Impact of Best Practice Hub (Bph) on Vegetable

IMPACT OF BEST PRACTICE HUB (BPH) ON VEGETABLE

PRODUCTION AMONG YOUTH FARMERS IN TANZANIA

Philipo Joseph Lukumay

A thesis submitted to the Faculty of the University of Delaware in partial fulfillment of the requirements for the degree of Master of Science in Agricultural and Resource Economics

Spring 2019

IMPACT OF BEST PRACTICE HUB (BPH) ON VEGETABLE

PRODUCTION AMONG YOUTH FARMERS IN TANZANIA

Philipo Joseph Lukumay

Approved: ______Leah H. Palm-Forster, Ph.D. Assistant Professor in charge of thesis on behalf of the Advisory Committee

Approved: ______Thomas W. Ilvento, Ph.D. Chair of the Department of Applied Economics and Statistics

Approved: ______Mark W. Rieger, Ph.D. Dean of the College of Agriculture and Natural Resources

Approved: ______Douglas J. Doren, Ph.D. Interim Vice Provost for Graduate and Professional Education ACKNOWLEDGMENTS

I am grateful to my supervisors Dr. Leah H. Palm-Forster and Dr. Justus Ochieng for the opportunity they granted me to learn from their intellectual expertise and experience in the field of agricultural economics. I got all the necessary academic support and guidance throughout my research process. I am also indebted to Jim and Marcia Borel for funding my studies through the Borel Global Fellowship program and for their warm moral support and encouragement during their frequent and lovely visits during my time at the University of Delaware. I deeply recognize and acknowledge academic support from Dr. Kelly Davidson and Dr. Ahsanuzzaman. I also acknowledge support from the World Vegetable Center –Eastern and Southern Africa for funding part of my research, giving me access to resources and hosting me during my thesis writing period. My accomplishments couldn’t have been possible without social, financial and intellectual support from both the University of Delaware, the Borel Global Fellowship, and the World Vegetable Center-Eastern and Southern Africa. Many thanks to my first mentor in the field of agricultural economics Dr. Victor-Afari-Sefa who proposed I apply for the fellowship and made sure that I succeed academically. I also thank my beloved wife Luciana Ismaely and son Lameck Philipo Lukumay for their patience during the whole time I have been out and busy. I appreciate the support from my parents Joseph Parmet Lukumay and Anna John Mollel for their warm encouragement in time of difficulties. Through the contributions from everyone, I was able to reach an extra mile in my career

iii

TABLE OF CONTENTS

LIST OF TABLES ...... vi LIST OF FIGURES ...... vii ABSTRACT ...... viii

Chapter

1 INTRODUCTION ...... 1

1.1 Research Problem ...... 1 1.2 General Objective ...... 4

1.2.1 Specific Objectives ...... 5

1.3 Organization of the Thesis ...... 5

2 LITERATURE REVIEW ...... 6

2.1 Vegetable Farming and the Problem of Synthetic Pesticide Use in SSA 6 2.2 Integrated Pest Management (IPM) ...... 7 2.3 Conceptual Framework and Hypotheses ...... 9

2.3.1 Conceptual Framework ...... 9

2.4 Research Hypotheses ...... 12

3 RESEARCH METHODOLOGY...... 13

3.1 BPH Program Description ...... 13 3.2 Placement of the BPH Program ...... 13 3.3 Sample Selection ...... 15 3.4 Data Collection ...... 16 3.5 Variable Selection ...... 16 3.6 Empirical Model ...... 20

3.6.1 Propensity Score Matching (PSM) ...... 21

4 RESULTS AND DISCUSSION ...... 27

4.1 Descriptive Statistics ...... 27

4.1.1 Summary Statistics Results for Outcome Variables ...... 28 4.1.2 Summary Statistics Results for Synthetic Pesticide Use and IPM ...... 31 4.1.3 Fruit Vegetables versus Leafy Vegetables ...... 33

4.2 Factors Influencing Participation into BPH Training Program ...... 36 4.3 Impact of BPH Training Program ...... 38

4.3.1 Testing for Bias Resulting from Observables ...... 38 4.3.2 Impact of the BPH Training Program ...... 42 4.3.3 Sensitivity Analysis ...... 46

5 CONCLUSION AND RECOMMENDATION ...... 49

REFERENCES ...... 52

Appendix

A MATCHING BALANCE ON OBSERVABLE CHARACTERISTICS .... 61 B IMPACT OF BPH ON DIRECT VERSUS INDIRECT ...... 64 C PERMISSION TO USE SECONDARY DATA ...... 65 D QUESTIONNAIRE: WORLDVEG-VINESA PROJECT ...... 66

LIST OF TABLES

Table 1: Hypotheses ...... 12

Table 2: Frequency and percentages of responses based on six protection gears farmers use in project location ...... 19

Table 3: The list of selected observable covariates and outcome variables used in the model...... 20

Table 4: Summary statistics for selected socioeconomics, institutional, farm characteristics and outcome variables ...... 30

Table 5: Summary statistics for vegetable yield (t/ha), use of synthetic pesticide and IPM ...... 32

Table 6: Summary statistics by vegetable categories ...... 35

Table 7: Factors influencing participation of vegetable farmers in the BPH training program ...... 37

Table 8: Estimation of the impact of BPH training program on direct participants .... 43

Table 9: Estimation of the impact of BPH training program on indirect participants . 45

LIST OF FIGURES

Figure 1: Conceptual diagram ...... 11

Figure 2: Map of Tanzania and part of Arumeru district showing project and control villages. Karangai and Maweni are recently formed villages within formerly large Kikwe village which later became Kikwe ward. Manyire and Kwa-Ugoro are not part of the study...... 14

Figure 3: Distribution of estimated propensity scores to test common support for direct participants’ farmers and control farmers ...... 40

Figure 4: Kernel density distribution to test balance between direct participants farmers and control farmers ...... 40

Figure 5: Distribution of estimated propensity scores to test common support for indirect participants’ farmers and control farmers ...... 41

Figure 6: Kernel density distribution to test balance between indirect participants’ farmers and control farmers ...... 41

vii

ABSTRACT

Sub-Saharan Africa (SSA) is experiencing an increase in the use of synthetic pesticide in a manner that escalates concerns over rising economic, public health, and environmental costs. Promotion of Integrated Pest Management (IPM) through Best

Practice Hubs (BPHs) is hypothesized to encourage farmers to use synthetic pesticide more judiciously along with non-chemical pest management practices such as pest resistant seeds, crop rotation, soil improvement, field hygiene, mulching etc. Vegetable farmers are expected to reduce the quantity of pesticide used, reduce pesticide expenditures, boost profits and reduce farmers’ vulnerability to health risks. To test these hypotheses, this study analyzes the impact of a BPH training program on the reduction of pesticide expenditures, increase in vegetable profits, and reduction of health risks using cross-sectional data collected from 441 youth vegetable farmers; 81 direct trained participants, 170 indirectly trained participants, and 190 control farmers in Tanzania. Propensity score matching models are employed to analyze the impact of

BPH training on pesticide use, yield, profits and farmers’ health risks. Two matching procedures – kernel matching (KM) and one-to-one nearest neighbor matching (NNM)

– are used to control for biases resulting from observables which could render results unreliable. Results suggest that direct trained farmers significantly reduced expenditures on synthetic pesticide, increased vegetable yield, vegetable profit, and had reduced vulnerability to health risks. Estimates of the average treatment effect indicate that synthetic pesticide expenditures were reduced by 51%, vegetable profits increased by 72%, and vulnerability to health risks reduced by 35%. No significant impact is found for indirect training done by trained farmers. This means that indirect training

viii through farmer-to-farmer interaction or learning through observation have little impact despite its widespread use in scaling improved technologies in most of the programs targeting smallholder farmers. Therefore, this study recommends promotion of direct training of farmers in training programs rather than indirect training when enough financial resources are available. Future research is needed to explore the best approach to improve indirect training outcomes.

Keywords: Best Practice Hub, integrated pest management, propensity score matching synthetic pesticide, Tanzania

Chapter 1

INTRODUCTION

1.1 Research Problem

Increase in food demand, thriving economic growth, and rising awareness about the importance of dietary diversification are the major causes of expansion in commercial vegetable farming in Sub-Saharan Africa (SSA) (Chauvin, Mulangu and

Porto 2012; OECD 2016). Nevertheless, surging crop pest pressure threatens production, and with the lack of integrated skills to control pests, small-scale farmers are forced to rely heavily on synthetic pesticide to reduce crop losses and maintain their livelihood (Williamson, Ball and Pretty 2008; Jepson et al. 2014; Christiaensen 2017).

Green revolution programs, agricultural commercialization, agro-inputs dealers, and farmers’ perceptions about good farming practices are key factors leading to the widespread use of improved inputs in SSA, including synthetic pesticide (Kelly,

Adesina and Gordon 2003; Schreinemachers and Tipraqsa 2012; Fan et al. 2015;

Riwthong et al. 2017; Jallow et al. 2017; Sheahan, Barrett and Goldvale 2017).

Despite the fact that synthetic pesticide prevents crop losses and boosts agriculture production, there exists negative effects to humans and non-target species when used irresponsibly at a large scale (Pimentel and Lehman 1993; Carson 2002;

Pimentel 2005). Irresponsible use of synthetic pesticide at the farm-level involves extensive application onto farms without considering quantity, frequency of application and environmental compliance issues like proper disposal of containers, proper handling and storage, and without wearing protective gear (Ngowi et al. 2007; Mrema et al. 2017; Mengistie, Mol and Oosterveer 2017). The resulting accumulation of

1 toxicants and prolonged/routine exposure to harmful chemicals from synthetic pesticide can lead to chronic health conditions and environmental pollution (Alavanja 2009).

Generally, there is an increase in costs related to use of synthetic pesticide at a global scale, billions of dollars in the United States (US) and Europe (Pimentel 2005; Calvert

2016). Similarly in SSA, the current estimates of potential cost of illnesses related to synthetic pesticide use amount to US$ 90 billion between 2005 and 2020, and this estimate does not include the cost of pest resistance or pollution to soil and water bodies

(United Nations Environment Program 2017). In addition, nearly 99% of global human death linked to synthetic pesticide use occurs in developing countries despite the fact that these countries consume only 25% of global pesticides produced, suggesting high rates of irresponsible use (Ncube et al. 2011).

Agriculture is among the leading sectors in the usage of synthetic pesticide in developing countries, including Tanzania. Considering poor farming practices which involve extensive application of synthetic pesticide for a long period of time and with limited use of protective gear, small-scale farmers are the primary and vulnerable victims of health risks linked to accumulation of harmful toxicants from synthetic pesticide (Sibanda et al. 2000; Ngowi et al. 2007; Damalas and Koutroubas 2016;

Mengistie et al. 2017). At the same time, there is an increase in pesticide expenditures which contribute to shrinking farm profit due to high production costs (Williamson et al. 2008). In addition, health risks linked to routine exposure to harmful chemicals from synthetic pesticide are increasing since the majority of small scale farmers do not use protective gear while spraying or handling synthetic pesticide (Sheahan et al. 2017).

Empirical studies confirm that farmers in northern Tanzania rarely wear protective

2 clothing while spraying synthetic pesticide and have been experiencing health issues such as neurological and skin problems among others due to routine pesticide application (Ngowi et al. 2007; Lekei, Ngowi and London 2016).

Irresponsible use of synthetic pesticide can result from lack of knowledge about alternative and easily available pests control techniques as well as farmers’ socioeconomic characteristics such as low income and low education level. Farmers with low education can hardly enroll into trainings and apply poor farming practices whereas low income might hinder access to improved inputs like pest resistant seed varieties which could reduce use of synthetic pesticide (Mengistie et al. 2017). Several studies recommend establishment of capacity building programs to promote IPM and equip farmers with knowledge about alternative pest control measures (Ngowi et al.

2007; Mengistie et al. 2017). Empirical studies conducted in other regions support these recommendations and show that there is a decrease in pesticide use and improvement in pesticide handling among farmers as a result of IPM adoption, which consequently improves farmer income (Sanglestsawai, Rejesus and Yorobe 2015; Sharma and Peshin

2016).

The Best Practice Hub (BPH) model is a capacity building approach established in four countries in SSA, including Tanzania, to promote the use of Integrated Pest

Management (IPM) and enable farmers to reduce their dependence on synthetic pesticide. Using the Farmer Field School (FFS) approach, BPH was specifically established in Tanzania, Ethiopia, Malawi and Mozambique between 2014 and 2017.

The main purpose is to influence farmers to complement synthetic pesticide with safer pest control alternatives such as use of improved seed varieties, field hygiene, crop

3 rotation, planting of barrier crops, and using insect traps, and if synthetic pesticide is to be used, then it must be used judiciously. Therefore, synthetic pesticide is still part of the IPM equation but used only occasionally as a last backup when all other approaches fail.

Despite the fact that there have been many capacity building programs like BPH in Sub-Saharan African and particularly in Tanzania, limited empirical studies have assessed the impact of training programs like BPHs, see for example the study by Davis et.al. (2012) that assessed the impact of the training program on overall agricultural productivity and poverty alleviation while Larsen and Lilleør (2014) assessed the impact of the training program on food security and poverty alleviation. No studies have assessed the impact of a capacity building program like BPH specifically on reducing synthetic pesticide expenditures in vegetable farming, on increasing vegetable profitability, and on reducing health risks linked to routine exposure of farmers to synthetic pesticide. Additionally, little is also known about who participates in the BPH program in Tanzania. This thesis seeks to fill the literature gap by using the cross- sectional data collected from a survey that reached 441 vegetable farmers in Tanzania to estimate the impact of the BPH training program on pesticide use.

1.2 General Objective

The general objective of this thesis is to assess the impact of the BPH training program on vegetable production among smallholder farmers in Tanzania.

1.2.1 Specific Objectives

i. To identify factors influencing vegetable farmers to participate into the BPH

training program

ii. To assess the impact of the BPH training program on pesticide expenditures

among vegetable farmers iii. To assess the impact of the BPH training on vegetable profitability among

vegetables farmers iv. To assess the impact of the BPH training on reducing vulnerability to health

risks among vegetable farmers

1.3 Organization of the Thesis

This thesis is organized as follows. Chapter 2 gives the review of literature about synthetic pesticide use in vegetable production in Tanzania, emphasizing the deviation from integrated best farming practices like IPM and the resulting dependence on synthetic pesticide, which leads to high synthetic pesticide expenditures, low vegetable profit and high health risks. The chapter presents a brief summary of IPM as an alternative approach promoted through BPH training and adopted by farmers to limit irresponsible use of synthetic pesticide. Chapter 2 also presents a conceptual framework and the research hypotheses. Chapter 3 presents methodology, Chapter 4 covers research results and the discussion and Chapter 5 presents the conclusion and recommendations.

Chapter 2

LITERATURE REVIEW

This chapter provides an overview of vegetable farming in SSA and the relationship between limited knowledge of IPM among small-scale vegetable farmers and the use of synthetic pesticide.

2.1 Vegetable Farming and the Problem of Synthetic Pesticide Use in SSA

Alliance for a Green Revolution in Africa (AGRA) predicts that vegetable production will nearly triple in SSA between 2010 and 2050 (AGRA 2017). The increase in productivity is more likely a result of land area expansion and not the use of scientifically-proven well-adapted production practices e.g. integrated plant protection approaches like IPM and use of improved seed varieties (Schreinemachers and Tipraqsa 2012). Farms with poor farming practices and with no integrated pest control methods, are more prone to pest infestation which ultimately encourage extensive use of synthetic pesticide (Abate, Huis and Ampofo 2000; Chauvin et al.

2012). Adoption of scientifically proven best farming practices (e.g., the use of improved seeds that are pest resistant) increases productivity and reduces expenditures of some inputs particularly synthetic pesticide, reduces vulnerability of farmers to health risk related to routine exposure to synthetic pesticide and the resulting environmental damage (Eigenbrode and Trumble 1994; Panda and Khush 1995; Stout and Davis 2009). A closer example is improved open pollinated varieties (OPV) of tomato and African eggplant, which generated economic gains of US $ 255 million and

US $ 5 million respectively between 1990 and 2014 in Tanzania (Schreinemachers,

Sequeros and Lukumay 2017).

Addition of organic matter into the soil inhibits survival of certain soil borne pathogens reduce crop damage, improve soil nutrition and texture, and thereby reduce expenditures on industrial fertilizer and synthetic pesticide (Bonanomi et al. 2010). The use of cover crop or mulch, soil solarization, barrier crops, traps crops, and sticky traps effectively reduce pest access to plants or survival, reduce frequency of application of synthetic pesticide, and total input expenditures which in turn boost farmers profit

(Stout and Davis 2009; Zhang, Zeiss and Geng 2015; Robačer et al. 2016).

Scientifically proven well-adapted integrated farming practices are therefore the best ways through which farmers can realize better livelihoods without compromising the ecosystem. One of those best farming practices is Integrated Pest Management (IPM).

2.2 Integrated Pest Management (IPM)

The United States Environmental Protection Agency (U.S. EPA) defines IPM as an effective and environmentally-sensitive approach used to manage pests by using common-sense practices like use of pests life cycles and their interaction with the environment to control their effect on plants and maintain the least possible hazard to people, property, and the environment (Ehler 2006; US EPA 2015). IPM takes advantage of all appropriate pest management options including responsible use of chemical pesticide. IPM practices can well be explained within the context of the following five categories.

Genetic method. This method involves growing pest resistance crop varieties that are well adapted to climatic condition minimizing pest/diseases incidences especially under unprotected field (Panda and Khush 1995; Stout and Davis 2009;

Karuppuchamy and Venugopal 2016). Resistance varieties of Tomato, African

7 eggplants, eggplant, sweet pepper, Ethiopian mustard, African nightshade, and amaranths are focus vegetables in the BPH training program being the most widely grown vegetables in SSA and Tanzania in particular.

Cultural method. Involves combining practices such as crop rotation, timing, field hygiene, plant nutrition, plant spacing, timing, mulching, soil tillage or heating that minimize the conditions pests need to survive (Katan 2000; Finch and Collier 2000;

Hester and Harrison 2007; Bonanomi et al. 2010; Santos et al. 2015; Robačer et al.

2016; Radicetti et al. 2016).

Physical/mechanical methods. Involves preventing pests’ access to crops, physical destruction of pest habitat and/or removal of pest. Most commonly used physical methods in SSA among small-scale farmers are barrier crops and trap crops surrounding vegetable field and use of sticky traps. Physical control measures such as use of greenhouses or net houses were part of the training but not considered in the survey as part of IPM option used by farmers due to the fact that adoption in the short run is not expected given high level of sophisticated technology and high investment costs (Nordey et al. 2017).

Use of synthetic/chemical pesticide. Involves the use of synthetic pesticide.

BPH emphasis farmers to use synthetic pesticide as the last IPM option when all other measures have failed and the farmer assessment show that the benefit outweighs the cost. Bio-pesticides regarded as environmental-friendly are not easily accessible in

Tanzania (Chandler et al. 2011; Moshi and Matoju 2017).

The BPH training program emphasized the use of the elaborated IPM practices assuming the knowledge enabled farmers to adhere to IPM principles and reduce the

8 use synthetic pesticide, reduce synthetic pesticide expenditures, boost vegetable profit and reduce their vulnerability to health risk resulting from routine exposure to synthetic pesticide when they realize the importance of using protective gear. Studies support that when farmers adopt IPM technologies, they reduce the quantity of pesticide used which in turn reduce pesticide expenditures (Yorobe, Rejesus and Hammig 2011).

2.3 Conceptual Framework and Hypotheses

2.3.1 Conceptual Framework

Transfer of best practice knowledge in vegetable farming depends on existing information transfer systems like public agricultural advisory services, agro-dealers and in most cases farmer-to-farmer interaction. These existing information transfer systems are not well-technically equipped and lack enough resources. With limited links to research institutions the information is not up to date, reliable or accurate (Anderson and Feder 2004; Davis 2008). These constrained systems therefore give rise to poor farming practices which lead to irresponsible usage of synthetic pesticide in vegetable farming. Irresponsible usage of synthetic pesticide is therefore a function of lack of or unreliable information from knowledge transfer systems which produce farmers with limited knowledge on issues like IPM practices specifically on knowledge about proper synthetic pesticide usage at the farm-level including safety precaution (see Figure 1).

For instance, untrained farmers often use unimproved seeds of local Open Pollinated

Varieties (OPV) - these are seeds that are self-extracted by farmers from previous harvests and are less pest resistant, thus providing a breeding ground for crop pests throughout the year. Limited IPM knowledge encourages overuse of synthetic pesticide, reduces farmers’ profits due to high synthetic pesticide expenditures, and if

9 farmers are not using protective clothing, exposure to harmful chemicals intensifies farmers’ health risks.

Capacity building initiatives like BPH training improves knowledge directly by exposing farmers to information about best practices and enhancing existing knowledge transfer systems to vastly impact farmer behavior related to farm input use, including pest management inputs (see Figure 1). The BPH training program is expected to change farmers’ perceptions about the integrated use of farm inputs and how interactions with synthetic pesticide might affect other social and economic systems.

Figure 1 below shows the impact of BPH training programs on farmers’ knowledge base and on existing knowledge transfer systems to achieve desirable farming practices.

It also tracks two groups of farmers, trained (direct and indirect participants) and control farmers whose outcomes are later compared. Letter T indicates the treatment in which

T=1 stands for both direct and indirect1 participants. T=0 indicates no treatment and this is the control group that is not affected by the BPH training program. Yi1 and Yi0 indicate outcomes for trained and control farmers, respectively, and these outcomes are compared to measure the impact of the BPH training program.

1 Indirect participants (BPH neighbors) were exposed into training through interaction with direct participants to BPH training program (TOTs approach) or through direct observation to field managed by direct participants.

Key: Direct influence

Indirect

Best Practice Hub

) influence (BPH) T= Trained 1 T= Untrained 0 Outcomes (Y )

i1 Social Change Social Farming Practices - Pesticides input costs Direct one interaction etc. interaction one -Vegetable yield - participant -IPM

on -Vegetable Profit - (T=1) -Incl. pesticides Drivers of Drivers - Occupation safety one use/safety

consideration

(Farmer groups, direct observation, observation, direct groups, (Farmer

Indirect Outcomes (Y ) i0 participants - Pesticides input (T=1) costs -Vegetable yield Control -Vegetable Profit (T=0) - Occupation safety consideration

Figure 1: Conceptual diagram

2.4 Research Hypotheses

Table 1: Hypotheses

Null hypothesis Expected results 1. None of the factors influence H0: βi At least one factor influence participation into the BPH = 0 participation into the BPH training training program program

2. No difference in pesticide H0: μ1 Trained vegetable farmers (direct or expenditures between trained = μ2 indirect) have less pesticides (direct or indirect) and control expenditures compared to control vegetable farmers vegetable farmers

3. No difference in vegetable H0: μ1 Trained vegetable farmers (direct or yield between trained (direct or = μ2 indirect) have higher vegetable yield indirect) and control vegetable compared to control vegetable farmers farmers

4. No difference in vegetable H0: μ1 Trained vegetable farmers (direct or profit between trained (direct or = μ2 indirect) have higher vegetable profit indirect) and control vegetable compared to control vegetable farmers farmers

5. No difference in vulnerability H0: μ1 Trained vegetable farmers (direct or to health risks related to routine = μ2 indirect) are less vulnerable to health exposure to synthetic pesticide risks related to routine exposure to between trained (direct or synthetic pesticide compared to indirect) and control vegetable control vegetable farmers farmers

Chapter 3

RESEARCH METHODOLOGY

3.1 BPH Program Description

This thesis is part of the project “Vegetable for Income and Nutrition in Eastern and Southern Africa (VINESA)” implemented in Tanzania, Ethiopia, Malawi and

Mozambique from 2014 to 2017. In this paper, this study analyze the impact of BPHs in Tanzania. The project aim was to practically train young vegetable farmers about best practices in vegetable production, specifically on the use of IPM firstly to improve vegetable yield and secondly to improve quality of vegetables through responsible use of synthetic pesticide, fertilizer and other inputs like improved seeds.

3.2 Placement of the BPH Program

BPH was established within a purposefully selected part of Arumeru district in

Tanzania which has a high potential for vegetable production. Arumeru district was selected due to rapid increase in vegetable farming which does not meet market demand due to smaller vegetable quantities produced by farmers on small plots of land and low quality of vegetables due to inefficient use of synthetic pesticide and fertilizer (AVRDC

2013). The BPH was the training site where farmers were trained from six selected satellite/research villages located within 10-15 kilometers from the BPH center:

Maweni, Uwiro, Nduruma, Manyata, Bangata and Kivulul villages. Nonparticipant farmers in these villages are considered indirect beneficiaries of the BPH training. Due to a funding shortage, Kivulul village was dropped from the project. Bangata village was not included in the endline survey because of the short time lag between graduation and data collection periods which leaves no time for participants to practice the skills.

20-25 vegetable farmers from each satellite village self-selected to form a single group that received BPH training for six consecutive months (or one cropping season).

Control villages were purposefully selected in the same district as the satellite villages. Consideration for selection of control villages was mainly based on pretreatment characteristics similar to those in satellite villages including agro-ecology, crops grown, vegetable markets and challenges in terms of access to water for irrigation and extension services. Control farmers were selected from Karangai, Mlangarini,

Olkung’wado and Lake Tatu villages. Figure 2 shows part of the district where satellite and control villages are located.

3.3 Sample Selection

The study involves three categories of farmers: (1) direct participants to BPH training, (2) indirect participants to BPH training, and (3) control farmers who reside in control villages. Neither direct participants, indirect participants nor control farmers were randomly selected. Financial and social feasibilities constrained the random process of assigning the BPH training program to vegetable farmers in target satellite communities and control villages. Direct participants were purposefully selected based on developed criteria to suit program requirements (e.g. youth farmers) as well as social and ethical consistency, the criteria were (i) the farmer must be between 15 and 35 years of age, (ii) be dependent on farm income, (iii) have access to land, (iv) be passionate about agriculture, (v) be an active member in the community, and (vi) commit to complete a six-month training at a BPH center and that vii) the participants also had to show willingness to share acquired skills with other farmers in their community.

Despite efforts, implementer’s criteria were not successfully used to select direct participants and farmers therefore self-selected into the program, e.g. almost half of enrolled farmers were older than 35 years.

Additionally, the sample of indirect participants and control farmers which was obtained before project inception in 2014 during the baseline survey could not be relied upon and thus new farmers were interviewed, firstly because no baseline information was collected from direct participants and secondly because the sample was adulterated given high attrition during the end line survey in 2017.

3.4 Data Collection

Cross-sectional data were collected from 441 vegetable farmers between

February and March 2017 in Arumeru district in Tanzania, in which 81 were direct participants, 170 indirect participants all from same villages, and 190 control farmers from control villages.

3.5 Variable Selection

The survey collected data for vegetable production and marketing as well as household socioeconomic characteristics. Vegetable production and marketing information regarding farmers’ use of inputs were necessary for generating synthetic pesticide expenditures and profit from vegetable sales. The data covered aspects such as quantities of inputs and inputs costs, farming constraints, risk or shocks and specifically information about the use of synthetic pesticide. Information regarding access to agricultural advisory and financial services were collected. For farming characteristics farmers were asked about types of vegetables they grow, inputs types, quantity and cost e.g. land, seeds, fertilizer both industrial and compost, synthetic pesticide, irrigation, labor (family and hired), machine rental and other costs, Table 3.

One of the outcomes analyzed is a continuous variable measuring synthetic pesticide expenditures in Tanzania shillings (TZS/ha)2. Synthetic pesticide expenditures were calculated from farm inputs information collected directly by asking

2 During analysis, all currency figures collected in Tanzania shillings (TZS) are converted into USD using the exchange rate as of the data collection quarter. Data were collected in February, 2017; the exchange rate published by the Bank of Tanzania for the quarter ending March 2017 is used, which is 1USD=2233.10. The report can be accessed in PDF at: http://www.bot.go.tz

farmers to recall per hectare quantities and costs of pesticide inputs used in the growing seasons in the previous year including, insecticides, fungicides and herbicides.

Vegetable profitability (TZS/ha) was calculated from input-output and marketing variables collected from last season recall by farmers. Synthetic pesticide expenditures were collected directly from farmers. Vegetable profitability is calculated by taking the difference between vegetable production revenue (TZS/ha) and total variable inputs costs (TZS/ha). Total variable input cost is obtained by summing up seed cost, the cost of synthetic pesticide (insecticide, fungicide and herbicide), fertilizer cost, manure cost, irrigation cost, labor (family and hired) cost, machine rental cost and other variable costs. Market prices for vegetables used to calculate vegetable revenue were tested for differences between trained and untrained farmers using t-tests. The vegetable market prices do not differ between trained and untrained farmers implying that farmers sell in similar markets, as shown in Table 5. Profit is calculated as shown in equation (1)

TZS TZS TZS Profit(π) in = Revenue ( ) − Total variable input cost ( ) (1) ha ha ha Vegetable profit is the outcome variable used to estimate the impact of BPH training on vegetable profitability. It is expected that BPH training encourages farmers to use

IPM, reduce pesticide expenditures and consequently lead to improved vegetable yield and profit. When synthetic pesticide expenditures decline and yield increase ceteris paribus, then vegetable profitability increase due to reduction in variable input costs and increase in quantity of vegetables supplied into the market. The assumption is that vegetable prices remain unchanged throughout project and control villages and more

yield are realized due to use of IPM practices relative to conventional methods resulting into increase in revenue and vegetable profit.

A dichotomous outcome variable (health risk; 1=negligible/low health risk assumed if a farmer uses necessary protection gear) was generated from a set of categorical variables that asked whether the farmer uses protection gear while applying synthetic pesticide in the vegetable field as the means to reduce vulnerability to health risks. Given local farming in Tanzania, six choices were developed based on previous experience on how farmers interact with synthetic pesticide, the six options were later used to generate analyzable binary variable, the choices were (1) wear all protective gear such as gloves, mask, overalls (full body suit), gumboots and consider general health safety like direction of the wind when spraying, (2) wear few protective gear e.g. only nose/mouth protection, (3) wear few protective gear and the wind direction, (4) wash hands with soap after chemical application, (5) consider opposite wind direction when spraying, and (6) no precaution taken (see Table 2 for the result about distribution of farmers based on occupation safety choices ).

Table 2: Frequency and percentages of responses based on six protection gears farmers use in project location Code # Classification of occupation safety gear/measures Freq. Percent 1 Wear all protection gear such as gloves, mask, 137 31.07 Overall, gumboots and consider general health safety 2 Wearing few protection gear e.g. only one like 57 12.93 nose/mouth protection 3 Few protection gear and wind direction 103 23.36 4 Washing hand with soap after chemical application 12 2.72 5 Consider opposite wind direction when spraying 39 8.84 6 No precaution taken, I just apply 93 21.09 Total 441 100

These six choices were used to generate four levels of a new variable: (3) full protection by considering a situation in which a farmer uses all necessary measures to avoid contact with synthetic pesticide, (2) limited protection where a farmer use few of the protective gear, (1) very limited protection if a farmer lacks many protective gear, and (0) no protection if a farmer reported to have not used any of the gear. Level 3 is considered safe and reduces exposure and vulnerability to health risks while levels 2, 1 and 0 expose farmers to high health risks. It is hypothesized that farmers who use full protection are well informed about synthetic pesticide and the health benefit of avoiding contact compared to those who use limited protection or no protection at all. To suit the suggested model, the dichotomous outcome variable is generated where the new variable is coded 1=not susceptible to synthetic pesticide exposure / less vulnerable to health risk; the other three options are coded 0=highly susceptible to synthetic pesticide

exposure/vulnerable to high health risk. Table 3 presents the list of outcome and independent variables along with their codes, definition and unit of measurements.

Table 3: The list of selected observable covariates and outcome variables used in the model

S/N Variable code Variable definition Unit Model 1. pestcophausd Synthetic pesticide expenditures TZS/ha PSM 2. profitphausd Vegetable profit TZS/ha PSM 3. healthrisk Health risk 1=less vulnerable PSM 4. educ_resp1 Education level of the farmer Years Pr , PSM 5. vegarea_ha Area allocated to vegetable farming Hectare (ha) Pr , PSM 6. hhsize Household size Number Pr , PSM 7. vfexp Veg. farming experience Years Pr , PSM 8. hhtype Lives in a male headed household 1=Yes Pr , PSM 9. credit Acquired credit in last 12 months 1=Yes Pr , PSM 10. f5 Household seek market 1=Yes Pr , PSM information 11. treatbc Direct participants 1=Yes Pr , PSM 12. treatcn Indirect participants 1=Yes Pr , PSM

Pr represents probit model, PSM represents the propensity score matching impact model

3.6 Empirical Model

In observational studies, evaluating the impact of treatment (BPH training program) on the outcome of an individual entails comparison of the potential outcomes of treated (participants) with and without the treatment (BPH training). A problem arises because it is only possible to observe the same individual in one state of the world

(either treated or not). Some studies are designed to collect data for a given individual

before and after the treatment is implemented, but this is often impossible due to research constraints (e.g., funding, time, etc.). Evaluating the impact in this case suggest the use of non-participants (untrained/control group) as the comparison proxy (Smith and Todd 2005). However, when random assignment of the treatment is not ensured, it is not statistically feasible to use outcomes of non-participants as an exact proxy of the counterfactual outcome of the participants in a state without the treatment because participants and non-participants tend to differ characteristically causing overt bias or selection bias (Caliendo and Kopeinig 2008). Selection bias produces a distribution of the treatment that is not determined by a specified random process. The treatment and the outcome are not conditionally independent and that the expected outcome for the non-participants will not equal the expected counterfactual outcome for participants in a state before the treatment (Austin 2011; Cerulli 2015).

The classical model of impact evaluation cannot be applied due to selection bias problem. Therefore, Propensity Score Matching (PSM) which addresses the selection bias is used to assess the impact of the BPH training program on pesticide use among vegetable farmers in Tanzania (Rosenbaum 2002; Rosenbaum 2010). Kernel

Matching (KM) and one-to-one Nearest Neighbor Matching (NNM) are the two matching approaches employed to estimate the impact.

3.6.1 Propensity Score Matching (PSM)

In non-experimental impact evaluation model, the critical question is about how the training participants would have performed had they not received the BPH training conditional on observed characteristics. The PSM is a widely used non-experimental causal impact model that matches participants and non-participants based on the

estimated probability of being trained namely propensity score (Dehejia and Wahba

2002; Austin and Stuart 2017). PSM is used to test hypothesis number 2 through 4 as presented in Table 1.

Let Ti be the binary treatment variable where T=1 for participants into the BPH training and T=0 for non-participants and Y0i be the observed outcome (continuous or binary) for the non-participants and Y1i be the observed outcome for the participants.

The propensity score 푝(x) is given by the expectation of the treatment variable (T=1) conditional on observed covariates (x) as in equation 2 below

푝(x) = Pr(T = 1|x) = E(T|x) (2)

Probit model. The propensity score is calculated from the parametric binary probit model. This model is the model of program participation used to estimate factors influencing participation into the BPH training program, it is therefore used to test hypothesis number 1 in Table 1. The model is as presented in equation 3 below.

푥푖β 1 퓏2 Pr(T푖 = 1|x) = Φ(푥푖β) = ∫ exp ( ) 푑퓏 (3) −∞ √2휋 2

Where the left side of the equation represent the probability to participate into the BPH training program and 푥푖 represents selected characteristics of observed vegetable farmers as listed in Table 3. Φ is the standard normal transformation which constrains the propensity to lie between 0 and 1.

After matching non-participants and participants based on the propensity scores within the region of common support, the impact of BPH training program is estimated directly. The general specifications for matching estimators, Average Treatment on the

Treated (ATET), average treatment on the non-treated (ATENT) and average treatment effect (ATE) are given by equations 4, 5 and 6 respectively following Cerulli (2015); however, this study uses ATET estimated on common support region to describe the impact of the BPH training program.

1 1 퐴푇퐸푇̂ = ∑ (푌푖 − 푌̂0푖) = ∑ 푌푖 − ∑ ℎ(푖, 푗) 푌푗 (4) 푁1 푁1 푖∈{푇=1}∩푆푝 푖∈{푇=1}∩푆푝 푗∈{퐶(푖)}∩푆푝

1 1 퐴푇퐸푁푇̂ = ∑ (푌̂1푖 − 푌푖) = ∑ ( ∑ ℎ(푖, 푗)푌푗 − 푌푖) (5) 푁0 푁0 푖∈{푇=0}∩푆푝 푖∈{푇=0}∩푆푝 푗∈{퐶(푖)}∩푆푝

1 1 퐴푇퐸̂ = ( ∑ 푇 ) . 퐴푇퐸푇̂ + ( ∑(1 − 푇 )) . 퐴푇퐸푁푇̂ (6) 푁 푖 푁 푖 푖 푖

Where in any case 푖 or 푗 may be a group of participant or non-participant vegetable farmers on a common support region respectively, common support region is denoted by ∩ 푆푝 and 푁푖 is the number of vegetable farmers in the common support region, 0 <

ℎ(푖, 푗) ≤ 1 are weights to apply to the single 푗 matched with 푖, and they generally increase as soon as 푗 is closer to i. 퐶(푖)is called the neighborhood of participants (푖) which is simply the set of non-participants (푗) matched with participants (푖).

Matching algorithms. After calculating the propensity score for participants and non-participants, the impact of BPH training program can be estimated using various matching algorithms. For the purpose of this thesis Kernel Matching (KM) and Nearest

Neighbor Matching (NNM) are selected. From the general specification of matching estimators in equations 4, 5 and 6, KM and NNM can be specified by changing the form

퐾푖푗 of weight ℎ(푖, 푗) and the set 퐶(푖) where for the KM the weight ℎ(푖, 푗) is and the ∑푗∈퐶 퐾푖푗 set 퐶(푖) represents all non-participants units (C). For one-to-one NNM the weight ℎ(푖, 푗) equals 1 and 퐶(푖) is the singleton 푗: 푚푖푛푗 ⃦ 푝푖 − 푝푗 ⃦.

KM is chosen because it is considered among the best matching approaches when potential controls from which to draw a matched group are insufficient (Berg

2011). KM uses all units in the untreated set and down-weights untreated observations that are more distant. For KM, every participant vegetable farmer is matched with a weighted average of all non-participant vegetable farmers with weights that are inversely proportional to the distance between the participants and the non-participant vegetable farmers, (Cerulli 2015). One-to-one NNM selects only one unit from the set of non-participants whose propensity score is the closest value to propensity score of participants according to a prespecified metric. A one-to-one NNM on the propensity score p(x) with replacements is used to check for robustness of the results and is chosen because it is among the best matching algorithms despite its limitation that it does not consider the level of the distance between matches, which allows for matching pairs even when they are far apart (Caliendo and Kopeinig 2008; Berg 2011). Allowing replacement for NNM increases quality of matches and reduces bias when propensity score distribution vary greatly between the participants and the non-participants

(Caliendo and Kopeinig 2008). NNM match each participant farmers with the closest non-participant farmers based on the distance metric over the propensity score p(x).

KM and NNM are selected because they are among the best matching estimators and the only two for which asymptotic properties are known (Cerulli 2015).

Test of PSM assumptions. PSM is built on three main assumptions which are the (i) Conditional Mean Independence (CMI) assumption, (ii) overlap or common support assumption, and (iii) unconfoundedness assumption over which the robustness of the estimator of the program impact is measured. Under the CMI, PSM assume that conditional on propensity scores p(x), placement of individuals into the BPH training program and into the control is independent of average synthetic pesticide expenditure, vegetable profit and health risk and their functional form. Under the common support assumption, PSM assume that each unit in the defined population has some chances to participate into the BPH training program and some chances of not being able to participate. Under the unconfoundedness, assumption, PSM assumes there is no effect from unobservable covariates on participation in the BPH training program and to synthetic pesticide expenditure, vegetable profit and health risk. This means assignment of the BPH training program is randomly done on the basis of observables.

When these assumptions hold, the comparison of observations/units with different treatments but identical pretreatment variables can be given a causal interpretation

(Heckman, Ichimura and Todd 1997; Imbens 2015). This study initially assume all stated assumptions hold, however, different tests are performed during analysis to check the assumptions and assess possible violations. Violation of the overlap and balancing assumptions mean comparing the incomparable and this leads to overt bias and that

ATE and ATET are biased and not reliable for inference. The two assumptions are tested by plotting the propensity score distribution and kernel function distribution

before and after matching. The test of possible deviation from the assumptions before and after estimating the treatment effects were therefore carried out accordingly.

Sensitivity analysis. The limitation of PSM is that it cannot control for the effect of unobservables. Violation of unconfoundedness assumption indicates an effect from unobservables first on the participation decision and second on the outcomes of interest.

When the effect from unobservables is present, the matching estimators such as ATET,

ATE and ATENT are therefore unreliable for inference. Sensitivity analysis provides the post-estimation test to assess the reliability of the matching estimators when the effect from unobservable covariates is expected. The test for the possible effect of unobservables is carried out using the Rosenbaum bounds in Stata 14 for both outcomes rbounds command for synthetic pesticide expenditures, vegetable yield and vegetable profit, and mhbounds command for health risk as proposed by Rosenbaum (2002).

Chapter 4

RESULTS AND DISCUSSION

4.1 Descriptive Statistics

Capacity building programs that teach farmers IPM skills promote the use of best farming practices to mitigate irresponsible input use, improve production, and enhance farmer livelihoods. Specifically, this thesis estimates the impact of participation in the BPH training program on the reduction of synthetic pesticide expenditures, increase in vegetable yield, profit and reduction in health risk. The impact is estimated by comparing the expected outcomes between farmers who directly or indirectly participated into the BPH training program against non-participant farmers.

T-tests were used for continuous variables and Chi-square (ꭓ2) tests were used for dichotomous variables. Table 4 presents results general summary statistics. Results of summary statistics show that direct participants to BPH training and control farmers were different in most selected socioeconomic characteristics such as age of the household head, education level of the farmer, vegetable farming experience, household size, whether a farmer seek market information, whether a farmer acquired a loan within 12 months before the survey, pesticide expenditures and whether the farmer planted fruit vegetables; however, similarities were found in factors such as proportion of female vegetable farmers, proportion of farmers in male-headed households, total household income excluding vegetable profit (USD3/year), easy

3 Figures expressed in 2017 exchange rate, 1USD=2233.10 TZS, exchange rate according to Bank of Tanzania. The report can be accessed in PDF at: http://www.bot.go.tz

access to extension services, yield losses due to pests among others as shown in Table

4. The fact that direct participants self-selected into the BPH training program and that no statistical difference is found between indirect participants and control farmers is likely to justify the difference.

4.1.1 Summary Statistics Results for Outcome Variables

Pesticide expenditures vary significantly between direct participants and control farmers in which control farmers use around 48% more in synthetic pesticide expenditures than direct participants (direct participants spent by average

93.432USD/ha/season compared to 163.724 USD/ha/season for synthetic pesticide) where as the comparison with indirect participants yield insignificant difference. On the other hand, vegetable profit for direct participants significantly exceeded that of control farmers by 95% where as the difference between indirect participants and control farmers show no significant difference.

No difference was found between direct participants and control farmers with regard to vulnerability to health risk, however, overall only 32% of farmers reported wearing necessary protective clothing, implying that 68% of vegetable farmers are highly vulnerable to health risks; however, a majority of direct participant (44%) reported to have used all necessary protective gear during the cropping season before the interview compared to only 29% of control farmers implying that over 70% of farmers who are not exposed to any IPM training are highly vulnerable to health risks compared to trained farmers. Exposure to health risks when applying synthetic pesticide is evident since harmful chemicals can easily contact farmers’ skin or get into internal organs through inhalation or even damage organs like eyes. Occupation safety rules

when spraying synthetic pesticide on crops requires that a farmer wear protective gear to avoid contact with harmful chemicals and reduce susceptibility to associated health risks.

Previous studies reveal that vegetable farmers in northern Tanzania rarely wear protective clothing while spraying synthetic pesticide and have been experiencing health risks such as neurological problems, skin problems among others due to routine pesticide application, (Ngowi et al. 2007; Lekei et al. 2016). Lack of significant differences between direct participants and control farmers imply either a small trickle down impact of the BPH training program given the short time lag toward impact evaluation or exogenous influence caused by selection bias, Table 4.

The simple mean comparison of the outcome variables between groups (as presented in Table 4, 6 does not control for the effects of other observable covariates and can thus not be relied upon for inference.

Table 4: Summary statistics for selected socioeconomics, institutional, farm characteristics and outcome variables

Variables Direct participants (1) Indirect participants Control Farmers (3) Pooled Sample Test Test (2) Mean SD Mean SD Mean SD Mean SD (1=3) (2=3) Socioeconomics characteristics Age of the household head (Years) 40.360 12.464 46.721 12.537 48.090 11.232 46.157 12.273 7.730*** 1.391 Education of respondent (Years) 7.907 1.960 7.093 1.465 7.320 1.970 7.338 1.807 -0.713** 0.122 Sex of respondent (1=Female) 0.240 0.430 0.118 0.324 0.169 0.375 0.162 0.369 -0.0479 0.072 Area allocated to vegetables (ha) 0.248 0.218 0.400 0.430 0.380 0.396 0.364 0.388 0.114* -0.0244 Vegetable farming experience (Years) 10.413 5.643 14.093 7.795 14.691 8.437 13.684 7.887 4.820*** 0.709 Household size (Number) 4.413 1.669 4.832 1.718 5.197 1.637 4.913 1.695 0.700** 0.311 Household decision made by female 0.227 0.421 0.211 0.409 0.185 0.390 0.203 0.403 -0.0423 -0.0272 (1=Yes) Lives in male headed household (1=Yes) 0.880 0.327 0.944 0.230 0.949 0.220 0.935 0.247 0.0582 0.000344 Total income excluding vegetable profit 314.131 367.235 321.142 450.985 598.139 1604.593 438.967 1107.055 286.3 252.1* (usd/year) 30 Institutional characteristics Access to extension services (1=Yes) 0.493 0.503 0.335 0.474 0.433 0.497 0.406 0.492 -0.0564 0.0873 Acquired credit in the last 12 months 0.547 0.501 0.304 0.462 0.287 0.453 0.341 0.474 -0.240*** -0.0167 (1=Yes) Household seek market information 0.189 0.394 0.092 0.290 0.073 0.260 0.101 0.302 -0.112** -0.0204 (1=Yes) Farm characteristics Yield losses due to pests (t/ha) 0.793 1.848 1.459 4.417 1.901 8.891 1.528 6.497 1.021 0.411 Pesticide intensive vegetables /PIVs 0.667 0.475 0.863 0.345 0.747 0.436 0.778 0.416 0.0741 -0.105* (1=Yes) Outcome variables Pesticide expenditures (USD/ha) 93.432 96.336 149.478 192.653 163.724 242.280 145.450 204.549 66.32* 14.71 Yield (t/ha) 17.196 48.119 14.817 17.812 16.718 25.459 16.084 28.785 -0.464 1.787 Vegetable profit (USD/ha) 513.341 1389.333 227.370 959.346 229.398 932.886 280.049 1042.809 -305.6* -11.88 Low health risk (1=Yes) 0.440 0.500 0.304 0.462 0.287 0.453 0.321 0.468 -0.122 -0.0101 N 81 170 190 441 Asterisks denote the level of significance for a ꭓ2 or t-test of difference in means, ***P < 0.01, **P < 0.05, *P < 0.1; SD, standard deviation

4.1.2 Summary Statistics Results for Synthetic Pesticide Use and IPM

Results for some important variables not included in the model of impact evaluation due to perceived effects on program participation and/or that the variable is affected by the BPH training program are also analyzed and presented in Table 5. For example, age of the participant was one of the implementers’ selection criteria and therefore influenced participation to some degree. On the other hand, factors such as group membership, use of synthetic pesticide (quantity and frequency), and vegetable yield and IPM practices were influenced by the BPH training program. Including these variables into the model introduces bias that would not otherwise have been present.

Rosenbaum (2010) suggest that such variables should not be matched. Descriptive results, however, show that most of these characteristics differ between direct participants farmers and control farmers and do not differ between indirect participants’ farmers and control farmers. For instance, there is a statistical difference between the proportion of farmers who are members of farmer groups between direct participants and control farmers.

Also, the quantity of synthetic pesticide used per crop cycle differed significantly between direct participants and control farmers in which control farmers used large quantities of around 15t/ha compared to around 10t/ha for direct participants farmers. This is also reflected in the frequency with which a farmer sprays synthetic pesticide on crops in which control farmers spray most frequently (up to 6 times per crop cycle) than direct and indirect participant farmers (around 5 times). Overall 73% of farmers reported to apply one or more IPM practices in which majority (88%) of direct participant farmers apply one or more IPM practices compared to 70% of control farmers, there was no difference between indirect participant farmers and control farmers with regard to all variables discussed (see Table 5).

Table 5: Summary statistics for vegetable yield (t/ha), use of synthetic pesticide and IPM

Variables Direct Indirect Control Pooled Sample Test Test participants (1) participants (2) Farmers (3) Mean SD Mean SD Mean SD Mean SD (1=3) (2=3)

Frequency of pesticide 4.551 3.052 5.604 5.139 6.514 10.341 5.801 7.643 1.892 0.957 application (number)

Quantity of synthetic 9.939 9.426 13.660 20.708 14.985 15.902 13.540 17.070 5.164** 1.679 32 pesticide applied (kg/ha)

Farmer used at least one 0.821 0.386 0.692 0.463 0.729 0.446 0.732 0.443 -0.101 0.0397 IPM practice (1=Yes)

Average vegetable price 0.284 0.238 0.263 0.391 0.236 0.297 0.255 0.327 -0.049 -0.0257 USD/kg

N 81 170 190 441

Asterisks denote the level of significance for a ꭓ2 or t-test of difference in means, ***P < 0.01, **P < 0.05, *P < 0.1; SD, standard deviation

4.1.3 Fruit Vegetables versus Leafy Vegetables

The data covered all vegetables grown in the district, with no specific target to one type of vegetable. From the list of vegetables whose data are collected, two categories of vegetables are created, fruit vegetables and leafy vegetables. As used in this study, fruit vegetables refer to those vegetables in which only fruit is consumed as a vegetable and leafy vegetables are those in which leaves are consumed. Fruits vegetables include tomato (Solanum lycopersicum), African eggplant (Solanum aethiopicum), sweet pepper (Capsicum annuum), eggplant (Solanum melongena) and okra (Abelmoschus esculentus). Leafy vegetables include amaranths (Amaranths spp.),

African nightshade (Solanum nigrum), Collard green (Brassica oleracea), and

Ethiopian mustard (Brassica carinata). The two categories of vegetables tend to differ significantly in terms of input demand and more specifically on the quantity of synthetic pesticide applied. In addition, there is the notable difference in labor demand most importantly gender distribution between the two vegetable categories where men dominate cultivation of fruit vegetables and women dominate cultivation of leafy vegetables (Dinssa et al. 2016; Fischer, Gramzow and Laizer 2018). Due to the underlying difference between the two categories of vegetables, the impact of BPH training program on synthetic pesticide expenditures, profitability and vulnerability to health risk is analyzed considering the differences.

Results show that farmers allocate more land to fruits vegetables (0.42ha) and less to leafy vegetables (0.18ha). Also, a farmer residing in a male-headed household is more likely to grow fruit vegetables than those residing in female-headed household.

Moreover, farmers growing fruit vegetables use synthetic pesticide more than double in terms of frequency of application and in terms of the quantity used. Farmers growing fruits vegetable record higher profit; however, leafy vegetable farmers had higher

income from non-vegetable income sources compared to fruits vegetable farmers,

Table 6. Also fruits vegetables record the high quantity of vegetable losses in the field caused by pest infestation in which around 2 t/ha is lost prior to harvest, Table 6. This imply that fruits vegetables are highly infested with pests and are therefore more sprayed making them pesticides intensive vegetables (PIVs).

Overall, only 18% of interviewed farmers were women, the majority of whom

(37%) grow leafy vegetables compared to only 13% growing fruits vegetables, Table

6. Among the factors reported to discourage women involvement into fruit vegetables farming are men dominance, more land requirement, frequent irrigation, high labor demand and growing fruits vegetables being pesticides intensive activity. Women in developing countries have less access to resources like land and irrigation facilities

(Fischer et al. 2018). The results support that of Fischer et al.,(2018) which found that fruits vegetables are regarded more commercial than leafy vegetables than leafy vegetables and have since been a male dominated farm business in Tanzania. On the other hand, leafy vegetables is the least commercial and regarded women business

(Dinssa et al. 2016). Results show that farmers growing fruit vegetables make more than double the profit compared to farmers growing leafy vegetables, Table 6. Land allocated to cultivation of fruit vegetables is more than double that of leafy vegetables, also pesticides expenditures for fruit vegetables is more than double that of leafy vegetables (171USD/ha versus 55USD/ha respectively implying higher demand of financial capital which majority of women have less access), Table 6.

Table 6: Summary statistics by vegetable categories

Variables Leafy vegetables (1) Fruits vegetables (2) Pooled Sample Test (1=2) Mean SD Mean SD Mean SD Gender of the farmer (1=Female) 0.371 0.486 0.133 0.340 0.183 0.387 0.204*** Land allocated to vegetables (ha) 0.180 0.192 0.416 0.402 0.366 0.380 -0.222*** Vegetable market price (USD/kg) 0.407 0.425 0.212 0.280 0.254 0.326 0.195*** Household farm decision making(1=Female) 0.191 0.395 0.196 0.397 0.195 0.396 -0.00273 Male headed household (1=Yes) 0.865 0.343 0.955 0.208 0.936 0.245 -0.0702* Total income excl. vegetable profit (USD/year) 714.762 2073.031 386.806 658.720 456.136 1122.656 274.6* Access to extension (1=Yes) 0.427 0.497 0.389 0.488 0.397 0.490 0.029

35 Access to credit (1=Yes) 0.404 0.494 0.322 0.468 0.340 0.474 0.0913

Household seek market information (1=Yes) 0.152 0.361 0.090 0.287 0.104 0.305 0.0442 Member to farmers group (1=Yes) 0.618 0.489 0.548 0.498 0.563 0.497 0.0657 Yield losses due to pests (t/ha) 0.458 1.342 1.699 7.078 1.437 6.334 -1.347 Pesticide application (Number) 2.483 1.778 6.666 8.313 5.781 7.619 -4.363*** Synthetic pesticide quantity (kg/ha) 6.771 8.627 15.299 18.230 13.496 17.019 -9.142*** Applied one or more of the IPM practices (1=Yes) 0.719 0.452 0.735 0.442 0.732 0.444 -0.079 Synthetic pesticide expenditures USD/ha) 54.677 64.328 170.540 220.245 146.046 203.321 -117.9*** Vegetable profit (USD/ha) 256.423 778.358 283.963 1111.950 278.141 1049.520 -56.01 Low health risk (1=Yes) 0.270 0.446 0.322 0.468 0.311 0.464 -0.0489

N 103 338 441 Asterisks denote the level of significance for a ꭓ2 or t-test of difference in means, ***P < 0.01, **P < 0.05, *P < 0.1; SD, standard deviation

4.2 Factors Influencing Participation into BPH Training Program

The propensity scores for matching are estimated using a probit model and the variables included in the model of program participation are selected socioeconomics, institutional and farming characteristics perceived to have not been affected by participation in BPH training program as presented in Table 4. The sample slightly reduced to 269 from 271 for direct participants and 358 from 360 for indirect participants due to missing data within covariates.

Estimation results indicate that factors such as area of land allocated to vegetable farming, vegetable farming experience of farmer, household size, whether a farmer acquired credit in the last 12 months from the date of survey and whether the farmer seek market information strongly influenced direct participation of farmers into the BPH training program. Household size was found to influence indirect participation into the BPH training program.

Specifically, an infinitesimal increase in land area allocated to vegetable farming decreased direct participation probability into the BPH training program by

20%, which could be associated to the fact that medium to large scale farmers hardly registered into the BPH training program which targeted youth who have less access to potential land for vegetable farming and depend mainly to resources from household head. This also supports descriptive results which show that land area allocated to vegetable farming was by average less than 0.5ha overall suggesting that majority of vegetable farmers are small scale. An infinitesimal increase in vegetable farming experience decreased participation probability by 1.7%. Access to credit increased direct participation probability into the BPH training program by 22.3%, suggesting the possibility that farmers seek better production techniques that could enable them to earn

income to repay their loan. Household size decreased indirect participation probability by 3.3%, Table 7.

Table 7: Factors influencing participation of vegetable farmers in the BPH training program

Direct Training Indirect Training Variables Coef. z Marginal Coef. z Marginal (SE) effect (SE) effect (dy/dx) (dy/dx) Education level of the 0.053 -0.039 1.02 0.0171 -1.030 -0.016 farmer (Years) (0.052) (0.038) Land area allocated to -0.625** 0.091 -2.01 -0.2004 0.540 0.036 vegetable farming (ha) (0.311) (0.168) Vegetable farming -0.053*** -0.004 -4.49 -0.0170 -0.440 -0.001 experience(Years) (0.012) (0.008) -0.118** -0.082* Household size (Number) -2.18 -0.0377 -1.890 -0.033 (0.054) (0.043) Member in male headed -0.494 0.041 -1.42 -0.1582 0.130 0.016 household (1=Yes) (0.349) (0.306) 0.696*** 0.116 Acquired credit (1=Yes) 3.84 0.2230 0.770 0.046 (0.181) (0.149) Household seek market 0.479* 0.188 1.81 0.1533 0.790 0.075 information (1=Yes) (0.264) (0.240) 0.604 0.551 Constant 1.01 1.220 (0.598) (0.451) Log pseudo-likelihood -134.110 -244.699 LR chi2(7) 50.380 5.57 Prob > chi2 0.000 0.591 Pseudo R2 0.1809 0.0117 Observations 269 358 Asterisks denote the level of significance for P>|z| where ***P < 0.01, **P < 0.05, *P < 0.1; Robust standard errors in parenthesis; SE stands for Robust Standard Error

4.3 Impact of BPH Training Program

The probit model is used to calculate the probability of being trained given the observable covariates (propensity scores) which is then used to match control farmers to direct participant farmers and indirect participant farmers. Results presented in Table

8 show the impact of BPH training program on synthetic pesticide expenditures

(USD/ha), vegetable yield (t/ha), vegetable profit (USD/ha) and reduction of health risks associated with farmers’ use or non-use of protective gear during pesticide application.

4.3.1 Testing for Bias Resulting from Observables

Because PSM is suited to eliminate overt biases which stem from lack of overlap and lack of balance between the covariates, it is always advisable to test the assumptions before assessing the impact of program participation. Weak overlap increases overt bias when some observable characteristics in the trained and control samples do not match.

Weak balancing also increases overt bias when some observable characteristics in the trained and control groups do not come from the same distribution (Rosenbaum 2010;

Cerulli 2015).

In this study, overlap is tested by generating the density distribution of the estimated propensity scores between direct participants and control farmers and between indirect participants and controls farmers, see Figures 3 and 5 respectively.

The distributions indicate that the common support conditions are achieved. The Kernel distributions of the propensity score between direct participants and control farmers and between indirect participants and control farmers before and after matching are plotted in the same graph and presented in figure 4 and 6 to assess the quality of the matching.

The distributions before matching indicate the lack of balance between propensity score distributions of participants and control farmers; however, the post matching

distributions indicate a great improvement between the groups, implying that full covariate balancing is achieved.

In addition to plotting the graph of distributions to test for overlap, the balancing test based on Kernel Matching is carried out for the selected observable characteristics between direct participants and control farmers and between indirect participants and control farmers, see appendix Table A1 and A2. The test shows that there is a statistically significant difference on observable characteristics between participant farmers (direct or indirect) and control farmers before matching, which is an indication of selection bias based on observables, but the standardized bias after matching was reduced to below 20%, Table A1. The standardized bias is the difference of sample mean in the trained and control (full or matched) subsamples as a percentage of the square root of the average of the sample variances in the trained and control groups

(Cerulli 2015). Matching is considered effective if reduction in standardized bias for all observable characteristics is below 20%, (Rosenbaum and Rubin 1985). For a similar application see Ochieng et al. 2018. Both tests prove to be successful and this allows estimation of the average treatment effect on the treated (ATET).

0 .2 .4 .6 .8 1 Propensity Score

Direct BPH Trainees Control Farmers

Figure 3: Distribution of estimated propensity scores to test common support for

direct participants’ farmers and control farmers

2.5 2.5

2 2

1.5 1.5

1 1

kdensity _pscore

.5 .5

0 0

0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1 Propensity Scores BEFORE Matching Propensity Scores AFTER Matching

Direct BPH Trainees Control Farmers

Figure 4: Kernel density distribution to test balance between direct participants farmers and control farmers

40 .3 .4 .5 .6 .7 Propensity Score

Indirect BPH Trainees Control Farmers

Figure 5: Distribution of estimated propensity scores to test common support for

indirect participants’ farmers and control farmers

kdensity _pscore

0 0

.3 .4 .5 .6 .7 .3 .4 .5 .6 .7 Propensity Scores BEFORE Matching Propensity Scores AFTER Matching

Indirect BPH Trainees Control Farmers

Figure 6: Kernel density distribution to test balance between indirect participants’ farmers and control farmers

41 4.3.2 Impact of the BPH Training Program

The results from KM and one-to-one NNM show similar trends of the impact; however, variation exists in whether the differences are statistically significant or not and in terms of the magnitude of the impact. The results show that the BPH training program reduced synthetic pesticide expenditures by around 50% from 138$/ha/season spent on synthetic pesticide to only 91$/ha/season. The impact of the BPH training program on vegetable profit is also noticed; however, where BPH training program increased vegetable profit by around 72% from 146$/ha/season to 524$/ha/season.

Significant impact on vegetable profit might be due to the significant impact on synthetic pesticides expenditures with 50% reduction when farmers substitute other

IPM techniques like improved seeds, proper soil nutrition among others which decrease capital allocated to purchase of synthetic pesticide and thereby boost profit margin,

Table 8. Moreover, the BPH training program had an impact on increasing the proportion of farmers who are less vulnerable to health risk, in which 40% of direct participants are less exposed harmful chemicals compared to 26% of non-participants suggesting that the BPH training program reduced health risk by 35%.

Table 8: Estimation of the impact of BPH training program on direct participants

Variable Direct BPH Control ATET S.E. T stat ATET as Γ Training % of PO KM PO Synthetic pesticide expenditures (USD/ha) 91.474 138.492 -47.018 24.199 -1.94* -51.401 1.05 Yield t/ha 16.941 16.391 0.550 6.164 0.09 3.246 1.05 Vegetable profit (USD/ha) 524.340 146.368 377.972 180.485 2.09** 72.085 1.05-1.10 Low health risk (1=Yes) 0.405 0.264 0.141 0.077 1.84* 34.887 3.85

43 NNM PO

Synthetic pesticide expenditures (USD/ha) 91.474 192.437 -100.963 44.732 -2.26** -110.374 1.05 Yield t/ha 16.941 25.668 -8.727 8.732 -1 -51.513 1.05 Vegetable profit (USD/ha) 524.340 116.190 408.150 197.191 2.07** 77.841 1.05-1.10 Low health risk (1=Yes) 0.405 0.266 0.139 0.108 1.29 34.375 3.85 Asterisks denote the level of significance for a t-test of difference in means, ***P < 0.01, **P < 0.05, *P < 0.1; Γ stands for gamma which measure the critical level of hidden bias, critical level of hidden bias is measured at 5% level of significance; S.E stands for standard error, PO stands for Potential Outcome; ATET-Average Treatment Effect on Treated.

The impact between indirect participants and control farmers show no significant impact; however, indirect participants record on average reduced synthetic pesticide expenditures, increased vegetable profit as well as increased proportion of farmers who are less vulnerable to health risk at a negligible degree, Table 9.

The comparison of impact between direct and indirect participants show that

BPH training program had significant impact on direct than indirect participants as presented in appendix Table B 3.

There is therefore a great similarity of results between indirect participants and control farmers suggesting negligible impact of indirect participation which make the sample of indirect participants less different from control farmers.

Table 9: Estimation of the impact of BPH training program on indirect participants

Variable Indirect BPH Control ATET S.E. T stat ATET as Γ Training % of PO KM PO Synthetic pesticide expenditures (USD/ha) 145.647 163.761 -18.114 23.049 -0.79 -12.437 1.1 Yield t/ha 72.769 23.277 49.492 58.730 0.84 68.013 1.05 Vegetable profit (USD/ha) 217.654 189.326 28.328 99.941 0.28 13.015 1.5 Low health risk (1=Yes) 0.296 0.283 0.012 0.049 0.25 4.200 2

45 NNM PO

Synthetic pesticide expenditures (USD/ha) 145.647 195.021 -49.374 36.062 -1.37 -33.900 1.1 Yield t/ha 72.769 15.472 57.297 58.477 0.98 78.738 1.05 Vegetable profit (USD/ha) 217.654 176.742 40.912 132.326 0.31 18.797 1.5 Low health risk (1=Yes) 0.296 0.290 0.006 0.066 0.09 2.000 2 Asterisks denote the level of significance for a t-test of difference in means, ***P < 0.01, **P < 0.05, *P < 0.1; Γ stands for gamma which measure the critical level of hidden bias, critical level of hidden bias is measured at 5% level of significance; S.E stands for standard error, PO stands for Potential Outcome; ATET-Average Treatment Effect on Treated.

4.3.3 Sensitivity Analysis

In addition to testing for the two assumptions on which PSM is built prior to assessing program impact, it is advisable to conduct sensitivity analysis after assessing program impact to test for the influence of hidden bias on the outcome of interest

(Becker and Caliendo 2007; Caliendo and Kopeinig 2008; Cerulli 2015). Hidden bias results from the effect of unobservable characteristics first on participation decision and second on the outcome of interest. In other words, presence of hidden bias defy the unconfoundedness assumption in a state where the researcher fail to observe all variables that simultaneously influence the participation decision and outcome variables (Becker and Caliendo 2007; Cerulli 2015). When hidden bias exist, results obtained from PSM are no longer robust and unreliable for inference. In this study, the hidden bias is assessed by employing the bounding approach for all outcome variables; synthetic pesticide expenditures, vegetable yield, vegetable profit, and health risk. Bounding approach is used to conduct sensitivity analysis for both continuous and categorical outcome variables as proposed by Rosenbaum (2002), details for their application are provided in (DiPrete and Gangl 2004; Cerulli 2015) and (Becker and Caliendo 2007) respectively.

According to Rosenbaum 2002, both matched individuals have the same probability of participating into the program only if the value of gamma (Γ) which is the log odds of differential assignment due to unobserved factors is one and the program have the significant impact only if the upper bound Hodges-Lehmann point

estimate is significant. Otherwise, if Γ is greater than one and insignificant, individuals who appear to be similar in terms of observable characteristics could differ in their odds of participating into the BPH training program by as much as a factor of

Γ due to the influence of unobservable characteristics. It was therefore important to check whether the unobservables characteristics would alter the results about the impact of BPH training program on the synthetic pesticide expenditures, vegetable profit and reduction of farmers’ health risk. Sensitivity analysis for continuous outcome variables ; synthetic pesticide expenditures, vegetable yield and profit are performed using rbounds command where as for binary outcome variable, health risk is performed using mhbounds command, all in in Stata 14. As required, the difference between the actual outcome and imputed outcome for each unit in the region of common support are initially calculated (Becker and Caliendo 2007; Rosenbaum

2010; Cerulli 2015).

Sensitivity analysis results for continuous outcomes. The sensitivity analysis test between direct participants show that synthetic pesticide expenditures and vegetable yield are insensitive to unobserved biases at smaller and large values of Γ from 1.05 onward for the upper bound significance level less than or equal to five percent (p < 0.05). The five percent upper significant value is observed from the value of Γ equal 1.05 which imply that the probability to be less exposed to health risk is

1.05 or more than 1.05 times higher for one unit than for another. This indicate that the matching can be sufficiently trusted and soundly reliable, Table 7. Also, vegetable

profit is less sensitive to biases resulting from unobservables at very smaller value of

Γ between 1.05 and 1.10 indicating high sensitivity to unobserved biases for values greater than 1.10, refer to Table 8.

For indirect participants versus control farmers the test shows that synthetic pesticide expenditures is insensitive to biases that could more than 1.05 increases the odds. This imply that synthetic pesticide expenditures is insensitive to unobserved biases even at large values of Γ. Vegetable profit is insensitive unobserved biases from the value of Γ equal 1.5 onward indicating that the variable is insensitive to unobservable biases at large values, Table 9.

Sensitivity analysis results for health risk. Assuming no hidden bias at a value of Γ equal one the test indicates the significant impact of the BPH training program on reducing health risk for direct participants. The test suggests five percent upper level significance (p < 0.05) for values of Γ greater than 3.85. This suggest that the study is insensitive to bias that would more than 3.85 times increase the odds making the matching as soundly reliable, Table 8.

Therefore, sensitivity results increase the confidence that there is no effect from unobservables on participation decision and on the estimated impact of the BPH training program for synthetic pesticide expenditures and vulnerability to health risk and/or there is the tolerable amount of unobserved biases. Therefore, the results can be regarded soundly reliable and valid for inference.

Chapter 5

CONCLUSION AND RECOMMENDATION

Minimizing the use of synthetic pesticide among small-scale vegetable farmers is a core goal of integrated pest management (IPM) initiatives to increase yield and farm incomes. Irresponsible use of synthetic pesticide costs farmers economically in terms of increased costs of production and also exposes farmers to high losses and vulnerability to health risks when they spray without adherence to occupation safety guidelines. The best practice hub (BPH) training approach aims at promoting IPM practices alongside other good agronomic practices that contribute to reduction in the use of synthetic pesticide to boost farmers’ income and reduce vulnerability to health risks in Tanzania. This thesis estimates the impact of the BPH training program on synthetic pesticide expenditures, yield, vegetable profit, and on reducing vulnerability to health risks of vegetable farmers in Tanzania using propensity score matching models to control for problems linked to selection bias and other observable differences which usually influence estimated outcomes. Promotion of IPM practices is hypothesized to play an important role in scaling the use of improved seeds varieties and other technologies including cultural, biological, and physical IPM techniques to control vegetable pests that otherwise threaten farmers yield, income, livelihoods and increase input costs like synthetic pesticide. If farmers reduce pesticide use they can reduce expenditure on synthetic pesticide, increase yield, profit and improve health.

Estimated results suggest that direct BPH training significantly reduced synthetic pesticide expenditures, increased vegetable profit, and reduced the proportion of farmers vulnerable to health risks. The results also show that indirect participation (farmer-to-farmer training) in the BPH training program had little impact on the outcome variables. This suggests that direct interactions of farmers with agricultural experts have greater impact than indirect interaction through farmer trainees of trainers (TOT). The policy implication is that indirect training through farmer-to-farmer interaction (TOTs) and/or learning through observation have little impact despite its widespread use in scaling improved technologies in most of the programs targeting smallholder farmers and implemented through farmer field schools (FFS). Although limited on the basis of financial feasibility, direct training remain the best approach to disseminate best farming practices and is thus recommended.

A limitation of this study is that the results may not be generalizable because the sample is not representative of the overall population of vegetable farmers in

Tanzania. Regardless of this limitation, the findings of this thesis contribute to the limited body of literature on the impact of BPH as a training approach on vegetable production among smallholder vegetable farmers in Tanzania.

A recommendation based on the results of this study is that IPM training programs be expanded to reach more small-scale vegetable farmers in developing countries to help mitigate the widespread use of synthetic pesticide, empower farmers

to improve their livelihoods through increased yield, household income and farmers’ knowledge on occupation safety to improve their health. The training programs would achieve better results if direct participation of farmers in the training is prioritized; however, where there are limited financial resources indirect participation (farmer-to- farmer training) can be considered but should be modified in order to achieve greater impact similar to direct training conducted by experts. Future research should consider estimating the difference in the extent of the impact of direct training participation relative to indirect training over sufficient time periods after the training intervention to ascertain whether indirect farmer training (farmer-to-farmer training approach) can be used effectively to promote the use of IPM practices.

REFERENCES

Abate, T., A. van Huis, and J.K.O. Ampofo. 2000. “Pest management strategies in traditional agriculture: An African perspective.” Annual Review of Entomology; Palo Alto 45:631. Available at: https://search-proquest- com.udel.idm.oclc.org/docview/222247257/citation/EB1B66628BBB4A75P Q/1 [Accessed February 2, 2018].

AGRA. 2017. “Africa Agriculture Status Report: The Business of Smallholder Agriculture in Sub-Saharan Africa.” No. Issue No. 5, Alliance for a Green Revolution in Africa (AGRA).

Alavanja, M.C.R. 2009. “Pesticides Use and Exposure Extensive Worldwide.” Reviews on environmental health 24(4):303–309. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2946087/ [Accessed July 16, 2018].

Anderson, J.R., and G. Feder. 2004. “Agricultural Extension: Good Intentions and Hard Realities.” The World Bank Research Observer 19(1):41–60. Available at: http://www.jstor.org.udel.idm.oclc.org/stable/3986492 [Accessed February 2, 2018].

Austin, P.C. 2011. “An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies.” Multivariate Behavioral Research 46(3):399–424. Available at: http://www.tandfonline.com/doi/abs/10.1080/00273171.2011.568786 [Accessed January 29, 2018].

Austin, P.C., and E.A. Stuart. 2017. “Estimating the effect of treatment on binary outcomes using full matching on the propensity score.” Statistical Methods in Medical Research 26(6):2505–2525.

AVRDC. 2013. “Improving Income and Nutrition in Eastern and Southern Africa by Enhancing Vegetable-based Farming and Food Systems.”

Becker, S.O., and M. Caliendo. 2007. “Sensitivity analysis for average treatment eﬀects.” The Stata Journal Number 1(7):71–83.

Berg, G.D. 2011. “An application of kernel-based versus one-to-one propensity score matching for a nonexperimental causal study: example from a disease management program evaluation.” Applied Economics Letters 18(5):439– 447. Available at: https://doi.org/10.1080/13504851003689692 [Accessed March 1, 2019].

Bonanomi, G., V. Antignani, M. Capodilupo, and F. Scala. 2010. “Identifying the characteristics of organic soil amendments that suppress soilborne plant diseases.” Soil Biology and Biochemistry 42(2):136–144. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0038071709003897 [Accessed January 15, 2018].

Caliendo, M., and S. Kopeinig. 2008. “Some Practical Guidance for the Implementation of Propensity Score Matching.” Journal of Economic Surveys 22(1):31–72. Available at: http://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-6419.2007.00527.x [Accessed November 24, 2018].

Calvert, G.M. 2016. “Acute Occupational Pesticide-Related Illness and Injury — United States, 2007–2011.” MMWR. Morbidity and Mortality Weekly Report 63. Available at: https://www.cdc.gov/mmwr/volumes/63/wr/mm6355a3.htm [Accessed August 23, 2018].

Carson, R. 2002. Silent Spring. Houghton Mifflin Harcourt.

Cerulli, G. 2015. Econometric Evaluation of Socio-Economic Programs. Berlin, Heidelberg: Springer Berlin Heidelberg. Available at: http://link.springer.com/10.1007/978-3-662-46405-2 [Accessed February 13, 2019].

Chandler, D., A.S. Bailey, G.M. Tatchell, G. Davidson, J. Greaves, and W.P. Grant. 2011. “The development, regulation and use of biopesticides for integrated pest management.” Philosophical Transactions of the Royal Society B: Biological Sciences 366(1573):1987–1998. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3130386/ [Accessed January 24, 2018].

Chauvin, N.D., F. Mulangu, and G. Porto. 2012. “Food production and consumption trends in sub-Saharan Africa: Prospects for the transformation of the agricultural sector.” UNDP Regional Bureau for Africa: New York, NY, USA.

Christiaensen, L. 2017. “Agriculture in Africa – Telling myths from facts: A synthesis.” Food Policy 67:1–11. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0306919217301112 [Accessed December 21, 2017].

Damalas, C.A., and S.D. Koutroubas. 2016. “Farmers’ Exposure to Pesticides: Toxicity Types and Ways of Prevention.” Toxics 4(1). Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5606636/ [Accessed June 12, 2018].

Davis, K. 2008. “Extension in sub-Saharan Africa: Overview and assessment of past and current models and future prospects.” Journal of International Agricultural and Extension Education 15(3):15–28.

Davis, K., E. Nkonya, E. Kato, D.A. Mekonnen, M. Odendo, R. Miiro, and J. Nkuba. 2012. “Impact of Farmer Field Schools on Agricultural Productivity and Poverty in East Africa.” World Development 40(2):402–413. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0305750X11001495 [Accessed December 21, 2017].

Dehejia, R.H., and S. Wahba. 2002. “Propensity score-matching methods for nonexperimental causal studies.” Review of Economics and statistics 84(1):151–161.

Dinssa, F.F., P. Hanson, T. Dubois, A. Tenkouano, T. Stoilova, J. d’A. Hughes, and J.D.H. Keatinge. 2016. “AVRDC - The World Vegetable Center’s women- oriented improvement and development strategy for traditional African vegetables in sub-Saharan Africa.” European Journal of Horticultural Science 81(2):91–105. Available at: https://www.pubhort.org/ejhs/81/2/3/index.htm [Accessed January 14, 2018].

DiPrete, T.A., and M. Gangl. 2004. “Assessing Bias in the Estimation of Causal Effects: Rosenbaum Bounds on Matching Estimators and Instrumental Variables Estimation with Imperfect Instruments.” Sociological Methodology 34(1):271–310. Available at: http://onlinelibrary.wiley.com/doi/abs/10.1111/j.0081-1750.2004.00154.x [Accessed February 8, 2019].

Ehler, L.E. 2006. “Integrated pest management (IPM): definition, historical development and implementation, and the other IPM.” Pest Management Science 62(9):787–789. Available at: http://doi.wiley.com/10.1002/ps.1247 [Accessed January 15, 2018].

Eigenbrode, S.D., and J.T. Trumble. 1994. “Host plant resistance to insects in integrated pest management in vegetable crops.” J. Agric. Entomol 11(3).

Fan, L., H. Niu, X. Yang, W. Qin, C.P.M. Bento, C.J. Ritsema, and V. Geissen. 2015. “Factors affecting farmers’ behaviour in pesticide use: Insights from a field study in northern China.” Science of The Total Environment 537:360– 368. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0048969715304915 [Accessed August 18, 2018].

Finch, S., and R.H. Collier. 2000. “Integrated pest management in field vegetable crops in northern Europe—with focus on two key pests.” Crop protection 19(8):817–824.

Fischer, G., A. Gramzow, and A. Laizer. 2018. “Gender, vegetable value chains, income distribution and access to resources: insights from surveys in Tanzania.” European Journal of Horticultural Science 82(6):319–327. Available at: https://www.pubhort.org/ejhs/82/6/7/index.htm [Accessed February 4, 2019].

Heckman, J.J., H. Ichimura, and P.E. Todd. 1997. “Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme.” The Review of Economic Studies 64(4):605–654. Available at: http://www.jstor.org.udel.idm.oclc.org/stable/2971733 [Accessed January 28, 2018].

Hester, R.E., and R.M. Harrison. 2007. Agricultural Chemicals and the Environment. Royal Society of Chemistry.

Imbens, G.W.I. 2015. “Matching Methods in Practice.” In p. 47.

Jallow, M.F.A., D.G. Awadh, M.S. Albaho, V.Y. Devi, and B.M. Thomas. 2017. “Pesticide risk behaviors and factors influencing pesticide use among farmers in Kuwait.” Science of The Total Environment 574:490–498. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0048969716320046 [Accessed June 12, 2018].

Jepson, P.C., M. Guzy, K. Blaustein, M. Sow, M. Sarr, P. Mineau, and S. Kegley. 2014. “Measuring pesticide ecological and health risks in West African agriculture to establish an enabling environment for sustainable intensification.” Philosophical Transactions: Biological Sciences 369(1639):1–18. Available at: http://www.jstor.org/stable/24501044 [Accessed April 4, 2018].

Karuppuchamy, P., and S. Venugopal. 2016. “Chapter 21 - Integrated Pest Management.” In Ecofriendly Pest Management for Food Security. San Diego: Academic Press, pp. 651–684. Available at: https://www.sciencedirect.com/science/article/pii/B978012803265700021X [Accessed January 15, 2018].

Katan, J. 2000. “Physical and cultural methods for the management of soil-borne pathogens.” Crop Protection 19(8):725–731.

Kelly, V., A.A. Adesina, and A. Gordon. 2003. “Expanding access to agricultural inputs in Africa: a review of recent market development experience.” Food Policy 28(4):379–404. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0306919203000629 [Accessed February 20, 2019].

Larsen, A.F., and H.B. Lilleør. 2014. “Beyond the Field: The Impact of Farmer Field Schools on Food Security and Poverty Alleviation.” World Development 64:843–859. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0305750X14002058 [Accessed December 21, 2017].

Lekei, E.E., A.V. Ngowi, and L. London. 2016. “Undereporting of acute pesticide poisoning in Tanzania: modelling results from two cross-sectional studies.” Environmental Health 15. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5129639/ [Accessed April 20, 2018].

Mengistie, B.T., A.P.J. Mol, and P. Oosterveer. 2017. “Pesticide use practices among smallholder vegetable farmers in Ethiopian Central Rift Valley.” Environment, Development and Sustainability 19(1):301–324. Available at: http://link.springer.com/10.1007/s10668-015-9728-9 [Accessed December 21, 2017].

Moshi, A.P., and I. Matoju. 2017. “The status of research on and application of biopesticides in Tanzania. Review.” Crop Protection 92:16–28. Available at: http://linkinghub.elsevier.com/retrieve/pii/S026121941630285X [Accessed January 24, 2018].

Mrema, E.J., A.V. Ngowi, S.S. Kishinhi, and S.H. Mamuya. 2017. “Pesticide Exposure and Health Problems Among Female Horticulture Workers in Tanzania.” Environmental Health Insights 11. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5484550/ [Accessed April 20, 2018].

Ncube, N.M., C. Fogo, P. Bessler, C.M. Jolly, and P.E. Jolly. 2011. “Factors associated with self-reported symptoms of acute pesticide poisoning among farmers in northwestern Jamaica.” Archives of environmental & occupational health 66(2):65–74. Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3914157/ [Accessed September 1, 2018].

Ngowi, A.V.F., T.J. Mbise, A.S.M. Ijani, L. London, and O.C. Ajayi. 2007. “Smallholder vegetable farmers in Northern Tanzania: Pesticides use practices, perceptions, cost and health effects.” Crop Protection 26(11):1617–1624. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0261219407000439 [Accessed January 13, 2018].

Nordey, T., C. Basset-Mens, H. De Bon, T. Martin, E. Déletré, S. Simon, L. Parrot, H. Despretz, J. Huat, Y. Biard, T. Dubois, and E. Malézieux. 2017. “Protected cultivation of vegetable crops in sub-Saharan Africa: limits and prospects for smallholders. A review.” Agronomy for Sustainable Development 37(6). Available at: http://link.springer.com/10.1007/s13593- 017-0460-8 [Accessed January 24, 2018].

Ochieng, J., V. Afari-Sefa, D. Karanja, R. Kessy, S. Rajendran, and S. Samali. 2018. “How promoting consumption of traditional African vegetables affects household nutrition security in Tanzania.” Renewable Agriculture and Food Systems 33(02):105–115. Available at: https://www.cambridge.org/core/product/identifier/S1742170516000508/typ e/journal_article [Accessed January 14, 2019].

OECD. 2016. “Agriculture in Sub-Saharan Africa: Prospects and challenges for the next decade.” In OECD-FAO Agricultural Outlook 2016-2025. OECD Publishing, pp. 59–95. Available at: http://www.oecd- ilibrary.org/agriculture-and-food/oecd-fao-agricultural-outlook-2016- 2025/agriculture-in-sub-saharan-africa-prospects-and-challenges-for-the- next-decade_agr_outlook-2016-5-en [Accessed April 16, 2018].

Panda, N., and G.A. Khush. 1995. “Host plant resistance to insects.” Host plant resistance to insects. Available at: https://www.cabdirect.org/cabdirect/abstract/19951111984 [Accessed January 27, 2018].

Pimentel, D. 2005. “‘Environmental and Economic Costs of the Application of Pesticides Primarily in the United States.’” Environment, Development and Sustainability 7(2):229–252. Available at: http://link.springer.com/10.1007/s10668-005-7314-2 [Accessed January 15, 2018].

Pimentel, D., and H. Lehman. 1993. The Pesticide Question: Environment, Economics and Ethics. Springer Science & Business Media.

Radicetti, E., R. Massantini, E. Campiglia, R. Mancinelli, S. Ferri, and R. Moscetti. 2016. “Yield and quality of eggplant (Solanum melongena L.) as affected by cover crop species and residue management.” Scientia Horticulturae 204:161–171. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0304423816301704 [Accessed January 15, 2018].

Riwthong, S., P. Schreinemachers, C. Grovermann, and T. Berger. 2017. “Agricultural commercialization: Risk perceptions, risk management and the role of pesticides in Thailand.” Kasetsart Journal of Social Sciences 38(3):264–272. Available at: http://linkinghub.elsevier.com/retrieve/pii/S245231511630039X [Accessed December 21, 2017].

Robačer, M., S. Canali, H.L. Kristensen, F. Bavec, S.G. Mlakar, M. Jakop, and M. Bavec. 2016. “Cover crops in organic field vegetable production.” Scientia Horticulturae 208:104–110. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0304423815303514 [Accessed January 15, 2018].

Rosenbaum, P.R. 2010. Design of observational studies. New York: Springer.

Rosenbaum, P.R. 2002. Observational Studies 2nd ed. New York: Springer-Verlag. Available at: https://www.springer.com/gp/book/9780387989679 [Accessed February 8, 2019].

Rosenbaum, P.R., and D.B. Rubin. 1985. “Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score.” The American Statistician 39(1):33–38. Available at: http://www.jstor.org/stable/2683903 [Accessed February 19, 2019].

Sanglestsawai, S., R.M. Rejesus, and J.M. Yorobe. 2015. “Economic impacts of integrated pest management (IPM) farmer field schools (FFS): evidence from onion farmers in the Philippines.” Agricultural Economics 46(2):149– 162. Available at: http://onlinelibrary.wiley.com.udel.idm.oclc.org/doi/10.1111/agec.12147/abs tract [Accessed December 22, 2017].

Santos, L.M.R., P. Munari, A.M. Costa, and R.H.S. Santos. 2015. “A branch-price- and-cut method for the vegetable crop rotation scheduling problem with minimal plot sizes.” European Journal of Operational Research 245(2):581– 590. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0377221715002428 [Accessed January 15, 2018].

Schreinemachers, P., T. Sequeros, and P.J. Lukumay. 2017. “International research on vegetable improvement in East and Southern Africa: adoption, impact, and returns.” Agricultural Economics 48(6):707–717. Available at: http://doi.wiley.com/10.1111/agec.12368 [Accessed January 13, 2018].

Schreinemachers, P., and P. Tipraqsa. 2012. “Agricultural pesticides and land use intensification in high, middle and low income countries.” Food Policy 37(6):616–626. Available at: http://linkinghub.elsevier.com/retrieve/pii/S030691921200070X [Accessed December 21, 2017].

Sharma, R., and R. Peshin. 2016. “Impact of integrated pest management of vegetables on pesticide use in subtropical Jammu, India.” Crop Protection 84:105–112. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0261219416300291 [Accessed December 21, 2017].

Sheahan, M., C.B. Barrett, and C. Goldvale. 2017. “Human health and pesticide use in Sub-Saharan Africa.” Agricultural Economics 48(S1):27–41. Available at: http://onlinelibrary.wiley.com/doi/10.1111/agec.12384/abstract [Accessed January 19, 2018].

Sibanda, T., H.M. Dobson, J.F. Cooper, W. Manyangarirwa, and W. Chiimba. 2000. “Pest management challenges for smallholder vegetable farmers in Zimbabwe.” Crop Protection 19(8–10):807–815. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0261219400001083 [Accessed August 23, 2018].

Smith, J., and P. Todd. 2005. “Does matching overcome LaLonde’s critique of nonexperimental estimators?” Journal of Econometrics 125(1):305–353. Available at: http://www.sciencedirect.com/science/article/pii/S030440760400082X [Accessed March 7, 2019].

Stout, M., and J. Davis. 2009. “Keys to the Increased Use of Host Plant Resistance in Integrated Pest Management.” In Integrated Pest Management: Innovation-Development Process. Springer, Dordrecht, pp. 163–181. Available at: https://link-springer- com.udel.idm.oclc.org/chapter/10.1007/978-1-4020-8992-3_7 [Accessed January 27, 2018].

United Nations Environment Programme. 2017. “Towards a Pollution-Free Planet Background Report.” United Nations Environment Programme. Available at: unenvironment.org/assembly [Accessed August 23, 2018].

US EPA, O. 2015. “Integrated Pest Management (IPM) Principles.” US EPA. Available at: https://www.epa.gov/safepestcontrol/integrated-pest- management-ipm-principles [Accessed January 29, 2018].

Williamson, S., A. Ball, and J. Pretty. 2008. “Trends in pesticide use and drivers for safer pest management in four African countries.” Crop Protection 27(10):1327–1334. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0261219408000884 [Accessed January 13, 2018].

Yorobe, J.M., R.M. Rejesus, and M.D. Hammig. 2011. “Insecticide use impacts of Integrated Pest Management (IPM) Farmer Field Schools: Evidence from onion farmers in the Philippines.” Agricultural Systems 104(7):580–587. Available at: http://linkinghub.elsevier.com/retrieve/pii/S0308521X11000813 [Accessed January 15, 2018].

Zhang, M., M.R. Zeiss, and S. Geng. 2015. “Agricultural pesticide use and food safety: California’s model.” Journal of Integrative Agriculture 14(11):2340– 2357. Available at: http://linkinghub.elsevier.com/retrieve/pii/S2095311915611261 [Accessed January 14, 2018].

Appendix

MATCHING BALANCE ON OBSERVABLE CHARACTERISTICS

Table A 1: Kernel matching results for the test of matching balance on observable characteristics between direct participants and control farmers Before Matching After Matching Variable Direct Control %bias Direct Control %bias BPH BPH Education level of the 8.000 7.286 35.7 8.013 8.131 -5.9 farmer (Years) Land allocated to 0.261 0.379 -37.2 0.263 0.266 -0.9 vegetables (ha) Vegetable farming 10.100 14.778 -65.5 10.190 10.138 0.7 experience(Years) Household size (Number) 4.500 5.180 -41.2 4.544 4.568 -1.4 Male headed household 0.888 0.947 -21.7 0.886 0.904 -6.3 (1=Yes) Access to credit (1=Yes) 0.538 0.291 51.4 0.532 0.458 15.4 Household seek market 0.188 0.074 34 0.177 0.192 -4.5 information (1=Yes)

Sample Ps R2 LR chi2 p>chi2 MeanBias MedBias B R %Var Before Matching 0.181 59.23 0 41 37.2 110.2* 0.75 50 After Matching 0.005 1.14 0.992 5 4.5 16.9 1.17 0 * if B>25%, R outside [0.5; 2]

Table A 2: Kernel matching results for the test of matching balance on observable characteristics between indirect participants and control farmers

Before Matching After Matching Variable Indirect Control %bias Indirect Control %bias BPH BPH Education level of the 7.166 7.286 -6.7 7.166 7.237 -4 farmer (Years) Land allocated to 0.397 0.379 4.3 0.397 0.382 3.6 vegetables (ha) Vegetable farming 14.148 14.778 -7.7 14.148 14.634 -5.9 experience(Years) Household size (Number) 4.858 5.180 -19.4 4.858 5.042 -11.1 Male headed household 0.947 0.947 -0.2 0.947 0.945 0.9 (1=Yes) Access to credit (1=Yes) 0.308 0.291 3.6 0.308 0.304 0.8 Household seek market 0.095 0.074 7.4 0.095 0.080 5.1 information (1=Yes)

Sample Ps R2 LR chi2 p>chi2 MeanBias MedBias B R %Var Before Matching 0.012 5.78 0.566 7 6.7 25.5* 1.02 25 After Matching 0.005 2.37 0.936 4.5 4 16.7 1.49 25 * if B>25%, R outside [0.5; 2]

2.5

1.5

kdensity _pscore

0 0

0 .2 .4 .6 .8 0 .2 .4 .6 .8 Propensity Scores BEFORE Matching Propensity Scores AFTER Matching

Direct BPH Trainees Indirect BPH Trainees

Figure A 1: Kernel density distribution to test balance between direct and indirect participants’ farmers

IMPACT OF BPH ON DIRECT VERSUS INDIRECT

Table B 3: Estimation of the impact of BPH training program for direct and indirect participants

Direct Indirect ATET S.E. T stat ATET as % Γ of PO KM PO Synthetic pesticides expenditures (USD/ha) 92.987 121.862 -28.876 23.891 -1.21 -31.054 1.25 Yield t/ha 17.072 38.417 -21.345 87.638 -0.24 -125.028 1.05 Vegetable profit (USD/ha) 529.928 185.759 344.169 189.746 1.81* 64.946 1.01-1.05 Low health risk (1=Yes) 0.410 0.251 0.160 0.076 2.11** 38.889 5 NNM PO

64 Synthetic pesticides expenditures (USD/ha) 92.987 120.891 -27.905 30.595 -0.91 -30.009 1.25

Yield t/ha 17.072 9.916 7.156 5.936 1.21 41.917 1.05 Vegetable profit (USD/ha) 529.928 125.393 404.535 196.664 2.06** 76.338 1.01-1.05 Low health risk (1=Yes) 0.410 0.205 0.205 0.098 2.09** 50.000 5 Asterisks denote the level of significance for a t-test of difference in means, ***P < 0.01, **P < 0.05, *P < 0.1; Γ stands for gamma which measure the critical level of hidden bias, critical level of hidden bias is measured at 5% level of significance; S.E stands for standard error, PO stands for Potential Outcome

PERMISSION TO USE SECONDARY DATA

QUESTIONNAIRE: WORLDVEG-VINESA PROJECT

Survey of Knowledge Access and Diffusion Process and Their Effects on Farmers’ Incomes, Nutrition and

Social Capital in Ethiopia, Malawi and Tanzania:

Farm Household Questionnaire Household [Use baseline ID if household was interviewed in 2014] ID: Region District Ward Village Date (dd/mm/yyyy): Survey Starting Time: Survey End Time: Longitude  ' " N / S (encircle) Latitude  ' " E Altitude (meters) Enumerator’s name Survey Checked by

(name of supervisor) Did you or any other member of your household participate in 1=YES 2=NO the 2014 survey? Category of 1=BPH training participant Households: 2=Non-participant but living in BPH village 3=Household in a control village

Definitions

Reference period. The reference period that will be used for the survey is DURING CROP YEAR

Dry season – end July, 2016 to Dec, 2016; Rainy season – end Dec, 2015 to July, 2016)

Currency: The type of currency unit might vary from country to country

Who should be interviewed?

Primary respondent: For intervention households, this is the person in the household who has received the VINESA training. For indirect beneficiaries and control households this is the person in the household who is the most likely to have received the training if it had been offered to them.

Secondary respondent: This is the person in the household who is usually in charge of meal preparations.

Informed Consent (enumerator copy) Before beginning the interview, it is necessary to introduce the household to the survey and obtain their consent to participate. Make it clear to them that their participation in the survey is voluntary. Please read the following statement in the language of interview: Thank you for the opportunity to speak with you. We are a research team from the World Vegetable Center in Arusha. We are conducting a survey to learn about vegetable production and consumption in this area. You have been selected to participate in an interview which includes questions on topics such as your family background, vegetable production, participation in training and knowledge obtained, and food and non-food expenditures. The survey includes a section to be asked about vegetable production and marketing which will be asked to a primary adult male or female in your household, and a section on vegetable consumption, which will be asked to the person in usually charge of meal preparation. These questions in total will take approximately 2 hours to complete and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or to skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information that identifies you with anyone. We will also interview other households in your community and in other communities in this district. After we collect all the information we will use the data to make a study about how to better organize vegetable training in this area. Do you have any questions about the study or what I have said? If in the future you have any questions regarding study and the interview, or concerns or complaints we welcome you to contact John Macharia, by calling +255 27 255 3125 / +255 768 268 093. We will leave one copy of this form for you so that you will have record of this contact information and about the study.

Please ask the participants (male and female) if they consent to the participation in the study (check one box):

Primary respondent: YES NO : Secondary respondent: YES NO

I ______, the enumerator responsible for the interview taking place on ______, 2017 certify that I have read the above statement to the participant and they have consented to the interview. I pledge to conduct this interview as indicated on instructions and inform my supervisor of any problems encountered during the interview process. If the household does not give consent to all of the data collection, stop the interview and inform your team leader. Team leaders will discuss the reason for this refusal and decide whether a partial data collection is possible for this household.

Informed Consent (respondent copy) Enumerator: Tear out this page, and leave it with the household. Thank you for the opportunity to speak with you. We are a research team from the World Vegetable Center in Arusha. We are conducting a survey to learn about vegetable production and consumption in this area. You have been selected to participate in an interview which includes questions on topics such as your family background, vegetable production, participation in training and knowledge obtained, and food and non-food expenditures. The survey includes a section to be asked about vegetable production and marketing which will be asked to a primary adult male or female in your household, and a section on vegetable consumption, which will be asked to the person in usually charge of meal preparation. These questions in total will take approximately 2 hours to complete and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or to skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information that identifies you with anyone. We will also interview other households in your community and in other communities in this district. After we collect all the information we will use the data to make a study about how to better organize vegetable training in this area. Do you have any questions about the study or what I have said? If in the future you have any questions regarding study and the interview, or concerns or complaints we welcome you to contact John Macharia, by calling+255 27 255 3125/ +255 768 268 093. We will leave one copy of this form for you so that you will have record of this contact information and about the study.

Please ask the participants (male and female) if they consent to the participation in the study (check one box):

Primary respondent: YES NO: Secondary respondent: YES NO

SECTION A: RESPONDENT IDENTIFICATION & HOUSEHOLD SOCIO-ECONOMIC CHARACTERISTICS A1. The person in the household managing vegetable production 1.1 HH ID. [______] (Same as page 1) 1.2 Name of primary respondent [______] 1.3 Cell phone/Mobile No. [______] 1.4 Name of the household head (if not respondent) [______] 1.5 Name of secondary respondent [______] 1.6 Type of farming? 1 = Contract only; 2 = Non-contract only; 3= Both [___] 1.6.1 Type of contract? 1=Formal/written contract 2= Informal/ verbal contract 1.7 Household head experience in vegetable production (No. of years) [___] 1.8 Farm decision making 1=head alone, 2=entire family [ ] 1.9 Categories of households 1. Male headed household 2. Female-headed household 3. Female co-head; the primary female decision maker in male-headed household ______1.10 Total household size, including respondents [____]

Household: A household is a group of people who live together and eat together. In our survey, a household member is someone who has lived in the household at least 6 months and at least half of the week in each week in those months. Even those persons who are not blood relations (such as servants, lodgers, or agricultural laborers) are members of the household if they have stayed in the household at least 3 months of the past 6 months and take food together. Generally, if one person stays more than 3 months out of the last 6 months outside the household, they are not considered household members. We do not include them even if other household members consider them as household members.

A2. Household roster (Kindly start with primary respondent). Member Name of HH Relationship In Male or Marital What is the What is ID member to the which female? Status highest your main household year See See level of occupation? head was codes codes formal this below below education person completed? born? (Number of years) 1 2 3 4 5 6 7 8 9 10 11 12

Marital status Relation to head (rhead) 8= son/daughter-in-law Sex: 1 = single 1= head 9= grandchild 2=monogamously married 2= spouse 10=other relative 1=Male; 3=polygamously married 3= own child 11=unrelated/ visitor 2=Female 4 = divorced 4= step child 12=brother /sister-in-law 5 = widowed 5= parent 13=parent-in-law 6 = separated 6= brother /sister 14=worker 7 = other, specify____ 7= nephew /niece 15=Other specify Occupation: Agriculture=1, Dairying=2, Commercial Poultry Farming=3, Fishery=4, Govt/private employee=5, Self-employed in crafts, shops etc.=6, Agri wage earner or day laborer=7, Non-agriculture wage earner or day laborer=8, Unemployed =9 Others=10 (mention)

A3.Social Capital 3.1 Characteristics of Groups that you’re a member of (it includes all type of groups for example , Women’s group,, Farmers’ group, cooperative society, youth group, primary society, rotating credit societies, dairy/cattle group) 3.1.1 How many groups (from a prompted enumeration of groups in the village) are you a member of (numbers)______3.1.2 The group details below Answers to questions below for up to three groups N Group On On Is the Does What Do other If Yes, Has there If yes, o Name average, what group the are groups/far which been an how? (a) how often extent registere group the mers come is the improvem do you do you d by the has grou for advice main ent in (j) participate particip ministry bank p from advice understan (Code) in the ate in of account activi members? they ding of activities the culture (Yes=1, ties ( seek? members of the group and No=2) Yes=1, (h) and group to decisio social (f) No=2) (Code) leaders’ which you n- services (e) (g) roles and belong in a making (Yes=1, responsibi month? ? No=2) lity? (b) (c) (d) (yes=1; Code no=2) (i) 1 2 3 Code for (c) 1= To a very small extent; 2= To a small extent; 3=neither a small nor large extent; 4=to a large extent; 5=to a very large extent; Code for (f) 1=Credit and savings 2=Collective marketing of vegetables 3=”gang” labor/labor sharing 4=Joint input purchase 5=Lobbying for district by laws 6=others, specify; Code for (h) 1=Irrigation 2. Seedling production 3=Making of compost 4=Pest and disease control 5=Fertilizer application 6=Marketing of vegetables 7=Other advise not related to agriculture 8=Other specify; Code for (j) 1= Aware of group bylaws 2=Financial reporting by leaders 3=Regular elections 4=Regular attendance to meetings 5=Others, specify

SECTION B: VINESA TRAINING PROGRAM (This section is answered by VINESA project participants) 4.1 Did you participate in VINESA training? Yes=1 No=2 (Note that this question is relevant only to BPH member) If [No] go to Section A5. 4.2 If [yes] please fill out the following table Training modules/topics A. Did you B. If attended, C. Have you attend? how useful did practiced on your Yes=1 No=2 you find the own farm what you (go to next training learned in this row); module? training module? 3=Don’t (codes) Yes=1; No=2; remember Already practiced it before the training=3 Module 1 – Safe and Efficient Vegetable Production 1. Raising seedlings 2. Land preparation 3. Transplanting 4. Direct sowing 5. Vegetable field management 6. Efficient water management Module 2 –How to produce quality Seeds 7. How to produce quality vegetable

seeds 8. Agronomic aspects 9. Quality control Module 3 – Reducing Post-harvest Losses 10. Proper vegetable harvesting and

postharvest handling 11. Prolonged vegetable storage (Increasing vegetable value (processing)) Module 4 – Increasing Intake of Vegetables 12. Improved nutrition and health of rural and urban consumers through

consumption of nutritious, diverse, and safe vegetables. 13. Vegetable recipe preparation and

preservations Module 5 – Becoming Successful Agri-business Entrepreneurs 14. Targeting high value markets 15. Becoming a preferred supplier 16. Maintain lasting relationship among

suppliers and buyers Module 6 – Increasing Women Role in Vegetable Farming 17. Gender in vegetable farming Module 7 – Forming Strong

Farmers’ Groups Key to strong farmers groups Codes for b: 4= very useful, 3=somewhat useful, 2=not very useful, 1= not at all useful.

4.3 Have you shared the knowledge you learned with neighbors or friends in your village? Yes=1 No=2 4.4 If yes, how many others have you shared your knowledge with? ______persons 4.5 What topics did you cover while training other people? ______4.5 If No, please explain why______4.6 Generally, do you think that you have benefited from VINESA project?______( 1 =Yes, No=2 4.6a If your answer above is NO, Explain why______4.6b If your answer above is YES, Explain how______

SECTION B1: INDIRECT BENEFITS FROM THE VINESA TRAINING (Only answered by indirect beneficiaries) 4.7 Do you think that you benefitted from other farmers who were trained by VINESA? Yes=1 No=2 4.8 If 4.7 is yes, have you also trained other farmers? Yes=1 No=2 4.9 If 4.8 is yes, how many have you trained so far? ______persons

SECTION C. LAND HOLDINGS AND CROPPING PATTERN C1. Description of land holdings and irrigation practices Description Land Irrigated Irrigation Distance Type of Rent in TZS per Remark area (acres) source between irrigation year per acre (acres) (b) water applied (a) source and field where vegetables are grown (km) Code (d) Code TZS/acre Share (h) (c) (e) (f) (%) (g) 1. Area owned (farm and non- farm area including homestead) 2. Area leased- out 3. Area leased- in 4. Area used to cultivate crops (excluding grazing and pasture areas) 5. Total number of plots (Numbers) Local unit code: * Source of irrigation code (c)– (1) Canal (2) pond/tank (3) surface (4) ground water (5) Others Type of irrigation code (e): 0=No irrigation used, 1=Furrow without ridges, 2=Furrow with ridges, 3=Manual from tube well 4=Manual from tank/lake 5=sprinkler 6= drip 7=pump with siphons, 8=Others (specify)

C2. Complete the following for all crops grown during the LAST SEASON in reference period (add extra sheets if required) Crop. Code Crop. Code Crop. Code Crop. Code Crop. ______Code____

Sowing. Sowing Sowing Sowing. Sowing. Activities Unit month: month: month: month: month: ______Harv.month: Harv.month Harv.month Harv.month: Harv.month ______Cropping 1=mono; 1 pattern 2=intercrop Contract 2 Yes=1; No=2 crop 3 Area Acres 1= Improved Variety 4 variety type 2=Local Name 5 -9 if unknown variety Crop Code: 1="Tomato" 2= "Amaranth" 3= "African Eggplant" 4 ="Night Shade" 5= "Irish Potato" 6= "Okra" 7 ="Cowpea leaves" 8= "Spider Plant" 9= "Cassava Leaves" 10= "Ethiopian Mustard" 11 ="Sweet Potato Leaves" 12= "Jute Mallow" 13= "Pumpkin leaves" 14= "Sorghum" 15= "Finger Millets" 16= "Maize" 17 ="Cassava tuber" 18= "Beans" 19= "Oilseeds e.g. sunflower, groundnut and others" 20= "Bitter gourd" 21= "Grapes" 22 ="Paddy rice" 23="Plantain Banana" 24 ="Okra" 25= "Cabbage" 26= "Broccoli" 27= "Eggplant" 28= "Onion" 29=”Sweet pepper” 30= Carrots 31=Pigeon pea 32=Lettuce 32= Pumpkin fruit 33=Other bananas 34=Kales 35=Others, specify

SECTION D: MAIN VEGETABLE PRODUCTION AND MARKETING D2.1 Seed production of major vegetable (note: it should be a major crop under Seed category) OUTPUT during LAST SEASON in reference period SNo Activities Unit Crop.code___ Output: Seed Did you produce vegetable seeds for marketing or home 1 1=Yes; 2=No consumption? (If yes, go to D2.3) 2 Area under vegetable seed production (Acres) 3 If Yes, what quantity of vegetable seed did you produce? Qty 4 Name of the Unit Name 5 Unit Conversion kg/Unit 6 Seed Production after conversion Qty inKg 7 Qty (seeds) given as gift to others (Gram) Qty in Kg 8 Qty for stored for own use (Gram) Qty in Kg 9 Qty by- product (kg)(Only if by-product is sold) Qty in Kg 10 Value for by- product(Only if by-product is sold) Val in TZS 11 Loss before Harvest Qty in Kg 12 Qty Wastage after harvest (Kg) Qty in Kg

D2.2 Crop (vegetable seed) marketing during LAST SEASON in reference period Marketing of Major Vegetable Seed SNo Activities Unit Crop. Code ____ 1 Qty Sold (Kg) Qty in Kg 2 No. of Transaction No. 3 Amount Received for seed production TZS 4 Source of Buyers Code 5 Reasons- buyers Code 6 Mode of payment Code 7 Time of Payment Code 8 Any input advance? Yes=1; No=2 9 If yes, how much? TZS 10 Sales location Code 11 Distance from home to sales location KM 12 Time between home to sales location Hrs 13 Transport mean Code 14 Transaction time on the sales location Hrs 15 Source of Price info Code 16 Packaging Cost TZS 17 Transportation Cost TZS 18 Loading and Off loading TZS 19 Payments at checkpoint or road-block TZS 20 Entry license fee at the market TZS 21 Weighing fees TZS 22 Grading TZS 23 Other expens:_____ TZS

D2.3 Crop (major vegetable (note: it should be a major crop under Vegetable category) OUTPUT during LAST SEASON in reference period S/No Activities Unit Crop. Code ___ Output: Vegetable 1 Area (Acres) 2 Vegetable Production (Unit) Qty 3 Name of the Unit Name 4 Unit Conversion Kg/Unit 5 Vegetable Production (Kg) Qty in Kg 6 Home Consumption (only vegetables) Qty in Kg 7 Qty (Vegetable) given as gift (Kg) Qty in Kg 8 Qty by- product (kg)(Only if by-product is sold) Qty in Kg 9 Value for by- product(Only if by-product is sold) Val in TZS 10 Loss before Harvest Qty in Kg 11 Qty Wastage after harvest (Kg) Qty in Kg

D3. Crop (major vegetable) marketing during LAST SEASON in reference period SNo Activities Unit Crop. Code___ 1 Qty Sold (Kg) Qty in Kg 2 No. of Transaction No. 3 Amount Received TZS 4 Source of Buyers Code 5 Reasons- buyers Code 6 Mode of payment Code 7 Time of Payment Code 8 Any input advance? Yes=1; No=2 9 If yes, how much? TZS 10 Sales location Code 11 Distance from home to sales location KM 12 Time btn. home to sales location Hrs 13 Transport mean Code 14 Transaction time on the sales location Hrs 15 Source of Price info Code 16 Packaging Cost TZS 17 Transportation Cost TZS 18 Loading and Off loading TZS 19 Payments at checkpoint or road-block TZS 20 Entry license fee at the market TZS 21 Weighing fees TZS 22 Grading TZS 23 Other expens:_____ TZS if multiple answer create a separate code for it and note down in your note book for the future reference. Code for buyer (Sec D2.2 &D3=Q4): 1=Collector in village (outside wholesale market); 2=Wholesaler on market; 3=processing firm; 4=Contractor; 5=modern retailers; 7=NGO; 9=Cooperative society; 10=Farmer co-op; 11=Retailer; 12=Consumers; 13=Hotels/restaurants; 14=Others:______Code reason buyer (Sec D2.2 &D3=Q5): 1=He gives higher prices; 2=He accepts large quantities; 3=He accepts small quantities; 4=He gives advances when needed; 5=He pays immediately; 6=He is close by; 7=He is always available 9=He gives better quality inputs; 10=No other option Code mode of payment (Sec D3&D4=Q6): 1=In cash; 2=In kind (agricultural input); 3=Partly in cash and partly in kind; 4=Cheque; 5=Others Code time of payment (Sec D3&D4=Q7): 1=Immediately on the day of the sale; 2=In the days after the sale; 3=A week after the sale; 4=Later than a week after the sale; 5=Weekly; 6=Monthly; 7=Quarterly; 8=Others: ____

Code sale location (Sec D3&D4=Q10): 1=Farmer’s field 2=Wholesale market; 3=Local retail market; 4=modern retail; 5=cooperative 6=farmers’ group; 7=Others____ Code main transport means (Sec D3&D4=Q13): 1=Porter/own carry; 2=Handcart; 3=Tractor; 4=Truck; 5=Car; 6=Bicycle; 7=Motorbike; 10=Others Code source of price info (Sec D3&D4=Q15): 1=Radio 2=Television 3=Newspaper 4= Government’s agricultural marketing information center 5= Any trader at the local market 6= Collector who comes to the farm 7= Other farmers 8= Extension officers 9= Internet 10= Cooperative/farmer’s association 11= Contract company 12= NGOs 13= Through mobile phone services 14= Other (specify)

D4. Details on inputs used for the major vegetable during LAST SEASON in reference period Vegetable seeds Vegetables only SNo Activities Unit Crop. Code__ Crop. Code__ 1 Did you purchase any seeds in last 12 months 1=Yes; 2=No 2 Total Seeds used– Qty Grams 3 Own Seed used – Qty Grams 4 If no, why code 5 Purchased Seeds– Qty Grams 6 Purchased Seeds cost – value TZS 7 When did you buy seeds Months 8 Source of Seeds Code 9 Major reason for choice of this vendor Code 10 Distance to vendor KM 11 Method of pay Code 12 Tagged product Code 13 If yes, tagged product price TZS 14 Branded Code 15 Package Code 16 Hybrid 1=Yes; 2=No 17 Satisfied purchase 1=Yes; 2=No 18 If No, why? Code 19 Manure-quantity Kgs 20 manure–value TZS 21 Inorganic fertilizer– quantity Kgs 22 Inorganic fertilizer– cost TZS 23 Inorganic Sellers Code 24 Pesticide (fungicides,insecticides, etc) - Qty Kg 25 No of times applied per season/crop cycle Numbers 26 At what growth stage Code 27 Pesticide (fungicides,insecticides etc) – cost TZS 28 Pesticide (fungicides,insecticides, l etc) Sellers Code 29 Herbicides - Qty Kg 30 No of times applied per season Numbers 31 At what growth stage Code 32 Herbicides – cost TZS 33 Herbicides Sellers Code 34 Source of info. on pesticides/herbicides Code 35 Cost of Irrigation TZS No. of times 36 Frequency of irrigation /season 37 Hired labor– quantity Man-days 38 Hired labor– value TZS 39 Family labor– quantity Man-days 40 Machine rental – value TZS 41 Other input costs – value TZS Code If no, why (Sec D4=Q4): 1=No need; 2=Unable to find the seeds at the right time; 3=Seeds were too expensive; 4=Did not find the required quality Source of Seed Code (Sec D4=Q8): 1=Other farmers 2= stockiest 3= Friends 4=company store; 5=contract company; 6=Others (specify) Codes reason for choice of vendor/seller (Sec D4-Q9): 1= He deliver better quality inputs; 2= He sell large quantities 3= He sell small quantities 4= He gives in credit when I don’t have money; 5= He deliver immediately; 6= He is close by;7= He is always available; 8=No other option Tagged product Code (Sec D4=Q12): 1=none; 2=seeds; 3=fertilizer; 4=chemical; 5=others (specify)

Branded Code (Sec C4=Q14): 1=Unbranded; 2=national/international brand; 3=local brand; 4=don’t know Package Code (Sec C4=Q15): 1=loose; 2=packaged with printed crop photo; 3=packaged with pasted sticker of crop photo If no, why Code (Sec C4=Q18): 1=Spurious or fake product; 2=under-weight; 3=others (Specify) Inorganic sellers code (Question 23): 1=Other farmers 2= stockiest 3= Friends 4=company store 5=Others (specify) At what growth stage code (Question 26, 31): 1= At seedling stage 2=Before flowering; 2=At harvest stage;3=Less than 14 days before harvest; 4= Any time (I don’t care timing)5= Every growth stage Codes for vendor/ seller (Sec D4 =Q33): 1= Agro dealers 2=Seed (producing) companies 3= Seed retailers in the village 4= Other (Specify)______Source of information on Pest/Insecticide (Question 34): 1=Other farmers 2= stockiest 3= Friends 4=company store 5=NGO/research institutes 6=Others (specify)

SECTION E: YIELD, INPUT USE FOR ALL OTHER CROPS (The questions in this section refer to all crops grown by the household).

E5 Harvested quantity and total output value of all crops in last 12 months Quantity Average price / Approximate harvested Prevailing price value of Crop enterprise and unit per unit production Unit codes: Qty Unit Price Value in TZS 1=90kg bag Cereals 2=kg Wheat 2 3=crate Rice 3 5=number Barley 4 6=bunches Sorghum 5 7=ton Other cereals (specify) 6 8=50kg bag Pulses& oil crops Dry beans 7 Cowpeas 8 Green grams 9 Pigeon peas 10 Groundnut 11 Other pulses & oil crops 12 (specify) Roots & tubers Irish potatoes 13 Sweet potatoes 14 Cassava 15 Other roots & tubers 17 (specify--) Vegetables African egg plant 18 Tomatoes 19 Chinese cabbages 20 Amaranths 21 Onions 22 Ethiopian Mustard 23 Other vegetables(specify---) 24 Other vegetables(specify---) 25 Other vegetables(specify--) 26 Other vegetables(specify--) 27 Fruits Bananas 28 Avocado 29 Mango 30 Passion fruit 31 Other fruits (specify----) 32 Cash crops Tobacco 33 Tea 34 Coffee 35 Sugarcane 36 Other cash crops (specify-) 37

E5:1 Quantity and value of total input use on all crops produced in last 12 months

Inputs Quantity (Unit Unit Total expenditure on Unit codes: codes) the input (TZS) 1=90kg bag 1. Seed and 2=kg seedlings 3=50 kg bag 2. Mineral fertilizer 4=litre 3. Animal manure 5= numbers 4. Other organic 6= 25 kg bag manure 7=10 kg bag 5. Lime 8=tonnes 6. Sticks, bags, 9=grams ropes, plastics 10=w/barrow 7. Insecticides 11=cart 8. Herbicides 12=5 kg bag 9. Fungicide 13=Other(specify)___ 10. Fuel, electricity (if used) 11. Cost of land preparation (e.g ploughing, tilling, disking, sowing, etc) if this service was bought. 12. Cost of hiring labor

SECTION F: PESTICIDE USE IN VEGETABLE PRODUCTION AND ACCESS TO MARKET INFORMATION F7. Pesticide use in vegetable production 7.1. What kind of precaution do you take before/during application of agricultural pesticides or insecticides? Please circle multiple correct responses. a. Wear all protection gear such as gloves, mask, Overall, gumboots =1 b. Wear of few protection gear e.g. only nose/mouth protection =2 c. Protection gear and wind direction =3 d. Washing hand with soap after chemical application =4 e. Milk taking after pesticide application =5 f. Milk taking after chemical application =6 g. Wind direction =7 h. No precaution taken, I just apply =8 7.2. How long do you usually wait after the last pesticide spraying to harvest the field (in days)?

7.3. How do you decide when to use the pesticides on vegetables? Please circle  one correct response a. At regular intervals throughout the season (calendar) b. When we see pests and /or diseases symptoms in the field (control) c. After field sampling and finding a certain number of pests or a certain level of damage (thresholds) d. When told by someone to apply a pesticide e. Other (please specify) 7.4 If someone told you to apply pesticide, who is that? 1. Extension agent, 2. Trader, 3. Stockist 4. Commission agent, 5. Wife, 6. Husband 7. Others (specify) (multiple answers possible)

F8. Access to market and price information for vegetable crop 8.1 In General, before choosing which crop/varieties to grow, do you seek market information (for example what to grow and where to sell to maximize product price) _____1=Yes 2=No 8.2 If Yes; Source of Information______8.3 Before growing the main crop, do you seek information on potential demand? _____1=Yes 2=No 8.4 Before harvesting, do you seek information on market prices for your main crop? _____1=Yes 2=No 8.5 If Yes, what are the most important sources of market prices information for your crops (Circle all that applies)? 1=radio; 2=TV; 3=news paper; 4=government’s agricultural marketing information center; 5=any trader at the local market; 6=collector who comes to the farm; 7=other farmers; 8=extension officers; 9=internet; 10=cooperative/farmers’ association; 11=contract company; 12=NGOs; 13=mobile phone; 14=others (specify)______8.6 How often do you obtain this information? 1=Daily; 2=once a week; 3=more than once a week; 4=once a month; 5=2-3 times a month; 6=once in 3 months; 7=once in a season 8.7 Are you satisfied with the accuracy of this information?_____1=Yes 2=No 8.8 If no, what is the main reason? 1= info. is not frequently available; 2=info. is inaccurate; 3=info. provided does not meet my interest; 4=info is too complex to understand; 5=others (specify)______8.9 Do you use mobile phone to get price, market and other information from other stake holders (other projects/NGOs, farmers etc)______1=Yes 2=No

SECTION G: POSTHARVEST AND POSTHARVEST LOSSES INFORMATION IN LAST SEASON FOR MAIN VEGETABLE CROP G1 General postharvest information G1.1 Where do you put your fresh produce 1. On ground in sun immediately after harvest? 2. On ground in shade 3. In basket (Tick only one.) 4. In crate 5. In cart 6. In plastic bag 7. In plastic sack 9. Other (Specify) G1.2 What do you do to prevent spoilage after 1. Harvest during cool weather harvest? 2. Careful handling so as not to damage (Tick all that applies) 3. Spray water on produce 4. Store under shade 5. Store in a cool place 6. Take care during transport by careful stacking 7.Use padding/cushioning material during transport 8. Harvest after buyer has been identified 9. Nothing 10. Other (Specify)

G1.3 Do you bring your harvested produce to the market? 1=Yes; 2=No G1.4 If yes, how is your produce brought from the 1. Baskets on foot field to the market? 2. Bicycle 3. Hand cart/push truck 4. Motor bike 5. Pick-up truck 6. Motorized tricycle 7.Other (Specify) 8. Not applicable G1.5 Do you do packaging of produce before Yes=1; No=2 selling? 1. Plastic Bag G1.6 If yes, how do you pack your 2. Sacks (woven plypropylene) produce? 3. Baskets 4. Wooden boxes 5. Large crates 6. Paper boxes/Cartons 7. Insulated/Styrofoam boxes 8. Loose 9. Other (Specify) G1.7 Do you do any processing of produce after Yes=1; No=2 harvest? 1. Grading 2. Sorting G1.8 What value addition activities do you do for 3. Cleaning each crop? 4. Packing 5. Labeling 6. Cooling

7. Storage 8. Transportation 9.Processing (Juicing, canning, drying, etc) G1.9 Do you have storage facilities for fresh Yes=1; No=2 produce? 2. Own Store room on farm 3. Use neighbors G2.0 If yes, what type of storage facilities do you 4. Rent/hire facilities have? 5.Use cooperative/association facilities Tick only one. 6. Outside/roadside 8 .Other (specify) G2.1 What is the normal/average length of storage Days

G2. Postharvest losses information G3.1 Did you have spoilage between harvesting and selling? 1=Yes; 2=No G3.2 If yes what percentage losses from the total produce in last season? (%) G3.3 Why did you have spoilage? 1. Hot weather 2. Diseases 3. Damage during harvest 4. Damage during transport 5. Delay in obtaining transport 6. Not able to find market 7. Poor quality of variety 8. Other (Specify)

H. TRAINING AND EXTENSION AND INFORMATION NEEDS H1. Extension Services 1. Did you use agricultural extension in the last twelve months? ____ 1=Yes>>8 2=No 2. In the last twelve months, was the wanted extension available?___ 1=Always available>>4; 2=Usually available; 3=Not available; 4=Did not want any 3. If yes, please fill out the table below for contact with an agricultural extension person Nr Trs Who provided the Distance to How often For what What type of agricultural information? place of did you crop information was extension have were mainly given? contact consul- with him in tations last 2 made? seasons? 1=International Research km Number of Crop 1=Use of Organization times code fertilizer 2=Government extension (if 2=Irrigation officer general, 3=New seed 3=Radio varieties code=88) 4=University/Directorate of 4=Diseases extension services problem 5=NGO 5=Soil problems 6=Donor project 6=Weather 7=TV problems 8=Extension agents from seed 7=Marketing company advice 9=Extension agent private 8=Help getting processing companies credit 10=stockiest 9=General 11=Other:______advice 1 2 3 4 5

4. If no, why did you not use any? ______1=No need; 2=Unable to find them at the right time; 3=they were too expensive; 4=Did not find the required quality; 5=not allowed by partner/spouse; 6=Breastfeeding; 7=pregnancy; 8=Illiteracy; 9=Others (specify)

H2. Financial services in the last twelve months 1. Do you own a Bank account? ____ 1=Yes 2=No 2. Did you receive any credit during the last twelve months? ____ 1=Yes (go to 5) 2=No 3. If no, why did you not receive any? ______1=No need; 2=Unable to find lender at the right time; 3=Interest rates are too high; 4=Did not have the collateral >>6 4. If yes, please fill out the table below for every credit transaction N Source Major Distance to Amount Annua What was Use of the o of credit reasons the lender borrowed in l the credit for the total? interes collateral choice t rate for the of credit loan? provider Code 1=He is km TZS/Kwacha/Bir % 1=Land 1=Seasonal credit close by s r 2=Equipmen agricultural provide 2=He t inputs (seeds, r gives the 3=Other fertilizer,…); best 4=No 2=Agricultura condition collateral l equipment; s 3= Land 3=He is purchase; reliable 4=Livestock 4=Alway purchase; s 5=Purchase available other assets; 5=No 6=Food other needs; option 7=Health 6=Low needs; interest 8=Education rates needs; 7=Others 9=Other , specify 1 2 3 4 5 6 Credit provider: 1. Private bank; 2. Government bank; 3. Cooperative society or District Cooperative Bank; 4. Regional rural bank; 5. Private money lender; 6. NGO;7. Input retailer; 8. Output trader; 9. Private processing company store; 10. Micro-finance institution; 11. Informal savings-credit group; 12. Other:______

I. ASSET HOLDINGS BY HOUSEHOLDS (ON THE DATE OF SURVEY) Name of item Ownership Number If you sold one Who owns it? Who takes care of What is the 1=Yes 2=No of assets of these today, 1=Man;2=Wom the assets? estimated (if no go to how much an 3=Man & 1=Man; 2=Woman lifespan the next asset) money could woman jointly; 3=Man and woman asset? you get? 4=Non-hh jointly; 4=Non- member household member A. Farm animals 1.Cows and calves 2. Sheep 3. Bullocks (Oxen) 4. Goats 5. Donkeys

6. Chicken, ducks

7. Pigs 8. Other livestock B.Machinery and other implements 9. Tractor 10.Power tiller 11. Power sprayer 12.Irrigation pump set 13. Carts

14.Bicycle 15.Motocycle 16. Wheel barrow

17. Water pump and hosepipe 18.Harvesting equip. 19. Knapsack sprayer 20.Other mechanical equipment C.Durable farm assets 21. Farm house 22. Animal house 23. storage house D. Other luxuries 24. Car 25. Electric gadgets

26. Furniture 27. TV 28. Radio 29. Refrigerator 30. Sewing Machine 31. Mobile phone

31. Other (specify)

J. LIVESTOCK INCOME J.1 what kind of farm animals did you sell in the last 12 months? SL Total revenue from Total Unit Amount of Number Animal selling number of TZS/kwacha/Birr/ (TZS/Kwacha/Birr) Sold Unit 1 Chicken 2 Ducks 3 Goats/Sheeps 4 Cattle 5 Animal manure 6 Pig (Swine) 7 Eggs 8 Fish 9 Milk

1. J2. Livestock rearing cost (Please provide the information about the livestock rearing cost of your household for the past 6 months (including those purchased only) Total expenditures Item (TZS/Birr/Kwacha) 1. Young animals 2.Animal feeds- grass/fodder/concentrates

grass/fodder/concentrates 3. Sheds, fencing, equipment 4. Medicine 5. Hired labor 6. Aquaculture/Fish 7. Others (specify) Note: Aquaculture cost includes breed, food, small, non-durable tools as fishing-net and others.

K. NON-FARM AND OFF-FARM INCOME K.1. Income from non-farm enterprises and commerce 1. In the last 12 months, did you have income from non-farm-activities:____ 1=Yes 2=No (if no, go to H.2) 2. How much income did you have in the last 12 months? Source of income 1=Yes 2=No Amount in TZS over the past 12 months Business Casual in company/school Farm laborer Income from renting out assets In kind (gift) Remittances from within the country (Ethiopia/Tanzania/Malawi? Remittances from abroad Pension Sale of assets( land, cars, bicycle etc) Salary from employment (police, teacher, banker etc) Dividend/bonus Other labor (e.g. construction, transport) Others, specify______

K2. Shocks in the last 5 years

Event Did this event [If yes, tick all years in happen over the which such event occurred] last five years? 2012 2013 2014 2015 2016 1. Yes; 2.No [If no, go to next line] 1 Major fire 2 Flood/cyclone 3 Drought 4 Major theft from the family 5 Major crop disease (that destroyed big part of crop) 6 Major animal disease (which killed animals) 7 Major (human) disease in the family 8 Deaths in the family 9 Divorce/separation 10 Too much rainfall 11 Other big shock (specify:______)

L. GENDER DIVISION OF LABOUR IN THE HOUSEHOLD L1. Labor force for MAIN vegetable crops cultivation (a) Wage rate for hired labor/day______Male ______Female Household Labor (person days) Hired Labor (person days) Male Female Total Male Female Total Adult Adult labor labor Labor labor labor labor days hired hired days days days (8hrs) labor labor (8hrs) (8hours (8hours days days ) if not ) if not (8hours (8hours used used ) if not ) if not enter enter (3) used used (6) zero zero enter enter (1) (2) zero zero (5) (4) 1. Land preparation 2. Direct Seeding /transplanting 3. Mulching 4. Weed control 5. Staking 6. Chemical fertilizer application 7. Manuring/composting 8. Pesticide application 9. Watering/irrigation 10. Harvesting 11. Packing/Transportatio n 12. Other (specify)

L3. Decision making by gender Who is the Who is the primary secondary Who was the main person in the household making the S/ decision decision following decisions? (List up to 3 persons) No maker on this maker on Use member ID from hh roster activity this activity (Code) (Code) 1 To buy land 2 To buy food items 3 What to prepare for dinner 4 To plant the following crops a)Staple crops b)Vegetables c)Others 5 Who to sell crops? a)Staple crops b)Vegetables c)Others 6 Who receive/or control income from crop sales a)Staple crops b)Vegetables c)Others 7 Selection of planting site 8 Buying inputs (seed, tools, fertilizer and pesticides) Who participate in community meeting or training 9 program? Who decide to participate in the community meeting or 10 training program? Who participate in agricultural training program 11 conducted by NGO/Government etc 12 Who participate farming study tour? 13 Who decide spending plan from credit received? 14 Who look after livestock at home? 15 Who sells livestock? 16 Who transport crop produced after harvest? Code: 1=HHD 2=Spouse 3=Male adult 15+ years 4= Female adult 15+ years 5=Male children (0-15years; 5=Female children (0-15years); 6=Other specify

M. PERCEPTION OF FARMERS (training AND non-trained) What is your perception about the following aspects compared to four years ago? No.1 Perception ( Codes) 1 Yes 2=No 3=Same 4=Don’t know 1 Do you feel that your income now is higher than 4 years ago? 2 Do you feel that your income is more stable now than 4 years ago? 3 Have you become more indebted as a result of vegetable farming? 4 Do you feel that the quality of your family’s meals has better now than four years ago? 5 Do you feel that your household members are healthier now than four years ago? 6 Do you now see vegetable farming as a profitable type of enterprise? 7 Would you prefer to work in the city instead of working on the farm? 8 Do you see a future for their family in vegetable farming? 9 Do you feel that your relationship with household and community members is better now than it was four years ago? 10 Do you feel that you able to sell your vegetables easier (lower risk and at better prices) than four years ago? 11 Do you think that your vegetable yields are higher now than four years ago? 12 Do you think that your post-harvest losses are lower now than four years ago?

N. RISK PERCEPTION/CONSTRAINTS Record this for all households Which of the following constraints to vegetable production and marketing you faced in the last 12 months? No. Risks Did your Did this household seriously affect experience any your normal of the living conditions following in the past 12 5=Very severely months? affected, 4= (Yes=1 ; No=2) Severely, 3=.. 1. Damage by pest and disease 2. Expensive inputs (pesticides, fertilizers and seeds) 3. Unavailability of quality seed 4. Lack of technical knowledge in production and processing 5. High cost of production 6. Lack of credit to pay for inputs 7. Unsuitable or poor quality soils 8. Transport and infrastructural bottlenecks 9. Lack of support from extension services 10. Lack of modern technologies in farming 11. Lack of labor to work in vegetable production 12. Shortage of water 13. Unpredictable rainfall 14. Poor crop yields 15. High post-harvest losses due to perishability of the vegetables 16. Lack of reliable market for vegetables 17. High fluctuation in vegetable prices 18. Risk of pesticides to farm workers’ health

SECTION O (Questions should be answered by secondary respondent if different from primary respondent) O: HOUSEHOLD FOOD CONSUMPTION AND DIETARY DIVERSITY (Asked to the person in charge of meal preparation within the household-Secondary respondent) O1. Food diversity and consumption Please describe the foods (meals and snacks) that the members of your household ate or drank yesterday (recall period 24 hours) during the day and night, whether at home or outside the home. Start with the first food or drink of the morning. Write down all foods and drinks mentioned. When composite dishes are mentioned, ask for the list of ingredients. When the respondent has finished, probe for meals and snacks not mentioned. Breakfast Snack Lunch Snack Dinner Snack

Households: include foods eaten by any members of the household, and include foods purchased and eaten outside the home

O2. When the respondent recall is complete, fill in the food groups based on the information recorded above: For any food groups not mentioned, ask the respondent if a food item from this group was consumed. Food Frequency – all the different foods that you’re household has eaten in the last 24hours Recall period: 24 hours Yes=1; Qn. No. Food Group (mention code from Qn.no) Examples No=2(All household corn/maize, rice, wheat, members ) Recall sorghum, millet or any other period: 24 hours grains or food made from these Yes=1; No=0 1 Cereals (e.g. bread, noodles, porridge or other grain products)+insert local foods i.e. ugali, porridge or paste white potatoes, white yam, 2 White Roots & Tubers white cassava, or other foods made from roots

pumpkin, carrot, squash, or sweet potato, that are orange 3 Vitamin A Rich Veg & Tubers inside + other locally available vitamin A rich vegetables (e.g. red sweet pepper)

dark green leafy veg, including wild forms + locally available 4 Dark Green Leafy Veg vitamin A rich leaves such as amaranth, cassava leaves, kale, spinach

other veg. (e.g. tomato, onion, 5 Other Veg eggplant)+other locally available veg

ripe mango, cantaloupe, apricot (fresh or dried), ripe papaya, dried peach, and 100% fruit 6 Vitamin A Rich Fruits juice made from these + other locally available vitamin A rich fruits other fruits, including wild fruits 7 Other Fruits and 100% fruit juice made from these

liver, kidney, heart or other 8 Organ Meat organ meats or blood-based foods

beef, pork, lamb, goat, rabbit, 9 Flesh Meats game, chicken, duck, other birds, insects

eggs from chicken, duck, guinea 10 Eggs fowl or any other egg

11 Fish & Seafood fresh or dried fish or shellfish dried beans, dried peas, lentils, 12 Legumes, Nuts & Seeds nuts, seeds, or foods made from these (eg hummus, peanut milk, cheese, yogurt or other 13 Milk & Milk Product butter) milk products

oil, fats or butter added to food 14 Oils & Fats or used for cooking sugar, honey, sweetened soda or sweetened juice drinks, sugary 15 Sweets foods such as chocolates, candies,cookies and cakes

spices (black pepper, salt), condiments (soy sauce, hot 16 Spices, Condiments, Beverages sauce), coffee, tea, alcoholic beverages

P. FOOD SECURITY/HUNGER COPING STRATEGIES P.1 Please answer the following and encircle the answer S.No Activities Code In the past four weeks, did you worry that your household would not have enough 1 food? IN the past four weeks, were you or any household member not able to eat the kinds 2 of foods you preferred because of a lack of resources? In the past four weeks, did you or any household member have to eat a limited variety 3 of foods due to a lack of resources? In the past four weeks, did you or any household member have to eat some foods that you really did not want to eat because of a lack of resources to obtain other type of 4 food? In the past four weeks, did you or any household member have to eat a smaller meal 5 than you felt you needed because there was not enough food? In the past four weeks, did you or any household member have to eat fewer meals in 6 a day because there was not enough food? In the past four weeks, was there ever no food to eat of any kind in your household 7 because of lack of resources to get food? In the past four weeks, did you or any household member go to sleep at night hungry 8 because there was not enough food? In the past four weeks, did you or any household member go a whole day and night 9 without eating anything because there was not enough food? In the past 12 months, were there months in which you did not have enough food to 10 meet your family's needs? If yes, which were the months in which you did not have enough food to meet your 11 family's need Code: 1=No; 2= Rarely (1-2 times in the past four weeks); 3=Sometimes (3-10 times in the past four weeks); 4=Often (>10 times in the past four weeks)

100

Q: HOUSEHOLD FOOD & NON-FOOD EXPENDITURE PATTERNS – 30 DAYS P1. In the last 30 days, how much did your household spent on food and fuel? If not If not If yes, purchase purchase how If yes, d, how d, how much how much much did Did did much did Did consume your your did consume your from househ house your from house your old hold Food item househ Food item your hold farm/gift purcha spend old farm/gift purch s from se? in the spend s from ase? friends (1) past in the friends etc (in 30 past 30 etc (in kilogram days? days? kilogram s) (2) s) TZS/ TZS/K 1=Yes KWA 1=Yes WACH

2=No CHA/ 2=No A/BIR BIRR R Milk and dairy Bread (1) products (9) Oil,fat, butter Rice (2) (10) Fruit and vegetables Maize (tomatoes, flour (3) eggplant, onions, okra) (11) Drinks-Soda and Millet (4) tea (12) Sorghum Tobacco and

(5) alcohol (13) Green Beans (6) gram/Mung bean (14) Sugar/salt Meat, chicken,

(7) fish (15) Fuel and light (LPG, kerosene, Eggs (8) electricity, firewood) (16) Dairy products* e.g. ghee, Yogurt, Cheese, milk powder, ice cream, sweets, etc Cereal products* e.g. bread, noodles

101

Q2. How much did your household spent on the following items? In the past 30 days In last 12 months If yes, how If yes, Did much did how much your your Did your did your house Item_code household Item_Code household household hold spend in the purchase? spend in purcha past 30 the past se? days? year? TZS/KW 1=Yes TZS/KWA 1=Yes; ACHA/BI 2=No CHA/BIRR 2=No RR Telephone, School/private tuition, Cellphone, internet School books & other (17) educational articles (24) Toilet articles (including Clothes/ Shoes toothpaste, hair oil, ((including children’s) shaving blades, etc) (25) (18) Household items (e.g electric bulb, tube light , Furniture and fixtures

glassware, bucket, (26) soap, insecticides, etc.) (19) Transport ( Bus, Crockery and utensils train, dada dada…) (includes casseroles, (20) thermos, etc) (27) House rent and rent Repair and maintenance other appliances (of residential buildings,

including water bathroom equipment) charges (21) (28) Non-agricultural Insurance premiums staff (domestic (29) servants) (22) Medical expenses Social functions (out-patient (marriage, funerals, services) (23) gifts, etc) (30)

102