Performance of nitrate compared with urea fertilizer in a semiarid climate of the northern Great Plains
Bibliographic record
Abstract
Fertilizer NO3-N may represent a benefit over NH4-N containing sources in semiarid regions where rainfall is often not sufficient to leach fertilizer-N out of crop rooting zones, denitrification concerns are not great, and when NH3 volatilization concerns exist. The objective of our study was to contrast plant-N derived from fertilizer-15N (15Ndff), fertilizer-15N recovery (F15NR), total N uptake, grain yield, and protein of wheat (Triticum aestivum L.) from spring-applied NaNO3 relative to urea and urea augmented with urease inhibitor N-(n-butyl)thiophosphoric triamide (NBPT). We established six fertilizer-N field trials widespread within the state of Montana between 2012 and 2017. The trials incorporated different experimental designs and 15N-labeled fertilizer-N sources, including NaNO3, NH4NO3, urea, and urea + NBPT. Overall, F15NR and 15Ndff in mature crop biomass were significantly greater for NaNO3 than urea or urea + NBPT (P < 0.05). Crop 15Ndff averaged 53.8%, 43.9%, and 44.7% across locations for NaNO3, urea, and urea + NBPT, respectively. Likewise, crop F15NR averaged 52.2%, 35.8%, and 38.6% for NaNO3, urea, and urea + NBPT, respectively. Soil 15N recovered in the surface layer (0–15 cm) was lower for NaNO3 compared with urea and urea + NBPT. Wheat grain yield and protein were generally not sensitive to improvements in 15Ndff, F15NR, or total N uptake. Our study hypothesis that NaNO3 would result in similar or better performance than urea or urea + NBPT was confirmed. Use of NO3-N fertilizer might be an alternative strategy to mitigate fertilizer-N induced soil acidity in semiarid regions of the northern Great Plains.
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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".