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Evapotranspiration Differences, Driving Factors, and Numerical Simulation of Typical Irrigated Wheat Fields in Northwest China

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Publicado en:Agronomy vol. 15, no. 8 (2025), p. 1984-2013
Autor principal: Yang, Tianyi
Otros Autores: Chen Haochong, Yu Haichao, Liao Zhenqi, Yang, Danni, Li, Sien
Publicado:
MDPI AG
Materias:
China
Energy balance
Irrigation scheduling
Border irrigation
Net radiation
Correlation analysis
Soil water
Evapotranspiration
Crops
Machine learning
Moisture content
Leaf area index
Computer simulation
Spring wheat
Water resources
Simulation
Precipitation
Water management
Algorithms
Arid regions
Water content
Agricultural resources
Wheat
Accuracy
Leaf area
Irrigation
Arid zones
Corn
Hydrology
Learning algorithms
Soil sciences
Bowen ratio
Irrigation efficiency
Hydrologic cycle
Drip irrigation
Mathematical models
Acceso en línea:Citation/Abstract
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Resumen:Wheat is a staple crop widely sown in Northwest China, and understanding and modelling evapotranspiration (ET) during the wheat-growing stage is important for irrigation scheduling and the efficient use of agricultural water resources. In this study, a four-year observation was conducted on a spring wheat field with border irrigation (BI) treatment and drip irrigation (DI) treatment, based on two Bowen ratio energy balance (BREB) systems. The results showed that the average ET across the whole growing stage scale was 512.0 mm for the BI treatment and 446.9 mm for the DI treatment, and the DI treatment reduced ET by 65.1 mm across the growing stage scale. The driving factors of the changes in ET in the two treatments were investigated using partial correlation analysis after understanding the changing pattern of ET. Net radiation (Rn), soil water content (SWC), and leaf area index (LAI) were the main meteorological, soil, and crop factors leading to the changes in ET in the two treatments. In terms of ET simulation, the SWAP model and different types of machine learning algorithms were used in this study to numerically simulate ET at a daily scale. The total ET values simulated by the SWAP model at the interannual scale were 11.0–14.2% lower than the observed values of ET, and the simulation accuracy varied at different growing stages. In terms of the machine learning simulation of ET, this study is the first to apply five machine learning algorithms to simulate a typical irrigated wheat field in the arid region of Northwest China. It was found that the Stacking algorithm as well as the SWAP model had the optimal simulation among all machine learning algorithms. These findings can provide a scientific basis for irrigation management and the efficient use of agricultural water resources in spring wheat fields in arid regions.
ISSN:2073-4395
DOI:10.3390/agronomy15081984
Fuente:Agriculture Science Database