Urease and Nitrification Inhibitors Impact on Winter Wheat Fertilizer Timing, Yield, and Protein Content
Bibliographic record
Abstract
Nitrogen fertilizer is an important input for winter wheat ( Triticum aestivium L.) production. However, the losses of applied N fertilizer are economically substantial and environmentally unsafe. Therefore, improved N fertilizer management practices are needed to increase yield, enhance wheat quality, and minimize negative consequences to the environment. The objective of this experiment was to determine the impact on two N fertilizer sources, two application times, and three placement methods on grain yield, protein concentration, and N uptake of winter wheat. The experiment was conducted in Montana, in a randomized complete block design. The mean grain yield, protein concentration, and N uptake in 2010/2011 were lower than 2012/2013 due to differences in soil fertility and inter‐seasonal variations in precipitation. Treatment effects were significant in 2010/2011 (wet season) but not in 2012/2013 (dry season). In 2010/2011, urea with agrotain (urease inhibitor) and N serve (nitrification inhibitor) broadcasted in spring (UANSBS) produced the highest yield (2630 kg ha −1 ). This yield increase was 29% more than fall applied urea. In 2010/2011, spring broadcasting of urea, urea with agrotain, Super Urea (urea with urease and nitrification inhibitors), and UANSBS produced similar but higher yield, grain protein concentration and grain N uptake than other treatments. Therefore, considering the erratic nature of precipitation in this dry land area, spring broadcasting of urea with or without inhibitors appeared to be practical N fertilizer management practices for the region. But economic analysis is needed to justify this suggestion.
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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".