Using Brassica Cover Crops as Living Mulch in a Vineyard, Changes over One Growing Season
Bibliographic record
Abstract
Farmers hoping to manage cropping systems sustainably are turning to cover crops to help mitigate plant pathogens. Plants with biofumigant properties are used to control soil-borne pathogens in agricultural settings, especially in till systems, where the brassicas are incorporated into the soil as green manure or seed meal. The effect of these crops is not well studied in no-till systems; thus, it is hard to know if they are as effective as green manure. Whether or not these cover crops can effect changes during a single growth season has not yet been studied. This study compared the response of the soil microbial community to four different brassica cover crops, two of which are commonly used in vineyards (Sinapis alba L. (white mustard) and Raphanus sativus (L.) Domin (tillage radish)) as well as two brassicas that are native or naturalized to the Okanagan (Capsella bursa-pastoris (L.) Medik. (Shepherd’s purse) and Boechera holboelli (Hornem.) Á. Löve and D. Löve (Holbøll’s rockcress)). Cover crops did not affect fungal species richness, but B. holboelli recover crops were associated with increased evenness among fungal taxa. Both C. bursa-pastoris and S. alba had lower levels of plant parasitic nematodes compared to non-brassica controls. These results were apparent only after a single growing season, which indicates growers could use this approach as needed, minimizing long-term exposure to biofumigants for beneficial soil microbes.
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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".