Improving farmers’ net revenue in traditional context: Analytic Hierarchy Process, Expert System, and Linear Programming

Abstract

The low yield of the agricultural sector in Sub-Saharan Africa (SSA) is not solely due to the type of agriculture (mainly traditional), but also to the crop selection process which is typically based on impressions or past experience. This approach cannot always ensure an optimal crop selection even for subsistence farming. To improve farmers’ net revenue, this work proposes a three-stage approach for crop selection in the context of traditional agriculture. Firstly, since crops’ yields are influenced by several environmental parameters, an analytic hierarchy process is used to set the weights of those parameters. Secondly, an expert system using a rule-based inference engine is designed to determine the appropriateness of crops depending on environmental and time constraints. Finally, the net revenue of the farmer is formulated as a linear programming problem, considering the operating account of the various crops selected during the previous stages. In addition, a web interface has been developed to allow farmers to benefit from the whole system. Scenarios have been designed from a collection of crop technical itineraries, and they have been compared with the outputs of the expert system. The result shown that the system can effectively help farmers to improve their net revenues.