Optimizing water and fertilizer management reduces carbon and water footprints for winter wheat production in China
Bibliographic record
Abstract
The grand challenge for sustainable farming systems is to maintain agricultural productivity in a changing climate with water resource constraints. Here, we present a spatiotemporal footprint framework to optimize agricultural activities in winter wheat systems, based on a 30-year integrated assessment from 1991 to 2020 in China. During this period, agricultural activities in China's winter wheat production system emitted 66.6 × 10 6 t CO 2 eq yr -1 and consumed 112 × 10 9 m 3 yr -1 of water annually. The Huang-Huai-Hai Plain had high greenhouse gas emissions and water consumption, yet maintained relatively low product-level footprints. From 2001 to 2020, synergistic reductions in carbon and water footprints were achieved by optimizing fertilizer practices for yield improvement. Scenario-based mitigation analysis revealed that substituting organic alternatives for chemical fertilizers reduced emissions by 12%, while powering irrigation equipment with renewable energy lowered emissions by 7.0%, and improving irrigation efficiency reduced water consumption by 3%, relative to the baseline scenario. Together, precision fertilization and energy-efficient irrigation were highly impactful, reducing carbon emissions by up to 20% and being a practical strategy to enhance food security and environmental sustainability. • China's winter wheat emitted 66.6 Mt CO 2 eq yr -1 , consumed 112 km 3 water yr -1 . • Huang-Huai-Hai Plain had high wheat emissions and water use, low product footprints. • Fertilizer optimization and yield increases drove 2001-2020 synergistic reductions. • Scenarios: N cut/substitution, irrigation efficiency, renewable reduced footprints.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".