Net Returns from Terrain‐Based Variable‐Rate Nitrogen Management on Dryland Spring Wheat in Northern Montana
Bibliographic record
Abstract
Agricultural producers can use variable‐rate application technology to vary N fertilizer within fields. This study was conducted to estimate changes in net returns from implementation of variable‐rate N management (VNM) on hard red spring wheat ( Triticum aestivum L.) in a summer‐fallow region in northern Montana. Net return from uniform N management (UNM) traditionally used by producers was compared with that from VNM in eight dryland fields between 1994 and 2004. Field experiments consisted of a replicated series of four to six N rates applied within strips oriented with the length of each field. Management zones (MZs) were created by dividing the fields into upper slopes, south‐facing middle slopes, north‐facing middle slopes, and lower slopes. Nitrogen recommendations for MZs were based on soil N testing and expected yields. Grain yield data were obtained using a production‐size combine equipped with a yield monitor. Mean grain protein and yield were similar between VNM and UNM. Yield differences were <223 kg ha −1 and averaged only 18 kg ha −1 . Grain yield did not differ significantly among N rates within MZs. In seven of the eight sites, net returns from VNM were up to US$27.97 ha −1 less than from UNM and were not profitable if Environmental Quality Incentive Program payments of US$6.36 ha −1 were considered as part of net income. Little evidence existed that VNM based on constructed MZs and predetermined N recommendations improves grain yields and profits or reduces N use in water‐limited, summer‐fallow systems of northern Montana.
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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 | 0.000 | 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 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".