HENNING BAUR, MICHAEL BOSCH, STEPHAN KRALL, THOMAS KUBY, ALISON LOBB-RABE, PAUL-THEODOR SCHÜTZ, ANDREAS SPRINGER-HEINZE
German Agency for Technical Cooperation (GTZ), Department of Agriculture, Fisheries and Food, Germany
Evaluators often try to quantify and prove the impact of agricultural research at a highly aggregated level such as farmers' welfare, household food security or the nutritional status of children. In many cases, however, attribution gaps that are caused by the existence of too many other significant factors make it impossible to isolate the effects of a single development intervention such as research.
Based on the critical review of an IITA impact study of soybean, this paper demonstrates the importance of establishing plausibility when inferring from the results of agricultural research to changes in the well-being of farm families and rural communities. It is argued that, to require impact assessment studies to establish more than plausible relationships between research results and developmental impact would force evaluators to gloss over much information and over-interpret the available data.
A reasonable compromise would be to trace out impact pathways and to establish plausible links between research investments and observed development impact(s). Seven standards are suggested as elements of good practice in impact assessment and evaluation feedback that should be included in impact studies: (1) A description of the agricultural research investment and its context; (2) The model or concept of innovation; (3) The objectives, scope and limitations of the evaluation; (4) The logic model underlying the project or programme; (5) The statement and testing of a concrete impact hypothesis; (6) A discussion of other factors that could have affected the observed changes; and, (7) A critical review and comment of the findings.
Applying these standards in impact assessment of agricultural research for development should help to strengthen the plausibility of impact claims at a justifiable cost.
Keywords: Agricultural research, impact assessment, innovative evaluation approach, plausibility