Evaluating Recommendations: Tips & Facts Sheet

A good place to start is to consider why many recommendations fail. Often, they are simply inappropriate. To ensure success, a thorough analysis should be conducted before making a recommendation. This analysis should involve disaggregating data by sex to determine if recommendations are appropriate for male and female farmers. The analysis should begin by determining whether the recommendation addresses a major problem farmers face. The importance of the problem should also be assessed. For example, what percent of the area is affected, and what percent of farmers are impacted? The frequency of occurrence, type, and extent of the problem should also be considered. Additionally, the solution should be evaluated for feasibility and appropriateness, taking into account gender constraints and needs. The environmental impact of the recommendation should also be assessed. Other factors, such as the effect on labor needs and household responsibilities, should also be considered. The recommendation should be easy to understand and adopt, and it should fit with the farming system. The potential for selling extra product and the impact on the environment should also be evaluated. The characteristics of the recommendation should be evaluated, including the type of benefit, amount of benefit, and risk involved. The availability of inputs and labor needs should also be assessed. The recommendation should be targeted at the right person, and labor needs should not impact other agricultural or household responsibilities. The ease of understanding and adoption of the recommendation should be evaluated, as well as the potential for selling extra product and the impact on the environment. The conclusion of the evaluation should be based on the importance of the problem and the effectiveness of the recommendation. The problem should be ranked as not important, important, or very important, and the recommendation should be evaluated as poor, good, or very good. This thorough analysis can help ensure the success of a recommendation and improve the probability of a successful outcome.