Diversifying crop rotation increases food production, reduces net greenhouse gas emissions and improves soil health
Bibliographic record
Abstract
Abstract Global food production faces challenges in balancing the need for increased yields with environmental sustainability. This study presents a six-year field experiment in the North China Plain, demonstrating the benefits of diversifying traditional cereal monoculture (wheat–maize) with cash crops (sweet potato) and legumes (peanut and soybean). The diversified rotations increase equivalent yield by up to 38%, reduce N 2 O emissions by 39%, and improve the system’s greenhouse gas balance by 88%. Furthermore, including legumes in crop rotations stimulates soil microbial activities, increases soil organic carbon stocks by 8%, and enhances soil health (indexed with the selected soil physiochemical and biological properties) by 45%. The large-scale adoption of diversified cropping systems in the North China Plain could increase cereal production by 32% when wheat–maize follows alternative crops in rotation and farmer income by 20% while benefiting the environment. This study provides an example of sustainable food production practices, emphasizing the significance of crop diversification for long-term agricultural resilience and soil health.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".