Peanut research and poverty reduction: impacts of variety improvement to control peanut viruses in Uganda.

Economic surplus results are presented first, followed by the results of how that surplus is allocated to individual households, and the impact on household income changes and poverty. Data for calculating the poverty indices and the adoption model were obtained from a national household survey conducted by the International Food Policy Research Institute (IFPRI) in collaboration with the Uganda Bureau of Statistics through the Uganda National Household Survey (UNHS) project of 1999-2000. The data set contains 2,949 households in the peanut growing region, enabling computation of the poverty indices and providing information on socioeconomic characteristics affecting technology adoption. A crop survey, a socioeconomic survey, and community survey questionnaires were all included. Information was obtained on household demographics, assets, labor allocation, yields and costs, and other agricultural production information. The surveys targeted representative households across Uganda (UNHS).

Economic Surplus Estimation

Data on expected yield and cost changes following adoption of the Rosette-resistant technology, and expert-opinion on expected adoption rates were obtained during a visit to Uganda in July 2003. A breeder responsible for the groundnut improvement program in Uganda, two extension workers, one a district extension officer in charge of Eastern Province, a farm management specialist, and several farmers were interviewed. University scientists conducting groundnut improvement research and buyers and processors of groundnuts were also interviewed. A questionnaire was designed and targeted at research managers, breeders, and extension agents who interact with farmers on a regular basis (Moyo). Questions were asked about groundnut research expenditures in Uganda, and expected adoption profiles, yield changes, and costs of production.

Information was collected on current peanut yields and costs of production for traditional and virus-resistant varieties (Serenut 3 and 4) as well as realized and projected adoption rates. The varieties were released in 2001 and therefore there was already some adoption (15% in the first two years) and higher adoption was expected in the coming years (experts estimated a maximum adoption rate of 50%). The Ugandan National Agricultural Research Organization (NARO) had been conducting research on Groundnut Rosette Virus when the Peanut CRSP (funded by USAID through the University of Georgia) brought the new virus-resistant variety, developed by ICRISAT in Malawi, to Uganda. Our analysis begins by estimating net returns from this research for a fifteen-year period starting from inception of Peanut CRSP activities in May 2001.

Parameters in the Surplus Analysis

Parameters for the economic surplus analysis were obtained from existing data and from expert opinion as indicated above. The model results can be sensitive to assumptions, and therefore sensitivity analysis was completed for elasticities, the adoption profile, and the discount rate. It would be straightforward to conduct sensitivity analysis on costs of the different groundnut technologies, about which there might be uncertainty.

A groundnut supply elasticity was not available for Uganda. Theory suggests that annual commodities using relatively little land and few other fixed factors will have relatively high elasticities of supply. Alston, Norton, and Pardey (1995) suggest that without other information, a supply elasticity of 1 is a good starting point since long-run elasticities for most commodities are greater than one, while short-run and intermediate elasticities are often close to 1. We assume it is 1. The demand elasticity is assumed to be -0.5, as groundnuts are a staple crop but preferred to many low cost starches.

Based on opinions of Ugandan scientists and other experts and on evidence from farmers who had already adopted, it is estimated that yield will increase by 67% following adoption (Moyo 2004). Input use is expected to increase by 50% per hectare upon adopting the technology, due to higher seed and other costs.

This per hectare cost change was converted to a per ton cost change and subtracted from the yield effect using the formula [K.sub.t] = [E(Y)/[epsilon] - E(C)/1 + E(Y)] p [A.sub.t] (1 - [[delta].sub.t]) (Alston, Norton, and Pardey 1995, p. 380) to arrive at a net per unit cost change of 37.1%. A three-year average border price for 1999 to 2001 was used as the base price in the economic surplus model, or $750/ton. Between the 1999 and 2001 agricultural seasons, Eastern Province farms produced an average of 43 thousand tons of peanuts (UNHS 2001), and this amount was used as the base quantity.

USAID, through the Peanut CRSR contributed $56,000 to the project, and other costs were incurred by the public sector in Uganda, by ICRISAT in Malawi, and by other U.S. universities. A 20% adjustment was made to account for Ugandan costs, including the salaries of breeders and other costs. The total cost (Ugandan plus USAID) was estimated to be $67,120 or $16,780 per annum, for the four-year period (2001-2004) in which the research was conducted. Other costs incurred by ICRISAT and Georgia were not included when calculating returns on the USAID/Uganda investment.

Aggregate Changes in Net Economic Benefits

The net present value of the research over the fifteen-year period for the open economy model is estimated to be US$43.0 and $35.6 million at 3 and 5% discount rates, respectively. These estimates represent aggregate net returns to the research. The gross benefits accrue to producing households in the Eastern Province, and the costs are borne by the research sponsors. In the closed economy case, net benefits are estimated to be US$41.1 and $34.0 million at 3 and 5% discount rates, respectively. In the closed economy case, the gross benefits accrue to producing and consuming households.

These estimates do not, however, indicate how poverty will change as a result of the research. Changes in poverty clearly depend on the characteristics of adopting households along with the per-household change in producer and consumer surplus.

Household-Level Incomes and Changes in Poverty

Poverty in Eastern Province is high, with the depth and severity indices indicating a significant shortfall in income below the poverty line and a high degree of inequality among the poor. Members of peanut-producing households are less poor than those in the full sample; the headcount of poverty is about four percentage points lower (table 1). The depth and severity indices indicate that peanut-producing households are more homogeneous than the full sample, as the percentage point gap between peanut producers and the full sample is higher for the depth and severity indices as compared to the headcount index. Poverty is much deeper and more severe among the nonproducing households than the headcount index alone indicates.

Determinants, of Adoption of New Technologies

All 2,949 Eastern Province households in the survey were asked about use of hybrid or improved seed but only 2,059 responded. Such seeds were adopted primarily for maize, but were adopted as well to a lesser extent for several other crops, including peanuts. Fewer households (499) reported using hybrid or improved seed than not using (1,560). Non-adopting households were headed by slightly older people, had fewer members, and lower income (table 2). Nonadopters were less likely to receive extension advice than adopters. Adopting households were mostly headed by married males. Adopting households had more (27%) people with postsecondary education than nonadopting households (14%). Adopting households had more access to land and were more likely to receive information related to crop production and marketing.

A probit model was used to estimate the probability of adopting new technologies, with the dependent variable the use of hybrid or improved seeds, and explanatory variables: sex and age of household head, marital status, education, access to extension services and market information, land tenure, household size, income, land holdings, and number of hoes owned (a proxy for farm capital). Results of the adoption model are summarized in table 3. Estimated probit coefficients are not directly interpretable, and therefore marginal effects were calculated, representing the marginal change in the probability of adoption given a unit change in each independent variable.

The signs for most coefficients are consistent with expectations and theory. For example, a positive relationship is expected between adoption of new technologies and level of education, access to information, income, and asset ownership. The older the household head, the less likely he or she is to adopt a new technology.

Male-headed households are about 9% more likely to adopt hybrid or improved seed than female-headed households. Households with junior high school as the highest education and those with secondary or higher are 8 and 9% more likely to adopt, respectively, than those with only primary education. An increase in the age of the household head by one year results in a decline in the probability of adoption of 0.13%. An increase in per capita income results in a significant but small increase in probability of adoption.

Impacts of Adoption of the Improved Peanut Variety on Aggregate Poverty