Assessment of nitrification and urease inhibitors on nitrate leaching in corn (<i>Zea mays</i> L.)
Bibliographic record
Abstract
Agricultural ecosystems are one of the largest global contributors to nitrate (NO3−) contamination of surface- and groundwater through fertilizer application. Improved fertilizer practices are needed to manage crop nutrient supply in corn (Zea mays L.) while minimizing impacts to clean water reserves. The goal of this study was to compare current nitrogen (N) fertilizer practices (urea at planting) with “packages” of improved management practices (a combination of right timing and product) that farmers potentially use. We conducted measurements in a continuous corn system from November 2015 to May 2017 at a large field scale (four 4 ha plots). Nitrate concentration was measured below the root zone and drainage estimated using a soil water budget approach in which evapotranspiration was measured using the eddy covariance method. The objective was to compare NO3−-N leaching from fields receiving urea vs. urea + combination of nitrification and urease inhibitors (NUI) fertilizer applications at planting, urea–ammonium nitrate (UAN) vs. UAN + NUI applied at sidedress, and a combination of these practices: urea + NUI at planting vs. UAN at sidedress. Drainage was only significant in the non-growing season. Neither fertilizer products applied with NUI at planting or sidedress proved to significantly reduce NO3−-N leaching. The combination of delaying fertilization to sidedress and applying UAN significantly reduced the soil water NO3−-N concentration compared with urea + NUI at planting (mean of 5.2 vs. 6.7 mg L−1) but only in 2015–2016. Based on these results, applying UAN at sidedress is recommended, although additional study years are needed to confirm those results.
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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".