Seasonal prediction of crop yields in Ethiopia using an analog approach

Abstract

As Ethiopia's population grows, crop yield predictions are becoming increasingly important for national food security. Accurate, easy-to-implement, and computationally efficient forecast approaches are desirable for broad applications in emerging regimes like Ethiopia. In this study, we develop and test an analog approach for pre-season crop yield prediction conditioned on antecedent precipitation and planting time soil moisture content indices to guide cultivation decision making. Historical planting time soil moisture at four selected sites were simulated using the Coupled Routing and Excess STorage (CREST) hydrological model and classified into five levels. Likewise, a historical crop yield database for each of the five classes of planting time soil moisture were constructed using the Decision Support System for Agrotechnology Transfer (DSSAT) agricultural model. Adopting maize as a representative crop, analog models based on different indexes or predictors with various lead times were constructed and used to conduct hindcasts during 1979–2014 and real-time forecast in 2018 and 2019. Both the hindcast and real-time forecasts were then evaluated against yield observations. To verify the applicability at locations with various environments, the analog models were then applied in different Agroecological Zones. The analog models were shown to be accurate and easy to implement, which may incentivize adoption by local extension agents and regional agricultural agencies to inform farmers’ crop choices.