Nitrogen and water requirements of fertigated cabbage in Ontario
Bibliographic record
Abstract
Increasing nutrient and water regulations have necessitated development of best management practices for application of nitrogen (N) and water. This study was conducted to determine if there was an optimal balance of N and water applied for late storage cabbage (Brassica oleracea L. var. capitata). Five rates of N and five irrigation rates arranged in a response surface design replicated three times were supplied to Huron cabbage grown on sandy loam soil to study the interaction of N and water applied. Plots were located at the University of Guelph, Simcoe Research Station, Ontario, Canada from 2003 to 2005. Total and marketable yields were maximized from a low of 278 kg ha-1 N in 2005 to above the highest rate tested (400 kg ha-1 N) in the other 2 yr. In 2005, there were 29 d above 30 °C and marketable yield was 49% lower than 2004, which had only 1 d above 30 °C. A target soil water value of 100% of field capacity was required to maximize yield in all 3 yr. More N is required as the water supply increases. The main influence of irrigation and N application was on head volume. Head density based on fresh weight was not influenced by irrigation or N application, but head density based on dry weight decreased with increased N application. Irrigation and N application should be managed concurrently to maximize yield and quality and N and irrigation efficiency for late storage cabbage. However, N and water will not prevent lost yield due to hot days, which suggests that late-cabbage yields are very sensitive to high air temperatures. Key words: Brassica oleracea var. capitata, cabbage, irrigation, fertigation, quality, nutrient management, air temperature
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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 | 0.001 |
| 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) | 0.001 | 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".