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Irrigation practice, tillage method, and nitrogen (N) management are the three most important agronomic measures for wheat (Triticum aestivum L.) production, but the combined effects on grain yield and wheat physiological characteristics are still poorly understood. We conducted a three-year split–split field experiment at the junction of the Loess Plateau and Huang-Huai-Hai Plain in China. The two irrigation practices (I0: non-irrigation and I1: one-off irrigation), three tillage methods (RT: rotary tillage, PT: plowing, and ST: subsoiling), and four N managements (N0, N120, N180, and N240) were assigned to the main plots, subplots, and sub-subplots, respectively. Irrigation practice, tillage method, N management, and most of their two-factor and three-factor interactions could significantly affect grain yield and the physiological characteristics of the leaves of winter wheat. One-off irrigation increased the grain yield by 46.9% by optimizing the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), the contents of proline (Pro) and soluble sugar (SS), and the net photosynthesis rate (Pn) in leaves during most growth stages of wheat. The improvement of grain yield and physiological characteristics under one-off irrigation was considerably affected by the tillage method and N management, and the effectiveness of one-off irrigation was improved under subsoiling and N180 or N240. One-off irrigation combining subsoiling and N180 had no significant difference relative to one-off irrigation combining subsoiling and N240, while it significantly increased grain yield by 47.1% over the three years, as well as increasing the activities of SOD, POD, and CAT, and Pn in wheat leaves by 23.2%, 41.2%, 26.1%, and 53.0%, respectively, and decreasing the contents of malondialdehyde (MDA), Pro, and SS by 29.2%, 65.4%, and 18.2% compared to non-irrigation rotary tillage combined with N240 across the two years and three stages. The wheat grain yield was significantly associated with the physiological characteristics in flag leaves, and the coefficient was greatest for POD activity, followed by SOD activity and Pn. Therefore, one-off irrigation combining subsoiling and N180 is an optimal strategy for the high-yield production of wheat in dryland regions where the one-off irrigation is assured.
Irrigation practice, tillage method, and nitrogen (N) management are the three most important agronomic measures for wheat (Triticum aestivum L.) production, but the combined effects on grain yield and wheat physiological characteristics are still poorly understood. We conducted a three-year split–split field experiment at the junction of the Loess Plateau and Huang-Huai-Hai Plain in China. The two irrigation practices (I0: non-irrigation and I1: one-off irrigation), three tillage methods (RT: rotary tillage, PT: plowing, and ST: subsoiling), and four N managements (N0, N120, N180, and N240) were assigned to the main plots, subplots, and sub-subplots, respectively. Irrigation practice, tillage method, N management, and most of their two-factor and three-factor interactions could significantly affect grain yield and the physiological characteristics of the leaves of winter wheat. One-off irrigation increased the grain yield by 46.9% by optimizing the activities of superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), the contents of proline (Pro) and soluble sugar (SS), and the net photosynthesis rate (Pn) in leaves during most growth stages of wheat. The improvement of grain yield and physiological characteristics under one-off irrigation was considerably affected by the tillage method and N management, and the effectiveness of one-off irrigation was improved under subsoiling and N180 or N240. One-off irrigation combining subsoiling and N180 had no significant difference relative to one-off irrigation combining subsoiling and N240, while it significantly increased grain yield by 47.1% over the three years, as well as increasing the activities of SOD, POD, and CAT, and Pn in wheat leaves by 23.2%, 41.2%, 26.1%, and 53.0%, respectively, and decreasing the contents of malondialdehyde (MDA), Pro, and SS by 29.2%, 65.4%, and 18.2% compared to non-irrigation rotary tillage combined with N240 across the two years and three stages. The wheat grain yield was significantly associated with the physiological characteristics in flag leaves, and the coefficient was greatest for POD activity, followed by SOD activity and Pn. Therefore, one-off irrigation combining subsoiling and N180 is an optimal strategy for the high-yield production of wheat in dryland regions where the one-off irrigation is assured.
The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R2 values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring.
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