Soil and Crop Parameters Related to Corn Nitrogen Response in Eastern Canada
Bibliographic record
Abstract
Information regarding the relationships between soil properties and the economic optimum N rate for crop yield is needed to ensure profitable use of N fertilizer. This study was conducted in 2007 and 2008 at 62 field sites in Québec (eastern Canada) to assess corn (Zea mays L.) response to N fertilizer, to calculate the economically optimum nitrogen rate (EONR) and corn yield (EOY), and to relate these two parameters with soil and crop‐based parameters. Yield response to N fertilizer rates (0–250 kg N ha−1) at each site was fitted to a linear, quadratic, or quadratic‐plus‐plateau model. The EONR and yield (EOY) were related to 12 soil and crop‐based parameters, and corn heat units (CHU). The quadratic‐plus‐plateau model best described the yield‐fertilizer relationship at 43 of the 62 field sites. The values of EOY varied from 7.4 to 13.3 Mg ha−1 in 2007 and from 5.2 to 11.2 Mg ha−1 in 2008, while EONR was between 73 and 235 kg N ha−1 in 2007 and from 48 to 200 kg N ha−1 in 2008. Correlation and principal component analysis showed that dissolved nitrogen (DNc) and dissolved carbon (DOCc) extracted with cold water and pre‐sidedress nitrate analyzed using nitrate test strips (PSNTts) were significantly and negatively related to EONR. In both years, PSNTts was consistently related to EONR, and CHU with EOY. While it remains challenging to predict EONR due to site‐specific variability and fluctuations in growing conditions, the PSNTts test shows promise in predicting the EONR for corn production in Québec.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".