Carrot Yield and Quality as Influenced by Nitrogen Application in Cut-and-Peel Carrots
Bibliographic record
Abstract
Root bulking, quality, and uniformity in cut-and-peel carrots (Daucus carota) are paramount for optimizing marketable yield and quality. Root bulking is an ecophysiological manifestation in response to inputs such as fertilizers. Understanding this ecophysioloical interaction will help to optimize yield, quality, and amount of inputs used. Three years of field trials were conducted in Kings County, Nova Scotia, to investigate the effects of varying levels of nitrogen (N) fertilizer on yield, recovery, and root and tissue N of two cut-and-peel varieties, Sugarsnax and TopCut. Seven levels of ammonium nitrate (34–0–0; 0, 50, 100, 150, 200, 300, and 400 kg N h−1) were hand broadcast in a split (60% pre-emergence and 40% 8 weeks after emergence) application. No significant interactive effects of N and variety in terms of gross yield or recovery were observed, though Sugarsnax total yields were 12.7% greater than those of TopCut. Overall, optimum yields were achieved at N rates of 150 kg N h−1 and further addition did not significantly improve yield or quality. Increased N significantly increased root and tissue N, but N concentration in both tissues peaked at the 300 kg N h−1 rate. However, neither root nor leaf tissue N had any effect on marketable or total yield. These results show that root bulking is not modulated by altering N applications, and the results also suggest that carrots may have high N-use efficiency or harness N from deeper zones.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".