Use of ammonium sulphate as a sulphur fertilizer: Implications for ammonia volatilization
Bibliographic record
Abstract
Abstract Ammonium sulphate is widely used as a sulphur (S) fertilizer, constituting about 50% of global S use. Within nitrogen (N) management, it is well known that ammonium‐based fertilizers are subject to ammonia (NH 3 ) volatilization in soils with pH > 7, but this has been overlooked in decision making on S fertilization. We reviewed 41 publications reporting measurements of NH 3 loss from ammonium sulphate in 16 countries covering a wide range of soil types and climates. In field experiments, loss was mostly <5% of applied N in soils with pH (in water) <7.0. In soils with pH > 7.0, there was a wide range of losses (0%–66%), with many in the 20%–40% range and some indication of increased loss (ca. 5%–15%) in soils with pH 6.5–7.0. We estimate that replacing ammonium sulphate with a different form of S for arable crops could decrease NH 3 emissions from this source by 90%, even taking account of likely emissions from alternative fertilizers to replace the N, but chosen for low NH 3 emission. For every kt of ammonium sulphate replaced on soils of pH > 7.0 in temperate regions, NH 3 emission would decrease from 35.7 to 3.6 t NH 3 . Other readily available sources of S include single superphosphate, potassium sulphate, magnesium sulphate, calcium sulphate dihydrate (gypsum), and polyhalite (Polysulphate). In view of the large areas of high pH soils globally, this change of S fertilizer selection would make a significant contribution to decreasing NH 3 emissions worldwide, contributing to necessary cuts to meet agreed ceilings under the Gothenburg Convention.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".