Climate change and variability are projected to alter the geographic suitability of lands for crop cultivation. In many developing countries, such as Kenya, information on the mean changes in climate is limited. Therefore, in this study, we model the current and future changes in areas suitable for rainfed maize production in the country using a maximum entropy (MaxENT) model. Maize is by far a major staple food crop in Kenya. We used maize occurrence location data and bioclimatic variables for two climatic scenarios-Representative Concentration Pathways (RCP) 4.5 and 8.5 from two general circulation models (HadGEM2-ES and CCSM4) for 2070. The study identified the annual mean temperature, annual precipitation and the mean temperature of the wettest quarter as the major variables that affect the distribution of maize. Simulation results indicate an average increase of unsuitable areas of between 1.9–3.9% and a decrease of moderately suitable areas of 14.6–17.5%. The change in the suitable areas is an increase of between 17–20% and in highly suitable areas of 9.6% under the climatic scenarios. The findings of this study are of utmost importance to the country as they present an opportunity for policy makers to develop appropriate adaptation and mitigation strategies required to sustain maize production under future climates.

This study examined the impact of land use/cover changes on soil erosion in western Kenya in the years 1995 and 2017. The study used the GIS-based Revised Universal Soil Loss Equation (RUSLE) modelling approach and remote sensing assessment. The results showed that the average soil loss through sheet, rill and inter-rill soil erosion processes was 0.3 t/ha/y and 0.5 t/ha/y, in the years 1995 and 2017, respectively. Of the total soil loss, farms contributed more than 50%, both in 1995 and 2017 followed by grass/shrub (7.9% in 1995 and 11.9% in 2017), forest (16% in 1995 and 11.4% in 2017), and the least in built-up areas. The highest soil erosion rates were observed in farms cleared from forests (0.84 tons/ha) followed by those converted from grass/shrub areas (0.52 tons/ha). The rate of soil erosion was observed to increase with slope due to high velocity and erosivity of the runoff. Areas with high erodibility in the region are found primarily in slopes of more than 30 degrees, especially in Mt. Elgon, Chereng’anyi hills and Elgeyo escarpments. This study forms the first multi-temporal assessment to explore the extent of soil erosion and seeks to provide a useful knowledge base to support decision-makers in developing strategies to mitigate soil erosion for sustainable crop production.

The western region of Kenya is experiencing remarkable land changes resulting from population growth and related impacts. The study used remote sensing and GIS techniques to analyse the land use/cover changes in the years 1995, 2001, 2010 and 2017. Multi-spectral Landsat (TM, ETM + and OLI) images were pre-processed and classified using maximum likelihood algorithm in ENVI 5.4. The overall classification accuracies in all the images was more than 80%. The results revealed major conversions of each land use/land cover type in varying trends and magnitudes. Between 1995 and 2001, there was an increase in built-up areas by 71%, forest cover by 43%, farms by 5%; and decrease in grassland by 47%. By 2017, the built-up areas had increased by 225% and farms by 17%; the forestland, grassland and water reduced by 38%, 10% and 11% respectively. The observed changes are characterised by increased settlements and encroachment of sensitive ecosystems.

Climate change poses a threat to crop production and livelihoods of rural farming communities in Kenya, a majority of whom are mainly dependent on rain-fed agriculture. The purpose of this study was to examine farm-level adaptation responses towards climate change and their influencing factors, using a case study of western Kenya. Structured questionnaire was administered to 210 farmers in selected locations in the region where households farm maize as the main crop. Logistic and multiple linear regression models were used to ascertain the factors that influence farmers’ adaptation practices. The results indicate that farmers perceived climate change as being responsible for the reduction in crop yield and production, crop failure and increase of fallow farms. The major adaptation strategies undertaken by the farmers included change in planting dates by either planting early or late during a season, diversification of crops, growing early maturing cultivars, use of drought-tolerant varieties and timely planting. The key determinants of adaptation strategies by the farmers included farm size, income and extension training. Understanding farmers’ responses to climate change in rain-fed crop production systems could assist in planning adaptation strategies towards sustainable crop production.

Consequences of global climate change are predicted to increase risks to crop production in the future. However, the possible broader impact of climate change on social-ecological systems still needs to be evaluated. Therefore, the present study focuses on one such globally important agricultural social-ecological system referred to as the Village Tank Cascade System (VTCS) in the dry zone of Sri Lanka. The VTCS has considerable potential to withstand seasonal climate variability mainly through continuous supply of water by the village tank storage throughout the year. The current study aimed to investigate trends of climate variability and possible impacts on paddy production in the North and North-central VTCS zone. Observed and projected rainfall and temperature data were analysed to evaluate the past variability trends (1970 to 2020) and model future (up to 2100) scenarios of climate variability and trends. Long-term observed rainfall and temperature data (1946 to 2020) were analysed to identify possible anomalies. The Maximum Entropy (MaxEnt) model has been used to predict the situation of future paddy farming (2050 and 2070) under two climate scenarios (RCP4.5 and RCP8.5) of the Intergovernmental Panel on Climate Change (IPCC). Six variables that would affect paddy growth and yield quality were used alongside the average monthly rainfall and temperature of two Global Climate Models (MIROC5 and MPI-ESM-LR). Climate suitability for two paddy cultivation seasons (Yala and Maha) were predicted for current and future climate scenarios. The findings revealed that observed and projected climate changes show considerable deviation of expected rainfall and temperature trends across the VTCS zone. Temperature exhibits warming of approximately 1.0 °C during the declared Global Warming Period (1970 to 2020) in the study area. In addition, there is a trend of significant warming by 0.02 °C/year, RCP4.5 and 0.03 °C/year, RCP8.5 from 1950 to 2100. Rainfall (1970–2020) shows high interannual variability but trends were not significant and less discernible. However, long-term projected rainfall data (1950–2100) analysis detected a significant (p = 0) upward trend (2.0 mm/year, RCP4.5 and 2.9 mm/year, RCP8.5), which is expected to continue up to the end of this century. Further, the study revealed some shifts in temperature towards higher values and positive anomalies in rainfall affecting seasonality and the likelihood of more extreme occurrences in the future, especially during the Maha cultivation season. The MaxEnt model predicts the following under future climate scenarios: (i) spatio-temporal shifts (conversions) in climate suitability for paddy farming in the VTCS zone; (ii) substantial low and moderate suitability areas that are currently suitable will remain unchanged; (iii) up to 96% of highly suitable and 38% of moderately suitable paddy growing areas in the VTCS zone will be at risk due to a decline in future climate suitability; and (iv) expansion of lower suitability areas by approximately 22% to 37%, due to conversion from moderate suitability areas. The study provides evidence that the continuous warming trend with increasing variability in rainfall and shifting seasonality could increase the vulnerability of future paddy farming in the VTCS. Thus, findings of this study will help planners to make more targeted solutions to improve adaptive capacity and regain the resilience to adjust the paddy farming pattern to deal with predicted climate variability and change.