Projected long-term climate change impacts on rainfed durum wheat production and sustainable adaptation strategies

Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 144980 - 144980

Опубликована: Фев. 1, 2025

Язык: Английский

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A Novel Transpiration Drought Index for Winter Wheat in the Huang-Huai-Hai Region, China: A Process-Based Framework Incorporating Improved Crop Water Supply–Demand Dynamics

Agronomy, Год журнала: 2025, Номер 15(3), С. 679 - 679

Опубликована: Март 11, 2025

Monitoring agricultural drought is crucial for mitigating yield losses in winter wheat, especially the Huang-Huai-Hai (HHH) region of China. Current indices often fall short accurately representing water supply–demand dynamics crops, neglect irrigation practices, and overemphasize intensity rather than its evolution overall impact. To address these concerns, we developed a novel transpiration index utilizing Water Balance Winter Wheat (WBWW) model. This integrated variations atmospheric conditions, soil moisture crop resistance, practices to enhance evaluation supply demand dynamics. The WBWW model was initially validated against field measurements, achieving an R2 0.7573, thereby confirming reliability subsequent analyses. create mechanistic understanding demand, adopted reduction rate actual potential identify events constructed joint probability distributions duration severity using copulas. led development Drought Assessment Index (WDAI). grade threshold WDAI established based on historical data from HHH through series statistical determination methods. Our findings showed that successfully identified 87.36% samples according their recorded grades, with 97.13% within one records. Comparative analyses retained regional existing indices—the Crop Deficit (CWDI) Relative Soil Moisture (RSMI)—further demonstrated effectiveness. study represents robust tool dynamic monitoring offers critical insights into practices.

Язык: Английский

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Development of a Drought Monitoring System for Winter Wheat in the Huang-Huai-Hai Region, China, Utilizing a Machine Learning–Physical Process Hybrid Model

Agronomy, Год журнала: 2025, Номер 15(3), С. 696 - 696

Опубликована: Март 13, 2025

Droughts, intensified by climate change and human activities, pose a significant threat to winter wheat cultivation in the Huang-Huai-Hai (HHH) region. Soil moisture drought indices are crucial for monitoring agricultural droughts, while challenges such as data accessibility soil heterogeneous necessitate use of numerical simulations their effective regional-scale applications. The existing simulation methods like physical process models machine learning (ML) algorithms have limitations: struggle with parameter acquisition at regional scales, ML face difficulties settings due presence crops. As more advanced complex branch ML, deep even greater limitations related crop growth management. To address these challenges, this study proposed novel hybrid system that merged model. Initially, we employed Random Forest (RF) regression model integrated multi-source environmental factors estimate prior sowing wheat, achieving an average coefficient determination (R2) 0.8618, root mean square error (RMSE) 0.0182 m3 m−3, absolute (MAE) 0.0148 m−3 across eight depths. RF provided vital parameters operation Water Balance Winter Wheat (WBWW) scale, enabling assessments combined Moisture Anomaly Percentage Index (SMAPI). Subsequent comparative analyses between system-generated results actual disaster records during two events highlighted its efficacy. Finally, utilized examine spatiotemporal variations patterns HHH region over past decades. findings revealed overall intensification conditions decline SMAPI rate −0.021% per year. Concurrently, there has been shift patterns, characterized increase both frequency extremity events, duration intensity individual decreased majority Additionally, identified northeastern, western, southern areas requiring concentrated attention targeted intervention strategies. These efforts signify notable application fusion techniques integration within big context, thereby facilitating prevention, management, mitigation

Язык: Английский

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Reducing water footprints: shaping transition to a net zero future agriculture

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 327 - 345

Опубликована: Янв. 1, 2025

Язык: Английский

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Co-applications of Biochar and Reduced Fertilizer Improved Soil Fertility, Nitrogen Use Efficiency, and Yield of Lycium chinense Mill: A Two-Year Field Trial

Journal of soil science and plant nutrition, Год журнала: 2025, Номер unknown

Опубликована: Апрель 4, 2025

Язык: Английский

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