Climate Change Effect on Water Use Efficiency under Selected Soil and Water Conservation Practices in the Ruzizi Catchment, Eastern D.R. Congo

peer reviewedConcerns have been raised on the effectiveness and sustainability of Soil and Water Conservation (SWC) practices as adaptation options to climate change and high intra– and inter–annual rainfall variabilities in eastern Democratic Republic of Congo (DRC). This study was conducted in the Ruzizi Plain, a dryland area, to assess the performance of maize (Zea mays L.) under two Representative Concentration Pathways (RCP 4.5 and 8.5) and two SWC practices (tied ridges and conventional tillage). The AgMIP’s Regional Integrated Assessment (RIA) approach was used to simulate Water Use Efficiency (WUE) under the Cropping System Model–Crop Environment Resource Synthesis (CSM–CERES–Maize) of the Decision Support System for Agro–technology Transfer (DSSAT). The model was calibrated using experimental data from nine cropping seasons (2011–2018) and 100 farms. The model sensitivity was assessed as a function of temperature, water, and SWC practices for the same environments. Initial conditions of crop management practices were used as input data for CSM–CERES–Maize. Current climate data were extracted from AgMERRA datasets corrected with local data for the period of 1980 to 2021. Future climate projections (2022–2099) were obtained after down−scaling the data from the 29 General Circulation Models (GCMS) of Coupled Model Intercomparison Project 5 (CMIP5) and subsetted to five GCMs based on climate regimes. GCMS results were a strong indicator that climate change in this DRC dryland will result in an increase in average annual temperatures for both RCP 4.5 and 8.5, with the highest increase (3.05 ◦C) under hot/dry conditions for RCP8.5 and the lowest (1.04 ◦C) under cool/dry conditions for RCP 4.5. All the models selected for five climate regimes for 2022–2099 showed no change in the rainfall trends for RCP 4.5 (p > 0.05). The models projected yield declines of 5–25%, with less yield losses under tied ridges as an adaptation practice. The use of efficient SWC practices could therefore be a promising strategy in reducing potential losses from climate change in drylands of eastern DRC

Current Practices and Prospects of Climate-Smart Agriculture in Democratic Republic of Congo: A Review

peer reviewedClimate-smart agriculture (CSA) is one of the innovative approaches for sustainablyincreasing the agricultural productivity, improving livelihoods and incomes of farmers, while at thesame time improving resilience and contributing to climate change mitigation. In spite of the factthat there is neither explicit policy nor practices branded as CSA in Democratic Republic of Congo(DRC), farmers are utilizing an array of farming practices whose attributes meet the CSA criteria.However, the intensity, distribution, efficiency, and dynamics of use as well as the sources of thesetechnologies are not sufficiently documented. Therefore, this review paper provides a comprehensiveevidence of CSA-associated farming practices in DRC, public and private efforts to promote CSApractices, and the associated benefits accruing from the practices as deployed by farmers in theDRC. We find evidence of progress among farming communities in the use of practices that canbe classified as CSA. Communities using these practices are building on the traditional knowledgesystems and adaptation of introduced technologies to suit the local conditions. Reported returns onuse of these practices are promising, pointing to their potential continued use into the future. Whileprogressive returns on investment are reported, they are relatively lower than those reported fromother areas in sub-Saharan Africa deploying similar approaches. We recommend for strategic supportfor capacity building at various levels, including public institutions for policy development andguidance, extension and community level to support uptake of technologies and higher educationinstitutions for mainstreaming CSA into curricula and training a generation of CSA sensitive humanresources