Broccoli growth in response to increasing rates of pre-plant nitrogen. II. Dry matter and nitrogen accumulation
Bibliographic record
Abstract
An understanding of plant nitrogen accumulation and soil nitrogen dynamics is needed to develop management practices that balance nitrogen requirements of vegetable crops with environmental protection. Field trials were conducted in 2001 and 2002 to determine the interaction of increasing rates of pre-plant nitrogen fertilizer with broccoli tissue nitrogen accumulation and soil nitrogen dynamics. Broccoli cultivars Decathlon and Captain were grown with seven rates of nitrogen (0, 50, 100, 150, 200, 300, 400 kg N ha -1 ) applied pre-plant as ammonium nitrate. Rate of nitrogen accumulation by the above-ground tissue biomass varied over time and among nitrogen treatments, ranging from 1 to 16 kg N ha -1 d -1 . At harvest, tissue nitrogen was high, but nitrogen use efficiency was low when high rates of nitrogen were applied. Soil NO 3 - -N content decreased from planting to harvest. At harvest, soil NO 3 - -N increased with increasing rates of nitrogen, with the majority of NO 3 --N found in the top 0 to 30 cm of the soil. At 200 kg ha -1 applied nitrogen, plants recovered essentially all of the estimated available nitrogen and there appears to be little risk of nitrogen loss during the growing season. Approximately 130 kg N ha -1 was supplied by the soil during the cropping season. Soil and crop residues at harvest ranged from 96 to 330 kg N ha -1 . This residual fertility poses a risk for nitrogen loss. Practical and cost-effective strategies are needed to manage residual nitrogen in the soil and crop residues to minimize loss and retain this nitrogen for subsequent crops. Key words: Brassica oleracea L. italica Plenck, nitrogen budget, nitrogen rates, nitrogen use efficiency, nutrient management
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".