Crop rotation differentially increases soil bacterial and fungal diversities in global croplands: a meta-analysis
Bibliographic record
Abstract
Crop rotation is widely practiced to improve agricultural sustainability, yet its impact on microbial diversity remains unclear. We conduct a global meta-analysis of 2406 paired observations to examine the effects of crop rotation on microbial diversity based on high-throughput sequencing data. We show that crop rotation significantly increases bacterial Shannon diversity and species richness, but has no effect on bacterial beta diversity. In contrast, crop rotation significantly increases fungal beta diversity without affecting fungal Shannon diversity and species richness. Changes in microbial communities are linked to soil pH, and available nitrogen and phosphorus. Notably, legume vs. non-legume, arbuscular mycorrhiza vs. non- arbuscular mycorrhiza, C3 vs. C4, and annual vs. perennial crop transitions, as well as climate and soil factors, affect the response ratios of microbial metics to crop rotation. Furthermore, the response ratios of bacterial Shannon diversity, bacterial species richness, and fungal species richness are positively related to the response ratio of crop yield. Our study reveals positive but differential effects of crop rotation on bacterial and fungal diversities, which are linked to improved crop productivity. Our findings thus have implications for using crop rotation to conserve soil microbial biodiversity, which is related to soil health and function, and enhance global food security. This study presents a global meta-analysis using high-throughput sequencing data to assess the effects of crop rotation on bacterial and fungal diversity and community structure. The results show that crop rotation significantly increases bacterial Shannon diversity and species richness, while having no effect on bacterial beta diversity.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.018 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".