Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture
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 Decisio...
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MDPI AG
2025-05-01
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| author | 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 Rakad Ta’any |
| author_facet | 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 Rakad Ta’any |
| author_sort | Safa E. El-Mahroug |
| collection | DOAJ |
| description | 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 (R<sup>2</sup> = 0.81) than in temperature-dominated cases (R<sup>2</sup> = 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. |
| format | Article |
| id | doaj-art-9fa0c0fb478e4b17bb06c9f054bd4b15 |
| institution | OA Journals |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-05-01 |
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| series | AgriEngineering |
| spelling | doaj-art-9fa0c0fb478e4b17bb06c9f054bd4b152025-08-20T02:33:39ZengMDPI AGAgriEngineering2624-74022025-05-017515610.3390/agriengineering7050156Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable AgricultureSafa E. El-Mahroug0Ayman A. Suleiman1Mutaz M. Zoubi2Saif Al-Omari3Qusay Y. Abu-Afifeh4Heba F. Al-Jawaldeh5Yazan A. Alta’any6Tariq M. F. Al-Nawaiseh7Nisreen Obeidat8Shahed H. Alsoud9Areen M. Alshoshan10Fayha M. Al-Shibli11Rakad Ta’any12Department of Land, Water and Environment, The University of Jordan, Amman 11942, JordanDepartment of Land, Water and Environment, The University of Jordan, Amman 11942, JordanDepartment of Chemistry, The University of Jordan, Amman 11942, JordanDepartment of Water and Environmental Engineering, Scientific Sustainable Vision Company, Amman 11194, JordanDepartment of Land, Water and Environment, The University of Jordan, Amman 11942, JordanDepartment of Land, Water and Environment, The University of Jordan, Amman 11942, JordanDepartment of Civil Engineering, The University of Jordan, Amman 11942, JordanDepartment of Civil Engineering, The University of Jordan, Amman 11942, JordanDepartment of Land, Water and Environment, The University of Jordan, Amman 11942, JordanDepartment of Land, Water and Environment, The University of Jordan, Amman 11942, JordanDepartment of Land, Water and Environment, The University of Jordan, Amman 11942, JordanDepartment of Land, Water and Environment, The University of Jordan, Amman 11942, JordanDepartment of Water Resources and Environmental Management, Al-Balqa’ Applied University, Al-Salt 19117, JordanClimate 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 (R<sup>2</sup> = 0.81) than in temperature-dominated cases (R<sup>2</sup> = 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.https://www.mdpi.com/2624-7402/7/5/156climate scenarioscrop–climate interactiondata-driven agriculturegradient boostingheat and water stressmachine learning |
| spellingShingle | 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 Rakad Ta’any Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture AgriEngineering climate scenarios crop–climate interaction data-driven agriculture gradient boosting heat and water stress machine learning |
| title | Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture |
| title_full | Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture |
| title_fullStr | Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture |
| title_full_unstemmed | Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture |
| title_short | Predictive Modeling of Climate-Driven Crop Yield Variability Using DSSAT Towards Sustainable Agriculture |
| title_sort | predictive modeling of climate driven crop yield variability using dssat towards sustainable agriculture |
| topic | climate scenarios crop–climate interaction data-driven agriculture gradient boosting heat and water stress machine learning |
| url | https://www.mdpi.com/2624-7402/7/5/156 |
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