Increasing crop rotational diversity can enhance cereal yields
Bibliographic record
Abstract
Abstract Diversifying agriculture by rotating a greater number of crop species in sequence is a promising practice to reduce negative impacts of crop production on the environment and maintain yields. However, it is unclear to what extent cereal yields change with crop rotation diversity and external nitrogen fertilization level over time, and which functional groups of crops provide the most yield benefit. Here, using grain yield data of small grain cereals and maize from 32 long-term (10–63 years) experiments across Europe and North America, we show that crop rotational diversity, measured as crop species diversity and functional richness, enhanced grain yields. This yield benefit increased over time. Only the yields of winter-sown small grain cereals showed a decline at the highest level of species diversity. Diversification was beneficial to all cereals with a low external nitrogen input, particularly maize, enabling a lower dependence on nitrogen fertilisers and ultimately reducing greenhouse gas emissions and nitrogen pollution. The results suggest that increasing crop functional richness rather than species diversity can be a strategy for supporting grain yields across many environments.
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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 | 0.001 |
| 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".