Abstract

Soybean (Glycine max (L.) Merr.), is a high value crop that can generate income for households. As a legume, soybean is incorporated in cropping systems to improve soil fertility. Soybean productivity is however limited by factors including declined soil fertility, climate change and partly due to inadequate land suitability information. This study aimed at identifying suitable land for soybean production in Kabwe district. Data layers of selected attributes relevant to soybean production were generated with slope and wetness data layers extracted from the digital elevation model (DEM). Elevation was used as a proxy for climate (rainfall and temperature) and was generated by reclassifying the elevation grid into elevation classes. Data layers for soil reaction (pH), soil organic carbon, phosphorus and texture were generated by inverse distance weighting interpolation method based on soil point data. A distance to roads layer was created using the euclidean distance tool. A spatial process model based on multi-criteria evaluation was used to integrate data layers in a weighted sum overlay to generate a soybean suitability map, whose quality was assessed using an error matrix. Results showed that 15.07% of the investigated area was highly suitable for soybean production, whereas 26.53% was suitable and 25.18% was moderately suitable. The other 20.57% was marginally suitable, 10.74% was currently not suitable and 1.92% was permanently not suitable. Based on ground truth data, the overall classification accuracy of the suitability map was 65%. The map was therefore good enough for use as a guide in selecting suitable sites for soybean production.

This work is licensed under a Creative Commons Attribution 4.0 License.