Within-farm spatiotemporal variability impacts carrot yield and quality more than preceding cover crop on a Canadian organic farm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it