Southern Africa proceedings. Nakuru, Kenya,

22-26 November 2004.

Azeez, A.A. and Madukwe, O.M. (2010). Cocoyam production and economic status of

farming households in Abia state, South-East, Nigeria. Journal of Agriculture and

Social Science. 6, 83–86

Bryant, Rob (1999). Agrochemicals in Perspective: Analysis of the Worldwide Demand for

Agrochemical Active Ingredients. Agranova, Allan Woodburn Associates Limited.

Kenya National Bureau of Statistics (2004), Economic Survey, Ministry of Planning

and National Development,Nairobi.

Congress on Allelopathy, Tsukuba, Japan, 2002. August 26-30.

74

Czovek, P., Kiraly, I. and Paldi, E. (2006). “Comparative Analysis of Stress Tolerance in

Aegilops Accessions and Triticum Wheat varieties to Detect Different Drought

Tolerance Strategies.” Acta Agronomica Hungarica, Vol. 54, No. 1, pp. 49 - 60.

Dobson H. (2005). Final Technical Report: Promoting Adoption of Integrated Pest Management

in vegetable production. Natural Resource Institute R8341 (ZA0059)

Doss, CR, Morris, ML, 2001. “How Does Gender Affect the aAdoption of Aagricultural

Innovations? The Case of Improved Maize Technology in Ghana”. Journal of

Agricultural Economics, 25, 27–39.

Egyir, I.S., (2008) “Assessing the Factors of Adoption of Agro-chemicals by Plantain

Farmers in Ghana: Using the ASTI analytical framework”. Paper presented at the

GIM Conference, Dalhousie University, Nova Scotia Canada

Export Processing Zones Authority, (2005), Grain Production Report, Nairobi.

Feder, G., & Umali, D. L. (1993). The Adoption of Agricultural Innovations: A Review,

Technological Forecasting and Social Change, 43, 215-239.

http://dx.doi.org/10.1016/0040-1625(93)90053-A

Fisher, Laing and Stoeckel, (1983). Research Designs for Family Planning: Field Studies.

South and East Asia Research Paper. Unpublished. Bangkok, Thailand.

75

Food and Agricultural Organization Statistical Database (FAOSTAT ), (Various

Puplications). Available on: http://apps.fao.org.

Food and Agriculture Organization (FAO, 2000).Food Outlook, No. 2 (April) and No. 4

(September).

Gamba, P., C. Ngugi, H. Verkuijl, W. Mwangi and F. Kiriswa (2002). Wheat Farmers’ Seed

Management and Varietal Adoption in Kenya. Mexico, D.F.: CIMMYT, Egerton

University, Njoro, Kenya, and KARI, Nairobi.

GOK (2008). Department of Crop Production: Statistics. Ministry of Agriculture.

GOK (2008). Uasin Gishu District Development Plan 2002 – 2008: Effective Management

for Sustainable Economic Growth and Poverty Reduction. Nairobi, National

Coordination Agency for Population and Development, Ministry of Planning and

National Development.

Hassan, R.M., W. Mwangi, and D. Karanja (1993). Wheat Supply in Kenya: Production

Technologies, Sources of Inefficiency and Potential for Productivity Growth.

CIMMYT Economics Working Paper No. 93-02. Mexico D.F. CIMMYT.

Hegedos, Z., Szentpetery, Z., Kassai, K. and Jolankai, M. (2002). “Protein and Wet Gluten

Contents in Winter Wheat Grain Samples.” Acta Agronomica Hungarica, Vol. 50

No. 3, pp. 383 - 387.

76

Idrisa YL, Gwary MM ,Shehu H (2008). Analysis of food security status among farming

households In Jere Local Government of Borno State, Nigeria.

Idrisa YL, Ogunbameru, BO (2008). Farmers’ assessment of the Unified Agricultural

Extension Service in Borno State, Nigeria. Journal of Agricultural Extension (11):

106-114

International Center for Soil Fertility and Agriculture (IFDC), (2006). Report, Vol. 31, pp.

1.

Jolankai, M., Birkas, M. (2007): Global climate change impacts on crop production in

Hungary. Agriculturae Conspectus Scientificus, 72 (1): 17-20.

Jolankai P., Z. Toth , T. Kismanyoky and I. Farkas (2008). Impacts of Agrochemical

Treatments in a Winter Wheat Monoculture. Hungary, Szent Istvan University:

Institute of Crop Production.

KARI (1989). Current Situation of Wheat Production in Kenya. Nairobi, Kenya: Kenya

Agricultural Research Institute.

Kazinczi G., Beres, I. and Horvath, J. (2002). Weed-crop Interferences in Hungary. Third

World

77

Knezevic, M., Ranogajec, L., Šamota D. (2008). “Effects of Soil Tillage and Herbicides on

Weeds and Winter Wheat Yields.” Cereal Research Communications, Vol. 36, pp.

1403 - 1406.

Lawlor, D.W. (2002): Carbon and nitrogen assimilation in relation to yield: mechanisms

are the key to understanding production systems. Journal of Experimental Botany,

53: 773-787.

Logistic Regression.Hp.Available: http://en.wikipedia.org/wiki/Logistic_regression. May

29th 2010

Maddala,G.S. (1983a) Limited Dependent and Qualitative Variables in Econometrics . New

York: Cambridge University Press

Maddala,G.S. (1983b) “Methods of Estimation for Models of Markets with Bounded Price

Variation”, International Economic Review, 24(2),361-378.

Moser,C.M., and C.B.Barett (2003). The Disappointing Adoption Dynamics of a Yield

Increasing, Low External Input Technology: The Case of SRI in Madagascar. Journal

of Agricultural Systems, 76(3), 1085-1100.

78

Mutuku, A. Mwanthi and Kimani, N. Violet (1993). “Patterns of Agrochemical Handling

and Community Response in Central Kenya”. Journal of Environmental Health, Vol.

55.

NPBRC. (1974, 1984,1987,1989). Annual Reports. Njoro, Kenya: National Plant Breeding

Research Center.

Nyakundi A(2008) .Factors affecting mushroom farming in western Kenya: A case study of

Mumias Division. Unpublished,Kenyatta University.

Nyangito H., and others, (2002). Performance of Kenya’s wheat industry and prospects for

Regional Trade in Wheat Products. Nairobi, KIPPRA.

Republic of Kenya (various years).Economic Survey, Government Printers, Nairobi.

Republic of Kenya (various years).Statistical Abstracts, Government Printers, Nairobi.

Rogers, E. M. (2003) Diffusion of innovations, fifth edition. Free Press, New York, U.S.A.

Rural Finance Services in Kenya. Hp. (2008). Available: www.ruralfinancenetwork.org.

May 27th, 2008.

Szentpetery, Zs., Hegedos, Z. and Jolankai, M. (2005a). “Impact of Agrochemicals on Yield

Quality and Pesticide Residues of Winter Wheat Varieties.” Cereal Research

Communications, Vol. 33, No. 3 pp. 635 - 640.

79

Szentpetery, Zs., Kleinheincz, Cs., Szoll!si, G. and Jolankai, M. (2005b). “Effect of Nitrogen

Topdressing on Winter Wheat Yield, Quantity and Quality.” Acta Alimentaria, Vol.

34 No. 2, pp. 177 - 185.

Tanacs, L., Krisch, J., Ger!, L., Monostori, T. and Petroczi, I. M. (2008). “Effects of New Type

Herbicides and Crop Year on Gluten, Rheological and Falling Number

Characteristics of Winter Wheat Varieties.” Cereal Research Communications, Vol.

36, pp. 74 - 77.

United States Department of Agriculture (Various publications). Crop estimates.

Uttley, Nigel (2008). Agrochemical Pricing Outlook, 2008: Enigma Marketing Research.

Cheshire, United Kingdom, Westfields House 2 Woodlands Drive Goostrey.

Wikipedia, (2011). http://en.wikipedia.org/wiki/Logistic_distribution

Zeller M., Diagne A., and Mataya C. (1997). Market Access by Smallholder Farmers in

Malawi: Implications for Technology Adoption, Agricultural Productivity, and Crop

Income. Food Consumption and Nutrition Division Discussion Paper Number 35.

Food Consumption and Nutrition Division, International Food Policy Research

Institute, 1200 Seventeenth Street, N.W. Washington, D.C. 20036-3006 U.S.A.

Retrieved on Jan 19, 2012 from:

http://www.ifpri.cgiar.org/sites/default/files/pubs/divs/fcnd/dp/papers/dp35.pdf

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APPENDICES

Appendix 1: Farmers’ Survey Questionnaire

Dear Respondent,

I am a postgraduate student at Moi University undertaking an Mphil research on, “An

Analysis of Use of Pre-emergence Wheat Herbicides in Uasin Gishu County,Kenya.”

This questionnaire is aimed at collecting data for the above mentioned research. The

research is purely for academic purposes and all information provided will be strictly

confidential and will be used for the purposes of this study only. Therefore, please feel free

to respond frankly. Your co-operation shall be highly appreciated. Please respond by putting

a tick or in writing where appropriate.

Thank You.

Yours Sincerely,

William Bett.

SERIAL NUMBER ________________________________

DISTRICT ________________________________

DIVISION ________________________________

LOCATION ________________________________

DATE ________________________________

1) Age Under 25 years [ ] 25 – 45 Years [ ]

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46 – 60 years [ ] Over 60 years [ ]

2) Education level Primary level [ ] O’ level [ ] A’ level [ ] College [ ]

Uneducated [ ]

3) Household size (Number of persons in the household) ____________________________

4) Farm machinery Family owned [ ] Hired [ ]

5) Land size (In Hectares) _____________________________________________________

6) Land tenure system Private ownership [ ]

Leasehold [ ]

7) What is your average income per year? Kshs. ___________________________________

8) For how long have you been in wheat production? _______________________________

9) Do you use agrochemicals in your wheat production? Yes [ ] No [ ]

10) Is your income sufficient for the purchase of agrochemicals you require in a season?

Yes [ ] No [ ]

10) Are there any extension services done in your area? Yes [ ] No [ ]

11) How often are these extension services carried out? _____________________________

__________________________________________________________________________

12) Who carries out the extension services in your area?

Extension agents from the Ministry of Agriculture [ ]

Extension agents from agrochemical companies [ ]

Others (Please specify) _________________________________________________

____________________________________________________________________

13) During the extension visits, are there any teachings or any information given concerning

the use of agrochemicals in wheat farming? Yes [ ] No [ ]

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14) What would you say about the teachings / information given during extension visits

concerning use of agrochemicals in wheat farming?

Very helpful [ ] Fairly helpful [ ] Helpful [ ] Not helpful [ ]

15) Have you ever attended any of the activities mentioned below?

Organized training sessions Yes [ ] No [ ]

Field days Yes [ ] No [ ]

Field demonstrations Yes [ ] No [ ]

16) a.) If yes in any of the above, are any of these activities carried out on the use of pre-

emergence wheat herbicides? Yes [ ] No [ ]

b.) Have you used pre-emergence wheat herbicides? Yes [ ] No [ ]

17) In your own opinion, how would you comment on the knowledge gained from the above

mentioned activities? Very helpful [ ] Fairly helpful [ ] Helpful [ ] Not helpful [ ]

18) In your area, are you aware of the existence of any wheat farmers association?

Yes [ ] No [ ]

19) Are you member of any of the wheat farmers association? Yes [ ] No [ ]

20) Do the farmers associations carry out any awareness activities on the use of

agrochemicals in wheat farming? Yes [ ] No [ ]

21) Do the awareness activities by the farmers associations on the use of agrochemicals

positively influence your decision to use agrochemicals? Yes [ ] No [ ]

22) Do you have a ready market for your wheat in your area? Yes [ ] No [ ]

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23) Where do you sell your wheat?

Cereals board [ ] Middle-men [ ] Local market [ ] Millers [ ]

24) Is there a readily available and adequate supply of wheat agrochemicals in your area?

Yes [ ] No [ ]

25) How far do you have to travel to get to the supply point of your wheat agrochemicals?

500 M [ ] 1 Km [ ] 2 Km [ ] 4 Km [ ] 10 Km and over [ ]

26) In your opinion, how would you comment on the costs involved in your acquisition of

agrochemicals?

Very cheap [ ]Cheap [ ] Fair [ ] Expensive [ ] Very expensive [ ]

27) In your purchase of agrochemicals, do you consider the company that produces a

particular agrochemical? Yes [ ] No [ ]

28) Have you ever applied for credit from any financial institution to facilitate your budget

for wheat production? Yes [ ] No [ ]

29) Were you able to access the credit? Yes [ ] No [ ]

30) Are there any agrochemical companies in your area that offer credit facilities to wheat

farmers? Yes [ ] No [ ]

31) Have you ever applied for credit from such companies? Yes [ ] No [ ]

32) Were you able to access the credit from these companies? Yes [ ] No [ ]

33) Are there farmers you would consider prominent in wheat production in your area?

Yes [ ] No [ ]

34) Have you ever paid such prominent farmers a visit to learn from them?

Yes [ ] No [ ]

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35) Have you used any agrochemical(s) as a result of witnessing it being successfully used

by other farmers? Yes [ ] No [ ]

36) What are the major constraints in the usage of wheat agrochemicals?

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

37) What are the remedies for these constraints?

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

__________________________________________________________________________

~~~~~~~~~~~~~~~~~~~~~~~~~ END ~~~~~~~~~~~~~~~~~~

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APPENDIX 2: Tables of Wheat Production, Importation and Demand Variables

Table A1: World Market Prices of Wheat for Years 2003/04 - 2007/08

Source 2003/04 2004/05 2005/06 2006/07 2007/08

U.S hard red winter 161 154 175 212 331

U.S soft red winter 149 138 138 176 303

Argentina Trigo Pan 154 123 138 188 300

Source: GOK, 2008

Table A2: Quantities and Values of Imported Pesticides 2004/05-2006/07

Category

2004/2005 2005/2006 2006/2007

Quantity in

tons

Value in

‘000’ Kshs

Quantity in

tons

Value in

‘000’ Kshs

Quantity in

tons

Value in

‘000’ Kshs

Insecticide 2,881 2,077 2,844 2,031 2,638 2,109

Fungicide 2,031 1,113 2,361 1,506 2,638 2,109

Herbicide 1,538 650 1,311 620 1,902 698

Others 597 133 1,192 337 748 205

Total 7,047 3,973 7,708 4,494 8,071 4,740

86

Source: GOK, 2008

Table A3: Wheat Import Vis a vis Local Production

Year Imports Unit of Measure Yield/HaLocal

ProductionLocal

consumption

1990 485 (1000 MT) 2 196 595

1991 293 (1000 MT) 2 220 571

1992 259 (1000 MT) 2 200 529

1993 618 (1000 MT) 2 150 608

1994 425 (1000 MT) 2 234 659

1995 196 (1000 MT) 2 297 593

1996 365 (1000 MT) 2 288 653

1997 513 (1000 MT) 2 250 713

1998 423 (1000 MT) 2 314 738

1999 683 (1000 MT) 1 135 818

2000 806 (1000 MT) 1 105 911

2001 633 (1000 MT) 2 230 863

2002 656 (1000 MT) 2 300 856

2003 419 (1000 MT) 2 196 815

2004 474 (1000 MT) 2 197 671

2005 629 (1000 MT) 2 225 854

2006 806 (1000 MT) 2 300 956

2007 550 (1000 MT) 2 225 900

Source: United States Department of Agriculture, (2007)

87

Table A4: Wheat Yield for Selected Countries in Tons per Hectare

Year EGYPT Switzerland S.Africa Norway N.ZEALAND

1990 6 3 6 5 5

1991 6 3 6 5 5

1992 5 3 4 4 5

1993 5 4 4 5 5

1994 6 3 2 3 5

1995 5 4 3 5 5

1996 6 4 3 5 6

1997 6 4 6 4 5

1998 6 4 6 5 5

1999 6 4 8 5 6

2000 5 4 5 5 6

2001 6 4 7 4 7

2002 6 4 6 4 7

2003 6 4 5 5 7

2004 7 4 3 5 7

2005 6 4 4 5 8

2006 6 5 3 5 8

2007 6 5 5 5 8

2008 6 5 5 5 8

Source: United States Department of Agriculture, (2007)

an analysis of factors affecting use of pre-emergence wheat herbicides in uasin gishu

Documents

list of tables table

farmers table

list of figures figure

table of contents declaration

household figure

wheat production

summary statistics

data analysis