Comparing soluble to controlled-release nitrogen fertilizers: storage cabbage yield, profit margins, and N use efficiency
Bibliographic record
Abstract
Optimizing N fertilizer applications involves maximizing N use efficiency (NUE) while minimizing losses but depends on complex interactions of crop, soil, weather, and management practices. One approach may be to use controlled-release fertilizer that synchronizes N availability with plant demand. A field experiment at two Ontario locations from 2007 to 2009 compared split-applied ammonium nitrate (ANs) to preplant-applied poly-coated urea (PCU) and soluble N at a ratio of 75:25 at five N rates on late-season storage cabbage (Brassica oleracea L. var. capitata). Maximum yield and profit margins were obtained at an average of 286 and >300 kg N ha −1 , but few differences among sources were observed. Compared with ANs, PCU did not affect plant N content, nor did PCU reduce soil nitrate or NUE, which indicates little differences in risk of environmental N losses between N sources. Dissolution from mesh bags indicated 5%–25% of various PCU formulations remained by harvest but 5%–10% remained by spring, which suggests conservation over the winter, a need to synchronize N release with crop uptake, and partially explains the lack of treatment differences. From agronomic, economic, and environmental perspectives, the tested PCU treatments for cabbage production in a humid, temperate climate were equivalent to the standard practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".