Using geopandas to assess land suitable for growing cocoa in Buikwe District

Abstract

This study integrated geospatial technologies with AHP (Analytical Hierarchical Process) algorithms in order to identify potential suitable areas for cocoa growing in Buikwe district. Despite the fact that previous studies have addressed land suitability, some of them like OseiGyabaah, A.P. et al. (2023) have only focused on the physical land characteristics when assessing the suitability of cocoa. However, this study goes ahead to look at the biophysical factors i.e. the soil chemical properties together with the physical land characteristics to assess the suitability of cocoa in Buikwe district. This study assessed a total of 19 soil chemical properties together with climate factors like rainfall, temperature, wind speed and other factors i.e. slope and land cover. Instead of the usual GIS softwares like Arcmap, QGIS, GIS-specific python library Geopandas was used with support from other python libraries like matplotlib, rasterio, xarray among others. The weighting of the factors was done with AHP within a python environment using python library numpy. The results of the GIS-MCDA study indicate that 20.73% of Buikwe district is highly suitable, 59.73% is moderately suitable, 15.72% is marginally suitable, and 3.82% is unsuitable. Higher cocoa yields can thus be achieved when farmers adjust their agronomic management to the requirements of the newly identified cocoa-growing areas. These results can thus be used as baseline information for more informed decision making thus strengthening the capacity for agricultural development. However, regions identified as moderately suitable and marginally suitable may have certain limitations to effective growth of cocoa thus it is advised and would be best to grow cocoa in areas identified as highly suitable.