A multi-criteria ananlysis for mapping soybean (glycine max (l.) Merr. land suitability in Kabwe district of Central Zambia
MetadataShow full item record
Land suitability is concerned with the process of estimating the fitness of land for alternative agricultural uses. Land suitability analysis for soybean (Glycine max (L.) Merr.) production culminates into suitability maps that can be used by farmers in decision making to enhance sustainable land use. Soybean is a high value crop with potential to generate income for households. Despite benefits associated with growing soybean, its production is limited by many factors that include decline in soil fertility, climate change and partly due to inadequate suitability information. It is against this background that this study was initiated. The objectives of the study were to (i) extract and map spatial attributes relevant to soybean production in Kabwe district (ii) apply a multi-criteria approach to generate a land suitability map for soybean and (iii) validate the quality of the suitability map generated for decision making. Spatial attributes relevant to soybean production including soil reaction, available phosphorus (P), soil organic carbon (SOC), soil texture, slope, drainage and climatic factors were assessed. Accessibility represented by distance to roads was also included since it affects suitability of an area. Data layers for slope and drainage (wetness) were extracted from the digital elevation model (DEM) using appropriate algorithms in ArcGIS. Elevation was used as a proxy for climate (rainfall and temperature) and was generated by reclassifying the elevation grid into elevation classes. The distance to roads dataset was generated using the euclidean distance tool. Data sets for soil parameters were generated by inverse distance weighting (IDW) based on soil samples collected from the field. A spatial process model based on multi-criteria evaluation was used to integrate selected spatial attributes in a weighted sum overlay to generate a soybean suitability map. The quality of the suitability map was assessed using an error matrix. Results showed that prediction maps were satisfactory for use in the suitability process model with prediction mean errors of -0.0101 for soil reaction, -0.1186 for P, 0.0012 for SOC and -0.0149 for texture. The suitability map show that 15.07 % of the area in Kabwe is highly suitable for soybean production, 26.53% is suitable and 25.18% is moderately suitable. The other 20.57 % is marginally suitable, whereas 10.74 % is currently not suitable and 1.92 % is permanently not suitable. Based on ground truth data, the suitability map was 65 % accurate, which is good enough for use as a guide in selecting suitable sites for soybean production.
University of Zambia
- Agricultural Sciences