Simulating adaptation strategies to offset potential impacts of climate variability and change on maize yields in Embu County, Kenya

Abstract

In this study, we assessed the possible impacts of climate variability and change on growth and performance of maize using multi-climate, multi-crop model approaches built on Agricultural Model Intercomparison and Improvement Project (AgMIP) protocols in five different agro-ecological zones (AEZs) of Embu County in Kenya and under different management systems. Adaptation strategies were developed that are locally relevant by identifying a set of technologies that help to offset potential impacts of climate change on maize yields. Impacts and adaptation options were evaluated using projections by 20 Coupled Model Intercomparison Project—Phase 5 (CMIP5) climate models under two representative concentration pathways (RCPs) 4.5 and 8.5. Two widely used crop simulation models, Agricultural Production Systems Simulator (APSIM) and Decision Support System for Agrotechnology Transfer (DSSAT) was used to simulate the potential impacts of climate change on maize. Results showed that 20 CMIP5 models are consistent in their projections of increased surface temperatures with different magnitude. Projections by HadGEM2-CC, HadGEM2-ES, and MIROC-ESM tend to be higher than the rest of 17 CMIP5 climate models under both emission scenarios. The projected increase in minimum temperature (Tmin) which ranged between 2.7 and 5.8°C is higher than the increase in maximum temperature (Tmax) that varied between 2.2 and 4.8°C by end century under RCP 8.5. Future projections in rainfall are less certain with high variability projections by GFDL-ESM2G, MIROC5, and NorESM1-M suggest 8 to 25% decline in rainfall, while CanESM2, IPSL-CM5A-MR and BNU-ESM suggested more than 85% increase in rainfall under RCP 8.5 by end of 21st century. Impacts of current and future climatic conditions on maize yields varied depending on the AEZs, soil type, crop management and climate change scenario. Impacts are largely negative in the low potential AEZs such as Lower Midlands (LM4 and LM5) compared with the high potential AEZs Upper Midlands (UM2 and UM3). However, impacts of climate change are largely positive across all AEZs and management conditions when CO2 fertilization is included. Using the differential impacts of climate change, a strategy to adapt maize cultivation to climate change in all the five AEZs was identified by consolidating those practices that contributed to increased yields under climate change. We consider this approach as more appropriate to identify operational adaptation strategies using readily available technologies that contribute positively under both current and future climatic conditions. This approach when adopted in strategic manner will also contribute to further strengthen the development of adaptation strategies at national and local levels. The methods and tools validated and applied in this assessment allowed estimating possible impacts of climate change and adaptation strategies which can provide valuable insights and guidance for adaptation planning.

Rainfall and Temperature Trends and Variability in Arid and Semi-arid Lands of Kitui County, Kenya

“…The higher rainfall variability in the OND seasonal rainfall compared to that in MAM seasonal rainfall in the research area could be a serious threat to food security since the OND seasonal rainfall is considered the main and most reliable season for crop production in the region. The results corroborate findings by Gummadi et al (2020) which indicated an increasing variability in the OND seasonal rainfall in Embu County, Kenya.…”

Assessment of Maize Yield Response to Agricultural Management Strategies Using the DSSAT–CERES-Maize Model in Trans Nzoia County in Kenya

“…Additionally, the simulation of phenological development and grain yield at maturity were within the acceptable thresholds (< 10%) and (< 20%), respectively, according to Jamieson et al, (1991). The accuracy of the DSSAT-CERES-Maize model for maize yield simulation aligned with other studies in Kenya that calibrated and evaluated the model for various applications (Aluoch et al, 2022;Gummadi et al, 2020). In addition, other studies conducted in the wider East Africa and SSA 1 3 regions found robust accuracies in the CERES-Maize model calibration and evaluation under various managements (Chisanga et al, 2020;Mourice et al, 2015;Volk et al, 2021).…”

“…In the present study, the phenological parameters (P1, P2, and P5) were calibrated by specifying 6000 runs, followed by a similar number of runs to estimate the growth parameters (G2, G3, and PHINT). The thresholds for phenological and growth parameters were guided by values obtained in other field experiments conducted in the East African region and subsequently adjusted for in the parameter file (Gummadi et al, 2020;Mourice et al, 2014).…”

Nematode assemblages, food web indices and metabolic footprints in maize-pigeon pea agro-ecosystems

“…This study was conducted in the lower midland (LM3), LM4 and inner lowland (IL5) zones, whereas Sharma et al (1993) conducted their study in LM3, LM4, LM5, upper midland (UM3) and UM4 AEZs. The AEZs LM3, UM3 and UM4 are high potential agricultural areas with an annual mean rainfall of >1000 mm, altitude of 1070-1460 m and mean temperature of 19.6-22.0 C. The LM4 and LM5 are low potential agricultural areas characterized by < 1000 mm of rainfall, mean annual temperature of 21.0-23.9 C and an altitude of 830-1220 m (Jaetzold et al, 2007;Gummadi et al, 2020). For the IL5, the annual mean rainfall is <900 mm, mean annual temperature is 24.0-25.4 C and the altitude is 600-850 m (Jaestzold et al 2007).…”