Abstract only

Impact assessment studies consistently show that the benefits of plant breeding research are large, positive, and widely distributed. Economic analysis consistently indicates that investment in crop genetic improvement generates attractive rates of return compared to alternative investment opportunities. Similarly, social impact analysis consistently indicates that welfare gains resulting from the adoption of modern varieties (MVs) reach both favored and marginal environments and are broadly shared by producers and consumers. Partially because of the large body of empirical evidence that supports these findings, governments, international lending agencies, philanthropic organizations, and private corporations have invested millions of dollars in plant breeding research. But how reliable are the results of studies that estimate the benefits of plant breeding research? Are the methods used to conduct such studies theoretically sound? And are the data sufficiently complete and accurate? This paper reviews methods specifically used to estimate the benefits of plant breeding research and discusses methodological issues and practical challenges that often receive inadequate attention in applied impacts work. The idea is not to question the validity of the broad conceptual frameworks used to estimate the benefits of plant breeding research (e.g., the economic surplus approach, the production function approach) or to examine the major issues in research evaluation in general. Rather, the objective of the paper is to examine and propose workable solutions to a number of problems that frequently arise when the widely accepted conceptual frameworks are used for empirical analysis of plant breeding research. Issues and challenges can be grouped into three general categories: 1. Measuring adoption of MVs:|*Defining the terms "improved germplasm" and "modern variety" in the presence of seed recycling, farmer selection, and creolization.|*Measuring the area planted to MVs (given the definitional problem).|*Measuring the area planted to MVs based on sample surveys, seed sales, and expert opinion.|*Consistency; given the limitations of data availability and reliability of estimates over time.|*Accounting for external factors that affect the rate and extent of MV adoption (e.g., cost and availability of improved seed and complementary inputs, farmers' knowledge and management skills, economic policies that affect the profitability of adopting MVs). 2. Estimating the benefits associated with adoption of MVs: Estimating farm-level productivity gains attributable to adoption of MVs.|*Consistency between estimates based on yield trials and aggregate production data.|*Dealing with different types of productivity gains (e.g., yield increases vs. yield losses foregone).|*Distinguishing between the effects of improved germplasm and the effects of improved crop management practices.|*Accounting for non-yield benefits (e.g., reduced growing cycle, improved grain quality, positive environmental externalities ).|* Estimating the longevity of benefits attributable to the adoption of MVs when the benefits erode through time. *Anticipating what would have happened to productivity in the absence of the plant breeding program (counterfactual scenario). 3. Assigning credit for plant breeding research:|*Dealing with research spillovers (assigning credit to different breeding programs when innovation results from collaborative research).|*Feasibility of evaluating returns to alternative plant breeding strategies.|CIMMYT researchers, in collaboration with colleagues from national agricultural research organizations, have conducted a series of studies designed to document and quantify the impacts of international maize and wheat breeding research. Based on lessons learned from the CIMMYT studies, each of these issues and challenges is discussed in detail, and practical guidelines are presented to help those interested in conducting applied impacts studies to avoid common pitfalls, which, if ignored, may lead to incorrect empirical results. We conclude with a discussion of the policy significance of improved empirical estimates.|

Socioeconomics Program

English

0309|R01CIMPU|AGRIS 0301|AL-Economics Program

Juan Carlos Mendieta

CIMMYT Staff Publications Collection