This study aims to investigate the impact of climate change on chickpea yield and explore best-fit adaptation strategies for yield optimization. Crop data from 2010 to 2017 were used for model calibration, and from 2020 to 2022 were used for model evaluation. The CROPGRO module under the Decision Support System for Agrotechnology Transfer (DSSAT) crop model was applied. Baseline (1971–2009) and future (2010–2039) weather data under the RCP4.5 emission scenario were downscaled from the Coordinated Regional Downscaling Experiment (CORDEX) Africa domain. Model evaluation results revealed the goodness of fit (R
2
) is 83% for flowering day, 84% for grain yield, and 86% for physiological maturity. At the end of 2040, the projected rainfall is expected to vary substantially, ranging from a 20% decrease to a 50% increase under the RCP4.5 emission scenario. In contrast, both maximum and minimum temperatures are projected to increase, with positive changes ranging from 0 to 20%. Days to flowering (DOF) are projected to decrease by an average of 5.5%, while days to maturity (DOM) decrease by an average of 2.8%. This indicates that increasing temperature accelerates chickpea development and reduces the crop growing season. At the Debre Zeit site, projected yield changes range from a 42.0% reduction to a 43.3% increase. Similarly, at the Cheffe Donsa site, yields are projected to vary between a 27.1% decrease and a 58.3% increase, while at the Minjar Shenkora site, yields are projected to vary between a 35.1% decrease and a 62.5% increase. This indicates that chickpea yield is expected to show considerable spatiotemporal variability across the years and sites. Planting on August 15 is optimum for the chickpea crop at the Debre Ziet site, while planting on August 20 and August 1 are suitable for the Cheffe Donsa and Minjar Shenkora sites, respectively. Planting time is an ideal adaptation strategy that can optimize chickpea yield, while applying fertilizers and tillage have little effect on chickpea yield.
Assessing climate change impacts on agriculture is essential for developing effective adaptation strategies, with crop simulation models providing valuable tools for evaluating future crop productivity. This study assessed climate change impacts on wheat and potato productivity in northwest Bangladesh using 27 Glob...
Md.Belal Hossain, K. Zeleke, Deng-Ke Liu et al.· Journal of Sustainable Agric...· 0 citations
Climate change poses growing risks to maize-based food systems, yet comparative evidence across climatically similar regions on different continents remains limited. This study compares climate change impacts and adaptation responses in rainfed maize systems in Nakuru County, Kenya, and Northwest China—two geographical...
Jackson K. Koimbori, Shuai Wang, Jie Pan et al.· Plants· 0 citations
We assess climate change impacts on maize yields across China’s Northeast Farming Region (NFR) for two future periods (2020–2049 and 2050–2079) using a province-scale statistical crop model driven by projections from 41 CMIP6 global climate models (SSP5-8.5 scenario). This model represents climate influences on maize y...
Andrew Cottrell, E. Pope, Tom Crocker et al.· Environmental Research Commu...· 0 citations
The simultaneous assessment of evapotranspiration and drought is imperative for adaptive water resource planning due to the growing challenges posed by climate change on agricultural production. A downscaling exercise was carried out using the SDSM model for three climate scenarios developed as part of the Sixth Assess...
Climate-driven changes in temperature and atmospheric CO2 may alter maize productivity, and their combined effects can differ among cultivars and sowing environments. This study used the CERES-Maize model within DSSAT to assess yield responses of NK-30, NK-7328 and NK-6110 under Raipur conditions. The model was calibra...
J. L. Chaudhary, Vikas Chandravanshi, Nandan Mehta et al.· International Journal of Env...· 0 citations
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