Synthetic Mulches in Organic Hardneck Garlic (Allium sativum subsp. ophioscorodon) Production
Bibliographic record
Abstract
The demand for certified organic garlic (Allium sativum) in Canada is increasing; however, garlic can be challenging to produce organically, as it does not compete well with weeds, requires relatively fertile soils, and is grown in a biennial cropping system. Synthetic mulches have been adopted in organic production as they can be an economical method to improve vegetable production by reducing weed pressure and modifying soil conditions. We hypothesize that garlic quality and overall yield will be improved when using synthetic mulches. In 2017-18, we conducted a randomized complete block design experiment to compare garlic production of black plastic, white plastic, and Kraft paper mulch treatments to a control with no mulch at a certified organic farm in Krestova, British Columbia. We evaluated garlic characteristics associated with yield and quality, changes in soil nutrition, and weed control of the mulch treatments. We found that plastic mulches had the best weed control, and all synthetic mulches increased minimum and maximum bulb diameter, clove count, and yield compared to the control. Mulching materials did not influence soil nitrate concentrations. The results support the hypothesis that synthetic mulches increase the quality and yield of the garlic compared to the control. Our findings suggest that synthetic mulching may be a key component of improving garlic production systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".