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Assessing Factors Affecting Adoption of Agricultural Technologies: TheCase of Integrated Pest Management (IPM) in Kumi District, Eastern

Uganda

Jackline Bonabana-Wabbi

Thesis submitted to the faculty of the

Virginia Polytechnic Institute and State University

in partial fulfillment of the requirements for the degree of

Master of Science

in

Agricultural and Applied Economics

Daniel B. Taylor, Chair

Valentine Kasenge

Michael Bertelsen

Anya McGuirk

November 18, 2002

Blacksburg, Virginia

Keywords:

Integrated Pest Management, Adoption, Multivariate logit, Uganda

Copyright 2002, Jackline Bonabana-Wabbi


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Assessing Factors Affecting Adoption of Agricultural Technologies:

The Case of Integrated Pest Management (IPM) in Kumi District, Eastern Uganda

Jackline Bonabana-Wabbi

(Abstract)

Improper pesticide use on crops causes adverse effects on humans, livestock, crops

and the environment. Integrated pest management practices emphasize minimal use

of pesticides in controlling pests, and their adoption by farmers can reduce the use of

pesticides and their adverse impacts. The introduction of IPM CRSP activities in

Uganda to institutionalize IPM methods focused on priority crops in the country. This

study analyzed adoption of eight IPM technologies on cowpea, sorghum and

groundnuts. Low levels of adoption (75%). Results indicate that

farmers participation in on-farm trial demonstrations, accessing agricultural

knowledge through researchers, and prior participation in pest training were

associated with increased adoption of most IPM practices. Size of farmers land

holdings did not affect IPM adoption suggesting that IPM technologies are mostly scale

neutral, implying that IPM dissemination may take place regardless of farmers scale of

operation. Farmers perception of harmful effects of chemicals did not influence

farmers decisions in regard to IPM technology adoption despite their high knowledge

of this issue, suggesting that these farmers did not consider environmental and health

impacts important factors when choosing farming practices. Farmers managerial

capabilities were not important in explaining cowpea IPM technology adoption.


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Dedication

To my dad

and

my late mom

And to Bobby


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iv

Acknowledgements

I would like to thank the United States Agency for International Development (USAID)

for funding this research through the Integrated Pest Management Collaborative

Research Support Program (IPM-CRSP), Grant Number LAG-G-00-93-00053-00. Many

thanks also go to the Office of International Research and Development at Virginia

Tech: its Director, Dr. S. K. De Datta, the management entity including Dr. Brhane

Gebrekidan, Dr Keith Moore, and Dr. Greg Luther for supporting this, and other

studies in Uganda. In addition, I am truly grateful to the IPM CRSP Uganda site chair

Dr. Mark Erbaugh at Ohio State University and the Uganda site coordinator Dr.

Samuel Kyamanywa for the opportunity to study at Virginia Tech.

I cannot say exactly how grateful I am to Prof Dan Taylor. His guidance in this study

was beyond measure. Dr. Taylor, I used to read acknowledgements by students you

have advised and always wondered how they could heap you so many endearments.

Now I know better. Your guidance is invaluable. Thank you also for providing me

facilities and various supplies that facilitated my comfortable study and stay at

Virginia Tech. In addition with Barbara, Alex and Claudia, I always had a family away

from home.

I would also like to extend my sincere thanks to Dr. Anya for reading through the last

draft and giving insightful comments. Many thanks go to Dr. Bertelsen for serving on

my committee and for providing valuable suggestions. Dr. Kasenges review of the first

three chapters was helpful in highlighting issues that would otherwise have gone un-

noticed.

Sincere thanks go to the farmers who volunteered to be interviewed. Without

sacrificing their valuable time to answer the survey questions, this study would not

have been possible. I am grateful to the field staff who assisted in collecting the data.

The District Agricultural Officer of Kumi Mr. Valdo Odeke was instrumental in making

the data collection process effective. Many thanks go to all my friends both in USA and

in Uganda. To my brothers Jacob and Josephat and sisters Joan and Dona, thank you

for encouraging me. Stella, your assistance with data entry is appreciated.


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Lastly I would like to express my deepest gratitude to my husband, Bobby, for his love,

care and patience. Bobby, your emotional support lifted me up every day, encouraged

me and gave me a reason to always look towards my goals. For these, I cannot thank

you enough.


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vi

Table of Contents

Abstract.. .ii

Dedication...iii

Acknowledgements...ivTable of Contents..vi

List of Tables..ix

List of Figures xi

CHAPTER1INTRODUCTION....1

1.1THE GENERALPROBLEM....................................................................................................... 11.1.1 IPM Interventions on Cowpea, Groundnuts and Sorghum:....................................... 51.1.2Rationale for IPM Interventions....................................................................................... 6

1.2PROBLEM STATEMENT........................................................................................................... 71.3OBJECTIVES.......................................................................................................................... 101.3.1 General Objective............................................................................................................. 101.3.2 Specific Objective ............................................................................................................. 101.4HYPOTHESIS ........................................................................................................................... 101.5SIGNIFICANCE OF THE STUDY ................................................................................................. 111.6SUMMARY OF RESEARCH METHODS.................................................................................. 111.7ORGANIZATION OFTHESIS .................................................................................................. 12

CHAPTER 2LITERATURE REVIEW ........................................................................................... 13

2.1OVERVIEW OF UGANDA ....................................................................................................... 132.1.1 Physical Characteristics ................................................................................................. 132.1.2 The Ugandan Economy................................................................................................... 132.2THE COLLABORATIVE RESEARCH SUPPORTPROGRAM (CRSP) ..................................... 192.2.1 The IPM CRSP .................................................................................................................. 192.2.2 Why IPM? .......................................................................................................................... 202.2.3 IPM in Uganda.................................................................................................................. 222.3TECHNOLOGY ADOPTION ..................................................................................................... 232.3.1 Measuring Adoption ........................................................................................................ 252.3.2 Determinants of Adoption .............................................................................................. 262.3.3 The Combined effect........................................................................................................ 33

2.4SUMMARY .............................................................................................................................. 34

CHAPTER 3METHODS............................................................................................................... 35

3.1THE STUDY AREA,SAMPLE, AND DATA COLLECTION TECHNIQUES ................................ 353.1.1 The Study Area................................................................................................................. 353.1.2 The Sample and Sampling Procedure .......................................................................... 373.1.3 Data Sources, Collection and Transformation ........................................................... 40


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3.2DATA ANALYSISTECHNIQUES AND THEIR LIMITATIONS ................................................... 433.2.1 Descriptive Analysis ........................................................................................................ 433.2.2 Crosstabs Chi-Square Tests .......................................................................................... 433.2.3 Discriminant Analysis: ................................................................................................... 433.2.4 Analysis of Variance (ANOVA) ....................................................................................... 43

3.2.5 Ordinary Least Squares (OLS) ....................................................................................... 443.2.6 Correlation Analysis ........................................................................................................ 443.2.7 Tobit, Logit and Probit Models ...................................................................................... 443.3DESCRIPTION OF CONCEPTUALMODELI .......................................................................... 463.4EMPIRICALMODELI ............................................................................................................ 503.4.1 Explanation of Variables and Apriori Expectations .................................................. 513.4.2 IPM Packages on Sorghum, Cowpea, and Groundnuts............................................ 533.4.3 Sorghum models:............................................................................................................. 543.4.4 Cowpea models ................................................................................................................ 553.4.5 Groundnut models .......................................................................................................... 563.5ATWOTIERED ANALYTICALPROCESS............................................................................... 56

3.5.1 Description of Conceptual Model II .............................................................................. 573.5.2 Empirical Model II ........................................................................................................... 583.6COLLINEARITY DIAGNOSIS................................................................................................... 593.7MODEL SELECTION............................................................................................................... 603.8ANALYTICAL SOFTWARE ....................................................................................................... 623.9SUMMARY .............................................................................................................................. 62

CHAPTER 4RESULTS................................................................................................................. 63

4.1GENERALDESCRIPTIVE ANALYSIS...................................................................................... 634.2ADOPTION OF IPMPRACTICES -UNIVARIATE ANALYSIS.................................................. 66

4.2.1 Sorghum............................................................................................................................ 674.2.2 Cowpea .............................................................................................................................. 724.2.3 Groundnut ........................................................................................................................ 764.3ADOPTION OF IPMPRACTICES -MULTIVARIATE ANALYSIS ............................................. 804.3.1 Multivariate analysis results: Sorghum ...................................................................... 804.3.2 Multivariate analysis results: Cowpea ......................................................................... 834.3.3 Multivariate analysis results: Groundnut................................................................... 864.4ADOPTION OF IPM-MODELFITTING ................................................................................ 884.4.1SORGHUM............................................................................................................................ 894.4.2 Cowpea .............................................................................................................................. 904.4.3 Groundnuts ...................................................................................................................... 91

4.5TECHNOLOGY ADOPTION INDICES....................................................................................... 924.6SUMMARY .............................................................................................................................. 95

CHAPTER 5DISCUSSION AND CONCLUSIONS ......................................................................... 96

5.1INTRODUCTION...................................................................................................................... 965.2SUMMARY OFTHESIS........................................................................................................... 965.3SUMMARY OF METHODS: .................................................................................................... 98


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5.4SUMMARY OF LEVEL OF IPMADOPTION: .......................................................................... 985.5SUMMARY OF FACTORS AFFECTING ADOPTION: ............................................................. 100a) Economic factors: ................................................................................................................ 101b) Social factors:....................................................................................................................... 102c) Management related factors: ............................................................................................. 102

d) Institutional factors: ........................................................................................................... 1035.6POLICY IMPLICATIONS AND CONCLUSIONS ...................................................................... 1035.7FUTURE RESEARCH DIRECTION ....................................................................................... 107

REFERENCES............................................................................................................................. 108

APPENDIX116

APPENDIX A: LIST OF ACRONYMS.116APPENDIX B: MAP OF UGANDA SHOWING VEGETATION (APPENDIX B1).117

MAP OF UGANDA SHOWING STUDY AREA (APPENDIX B2)....118MAP OF KUMI DISTRICT SHOWING LOCATION OF FOCAL POINTS AND

SURROUNDING AREAS (APPENDIX B3)...119APPENDIX C: INTRODUCTORY LETTER120APPENDIX D: SURVEY FORM...121APPENDIX E: SOURCES OF OFF-FARM INCOME (E1).133

SORGHUM VARIETIES GROWN IN KUMI DISTRICT (E2)...133WEED SPECIES IN SORGHUM IN THE STUDY AREA (E3)....134COLLINEARITY DIAGNOSTIC RESULTS (E4)..134

VITA...135


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List of Tables

TABLE 1.1:STATUS OF POPULATION AND FOOD AVAILABILITY IN DEVELOPING COUNTRIES ......................2

TABLE 2.1:CULTIVATED AREA OF MAJORFOOD CROPS IN UGANDA .......................................................17

TABLE 3.1:SUMMARY OF SOCIAL,DEMOGRAPHIC AND ECONOMIC CHARACTERISTICS OF KUMI DISTRICT

FARMING SYSTEM ..............................................................................................................................37

TABLE 3.2:DESCRIPTION OF VARIABLES USED IN THE ANALYSES .............................................................42

TABLE 4.1:SUMMARY STATISTICS OF CONTINUOUS VARIABLES ...............................................................65

TABLE 4.2:SUMMARY STATISTICS OF NON-CONTINUOUS VARIABLES .......................................................65

TABLE 4.3:CROPS GROWN:SUMMARY STATISTICS...................................................................................66

TABLE 4.4:PEST INCIDENCE ON SORGHUM ...............................................................................................68

TABLE 4.5:CHARACTERISTICS OF FERTILIZERADOPTERS ANDNON-ADOPTERS IN SORGHUM

PRODUCTIONCONTINUOUS VARIABLES .........................................................................................69

TABLE 4.6:CHARACTERISTICS OF FERTILIZERADOPTERS ANDNON-ADOPTERS IN SORGHUM

PRODUCTIONNON-CONTINUOUS VARIABLES .................................................................................69

TABLE 4.7:STRIGA ADOPTERS VERSUS NON- ADOPTERSCONTINUOUS VARIABLES.................................70

TABLE 4.8:STRIGA ADOPTERS VERSUS NON- ADOPTERSCOMPARISON WITH NON-CONTINUOUS

VARIABLES .........................................................................................................................................70

TABLE 4.9:CHARACTERISTICS OF CROP ROTATORS AND NON-CROP ROTATORSCONTINUOUS VARIABLES

............................................................................................................................................................71

TABLE 4.10:CHARACTERISTICS OF CROP ROTATORS AND NON-CROP ROTATORSNON-CONTINUOUS

VARIABLES .........................................................................................................................................71

TABLE 4.11:DISTRIBUTION OF COWPEA VARIETIES IN STUDY AREA ......................................................... 73

TABLE 4.12: CHARACTERISTICS OF ADOPTERS AND NON-ADOPTERS OF TIMELY PLANTING FOR COWPEA

PRODUCTIONCONTINUOUS VARIABLES...........................................................................................73

TABLE 4.13:CHARACTERISTICS OF ADOPTERS AND NON-ADOPTERS OF TIMELY PLANTING FOR COWPEA

PRODUCTIONNON-CONTINUOUS VARIABLES ...................................................................................74

TABLE 4.14:CHARACTERISTICS OF ADOPTERS AND NON-ADOPTERS OF INTERCROPPING WITH CEREALS

FOR COWPEA PRODUCTIONCONTINUOUS VARIABLES .....................................................................74

TABLE 4.15:CHARACTERISTICS OF ADOPTERS AND NON-ADOPTERS OF INTERCROPPING WITH CEREALS

FOR COWPEA PRODUCTIONCATEGORICAL VARIABLES ...................................................................75

TABLE 4.16:CHARACTERISTICS OF ADOPTERS AND NON-ADOPTERS OF IMPROVED VARIETY FOR COWPEA

PRODUCTIONCONTINUOUS VARIABLES...........................................................................................75


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TABLE 4.17:CHARACTERISTICS OF ADOPTERS AND NON-ADOPTERS OF IMPROVED VARIETY FOR COWPEA

PRODUCTIONNON-CONTINUOUS VARIABLES ...................................................................................76

TABLE 4.18:GROUNDNUT VARIETIES, AND THEIR PERFORMANCE IN THE STUDY AREA ............................77

TABLE 4.19:CHARACTERISTICS OF ADOPTERS AND NON-ADOPTERS OF CLOSE SPACING IN GROUNDNUT

PRODUCTION -NON-CONTINUOUS VARIABLES...................................................................................77

TABLE 4.20:CHARACTERISTICS OF ADOPTERS AND NON-ADOPTERS OF CLOSE SPACING IN GROUNDNUT

PRODUCTION - NON-CONTINUOUS VARIABLES ...................................................................................78

TABLE 4.21:CHARACTERISTICS OF ADOPTERS ANDNON-ADOPTERS OF IMPROVED VARIETY IN

GROUNDNUT PRODUCTIONCONTINUOUS VARIABLES ....................................................................78

TABLE 4.22:COMPARISON OF PEST OCCURRENCE AND CONTROL EFFORTS AMONG SORGHUM, COWPEA

AND GROUNDNUT CROPS ....................................................................................................................79

TABLE 4.23A.MAXIMUM LIKELIHOOD ESTIMATES FORFERTILIZER(FTIS)ADOPTION MODEL ..............81

TABLE 4.23B.MAXIMUM LIKELIHOOD ESTIMATES FORECAT(CELOSIA)ADOPTION MODEL.................82

TABLE 4.23C.MAXIMUM LIKELIHOOD ESTIMATES FORROTN(CROP ROTATION)ADOPTION MODEL....83

TABLE 4.24A.MAXIMUM LIKELIHOOD ESTIMATES FORTPCP(TIMELY PLANTING)ADOPTION MODEL . 84

TABLE 4.24B.MAXIMUM LIKELIHOOD ESTIMATES FORICCP(INTERCROPPING)ADOPTION MODEL.......85

TABLE 4.24C.MAXIMUM LIKELIHOOD ESTIMATES FORICPV(INTERCROPPING)ADOPTION MODEL ......86

TABLE 4.25A.MAXIMUM LIKELIHOOD ESTIMATES FORCLSP(CLOSE SPACING)ADOPTION MODEL ......87

TABLE 4.25B.MAXIMUM LIKELIHOOD ESTIMATES FORIGNV(IGOLA)ADOPTION MODEL .....................88

TABLE 4.26:MAXIMUM LIKELIHOOD ESTIMATES FOR THE FITTED SORGHUM IPMADOPTION MODELS..89

TABLE 4.27:SUMMARY GOODNESS-OF-FIT TESTS FOR SORGHUM MODELS ............................................... 89

TABLE 4.28.MAXIMUM LIKELIHOOD ESTIMATES FOR THE FITTED COWPEA IPMADOPTION MODELS ....90

TABLE 4.29:SUMMARY GOODNESS-OF-FIT TESTS FOR COWPEA MODELS..................................................90

TABLE 4.30:MAXIMUM LIKELIHOOD ESTIMATES FOR THE GROUNDNUT IPMADOPTION MODELS .........91

TABLE 4.31:SUMMARY GOODNESS-OF-FIT TESTS FOR GROUNDNUT MODELS ...........................................91

TABLE 4.32:DISTRIBUTION OF TECHNOLOGIES..........................................................................................92

TABLE 4.33:CUMULATIVE LOGIT MODEL ESTIMATES FORADOPTION OF ONETECH AND TWOTECH

SORGHUM TECHNOLOGIES .................................................................................................................92

TABLE 4.34:CUMULATIVE LOGIT MODEL ESTIMATES FORADOPTION OF ONETECHTWOTECH AND

THREETECHCOWPEA TECHNOLOGIES.........................................................................................93

TABLE 4.35:CUMULATIVE LOGIT MODEL ESTIMATES FORADOPTION OF ONETECH AND TWOTECH

GROUNDNUT TECHNOLOGIES ............................................................................................................94


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List of Figures

FIGURE 2.1:THE ADOPTION CURVE ............................................................................. 25

FIGURE 3.1:RESPONDENTSELECTION .......................................................................... 39

FIGURE 3.2:LOGISTIC REGRESSION CURVE FOR [0,1]RESPONSE MODELS......................... 49

FIGURE 3.3:COMPONENTS OFTHE IPMPACKAGES ON COWPEA,SORGHUM,AND

GROUNDNUTS. .................................................................................................... 54

FIGURE 3.4:MODELBUILDING PROCEDURE................................................................... 61

FIGURE 4.1:FARM INPUTACQUISITION:DISTRIBUTION OF PURCHASE DECISIONS................ 67

FIGURE 4.2:DISTRIBUTION OF SORGHUM VARIETIES AS APERCENTOFTOTALSORGHUM

ACREAGE FORTHE SAMPLE .................................................................................. 67

FIGURE 4.3:REASONS FOR COWPEA DEFOLIATION ......................................................... 72

FIGURE 4.4:PESTOCCURRENCE AND CONTROL............................................................. 79

FIGURE 5.1: LEVELS OF IPM ADOPTION.....98

FIGURE 5.2: FACTORS AFFECTING IPM TECHNOLOGY ADOPTION.....100


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Chapter 1 Introduction

1.1 The General Problem

In the past several years following the advent of the green revolution, concerted efforts to

raise food production resulted in substantial increments in global food output. Thedistribution of the increase was heavily skewed towards the more developed nations while

other regions of the globe realized less than impressive increments. Food output in Africa

lags behind the rest of the worlds production levels. In the last decade, the continents

share of world food production was a meager 3.9%. By comparison, Asia, North America

and Europe produced 47.7%, 14.8% and 12.2% respectively (Oerke, et al., 1994). By 1990,

Africas population was 615 million and was projected to increase to 813 million by the

end of 2002 (FAOSTAT, 2002), a 32% population increase in just over a decade. Moreover,

even within Africa, there are variations in these trends with some countries exhibiting

higher population growth with low agricultural development. Sub-Saharan Africas

agricultural performance has been variably called the worlds foremost global challenge

(United Nations, 1997) and as still very far behind the rest of Africa (Odulaja and Kiros,

1996 p.86). Moreover, the regions population is increasing, and is expected to account for

30% of the underdeveloped world by the year 2010 (Table1.1).

Low food production and high population growth rates inevitably lead to problems of per

capita consumption. Not surprising, the worlds most hungry people also live in the Sub-

Saharan region of the continent (von Braun, Teklu and Webb, 1999; Wilson, 2001).

According to FAO (The Food and Agricultural Organization), Sub-Saharan Africa is

expected to have 264 million chronically1 undernourished people by the year 2010 (FAO,

1996). Several demographers have studied the situation and hypothesized numerous ways

of avoiding the Malthusian trap that is likely to envelop the continent. No wonder world

organizations such as FAO, the World Bank, and IFPRI (International Food Policy and

Research Institute) have defined their core objective towards increasing food output and

improving the quality of life for the rural poor on the continent.

IFPRI suggests that the supply of food will need to rise by around 70% by the year 2020 if

the 6.5 billion people who are expected to be living in developing countries, including

Uganda, are going to be food secure (Leisinger, 1996). With only 18 years to this deadline,

1 Chronically undernourished people are defined by FAO as those whose estimated annual foodenergyintake fallsbelow that required to maintain body weight and support light activity.


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food production has remained stagnant, or declined in most of Sub-Saharan Africa (von

Braun, Teklu and Webb, 1999; McCalla, 1999). IFPRI2 already realizes that food problems

in Sub-Saharan Africa will persist well beyond 2025 (McCalla, 1999).

Table 1.1 Status of population and food availability in developing countriesNumber of chronicallyundernourished people

(Millions)

Share of regionsPopulation(Percent)

Share of totalundernourished

Population(Percent)

Region 1990-92 2010 1990-92 2010 1990-92 2010East Asia 268 123 16 6 32 18South Asia 255 200 22 12 30 29Sub-Saharan Africa 215 264 43 30 26 39Latin America and theCaribbean

64 40 15 7 8 6

Middle East and N.Africa

37 53 12 10 4 8

Total 839 680 21 12 100 100Sour ce : FAO (19 96 )

The goal of increasing food production is both externally and internally challenged by

various factors. External factors such as natural calamities like droughts and floods are

well beyond the control of the local subsistence farmer. Other broad external factors

include poor farming technologies and bad government policies. Internal factors include

pests, soil infertility, land availabilityand population increase with a subsequent rise in

food demand. Although these broad external and internal factors may not be directly

controllable, they can be influenced by human behavior. While the increase in population

will exacerbate, rather than improve the food availability situation (Wilson, 2001) -

especially if the population is malnourished - this study confines itself to perhaps what is

considered the most limiting factor to food production increase, that is insect pests and

diseases.

Africas overall crop loss due to pests stands at an astonishing 96.2% of its production

(Oerke et al., 1994).In Uganda, although literature does not provide quantitative losses, it

is estimated that crop losses due to pests are larger than those causes by drought, soil

infertility, or poor planting material (Kyamanywa, 1996). As a result, addressing the effects

of pests on Ugandas agricultural production captures a lot of attention from both local

and international bodies. Not surprisingly, a number of agricultural research efforts are

2 Appendix A contains a list of acronyms used in this thesis.


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currently underway aimed at reversing the trend of pest damage to Ugandas agricultural

produce.3 As noted above, Ugandas current 4.8% annual increase in crop production is

perhaps most attributable to the numerous agricultural research activities (Odulaja and

Kiros, 1996; United Nations, 1997) in the country, many of which encompass pest control

programs.

The aforementioned agricultural research is mainly supported through governmental, non-

governmental, private and other funding sources. Moreover, this research requires

sustained investment of resources. The success or failure of this research inevitably plays

a pivotal role in the continued investment in such programs in the country. Needless to

say, prioritization of funding for these research programs is necessary due to the limited

availability of scarce resources and competing uses of these resources for other

investments. As Alston, Norton and Pardley (1995) correctly state, research administratorsare increasingly facing sharper pressure to justify budgets and prioritize programs.

Probably the most important determinant of the effectiveness of such programs is the level

of adoption of innovations that these programs generate, and on their profitability

(Griliches, 1957; Caswell et. al, 2001). In addition, the faster the research can be

completed, the higher the turnover of benefits. Moreover, the more evident research results

are, the easier it is to justify the implementation of, and continued investment in research

programs. A common problem for many individuals and organizations is how to speed up

the rate of diffusion of a research programs innovations (Rogers, 1995). Yet, speeding up

the rate of adoption of technologies requires knowledge of the underlying factors that

influence adoption decisions. It is therefore not unexpected that economists and others

conduct studies to determine these factors.

Rogers (1995) demonstrates that adoption of technologies depends on their characteristics:

compatibility with the existing values and norms, complexity, observability, trialability,

and relative advantage. This definition pertains to technologies in a variety of disciplines,

and may be as relevant in other fields as it is in agricultural related technologies. In dairy

production in 5 states in the US for instance, El-Osta and Morehart (1999) identify age of

operator, size of operation and specialization as important factors in increasing likelihood

3 This is in spite of IFPRIs recent statement that international and national support for agricultural research is

eroding due to perceptions of agriculture as a major source ofenvironmental pollution (IFPRI, 2001) implying thatthose who fund research may shift their emphasis from agricultural research to natural resource management(Hassan, 1998; Wilson, 2001).


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of technology adoption, while research by Caswell, et al., (2001) ascertains that high levels

of farm operator education are likely to induce adoption of management technologies.

Others say lack of adequate inputs and active information4 (Feder and Slade, 1984) may

be obstacles to adoption. These studies pertain to technologies in the developing countries

but could apply to less developed countries.

In developing countries, studies related to Integrated Pest Management (IPM) have not

been as prevalent as in developed countries. This realization has led to a number of recent

studies on IPM being done in the Philippines (Tjornhom, 1995), Jamaica (Ogrodowczyk,

1999; Patterson, 1996), Ecuador (Yamagiwa, 1998) and other developing nations. The

Ecuadorian study identifies over-valuation of the local currency for pesticide importers,

lowering the cost of pesticides, subsidized credit to farmers and exemption from sales

taxes as policies that encourage pesticide use and are thereby limiting adoption of pestcontrol alternatives such as IPM. The Ecuadorian study is in agreement with the

Philippines study which found that among others, the lower the cost of pesticides, the

more likely it is for farmers to use pesticides instead of IPM technologies.

No such study has however been done for adoption of IPM technologies in Uganda. IPM is

a set of technologies that aims at reducing pest damage to crops while emphasizing non-

chemical pest control methods. In Uganda, the rejuvenation of IPM activities through the

Integrated Pest Management Collaborative Research Support Program (IPM CRSP)5 efforts

was welcomed with much enthusiasm with expected benefits including higher yields,

reduced pests and reduced expenditures on pesticides. At the time, it was anticipated that

pioneering participating farmers would act as role models and other farmers would adopt

the practices thereafter. The IPM practices were thus expected to diffuse beyond the

original area of operation.

However, several years after its introduction, the activities of the IPM CRSP program have

not been evaluated in terms of adoption. According to a recent study evaluating farmer

knowledge and awareness of IPM in Uganda, IPM CRSP program involvement and

exposure by potential adopters was thought to explain the trend of adoption of targeted

4 Active information is that obtained purposively. Unlike passive information, active information involves costs tothe information seeker in terms of time, cash or both.5 More about the IPM CRSP is explained in later sections.


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practices (Erbaugh et al., 2001). However, no attempt to quantify these assertions was

made.

Since the introduction of IPM CRSP research in Uganda, researchers have developed

several pest control strategies for important crops including cereals, legumes, vegetablesand other horticultural crops. In Ugandas agricultural production arena, cereal and

legume crops are of major importance. In Eastern Uganda, three such crops include

sorghum (Sorghum bicolor), groundnut (Arachis hypogaea) and cowpea (Vigna unguiculata).

Their priority status is due to both acreage planted and their nutrition content.

Groundnuts and Cowpea are the second and third most important legume food crops in

Uganda after beans (IPM CRSP Annual Report, 2001), Sorghum is among the most

important cereal crops ranked third to maize and millet in providing for the carbohydrate

needs of the Ugandan diet (FAOSTAT, 2002). Current statistics (FAOSTAT, 2002) estimate64,000ha, 208,000ha and 282,000ha as national acreage for cowpea, groundnuts and

sorghum while production levels are estimated at 64,000 Mt, 146,000 Mt and 423,000 Mt

respectively. These crops are often said to be food-security crops because of their drought

resistance. However, these crops are not without their share of problems. Pest attacks on

these crops calls for control strategies. A brief review of pest control activities and the

rationale for IPM intervention on the three crops is given below.

1.1.1 IPM Interventions on Cowpea, Groundnuts and Sorghum:

The IPM CRSP field monitoring of 1996 revealed high pest levels on cowpea in easternUganda. Major insect pests on cowpea identified included blister beetles (Epicauta spp.),

aphids (A. craccivora Koch), pod-borers (Maruca testularis) and thrips (Megalurothrips

sjostedti) and leafhoppers (IPM CRSP Annual Report, 1996). Aphids (A. craccivora) cause

damage by sucking plant sap and damage pods by forming honeydew deposits. The pod-

borer (M. testularis) is reportedly worst during the rainy season. In Kumi district, pest

damage contributes to 24-48% of the total variation in cowpea grain yield with thrips ( M.

sjostedti) accounting for the greatest damage (Karungi et al., 1999). In addition, cowpea is

one of the crops that are consistently sprayed by farmers probably because pesticideapplication has a significant effect on limiting the severity of diseases (Adipala et al, 1999).

By 1995, 92% of cowpea farmers in Kumi were using insecticides as their main pest

control strategy (IPM CRSP Annual Report, 1996). And, as an IPM practice, farmers are

increasingly planting with the first sign of rains to enable the cowpea crop to escape

damaging populations of certain pests by harvesting before peak pest populations.


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Strigais considered a major pest of Sorghum (Parker, 1980) and was found to be the most

serious weed affecting sorghum yields in Uganda. In Kumi district, the parasitic weed has

a widespread distribution. Ninety seven percent of sampled farmers involved in the 1996

IPM CRSP participatory assessment were able to identify it (Erbaugh et al, 2001). Thegravity of the Striga problem is thought to stem from the fact that the seed evolved in such

a way that it only germinates naturally when in the vicinity of a sorghum root (Parker,

1980). Furthermore, the seed is very small and can persist for many years in the soil.

Two diseases are of major consequence to groundnut production in Kumi namely

groundnut rosette (GRV) and cercospora leafspot (Cercospora arachidicola) which

frequently lead to total crop failures. Major groundnut pests include aphids (A. craccivora),

thrips (M. sjostedti) and leaf miners (Aroarema modeicella) (IPM CRSP Annual Report,

2001).

1.1.2 Rationale for IPM Interventions

Disease and insect infestation on many crops occurs simultaneously. Sorghum,

groundnuts and cowpeas are no exceptions. Therefore controlling insects and diseases

simultaneously necessarily calls for an integrated approach, which IPM packages address.

For cowpea, a number of studies revealed that cowpea production could be improved andincreased through well-defined IPM systems (Isubikalu, Erbaugh and Semana, 1997;

Jackai et al., 1985). Among the most promising technologies developed by IITA are

varieties resistant to Striga,6 aphids (A. craccivora Koch), and bruchids (Callosobruchusmaculates), improved storage techniques using solar drying, and the use of botanical

pesticides in the field and in storage (CGIAR, 2002). Current IPM CRSP practices

disseminated to farmers in Uganda for control of Cowpea insect pests have included close

spacing, and strategic insecticide application. In addition, well-timed defoliation, and

intercropping with Sorghum are encouraged.

In regard to Sorghum, although farmers are generally less likely to use pesticides on

cereals, heavy weed infestations cause considerable crop loss and therefore provide an

incentive for weed control. It is suggested in literature (Parker, 1980 and IPM CRSP

6 In West Africa, Striga is a problem on cowpea (CGIAR, 2002)


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Annual Report, 1999) that crop rotation is one of the most potent methods for reducing

striga. Planting a more rapidly growing cultivar that suppresses and shades weeds,

growing resistant varieties and good irrigation, in addition to high nitrogen levels are

thought to reduce the weed. However, because of the nature of the weed, several studies

have demonstrated that no purely cultural control system is fully effective thereby callingfor an integrated approach. IPM CRSP measures on strigain Uganda include intercropping

Sorghum and silver leaf desmodium, a legume that suppresses striga weeds; planting

resistant genotypes (such as Sekedo); crop rotations (Cotton/Sorghum/Cowpea); and

recommended fertilizer application (40-8-8 kg of NPK/ha). Other measures include seed

coating with herbicide, two weedings and other cultural practices involving modified

planting dates and crop management practices (IPM CRSP Annual Reports, 1998-2000).

On Groundnuts, practices developed by researchers include: early planting, manipulationof plant density, planting a resistant variety, and minimum spray schedule of 2-3

Dimethoate or 1-2 sprays of Dimethoate and Dithane M45. The crop is also often

intercropped with maize as a control strategy (IPM CRSP Annual Reports, 1998-2000).

IPM CRSP researchers hypothesize that these alternative methods (IPM activities) can be

disseminated to more farmers through establishing field schools and through interacting

with other partners such as non-governmental organizations (IPM CRSP Annual Report,

1999). The effectiveness of this dissemination approach however, greatly depends on how

farmers perceive IPM CRSP activities. Moreover, these alternative methods require farmers

to abandon age-old methods involving the use of conventional non-farm inputs including

reducing their dependence on reliable pesticides for control.

1.2 Problem Statement

In many countries, including Uganda, non-farm chemical inputs play a large role in

agricultural production, especially because of the need to increase production.

Unfortunately the use of some of these inputs is associated with degradation of the

environment, and health of living organisms, including humans. Mitigating the effects of

these necessary evils therefore became a focus for many research programs. Alternative

methods of production that reduce negative effects of chemicals and yet maintain at least

the same level of production are continuously sought. Alternatives such as cultural


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methods, organic, and biological control methods are increasingly emphasized to improve

land productivity and control of pests.

One such alternative is Integrated Pest Management. As mentioned earlier, the IPM

approach emphasizes the use of non-chemical inputs and judicious use of chemical inputsin production to reduce pest incidence on crops, thereby increasing farmers yields and

returns. This approach is recommended globally for increasing agricultural production

without upsetting the balance of nature while controlling pests. Although some literature

indicates uncertainty of IPM profitability (Abara and Singh, 1993), or profitability of some,

but not all parts of the total package (Smith, Wetzstein and Douce, 1987), several studies

demonstrate that benefits accrue from IPM. These include its effect on reducing pesticide

residue on crops, lessening the negative impacts of pesticides on the environment and

humans, lowering production costs, and increased pest management effectiveness.

A linear programming model developed in 1982 on a national level indicated that

widespread adoption of farming practices without the use of pesticides (and fertilizers)

would increase net farm incomes in the US (Olson, Langley and Heady, 1982). In an

evaluation of pest management characteristics Smith, Wetzstein and Douce (1987) showed

that different characteristics of pest management affected net benefits in Georgia, USA.

They specifically found that proper spraying7 and using beneficial insects significantly

increased net returns. In Virginia, Mullen, Norton and Reaves (1997) quantified annual

environmental returns of approximately $844,000 from implementation of the Virginia

peanut IPM program, while on Jamaican vegetable crops, IPM led to increase in profits on

all the three crops studied by Ogrodowczyk (1999). Furthermore, because of the high

potential IPM has in the Near East8, IPM implementation was stated as a necessary

requirement to improve crop protection in vegetable cultivation (Alebeek and Lenteren,

1992). In addition, in a study on the environmental and economic consequences of IPM in

Viticulture (Fernandez-Cornejo, 1996), IPM adoption was found to positively affect both

yields and profits in grape production.

7 They define proper spraying as (number of sprays after the threshold - number of improper sprays)/total numberof sprays.8 Countries in the region referred to as The Near East include Turkey, Jordan, Egypt, Tunisia and Morocco


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Similar studies in Uganda show potential benefits from IPM adoption. Bashaasha et al.,

(2000) establish benefits ranging between Shs 101,378 and Shs 255,9089 from adopting

IPM systems in the control ofstriga in sorghum fields in Kumi district. In another study

assessing IPM systems in Groundnuts, Bonabana et al., (2001) established a Marginal

Rate of Return of 870% in adopting a disease resistant variety as an IPM strategy forcontrol of major groundnut insect pests in the same district. These net benefits translate

into profits for farmers. Therefore IPM has been demonstrated to be potentially profitable

and in such cases society can benefit from its adoption.

Economic theory suggests that practices proved to be profitable are likely to be adopted by

producers. Yet according to Giliomee (1994), IPM, a profitable venture, has not been widely

adopted. For instance, only 4% of all US farms are said to practice true IPM (Ehler and

Bottrell, 2000). This pattern is also found in small-scale farming communities in Jamaica(Patterson, 1996). In Uganda, only a few farmers use complete IPM packages (Kyamanywa,

1996). Moreover, extent and level of IPM use in Uganda is still largely unknown. As such,

several questions arise: What is the current level of adoption of IPM? How can adoption be

accelerated? What factors influence IPM adoption?

There is a general lack of understanding of the factors affecting the adoption of IPM

technologies in farming systems in Uganda. Moreover, as noted above, although economic

analyses show potential benefits, no attempt has been made to ascertain reasons for the

observed levels of adoption. Only with a thorough understanding of these factors can

further insight be developed concerning strategies to promote IPM.

Most of those who attempt to explain the adoption of IPM in Uganda base their assertions

on subjective beliefs about the conventional practices of smallholder farmers, and not on

analytical evidence. Therefore, an empirical description regarding factors affecting

adoption is necessary. Several underlying factors may be the cause of the observed level of

adoption. For example a complex set of interactions or conditions involving the technology

(IPM), the institution (administration), the potential/targeted adopter (the farmer) or the

general setting in which the technology is introduced may affect adoption. As Diebel,

Taylor and Batie (1993) state, these factors may either be barriers or enhancers of

adoption. It is therefore imperative to study these conditions farmers social

9 1US$=1760UGShs (May 2001)


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characteristics, economic setting, institutional factors and managerial aspects to identify

the conditions that are affecting IPM adoption.

1.3 Objectives

1.3.1 General Objective

This studys objective is to establish whether social, economic, management and

institutional factors that affect adoption of IPM technologies on three major crops in Kumi,

Eastern Uganda.

1.3.2 Specific Objective

The specific objectives of the study include the following:

(i) To establish factors that affect adoption of IPM practices specific on cowpea,

groundnuts and sorghum.

(ii) To estimate the relative contribution of each factor in affecting adoption, thereby

establishing the factors that have the greatest impact on technology adoption.

(iii) To establish the level of adoption of eight IPM technologies in Kumi

Achieving the above objectives will be a major step towards designing a system that can

encourage adoption in the study area and up-scaling the adoption pattern to other

geographical areas with similar agro-ecological characteristics.

1.4 Hypothesis

The following hypotheses will be tested:

(i) Cost of a technology negatively influences its adoption while per capita farm income

positively influences technology adoption.

(ii) Farm size and education level of farmers positively influence technology adoption.

(iii) Adoption is negatively influenced by length of farming experience, farmers age and

household labor.

(iv) There is no significant difference in IPM adoption between men and women.


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1.5 Significance of the study

By pointing out the factors that influence IPM technology adoption, this study will provide

guidance to the IPM administrators and researchers for enhancing the programs

effectiveness. The added knowledge on which factors have the greatest influence on IPM

adoption will help administrators make more informed decisions on how to promote IPM

adoption.

Another benefit from the research will be provision of an explanation of the current state of

technologies used by farmers. Moreover, since IPM involves a variety of practices that are

specific to individual crops, measuring its adoption on various crops may provide a strong

case for increasing investment in various IPM research.

Also because of the importance of cowpea, sorghum and groundnut in the Eastern region,it is envisioned that technological spillovers are likely outside of this study area. IPM

adoption on these three crops outside the study area could be projected.

In addition, this study will provide a basis for gauging how policy changes may affect

farmers. Policy issues that constrain or enhance the provision of inputs that are required

to carry out IPM practices have a direct effect on how IPM farmers react to them. The

results will provide useful information to enhance the success of the IPM CRSP project,

and indeed any other related program that attempts to introduce practices for adoption in

settings that are similar to those in this study area. Results of this study will thus have

implications well beyond the confines of the study area.

Finally, in Uganda the IPM CRSP is an externally funded project whose continued support

is dependent on the effectiveness of the program. Therefore, for continued funding, the

IPM CRSP must demonstrate benefits. Yet these benefits do not accrue if farmers do not

adopt the practices. A crucial step therefore seems to be to identify the forces that enhance

IPM adoption. This thesis aims to fulfill this important task.

1.6 Summary of Research Methods

Survey data were collected from a random sample of farmers in the study area. Using

statistical methods yield differences between adopters and non-adopters are obtained. A


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multivariate logit analysis identifies factors and their relative importance in explaining

adoption of eight IPM technologies.

1.7 Organization of ThesisThe thesis is organized into five main chapters. Chapter 1 has presented an introduction

to the problem - the main thrust of the study, a delineation of underlying assumptions and

objectives of the study. Chapter 2 addresses the general theory and description of the

agricultural system in Uganda. Chapter 3 provides the methods of data collection and data

sources; a description of the study area, the sampling and analysis techniques, and

develops a conceptual framework used to analyze the empirical data, while Chapter 4

comprises the empirical results of the study and discussion. The final chapter gives a

summary, policy implications and conclusions of the study.


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Chapter 2 Literature Review

This chapter describes Ugandas economy with major emphasis on the agriculture sector

and IPM activities in the country. It also examines relevant literature on technology

adoption: trends, the process of adoption, measurement of adoption, and factors affecting

adoption.

2.1 Overview of Uganda

2.1.1 Physical Characteristics

Uganda is a landlocked country located between 1.450N 29.940E and 4.250N 35.010E in

the Great Lakes region in Sub-Saharan Africa. The country has substantial natural

resources, including fertile soils, regular rainfall, and sizeable mineral deposits of copper

and cobalt. It has five major lakes, two major rivers and several seasonal and non-

seasonal rivers, swamps and wetlands providing a variety of fish species all year round.

The natural vegetation is mainly savanna grassland, woodland, bush land and tropical

high forest. See Appendix B1 for a map of geographical features of Uganda. Uganda has a

tropical climate, with two rainy seasons from December to February, and from June to

August, providing two crop-growing seasons. There are slight variations in rainfall from

one region to another. Over the last several years, the mean annual rainfall was 750mm in

the North East and 1,500mm in the high rainfall areas of the shores of Lake Victoria,

around the highlands in the East and southwestern region. Average temperatures of 210C

(70F) have prevailed in the last decade. The tropical climate with fertile soils, regular

rainfall and favorable temperatures enable production of a diversity of crops and livestock

(EIU, 2001).

2.1.2 The Ugandan Economy

Uganda has had wide fluctuations in its economic performance. In the late 1960s political

instability caused by a dictatorial government and state-run intervention in almost all

sectors of the economy destroyed the countrys physical infrastructure and many

economic and social amenities. By the early 1980s, Uganda had become one of the

poorest countries in the world. At that time, education and health systems broke down,


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human indices10 were poor, and the civil service had been destroyed by low wages and

poor morale. Consequently, real GDP per capita was at its lowest (EIU, 2001).

Around the mid 1980s a new government came into power. This government, with the help

of donors mainly the IMF and World Bank embarked on an economic recovery programaimed at reducing poverty by rehabilitating infrastructure (economic, social and

institutional). Further, the recovery program encompassed civil service reform, revised

investment and incentive structures, and made a rapid move to a market-determined

exchange rate thereby giving the country a robust economic performance (EIU, 2001).

From the mid 1990s, an economic downturn set in again. Prior to this period, specifically

around 1994, Uganda had attained a GDP growth of 11.5%. By the end of 1996 real GDP

growth had dropped to 7%. Although this was one of the highest in the Sub-Saharanregion at the time (UN, 1997), it still represents a significant drop. This downturn has

continued to the present. In 1999 GDP was estimated at US $6.3 billion but by 2000, it

had dropped to US $5.9 billion and to US $5.7 billion by the end of 2001 (World Bank,

2002). Export of goods has been declining from US $639.2 millionin 1996 to US $390.8

million in 2000, and remaining constant in 2001 (World Bank, 2002). The significant

decline in the 2000 exports was attributed to a drought in the third quarter of 2000 that

resulted in a low level of coffee production (from 236,200 tons in 1999 to 186,000 tons in

2000). Considering that coffee accounts for over 40% of the countrys export receipts, the

poor harvest severely depressed export receipts. In addition, coffee prices are falling. This

decline in coffee production is attributed to excess production (internationally) that

currently outstrips demand. The EIU (2001) estimated Ugandas earnings from coffee fell

from US $162 million in 2000 to US $149 million in 2001.

In 2002 coffee export earnings continue to fall due to depressed prices. However,

predictions of a more robust economic performance indicate that real GDP growth may

increase to 6.6% in 2002 due to increased output in other agricultural activities: cotton,

tobacco, fish, maize and flowers. In addition non-agricultural sector development, which is

largely funded by donor inflows, is increasingly supporting growth (EIU, 2001).

10 Human indices include infant mortality rate, maternal mortality rates, birth weight, average life expectancy,literacy rates and percentages below/above poverty line.


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Ugandas terms of trade continue to deteriorate. In addition, the total outstanding debt is

increasing. While the total debt was estimated at US $3.48 billion in 1999 (World Bank,

2000), it had reached US $3.7 billion by 2000 (EIU, 2001). It is probably because of these

deteriorating terms of trade that Uganda was the first country to benefit from the Heavily

Indebted Poor Countries (HIPC) Initiative of the World Bank given that it was faced with aserious debt problem (World Bank, 2000). Under the initiative, the country would be able

to reduce its external debt by 20% of the net present value (Uganda, 1998a) and to redirect

resources to priority poverty reduction efforts. HIPC relief in 2002 is expected to reduce

debt-service payments to US $106 million (EIU, 2001). Moreover, reports (Uganda, 1998a)

show that Ugandas poverty reduction strategy is very effective, which has prompted the

World Bank to shift emphasis from project-based assistance to direct budgetary support

for the governments poverty eradication plan.

Ugandas current (mid-2002) population of 23.9 million (US Bureau of the Census, 2002)

is increasing, expected to reach 28 million by 2010. In 1991, the total population was 16.7

million and by 1999, it had reached 21.5 million. Between the period 1969 and 1980,

Ugandas population grew at an average rate of 2.7% per annum and was expected to grow

even faster. However, the period 1980-1991 saw a decline in the growth rate to 2.5% per

annum. The current annual population growth rate of 2.9% partially outstrips the labor

force, which is lagging behind at a 2.7% annual increase. If this trend continues, it might

suggest an increase on stress caused by dependant populations on the working

population. However, Ugandas urban population (12.5% in 1995) as a percentage of the

total population was about the lowest in Sub-Saharan Africa and other low-income

countries. This could present a higher opportunity for success and effectiveness of rural

based programs (CIA, 2000).

It is not possible to talk about Ugandas economy without mentioning the countrys

agricultural sector. Agriculture accounts for the biggest proportion of the countrys GDP.

Its performance closely predicts the economys overall behavior. Agriculture has been

referred to as the backbone of the Ugandan economy. It is not surprising that the

devastating effects of the 1997 El Nino weather phenomenon on Ugandas agricultural

output affected the whole economy. Agriculture provides food for domestic consumption,

raw materials for local industries, is the major source of export earnings, and employment.

In 1990, approximately 80% of the countrys total work force was employed in agriculture.

Ugandas principal exports are coffee, fish and fish products, tea, cotton and tobacco. In


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1999, the country generated US $549 million in exports (FoB) from agricultural trade.

Owing to the great contribution of this sector, more will be mentioned in the next section.

The second most important economic sector in Uganda is the service industry (including

but not limited to tourism, construction, hotels, and transportation). The service industryprovides critical support to the other sectors of the economy. This sector employs about

13% of the total work force. In a span of 10 years, its contribution to the economy has

been increasing from 28.7% in 1987, to 30.3% in 1992, and to 34.2% in 1997 (Uganda,

1998a). In 2001, the service industry accounted for over 70% of the value added to

manufactured goods in the country (Uganda Export Promotion Board, 2002).

During the same time frame, the share of the manufacturing sector (the third most

important sector) in the total national GDP steadily rose. In 1987, it contributed 4.9%, and6.1% and 9.0% to GDP in 1992 and 1997 respectively. The manufacturing sector employs

about 6% of the total work force in Uganda. This sector is, however, heavily dependent on

imports of materials. Therefore increases in prices of imports hinder growth.

2.1.2.1 The Uganda Agricultural Sector

As mentioned in section 2.1.2.0 above, the agricultural sector makes the largest

contribution to Ugandas economy. In 1989 it contributed 56.8% of the national GDP, a

percentage that has, however, been declining since. In 1999 agricultures share of GDP

was 41.9%, which represents a drop of over 14% in just a decade. This is despite its

increasing annual growth rate from 4.0% in 1990 to 4.6% in 1996. This is confirmation

that the value of agricultural production has declined both in absolute terms and in

relation to other sectors (Uganda, 1998a).

Of the countrys total area of approximately 236,046 sq. km, land area accounts for over

199,710sq. km of which 25% is arable, 9% is under permanent crops, another 9% under

permanent pasture and the rest under other uses (roads, buildings and other

infrastructure). Available information (World Bank, 1993) suggests that only 30% of the

total cultivable area is under use. In 1990, an estimated 4.6 million ha were cultivated,

and of this, 36% was under Coffee, Banana, Tea, and Sugar, while cereal crops like maize,

millet, and sorghum took up about 23%. Table 2.1 shows selected 1981 to 2001 figures of

land area under various food crops in the country.


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The amount of cultivated area currently fluctuates around 3.6 million ha from its high of

5.5 million ha in 1978. The majority of farm production (80%) is carried out on an average

of one - two hectare farms. Cultivated area per farm shows a substantial increase in farm

size in the central and southwestern region.

Table 2.1: Cultivated Area of Major Food Crops in Uganda ('000 ha)

Crop 1981 1986 1989 1990 1992 1994 1996 1998 2000 2001

Millet 293 341 374 373 396 412 400 401 384 389

Maize 260 321 566 401 438 563 584 616 629 652

Sorghum 170 207 245 240 250 260 271 280 280 282

Rice 12 18 73 39 50 55 58 64 72 76

Wheat 4 5 4 2 5 5 5 5 7 8

Sweet potato 33 407 300 413 442 473 516 544 555 572

Irish potato 24 19 35 32 37 44 53 60 68 73Cassava 309 361 495 412 362 320 335 356 401 390

Beans 289 396 431 495 536 574 615 645 699 731

Field peas 18 17 27 24 26 28 29 31 29 36

Cowpeas 40 49 46 49 49 53 56 60 64 64

Groundnut 110 176 217 186 184 189 195 200 199 208

Pigeon peas 54 66 67 62 62 67 71 74 78 78

Soybeans 5 11 20 37 59 68 76 80 106 127

Sesame seed (Simsim) 70 70 133 124 143 158 172 179 194 203

Sour ce : Th e Wor ld Ba nk (19 93 ), FAOSTAT (20 02 )

More than 70% of the farms are primarily crop-production oriented. In the western areas,

over 90% of crop production farms are in monocrop stands while in the other regions

mixed cropping systems predominate (World Bank, 1993). Labor is primarily from family

sources. During peak seasons like land preparation, weeding and harvesting, hired labor

is used especially in the central region. In the northeastern region, labor-sharing

arrangements are common while in the north, communal labor is widely used.

Food crop production increased over the last decade in almost all crop categories with the

highest increase noticeable in cereal crops (FAOSTAT, 2002). In the same period, the

livestock sector experienced moderate increases and accounted for 17% of agricultural

GDP. More than 90 percent of agricultural output is consumed domestically.


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Generally, cash crop production has experienced wide fluctuations. Coffee yields have

been low and declining over the past fifteen years. Recently there has been some increase

in cash crop production (mainly cotton). In January August 2000, tea output increased

by 22% compared with the same period in 1999 (CIA, 2000). In 2001, tea production

experienced a 12% reduction. Coffee production increased from 196,800 tons in 1998 to198,000 tons in 1999, but dropped to 186,000 tons (EIU, 2001). Cottons increase was

from 45,100 tons in 1998 to 46,000 tons in 1999.

Part of the increase in crop production was prompted by the high urban demand for food

(World Bank, 1993), favorable government policies (McCalla, 1999; CIA, 2000; EIU, 2001)

and the expansion in cultivated area for food crops (Odulaja and Kiros, 1996; also

illustrated in Table 2.1). Sound government policies, including the continued investment

in the rehabilitation of infrastructure, improved incentives for production and exports, andreduced inflation also led to boosting of production. Incentives for production and export

included subsidizing producers of export crops (CIA, 2000).

2.1.2.2 The Ugandan Agricultural Research and Extension Network

Numerous agricultural research activities on major crops in the country are one of the

biggest contributing factors to Ugandas increase in agricultural production. Uganda has

had a long tradition of crop research. Agricultural research began in 1908, the major focus

then, being the improvement of production of major export crops (such as cotton and

coffee) to increase Ugandas share of these crops in the international market (Uganda,

1988). Progressively, research focus shifted, and grain crops such as beans and maize

were introduced to the research arena. The establishment of the National Agricultural

Research Organization (NARO) in 1992 was aimed at increasing the amount of research on

all major crops in the country (Kyamanywa, 1996).

Prior to the establishment of NARO, agricultural research activities were scattered and

uncoordinated in three ministries: the Ministries of Agriculture and Forestry, Animal

Industry and Fisheries, and Regional Cooperation (Uganda, 1988). However, with the

launching of the Rehabilitation and Development Plan 1987-1999, which aimed at a rapid

recovery of the agricultural sector and the improvement and stabilization of its

contribution to the GDP, the need for organized research to contribute more effectively

and efficiently to development became even more urgent (Uganda, 1988, p.1). As such,

NARO was formed to act as a catalyst in the development process of Uganda. Its aim was


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to improve the efficiency of the utilization of resources allocated to research and enhance

research activities in the agricultural sector. In carrying out these duties, NAROs board

was expected to pay special attention to obtaining, through government and other

appropriate sources, financial and other resources required for the implementation of the

National Agricultural Research (NAR) strategy and plans. These resources are obtainablefrom donors, the international community, research agencies and the public.

Donor sources of funding are important to many developing countries, in part because of

the component of foreign exchange that they represent (Uganda, 1988). In the agricultural

sector, under the umbrella of NARO, a number of donor sources were identified. The

extent of investment by each varies depending on the scope of the activity and the progress

of the sector involved. The World Bank and other donors reacted positively to Ugandas

economic reform effort and mounted an expanded level of donor support (World Bank,2000).

Clearly, Ugandas agricultural research stands to benefit from this expanded support.

Uganda currently is a member of a number of local, regional and international agricultural

organizations. These provide support to agricultural research in many forms. Some are

purely donors while many still are in collaborative agreements and/or partnerships

between the major funding body and the host countrys private and public institutions.

2.2 The Collaborative Research Support Program (CRSP)

2.2.1 The IPM CRSP

Collaborative Research Support Programs were created by the United States Agency for

International Development (USAID) and the Board for International Food and Agriculture

Development (BIFAD) as a long-term mechanism to focus capabilities of US Land Grant

Colleges to carry out the international food and agricultural research mandate of the US

Government (IPM CRSP, 2001). In September 1993, the IPM CRSP was initiated under the

International Development and Food Assistance Act of 1975 (IPM CRSP, 2001), funded by

USAID and participating universities. In the USA, participating institutions include Ohio

State, Purdue, University of Georgia, Penn State, Montana State, USDA Vegetable lab and

Virginia Tech. Virginia Tech serves as the Management Entity for the IPM CRSP for the

program.


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The IPM CRSP engages in research, education/training and information exchange through

collaborative partnerships among US and developing country institutions. These

developing countries include Bangladesh and Philippines in Asia, Albania in Eastern

Europe, Jamaica in the Caribbean, Ecuador and Guatemala in Latin America, and Mali

and Uganda in Africa. As some of its major objectives the IPM CRSP seeks to evaluateappropriate participatory IPM approaches, describe technical factors affecting IPM and

identify and describe the social, economic and institutional factors affecting pest

management. In doing these, the IPM CRSP supports research activities in the host

countries. In Uganda, Makerere University and National Agricultural Research

Organization (NARO) are the participating institutions.

2.2.2 Why IPM?

The concept of Integrated Pest management (IPM) was first conceived after World War II

when it was determined that a control system was required to check overuse or abuse of

pesticides used to control major pests of cotton in the USA. It required a compatible

control strategy, which was a mix of biological and reduced chemical control tactics. In

1972, IPM was formulated into national policy and under US president Jimmy Carter; an

interagency coordinating committee was formed in 1979 to ensure development and

implementation of IPM practices (Ehler and Bottrell, 2000).

The focus of IPM research is to reduce pesticide usage on crops while maintaining a high

level of pest control. In general, IPM calls for a much greater reliance on non-chemicalapproaches to pest management (IFPRI, 1998) while maintaining agricultural production

and preserving profitability (Mullen, Norton and Reaves 1997). In doing this, IPM

encourages strategies that include greater dependence on biological approaches, cultural

approaches and judicious use of some pesticides. A broader definition of IPM is that given

by Wightman (1998):

IPM consists of management activities carried out by farmers that maintain

the intensity of potential pests at levels below which they become pests, withoutendangering the productivity and profitability of the farming system as a whole,

the health of the farm family and its livestock and the quality of the adjacent

and down stream environments. (Wightman, IFPRI homepage, 1998).


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Pests have been known to attack crops virtually at every stage of crop development: at pre-

germination, budding, flowering, harvest and in post harvest/storage thereby leaving the

crop with no "breathing space." This necessarily calls for pest control. Various methods of

pest control may be employed and can be categorized into two broad groups: Chemical and

non-chemical - each with its advantages and disadvantages.

The range of non-chemical options is diverse, including biological control, cultural control,

plant host resistance, sanitation and genetic transformations. Biological control, the use of

natural enemies of pests and entomopathogens is somewhat limited in its applicability and

its application for subsistence level farming although the potential for expanding its use is

great (Jackai, et al., 1985; Pimentel, 1986).

Chemical means have a number of benefits like ease in application (although notnecessarily safe application), effectiveness and fast action on target pests. However, their

disadvantages, especially in interfering with the ecosystem, are well documented.

Cultural methods include manipulation of planting dates and cropping patterns, such as

crop diversity and crop rotation. These methods achieve their pest control abilities from

having one or more crops in the rotational sequence that are resistant to a key pest. For

weed suppression, the success of rotation systems appears to be based on the use of crop

sequences that create varying patterns of resource competition, soil disturbance, and

mechanical damage to provide an unstable and frequently inhospitable environment that

prevents the proliferation of a particular weed species, (Liebman and Dyck, 1993).

Rotations offer an opportunity to increase production, either through direct yield increases

or through reductions in some of the inputs required for the present or next crop. Greater

benefits are usually obtained by rotating two distinctly unrelated crops. Crop diversity

makes the environment less favorable to certain pests while manipulation of planting time

avoids reduction in yields caused by pests. In addition, cultural controls are far less

ecologically disruptive than the standard chemical control practices.

However, cultural methods are often labor intensive (Pimentel, 1986). Considering that

most subsistence farms use family labor, one might infer that this should not be a

problem. However, with the fast paced life that is expected in the near future, and the

subsequent value of time, these two resources: time and labor will become constraints to

cultural control means. Furthermore in subsistence production systems, family labor is


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often in short supply at times such as sowing, weeding and harvesting. In addition, these

methods may have added risks. For instance, in a bid to control the known pests, altering

planting time may create a more favorable environment to more destructive pests. Also

planting time manipulations may be constrained by climatic changes. Moreover the

effectiveness of these cultural methods is highly unpredictable (Pimentel, 1986).

In general, each method (biological, cultural or chemical) may contribute to pest

suppression. However, according to Jackai et al., (1985), no one method provides

satisfactory results. Hence, an integrated approach that avoids the use of a single control

tactic is necessary. In effect when several methods are employed, the amount of each

component (biological, cultural, chemical), including the use of pesticides in the package

may be reduced - this is the basic principle in Integrated Pest Management programs.

2.2.3 IPM in Uganda

In many developing countries, IPM systems and practices have been pursued for over two

decades. In Uganda, early IPM practices were focused on coffee and cotton. This was

probably because of these crops importance as major cash crops and foreign exchange

earners for the country and hence the urgent need to protect them from devastating yield

loss due to pests. Post harvest systems were also developed under these early Uganda IPM

efforts by various agricultural research institutes in the country. Both cultural and

chemical methods were used to control pest populations on these crops. The system was

based on a careful analysis of pest populations and pest patterns and determining asuitable strategy for their control. However, the period of political and civil strife saw the

collapse of this otherwise effective IPM system.

However, this was not the end of IPM efforts in Uganda. Kyamanywa (1996) mentions that

efforts to rejuvenate IPM were pursued in 1994 when under funding from the IPM CRSP,

the Uganda IPM Network was formed. Its initial focus was directed towards raising

knowledge and awareness of fundamental IPM concepts. Subsequently, efforts to develop

pest management alternatives for priority pests with an added emphasis on environmentalquality were incorporated and more aspects of agricultural production were considered.

The new IPM CRSP crop focus expanded to include key food crops many of which were

grain crops - Beans, Maize, Cowpeas, Sorghum and Groundnut. Other additions to IPM

CRSP trials in Uganda included disease and pest control strategies on two high-value


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horticultural crops: tomatoes and potatoes. Mold incidence on stored maize and

groundnuts and coffee wilt incidence are currently being investigated. Among the crops the

IPM CRSP has had active programs on a long-term basis include sorghum, groundnuts

and cowpeas. Thus this study focuses on these crops.

2.3 Technology Adoption

Various authors define the term technology in a variety of ways. Rogers (1995) uses the

words technology and innovation synonymously and defines technology as the design for

instrumental action that reduces the uncertainty in the cause-effect relationship involved

in achieving a desired outcome.

A more meaningful definition may be that a technology is a set of new ideas. New ideasare associated with some degree of uncertainty and hence a lack of predictability on their

outcome. For a technology to impact on the economic system, blending into the normal

routine of the intended economic system without upsetting the systems state of affairs is

required. This entails overcoming the uncertainty associated with the new technologies. It

therefore comes as no surprise that several studies set out to establish what these factors

are, and how they can be eliminated (if constraints) or promoted (if enhancers) to achieve

technology adoption.

Perhaps a clearer definition of the term technology can be obtained from the work by

Enos and Park (1988), who, in their study of adoption of imported technology, define

technology as the general knowledge or information that permits some tasks to be

accomplished, some service rendered, or some products manufactured (p.9). Abara and

Singh (1993) explain that it is the actual application of that knowledge that would be

termed technology. Although in the Enos and Park (1988) study, the focus was non-

agricultural, this definition fits agricultural technologies too. From their definition, it is

clear that technology is aimed at easing work of the entity to which it applies. Most

technologies are therefore consequently termed labor-saving, time-saving, capital-saving

or energy-saving and so forth. To economists this implies saving on resources that are

scarce.

Adoption is an outcome of a decision to accept a given innovation. Feder, Just and

Zilberman (1985) while quoting Rogers earlier work of 1962 define adoption as a mental


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process an individual passes from first hearing about an innovation to final utilization

p.256. Much scholarly interest on adoption falls in two categories: rate of adoption, and

intensity of adoption. It is usually necessary to distinguish between these two concepts as

they often have different policy implications. Rate of adoption, the relative speed with

which farmers adopt an innovation, has as one of its pillars, the element of time. On theother hand, intensity of adoption refers to the level of use of a given technology in any time

period.

Clearly, a technology that is being adopted has an edge over conventional practices.

Usually, a technological innovation encompasses at least some degree of benefit for its

potential adopters (Rogers, 1995). In this study, a technology, as it relates to IPM, is a set

of practices (new or old) integrated into a package that aims to control specific pests on

select crops in a manner that is proven more effective than the conventional means.

Several stages precede adoption. Awareness of a need is generally perceived as a first step

in adoption process (Rogers, 1983). The other stages are: Interest, Evaluation, Acceptance,

Trial, and finally, Adoption (Lionberger, 1960). The Lionberger analysis also notes that

these stages occur as a continuous sequence of events, actions and influences that

intervene between initial knowledge about an idea, product or practice, and the actual

adoption of it. However, not all decisions involve a clear-cut sequence. In fact most recent

literature suggests that these stages may occur concurrently and some may/not occur in

adoption decision processes.

According to Cameron (1999) the dynamic process of adoption involves learning about a

technology over time. In fact many innovations require a lengthy period often of many

years from the time they become available to the time they are widely adopted (Lionberger,

1960; Rogers, 1995; Enos and Park, 1988). The average time between initial information

and final adoption varies considerably by person, place and practice. Alston, Norton and

Pardley (1995) demonstrate that the time after the initial investment in research through

the generation of pre-technology knowledge up to maximum adoption by producers

involves many long, variable and uncertain lags.

The literature on this subject (Griliches, 1957; Lionberger, 1960; Rogers, 1983; Alston,

Norton and Pardley, 1995), describes the process of adoption as taking on a logistic

nature. It increases with time (as the stock of knowledge increases), reaches a maximum


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level, and later decreases as the technology depreciates or becomes obsolete. Fig 2.1 below

shows the shape that most adoption processes take.

Figure 2.1: The adoption curve Source: Alston, Norton and Pardley (1995)

The research stage may take up to 5 years and the development stage another five years.

However, this is a generalization. Some adoption processes are shorter or longer than the

model shown above. Underlying conditions may shorten or lengthen this period.

2.3.1 Measuring Adoption

Although studies by Mullen, Norton and Reaves (1997), suggest that adoption of IPM is

usually a matter of degree, this is not to state that the measurement of adoption is simple.

In fact, in his study, Nowak, (1996) did agree that measuring the adoption of IPM can be

more complex than it sounds: At first glance, it appears to be nothing more than a

question of whether a grower is or is not using a specific practice. Yet this simplistic view

quickly changes as one begins to assess how it is being used, where it is being used, and

the appropriateness of that use relative to actual pest conditions (Nowak, 1996, p. 99).

Much more work is needed in refining methods and in compiling the data needed to

credibly measure and monitor IPM adoption.

The rate of adoption is usually measured by the length of time required for a certain

percentage of members of a system to adopt an innovation. Extent of adoption on the other

hand is measured from the number of technologies being adopted and the number of

10

30

Adoption ProcessResearch and

Development lag

Adoptionofnew

Technology

Time

(Years)


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producers adopting them. The current study focuses on the extent of adoption and the

factors affecting it.

Depending on the technology being investigated, various parameters may be employed to

measure adoption. Measurements also depend on whether they are qualitative orquantitative. For instance in the study investigating the adoption of improved seed and

fertilizer in Tanzania, Nkonya, Schroeder and Norman (1997) estimated the intensity of

adoption by examining the area planted to improved seed and the area receiving fertilizer.

For another study that investigated the adoption o

technology adoption facto

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