Applicability, scope, and threshold determination of the cotton water stress characterization index: Prediction based on machine learning algorithms and validated by field experiments
文献类型： 外文期刊
作者： Hang Wang ; Wenting Qi ; Xiaopeng Ma; Jiandong Wang* ; Chuanjuan Wang
关键词： Cotton water stress diagnosis; Crop water stress index (CWSI); Plant water deficit Index (PWDI); Machine learning; Leaf water conten
期刊名称： Agricultural Water Management
ISSN： 0378-3774
年卷期： 2025 年
页码：
收录情况： SCIE（2025版） ; ; EI（2025版）
摘要： Accurate prediction of crop water diagnostic indicators is crucial for improving crop water productivity; how-ever, the applicability of water stress indices for diagnosing crop water status requires study. We analyzed theaccuracy of machine l earning (ML) algorithms in predicting the Crop Water Stress Index (CWSI) and transpiration-based Plant Water Deficit Index (PWDI). Their applicability across the growth cycle and threshold values was determined. Using field experiment data (n = 2 years) from cotton under mulch drip irrigation in Xinjiang, we evaluated the predictive performance of four ML algorithms for the cotton CWSI and PWDI. Using XGBoost’s variable importance analysis and the random forest (RF) two-dimensional partial dependence anal-ysis, we determined responses of indices to key factors and their applicability. Tree-based models, specifically RF and XGBoost, performed better than other models. The best CWSI predictors were midday air temperature (Ta-13:00), leaf temperature (Tc), and vapor pressure deficit (VPD). Key variables PWDI factors included stem sap flow rate (V), transpiration rate (Tr), and solar radiation (SR). CWSI was most accurately predicted when Ta-13:00 < 27◦C and Tc < 27◦C, whereas for PWDI, optimal Ta-13:00 > 27◦C, root-zone soil water content (SWC-RZ) < 0.175 cm³ cm⁻³ , and SR < 400 W m⁻². Threshold analyses suggested CWSI values for the squaring and boll stages of 0.79 and 0.80, respectively, but 0.65 for PWDI across stages. Using the indices, critical insights were determined for developing and implementing irrigation strategies to optimize crop water-use efficiency and precise water management in agricultural systems.
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