Effect of nitrogen fertilization and topography on soft red winter wheat yield and protein content in two Ontario landscapes
Bibliographic record
Abstract
Identification of suitable management units for the variable application of fertilizer N is an essential component of a site-specific N management program. Topography and/or soil N test results are examples of two parameters that could potentially delineate N management units for improved grain yield or protein concentration. Field studies were conducted on soft red winter wheat (Triticum aestivum L.) to examine the effects of mineral soil N, soil organic carbon (SOC), and N fertilization on grain yields and protein concentrations in two variable landscapes in southwestern Ontario, Canada. Six N rates (0 to 145 kg N ha-1) were applied to plots (3 × 400 m long), arranged in a randomized complete split block design with four replicates. Sampling on a 3 × 20 m grid indicated slope position affected soil nitrate (NO3-N) and SOC levels at site 1, but not at site 2. The range of spatial correlation for NO3-N was 11.5 m at both sites and for SOC was 16.0 m and 32.4 m at site 1 and 2, respectively. Yields decreased and protein concentrations increased while moving upslope at both sites. Yield response to applied fertilizer N was characterized using a quadratic model. The most economic rate of N (MERN) for site 1 was 105 kg ha-1, and did not vary with topography. At site 2, yield response to applied fertilizer N varied with topography and MERN increased while moving downslope (91, 104, 120 kg N ha-1 for upper, mid, and lower, respectively). Protein concentration responses to applied N were fitted to cubic models, and the nature of the response to applied N did not vary with slope position. Based simply on grain yield, the potential to use topography and/or mineral N in the soil to identify management units for variable application of N was limited at these two sites; however, variably applying N may reduce the risk of exceeding the protein limits for export markets on the upper slope positions without sacrificing yield. Key words: Site-specific crop management, nitrogen application, N fertilizer, variogram, topography
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".