Climate-smart agriculture (CSA) is one of the innovative approaches for sustainably increasing the agricultural productivity, improving livelihoods and incomes of farmers, while at the same time improving resilience and contributing to climate change mitigation. In spite of the fact that 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 these technologies are not sufficiently documented. Therefore, this review paper provides a comprehensive evidence of CSA-associated farming practices in DRC, public and private efforts to promote CSA practices, and the associated benefits accruing from the practices as deployed by farmers in the DRC. We find evidence of progress among farming communities in the use of practices that can be classified as CSA. Communities using these practices are building on the traditional knowledge systems and adaptation of introduced technologies to suit the local conditions. Reported returns on use of these practices are promising, pointing to their potential continued use into the future. While progressive returns on investment are reported, they are relatively lower than those reported from other areas in sub-Saharan Africa deploying similar approaches. We recommend for strategic support for capacity building at various levels, including public institutions for policy development and guidance, extension and community level to support uptake of technologies and higher education institutions for mainstreaming CSA into curricula and training a generation of CSA sensitive human resources.

Experimental evidence of nutrient limitations on primary productivity in Afrotropical forests is rare and globally underrepresented yet are crucial for understanding constraints to terrestrial carbon uptake. In an ecosystem‐scale nutrient manipulation experiment, we assessed the early responses of tree growth rates among different tree sizes, taxonomic species, and at a community level in a humid tropical forest in Uganda. Following a full factorial design, we established 32 (eight treatments × four replicates) experimental plots of 40 × 40 m each. We added nitrogen (N), phosphorus (P), potassium (K), their combinations (NP, NK, PK, and NPK), and control at the rates of 125 kg N ha⁻¹ year⁻¹, 50 kg P ha⁻¹ year⁻¹ and 50 kg K ha⁻¹ year⁻¹, split into four equal applications, and measured stem growth of more than 15,000 trees with diameter at breast height (dbh) ≥1 cm. After 2 years, the response of tree stem growth to nutrient additions was dependent on tree sizes, species and leaf habit but not community wide. First, tree stem growth increased under N additions, primarily among medium‐sized trees (10–30 cm dbh), and in trees of Lasiodiscus mildbraedii in the second year of the experiment. Second, K limitation was evident in semi‐deciduous trees, which increased stem growth by 46% in +K than –K treatments, following a strong, prolonged dry season during the first year of the experiment. This highlights the key role of K in stomatal regulation and maintenance of water balance in trees, particularly under water‐stressed conditions. Third, the role of P in promoting tree growth and carbon accumulation rates in this forest on highly weathered soils was rather not pronounced; nonetheless, mortality among saplings (1–5 cm dbh) was reduced by 30% in +P than in –P treatments. Although stem growth responses to nutrient interaction effects were positive or negative (likely depending on nutrient combinations and climate variability), our results underscore the fact that, in a highly diverse forest ecosystem, multiple nutrients and not one single nutrient regulate tree growth and aboveground carbon uptake due to varying nutrient requirements and acquisition strategies of different tree sizes, species, and leaf habits.

Sustainable land use systems planning and management requires a wider understanding of the spatial extent and detailed human-ecosystem interactions astride any landscape. This study assessed the extent of historical, current, and future land use systems in Uganda. The specific objectives were to (i) characterize and assess the extent of historical and current land use systems, and (ii) project future land use systems. The land use systems were defined and classified using spatially explicit land use/cover layers for the years 1990 and 2015, while the future prediction (for the year 2040) was determined using land use systems datasets for both years through a Markov chain model. This study reveals a total of 29 classes of land use systems that can be broadly categorized as follows: three of the land use systems are agricultural, five are under bushland, four under forest, five under grasslands, two under impediments, three under wetlands, five under woodland, one under open water and urban settlement respectively. The highest gains in the land amongst the land use systems were experienced in subsistence agricultural land and grasslands protected, while the highest losses were seen in grasslands unprotected and woodland/forest with low livestock densities. By 2040, subsistence agricultural land is likely to increase by about 1% while tropical high forest with livestock activities is expected to decrease by 0.2%, and woodland/forest unprotected by 0.07%. High demand for agricultural and settlement land are mainly responsible for land use systems patchiness. This study envisages more land degradation and disasters such as landslides, floods, droughts, and so forth to occur in the country, causing more deaths and loss of property, if the rate at which land use systems are expanding is not closely monitored and regulated in the near future.

Long-term trend analysis at local scale for rainfall and temperature is critical for detecting climate change patterns. This study analysed historical (1980–2009), near future (2010–2039), mid- (1940–2069) and end-century (2070–2099) rainfall and temperature over Karamoja sub-region. The Modern Era-Retrospective Analysis for Research and Applications (MERRA) daily climate data provided by the Agricultural Model Inter-comparison and Improvement Project (AgMIP) was used. The AgMIP delta method analysis protocol was used for an ensemble of 20 models under two representative concentration pathways (RCPs 4.5 and 8.5). Historical mean rainfall was 920.1 ± 118.9 mm and minimum, maximum and mean temperature were 16.8 ± 0.5 °C, 30.6 ± 0.4 °C and 32.0 ± 0.7 °C, respectively. Minimum temperature over the historical period significantly rose between 2000 and 2008. Near future rainfall varied by scenario with 1012.9 ± 146.3 mm and 997.5 ± 144.7 mm for RCP4.5 and RCP8.5 respectively; with a sharp rise predicted in 2017. In the mid-century, mean annual rainfall will be 1084.7 ± 137.4 mm and 1205.5 ± 164.9 mm under RCP4.5 and RCP8.5 respectively. The districts of Kaabong and Kotido are projected to experience low rainfall total under RCP4.5 (mid-century) and RCP8.5 (end-century). The minimum temperature is projected to increase by 1.8 °C (RCP4.5) and 2.1 °C (RCP8.5) in mid-century, and by 2.2 °C (RCP4.5) and 4.0 °C (RCP8.5) in end-century.

Climate change is a serious threat to the livelihoods of rural communities, particularly in mountainous areas because they are very sensitive to such changes. In this study, we assessed the household determinants to climate change adaptation drawing from a case study of agricultural adaptation in the Mount Rwenzori area of South Western Uganda. The study identified the major adaptation practices that are adopted by farmers to cope with the impacts of climate change and using available on-farm technologies. A total of 143 smallholder farmers were sampled and interviewed using field based questionnaires, field observations, and key informant interviews. Data was cleaned, entered and analysed using SPSS and Stata software for descriptive statistics. Thereafter, a Multinomial logistic regression model was used to assess the drivers of farmers’ choice for adaptation practices, factors influencing the choice of adaptation, and barriers. The major adaptation practices that were identified included; use of different crop varieties, tree planting, soil and water conservation, early and late planting, and furrow irrigation. Discrete choice model results indicated the age of the household head, experience in farming, household size, climate change shocks, land size, use of agricultural inputs, landscape position (location), and crop yield varied significantly (p > 0.05), which influenced farmers’ choice of climate change adaptation practices. The main barriers to adaptation included inadequate information on adaptation methods and financial constraints, leading us to conclude that contextual adaptation practices are more desirable for adoption to farmers. Adapting to climate change needs support from government and other stakeholders, however the implementation is more successful when appropriate and suitable choices are employed.

The impact of climate and land use/land cover (LULC) change continues to threaten water resources availability for the agriculturally used inland valley wetlands and their catchments in East Africa. This study assessed climate and LULC change impacts on the hydrological processes of a tropical headwater inland valley catchment in Uganda. The hydrological model Soil and Water Assessment Tool (SWAT) was applied to analyze climate and LULC change impacts on the hydrological processes. An ensemble of six regional climate models (RCMs) from the Coordinated Regional Downscaling Experiment for two Representative Concentration Pathways (RCPs), RCP4.5 and RCP8.5, were used for climate change assessment for historical (1976–2005) and future climate (2021–2050). Four LULC scenarios defined as exploitation, total conservation, slope conservation, and protection of headwater catchment were considered. The results indicate an increase in precipitation by 7.4% and 21.8% of the annual averages in the future under RCP4.5 and RCP8.5, respectively. Future wet conditions are more pronounced in the short rainy season than in the long rainy season. Flooding intensity is likely to increase during the rainy season with low flows more pronounced in the dry season. Increases in future annual averages of water yield (29.0% and 42.7% under RCP4.5 and RCP8.5, respectively) and surface runoff (37.6% and 51.8% under RCP4.5 and RCP8.5, respectively) relative to the historical simulations are projected. LULC and climate change individually will cause changes in the inland valley hydrological processes, but more pronounced changes are expected if the drivers are combined, although LULC changes will have a dominant influence. Adoption of total conservation, slope conservation and protection of headwater catchment LULC scenarios will significantly reduce climate change impacts on water resources in the inland valley. Thus, if sustainable climate-smart management practices are adopted, the availability of water resources for human consumption and agricultural production will increase.