Agronomic Effectiveness of Granular Nitrogen/Phosphorus Fertilizers Containing Elemental Sulfur with and without Ammonium Sulfate: A Review
Bibliographic record
Abstract
Deficiency of S in soils has become a soil fertility issue worldwide because of a decrease in S deposition from air to soil due to legislation and increased crop removal. Continuous use of high‐analysis nitrogen/phosphorus (NP) fertilizers lacking in S further exacerbates the S deficiency for crop production. Several newly developed granular NP fertilizers such as monoammonium phosphate (MAP), diammonium phosphate (DAP), and triple superphosphate (TSP) containing micronized elemental sulfur (ES) with/without ammonium sulfate (AS) have been marketed to farmers. It is claimed that these products can provide available SO4–S through AS and ES oxidation during the growing season. The objective of this review was to carefully examine the literature that addresses the agronomic effectiveness of the granular NP–ES or NP– (ES+AS) fertilizer products. The review shows that oxidation of ES particles in granular NP fertilizers is generally nil or inadequate to provide available S to seasonal (or first) crops in greenhouse studies. This is due to the negative locality effect on granular ES oxidation. In contrast, available S can be obtained from the associated AS component of the granular (ES+AS). Under field conditions, limited studies showed these granular (ES+AS) were as effective as SO4–based sources at a high single S rate, but lack of data at multiple S rates. The detailed evaluation of available data so far often shows that the granular NP fertilizers containing ES or (ES+AS) cannot provide available S as compared with traditional SO4–based S sources for season‐long or first field crops. Sulfur nutrient is important to crop growth to produce maximum crop yield. Several new S fertilizers are marketed to farmers often without scientific data to support the fertilizer producers’ claims. This review article examines the available data to check the claims; the results often cannot validate the claims.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".