Comparing and contrasting the three studies reviewed in this paper produced lessons in several areas, including the conceptual approaches and methods used, the policy implications that resulted, and the future subregional priorities for agricultural research in SSA.
First, the degree to which the various models were integrated in the analyses remains limited, despite efforts to improve such integration with each successive study. Further work is needed to improve the integration between the EMM and DREAM models, for example, by incorporating the dynamic technology adoption features into the EMM model. Moreover, construction of the spillover matrixes could be improved upon and incorporated into the model.
Second, sufficient and accurate data is often lacking, especially agricultural and socioeconomic data at lower administrative levels (for example, on production, consumption, prices, and incomes). An even more serious problem in some countries is incomplete information on research resources, capacities, and expenditures by commodity or research discipline (to determine unit cost of research), although ASTI is continuing efforts to fill such gaps. Moreover, better information is needed on the probability of technology adoption and diffusion based on past observations, including yield performance by technology type or farming system (both actual and on-farm trials). Such information would improve future applications of the DREAM model, especially if well-constructed spillover matrixes could also be incorporated. With this in mind, more work is needed to improve the data systems on commodity-specific costs, capacities, and adoption and outcome variables, such as time lags, probabilities of adoption and diffusion, rates of return estimates for R&D, and yield effects. ASTI might consider pursuing such information to improve its own databases on agricultural R&D in Africa.
Despite these modeling and data limitations, the results of all three studies proved to be highly policy relevant. This underscores the usefulness of such analysis to inform priority-setting processes for subregional R&D strategies. The study results are also a testament to the value of evidence in setting research priorities, and therefore to the need to build regional analytical research capacities in priority setting and impact assessment. Support from universities, donors, and international institutions could play a key role in further developing this capacity within Africa.
Also on the policy front, to exploit the benefits of cross-country cooperation in R&D, the three regional strategies must overcome many institutional and administrative barriers to managing and coordinating such systems across many national ones (Pardey et al. 2007). Indeed, cooperation across complex systems can incur high transaction costs, especially if some systems are far more advanced than others. Moreover, cross-country collaboration is affected by each country’s own program needs and desire to maintain a bargaining position for domestic resources. Even if collaboration is desirable, any likely transaction costs must be weighed against potential benefits (Pardey et al. 2007).
Finally, donors have traditionally dominated the attention and support of regionally based research organizations such as ASARECA and CORAF/WECARD. Once the benefits of greater regional cooperation and economic integration become more evident and understood, the political will of member states to commit national resources to regional efforts is likely to increase. Growing national interest is already evident in the support of individual countries to programs such as the West African Agricultural Productivity Program and the East African Agricultural Productivity Program.