Livestock–cropland re-coupling and intensive farming: strategies for enhancing greenhouse gas mitigation and eco-efficiency in wheat–maize production in North China Plain
Bibliographic record
Abstract
Abstract Using manure compost can be an effective strategy to sustain crop production, mitigate greenhouse gas (GHG) emissions, and promote soil organic carbon (SOC) sequestration. However, in the North China Plain (NCP)—a key food hub in China—the disconnect between livestock farms and croplands limits manure recycling, obscuring its potential environmental benefits and economic costs. This study employs a life cycle assessment method to quantify GHG and ammonia emissions, SOC sequestration, economic performance, and the eco-efficiency of wheat–maize production in the NCP across six livestock–cropland coupling scenarios: farmers’ practice (FP), traditional household farming (HF), modern intensive decoupled systems with low (L), medium (M), and high (H) manure returning rates, and an intensive coupled system with optimum manure returning rate (IC). The results show that increasing manure return rates in intensive systems decreases the net global warming potential (NGWP), emphasizing the importance of livestock–cropland re-coupling. Emissions embodied in the field input supply chain was identified as a major NGWP contributor, while SOC accumulation significantly contributed to net GHG mitigation. The IC scenario is both the most economically viable ($322.8 (t grain)−1) and eco-efficient (1.03 kg CO2-eq USD−1) system. With the same compost application rates, intensive farming reduced the NGWP by 26.1% compared to household farming, despite trade-offs between GHG and NH3 emissions. The FP scenario had the highest climate impact (722.8 kg CO2-eq (t grain)−1) and the lowest eco-efficiency (4.91 kg CO2-eq USD−1). These insights advance our understanding of sustainable management practices for pursuing synergistic progress in economic gains, environmental conservation, and sustainable agricultural production.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".