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
Aug 04, 2015
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.
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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
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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
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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
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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
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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
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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.
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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.
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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
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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
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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
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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.
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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
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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.
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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
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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
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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:
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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.
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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.
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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
E, all other factors.
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
E, all other factors.
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
Source: Author’s Survey Data, 2010
Below mean income
88%
Above mean income
12%
Figure 4.1: Distribution of Income around Mean
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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.
__________________________________________________________________________
Source: Author’s Survey Data, 2010
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
Source: Author’s Survey Data, 2010
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
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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 [ ]
81
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 [ ]
83
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)