How Well Do Critical Nitrogen Concentrations Work for Cabbage, Carrot, and Onion Crops?
Bibliographic record
Abstract
With the introduction of nutrient management legislation in Ontario, there is a need to improve the efficiency of nitrogen (N) utilization. One possibility is to use critical nutrient concentrations in plant tissue as an indicator of the N nutritional status of the crop. Plant tissue analysis was used to determine the total N and nitrate-N (NO 3 -N) concentrations of cabbage ( Brassica oleracea var. capitata L. ) , carrots ( Daucus carota L.), and onions ( Allium cepa L.) grown in Ontario. The tissue samples were collected from plants as part of N fertilization studies from 1999 to 2001 on the organic soils in the Holland/Bradford Marsh area and the mineral soils near Simcoe, Ontario. Yield was assessed at harvest as an indicator of the N requirement of the crop. Testing the usefulness of critical NO 3 -N concentrations to indicate the N requirement of the crop was problematic because: 1) few published references were available to indicate a critical level of NO 3 -N in these crops; 2) tissue NO 3 -N concentrations were highly variable; and 3) field data rarely matched published references. Tissue total N concentrations from the trials corresponded to published critical N concentrations in some cases, however, the use of published critical N concentrations would have resulted in either over or under-application of fertilizer to the crops. Cultivar, soil type, and climate were shown to affect tissue N concentrations. Based on these results it was concluded that local research and field verification is required before tissue N critical nutrient concentrations become useful for determining fertilizer needs of cabbage, carrots, and onions grown in Ontario.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".