Achieving comprehensive water productivity improvement: a multi-objective simulation-optimization model for water productivity-oriented irrigation water management
Improving water productivity (WP) from multiple perspectives is crucial for irrigated agriculture in arid areas, particularly due to challenges such as low crop WP, limited economic returns, and secondary soil salinization. In this study, a multi-objective simulation-optimization model is establishe...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2025
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| Acceso en línea: | https://hdl.handle.net/10568/173410 |
| _version_ | 1855513097540730880 |
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| author | Li, G. Zhang, C. Huo, Z. Liu, Y. |
| author_browse | Huo, Z. Li, G. Liu, Y. Zhang, C. |
| author_facet | Li, G. Zhang, C. Huo, Z. Liu, Y. |
| author_sort | Li, G. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Improving water productivity (WP) from multiple perspectives is crucial for irrigated agriculture in arid areas, particularly due to challenges such as low crop WP, limited economic returns, and secondary soil salinization. In this study, a multi-objective simulation-optimization model is established for maximizing irrigation water productivity (IWP), economic water productivity (EWP), and nutritional water productivity (NWP) concurrently. Moreover, the Environmental Policy Integrated Climate (EPIC) crop growth model and water-salt balance equations are incorporated to readily simulate daily physical processes of crop growth and dynamic water and salt movement. The crop parameters required for simulating these physical processes are parameterized and calibrated based on existing studies. Subsequently, it’s implemented in a case study within the Jiefangzha Irrigation Subarea, which is divided into 44 irrigation subsystems (basic irrigation decision-making units) to reflect the spatial distribution of input data. The Non-dominated Sorting Genetic Algorithm-III (NSGA-III) and Elite Opposition-Based Learning (EOBL) strategy are used to solve the problem and enhance the diversity of the randomly generated initial population accordingly, thus optimal solutions can be generated for supporting high-efficiency irrigation water use. Results indicate that (1) Optimal objectives (mean of the 44 irrigation subsystems) for IWP, EWP, and NWP are 6.72 Yuan/m3, 2.36 Kg/m3, 20041.7 Kcal/m3, with IWP increasing by 32.58 % over the status-quo. (2) Irrigation subsystems 41, 42, and 44 suffer severe salt accumulation, where salt-tolerant sunflowers should be prioritized for planting. (3) Crop root growth processes, sowing time, and growing periods have a strong influence on the salt concentration of actual root zone. Moreover, the proposed model emphasizes the influence of dynamic water-salt movement processes on crop growth and WP. Therefore, the study expands the research on multifaceted crop WP concepts and applications in the arid region, offering scientifically optimal solutions for the efficient utilization of irrigation water and effective salinity control. |
| format | Journal Article |
| id | CGSpace173410 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1734102025-10-26T13:01:49Z Achieving comprehensive water productivity improvement: a multi-objective simulation-optimization model for water productivity-oriented irrigation water management Li, G. Zhang, C. Huo, Z. Liu, Y. Improving water productivity (WP) from multiple perspectives is crucial for irrigated agriculture in arid areas, particularly due to challenges such as low crop WP, limited economic returns, and secondary soil salinization. In this study, a multi-objective simulation-optimization model is established for maximizing irrigation water productivity (IWP), economic water productivity (EWP), and nutritional water productivity (NWP) concurrently. Moreover, the Environmental Policy Integrated Climate (EPIC) crop growth model and water-salt balance equations are incorporated to readily simulate daily physical processes of crop growth and dynamic water and salt movement. The crop parameters required for simulating these physical processes are parameterized and calibrated based on existing studies. Subsequently, it’s implemented in a case study within the Jiefangzha Irrigation Subarea, which is divided into 44 irrigation subsystems (basic irrigation decision-making units) to reflect the spatial distribution of input data. The Non-dominated Sorting Genetic Algorithm-III (NSGA-III) and Elite Opposition-Based Learning (EOBL) strategy are used to solve the problem and enhance the diversity of the randomly generated initial population accordingly, thus optimal solutions can be generated for supporting high-efficiency irrigation water use. Results indicate that (1) Optimal objectives (mean of the 44 irrigation subsystems) for IWP, EWP, and NWP are 6.72 Yuan/m3, 2.36 Kg/m3, 20041.7 Kcal/m3, with IWP increasing by 32.58 % over the status-quo. (2) Irrigation subsystems 41, 42, and 44 suffer severe salt accumulation, where salt-tolerant sunflowers should be prioritized for planting. (3) Crop root growth processes, sowing time, and growing periods have a strong influence on the salt concentration of actual root zone. Moreover, the proposed model emphasizes the influence of dynamic water-salt movement processes on crop growth and WP. Therefore, the study expands the research on multifaceted crop WP concepts and applications in the arid region, offering scientifically optimal solutions for the efficient utilization of irrigation water and effective salinity control. 2025-03 2025-02-27T08:37:47Z 2025-02-27T08:37:47Z Journal Article https://hdl.handle.net/10568/173410 en Open Access Elsevier Li, G.; Zhang, C.; Huo, Z.; Liu, Y. 2025. Achieving comprehensive water productivity improvement: a multi-objective simulation-optimization model for water productivity-oriented irrigation water management. Agricultural Water Management, 309:109316. [doi:https://doi.org/10.1016/j.agwat.2025.109316] |
| spellingShingle | Li, G. Zhang, C. Huo, Z. Liu, Y. Achieving comprehensive water productivity improvement: a multi-objective simulation-optimization model for water productivity-oriented irrigation water management |
| title | Achieving comprehensive water productivity improvement: a multi-objective simulation-optimization model for water productivity-oriented irrigation water management |
| title_full | Achieving comprehensive water productivity improvement: a multi-objective simulation-optimization model for water productivity-oriented irrigation water management |
| title_fullStr | Achieving comprehensive water productivity improvement: a multi-objective simulation-optimization model for water productivity-oriented irrigation water management |
| title_full_unstemmed | Achieving comprehensive water productivity improvement: a multi-objective simulation-optimization model for water productivity-oriented irrigation water management |
| title_short | Achieving comprehensive water productivity improvement: a multi-objective simulation-optimization model for water productivity-oriented irrigation water management |
| title_sort | achieving comprehensive water productivity improvement a multi objective simulation optimization model for water productivity oriented irrigation water management |
| url | https://hdl.handle.net/10568/173410 |
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