Using Soil Water to Control Ammonia Emission from Acid Soils with and Without Chicken Litter Biochar
Bibliographic record
Abstract
Although urea use in agriculture is on the increase, increase in pH at soil microsite due to urea hydrolysis which causes ammonia emission can reduce N use efficiency. Among the interventions used to mitigate ammonia loss include urease inhibitors, clinoptilolite zeolite, coated urea, and biochar but with little attention to the use of soil water levels to control ammonia volatilization. The objective of this study was to determine the effects of soil water levels on ammonia volatilization from soils with and without chicken litter biochar. Dry soils with and without chicken litter biochar were subjected to 0%, 25% 50%, 75%, 100%, and 125% soil water. There was no urea hydrolysis in the soil without water. Chicken litter biochar as soil amendment effectively mitigated ammonia loss at 1% to 32% and 80% to 115% field capacity. However, urea used on soil only showed lower ammonia loss at 33% to 79% and 116% to 125% field capacity compared with the soils with chicken litter biochar. At 50% field capacity ammonia loss was high in soils with and without chicken litter biochar. Although chicken litter biochar is reputed for improving soil chemical properties, water levels in this present study affected soil chemical properties differently. Fifty percent field capacity, significantly reduced soil chemical properties. These findings suggest that timely application of urea at the right field capacity can mitigate ammonia emission. Therefore, whether soils are amended with or without chicken litter biochar, urea application should be avoided at 50% field capacity especially in irrigated crops.
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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) | 0.001 | 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".