Geospatial characterization of climate-smart agroforestry in two contrasting physiographic zones of Rwanda

Abstract

Abstract The unmatched world population growth with production has increased human demand causing starvation consequent to food shortage. Climate-smart agroforestry (CSAF) among other options can enhance productivity, improve income and food security, and stabilize the environment. This study investigates the land suitability for CSAF in the Bugesera and Rulindo regions of Rwanda. After searching the literature and the local expert knowledge and opinions, nine variables were considered for investigation in the study viz. elevation, slope, soil type, rainfall, temperature, LU/LC, distance from roads and trade centers, and landslide risks. The analysis used two commonly known techniques (AHP and GIS) integrated to classify and sort out the suitable land for CSAF practices and development. Results identified three CSAF suitability zones, ranging from 1,662.82ha (1.60%) as most suitable and 90,123.78ha (86.62%) suitable to 12,262.50ha (11.78%) unsuitable zones in Bugesera. In Rulindo, suitability zones range from 709.92ha (9.69%) as most suitable and 6,514.56ha (88.92%) suitable to 102.24ha (1.39%) unsuitable land for CSAF. Results further showed that the available means suitable land for CSAF are 34,683.03ha in Bugesera (34,683.03 ± 48,304.71) and 2,442.24ha in Rulindo (2,442.24 ± 3,539.79). Land suitability scores for CSAF largely varied across sites (F = 1.33, p = 0.31). Cross-validation using ground-truthing information (field visit and collection of GPS-based ground coordinates of random locations of actual CSAF) and evidence from literature about existing CSAF mostly supported the generated CSAF suitability maps (nearly 91% of ground-based locations supported the model output). These results reveal the extent of implementation of CSAF practices in the targeted areas. In areas such as Bugesera and Rulindo where investigations on CSAF are scanty, suitability maps in this study would allow identifying sites with high potential for CSAF. The cross-site suitability mapping and analysis for CSAF would provide an opportunity to policy-makers for location-specific land use planning for expanding and implementing CSAF-based models. Those would assist in addressing ecosystem restoration, optimum farm production, increased income, and enhanced food security. This study will pave the way for further studies on the potential CSAF and possibly required interventions for the assessed areas.