Assessment of Chlorophyll and Nitrate Meters as Field Tissue Nitrogen Tests for Cabbage, Onions, and Carrots
Bibliographic record
Abstract
Nutrient management legislation has prompted an evaluation of alternative nitrogen (N) management techniques. SPAD (Soil Plant Analysis Development) chlorophyll and Cardy nitrate (NO 3 - ) meters were evaluated for their potential as tissue nitrogen tests in cabbage ( Brassica oleracea var. capitata ), onions ( Allium cepa ), and carrots ( Daucus carota subsp. sativus ). Cabbage, carrots, and onions were grown on both organic and mineral soils in Ontario, Canada in 2000 and 2001. Nitrogen was applied at five rates to cabbage and carrots and three rates to onions ranging from 0 to 200% of current provincial recommended N rates. In an additional treatment, 50% of the recommended rate was applied preplant and sidedress N applications of 40 kg·ha -1 (35.7 lb/acre) were applied when SPAD chlorophyll meter readings fell below 95 (2000) and 97% (2001) of the highest N rate treatment. Yields were generally unaffected by N rate, except in cabbage in 2000, suggesting adequate N was present in most treatments. SPAD chlorophyll meter readings were highly variable among soil types, cultivars, and years. Chlorophyll readings reached a plateau in adequately fertilized crops in many instances. Cardy readings were less variable among soil types, cultivars, and years. The relationship between N rate and sap NO 3 -N concentration was generally linear. The results suggest that the use of a well-fertilized reference plot is most appropriate for the SPAD meter on these vegetable crops, while the use of region-specific critical NO 3 -N concentrations was most appropriate for the Cardy NO 3 -meter. Both meters would be cost advantageous when over 500 samples are tested. The meters were generally easy to use, except for the SPAD meter on carrots. The meters have potential for N management of vegetable crops under Ontario growing conditions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".