(220) Determination of Water and Nitrogen Requirements of Cabbage using Fertigation
Bibliographic record
Abstract
Fertigation is a promising strategy to improve nitrogen use efficiency, yield, and quality of cabbage ( Brassica oleracea var. capitata ), but there is a lack of data relevant to growers in Ontario. Field trials were conducted in 2003 and 2004 to determine the optimum rate of water and nitrogen application in terms of yield and quality of `Huron' cabbage. Treatments consisted of combinations of target soil moisture levels (25% to 100% field capacity) and nitrogen fertilizer (0–400 kg·ha -1 N) as dictated by a central rotatable composite design. Nitrogen applications were split with 50% broadcast and incorporated before planting and the remaining split into weekly applications via a trickle irrigation system. Water was applied two to three times per week to bring soil moisture up to the target levels. Maximum marketable yield was reached at a combination of 400 kg·ha -1 N and a soil moisture target of 100% field capacity. Many heads were undersized or undeveloped at low rates of nitrogen. Applications of nitrogen required for high yield and quality can pose a risk of leaching; however, use of fertigation minimizes potential in-season leaching. Estimated total residual nitrogen at harvest ranged from 83–211 kg·ha -1 N, which could have a negative impact on the environment. Thus, there is a considerable challenge to reduce environmental impact without economic losses. Improved knowledge of in-season nitrogen requirements might further reduce the levels of nitrogen applied without reducing yield and quality.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".