Boosting economic growth and shaping livelihoods with machine learning

This paper expands on how the use of geospatial datasets on climatic variables, the Ecocrop modelling tool and the Random Forest algorithm helped to determine suitable crop growth areas in Malawi and Kenya.


70% of Africans rely on agriculture for their livelihoods. However, the extent to which land use is mapped and monitored is largely ineffective in many countries. This often leads to inaccurate identification of available land in advising new investment. Suitability analysis based on ground survey training data can be used to identify potential crop-growing areas and thus enable agricultural investments in areas with the greatest potential for successful crop yields. This study successfully predicted potential areas that can grow tea and cranberry in Malawi, where the agricultural sector is characterised by limited private investments. The two value chains were...

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