The demand for verifiable evidence ofresults and impacts of development agricultural programs andprojects is growing. However, most of the indicators thatdevelopment practitioners have traditionally used intracking progress toward achieving projects' objectivesfocus on the workings of the development operation itself.These performance indicators relate mainly to lower-levelinputs and outputs and are used to populate managementinformation systems. Higher-level indicators are used tomeasure progress in achieving the ultimate objectives ofprojects, and in bringing about larger outcomes and impacts.The ability to measure and demonstrate outcomes and impactsrelies on the use of indicators that are based on reliabledata and on the capacity to systematically collect andanalyze that information. The conditions in which monitoringand evaluation (M&E) are carried out vary widely,depending on the demand for information, the extent to whichit is used to inform decision-making, and the reliability ofthe systems that are in place to capture and convey thatinformation. Throughout much of the developing world theseconditions are "less-than-ideal," and informationis irregular and often lacking altogether. In theseconditions there is a lack of effective demand forinformation on the part of policy makers. The conditions areoften especially pronounced for data related to rural areas,where the costs of data collection are high and the qualityof existing data is particularly low. Building data systemsand developing and supporting capacity for M&E in theseconditions is, therefore, a pressing imperative forinterventions in the agriculture and rural developmentsector. Strengthening capacity for M&E begins at thenational and sub-national levels, where addressing theweaknesses of national statistical systems is a commonpriority. The data collected and reported within countriesmust not only be of sufficient quality to inform planningand policy formulation but must also be consistent between countries.
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