Coupled weather and crop simulation modeling for smart irrigation planning: a review

被引：2
作者：
Naziq, Mohamed S. [1 ]
Sathyamoorthy, N.K. [1 ]
Ga, Dheebakaran [1 ]
Pazhanivelan, S. [2 ]
Vadivel, N. [3 ]
机构：
[1] Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore
[2] Centre for Water and Geospatial Studies, Tamil Nadu Agricultural University, Coimbatore
[3] Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore
来源：
Water Supply | 2024年 / 24卷 / 08期
关键词：
crop simulation models; irrigation scheduling; optimization; water productivity; weather forecasting;
D O I：
10.2166/ws.2024.170
中图分类号：
学科分类号：
摘要：
Efficient irrigation scheduling is essential for optimizing crop yields and water-use efficiency. This review examines crop simulation models and methods for improving irrigation management, with a focus on integrating weather forecast data. The FAO (Food and Agriculture Organization) developed models such as AquaCrop, WOFOST (WOrld FOod Studies), DSSAT (Decision Support System for Agrotechnology Transfer), and APSIM (Agricultural Production Systems sIMulator), exploring the incorporation of forecasted ETo (reference evapotranspiration) calculated based on forecasted values of weather through the Penman-Monteith method and rainfall data into the models using modified rule-based approaches with various forecast horizons, which enhances irrigation planning. Optimization methods, including genetic algorithms coupled with crop models, are also assessed and have shown significant water savings and profit gains compared with traditional farming practices. Emerging real-time irrigation scheduling tools, including simulation-optimization, field data assimilation, and human–machine interactions, further improve productivity and water conservation. Studies have also shown that web-based decision support using satellite remote sensing and crop models can be used to effectively monitor crop water status and predict real-time irrigation needs. Ongoing innovations like coupling crop models with optimization techniques, weather forecasting, remote sensing, and recommendations based on field experiments have shown promise for transforming irrigation planning and management. © 2024 The Authors.
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页码：2844 / 2865
页数：21
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