Use of a Nitrogen Budget to Predict Nitrogen Losses in Processing Butternut Squash with Different Nitrogen Fertilization Strategies
Bibliographic record
Abstract
With rising input costs and environmental concerns, growers are seeking methods to minimize nitrogen (N) inputs and off-field N losses while maintaining crop yields. Field studies on processing butternut squash ( Cucurbita moschata Duchesne ex Poir.) were conducted in 2004–2007 at 11 locations in Ontario, Canada, to determine the optimal N rate and estimate potential N losses. Preplant broadcast ammonium nitrate was applied at five rates between 0 and 220 kg N/ha. In contrasting years (i.e., cool/wet versus warm/dry versus average), 64% of sites were nonresponsive to N fertilizer as indicated by no differences in marketable squash yield. In responsive sites, the most economical rate of N (MERN) was between 105 and 129 kg N/ha of N fertilizer, indicating that the Ontario-recommended rate of 110 kg N/ha seems appropriate for responsive sites. At 110 kg N/ha, no yield advantage resulted from using a controlled-release N (CRN) or split-applying ammonium nitrate at preplant and vine elongation at 65 + 45 kg N/ha, respectively, compared with the same amount applied preplant. Apparent N losses (N inputs – N outputs) at harvest were 83 and 29 kg N/ha greater at a fertilizer application rate of 220 kg N/ha than at 0 and 110 kg N/ha, respectively. At 110 kg N/ha, crop removal balance and apparent N loss calculations suggest relatively low risk of N loss from the field during the growing season and after harvest, respectively. However, environmental and economical risks would be minimized if nonresponsive sites could be identified before N fertilizer application.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".