Land suitability analyses are crucial for identifying sustainable areas for agricultural crops and developing appropriate land use strategies. Thus, the present study aims to analyze the current and future land suitability for wheat (Triticum aestivum L.) cultivation in Ethiopia. Twelve variables including soil properties, climate variables, and topographic characteristics were used in the evaluation of land suitability. Statistical methods such as Rotated Empirical Orthogonal Functions (REOF), Coefficient of Variation (CV), correlation, and parametric and non-parametric trend analyses were used to analyze the spatiotemporal variability in current and future climate data and identified significant patterns of variability. For future projections of land suitability and climate, this study employed climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) framework, downscaled using regional climate model version 4.7 (RegCM4.7) under two different Shared Socioeconomic Pathway (SSP) climate scenarios: SSP1 (a lower emission scenario) and SSP5 (a higher emission scenario). Under the current condition, during March, April, and May (MAM), 53.4% of the country was suitable for wheat cultivation while 44.4% was not suitable. In 2050, non-suitable areas for wheat cultivation are expected to increase by 1% and 6.9% during MAM under SSP1 and SSP5 climate scenarios, respectively. Our findings highlight that areas currently suitable for wheat may face challenges in the future due to altered temperature and precipitation patterns, potentially leading to shifts in suitable areas or reduced productivity. This study also found that the suitability of land for wheat cultivation was determined by rainfall amount, temperature, soil type, soil pH, soil organic carbon content, soil nitrogen content, and elevation. This research underscores the critical importance of integrating spatiotemporal climate variability with future projections to comprehensively assess wheat suitability. By elucidating the implications of climate change on wheat cultivation, this study lays the groundwork for developing effective adaptation strategies and actionable recommendations to enhance management practices. The findings support the county’s commitment to refining agricultural land use strategies, increasing wheat production through suitability predictions, and advancing self-sufficiency in wheat production. Additionally, these insights can empower Ethiopia’s agricultural extension services to guide farmers in cultivating wheat in areas identified as highly and moderately suitable, thereby bolstering production in a changing climate.

Narok County in Kenya is the home to the Maasai Mara Game Reserve, which offers important habitats for a great variety of wild animals, hence, a hub for tourist attraction, earning the county and country an extra income through revenue collection. The Mau Forest Complex in the north is a source of major rivers including the Mara River and a water catchment tower that supports other regions as well. Many rivers present in the region support several activities and livelihood to the people in the area. The study examined how the quantity of surface water resources varied under the different climate change scenarios, and the sensitivity of the region to a changing climate. Several datasets used in this study were collated from different sources and included hydro–meteorological data, Digital Elevation Model (DEM), and Coordinated Regional Downscaling Experiment (CORDEX) climate projections. The WEAP (Water Evaluation and Planning) model was applied using the rainfall–runoff (soil moisture method) approach to compute runoff generated with climate data as input. All the calculations were done on a monthly time step from the current year account to the last year of the scenario. Calibration of the model proceeded using the PEST tool within the WEAP interface. The goodness of fit was evaluated using the coefficient of determination (R²), percentage bias (PBIAS), and Nash–Sutcliffe efficiency (NSE) criterion. From the tests, it was clear that WEAP performed well in simulating stream flows. The coefficient of determination (R²) was greater than the threshold R² > 0.5 in both periods, i.e., 0.83 and 0.97 for calibration and validation periods, respectively, for the monthly flows. A 25-year mean monthly average was chosen with two time slices (2006–2030 and 2031–2055), which were compared against the baseline (1981–2000). There will be a general decrease in water quantity in the region in both scenarios: −30% by 2030 and −23.45% by 2055. In comparison, RCP4.5 and Scenario3 (+2.5°C, +10% P) were higher than RCP8.5 and Scenario 2 respectively. There was also a clear indication that the region was highly sensitive to a perturbation in climate from the synthetic scenarios. A change in either rainfall or temperature (or both) could lead to an impact on the amount of surface water yields.

Wambui MB, Opere A, Githaiga MJ, Karanja FK. 2017. Assessing the impacts of climate variability and climate change on biodiversity in Lake Nakuru, Kenya. Bonorowo Wetlands 1: 13-24. This study evaluates the impacts of the raised water levels and the flooding of Lake Nakuru and its surrounding areas on biodiversity, specifically, the phytoplankton and lesser flamingo communities, due to climate change and climate variability. The study was to review and analyze noticed climatic records from 2000 to 2014. Several methods were used to ascertain the past and current trends of climatic parameters (temperature, rainfall and evaporation), and also the physicochemical characteristics of Lake Nakuru (conductivity, phytoplankton, lesser flamingos and the lake depth). These included time series analysis, and trend analysis, so the Pearson’s correlation analysis was used to show a relationship between the alterations in lake conductivity to alterations in population estimates of the lesser flamingos and the phytoplankton. Data set extracted from the Coupled Model Intercomparison Project Phase 5 (CMIP5) (IPCC Fifth Assessment Report (AR5) Atlas subset) models were subjected to time series analysis method where the future climate scenarios of near surface temperature, rainfall and evaporation were plotted for the period 2017 to 2100 (projection) for RCP2.6 and RCP8.5 relative to the baseline period 1971 to 2000 in Lake Nakuru were analysed. The results were used to evaluate the impact of climate change on the lesser flamingos and phytoplankton abundance. It was noticed that there was a raise in the mean annual rainfall during the study period (2009 to 2014) which brought the increment in the lake’s surface area from a low area of 31.8 km² in January 2010 to a high of 54.7 km² in Sept 2013, indicating an increment of 22.9 km² (71.92% surface area increment). Mean conductivity of the lake also lessened leading to the loss of phytoplankton on which flamingos feed making them to migrate. A strong positive correlation between conductivity and the lesser flamingo population was noticed signifying that low conductivity affects the growth of phytoplankton and since the lesser flamingos depend on the phytoplankton for their feed, this subsequently revealed that the phytoplankton density could be a notable predictor of the lesser flamingo occurrence in Lake Nakuru. There was also a strong positive correlation noticed between phytoplankton and the lesser flamingo population which confirms that feed availability is a key determining factor of the lesser flamingo distribution in the lake. It is projected that there would be an increment in temperatures, rainfall and evaporation for the period 2017 to 2100 under RCP2.6 and RCP8.5 relative to the baseline period 1971 to 2000 obtained from the Coupled Model Intercomparison Project phase 5 (CMIP5) multi-model ensemble. As a result, it is expected that the lake will further increment in surface area and depth by the year 2100 due to increased rainfall thereby affecting the populations of the lesser flamingos and phytoplankton, as the physicochemical factors of the lake will alter as well during the projected period.

Climate change and variables pose a significant risk for agro-pastoral production systems, where mixed crop–livestock production is a primary livelihood strategy. Therefore, this study aims to analyze agro-pastoralists’ perceptions of climate change, the perceived impacts on their production systems, and the adaptation strategies used by mixed crop–livestock producers in the lowland areas of Ethiopia. Data was collected using household surveys, focus group discussions (FGDs), and key informant interviews. A survey was conducted in three major mixed crop–livestock producer woredas (Teltale, Yabello, and Gomole) with a total of 396 respondents. A multi-stage sampling approach was used for selecting the sample household for the study. The data were analyzed using descriptive statistics and a chi-square test. The study found that agro-pastoralists perceive climate change as a significant threat to their livelihoods. The results revealed that 95.9% and 91.4% of respondents across the three woredas perceived a decline in rainfall and increased temperatures, respectively. The most widely adopted adaptation strategies were livelihood diversification (96.4%), livestock diversification (89.3%), and crop diversification (70.4%). Notably, Teltale had the highest adoption rate of crop diversification, while Yabello exhibited a higher prevalence of non-farming activities. The findings highlight the need for targeted interventions to enhance adaptive capacity, improve access to climate-smart technologies, and strengthen capacity for climate adaptation in mixed crop–livestock systems. This study contributes to climate risks, their impacts on their production systems, and the adaptation strategies for designing targeted interventions that enhance the resilience of mixed crop–livestock production systems in the face of climate change.