Effects of Initial Soil Moisture, Clod Size, and Clay Content on Ammonia Volatilization after Subsurface Band Application of Urea
Bibliographic record
Abstract
Ammonia losses from broadcast urea vary based on soil physical and chemical properties; however, less is known about how soil properties affect NH3 losses after subsurface banding of urea. Therefore, three field trials were established to determine how initial soil moisture, clod size, and clay content affect NH3 volatilization from subsurface‐banded (0.025‐m depth) urea using wind tunnels. The first study measured volatilization after banding in a loamy mixed frigid Typic Humaquept at 50, 100, 150, 200, or 250 g kg−1 gravimetric water content (WC). Study 2 measured volatilization from the same soil after covering the bands with soil clods that ranged from <2 to >24 mm in diameter, whereas Study 3 measured volatilization from transplanted, acidic soils with clay contents ranging from 5 to 57%. Cumulative 17‐d NH3 losses for study one ranged from 8.3 to 20.8% of applied N, with the soil wetted to 200 g kg−1 WC experiencing the greatest losses. For Study 2, cumulative NH3 volatilization losses ranged from 10.8 to 20.8% of applied N, with the greatest losses from the largest clod sizes. For Study 3, NH3 losses ranged from 2.5 to 51.7% of applied N, with the NH3 losses correlated to the maximum pH measured in the band (P < 0.001), and to the soil cation exchange capacity (P = 0.054), titratable acidity (P = 0.072), and clay content (P = 0.100). However, the soil with high silt, not sand, content had the highest volatilization losses, suggesting that high silt soils may have the greatest potential for NH3 volatilization. Core Ideas Greater NH3 losses from soils at 200 g kg−1 water content than at ≤150 g kg−1. Covering the urea band with clods <6 mm reduced NH3 losses. Soil NH3 losses correlated closely to the maximum pH reached in the band and soil CEC. The highest NH3 losses were from soils with low clay and high silt content. Previous Canadian studies on NH3 loss from banded urea may overestimate losses.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".