Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture - Safa E. El-Mahroug; Ayman A. Suleiman; Mutaz M. Zoubi; Saif Al-Omari; Qusay Y. Abu-Afifeh; Heba F. Al-Jawaldeh; Yazan A. Alta’any; Tariq M. F. Al-Nawaiseh; Nisreen Obeidat; Shahed H. Alsoud; Areen M. Alshoshan; Fayha M. Al-Shibli; et al. (AgriEngineering)
Climate change poses a significant threat to agricultural productivity, particularly in regions vulnerable to extreme temperatures and water scarcity, such as Irbid, Jordan. This study assesses the future impacts of projected shifts in precipitation and temperature on wheat yields, using the Decision Support System for Agrotechnology Transfer (DSSAT) model for calibrating and validating under local agro-environmental conditions. Two shared socioeconomic pathways (SSP3-7.0 and SSP5-8.5), representing high-emission and fossil-fuel-intensive futures, were evaluated across mid- and late-century periods (2030–2060 and 2070–2100). The DSSAT model was calibrated using local field data to simulate crop phenology, biomass accumulation, and nitrogen dynamics, showing strong agreement with observed grain yield and harvest index, thereby confirming its reliability for climate impact assessments. Yield projections under each scenario were further analyzed using machine learning algorithms—random forest and gradient boosting regression—to quantify the influence of individual climate variables. The results showed that under SSP5-8.5 (2030–2060), precipitation was the dominant factor influencing yield variability, underscoring the critical role of water availability. In contrast, under SSP3-7.0 (2070–2100), rising maximum temperatures became the primary constraint, highlighting the growing risk of heat stress. Predictive accuracy was higher in precipitation-dominated scenarios (R2 = 0.81) than in temperature-dominated cases (R2 = 0.65–0.73), reflecting greater complexity under extreme warming. These findings emphasize the value of integrating well-calibrated crop models with climate projections and machine learning tools to support climate-resilient agricultural planning. Moreover, practical adaptation strategies, such as adjusting planting dates, using heat-tolerant varieties, and optimizing irrigation, are recommended to enhance resilience. Emerging techniques such as seed priming show promise and merit integration into future crop models. The findings support SDG 2 and SDG 13 by informing climate-resilient food production strategies.