Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditions
Crop growth simulation models are valuable for understanding and managing agro-ecological systems, especially in arid regions. The DSSAT-CERES-Wheat model is widely used to simulate wheat growth and development, but its accuracy diminishes under water stress conditions. This study evaluated the effe...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0378377424005717 |
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| author | Ning Yao Yingnan Wei Kunhao Jiang Jian Liu Yi Li Hui Ran Tehseen Javed Hao Feng Qiang Yu Jianqiang He |
| author_facet | Ning Yao Yingnan Wei Kunhao Jiang Jian Liu Yi Li Hui Ran Tehseen Javed Hao Feng Qiang Yu Jianqiang He |
| author_sort | Ning Yao |
| collection | DOAJ |
| description | Crop growth simulation models are valuable for understanding and managing agro-ecological systems, especially in arid regions. The DSSAT-CERES-Wheat model is widely used to simulate wheat growth and development, but its accuracy diminishes under water stress conditions. This study evaluated the effects of different types of water stress response functions on the CERES-Wheat model, focusing on unit grain weight, biomass, and yield to improve the model's accuracy under water stress conditions. Our findings have disclosed that the calibration process demonstrated average Relative Mean Absolute Error (RMAE) and Relative Root Mean Square Error (RRMSE) values hovering at 5 %, whereas the evaluation process values surpassed 15 %. It was found that water stress occurring before the jointing stage significantly influenced model simulations of biomass and grain yield. This study assesses the performance of modified water stress response functions stepwise. Optimal parameters for these functions were identified, with the smallest RMAE and RRMSE observed at specific parameter values for different curve types. Comparative analyses using the modified CERES-Wheat model with six water stress response curves (WSRF) demonstrated improved WSRF 1, 2, 3, and 6 performance over the default function. The Penman-Monteith method for estimating potential evapotranspiration (E0) outperformed the Priestley-Taylor method, particularly in simulating unit grain weight, aboveground biomass, and grain yield. Convex functions (WSRF 1–3) were superior to concave functions (WSRF 4–6), enhancing overall simulation accuracy by 13.7 %. Furthermore, time-series output variables, including soil water and biomass dynamics, were evaluated. The original model underestimated early soil moisture and overestimated soil water stress, leading to significant biomass simulation errors. The modified model showed substantial improvements, particularly under low irrigation. However, simulation errors persisted under severe early-stage water stress. Validation results using DSSAT v4.7 databases confirmed the enhanced performance of the modified model under water stress conditions. The finding of this study proved that the improved model for the vast arid regions of China and similar environments globally can offer valuable insights and improve the accuracy of simulations, aiding in more effective agricultural management and planning under water-limited conditions. |
| format | Article |
| id | doaj-art-1a81cf2fb59a4a47a98c416191c89809 |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-1a81cf2fb59a4a47a98c416191c898092025-01-07T04:16:50ZengElsevierAgricultural Water Management1873-22832025-02-01307109235Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditionsNing Yao0Yingnan Wei1Kunhao Jiang2Jian Liu3Yi Li4Hui Ran5Tehseen Javed6Hao Feng7Qiang Yu8Jianqiang He9College of Water Resources and Architectural Engineering/Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, PR ChinaCollege of Water Resources and Architectural Engineering/Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, PR ChinaCollege of Water Resources and Architectural Engineering/Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, PR ChinaCollege of Water Conservancy & Architectural Engineering, Shihezi University, Shihezi, Xinjiang 832003, PR ChinaCollege of Water Resources and Architectural Engineering/Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, PR China; Correspondence authors.Key Laboratory of Eco-Environment of Three Gorges Region, Ministry of Education, Chongqing University, Chongqing 400044, PR ChinaDepartment of Environmental Sciences, Kohat University of Science and Technology, Kohat 26000, PakistanCollege of Soil and Water Conservation Science and Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, PR ChinaCollege of Soil and Water Conservation Science and Engineering, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, PR ChinaCollege of Water Resources and Architectural Engineering/Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Northwest Agriculture and Forestry University, Yangling, Shaanxi 712100, PR China; Correspondence authors.Crop growth simulation models are valuable for understanding and managing agro-ecological systems, especially in arid regions. The DSSAT-CERES-Wheat model is widely used to simulate wheat growth and development, but its accuracy diminishes under water stress conditions. This study evaluated the effects of different types of water stress response functions on the CERES-Wheat model, focusing on unit grain weight, biomass, and yield to improve the model's accuracy under water stress conditions. Our findings have disclosed that the calibration process demonstrated average Relative Mean Absolute Error (RMAE) and Relative Root Mean Square Error (RRMSE) values hovering at 5 %, whereas the evaluation process values surpassed 15 %. It was found that water stress occurring before the jointing stage significantly influenced model simulations of biomass and grain yield. This study assesses the performance of modified water stress response functions stepwise. Optimal parameters for these functions were identified, with the smallest RMAE and RRMSE observed at specific parameter values for different curve types. Comparative analyses using the modified CERES-Wheat model with six water stress response curves (WSRF) demonstrated improved WSRF 1, 2, 3, and 6 performance over the default function. The Penman-Monteith method for estimating potential evapotranspiration (E0) outperformed the Priestley-Taylor method, particularly in simulating unit grain weight, aboveground biomass, and grain yield. Convex functions (WSRF 1–3) were superior to concave functions (WSRF 4–6), enhancing overall simulation accuracy by 13.7 %. Furthermore, time-series output variables, including soil water and biomass dynamics, were evaluated. The original model underestimated early soil moisture and overestimated soil water stress, leading to significant biomass simulation errors. The modified model showed substantial improvements, particularly under low irrigation. However, simulation errors persisted under severe early-stage water stress. Validation results using DSSAT v4.7 databases confirmed the enhanced performance of the modified model under water stress conditions. The finding of this study proved that the improved model for the vast arid regions of China and similar environments globally can offer valuable insights and improve the accuracy of simulations, aiding in more effective agricultural management and planning under water-limited conditions.http://www.sciencedirect.com/science/article/pii/S0378377424005717Nonlinear water stress responseDSSAT-CERES-Wheat modelWater deficit conditionsCrop simulation accuracyArid and semi-arid agricultureWheat growth modeling |
| spellingShingle | Ning Yao Yingnan Wei Kunhao Jiang Jian Liu Yi Li Hui Ran Tehseen Javed Hao Feng Qiang Yu Jianqiang He Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditions Agricultural Water Management Nonlinear water stress response DSSAT-CERES-Wheat model Water deficit conditions Crop simulation accuracy Arid and semi-arid agriculture Wheat growth modeling |
| title | Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditions |
| title_full | Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditions |
| title_fullStr | Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditions |
| title_full_unstemmed | Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditions |
| title_short | Nonlinear water stress response functions can improve the performance of the DSSAT-CERES-Wheat model under water deficit conditions |
| title_sort | nonlinear water stress response functions can improve the performance of the dssat ceres wheat model under water deficit conditions |
| topic | Nonlinear water stress response DSSAT-CERES-Wheat model Water deficit conditions Crop simulation accuracy Arid and semi-arid agriculture Wheat growth modeling |
| url | http://www.sciencedirect.com/science/article/pii/S0378377424005717 |
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