Optimizing irrigation scheduling for winter wheat using the AquaCrop model in Xinjiang, China
Bibliographic record
Abstract
Introduction: Establishing an appropriate irrigation schedule is fundamental for the sustainable management of agricultural water resources, effectively alleviating water scarcity and ensuring regional food security. Methods: In this study, the AquaCrop model was calibrated and validated using field experimental data of winter wheat collected from 2022 to 2024. Irrigation schedules for three typical rainfall years-wet, normal, and dry-were determined, and a multi-objective optimization approach was proposed by coupling the AquaCrop model with the entropy weight method. Results: The results showed that the AquaCrop model accurately simulated canopy cover, aboveground biomass, soil water storage, and yield of winter wheat. To achieve the maximum yield, 15, 16, and 18 irrigation events were required in wet, normal, and dry years, respectively, with an irrigation quota of 30 mm per event and a lower soil water content threshold maintained at 50% of readily available water (RAW). In contrast, when the objective shifts from maximizing yield to maximizing water use efficiency (WUE), the highest WUE was achieved with 3, 4, and 5 irrigations in wet, normal, and dry years, respectively, with RAW thresholds of 90%, 90%, and 80%, and an irrigation quota of 80 mm. When considering multi-objective optimization to minimize irrigation water while maximizing yield and WUE, the recommended irrigation schedules were 3 irrigations for wet years and 4 irrigations for both normal and dry years, with RAW thresholds of 90%, 90%, and 110%, respectively, and an irrigation quota of 80 mm. Discussion: The findings provide a theoretical basis and technical support for developing optimized irrigation schedules and making informed irrigation decisions for winter wheat in arid regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".