Improving Complementarity Effect of Legume Intercrop by Earthworm Facilitation for Wheat Performance
Bibliographic record
Abstract
Intercrops and crop mixtures are considered to be a way to increase nitrogen use efficiency by promoting niche complementarity and facilitation, reducing the input of fertilizers and herbicides, which are important factors when considering the effects of climate change. However, interactions between crop communities and soil functional diversity also have major effects on crop cover function. Our study aimed to investigate the simultaneous effects of plant composition and presence of earthworms on the growth (roots and shoots) of wheat (Triticum aestivum L.). Mesocosms filled with soil were sown with either 6 wheat plants of the same cultivar, or 6 plants of 3 different wheat cultivars, or 3 wheat plants of 3 different cultivars with 3 clover plants (Trifolium hybridum L.). A part of the mesocosms was inoculated with either endogeic earthworms (Aporrectodea caliginosa S.) or a mixture of endogeic and anecic earthworms (Lumbricus terrestris S.). A relative interaction index was calculated to highlight competition strength between plants with or without earthworms. The presence of different cultivars had no influence on wheat performance, but with clover, plant competition decreased to the benefit of wheat biomass and N accumulation. Earthworms also reduced the competitive strength between wheat plants in mixed-cultivar mesocosms and in intercropping. In intercrops with clover, wheat performance was improved as a result of niche complementarity and earthworm facilitation for N resource. Our results suggest that the plant functional group, such as legumes, and earthworm communities work synergistically to improve wheat yields.
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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) | 0.001 | 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".