Household-specific targeting of agricultural advice via mobile phones: Feasibility of a minimum data approach for smallholder context
This article (PDF) in the Computers and Electronics in Agriculture journal explores the feasibility of an automated advisory service that collects household data from farmers, for example through the keypads of conventional mobile phones, and uses this data to prioritize agricultural advisory messages accordingly. In recent years, agricultural extension services in developing countries have increasingly introduced modern ICT to deliver advice. But to realize efficiency gains, digital applications may need to address heterogeneous information needs by targeting agricultural advisory contents in a household-specific way. Based on socio-economic variables, models were created to predict household-specific rankings of information options based on 2-4 variables, requiring the farmer to answer questions through an ICT interface. These predicted rankings informed household-specific prioritizations of advisory messages in a digital agro-advisory application. Household-specific “top 3” options suggested by the models were better-fit to farmers’ preferences than a random selection of 3 options by 48-68%, on average. The analysis shows that relatively limited data inputs from farmers, in a simple format, can be used to increase the client-orientation of ICT-mediated agricultural extension. This suggests that household-specific prioritization of agricultural advisory messages through digital two-way communication is feasible. In the future, research may produce more generalizable insights about which data-sparse indicators can serve as predictors of farmers’ information needs. Small standard sets of questions that efficiently capture the factors behind farmers’ information needs will likely be useful for a wide range of digital applications in agricultural advisory. For digital agricultural advisory applications, collecting little data from farmers at each interaction may feed learning algorithms that continuously improve the targeting of advice.