The role of impact assessment in evaluating agricultural investments in Eastern and Southern Africa
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Google URL: http://books.google.com/books?id=RPZkzs-4-BIC
Eastern and Southern Africa, in particular, have been burdened with chronic food insecurity, pervasive rural poverty and natural resource degradation. In many countries, agricultural research and Development (R&D) has had limited success in improving agricultural productivity and the livelihoods of resource-poor households. Impact assessment (IA) can measure and quantify the benefits and consequences of R&D investments. Although IA of agricultural R&D gained momentum in the 1990s in Eastern and Southern Africa, relatively few of the studies in Africa have been published. This chapter synthesizes the results of the IA studies included in this book and compares them with similar studies in other regions of the world. The available evidence indicates that rate of return for many R&D investments in Eastern and Southern African countries has been high for a wide range of programmes and commodities. Several challenges remain. There has been little methodological and practical work in assessing the economic impact of non research outputs such as training, networking, advisory services, and policy and institutional reforms. Many assessments of the environmental impacts of R&D programmes are using qualitative methods because of the lack of data. At present, IA results in Eastern and Southern Africa are mainly used to satisfy external accountability and Reporting. Few agricultural research managers use evaluation results to support their resource allocation decisions. Fewer institutions have developed formal institutional evaluation systems to support decision-making and institutional learning. IA remains relatively new and externally driven. Institutional and human capacity is still lacking to assess impact regularly, incorporate results in a continuous process of planning research, update research outcomes and revise research plans. National R&D systems need to pay greater attention to internal learning, accountability and institutionalizing feedback mechanisms in developing IA procedures.
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