Within-farm spatiotemporal variability impacts carrot yield and quality more than preceding cover crop on a Canadian organic farm
Bibliographic record
Abstract
Abstract Carrot (Daucus carota) is an important crop grown in Canada and globally. Fresh market carrots have strict cosmetic requirements to command full value at “Grade A” and are frequently downgraded for irregular shape, size, or pest damage. Organic farming presents challenges for nutrient management, soil health and pest control, which may be mitigated with cover cropping. A 3-year field experiment was conducted on a commercial organic farm to 1) test the effects of six preceding-year cover crop treatments compared to a weedy fallow control on carrot yield and quality, wireworm damage, reasons for downgrading, and populations of plant parasitic nematodes, and 2) characterize within-farm spatiotemperal variability in production to identify strategies to improve and stabilize economic return. Carrot yield (42–55 Mg ha−1), quality (39–92% Grade A) and market value (183–221 $1000 Canadian dollars ha−1) varied drastically across years, and blocks within years (≤ 20% differences), but cover crops had no impact on these metrics. The dominant reasons for downgrading were morphological, affecting 7–74% of carrots each year and varying with cover crop only once, where carrots following buckwheat (Fagopyrum esculentum) had fewer shape flaws. Nematodes had no relationship to cover crop or any carrot metric and wireworms damaged only 2% of carrots across all three years. This study found virtually no effect of cover crop species composition on next year’s carrots on this farm, and that the farmer-collaborators can optimize their operation by improving crop establishment across space and time, reducing morphological flaws, and seeking higher value for downgraded produce.
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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 | 0.001 | 0.001 |
| 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".