AquaCrop Plug-in-PSO: A novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in-season weather forecast and crop yield estimation
Maize is among the most important crops in Iran. Enhancing maize irrigation management is critical for alleviating the pressure on limited freshwater resources in Khorasan Razavi province. The main objective of this study was to develop a novel irrigation scheduling optimization framework (AquaCrop...
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Elsevier
2024-12-01
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| Series: | Agricultural Water Management |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S037837742400489X |
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| author | Mahdi Gholami Sharafkhane Ali Naghi Ziaei Seyed Mohammadreza Naghedifar Amir Akbari Amir Verdi |
| author_facet | Mahdi Gholami Sharafkhane Ali Naghi Ziaei Seyed Mohammadreza Naghedifar Amir Akbari Amir Verdi |
| author_sort | Mahdi Gholami Sharafkhane |
| collection | DOAJ |
| description | Maize is among the most important crops in Iran. Enhancing maize irrigation management is critical for alleviating the pressure on limited freshwater resources in Khorasan Razavi province. The main objective of this study was to develop a novel irrigation scheduling optimization framework (AquaCrop plug-in-PSO) for maize, based on in-season weather forecasts integrated with the AquaCrop plug-in model and the particle swarm optimization (PSO) algorithm. During the growing season, weather forecasts combined with AquaCrop plug-in-PSO algorithm create optimal irrigation plans for different crop growth stages, maximizing crop water productivity (WPC). A two-year (2021–2022) maize field irrigation experiment was conducted in Mashhad, Iran, to collect all necessary data for calibration (2021 data) and validation (2022 data) of the AquaCrop model. Three irrigation cases were then simulated (i.e., full irrigation and deficit irrigation: 70 % and 90 % of full irrigation) to evaluate the performance of the AquaCrop plug-in-PSO approach against the typical irrigation management of local farmers, as well as ET-based and soil moisture-based irrigation scheduling methods. Additionally, AquaCrop plug-in-PSO was used to evaluate the algorithm’s performance with historical weather data. The simulation results showed that the AquaCrop Plug-in-PSO, when used with weather forecast data (AquaCrop plug-in-PSO dynamic approach), outperformed all other irrigation scheduling methods for both deficit and full irrigation cases, achieving the highest WPC, ranging from 2.21 to 3.12 kg m−3. Our simulation results demonstrate that the AquaCrop plug-in-PSO dynamic approach can be effectively used for efficient autonomous full and maize deficit irrigation scheduling in arid and semiarid regions. |
| format | Article |
| id | doaj-art-c47591e82d844b2eaf173e14bcd6322c |
| institution | Kabale University |
| issn | 1873-2283 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Agricultural Water Management |
| spelling | doaj-art-c47591e82d844b2eaf173e14bcd6322c2024-12-14T06:29:37ZengElsevierAgricultural Water Management1873-22832024-12-01306109153AquaCrop Plug-in-PSO: A novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in-season weather forecast and crop yield estimationMahdi Gholami Sharafkhane0Ali Naghi Ziaei1Seyed Mohammadreza Naghedifar2Amir Akbari3Amir Verdi4Department of Water Science and Engineering, College of Agriculture, Ferdowsi University of Mashhad (FUM), Mashhad 9177948974, IranDepartment of Water Science and Engineering, College of Agriculture, Ferdowsi University of Mashhad (FUM), Mashhad 9177948974, IranDepartment of Water Science and Engineering, College of Agriculture, Ferdowsi University of Mashhad (FUM), Mashhad 9177948974, IranDepartment of Water Science and Engineering, College of Agriculture, Ferdowsi University of Mashhad (FUM), Mashhad 9177948974, IranDepartment of Environmental Sciences, University of California Riverside, Riverside, CA 92521, USA; Corresponding author.Maize is among the most important crops in Iran. Enhancing maize irrigation management is critical for alleviating the pressure on limited freshwater resources in Khorasan Razavi province. The main objective of this study was to develop a novel irrigation scheduling optimization framework (AquaCrop plug-in-PSO) for maize, based on in-season weather forecasts integrated with the AquaCrop plug-in model and the particle swarm optimization (PSO) algorithm. During the growing season, weather forecasts combined with AquaCrop plug-in-PSO algorithm create optimal irrigation plans for different crop growth stages, maximizing crop water productivity (WPC). A two-year (2021–2022) maize field irrigation experiment was conducted in Mashhad, Iran, to collect all necessary data for calibration (2021 data) and validation (2022 data) of the AquaCrop model. Three irrigation cases were then simulated (i.e., full irrigation and deficit irrigation: 70 % and 90 % of full irrigation) to evaluate the performance of the AquaCrop plug-in-PSO approach against the typical irrigation management of local farmers, as well as ET-based and soil moisture-based irrigation scheduling methods. Additionally, AquaCrop plug-in-PSO was used to evaluate the algorithm’s performance with historical weather data. The simulation results showed that the AquaCrop Plug-in-PSO, when used with weather forecast data (AquaCrop plug-in-PSO dynamic approach), outperformed all other irrigation scheduling methods for both deficit and full irrigation cases, achieving the highest WPC, ranging from 2.21 to 3.12 kg m−3. Our simulation results demonstrate that the AquaCrop plug-in-PSO dynamic approach can be effectively used for efficient autonomous full and maize deficit irrigation scheduling in arid and semiarid regions.http://www.sciencedirect.com/science/article/pii/S037837742400489XAquaCropDeficit irrigationOptimizationPSO algorithmWeather forecasts |
| spellingShingle | Mahdi Gholami Sharafkhane Ali Naghi Ziaei Seyed Mohammadreza Naghedifar Amir Akbari Amir Verdi AquaCrop Plug-in-PSO: A novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in-season weather forecast and crop yield estimation Agricultural Water Management AquaCrop Deficit irrigation Optimization PSO algorithm Weather forecasts |
| title | AquaCrop Plug-in-PSO: A novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in-season weather forecast and crop yield estimation |
| title_full | AquaCrop Plug-in-PSO: A novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in-season weather forecast and crop yield estimation |
| title_fullStr | AquaCrop Plug-in-PSO: A novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in-season weather forecast and crop yield estimation |
| title_full_unstemmed | AquaCrop Plug-in-PSO: A novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in-season weather forecast and crop yield estimation |
| title_short | AquaCrop Plug-in-PSO: A novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in-season weather forecast and crop yield estimation |
| title_sort | aquacrop plug in pso a novel irrigation scheduling optimization framework for maize to maximize crop water productivity using in season weather forecast and crop yield estimation |
| topic | AquaCrop Deficit irrigation Optimization PSO algorithm Weather forecasts |
| url | http://www.sciencedirect.com/science/article/pii/S037837742400489X |
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