Variability in Corn Yield Response to Nitrogen Fertilizer in Eastern Canada
Bibliographic record
Abstract
Core Ideas A 8‐yr study of corn N fertilization on high‐yielding fields in Québec, eastern Canada. Grain yield response to N rates varied among site‐years. The economically optimal N rate was affected by soil textural classes, planting date, and rainfall. Averaged across textures, planting date, and weather, economically optimal N rate was 195 kg N ha−1. Nitrogen applications at rates above the current N recommendation increased grain yield. Corn (Zea mays L.) yield response to N has been found to vary spatially within a field. The objective of this study was to examine how grain corn yield response to N varies with planting date, soil texture, and spring weather across sites and years in the Montérégie region. Trials were conducted from 2002 to 2004 and 2006 to 2010, at 11 sites with 23 hybrids and four N application rates, for a total of 45 site‐years. Each site‐year involved five or six N rates ranging from 80–90 to 240 kg N ha−1. Grain yield response to N rates varied among site‐years. Trials were separated into two groups based on optimal and late planting dates. Significant differences in grain yield among the applied N rates were observed in all of the site‐years planted at optimal dates (from 8.8–14.7 Mg ha−1), and in most of those planted late (8.5–12.8 Mg ha−1). Economic optimum nitrogen rates (EONR) ranged less widely for site‐years planted on optimal dates (180–237 kg N ha−1) than for those planted late (132–237 kg N ha−1). The EONR was affected by soil textural classes and rainfall. On coarse‐textured soils, more N was needed to optimize grain yield in years with wet growing seasons. These results suggest that the current N recommendations for corn in Quebec should consider the variability in response associated with site‐specific effects of planting date, soil texture, and weather.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".