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Assessment of the impact of Improved Pigeonpea Development in Northern Tanzania

Report No. 1

mpactAssessment

Science with a human face

ICRISAT-India (Headquarters)Patancheru, Telangana, [email protected]

ICRISAT-Liaison OfficeNew Delhi, India

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ICRISAT-NigerNiamey, [email protected]

ICRISAT-EthiopiaAddis Ababa, [email protected]

ICRISAT works in agricultural research for development across the drylands of Africa and Asia, making farming profitable for smallholder farmers while reducing malnutrition and environmental degradation.

We work across the entire value chain from developing new varieties to agri-business and linking farmers to markets.

ICRISAT appreciates the support of CGIAR donors to help overcome poverty, malnutrition and environmental degradation in the harshest dryland regions of the world. See http://www.icrisat.org/icrisat-donors.htm for full list of donors.

About ICRISAT: www.icrisat.org ICRISAT’s scientific information: EXPLOREit.icrisat.org

We believe all people have a right to nutritious food and a better livelihood.

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Citation: Dalton TJ and Regier G. 2016. Impact Assessment Report No. 1. Assessment of the impact of Improved Pigeonpea Development in Northern Tanzania. Patancheru 502 324, Telangana, India: International Crops Research Institute for the Semi-Arid Tropics. 52 pp.

AcknowledgmentsThis study was commissioned by ICRISAT under the supervision of Dr Kizito Mazvimavi, Head Impact Assessment Office. The CRP Grain Legumes and the Bill and Melinda Gates Foundation through the Tropical Legumes II Project financially supported the study. ICRISAT Nairobi staff offered logistical and administrative support, which enabled the fieldwork and the assembly of documents. The authors gratefully acknowledge the technical support of research and field staff of ICRISAT Kenya; the guidance and contributions of Dr. Said Silim and Dr. Alastair Orr; and the data collection and management of Bernard G Munyua. The authors are grateful to Mr Steven Lyimo and staff at the Selian Agricultural Research Institute (SARI) for the material and logistical support rendered throughout the course of the study. Further, we recognize and appreciate the efforts of the enumerators for data collection and those farmers who were willing to provide us with the required information.

© International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), 2016. All rights reserved.

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About the authorsTimothy J Dalton Associate Professor, Department of Agricultural Economics, Kansas State UniversityGreg Regier Graduate Assistant, Department of Agricultural Economics, Kansas State University

2015

Impact Assessment Series No. 01


Assessment of the impact of improved pigeonpea development by ICRISAT in

northern Tanzania

Timothy J DaltonGreg Regier

ContentsAcronyms and Abbreviations ..........................................................................................................................vi

Executive Summary ........................................................................................................................................ 1

Section I: Introduction, Methods and Descriptives ........................................................................................ 2The DREAM model ................................................................................................................................ 2

Project overview and data collection ............................................................................................................. 2Demographic structure of the households ............................................................................................ 2Gender issues ........................................................................................................................................ 5Wealth indicators .................................................................................................................................. 6Pigeonpea production system ............................................................................................................... 9Improved pigeonpea utilization and knowledge ................................................................................. 14Yield, cost and net returns .................................................................................................................. 16Five-year pigeonpea impact-stated response ...................................................................................... 19Household dietary diversity................................................................................................................. 20Household food security ..................................................................................................................... 21Summary of household surveys .......................................................................................................... 21

Vendor survey .............................................................................................................................................. 21Introduction ......................................................................................................................................... 21Pigeonpea trading ............................................................................................................................... 22Pigeonpea purchases and sales .......................................................................................................... 22Summary of trader surveys ................................................................................................................. 23

Section II: Quantitative Assessment of Impact ............................................................................................. 24Introduction and approach .................................................................................................................. 24

Adoption of improved varieties and output market participation ............................................................... 25Descriptive statistics of variables used in regression analysis ............................................................. 25Econometric analysis of factors affecting adoption and marketing decisions ..................................... 28Panel data evaluation .......................................................................................................................... 31Impact evaluation ................................................................................................................................ 32Social impact using the DREAM model ............................................................................................... 36

Summary and Conclusions ........................................................................................................................... 40

References .................................................................................................................................................... 41

Appendix 1: Population pyramids by district ................................................................................................ 42

Appendix 2: DREAM model .......................................................................................................................... 43

Table 1. Number of households surveyed, 2008–2012. .................................................................................3

Table 2. Descriptive statistics on household demographics by district. .........................................................4

Table 3. Four domains of empowerment. ......................................................................................................6

Table 4. Percentage of women empowered by indicator. ..............................................................................6

Table 5. Assets per household, 2012. .............................................................................................................7

Table 6. Total assets and land, 2008–2012. ....................................................................................................7

Table 7. Household primary residence wall and roof material (%), 2008–2012. ............................................7

Table 8. Crop utilization, 2012. .......................................................................................................................8

Table 9. Crop area by main crops, 2008 and 2012. .........................................................................................8

Table 10. Other income (TZS), 2012. ..............................................................................................................9

Table 11. Pigeonpea variety adoption by plot (frequency of plots). ..............................................................9

Table 12. Estimate of improved variety adoption using physiological plant characteristics (%).................. 10

Table 13. Improved pigeonpea adoption by area, 2008–2012 (% of acres). ............................................... 11

Table 14. Improved pigeonpea adoption by area with re-interviewed farmers, 2008–2012 (%). ............... 11

Table 15. Pigeonpea seed prices and farmer purchases. ............................................................................. 11

Table 16. Impact of crop stresses on pigeonpea yield (%). .......................................................................... 11

Table 17. Soil characteristics of pigeonpea plots (%). .................................................................................. 12

Table 18. Stated impact of pigeonpea on erosion and soil fertility (%). ...................................................... 12

Table 19. Biochemical and mechanical input prices and quantities. ........................................................... 13

Table 20. Labor use, 2012 (days/acre). ........................................................................................................ 13

Table 21. Labor wages 2012 (TZS/day). ....................................................................................................... 14

Table 22. Labor use, 2008–2012 (days/acre). .............................................................................................. 14

Table 23. Pigeonpea knowledge, adoption and dis-adoption. .................................................................... 15

Table 24. Pigeonpea utilization. ................................................................................................................... 16

Table 25. Pigeonpea yield assessment by district (%), 2012. ....................................................................... 16

Table 26. Yield and net revenue. ................................................................................................................. 17

Table 27. Pigeonpea marketing, 2008–2012. .............................................................................................. 18

Table 28. Pigeonpea buyer, 2008–2012 (%)................................................................................................. 18

Table 29. Off-farm transactions, 2008–2012. .............................................................................................. 19

Table 30. Sorting and price premiums, 2012. .............................................................................................. 19

Table 31. PMG participation, access to extension and markets. ................................................................. 19

Table 32. Stated pigeonpea acreage per household from 2010 to 2012. .................................................... 20

Table 33. Stated contribution of pigeonpea to overall well-being of household, past 5 years (%). ............ 20

List of Tables

Table 34. Access to the primary food groups (%). ....................................................................................... 21

Table 35. Descriptive statistics of variables used in regression analysis, 2012. ........................................... 26

Table 36. Mean values of variables by region, 2012. ................................................................................... 27

Table 37. Comparison of variables by adopters, 2008. ................................................................................ 27

Table 38. Mean values of variables by year, 2008–2012. ............................................................................ 28

Table 39. Estimation results of seemingly unrelated bivariate probit model and recursive binary probit model. ............................................................................................................................... 29

Table 40. Conditional and average marginal impacts. ................................................................................. 30

Table 41. Average Marginal Effects of Recursive Binary Probit models. ...................................................... 31

Table 42. Food security classification by district (% of households). ........................................................... 33

Table 43. Poisson and GMM IV Poisson parameter estimates for household dietary diversity. .................. 33

Table 44. Marginal impacts derived from yield function estimates using linear and quadratic function forms. ............................................................................................................................. 35

Table 45. Mean differences of yield (kg/ha) of improved varieties over local using quadratic functional form and panel data specification............................................................................... 36

Table 46. Linear unrestricted cost equation estimates (TZS/ha). ................................................................ 37

Table 47. Predicted cost of production differences between improved and traditional varieties (TZS/ha) ...37

Table 48. Yield and cost assumptions for DREAM model scenarios (output/ha or TZS/ha). ....................... 38

Table 49. Simulation results of the social benefits to pigeonpea improvement under three scenarios. .... 39

List of FiguresFigure 1. Population structure of households sampled in 2012. ..................................................................3

Figure 2. Pigeonpea variety adoption by plot (%). .......................................................................................9

Figure 3. Cumulative distribution functions of pigeonpea yields, 2012. ....................................................15

Figure 4. Cumulative distribution functions of pigeonpea net returns, 2012. ...........................................17

Figure 5. Stated pigeonpea output per household, 2010–2012. ................................................................20

Acronyms and AbbreviationsDREAM Dynamic Research EvaluAtion for Management

HDDS household dietary diversity score

HFIAS household food insecurity access scale

HHS household hunger scale

ICRISAT International Crops Research Institute for the Semi-Arid Tropics

IFPRI International Food Policy Research Institute

IMOD Inclusive Market-Oriented Development

NGO nongovernmental organization

OPHI Oxford Poverty and Human Development Initiative

PMG producer marketing group

SARI Selian Agricultural Research Institute

TZS Tanzanian shilling

USAID United States Agency for International Development

WEAI Women’s Empowerment in Agriculture Index

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Executive SummaryICRISAT’s program to improve pigeonpea in northern Tanzania has produced numerous new varieties with desirable traits that have been highly or moderately adopted by smallholder farmers in Arumeru, Babati, Karatu and Kondoa districts. The end-use traits favored by farmers are equally valued by brokers, assemblers and middlemen who often resell the varieties on the international market.

Using data collected in 2012, and combining it with data from 2008, we found both quantitative and qualitative evidence to support economic surplus modeling. Using the DREAM model and very restrictive assumptions on the cost savings and yield impacts, we found that between US$1.03 million and US$5.06 million of net social benefits are generated with the largest proportion accruing to consumers; it’s consistent with previous studies. The lowest benefit-to-cost ratio is 2.18. Even under highly unrealistic and extremely conservative assumptions on the conditions affecting the calculation of net social benefits, the public investment in pigeonpea improvement and market development is justified. It is even more strongly justified when highly conservative assumptions are relaxed. Under the intermediate and optimistic scenarios the IRRs range from 21.9% to 25.5%, and benefit-to-cost ratios range between 4.9 and 6.8 times the cost of public investment.

Quantitative analysis of the farm-level impacts of adopting improved varieties is complicated by a high degree of heterogeneity of yield, cost and net revenue effects even when district-level differences are taken into consideration. Improved pigeonpea varieties, while exhibiting performance traits similar to those of other modern seeds with high yield potential, are liable to attract low net revenues, especially when yields are low. Using stochastic dominance analysis of net returns in 2012, we were unable to determine that improved pigeonpea varieties dominated local varieties. This analysis was made even more difficult by the high numbers of farmers who grew pigeonpea variety Bangili, which is regarded as a ‘mixed’ variety containing traits of both local and improved varieties.

Market trader surveys were conducted with large-scale brokers, rural assemblers, retailers and farmers concerning pigeonpea marketing activities. Overall, there was little evidence to suggest that the market is becoming more sophisticated. At present, demand for quality- or trait-differentiated varieties is very limited. Traders seek uniform quality of pigeonpea free from foreign matter, impurities or damaged seeds. Color and uniform seed size were less frequently cited as important characteristics. It appears that varietal differentiation among traders is limited and that demand for specific traits is negligible. Future breeding efforts leading to higher earnings for producers should therefore focus on a reduced set of traits; specifically, yield under district-specific biotic and abiotic stresses, size, and seed weight.

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Section I: Introduction, Methods and DescriptivesICRISAT began a regional pigeonpea breeding research program in 1991 in response to Fusarium wilt, a soil-borne fungal disease that was devastating Tanzania’s pigeonpea crop. With initial funding from the African Development Bank, research was initiated by ICRISAT in collaboration with the Selian Agricultural Research Institute (SARI) in Arusha. Other partners were TechnoServe and Catholic Relief Services.

The research focused on developing pigeonpea with at least four qualities: ■ Resistance to Fusarium wilt ■ Early maturation to escape drought and avoid Fusarium wilt ■ High yield ■ Cream-colored seeds to meet market demand

Twenty-one pigeonpea varieties were developed and tested on station in 1997. ICEAP 00040, the most adopted variety to date, was released in 2002 (Shiferaw et al. 2005; Jones et al. 2002).

In Tanzania, pigeonpea with cream-colored grain is in high demand. To assist smallholder producers in capturing this market, TechnoServe started organizing producers into producer marketing groups (PMGs) in 1999. Training in grain cleaning and handling was provided, and the PMGs were linked directly to exporters (Jones et al. 2002). TechnoServe also provided business and marketing training to farmer cooperatives, local seed enterprises, and input stockists.

The impact of improved pigeonpea varieties was initially assessed by sampling 240 households in Babati during the 2003 pigeonpea season (Shiferaw et al. 2005; Shiferaw et al. 2008). More recent studies have estimated the impact of improved pigeonpea using data collected from the 2008 season from four districts in northern Tanzania: Arumeru, Babati, Karatu and Kondoa (Asfaw et al. 2012). Households from each of the four districts were resurveyed in 2010 and again in 2012, providing the panel data used in this research.

The DREAM modelDREAM, or Dynamic Research EvaluAtion for Management, is a menu-driven software package developed by the International Food Policy Research Institute (IFPRI) for evaluating the economic impacts of agricultural research and development. DREAM was used in this survey to define a range of technology investment, development, and adoption scenarios and save them in an integrated database. DREAM’s multiple-region specification was particularly useful in preparing this report because of its capability of simulating various technology spillover scenarios that enable it to collect research results from various regions. More information about DREAM is included as Appendix 2.

Project overview and data collectionA total of 731 households were surveyed in a random sampling of villages in the four districts, all of which are located in the northern zone of Tanzania. Data was collected in November and December 2012 by either experienced or well-trained enumerators in collaboration with ICRISAT-Nairobi and SARI. This survey was designed to capture the same information as similar studies that took place in 2008 and 2010 in order to create a panel data set. The number of households surveyed is shown in Table 1 below. Although the same households were targeted for interview each year, less than 50% of them (n=276) were interviewed all 3 years.

Demographic structure of the householdsThis section describes the demographic structure of the households in late 2012. This information can be used to look at household and individual characteristics that might relate to adoption, production, marketing and income generating activities.

3

Consistent with many low-income nations undergoing rapid population growth, the households sampled in this survey have a high percentage of youths relative to adults. This is often characterized by a population pyramid with a wide base that rapidly declines into early and middle adulthood (Figure 1). The structure of this pyramid suggests rapid population growth of the overall sample. When the age structure is broken into district-level subgroups, very similar age structures are produced. Survey results suggest that there are even more children relative to adults in Babati district (Appendix 1).

This hypothesis is investigated by examining family size and the breakdown between adults, children and the elderly by generating three alternative dependency ratio measures. The first measure is commonly referred to as the youth/adult ratio, which provides the percentage of the population below 15. The youth dependency ratio in many low-income countries is often around 30%, although it has reached 43% in Ethiopia and 45% in Nigeria. The youth/adult ratio is followed by two dependency ratios.

The first dependency ratio is constructed as the ratio of youth (15 and under) to adults (15 to 64). The second, the standard dependency ratio, combines youth and the elderly (65 and over) relative to the

Figure 1. Population structure of households sampled in 2012.

Table 1. Number of households surveyed, 2008–2012.District 2008 2010 2012 TotalArumeru 153 153 150 456Babati 156 152 222 530Karatu 150 150 210 510Kondoa 154 150 149 453Total 613 605 731 1949

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Table 2. Descriptive statistics on household demographics by district.

District Variable N Mean MedianStandard Deviation Minimum Maximum

Arumeru Family size 150 5.47 5.00 1.81 1.00 11.00

Youth/adult ratio 150 .34 .33 .22 .00 .71

Youth dependency ratio 147 .74 .50 .65 .00 2.50

Dependency ratio 147 .83 .67 .64 .00 2.50

Agricultural dependency ratio 150 .57 .25 .74 .00 2.50

Full-time agricultural dependency ratio

144 .76 .50 .84 .00 4.00

Babati Family size 222 6.53 7.00 2.18 2.00 13.00


Youth dependency ratio 218 1.00 .80 .79 .00 3.50

Dependency ratio 218 1.05 1.00 .79 .00 3.50



216 1.35 1.00 1.14 .00 6.00

Karatu Family size 210 6.29 6.00 2.47 1.00 15.00






206 .93 .60 1.07 .00 6.00

Kondoa Family size 149 5.97 6.00 2.04 1.00 13.00


Youth dependency ratio 149 .88 1.00 .75 .00 5.00

Dependency ratio 149 .99 1.00 .84 .00 6.00



144 1.13 1.00 1.08 .00 7.00

Total Family size 731 6.13 6.00 2.20 1.00 15.00






710 1.06 1.00 1.07 .00 7.00

adult population. Both of these measures focus on age as the defining characteristic of dependency. The comparison between the youth dependency ratio and the dependency ratio illustrates the proportion of the elderly to the total population.

A third measure is to develop the ratio of family members who actively contribute to agricultural production against those that do not, to more directly measure household labor against those considered dependents. Results of these calculations are presented in Table 2.

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The demographic characteristics presented in Table 2 show that Karatu and Kondoa are very similar in their overall demographic structure, while Arumeru and Babati differ from each other and the other two districts. In addition, they differ from the global average in nearly all demographic categories.

Families in Arumeru are generally smaller by nearly one person (p<0.001), have fewer children (p<0.01) and a lower dependency ratio (p<0.01) than the average, while families in Babati are larger than the average and have more children (significant at the p<0.001 and P<0.05 levels, respectively). Karatu and Kondoa are not significantly different from the mean or each other, indicating three subgroups of household demographic structures in the sample:

■ Kondoa and Karatu representing the average ■ Babati with larger families ■ Arumeru with smaller families

With this in mind, we can focus on the average and keep Babati and Arumeru as the extremes of the overall distribution. The average of the youth/adult ratio is 38%, but the district average ranges from 34% to 42%. There are some households without youths and others where 80% of the household is under the age of 15. When the dependency ratio is reformulated to reflect the combined group of children and elderly as dependents relative to adults, the average is not statistically different from 1, indicating that each adult is supporting at least one other person. The demographic importance of the elderly group is limited and increases the average dependency ratio by only 0.09 of a person (p<0.001). This reflects the very narrow band of the population above the age of 64 in Figure 1. Only 3.3% of the sample is above the age of 64.

The final measure of dependency is the agricultural dependency ratio and the full-time agricultural dependency ratio. Both ratios use self-reported measures of whether the individual worked full time, part time or not at all in agriculture. The agricultural dependency ratio combines both categories of workers into one group. The non-agriculturally active group is divided by the combined active group to indicate the percentage of household members that are dependent on those active in agriculture. A second variant of the measure includes only those who are active full time in agriculture.

The agricultural dependency ratio averages 0.75 for the combined full-time and part-time workers and 1.06 for the full-time workers. The combined total of 0.75 is lower than the age-class dependency ratio, but the full-time ratio is higher (both significant at p<0.001) suggesting that youths in these households are providing part-time agricultural labor on the household farm. Cross-tabulation of farm labor participation and age-class indicates that 91% of full-time workers are adults, 5% are elderly, and the remaining 4.2% are youths. The part-time agricultural work force consists of 59.3% adults and 39% youth. Nearly 40% of those surveyed in the sample were active in agricultural production full time, 26.1% part time and 34.3% not active at all. These proportions did not differ by district.

Only a small percentage of the sample played any leadership role in the community (7.4%). Of those that did, the most common role was as religious leader (27%), followed by a village leader (26%) and third as a committee member (20%). The remainder were dispersed between teachers, members of ‘ten cells’ and political leaders.

Gender issuesWe attempt to examine women’s empowerment through several questions aimed at examining autonomy and agency in decision making and resource ownership. The four domains – production, resources, income and leadership – comprise eight indicators (Table 3). These areas were selected to be similar to the indicators used to construct the Women’s Empowerment in Agriculture Index (WEAI) used by the United States Agency for International Development (USAID), the International Food Policy Research Institute (IFPRI), and the Oxford Poverty and Human Development Initiative (OPHI) (IFPRI 2012).Each indicator is measured by asking the primary respondent to name the top two household members responsible for decision making, selection of varieties, and so on. If a female is listed as at least one of the top two

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Table 4. Percentage of women empowered by indicator.

Domain Indicator Local Improved

Production Decision in purchasing inputs 50 50

Selection of pigeonpea variety 34 37

Resources Ownership of land 28 30

Responsible for sale of pigeonpea 51 49

Negotiation of prices 44 46

Amount of pigeonpea to save 77 80

Income Control over use of income 56 69

Leadership Leadership role in community 9 19

1. US$1 = TZS 1,579.82 TZS on 1 Dec 2012. (On 22 Oct 2013 US$1 = TZS 1,608.47.)

Table 3. Four domains of empowerment.

Domain Indicator

Production Decision in purchasing inputs

Selection of pigeonpea variety

Resources Ownership of land

Responsible for sale of pigeonpea

Negotiation of prices

Amount of pigeonpea to save

Income Control over use of income

Leadership Leadership role in community

household members responsible, the woman is considered empowered in that area. Table 4 indicates the percentage of women empowered to make decisions or control resources. Little differentiation is noted between adopters and non-adopters.

Wealth indicators

Assets, including household and land holdings

The average total non-livestock assets per household are slightly over 1 million Tanzanian shillings (TZS) (US$615) per household (Table 5).1 Of these, more than 60% come from tractors and other motorized vehicles. However, this is a bit misleading as only 3% of households have a tractor and 6% have other motorized vehicles.

Total assets nearly doubled between 2008 and 2012 (Table 6). However, this is likely due at least in part to the method used to capture assets value. The total amount of land cultivated has changed very little over the past 4 years, and uncultivated land varies only slightly between years.

In the past 4 years, householders are more likely to build their primary residence out of burned bricks and earth, and much less likely to construct them of timber. They are also more likely to use corrugated iron sheets for roofing instead of the traditional grass thatch (Table 7).

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Table 5. Assets per household, 2012.Asset Total value (TZS) Percent of total assetsOx plow 89,000 8.9Oxcart 75,417 7.5Sickle 1,441 0.1Machete 5,477 0.5Axe 4,658 0.5Spade 3,685 0.4Jembe/hoe 12,992 1.3Sprayer 9,954 1.0Wheelbarrow 5,334 0.5Bicycle 91,515 9.1Tractor 437,503 43.7Other motorized vehicles 168,375 16.8Radio 28,230 2.8Mobile phone 51,031 5.1Television 16,763 1.7Total 1,001,300 100.0

Table 6. Total assets and land, 2008–2012.Year Total assets (TZS) Total land holdings (acres) Uncultivated/ fallow land (%)2008 520,716 5.76 14.42010 869,717 5.38 3.22012 1,001,942 5.30 11.3N: 2008 = 611; 2010 = 600; 2012 = 731

Table 7. Household primary residence wall and roof material (%), 2008–2012.Wall material 2008 2010 2012Burned brick 63.9 70.2 74.9Unburned brick 7.4 5.3 4.7Stone 0.3 0.0 0.1Earth 12.4 14.1 17.2Wood (timber) 12.6 6.5 1.2Concrete block 3.3 3.5 1.9Other 0.2 0.5 0.0Roof materialGrass thatch 17.0 14.1 14.0Corrugated iron sheet 80.7 83.9 85.7Tile 0.2 0.0 0.3Tin 0.3 0.2 0.0Other 1.8 1.8 0.0N: 2008 = 612; 2010 = 604; 2012 = 729

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Table 8. Crop utilization, 2012.

CropArea planted

(acres)2Quantity produced

(kg/acre)cPrice

(TZS/kg)3Percent

sold3Gross income per

acre2

Pigeonpea1 3.3 160 670 73.5 84,137Maize 3.9 425 271 14.8 36,644Beans 1.1 142 569 24.8 18,643Sunflowers 0.9 203 589 37.2 36,456All other crops 0.7 - - - -1 Most pigeonpea and beans are intercropped with maize 2 All households (N=730) 3 For households producing the particular crop (N: pigeonpea=693; maize=714; beans=314; sunflowers=191)

Table 9. Crop area by main crops, 2008 and 2012.Full sample Partial sample of repeat households

Crop 2008 (n=613) 2012 (n=731) 2008 (n=276) 2012 (n=276)Pigeonpea 2.93 3.28 2.97 3.11Maize 3.59 3.87 3.72 3.93Beans 0.81 1.06 0.93 0.96Sunflowers 0.81 0.92 0.86 0.84All other crops 1.33 0.68 1.57 0.66N: 2008 = 613, 2012 = 731

Production area and crops/value of crop production and livestock

More than 98% of pigeonpea are intercropped, the vast majority (93%) with maize. Plots were an average of 1.5 km from households and more than 86% of plots were owned by farmers. Little variation between districts was noted. Farmers plant an average of nearly 4 acres2 of maize, followed by pigeonpea and beans, which are often intercropped with maize (Table 8). Farmers also plant about 1 acre of sunflowers and less than one tenth an acre of each of the following crops: sorghum, wheat, groundnut, chickpea, millet, cowpea, and vegetables. Improved pigeonpea were sown at an average elevation of 1,340 meters compared to 1,417 meters for local varieties. This may be in part to the difference in elevation between districts from 1,082 meters in Arumeru up to 1,502 meters in Karatu. Although maize yields more than double the yield of pigeonpea per acre, only 15% is sold compared to nearly 75% of pigeonpea. Gross income per acre is calculated based on produce that was actually sold. Sunflower provides the highest gross income per acre, followed by pigeonpea. Should 100% of the crop be sold, all crops have the potential to provide between TZS 80,000 (US$49) and TZS 140,000 (US$86) per acre of gross income.

Total gross income from crops is TZS 726,671 (US$447) per household. In comparison, income from livestock is TZS 212,253 (US$131) per household and TZS 182,536 (US$112) from milk production. We estimated total livestock assets at TZS 2.2 million (US$1,354) per household, more than twice the value of all other assets. Livestock assets varied from TZS 1.4 million (US$861) in Kondoa to TZS 3.1 million (US$1,907) in Karatu.

In the past 4 years, the typical household has increased acreage of pigeonpea, maize, and bean and sown less area to other crops such as groundnut, chickpea, rice, and millet (Table 9). This holds true for the partial sample of households interviewed in both 2008 and 2012, possibly because of the design of the questionnaire, which asked farmers to report crops from a much larger list of options.

2. Because Tanzanian farmers measure land area in acres, this measurement has been adopted for use throughout this report (1 acre = 0.4ha).

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Table 10. Other income (TZS), 2012.

DistrictFarm-

relatedOn and off-farm labor Business

Govt payments

Remit- tances Other

Total other income

Arumeru 5,927 118,013 291,477 22,000 15,333 192,600 647,725Babati 12,477 100,077 178,608 1,441 11,252 10,045 313,901Karatu 39,310 214,186 279,205 9,354 2,048 188,548 735,392Kondoa 7,493 56,764 44,365 5,473 13,699 27,912 142,152Total 17,840 127,807 203,402 8,759 9,929 102,631 469,663N = 730

Other income

Most other sources of income are derived from small businesses and off-farm labor (Table 10). Government payments and remittances provide very little household income. Households in Karatu and Arumeru earn a much greater amount of income outside of crops and livestock compared with households in the other districts.

Pigeonpea production system

Plot characteristics

The 2012 survey measured information about improved varieties using three methods. The first method was farmer-stated response (identical in the 2008 and 2010 surveys), which we used for comparison. The most popular improved variety was still ICEAP 00040, followed by 00053 and 00557. Babati White and Bangili remained the most common local varieties (Table 11). Combining the 2012 results with 2008 and 2010 data reveals that adoption of improved pigeonpea varieties occurred rapidly, increasing from 23% to 46%, especially in Karatu (+59.4%), Arumeru (+40.6%), and Kondoa (+23.9%). However, in Babati, the area of the most direct intervention, the adoption of improved pigeonpea was much lower and actually decreased slightly between 2008 and 2012 (Figure 2). This is likely due to farmers planting Bangili, which is a cross between improved and local varieties.

Two other methods are used to measure the adoption of improved pigeonpea varieties. Rather than attempting to extract individual varieties using the protocol, basic questions were asked to identify varieties as ‘local’ or ‘improved’. The protocol asked farmers the following three questions regarding pigeonpea’s physiological characteristics:3

■ What are your sowing and harvesting dates? ■ Does the plant spread out when it grows? ■ Does the plant have solid yellow flowers?

Table 11. Pigeonpea variety adoption by plot (frequency of plots).Variety Arumeru Babati Karatu Kondoa Total00040 84 55 177 51 36700053 24 39 4 0 6700576-1 1 1 1 0 300554 1 0 1 1 300557 0 0 8 0 8Babati White 22 49 46 25 142Bangili 0 242 0 78 320Total 132 386 237 155 1,014

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Answers to the first question helped us estimate maturation time. If the maturation time was 9 months or less, the variety was identified as ‘improved’. The pigeonpea variety is also considered improved if the farmer answered ‘no’ to either of the last two questions. The results are presented in Table 12 below.

The purpose of the protocol is to accurately measure the current rates of adoption of improved pigeonpea varieties. Pigeonpea varieties cross very easily, and we hypothesize that beneficial characteristics from improved varieties may have crossed over into local varieties. Nearly half of all varieties identified by the farmers as local have a maturation time of less than 9 months, suggesting that these local varieties have indeed taken on improved variety traits. Crossing of pigeonpea varieties goes both ways, however, as indicated by the large number of varieties with local traits, even though farmers reported the varieties as improved. We are cautious to place too much confidence in these results, however, the protocol should not be scaled up because of the unanticipated complexity of classifying varieties of pigeonpea.

A final measure of adoption is based on area rather than number of plots. The results are very similar using both measurements (Tables 12 and 13). Adoption was most rapid in Arumeru and Karatu, and dis-adoption occurred in Babati in 2010 followed by a return to improved varieties in 2012.

In order to check the robustness of these results, we compared only the 276 farmers interviewed in the three previous surveys. The results, once again, were similar to previous adoption estimates, although there appeared to be less dis-adoption in Babati and somewhat less overall adoption in Arumeru (Table 14).

Table 12. Estimate of improved variety adoption using physiological plant characteristics (%). Arumeru Babati Karatu Kondoa TotalMaturation time Local 15.7 38.2 15.3 20.9 26.0 Improved 84.3 61.8 84.7 79.2 74.0Yellow flowers, spreads out

Local 92.7 39.7 90.9 59.7 63.8

Improved 7.4 60.3 9.1 40.3 36.2N = 991

3. A fourth question: Does the crop have straight pods? was also part of the protocol, but was removed during testing of the survey because farmers were unable to recall this information.

Figure 2. Pigeonpea variety adoption by plot (%).

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Pigeonpea seed

In 2008, 2010 and 2012, farmers applied less seed per acre to plots with improved varieties, and paid a higher price for improved seed (Table 15). Plots sown to improved pigeonpea varieties were more likely to use purchased seed in 2012; also, seed prices for improved varieties were much lower in 2012, which may have induced a greater percentage of farmers to purchase seed that year.

Crop stresses and soil characteristics

A 2005 study revealed that the traits farmers consider when choosing a variety, starting with the most important, are high yield, disease resistance, and pest resistance (Shiferaw et al. 2005). One of ICRISAT’s objectives in Tanzania was to develop improved varieties resistant to Fusarium wilt. Farmers stated that improved pigeonpea are less likely to have yield loss due to Fusarium wilt than local pigeonpea varieties. However, local varieties appear to be more drought-tolerant. Yields of local and improved varieties are equally impacted by pests and weeds (Table 16). These trends are consistent across districts.

Table 14. Improved pigeonpea adoption by area with re-interviewed farmers, 2008–2012 (%).Year Arumeru Babati Karatu Kondoa Total2008 42.2 21.0 24.9 0.0 20.72010 61.0 17.5 49.9 17.0 41.62012 70.4 24.4 85.3 22.2 48.9

Table 15. Pigeonpea seed prices and farmer purchases.Year Seed (kg/acre) Seed price (TZS/kg) Farmer purchased seed (%)2008 Local 4.75 899 4

Improved 4.33 1,561 42010 Local 7.30 2,387 7 Improved 4.75 3,994 82012 Local 5.08 1,805 7

Improved 4.48 2,103 25N: 2008 = 646; 2010 = 542; 2012 = 517

Table 16. Impact of crop stresses on pigeonpea yield (%).Fusarium wilt Drought Pests Weeds

Local Improved Local Improved Local Improved Local ImprovedNo yield loss 42 58 28 10 5 11 81 91Yield loss of 0–25% 47 38 33 34 37 45 19 9Yield loss of 26–49% 10 3 29 32 47 39 1 0Yield loss of 50% or more 1 0 10 23 11 6 0 0

Table 13. Improved pigeonpea adoption by area, 2008–2012 (% of acres).Year Arumeru Babati Karatu Kondoa Total2008 41.8 37.3 18.7 1.0 23.22010 70.5 22.7 52.5 17.8 45.42012 82.6 26.9 82.1 27.8 49.6

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Soil characteristics were estimated subjectively through observation and farmer-stated response. Soil characteristics may control for yield differences and provide insight into where farmers are sowing local and improved varieties. For example, if a farmer pays more for improved pigeonpea seed, we would expect that they will sow the seed on their best plot. Soil characteristics are basically the same between local and improved varieties; thus, they do not provide any initial insight into where farmers sow pigeonpea (Table 17).

Because pigeonpea is a legume, it fixes nitrogen, thus enriching the soil. The crops can also be used as erosion control because they continue to grow long after the maize is harvested, and their roots often remain in the ground. While it was not feasible to measure the actual impact of pigeonpea on erosion and soil fertility in this study, we did examine farmers’ stated perceptions of pigeonpea’s impact on erosion and soil fertility. Results showed that farmers sowing local varieties were more likely to state that pigeonpea decrease erosion and improve soil fertility (Table 18).

Table 17. Soil characteristics of pigeonpea plots (%). Local Improved

Soil fertility Poor 4 2

Good 46 59

Medium 50 37

Soil depth Deep 23 21

Medium 72 73

Shallow 5 4

Soil type Black 32 39

Brown 4 6

Red 38 41

Gray 27 12

Soil slope Gentle slope 47 56

Medium slope 51 41

Steep slope 1 1

Soil water conservation

No 42 49

Yes 58 51

Water logging No 95 85

Yes 5 15

Irrigated No 98 97

Yes 2 3

Table 18. Stated impact of pigeonpea on erosion and soil fertility (%).Arumeru Babati Karatu Kondoa Local Improved Total

What is the impact of pigeonpea on erosion?

Increases 0 0 0 4 2 0 1

No impact 23 0 24 9 10 17 13

Decreases 77 100 76 87 89 82 86

How has soil fertility changed in the past 5 years?

Improved 76 95 75 89 88 81 84

Remained the same 24 5 25 11 12 19 16

Declined 0 0 0 0 0 0 0

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Table 19. Biochemical and mechanical input prices and quantities.

Year

Chemical price (TZS/

liter)Chemicals

(liters/ acre)

Manure price (TZS/

ton)1Manure

(tons/ acre)

Fertilizer price (TZS/

ton)2Fertilizer

(tons/acre)2

Hired oxen price (TZS/

acre)

2008 Local 9,376 0.57 11,063 0.2 337 47 18,285

Improved 7,193 0.68 20,625 0.19 392 34 23,756

2010 Local 11,516 0.89 13,774 0.51 280 35 16,909

Improved 10,628 0.43 14,576 0.47 - 27 15,241

2012 Local 14,178 0.82 19,118 0.48 - - 34,274

Improved 16,246 0.63 16,632 0.34 1,065 37 32,180

1 In this report, the spelling ‘ton’ is used to signify metric ton (1,000 kg). 2 Although information was collected on fertilizer price and quantity, the number of observations was too small to report

Table 20. Labor use, 2012 (days/acre).1

Land prep

and sowing WeedingChemical

application Harvest Threshing Sorting Total

Local Family 2.8 4.1 0.1 1.8 0.9 0.4 10.2Hired 1.1 4.4 0.1 2.3 0.6 0.1 8.6Total 3.9 8.5 0.2 4.1 1.5 0.5 18.7

Improved Family 3.7 5.5 0.1 3.2 1.6 0.7 14.7***Hired 1.0 4.1 0.1 2.0 0.7 0.3 8.2Total 4.7 9.6 0.2 5.2 2.3 1.0 22.8**

1 One working day is 8 hours; top dressing labor was removed due to lack of observations. * indicates significantly higher labor use at the 0.10 level, ** at the 0.05 level, and *** at the 0.01 level. N = 675

Non-seed purchased inputs

Almost 97% of producers paid for inputs with cash and only 1.5% used credit. There may be several reasons that few farmers use credit to purchase inputs. First, only a little more than 13% have access to credit or a bank account, and second, most purchase few inputs for growing pigeonpea. For example, 75% of producers saved and used their own pigeonpea seed, and very few purchased manure or fertilizer.

Farmers tend to apply similar quantities of biochemical and mechanical inputs, regardless of whether they are planting a local or improved variety (Table 19). The only exceptions to this are the use of chemicals, applied in greater quantity in 2010 to local pigeonpea varieties (p<0.05), and manure, with more applied to local varieties in 2012 (p<0.01). The prices of chemical and hired oxen appear to be trending upward from 2008 to 2012, but this may be in part due to inflation.

Labor

Labor data was collected by asking farmers to recall labor use from the 2011/12 season in November and December 2012. Because this is recall data, any conclusions from the data must be taken cautiously. In an attempt to reduce recall bias, labor data was only collected from the largest pigeonpea plot. An additional 34 observations were dropped due to incomplete data and 22 observations dropped because the variety is unknown.

Pigeonpea farmers use significant family and total labor per acre on improved pigeonpea plots. Tasks include land preparation, weeding, harvesting and threshing (Table 20). Both local and improved varieties

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use equal amounts of hired labor. Weeding labor constitutes almost half of total labor for both local and improved varieties.

The 2012 survey also collected wage information from hired labor (Table 21). Labor was the most expensive for chemical application (TZS 9,108/day) and the least expensive for harvesting (TZS 3,211/day). Plots containing improved pigeonpea often paid a higher wage rate, but wage rates vary significantly between regions.

The results of the panel using available data from 2008–2012 are presented in Table 22. Little variation exists between years and varieties, with the exception of weeding and hired labor, both of which were much higher in 2012. This was most likely the result of asking more detailed questions. For example, the 2012 survey asked how many hours farmers spent during the first and second weedings, whereas the 2008 and 2010 surveys only asked for weeding labor once.

Improved pigeonpea utilization and knowledge

Experience, knowledge, adoption and dis-adoption of varieties

The average farmer has slightly more than 18 years of experience growing pigeonpea. Babati farmers have the most experience (22.2 years) and Arumeru farmers the least (14.8 years). Farmers sowing primarily local varieties have more experience (19.6 years) than those planting improved varieties (17.1 years).

The most widely known pigeonpea variety is ICEAP 00040, followed by Babati White, Bangili, and other local varieties (Table 23). Of these popular varieties, more than two thirds of respondents who knew of Bangili and ICEAP 00040 sowed these varieties in 2011/12. The main reason that these varieties were not sown is that farmers could not acquire seed (67%), followed by lack of cash (11%). According to the 2012 survey, Babati White was the only variety that less than half of non-adopters planned to sow in the future. The main reasons given were potential for low yield (41%) and susceptibility to disease and pests (40%).

Table 21. Labor wages 2012 (TZS/day).

Variety Arumeru Babati Karatu Kondoa

Local 6,064 3,489 4,066 4,311Improved 6,697 3,335 5,117 5,039Total 6,566 3,450 4,915 4,616N = 675

Table 22. Labor use, 2008–2012 (days/acre).

Year Land preparation

and sowing WeedingChemical

application Harvest Threshing Total hired

2008 Local 3.9 5.2 0.1 3.3 2.4 2.1 Improved 4.9 5.5 0.1 4.3 3.0 2.12010 Local 3.7 5.0 0.1 4.5 2.8 1.3 Improved 2.7 3.4 0.2 3.3 2.4 1.52012 Local 3.9 8.5 0.2 4.1 1.4 8.6

Improved 4.7 9.6 0.2 5.2 2.2 8.2

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Table 23. Pigeonpea knowledge, adoption and dis-adoption.

Respondents who know variety (%)

Varieties sown by those that

know them (%)

Average number of years planted variety

Variety sown in 2011/12

(%)

Variety dis-adopted in 2012 (%)

If not planted in 2012, will plant in future?

(%; 1 = yes)

ICEAP 00040 63.7 79.0 5.8 67.0 12.0 90.2ICEAP 00053 18.9 59.4 5.8 39.9 19.6 79.0ICEAP 00576-1 0.4 33.3 13.0 33.3 0.0 100.0ICEAP 00554 1.1 50.0 4.5 12.5 37.5 100.0ICEAP 00557 1.6 83.3 2.7 58.3 25.0 60.0Babati White 50.5 90.0 18.1 32.5 57.5 31.0Bangili 42.4 87.1 10.5 70.6 16.5 79.1Other (mostly local) 45.4 96.4 20.6 28.6 67.8 10.0

Figure 3. Cumulative distribution functions of pigeonpea yields, 2012.

N=675; Local=322; Improved=353

The most popular sources of pigeonpea variety information were: ■ Farmers or neighbors (71%) ■ Government extension (9%) ■ Research centers through on-farm trials, demonstrations, and field days (8%)

In order to obtain pigeonpea variety for the first time, most farmers bought or exchanged seed with other farmers (38%), obtained from family (27%), or from local traders or agro-dealers (13%). In order to obtain a particular variety, 41% obtained seed with cash, 40% received it free, and 14% exchanged with other seed.

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Pigeonpea utilization

Although about three quarters of the pigeonpea harvest was sold in 2012, a majority of producers also kept pigeonpea for home consumption (Table 24). However, less than 10% of the households purchased pigeonpea for the purpose of home consumption during the past year. Almost all producers save the pigeonpea stems and used them as fuel. Pigeonpea stems replaced firewood for an average of 4 months, saving an estimated TZS 55,000 (US$34) during the 2012 season.

Yield, cost and net returns Farmers stated that the 2012 pigeonpea harvest was not a great one; in fact, close to three quarters of pigeonpea producers in Arumeru and Karatu considered the 2012 yield ‘poor’ (Table 25). This information was taken into consideration when comparing yields using our panel data, especially since farmers tend to consider local pigeonpea varieties as drought-tolerant.

The results of the cumulative distribution functions of pigeonpea yields in 2012 reveals multiple lower-tail crosses between pigeonpea varieties until a cumulative probability of 0.50 is reached (Figure 3). This implies that neither improved nor local varieties are first-degree or second-degree stochastically dominant. However, improved pigeonpea appear to have greater potential to produce higher yields.

Pigeonpea yield, cost, and net returns vary depending on year (Table 26). Yields of improved varieties outperformed local varieties in 2008 and 2012, but the opposite occurred in 2010. There is no explanation why this is the case. In both 2008 and 2012, the total costs of improved varieties were significantly higher, but in 2008 the higher yields and prices led to significantly higher net returns.4

The average wage rate is calculated from 2012 survey data for each household by taking the total cost of hired labor over the total number of hired days. The average wage rate in 2012 is TZS 4518 /day. This value is multiplied by total labor days in 2008, 2010 and 2012 to estimate labor costs as reported in Table 26, as no wage data is available for 2008 or 2010.

The cumulative distribution functions of pigeonpea net returns in 2012 shows that neither improved nor local varieties are first-degree or second-degree stochastically dominant (Figure 4). The distribution of the

Table 25. Pigeonpea yield assessment by district (%), 2012.

Arumeru Babati Karatu Kondoa Total

Poor 73 52 79 42 59

Average 26 33 16 42 30

Good 1 15 5 15 11

4. Previous study with the DREAM model reported 10% lower costs and 18–34% higher yields.

Table 24. Pigeonpea utilization.

Pigeonpea saved for consumption

(1=yes)

Purchased pigeonpea?

(1=yes)

Pigeonpea stems used for fuel?

(1=yes)

Months pigeonpea stems

lasted as fuel

Estimated value of fuelwood saved

(TZS)

Local 87.6 12.7 95.3 4.6 61,291Improved 71.5 5.4 94.3 3.6 48,749Total 75.5 9.3 94.6 4.1 54,566N = 675

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Table 26. Yield and net revenue.

r Yield (kg/

acre)

Pigeonpea price (TZS/

kg)1Revenue

(TZS/acre)Input cost (TZS/acre)

Labor cost (TZS/acre)

Total cost (TZS/acre)

Net Returns (TZS/acre)

2008 Local 159 474 77,978 11,089 67,138 78,227 -98

Improved 244*** 491** 120,033*** 15,072*** 79,992** 95,064** 24,969***

2010 Local 283** 808 245,091** 12,174 72,955*** 85,129*** 15,7075

Improved 243 814 204,520 15,879*** 53,996 69,875 133,125

2012 Local 155 727 112,918 30,944 82,006 112,950 -269

Improved 180** 722 133,811** 36,044 98,736** 134,780** -9691 Based on the average price received by each farmer; if farmer didn’t sell pigeonpea the mean district price is used. * indicates significantly higher labor use at the 0.10 level, ** at the 0.05 level, and *** at the 0.01 level.

Figure 4. Cumulative distribution functions of pigeonpea net returns, 2012.

net returns of improved pigeonpea is much wider than the net returns of local pigeonpea. This suggests that improved pigeonpea have the potential for higher net returns, but may also result in much lower net returns than local varieties.

Marketing and PMG participation

Data was collected on the marketing of pigeonpea based on the number of marketing transactions. In 2012, 8% of farmers reported that the quality of the crop was poor as opposed to 0% in 2008 and 2010. This could be one reason that the price in 2012 was lower than 2010 (Table 27), although local prices may also be effected by global pigeonpea prices especially since many of this pigeonpea is sold for export. Transaction size has gone down slightly each year as has the number of transactions. Period to payment is slightly higher, as well as sales tax.

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Table 27. Pigeonpea marketing, 2008–2012.

YearTransaction per

householdQuantity sold per transaction (kg) Price (TZS/kg)

Period to payment (weeks)

Sales tax (TZS/kg)

2008 1.6 565 467 0.02 0.112010 0.8 497 850 0.03 0.192012 0.9 483 729 0.06 0.53N: 2008 = 1,002; 2010 = 459; 2012 = 660

Table 28. Pigeonpea buyer, 2008–2012 (%).

2008 2010 2012 Total

Farmer group 0 1 0 0Farmer union or coop 0 0 1 0Consumer or other farmer 2 6 3 3Rural assembler 16 10 18 15Broker/middlemen 76 77 68 74Urban grain trader 6 5 4 5Exporter 0 1 1 0Tanzania breweries 0 0 0 0Kilimo Markets Ltd 0 0 4 1N: 2008 = 1,002; 2010 = 459; 2012 = 660

Nearly 90% of transactions were sold to rural assemblers and middlemen in all three seasons and 4% of producers sold to Kilimo Markets Ltd in 2012 (Table 28). Kilimo Markets Ltd is based in Karatu, where the company captured 16% of all transactions in that district. Farmers reported that they sold to their preferred customer more than 95% of the time in each of the 3 years.

The percentage of off-farm transactions increased from 11.3% to 28.5% between 2008 and 2012 (Table 29). At the same time, the distance and time taken to sell the pigeonpea did not change significantly between the three seasons, and transport costs generally trended downward.

The 2012 survey revealed that more than 81% of farmers sold their pigeonpea harvest, and that they were able to sell 92% of their production. As was mentioned above, TechnoServe organized farmers into PMGs in the early 2000s to encourage producers to clean their grain prior to selling to exporters to increase their profits (Jones et al. 2002). At this time, less than 1% of producers across all districts are organized into Producer Marketing Groups. Less than 1% of farmers sorted pigeonpea prior to selling (Table 30). Ninety-eight percent of farmers claimed that they did not sort pigeonpea because there is no price premium for doing so. The price that farmers were paid was slightly higher for improved varieties, and a greater percentage of improved varieties were sold as seed. More than 62% of farmers claimed that the price they received was lower than expected, which may indicate that the 2012 price was lower than in previous years. Most farmers get price information from other farmers and not cell phones or radios.

Markets/cooperatives/extension

Most producers are located relatively close to the nearest ward or village agricultural extension office (Table 31). Producers in Arumeru are the farthest from the village market (5.8 km), which is reflected by a much higher cost to reach the village market (TZS 1,333 per person). However, both the village and main market are accessible in Arumeru nearly 12 months each year compared with only about 10 months a

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Table 29. Off-farm transactions, 2008–2012.

YearOff-farm

transactions (%)Distance to point

of sale (km)Time taken to sell (minutes)

Transport cost (TZS)

Mode of transportation (%)

Ox cart Hired truck Bicycle

2008 11.3 27 32 18,792 31 38 6

2010 21.8 23 28 6,414 39 24 18

2012 28.5 28 31 13,273 34 32 21N: 2008 = 113; 2010 = 103; 2012 = 188

Table 30. Sorting and price premiums, 2012.

Price (TZS/kg)Sorted pigeonpea prior to selling (%)

Pigeonpea sold as seed (%)

Source of price information (%)

Other farmers Cell phone Radio

Local 715 0.0 1.0 88.8 3.8 0.3Improved 742 1.7 3.2 75.5 7.8 1.4N: Local = 313, Improved = 347;Total = 660

Table 31. PMG participation, access to extension and markets.

Distance to extension office (km)

Distance to village market

(km)

Cost to village market (TZS/

person)

Months passable to

village market during year

Distance to main market

(km)

Cost to main market (TZS/

person)

Months passable to

main market during year

Arumeru 2.1 5.8 1,333 11.9 7.1 1,612 11.8Babati 1.6 1.5 36 11.0 4.0 416 11.2Karatu 2.2 3.6 328 11.6 13.8 1,353 11.9Kondoa 0.0 1.3 379 9.9 5.7 1,053 10.5

year in Kondoa. Roads to the main market are the worst in Karatu and Arumeru, where only about 33% are paved with gravel or asphalt compared with 61% in Kondoa and 74% in Babati.

Five-year pigeonpea impact-stated responsePigeonpea farmers were asked to give the number of acres planted to pigeonpea during the past three seasons, as well as their pigeonpea output. This information complements the panel data set. Producers stated that acres sown to pigeonpea remained constant over the three seasons and this trend is consistent in all four districts (Table 32).

At the same time, total farm output was unchanged or somewhat lower in all four regions (Figure 5). This was likely caused by drought and pest damage as indicated in Table 16.

Pigeonpea producers have mixed opinions about the impact of pigeonpea on their households over the last 5 years (Table 33). Nearly 50% of producers in Babati think that pigeonpea have had a considerable or very significant impact on their household, compared with only 25% of producers in Kondoa.

More than 85% of households claimed that they made investments during the past 5 years with income from pigeonpea. The average investment was TZS 1.13 million (US$695), varying between TZS 680,000 (US$418) in

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Kondoa and TZS 1.62 million (US$996) in Babati. Producers planting local and improved varieties made nearly equal investments of TZS 1.11 million (US$683) in Kondoa and TZS 1.17 million (US$720) in Babati.

Household dietary diversityThe Household Dietary Diversity Score (HDDS) was used to assess the economic ability of households to access a variety of foods (Kennedy et al. 2011). Food items consumed in the household were collected from respondents over a 24-hour recall period. More than 84% of households had good access to cereals, vegetables, oils and fats, but less than 20% had access to fish, eggs and meat (Table 34).

Access to each of the 12 food groups cited in Table 34 is aggregated to create the HDDS, which has a score from 0 to 12. The range of HDDS scores is 1 to 12 with a mean value of 6.15. Kondoa had the highest mean score of 6.4 while Babati has the lowest score (5.9). Producers who planted improved varieties of

Figure 5. Stated pigeonpea output per household, 2010–2012.

Table 32. Stated pigeonpea acreage per household from 2010 to 2012.

Acres 2010 2011 2012Arumeru 2.0 1.9 2.1Babati 4.2 4.1 4.1Karatu 2.9 3.0 3.1Kondoa 3.6 3.7 3.5Total acres 3.3 3.3 3.3

Table 33. Stated contribution of pigeonpea to overall well-being of household, past 5 years (%).

Arumeru Babati Karatu Kondoa TotalLittle or no impact 39 14 33 23 26Somewhat 22 42 32 52 37Considerable or very significant 39 44 36 25 37

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Table 34. Access to the primary food groups (%).Food group Access (1=yes)

Cereals 100

White tubers and roots 12

Vegetables 84

Fruits 32

Meat 20

Eggs 15

Fish and other seafood 14

Legumes, nuts and seeds 53

Milk and milk products 56

Oils and fats 85

Sweets 68

Spices, condiments and beverages 77

pigeonpea have a significantly higher dietary diversity of 6.3 (p<0.01) compared to those planting local pigeonpea (6.0).

Household food securityHousehold Food Insecurity Access Scale (HFIAS) score is estimated at the household level. It is based on nine questions that ask the level of food security in the household (Coates et al. 2007). The scores ranged from 0 to 27 and the mean was 4.6. Kondoa had the highest food insecurity with an HFIAS of 5.8, while all other districts had scores 4.5 or below. Producers sowing local varieties also had higher scores of 5.0 (p<0.01) than those sowing improved varieties (4.2).

Summary of household surveysThis section has described important descriptive information about the households surveyed in 2012. In general, statistical analysis detected significant differences between districts and between producers of improved pigeonpea varieties and local varieties. These differences will be examined in multivariate regression analysis in the second part of this report.

Vendor surveyIntroductionIn addition to household level surveys, market surveys were conducted in each of the four districts. Overall, 15 responses from retailers were obtained from Babati, Karatu and Kondoa, as well as 13 from Arumeru, a total of 58 overall. The sample was divided between broker/rural assemblers (62%) and retailers (38%). Most grain vendors indicated that they participate in more than one type of outlet, described either as an urban retailer, a rural retailer or market retailer. Some dealt directly with farmers. Eighty-five percent of the vendors were male and the average age was 41 with a standard error of 1.22 years. More than half the respondents (59%) indicated that grain trading was their primary occupation and 83% of those that did not claim grain trading as their primary occupation indicated farming. On average, traders have been active in the pigeonpea market for 9.4 years with a standard error of 0.84 years.

Investigations into the motivation behind traders’ involvement in the pigeonpea market identified three main reasons. These are identified as follows, along with the percentage of each.

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1. Pigeonpea is an important source of cash income (63%).

2. The pigeonpea market is reliable and prices are high (25%).

3. Desire to diversify business activities (12%).

The distribution of responses between vendors and assemblers were roughly equal for (1) and (2). The responses for (3) were expressed only by brokers. Only one of the vendors indicated that they first heard about pigeonpea trading from ICRISAT, three others cited NGOs or field days, a few from friends and other farmers, but the most common source of initial information about the market came from other vendors, traders and brokers.

Pigeonpea tradingThe annual amount (by weight) of pigeonpea transactions averaged around 44 kg for retailers and 4,319 kg for brokers with considerable variation among both groups. All vendors indicated that purchases were based on either physical characteristics (size, color, free from physical defects), cleanliness (free from foreign matter and pests), volume, and specific variety types. Overall, grain quality was the most important trait for brokers and retail vendors (60% of all responses), while cleanliness ranked second (34%). Despite the overall dominance of these two characteristics, the two groups did not agree on which was the most important, with brokers placing greater emphasis on cleanliness over grain characteristics (Chi-Square P-value=0.003). Only 7 of 58 respondents (12%) indicated that they store pigeonpea to obtain higher prices with no significant difference between retailers and brokers. Only one respondent indicated that price depended upon grain quality, while most set prices based upon prevailing market conditions, wholesale and export prices, and transportation charges.

Pigeonpea purchases and sales In order to assess the demand for specific pigeonpea varieties, traders were asked whether they purchased specific pigeonpea varieties. Only 3 of 21 retailers and 7 of 36 brokers indicated that they purchased specific varieties. The most commonly cited variety was ICEAP 00040 (eight times), ICEAP 00557 was cited twice, and ICEAP 00053, ICEAP 00932, ICEAP 00936, Babati White and Bangili received one citation each. Based upon the limited number of responses, traders estimated that approximately 285 tons of ICEAP 00040, 136 tons of ICEAP 00557, 132 tons of Babati White, 72 tons of ICEAP 00936, 64 tons of ICEAP 00932, 60 tons of Bangili and 4 tons of ICEAP 00053 were purchased, with the remaining volume from unspecified varieties. Based upon the limited number of observations in the vendor survey, it is impossible to assess whether markets are developing for specific variety types. It is therefore safe to suggest that pigeonpea can be regarded as an undifferentiated commodity, although a more targeted market survey would be required to thoroughly test this hypothesis. Unfortunately, traders were unwilling to share pricing information (only six observations were obtained) so it was impossible to determine whether specific varieties received a price premium. Traders indicated that farmers were their primary source of pigeonpea.

The three most frequently cited purchasers of pigeonpea were wholesalers/brokers (55.2%), exporters (27.6%), and processors (8.6%). The most frequently cited reason for selecting one of these groups was because they could ‘sell all at once to a buyer’ (58.6% of responses) or because ‘the buyer offers a good price’ (26.8% of responses). Sellers received an average price of TZS 785/kg for their pigeonpea with some variation by district. Sellers in Kondoa received TZS 865/kg for pigeonpea, Arumeru received an average price of TZS 800/kg, while farmers received TZS 763/kg in Babati and TZS 690/kg in Karatu.

Unfortunately, too few observations were undertaken to test for statistically significant differences between regions. Only three of the sellers indicated that they clean the pigeonpea before selling them by sorting for size or damaged seeds.

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Traders were asked to describe how the market has changed over the past 5–10 years or since they became active. Comments were nearly equally divided between negative (27) and positive (30) with one neutral. Positive comments indicated that buyers have increased, that the market is expanding and becoming more reliable, that pigeonpea is a profitable crop, and that demand is high. Negative comments indicated that the market is unreliable, that prices fluctuate or that they have decreased, and that market power is held by just a few large buyers. When traders were asked to consider the future outlook for pigeonpea, the overwhelming majority of comments (39 of 58) were positive. Traders indicated that they see a bright future because exporters are now paying premium prices for sorting by color or large seed types with high weights. They anticipate greater market opportunities with the emergence of processing industries, and that with increased production the crop can emerge as one of the leading cash crops alongside sunflowers. On the negative side, traders were concerned that drought and unpredictable weather could threaten the crop, and that the market is unpredictable and influenced by a monopoly.

Summary of trader surveysMarket trader surveys were conducted with large-scale brokers, rural assemblers and also with retailers and farmers about pigeonpea marketing activities. Evidence that the market for pigeonpea is becoming more sophisticated is inconclusive. Demand for quality- or trait-differentiated varieties remains very limited. Traders were concerned with obtaining ‘fair average quality’ pigeonpea free of foreign matter, impurities or damaged seeds. Color and uniform seed size was less frequently cited as an important characteristic. The survey consisted of responses by 58 traders spread nearly equally between the four regions. There appeared to be few differences between the districts aside from some potential differences in prices. Future researchers may wish to conduct anonymous purchase and sale of pigeonpea with varying quality attributes, and in varying quantities, in order to develop a hedonic pricing model of pigeonpea traits. In the absence of that model, it appears that limited varietal differentiation among traders and demand for specific traits is limited.

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Section II: Quantitative Assessment of ImpactIntroduction and approachThis section of the report presents quantitative assessment of the impacts of the program. The program attempted to improve farmer welfare through the development of new pigeonpea cultivars and through product marketing innovations. Since both components are complementary, the relationship between the two is analyzed. In addition, this analysis conducts cross-sectional and time-series analysis on a subset of respondents of the 2012 survey. Where applicable, attempts are made to link the data sets from 2008 and 2010 with the one collected in 2012.

ICRISAT’s primary intervention in Tanzania was the regional pigeonpea breeding research program, while the second intervention was to develop a strategy focused on increasing the value of pigeonpea through market development. ICRISAT intervened in several different areas to reduce the transaction costs limiting farmer participation in markets. Activities also focused on strengthening the role of the marketing sector by identifying varieties with desirable end-use characteristics in the export market and aligning these middlemen with farmers in order to stimulate an increase in the volume and quality of pigeonpea supply. Few studies have quantified the facilitating role of market development or traced the impact of market development back to the household level. At the household level, we focus on market exchanges between farmers and aggregators in the voluntary transaction of grain, and more specifically whether these transactions are of an improved pigeonpea variety developed by ICRISAT.

Initial analyses focused on determining factors affecting the adoption of new varieties and output marketing. These decisions were initially modeled using the cross-sectional data collected in 2012 with a utility maximization model. The decision to adopt varieties or marketing practice by farmer i in time period t occurs when the utility of adoption is greater than the utility of non-adoption , or simply . Since utility is random, the decision to adopt can be parameterized using a discrete choice model in the form:

Equation 1:

Equation 2:

Since the utility of adoption cannot be directly observed, we can only model the probability of adoption as:

Equation 3:

where Φ is the cumulative distribution function of the standard normal distribution and Xt is a vector of explanatory variables. In the case of the probit model, the parameters are estimated using maximum likelihood of the probability that the household i adopts the improved variety or marketing practice (Y) viz:

Equation 4:

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A second line of inquiry was to determine whether farmers adopted new varieties and sold pigeonpea on the output market. In such a case, we wanted to determine:

■ if packages of practices such as those promoted by the program were adopted simultaneously; and ■ if any causality existed in the adoption decisions.

The presentation above can be generalized to a bivariate model to examine joint decisions or multinomial models by expanding the dichotomous adoption decision into three or more categorical choices: for example, where panel data allows the examination of non-adoption, adoption and subsequent dis-adoption.

Modeling two decisions simultaneously requires generalizing the model above and relating the two equations. Simple bivariate models can help determine whether the decisions are related. Similarly, recursive models, where the dependent variable of one equation is used as an explanatory variable, can help to shed light on the relationship.

The bivariate Probit regressions model the decision to adopt the new varieties (Ait) and the marketing practices, for producer i, (Yit=Ait) simultaneously with the marketing innovation decision (Yit=Mit):

Equation 5:

Although the two decisions can be explained using a similar set of explanatory variable, we attempted to isolate specific factors to explain both decisions. Bivariate probit models with treatment effects are estimated using full information and maximum likelihood. We were interested in the sign and magnitude of the correlation coefficient ρ between the error term in adoption equations (ε) and participation (ν) to indicate the relationship between varietal adoption and marketing practices. The hypothesis of no relationship can be tested by evaluating the significance of the hypothesis that ρ=0. If a relationship is detected, recursive models are warranted to determine whether any causal relationship exists.

Adoption of improved varieties and output market participationBased on ICRISAT’s Inclusive Market-Oriented Development (IMOD) model, access to inputs and market opportunities is a key driver of economic growth that occurs as farmers’ transition from subsistence to market-oriented production (ICRISAT 2011). For that reason, in this section we address two research topics:

■ The determinants output market participation ■ Varietal adoption

In a subsequent section, we measure the impact of market participation on one of the expected development outcomes derived from the IMOD model: food security.

Descriptive statistics of variables used in regression analysisThe descriptive statistics used in the regression models are presented in Table 35. The results show that 56% of producers in 2012 sowed improved pigeonpea varieties, with 86% of farmers having full access to improved pigeonpea varieties. More than 82% of farmers sold pigeonpea, but only 21% sold pigeonpea off-farm. On average, these farmers had been sowing pigeonpea for more than 17 years. More than three fourths of respondents were male, with an average age of 47 years and nearly 6.5 years of education. While the average non-livestock assets were TZS 1 million and animal assets TZS 2 million, variation between households was significant. Total land holdings per household were more than 5 acres, and ownership of bicycles, radios and mobile phones were all quite high.

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Table 35. Descriptive statistics of variables used in regression analysis, 2012.

Variables Mean Std Dev Min MaxQuestionnaire number 3,366 211 3,001 3,731Pigeonpea variety sown (1 = Improved, 0 = Local) 0.56 0.50 0 1Output market participation (1 = Yes, 0 = No) 0.82 0.39 0 1Output market participation off-farm (1 = Yes, 0 = No) 0.21 0.40 0 1Input market participation (1 = Yes, 0 = No) 0.27 0.44 0 1Pigeonpea experience (years) 17.58 10.22 0 56Access to bank account (1 = Yes, 0 = No) 0.14 0.35 0 1Access to bank credit (1 = Yes, 0 = No) 0.14 0.34 0 1Access to improved seed 0.86 0.35 0 1Agricultural dependency ratio 0.75 0.82 0 6Gender (1 = Male, 0 = Female) 0.76 0.43 0 1Age (years) 47.35 12.03 19 82Education of household head (years) 6.45 2.44 0 15Salaried and off-farm income (1 = Yes, 0 = No) 0.05 0.22 0 1Non-livestock assets (TZS) 1,000,572 3,694,430 0 42,200,000Total animal assets (TZS) 2,031,405 3,426,766 0 62,800,000Total land holdings (acres) 5.30 5.47 0 63Ownership of oxcart (1 = Yes, 0 = No) 0.24 0.43 0 1Ownership of bicycle (1 = Yes, 0 = No) 0.67 0.47 0 1Ownership of radio (1 = Yes, 0 = No) 0.78 0.41 0 1Ownership of mobile phone (1 = Yes, 0 = No) 0.86 0.35 0 1Distance to agricultural extension office (km) 2.34 2.61 0 15Distance to main market (km) 7.78 8.82 0 150Transport cost to main market (TZS/person) 1,060 1,002 0 15,000(1 = Yes, 0 = No) 0.20 0.40 0 1(1 = Yes, 0 = No) 0.29 0.45 0 1(1 = Yes, 0 = No) 0.30 0.46 0 1(1 = Yes, 0 = No) 0.21 0.40 0 1N = 731

Adoption of improved varieties of pigeonpea was highest in Karatu and Arumeru, and much lower in the other districts (Table 36). Off-farm output market participation was also higher in Karatu and Arumeru, even while transportation costs to the main market were the highest in these districts. Output market participation, however, was lower. Access to improved seed was below 65% only in Babati, and the agricultural dependency ratio was the highest among districts, which may explain the overall low adoption rate in Babati. Households in Karatu had the highest livestock and non-livestock assets and the highest percentage of households with a source of off-farm income. The opposite was true for households in Kondoa, although Kondoa had the highest land holdings.

The differences between adopters and non-adopters were less subtle than the differences between districts. Adopters of improved varieties of pigeonpea tended to be significantly more likely to participate in both output and input markets, to have access to credit and improved seeds, and to have more years of education (Table 37). Adopters also had more valuable assets, greater land holdings, and higher ownership of oxcarts, radios, and mobile phones. Non-adopters had significantly more experience sowing pigeonpea, and were closer to the main market, resulting in lower transport costs.

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Table 36. Mean values of variables by region, 2012.Variables Arumeru Babati Karatu KondoaPigeonpea variety sown (1 = Improved, 0 = Local) 0.72 0.34 0.82 0.34Output market participation (1 = Yes, 0 = No) 0.63 0.98 0.74 0.85Output market participation off-farm (1 = Yes, 0 = No) 0.21 0.19 0.23 0.17Input market participation (1 = Yes, 0 = No) 0.47 0.05 0.47 0.11Pigeonpea experience (years) 14.39 21.75 16.23 16.46Access to bank account (1 = Yes, 0 = No) 0.29 0.10 0.16 0.03Access to bank credit (1 = Yes, 0 = No) 0.23 0.14 0.14 0.03Access to improved seed 0.99 0.64 0.99 0.88Agricultural dependency ratio 0.57 0.95 0.62 0.82Gender (1 = Male, 0 = Female) 0.67 0.76 0.82 0.78Age (years) 47.72 44.90 47.20 50.84Education of household head (years) 6.59 6.55 6.37 6.26Salaried and off-farm income (1 = Yes, 0 = No) 0.07 0.02 0.09 0.02Non-livestock assets (TZS) 1,044,451 654,676 1,801,225 343,322Total animal assets (TZS) 1,563,643 2,339,007 2,491,061 1,396,161Total land holdings (acres) 5.30 4.80 5.21 6.20Ownership of oxcart (1 = Yes, 0 = No) 0.23 0.25 0.33 0.10Ownership of bicycle (1 = Yes, 0 = No) 0.53 0.81 0.62 0.67Ownership of radio (1 = Yes, 0 = No) 0.81 0.86 0.71 0.73Ownership of mobile phone (1 = Yes, 0 = No) 0.87 0.87 0.88 0.81Distance to agricultural extension office (km) 2.13 1.63 2.20 3.81Distance to main market (km) 7.15 3.95 13.75 5.72Transport cost to main market (TZS/person) 1,612 416 1,353 1,053N = 731,

Table 37. Comparison of variables by adopters, 2008.Variables Non-adopters Adopters

Output market participation (1 = Yes, 0 = No) 0.78 0.84**Output market participation off-farm (1 = Yes, 0 = No) 0.15 0.25**Input market participation (1 = Yes, 0 = No) 0.18 0.34**Pigeonpea experience (years) 18.53** 16.82Access to bank account (1 = Yes, 0 = No) 0.10 0.18**Access to bank credit (1 = Yes, 0 = No) 0.11 0.16**Access to improved seed 0.74 0.96***Agricultural dependency ratio 0.84*** 0.68Gender (1 = Male, 0 = Female) 0.74 0.78Age (years) 47.89 46.92Education of household head (years) 6.13 6.70***Salaried and off-farm income (1 = Yes, 0 = No) 0.04 0.06Non-livestock assets (TZS) 596,067.00 1,324,375.00***Total animal assets (TZS) 1,642,655.00 2,342,596.00***Total land holdings (acres) 4.63 5.85***Ownership of oxcart (1 = Yes, 0 = No) 0.15 0.31***Ownership of bicycle (1 = Yes, 0 = No) 0.64 0.69Ownership of radio (1 = Yes, 0 = No) 0.73 0.82**Ownership of mobile phone (1 = Yes, 0 = No) 0.80 0.90***Distance to agricultural extension office (km) 2.55** 2.17Distance to main market (km) 5.94 9.26***Transport cost to main market (TZS/person) 763.00 1,298.00***N = 731, Using a one-sided t-test, * indicates significantly higher at the 0.10 level, ** at the 0.05 level, and *** at the 0.01 level.

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A comparison of the panel data in Table 38 reveals several key trends. As noted earlier, adoption of improved varieties of pigeonpea increased rapidly from 2008 to 2012. During that same time period, output market participation and access to improved seed declined slightly. Farmer characteristics remained mostly unchanged during this 4-year period. However, household non-livestock assets increased nearly ten-fold, due in part to inflation, while total land holdings decreased. Ownership of oxcarts and mobile phones increased during this period while ownership of bicycles and radios remained mostly unchanged.

Econometric analysis of factors affecting adoption and marketing decisionsThe data described in the previous section is used to estimate the determinants affecting the adoption of improved varieties and the decision to market pigeonpea output. Observations where a household indicated that they adopted Bangili were removed from the analysis because of their intermediate nature as a ‘mixed’ variety. All subsequent analyses assume the same in order to highlight the differences between local and improved varieties – hence the importance of the improvement process. Table 39 presents regression estimates for the seemingly unrelated bivariate probit and the recursive models. The first columns present the results of a seemingly unrelated bivariate probit model following Equation 5, while the right-hand side of the table presents recursive binary probit models following Equation 4. Several overall findings are of interest. Marginal impacts of these variables are presented in Table 40.

One finding of interest is that the bivariate model indicates that the decisions to adopt improved pigeonpea varieties and sell partial production (either in off-farm markets or at the farmgate) are not related. The null hypothesis, rho=0, is not rejected at any conventional p-value. However, this is not a rigorous test for causality. Since there was no significant correlation of the error terms in the bivariate

Table 38. Mean values of variables by year, 2008–2012.

Variables 2008 2010 2012

Pigeonpea variety sown (1 = Improved, 0 = Local) 0.21 0.33 0.56Output market participation (1 = Yes, 0 = No) 0.87 0.75 0.81Pigeonpea experience (years) 14.37 14.58 17.55Access to improved seed 0.90 0.86 0.86Gender (1 = Male, 0 = Female) 0.89 0.89 0.91Age (years) 46.83 48.58 48.40Education of household head (years) 5.82 6.04 6.43Salaried and off-farm income (1 = Yes, 0 = No) 0.05 0.07 0.07Total assets (TZS) 108,345 877,639 1,002,459Total land holdings (acres) 5.76 5.40 5.30Ownership of oxcart (1 = Yes, 0 = No) 0.17 0.21 0.24Ownership of bicycle (1 = Yes, 0 = No) 0.61 0.61 0.67Ownership of radio (1 = Yes, 0 = No) 0.76 0.79 0.78Ownership of mobile phone (1 = Yes, 0 = No) 0.50 0.72 0.86Distance to agricultural extension office (km) 11.88 4.61 2.33Distance to main market (km) 7.35 8.89 7.80Transport cost to main market (TZS/person) 847 959 1059Kondoa (1 = Yes, 0 = No) 0.25 0.25 0.20Karatu (1 = Yes, 0 = No) 0.24 0.25 0.29Babati (1 = Yes, 0 = No) 0.25 0.25 0.30Arumeru (1 = Yes, 0 = No) 0.25 0.25 0.20N = 1949 (2008 = 613; 2010 = 605; 2012 = 731)

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Table 39. Estimation results of seemingly unrelated bivariate probit model and recursive binary probit model.

Seemingly Unrelated Bivariate Probit

Recursive Binary ProbitPigeon

adoptionOutput Market

ParticipationPigeon

adoptionOutput Market

Participation

Coef.Robust

Std Error Coef. Robust



Std Error

Gender 0.033 0.141 -0.008 0.156 0.031 0.142 -0.010 0.157

Age 0.002 0.007 -0.011 0.007 0.003 0.007 -0.012 0.007

Education 0.084*** 0.027 0.097*** 0.030 0.078*** 0.027 0.090*** 0.030

Experience 0.004 0.007 0.007 0.008 0.004 0.007 0.007 0.008

Dependency ratio -0.010 -0.140 -0.003 -0.040 -0.008 0.071 -0.003 0.081

Total assets (TZS’000) 0.000 0.000 0.000* 0.000 0.000 0.000 0.000* 0.000

Total animal assets (TZS ‘000) 0.000*** 1.030 0.000*** 1.120 0.000 0.000 0.000 0.000

Total land (acres) 0.023 0.017 -0.002 0.016 0.023 0.017 -0.003 0.016

Oxen cart (1=yes) 0.383** 0.153 0.699*** 0.213 0.345** 0.157 0.679*** 0.215

Bicycle (1=yes) 0.268** 0.136 0.158 0.143 0.256* 0.136 0.136 0.144

Radio (1=yes) 0.393*** 0.145 -0.127 0.162 0.404*** 0.146 -0.163 0.163

Labor income (TZS) -0.180 0.275 -0.077 0.280 -0.184 0.279 -0.063 0.284

Improved seed access (1=yes) 1.098 5.970 0.378 1.470 1.087*** 0.184 0.298 0.262

Distance to market (km) -0.002 0.007 -0.040*** 0.013 0.000 0.007 -0.041*** 0.013

Trasport cost to market 0.000*** 0.000 0.000*** 0.000 0.000** 0.000 0.000*** 0.000

Bank account (1=yes) 0.172 0.279 0.101 0.306 0.168 0.280 0.093 0.307

Bank credit (1=yes) -0.100 0.274 0.236 0.324 -0.114 0.275 0.243 0.326

Distance to extension office (km) -0.009 0.024 -0.007 0.026 -0.009 0.023 -0.005 0.025

Kondoa -1.263*** 0.201 0.824*** 0.215 -1.345*** 0.208 0.976*** 0.227

Karatu 0.173 0.196 0.393* 0.210 0.149 0.197 0.385* 0.210

Babati -1.054*** 0.226 1.972*** 0.310 -1.167*** 0.233 2.117*** 0.318

Constant -1.789*** 0.520 -0.445 0.558 -1.992*** 0.535 -0.493 0.562

Adoption of pigeon pea varieties 0.322* 0.167

Output market participation 0.343** 0.169

Number of observations 703 703 703 703

Log-likelihood function -562.13*** -333.03*** -230.09***

Rho 0.1884** * indicates significantly higher labor use at the 0.10 level, ** at the 0.05 level, and *** at the 0.01 level.

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probit, the average marginal impacts did not differ from the conditional marginal impacts, and it was not possible to identify the causality of the relationship. Recursive binary probit models, where each of the decisions were used as an explanatory variable, produced insignificant parameter estimates. It is likely that the simultaneous introduction of these activities created intangible circumstances to identify a causal relationship from the cross-sectional data.

Variables from three areas are consistently significant across the analysis of the 2012 data. Some variables in the farmer characteristics category were significantly different from zero (Tables 39 and 40). Of those that were significant, the educational level of the farm household was positive and significant, indicating an increase of approximately 2% in the probability of participating in the output market for each additional year of schooling (according to the average marginal effects of the bivariate model).

Adoption and output market participation are positively correlated with several measures of assets and wealth. The probability of adopting improved pigeonpea varieties was approximately 9.4% higher if the farmer owned a radio. By contrast, output market participation was only affected by the ownership of an oxcart, and those who did own one had an 11% higher probability of market participation. Market participation was also negatively correlated with total asset value indicating that more wealthy households were less likely to participate in output markets. This is consistent with previous studies which suggest that wealthier households have higher opportunity costs of marketing pigeonpea, indicating that this is a pro-poor strategy to increase household income.

Table 40. Conditional and average marginal impacts. Seemingly Unrelated Bivariate Probit Recursive Binary Probit

Conditional Marginal Conditional Marginal

Adoption Output Market Adoption Output Market

Gender 0.009 -0.002 0.007 0.077**

Age 0.001 -0.002 0.001 0.001

Education 0.020*** 0.015*** 0.022*** 0.030***

Experience 0.001 0.001 0.001 0.000

Dependence Ratio -0.003 0.000 -0.003 -0.005

Total Assets (TZS’000) 0.000 0.000* 0.000 0.000

Total Animal Assets (TZS’000) 0.000 0.000 0.000 0.000

Total land (acres) 0.006 -0.001 0.006 0.002

Oxen Cart (1=yes) 0.090** 0.109*** 0.100** 0.088**

Bicycle (1=yes) 0.067* 0.022 0.071* 0.049

Radio (1=yes) 0.104*** -0.027 0.103*** -0.001

Labor Income (TZS) -0.046 -0.010 -0.056 0.057

Improved Seed Access (1=yes) 0.280*** 0.046 0.292*** -0.070

Distance to market (km) 0.000 -0.007*** -0.001 0.003

Transport cost to market 0.000** 0.000*** 0.000** 0.000

Bank account(1=yes) 0.043 0.014 0.046 -0.016

Bank Credit(1=yes) -0.029 0.040 -0.026 0.017

Distance to extension office(km) -0.002 -0.001 -0.002 -0.019

Adoption of pigeon pea varieties 0.035

Output market participation 0.023* indicates significantly higher labor use at the 0.10 level, ** at the 0.05 level, and *** at the 0.01 level.

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Transaction costs also play a role in adoption and marketing decisions. The regression results indicated a negative relationship between the adoption of new varieties and the cost of transportation to the marketplace, suggesting that transaction costs affect adoption. A non-intuitive result was identified in the marketing decision where the cost of transport to the market was positively related to participation, suggesting that fixed transaction costs may dominate. By contrast, a negative relationship between the distance to the market and the marketing decision was also identified. The probability of marketing pigeonpea decreased by 0.7% for each additional kilometer between the household and marketplace above the mean distance of 7.8 km.

Statistical analysis indicates very high availability and access to improved pigeonpea – 88% in Kondoa, 99% in Karatu, 99% in Arumeru – but only above average (64%) in Babati (Table 36). Based upon these results, it is apparent that while availability of pigeonpea seed is not a formidable constraint in the first three districts, the problem persists in Babati. However, regression results indicate that availability of pigeonpea seed is not a significant factor in the adoption of improved varieties.

To capture additional information related to geographical differences, such as supply characteristics, inadequate infrastructure, thin markets, and isolation, we included dummy variables and compared them with our base of Arumeru. Consistent with statistical results, we found no significant differences between the regional dummy for Karatu and Arumeru with respect to adoption, in contrast to Kondoa where we found statistically significant and negative impacts. The negative effects may likely be the impact of time and reflect the different starting times for project activities in each of the locations. We also found significant differences between all districts with respect to marketing decisions.

Panel data evaluationBecause the correlation of adoption and marketing decisions was inconclusive, we attempted to take advantage of the panel of observations from 2008, 2010 and 2012. Overall, when all three years of data

Table 41. Average Marginal Effects of Recursive Binary Probit models. Recursive Binary Probit

Average Marginal Effects Adoption Output market

Age 0.0058 -0.0123*Education 0.0982*** 0.0241Experience 0.0042 0.0061Total assets (TZS’000) 0.000 0.000*Total animal assets (TZS ‘000) -0.0058 0.0204Oxen cart (1=yes) 0.5536*** 0.1702Bicycle (1=yes) 0.1981 0.0789Radio (1=yes) -0.046 0.0702Labor income (TZS) 0.3481 -0.1039Trasport cost to market 0.0001* 0Distance to extension office (km) -0.0456*** 0.0159Kondoa -1.5682*** 0.3544*Karatu -0.1601 0.3578*Babati -1.1713*** 1.0846 ***Output market participation 0.1601Adoption of pigeon pea varieties -0.1143* indicates significantly higher labor use at the 0.10 level, ** at the 0.05 level, and *** at the 0.01 level.

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were combined, the total sample of observations was 1,742. The panel is unbalanced, however, and 1,016 of the observations appeared in only one or two rounds of the survey. These observations are held aside to look at the subsample of 726 households that appear in each of the three rounds.

The panel estimates are largely consistent with the results from 2012 but are more responsive, consistent with greater elasticity of response in the long run over the short. We find strong, significant and positive impacts of education on adoption, while negative impacts on the age variable for marketing. Marketing decisions are positively related to household wealth. The ownership of an oxcart increased the probability of adoption by over 50%. This may be an important parameter of household wealth.

Unlike the 2012 estimates, we found a strong and significant impact when households were located close to an agricultural extension office. Table 36 shows that the distance between households and agricultural extension offices declined between the three periods, perhaps reflecting government re-investment in capacity. The probability of adoption declined by 4.5% for each additional kilometer beyond the mean distance of 6.4 km from the household to the extension office. The fact that this is insignificant in analysis of the 2012 data while significant in the panel may indicate that access to extension services is now optimally located.

Consistent with previous analyses, we do not gain any additional insight into the relationship between the adoption of new varieties and marketing. Neither the parameter estimates nor the marginal effects are significantly different from zero.

Impact evaluationThe relationship between the adoption of improved varieties and participation in output markets complicates the assessment of the impact of these interventions. The inability to clearly segregate the two impacts indicates that the individual impacts are likely to be confounded.

Household dietary diversity and household food security

Low-cost metrics to evaluate the status of food security and household dietary diversity are available. These metrics have been developed to measure the health status of households in a low-cost, time-effective manner. Rather than conducting an in-depth food consumption recall survey, these instruments were designed to capture both macro and micronutrient intake in a single index of dietary diversity (Kennedy et al. 2011). Follow-up studies have determined that dietary diversity correlates with food security and specifically with energy intake, micronutrient adequacy, and household well-being (Kennedy et al. 2011). This analysis draws on the HDDS, which is a composite metric designed to collect information on the consumption of 12 food groups (Kennedy et al. 2011).

In addition to the HDDS, household food insecurity was measured through the household hunger scale (HHS). The HFIAS asked three questions about whether hunger has occurred and the level of frequency over a 30-day recall period. It was therefore a measure of the quantity of food consumed. Overall, variation in the HHS was limited, with 722 of the households (98.7%) reporting little or no hunger. We therefore did not pursue analysis of the HHS and instead focused attention on the HDDS, which is a composite measure of food quantity and quality.

Households are considered food secure when 10 or more of the food groups are consumed over the recall period, moderately food secure when they consume between six and nine of the food categories, and food insecure when they consume five or fewer food categories. Few households in the study region can be considered food secure according to their consumption patterns, while the overall majority (64.7%) are considered moderately food secure (Table 42). Nearly one third of the sample is considered food insecure.

We modelled the household count of consumed food categories and corrected for the endogeneity of varietal adoption and output market participation using both a Poisson model and a GMM IV Poisson

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Table 42. Food security classification by district (% of households).

Food insecure Moderately food secure Food secure

Arumeru 19.3 77.3 3.3Babati 35.6 61.7 2.7Karatu 38.1 58.6 3.3Kondoa 24.2 75.8 0

Table 43. Poisson and GMM IV Poisson parameter estimates for household dietary diversity.Poisson GMM IV Poisson

Coefficient Standard Error Effect Standard Error Coefficient Standard ErrorGender 0.030 0.029 0.185 0.181 -0.009 0.048Age 0.001 0.001 0.008 0.009 -0.003 0.002Education 0.007 0.005 0.042 0.034 0.008 0.012Total Assets (TZS ‘000) 5.96E-06* 3.14E-06 3.69E-05* 1.95E-05 -8.25-06 6.02E-06Total land (acres) 0.001 0.002 0.005 0.012 0.001 0.003Labor Income (TZS) 0.087** 0.042 0.536** 0.258 -0.110* 0.060HH Members 0-5 0.003 0.017 0.017 0.103 -0.008 0.022HH Members 6-14 -0.008 0.009 -0.053 0.055 0.004 0.013Adult HH Members 0.001 0.007 0.005 0.041 0.007 0.010HH Members >65 -0.035 0.030 -0.219 0.184 0.035 0.040Kondoa 0.027 0.032 0.169 0.196 -0.187 0.146Karatu -0.056* 0.030 -0.349* 0.185 0.060 0.036Babati -0.047 0.037 -0.292 0.229 0.011 0.083Adoption of pigeonpea varieties

0.033 0.028 0.205 0.173 -0.599 0.484

Output market participation 0.091** 0.036 0.566** 0.220 -0.231 0.196Constant 1.618*** 0.090 -1.070** 0.416N=509*indicates significantly higher labor use at the 0.10 level, **at the 0.05 level, and ***at the 0.01 level

model. We used both models for comparison purposes. The count of consumed food groups was modeled as a function of characteristics of the household head (gender, age and education), the age distribution of household members (0–5 years, 6–14 years, 15–65 years and 65 years or older), wealth and income correlates (total assets, total land holdings and off-farm labor income) plus district-level controls. The adoption of improved pigeonpea varieties and output market participation variables were also included in the regressions. These variables were instrumented using the distance to the nearest agricultural extension office, the distance to the nearest market, and the transportation costs to the nearest market.

Table 43 presents results of the Poisson and GMM IV Poisson regressions. Marginal effects and coefficient estimates are presented for the Poisson model, while only coefficient estimates are presented for the GMM IV Poisson model. The Poisson model indicates that participation in the output market, higher asset values, and labor income increase the dietary diversity of the household. The impact of adopting an improved variety does not affect dietary diversity. The marginal value of the impact of participation in the output market is similar to the magnitude of labor income and the score increase by over one half of a point. There was no direct impact of adopting improved pigeonpea varieties on household dietary diversity, but based on the associations established in the adoption section, it is plausible that the adoption of improved varieties indirectly affects dietary diversity through output market participation. Overall, district differences were noted with overall dietary diversity lower in Karatu. The GMM Poisson model performed poorly.

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Impacts on productivity and profitability

Section I established the heterogeneity of yield, cost and net revenue differences. In this section, we attempt to isolate the impact of new pigeonpea varieties on these components of farm revenue through an econometric approach. This analysis is based on identifying the productivity differential of new pigeonpea varieties through the development of a yield function that controls for producer heterogeneity of production practices and skill. Pigeonpea yield of farmer i and plot j is (yij). This is estimated as a function of crop production inputs xlij – seed (kg/ha), manure (t/ha), fertilizer (kg/ha), chemicals (l/ha), labor (days/ha), oxen services (TZS/ha) – as well as human capital M and farm characteristics (HCij), including the number of years of experience growing pigeonpea, the gender of the household head, the age of the household head, the education level of the household head, the total value of non-animal assets, N field level attributes (PCij) including the perceived level of soil fertility, soil depth, soil type, slope of the field and whether the field was irrigated, D district and time dummy variables (Zij) (Kondoa, Karatu and Babati plus 2008 and 2010, leaving Arumeru and 2012 as the omitted categories). Pigeonpea variety adoption is instrumented using distance to the nearest extension office and the distance to the nearest market in a Heckman two-step instrumental variable approach. This variable is also interacted with region and time variables (TZp) in order to assess the heterogeneity of impacts (Equation 6).

Equation 6:

This yield function is estimated as a linear functional form and a quadratic under two differing econometric assumptions. First, we estimate models as a pooled cross-section that assumes no relationship between observations. Second, we model the relationship as a panel data set and control for unobserved farmer heterogeneity by controlling within farmer variability for households where data was collected for more than one season. This approach produces an unbalanced panel. Because variety Bangili is a mix of local varieties that have out-crossed with improved varieties, and hence an intermediate between improved and local varieties, it was not included in this analysis. Our focus was to establish the yield and cost differences between local and improved varieties. Table 44 presents the marginal effects of the two functional forms and the econometric approach.

The adoption of improved pigeonpea varieties had a positive effect on yield, although the significance of the impact depends on the district and the year. A test of the hypothesis that time and region affect varietal adoption was rejected at a P-value of less than 1% for all equations. Moreover, controlling for the unbalanced panel nature of the data affects the yield impact of the new varieties, as does the functional form specification.

In general, the regressions indicate a positive marginal product to additional labor ranging from 1.47 to 2.53 kg/ha for each additional day of labor above the mean. There is an even greater marginal impact to increasing seeding rates. For each additional kilogram of seed, 6.96 to 10.53 kg of additional pigeonpea will be produced. By contrast, and only in the quadratic models, fertilizer is overused, probably because it is intercropped with maize.

All specifications showed a positive marginal impact to additional education except in the cross-sectional quadratic specification. Soil characteristics, and in particular the soil type, affected yield and a strong marginal impact to irrigation was reported. Irrigated fields produced between 204 kg/ha and 212 kg/ha more than unirrigated fields. The district and year controls, plus interactions with the adoption of improved varieties, were frequently significant, highlighting the heterogeneity of yield response to location and time.

This information is important in order to capture the interactions between costs and revenues over time that contribute to assessing aggregate welfare impacts. In order to estimate average yield effects, the predicted yield levels were calculated for improved and local varieties and tests of mean differences were

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Table 44. Marginal impacts derived from yield function estimates using linear and quadratic function forms.Linear Quadratic

Cross-sectional Panel Cross-sectional Panel

Coefficient/Marginal Effect

Robust Std. Error


Robust Std. Error

Marginal Effect

Robust Std. Error

Marginal Effect

Robust Std. Error

Improved variety adoption

291.53 181.80 66.58 51.25 322.38* 186.84 70.29 52.68

Crop inputs

Total Labor (D/ac) 1.47*** 0.37 1.49*** 0.37 2.53*** 0.55 2.50*** 0.55

Manure (t/ac) -0.65 5.49 -0.87 5.41 12.48 11.37 11.66 11.48

Fertilizer (kg/ac) 0.19 0.98 -0.05 1.01 -4.43*** 1.24 -4.57*** 1.21

Chemicals (l/ac) 28.62* 15.77 27.84* 16.01 34.99 28.65 34.01 29.58

Seed (kg/ac) 6.96*** 1.74 7.10*** 1.78 10.02*** 2.53 10.53*** 2.67

Hired Oxen Service (TZS/ac)

1.3E-04 1.1E-04 1.3E-04 1.1E-04 4.1E-04 3.8E-04 5.0E-04 3.7E-04

Human Capital

Pigeonpea Experience (year)

-1.73 1.37 -2.20* 1.35 -1.46 1.37 -1.99 1.32

Gender 50.20 36.47 46.30 36.96 47.61 36.26 42.82 37.11

Age -0.17 1.15 -0.25 1.17 -0.14 1.16 -0.26 1.17

Education 9.14* 5.39 9.39* 5.36 9.05 5.51 9.13* 5.50

Total Assets (TZS ‘000) 5.9E-04 2.8E-03 -4.2E-04 3.0E-03 6.2E-04 2.9E-03 -6.6E-04 3.2E-03

Field Characteristics

Soil Fertility category -7.56 22.41 -6.59 22.02 -6.35 22.06 -5.23 21.63

Soil Depth category 15.02 23.44 16.53 23.42 25.14 23.70 27.58 23.93

Soil Type Category -34.37** 14.66 -34.68** 15.08 -32.29** 14.78 -32.93** 15.25

Soil Slope Category -1.33 23.96 -0.79 23.97 -2.61 23.90 -1.24 23.78

Irrigate (1=yes) 206.46*** 72.68 203.51*** 72.60 211.54*** 72.17 207.37*** 71.40

Time and Region Shifters

Kondoa 105.60** 45.26 92.38** 45.18 108.83** 47.66 94.60** 48.14

Karatu 63.50 51.74 65.02 52.72 56.27 52.09 56.17 53.32

Babati 184.22*** 59.67 181.91*** 60.77 162.17*** 61.85 158.30** 63.48

2008 191.82*** 46.55 172.01*** 43.24 204.49*** 47.18 185.28*** 44.03

2010 278.70*** 41.52 280.55*** 39.70 286.14*** 43.91 292.06*** 41.80

Adoption interactions

Kondoa 126.77** 57.64 136.46** 55.26 125.51** 57.53 137.86** 54.70

Karatu 69.46 52.31 72.05 55.55 69.43 52.19 73.31 56.12

Babati 208.11*** 69.08 198.52*** 69.04 218.84*** 70.74 209.19*** 70.87

2008 18.29 60.53 28.78 58.28 19.32 61.46 27.32 58.82

2010 -90.95* 53.85 -91.57* 52.12 -88.21 55.41 -90.18* 53.46N=1,190 in cross-section and 1,190 in the panel with 865 groups

*indicates significantly higher labor use at the 0.10 level, **at the 0.05 level, and ***at the 0.01 level

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calculated (Table 45). Consistent with the results of the production functions, differences in the predicted yields were heterogeneous – in most cases positive and significantly different from zero. Positive yield differences ranged from 11% to 56%. In one case, local varieties out-yielded the improved varieties by approximately 14% and there were cases where the yields of the improved and local varieties were not significantly different.

Cost differences

Unrestricted total cost functions were estimated for local and improved varieties using a similar formulation as the production functions (Equation 7). Prices for the inputs were regressed against total cost with the same set of intercept shifters (Table 46). Due to the high degree of collinearity between input prices and the second-order terms, quadratic functions were not estimable.

Equation 7:

Several parameter estimates of the cost functions were significant, including time and district interaction variables. A joint test of the hypothesis that time and region interact with varietal adoption was rejected at a P-value of less than 1% for all equations, indicating that costs were heterogeneous. For this reason, costs estimates are predicted for each region and year and tests of mean differences between the cost of production of local and improved varieties are calculated (Table 47).

Tests of mean differences identified no differences in the systematic cost of production except in 2010 in Karatu, Babati and Arumeru. In these locations in that year, the predicted cost of production difference between improved and local varieties indicated that it was less costly to produce improved varieties than local varieties by 16% to 20%. These findings are unexpected but could reflect differences in labor use that may occur because of the uniform maturity of improved varieties. Uniform maturity requires fewer field visits for harvest; hence less labor. These results are also consistent with lower costs of production presented in Table 26 where the average cost of production was 17.9% lower for improved varieties across the entire sample in 2010.

Social impact using the DREAM model

In this section the econometric results on yield and cost effects are integrated into the DREAM model in order to estimate the social returns to investment. These results are estimated under varying assumptions that represent both conservative and optimistic estimates of impact, as well as an intermediate between the two extremes. In addition, these models build on earlier analyses in order to assist with comparison with previous research. The current models differ from previous assessments in several specific ways:

■ The horizontal market model assumption was expanded to include the four districts surveyed and each district was specified with yield impacts, cost savings, maximal adoption rates, and technology development timelines derived from the preceding econometric analysis.

Table 45. Mean differences of yield (kg/ha) of improved varieties over local using quadratic functional form and panel data specification.Location 2008 2010 2012Kondoa NA 69.0* 132.6***Karatu 123.2*** -4.6 118.3***Babati 286.4*** 40.8 187.4***Armeru 39.6** -67.2*** 21.7*indicates significantly higher labor use at the 0.10 level, **at the 0.05 level, and ***at the 0.01 level Note: The number of observations on the yield of improved varieties in Kondoa in 2008 was insufficient to calculate the mean.

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Table 47. Predicted cost of production differences between improved and traditional varieties (TZS/ha).

2008 2010 2012

Arumeru 10,036 -44,700*** 25,292Babati 5,481 -36,797*** -8,191Karatu 20,203 -36,843*** -28,648Kondoa 39,334 -20,924 -65,902

Table 46. Linear unrestricted cost equation estimates (TZS/ha).Cross-sectional Panel

Coefficient/ Marginal Effect

Robust Std. Error


Robust Std. Error

Improved variety adoption 57,769.3 -40,691.3 57,282.5Wage rate labor (TTS/day) -0.7 1.0 -0.5 0.9Manure (TZS/t) 2.2** 1.1 - 19,123.2 1.2Fertilizer price(TZS/kg) 45.1 77.0 25.9 67.4Chemical price (TZS/I) 0.6 0.6 1.0 0.7Seed price (TZS/kg) 14.0*** 3.6 14.3*** 3.4Hired Oxen Service (TZS/ha) 2.7*** 0.2 2.7*** 0.2Human CapitalPigeonpea Experience (years) -1,020.8* 547.8 -1,031.1* 566.3Gender -2,626.0 20,734.4 5,801.8 15,289.2Age 1336.5** 573.3 1,439.1** 664.5Education 1,903.4 2,486.9 -168.3 3,583.6Total Assets (TZS '000) -1.0 0.7 0.0 0.0Field CharacteristicsSoil Fertility category 8,860.7 12,066.4 10,706.9 13,047.0Soil Depth category -56,171.4*** 10,490.9 -54,350.8*** 10,591.4Soil Type category -20,074.6*** 5,445.3 -19,737.4*** 5,700.1Soil Slope Category -4,525.8 10,743.4 -3,372.5 9,797.6Irrigate (l=yes) 45,812.1 69,628.3 37,768.9 62,540.1Time and Region ShiftersKondoa -54,249.7** 22,567.3 -59,022.7** 25,523.5Karatu -79,120.9*** 22,073.7 -89,882.5*** 25,757.9Babati -34,594.7 23,961.5 -51,747.6* 27,493.72008 -52,309.7*** 19,229.0 -59,706.8*** 20,173.82010 -11,143.8 15,804.3 -12,499.5 15,439.2Adoption interactionsKondoa -13,500.3 21,869.3 -22,131.3 22,100.3Karatu -88.9 14,175.1 -2,976.8 15,251.8Babati -37,429.7* 20,618.3 -31,122.2 23,017.12008 22,908.6 18,075.9 33,151.2 21,749.72010 -50,675.0*** 15,058.1 -44,025.0*** 16,700.7Constant 331,120.3*** 61,372.1 340,917.5*** 63,438.2N = 1,190 in cross-section and 1190 in the panel with 865 groups * indicates significantly higher labor use at the 0.10 level, ** at the 0.05 level, and *** at the 0.01 level.

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■ We did not include any spillover effects to other regions of Tanzania given the lack of evidence of spillover effects.

■ Time lags to technology development were lengthened. ■ Dis-adoption occurred at a faster pace than previously assumed. ■ Due to the absence of disaggregated costs of program development and implementation by district, the

same magnitude and time horizon was assumed for Arumeru, Kondoa and Karatu as for Babati. Hence the total cost of program was quadrupled and no economies of size in technology development and dissemination were assumed. Only start dates of program activities were altered.

The disaggregation of the market model was justified based on the econometric estimates of yield and cost effects, and specifically the rejection of the null hypothesis of no time or district level effects. The lack of spillover effects was based on the findings described in Mponda et al. (2013) who reported no evidence of improved technology usage in southern Tanzania. An adjustment was made to reflect evidence that spillover effects between the districts of study were not as linear as expected and that inter-district transfer of improved varieties was not always successful, especially in Kondoa. A further adjustment was made to reflect survey evidence that indicated that dis-adoption of improved varieties in Babati was occurring sooner and that farmers were sowing varieties that were a mixture of improved and local germplasm.

A major difference between this study and previous ones reflects the statistical evidence that some of the yield and cost-saving effects were not statistically different from zero. This allows the study to set bounds on what might be considered the upper limit and lower limit of the social impact of the pigeonpea development program (Table 48). Models are estimated where consistent yield advantages and costs savings can be observed, where yield effects but no cost savings are observed, and where cost savings are achieved without significant yield effects. All other assumptions on the supply and demand elasticities, price transmission, growth in supply and demand and public policy are the same as previous studies.

The purpose of these three scenarios is not to take issue with whether or not improved varieties have higher yields or not or whether they reduce cost or not, but to acknowledge the limitations of the DREAM model to incorporate stochastic technology and cost savings that are condition-specific. The first scenario assumes that there is always a positive yield effect and a cost savings, but because the statistical evidence does not support this assumption it is likely that the true social impact is below the estimated level. The other two scenarios acknowledge that during some years no evidence of cost savings or yield effects is observed, and during others those impacts do take place. Each of these scenarios can be considered as lower limits of social impact if these assumptions are maintained throughout the 30-year planning horizon of the DREAM model. This is also an oversimplification since the econometric results show that there are years in which both yield and cost savings occur.

A fourth scenario without yield effects or cost savings is not estimated because the evidence does not support this, especially the evidence on adoption rates that indicates deep penetration of improved varieties in Karatu and Arumeru and moderate adoption rates in Kondoa and Babati. These adoption rates may signify that the true impact is likely a combination of the yield and cost savings effects since few other plausible explanations exist.

Table 48. Yield and cost assumptions for DREAM model scenarios (output/ha or TZS/ha).Yield and cost effects No cost effect No yield effect∆TC ∆Y ∆TC ∆Y ∆TC ∆Y

Kondoa -11.4 47.7 0 47.7 -11.4 0Karatu -20.2 44.0 0 44.0 -20.2 0Babati -16.4 56.0 0 56.0 -16.4 0Arumeru -16.7 11.1 0 11.1 -16.7 0

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Results of the analysis conducted in DREAM are presented in Table 49. These results should be interpreted as an update of previous studies with new empirical evidence on yield and cost benefits disaggregated into four districts of intervention instead of one, a relaxation of the assumption of spillover benefits to other regions of Tanzania, a lengthening of the time lags to technology development, and a shortening of the time to dis-adoption. Each of the last three points should decrease the value of the impacts over previous studies. There is no point of reference for the first change since previous studies were not disaggregated to the same districts.

Despite the changes, the results indicate a similar magnitude of effects as previous reports. Under the most optimistic scenario, where yield and cost-saving effects occur, the estimated internal rate of return (IRR) is 25.5% for the program, which is very similar to previous analyses despite a more conservative set of assumptions. Moreover, if we assume consistent yield effects to the varieties without cost savings, the IRR drops to 21.9%. Under the most restrictive assumption, where there is no yield effect and only a cost savings (for which the evidence is weak), the IRR drops to 13.5%.

Table 49. Simulation results of the social benefits to pigeonpea improvement under three scenarios.

Region

Present value of benefits (B) to and costs (C) in USD '000

Producers Consumers Government Total Costs (B-C) B/C RR

Yield and cost-saving effects

Kondoa 746.5 0.6 3.4 750.6 171.4 579.1 4.37 21.20%

Babati 858.8 0.6 3.9 863.4 257.9 605.4 3.34 18.20%

Karatu 3545.1 0.6 14.6 3560.4 221.4 3338.9 16.07 36.40%

Arumeru 706.5 0,6 3.2 710.3 221.4 488.8 3.2 17.80%

Rest of World -5174.3 5231 0 56.7 0 56.7

Rest of Tanzania -34.6 21.9 -0.1 -12.8 0 -12.8

Total 648.1 5255.4 25 5928.6 872.2 5056.3 6.79 25.50%

Yield effect but no cost-saving effect

Kondoa 637.3 0.4 2.9 640.7 171.4 469.3 3.73 19.50%

Babati 714.4 0.4 3.3 718.2 257.9 460.3 2.78 16.20%

Karatu 2580.6 0.4 11.1 2592.2 221.4 2370.7 11.7 32.60%

Arumeru 283.9 0.4 1.3 285.7 221.4 64.3 1.29 8.10%

Rest of World -3843.9 3885.2 0 41.3 0 41.3

Rest of Tanzania -25.7 16.3 -0.1 -9.5 0 -9.5

Total 346.7 3903.3 I8. 6 4268.7 872.2 3396.5 4.89 21.90%

No yield effect but a cost-saving effect

Kondoa 142.9 0.2 0.6 143.8 171.4 -27.5 0.83 3.60%

Babati 198.2 0.2 0.9 199.4 257.4 -58 0.77 2.80%

Karatu 1100.7 0.2 5.1 1106 221.4 884.6 4.99 22.70%

Arumeru 439.5 02 2 441.7 221.4 220.3 1.99 12.70%

Rest of World -1811 1829.9 0 18.9 0 18.9

Rest of Tanzania -12.1 7.6 0 -4.5 0 -4.5

Total 583 1838.4 8.7 1905.5 871.7 1033.7 2.18 13.50%

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Under all of these assumptions between US$1.03 million and US$5.06 million of net social benefits (B-C) are generated with the largest proportion accruing to consumers, consistent with previous studies. The lowest benefit-to-cost ratio (B/C) is 2.18. Even under highly unrealistic and extremely conservative assumptions on the conditions affecting the calculation of net social benefits, the public investment in pigeonpea improvement and market development is justified. It is even more strongly justified when highly conservative assumptions are relaxed. Under the intermediate and optimistic scenarios the IRRs range from 21.9% to 25.5%, and benefit-to-cost ratios range between 4.9 and 6.8 times the cost of public investment.

Summary and Conclusions

This report has updated previous studies on the adoption, utilization and impact of ICRISAT’s program on pigeonpea improvement in northern Tanzania. This study differs from previous ones in that it examines the impact across four districts, and whenever possible it documents the evolution of indicators by comparing the results of a household level survey conducted in 2012 with previous rounds of similar surveys in 2008 and 2010. In addition, focus group discussions with pigeonpea assemblers and traders provided insight into the development of the domestic and export markets.

Empirical evidence indicates that there are regions with extremely high rates of adoption of improved pigeonpea varieties and others with moderate rates. The program has been successful in developing improved varieties adapted to local conditions that have stimulated demand by farmers. Quantitative analysis of the farm-level impacts of adopting improved varieties is complicated by a high degree of heterogeneity of yield, cost and net revenue effects even when district-level differences are taken into consideration. It appears that improved pigeonpea varieties exhibit similar performance traits to many other modern seed technologies with higher yield potential, but lower net revenues, especially under low yield conditions. Stochastic dominance analysis of net returns in 2012 could not determine whether improved pigeonpea varieties first- or second-order stochastically dominated local varieties. This analysis was made even more difficult by the high numbers of farmers who indicated that they grew a pigeonpea variety named Bangili, a mixed variety containing traits of local and improved varieties.

Market trader surveys were conducted with large-scale brokers, rural assemblers, retailers and farmers about pigeonpea marketing. Overall, evidence was limited that the market for pigeonpea is becoming more sophisticated. There is clearly demand, albeit limited, for quality- or-trait-differentiated varieties. Traders seek a uniform quality of pigeonpea free of foreign matter, impurities or damaged seeds. Color and uniform seed size were less frequently cited as important characteristics. Varietal differentiation among traders and demand for specific traits is limited.

The second section of the report focused on multivariate analysis of the data collected in 2012, and where possible, integrated the data collected in 2008 and 2010 into an unbalanced panel. The first important finding is that it was difficult to identify any causal relationship between the adoption of improved varieties and the household’s decision to sell pigeonpea. The correlation between the decision to adopt new pigeonpea varieties and participate in the output market was not significantly different from zero. By contrast, limited evidence indicated that the decision to participate in the output marketplace improved household food security as measured by the household diversity scale in 2012.

The panel data of improved and local varieties was used to estimate yield effects and total cost of production differences while controlling for the endogeneity of the adoption decision using a two-stage, least-square instrumental variable regression approach. Each of these regressions controlled for district and time differences. Results indicated a high degree of heterogeneity and that an F test of the hypothesis that the joint effects of regions and time were equal to zero was rejected in all regressions. Based on this finding, and on the overall performance of the yield and cost regressions, separate effects on the average

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impact of adopting improved varieties were derived. Results of these estimates were not consistent and some effects on yields and cost saving were not always significantly different from zero.

Based upon these findings, economic surplus calculations were estimated for three different scenarios using the DREAM simulation model. These three scenarios build on previous economic surplus estimates by incorporating empirical evidence obtained in the 2008, 2010 and 2012 surveys and by relaxing several assumptions in previous models that were not supported by empirical evidence. Despite these changes, the simulation results produced estimates that are close in magnitude to previous studies. The simulation results indicate that the rate of return to investment in pigeonpea technology and market development generated an internal rate of return (IRR) that could be as high as 25.5% per year. Under the most restrictive set of assumptions the IRR was 13.5% per year while for an intermediate result the IRR was 21.9% per year. Based upon the increasing trends in adoption of improved varieties over time, high adoption rates of improved varieties in some regions and moderate rates in others, it appears that the qualitative evidence suggests that the true IRR lies in the neighborhood of the most optimistic result which is 25.5% per year. Given this qualitative argument and the quantitative estimates of benefits and costs, the results suggest that investment in the pigeonpea improvement program has generated social benefits 4.9 to 6.8 times the cost of the program.

ReferencesAsfaw S, Shiferaw B, Simtowe F and Lipper L. 2012. Impact of modern agricultural technologies on smallholder welfare: evidence from Tanzania and Ethiopia. Food Policy 37:283–295.

Coates J, Swindale A and Bilinsky P. 2007. Household food insecurity access scale (HFIAS) for measurement of food access: indicator guide. Washington DC: Food and Nutrition Technical Assistance Project (FANTA).

ICRISAT. 2011. Inclusive market-oriented development (IMOD). Patancheru 502 324, Telangana, India: International Crops Research Institute for the Semi-Arid Tropics.

IFPRI. 2012. Women’s empowerment in agriculture index. Washington DC: International Food Policy Research Institute.

Jones R, Freeman HA and Monaco GL. 2002. Improving the access of small farmers in eastern and southern Africa to global pigeonpea markets. AgREN Network Paper No.120.

Kennedy G, Ballard T and Dop M. 2011. Guidelines for measuring household and individual dietary diversity. Rome: EC-FAO.

Mponda O, Kidunda B and Bennett B. A value chain analysis for pigeon pea in the southern regions of Tanzania. Unpublished manuscript. ICRISAT 2013.

Shiferaw BA, Kebede TA and You L. 2008. Technology adoption under seed access constraints and the economic impacts of improved pigeonpea varieties in Tanzania. Agricultural Economics 39:309–323.

Shiferaw B, Silim S, Muricho G, Audi P, Mligo J, Lyimo S and Christiansen JL. 2005. Assessment of the adoption and impact of improved pigeonpea varieties in Tanzania. Journal of SAT 5(1).

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Appendix 1: Population pyramids by district

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Appendix 2: DREAM modelDREAM, or Dynamic Research EvaluAtion for Management, is a menu-driven software package for evaluating the economic impacts of agricultural research and development (R&D). Users can simulate a range of market, technology adoption, research spillover, and trade policy scenarios based on a flexible, multi-market, partial equilibrium model.

With DREAM you can define a range of technology investment, development, and adoption scenarios and save them in an integrated database. Scenarios are described using market, R&D, and adoption information for any number of separate ‘regions’. Some factors, such as taxes, subsidies, growth rates, and price elasticities, can be specified as constant or as changing over the analysis period. Each region in which production takes place may have its own pattern of technology adoption. After specifying the initial conditions for each region, you can simulate the likely effects of technology development and adoption on:

■ prices; ■ quantities produced, consumed, and traded; and/or ■ the flow of economic benefits to producers, consumers, and government.

DREAM handles simple to relatively complex evaluation problems using a standardized interface. A number of market assumptions are possible: small open economy, closed economy, vertically integrated farm and post-harvest sectors in a single economy, or multiple trading regions. The software also accommodates technology-driven shifts in supply or demand, and users may specify constant or variable shift effects over time in farmers’ fields.

Importantly, DREAM’s multiple-region specification can simulate various technology spillover scenarios wherein a technology may be adopted in more than one region. Changes in the pattern of technology spillovers can significantly alter the size and distribution of R&D benefits.

DREAM has been applied to the evaluation of individual projects in a national context as well as to entire commodity sectors at a subcontinental or continental scale. And while it was designed primarily to evaluate options for R&D yet to be undertaken (ex-ante assessments), DREAM has also been successfully applied to analyzing the effect of past research (ex post assessments).

Specifications

DREAM is designed to run under Windows 95 or Windows NT. As a minimum the program requires a 486DX 66Mhz processor (but in practice a Pentium 150Mhz or higher is strongly recommended), a mouse, 16MB RAM and 12MB of hard disk space (allowing for the required Visual Foxpro 5.0 modules and the data directories “\database” and “\examples”). A minimum screen resolution of 640x480 is required, but 800x600 is preferred, and the display system should be capable of displaying 256 colors.

The program does not use any proprietary hardware or software requiring third party approval, but is protected by copyright.

Producer: International Food Policy Research Institute

Size: 8.0 MB

For more information, see the IFPRI blog at http://www.ifpri.org/dataset/dream-dynamic-research-evaluation-management.

http://www.ifpri.org/dataset/dream-dynamic-research-evaluation-management

http://www.ifpri.org/dataset/dream-dynamic-research-evaluation-management

Assessment of the impact of Improved Pigeonpea Development in Northern Tanzania

Report No. 1

mpactAssessment


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ICRISAT works in agricultural research for development across the drylands of Africa and Asia, making farming profitable for smallholder farmers while reducing malnutrition and environmental degradation.

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We believe all people have a right to nutritious food and a better livelihood.

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