Nitrogen management in wheat (Triticum aestivum L.): grain yield and quality as influenced by topography and fertilization
Bibliographic record
Abstract
Excessive N fertilization of soft red winter wheat (' Triticum aestivum' L.) may lead to undesirable protein concentrations. The inter-relationships between wheat grain yield, grain protein concentration, soil nitrogen (N) levels and N fertilization on two variable landscapes (site 1 and 2) in Southwestern Ontario were examined. Six N rates (0 to 145 kg N/ha) were applied to plots (400 m long), arranged in a randomized complete split block design with four replicates. Along each plot, samples for soil N test and grain yield were collected on a 20-m interval resulting in a 3 x 20 m grid-sampling pattern (456 sampling points/site). Upper slope positions typically had lower yields and higher protein concentrations than the lower slope positions. For each field the most economic rate of N (MERN) for yield calculated from quadratic regression models was determined to be 103.1 and 105.2 kg N/ha at sites 1 and 2, respectively. The MERN varied with slope position at site 2 suggesting the potential to variably apply N. Protein concentrations followed a sigmoidal response to applied N. Although the response was similar for each slope position, there was a greater risk of exceeding the desirable protein concentration at the upper slope positions.
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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) | 0.000 | 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 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".