The aim of this study was to develop a suitability model for Green gram production in Kitui County using GIS-based multi-criteria evaluation. Soil and topography were chosen as the main criteria for analysis and 6 sub criteria (soil texture, depth, pH, cation exchange capacity drainage and slope). The criteria were selected based on crop experts' knowledge and available Green gram requirements literature. The criteria maps were reclassified into 4 suitability levels Highly (S1), Moderately (S2), Marginally (S3) and not suitable (N) based on FAO guidelines. The Analytical Hierarchy Process decision making tool was used to determine the perceived weights or influence that each criteria carries. The weights were then used as inputs in the weighted overlay and a suitability map generated. Based on the findings all land is suitable for Green gram production with varying degrees of suitability where 32.7%, 23.7% and 43.6% as highly, moderately and marginally suitable respectively. Major limitations that prevent land from being highly suitable include acidity, alkalinity and poor drainage in soils and in some cases steep slopes.

This study sought to determine the spatial and temporal variability of rainfall under past and future climate scenarios. The data used comprised stationbased monthly gridded rainfall data sourced from the Climate Research Unit (CRU) and monthly model outputs from the Fourth Edition of the Rossby Centre (RCA4) Regional Climate Model (RCM), which has scaled-down nine GCMs for Africa. Although the 9 Global Climate Models (GCMs) downscaled by the RCA4 model was not very good at simulating rainfall in Kenya, the ensemble of the 9 models performed better and could be used for further studies. The ensemble of the models was thus bias-corrected using the scaling method to reduce the error; lower values of bias and Normalized Root Mean Square Error (NRMSE) were recorded when compared to the uncorrected models. The bias-corrected ensemble was used to study the spatial and temporal behaviour of rainfall under baseline (1971 to 2000) and future RCP 4.5 and 8.5 scenarios (2021 to 2050). An insignificant trend was noted under the baseline condition during the March-May (MAM) and October-December (OND) rainfall seasons. A positive significant trend at 5% level was noted under RCP 4.5 and 8.5 scenarios in some stations during both MAM and OND seasons. The increase in rainfall was attributed to global warming due to increased anthropogenic emissions of greenhouse gases. Results on the spatial variability of rainfall indicate the spatial extent of rainfall will increase under both RCP 4.5 and RCP 8.5 scenario when compared to the baseline; the increase is higher under the RCP 8.5 scenario. Overall rainfall was found to be highly variable in space and time, there is a need to invest in the early dissemination of weather forecasts to help farmers adequately prepare in case of unfavorable weather. Concerning the expected increase in rainfall in the fu-

Green gram is considered as one of the legumes suitable for cultivation in the Arid and Semi-Arid Lands (ASALs) of Kenya. However, climate change may alter the areas suitable for green gram production. This study sought to model green gram suitability in Kenya under present and future conditions using bias-corrected RCA4 models data. The datasets used were: maps of soil parameters extracted from Kenya Soil Survey map; present and future rainfall and temperature data from an ensemble of nine models from the Fourth Edition of the Rossby Centre (RCA4) Regional Climate Model (RCM); and altitude from the Digital elevation model (DEM) of the USGS. The maps were first reclassified into four classes of suitability as Highly Suitable (S1), Moderately Suitable (S2), Marginally Suitable (S3), and Not Suitable (N). The classes represent the different levels of influence of a factor on the growth and yield of green grams. The reclassified maps were then assigned a weight generated using the Analytical Hierarchy Process (AHP). A weighted overlay of climate characteristics (past and future rainfall and temperature), soil properties (depth, pH, texture, CEC, and drainage) and altitude found most of Kenya as moderately suitable for green gram production during the March to May (MAM) and October to December (OND) seasons under the baseline, RCP 4.5 and RCP 8.5 scenarios with highly suitable areas being found in Counties like Kitui, Makueni, and West Pokot among others. During the MAM season, the area currently highly suitable for green gram production (67,842.62 km 2) will increase slightly to 68,600.4 km 2 (1.1%) during the RCP 4.5 and reduce to 61,307.8 km 2 (−9.6%) under the RCP 8.5 scenario. During the OND season, the area currently highly suitable (49,633.4 km 2) will increase under both RCP 4.5 (22.2%) and RCP 8.5 (58.

No abstract

This study purposed to evaluate the impact of climate change on green gram yield, biomass and days to maturity under the baseline and future climate scenarios in Kitui County, Kenya. A field experiment was conducted during the March–April–May (MAM) and October–November–December (OND) planting seasons of 2018 and 2019 in the South Eastern Kenya University (SEKU) farm. Data on soil physical and chemical properties, daily climate data on rainfall, maximum and minimum temperature, and solar radiation, and green gram phenology dates were collected from the site and used in the calibration and validation of the APSIM model for four varieties of green gram, namely Biashara, Tosha, N26, and KS20 varieties. The calibrated green gram model captured the observed yield, biomass and days to maturity of the four varieties of green gram well. The calibrated green gram model was used to simulate the effects of climate change using daily climate data from an equal-weight ensemble of the nine CORDEX RCA4 models under the baseline scenario (1971 to 2000), and the future RCP 4.5 and 8.5 scenarios (2021 to 2050). During the MAM and OND seasons, a statistically significant decline in yield, biomass, and days to maturity is expected under both the RCP 4.5 and RCP 8.5 scenarios. The high variability in rainfall amount under both the RCP 4.5 and RCP 8.5 scenarios will translate to a lower yield and biomass. The increase in temperature under both the RCP 4.5 and RCP 8.5 scenarios will reduce the days to maturity for green grams in Kitui County. A decline in green gram yield is expected under future climate scenarios in one of the highly suitable zones for Kitui County, Kenya. Given that the government aims to revive farming in the ASALs by promoting climate-smart agriculture through planting drought-resistance crops, there is a need to develop green gram varieties which are more tolerant to the expected change in climate.