Mitigating Ammonia Volatilization from Waterlogged Acids Soils Using Organic Amendments
Bibliographic record
Abstract
In production agriculture, granular urea is the most used nitrogen fertilizer in crop production. However, increase in soil pH following application of urea causes ammonia volatilization and reduces N use efficiency. To minimize ammonia loss, organic amendments are used, however, type of organic amendment use could affect urea use efficiency. This study was to determine the effects of organic amendments derived from forest litter, Leucaena leucocephala, chicken litter, and cow dung on ammonia volatilization and chemical properties of a waterlogged acid soil. Treatments evaluated were: (i) T1, Soil only, (ii) T2, Existing recommended fertilization, (iii) T3, Biochar-forest litter compost, (iv) T4, Biochar-chicken litter compost, (v) T5, Biochar-cow dung compost, (vi) T6, Biochar-Leucaena compost, and (vii) T7, Biochar-Leucaena - chicken litter compost. Standard procedures were used to quantify ammonia volatilization and soil chemical properties. The findings of this present study also revealed that the total amount of ammonia loss from urea over a period of forty-two days depends on the influence of the organic amendments on urea hydrolysis. Emissions of ammonia from T6 and T7 were significantly higher because, the decomposition of Leucaena leucocephala favours urea hydrolysis compared with those of T3, T4, and T5. Therefore, Leucaena leucocephala composts should be carefully co-applied with urea to minimize ammonia loss if the aim of using this type of amendments is to improve N use efficiency and soil and crop productivity.
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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 | 0.001 | 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".