Slope Position, Nitrogen Fertilizer, and Fungicide Effects on Diseases and Productivity of Wheat on a Hummocky Landscape
Bibliographic record
Abstract
Precision agriculture technology allows growers to selectively apply inputs to different management units within a single field. A 4‐yr study consisting of a split‐split block experiment was conducted in the northern prairies to determine the effects of foliar fungicide (FU) and N fertilizer application to slope (SL) position based management units across a hummocky landscape on leaf spot and root diseases, biomass and seed yield, and seed quality of wheat (Triticum aestivum L.). Fertilizer rates (0, 40, 80, and 120 kg N ha−1) were applied in strips across two SL positions (upper and lower) as main plots with FU application (treated and untreated) as subplots. Leaf spot diseases were consistently more severe on the upper than lower SL and reduced by FU treatment but varied inconsistently with changes in N rate. Biomass and seed yield in the two dry years were greater on the lower than upper SL. They were increased by FU treatment in 3 of 4 yr and N rate in 1 yr. Thousand‐kernel weight (TKW) and grain test weight (TW) were usually greater on the upper than lower SL, and TKW was increased by FU application in 2 yr. Protein content usually increased with increasing N rate, but the effect of SL and FU varied among years. The paucity of interactions among treatment factors indicated that selective application of FU and N fertilizer to SL position based management units for improvement of yield and seed quality was not warranted.
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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".