Weather effects on corn response to in-season nitrogen rates
Bibliographic record
Abstract
Xie, M., Tremblay, N., Tremblay, G., Bourgeois, G., Bouroubi, M. Y. and Wei, Z. 2013. Weather effects on corn response to in-season nitrogen rates. Can. J. Plant Sci. 93: 407–417. The response of corn yield to in-season nitrogen rate (ISNR) fertilizer applications in a temperate humid climate is conditioned to a great extent by prevailing weather conditions, which affect nitrogen use efficiency and raise the level of uncertainty for making management decisions. A better understanding of the effects of temperature, expressed as accumulated corn heat units (CHU), and precipitation would help to ensure that a “closer-to-optimal” nitrogen rate is supplied at side-dressing. A meta-analysis was performed using a database of nitrogen response trials conducted from 1997 to 2008 in 60 locations in the corn grain production area of Québec, in conjunction with a weather database. Meta-analysis is a statistical procedure for combining results from a series of studies that is used in many fields of research. It is used to assess treatment effect (also called effect size) in a group of studies or experiments. Corn yield response to ISNR was negatively correlated with overall CHU accumulation, but positively correlated with CHU accumulation before side-dressing. Responses also showed a stronger relationship with cumulative precipitation (PPT) before side-dressing than after side-dressing. High and evenly distributed precipitation before side-dressing tended to increase responses to ISNR. It can be concluded that low CHU, low precipitation and low precipitation evenness before side-dressing reduce the impact of ISNR on corn yield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".