Phosphorus diffusion from monocalcium phosphate co-applied with salts in a calcareous soil
Bibliographic record
Abstract
Mixing non-phosphatic salts with fertilizer P influences the solubility and mobility of P in soils. Little evidence, however, is available regarding the mechanisms causing such effects. The object ives of this study were to investigate the effects of mixing fertilizer P with (NH4)2SO4, MgSO4 or (NH2)2CO on the diffusion of P in a calcareous soil (Gleyed Rego Black Chernozem), and to identify the causes for such effects. To the surface of 50-mm-long soil columns, maintained at field capacity water content, 32P-labelled monocalcium phosphate (MCP) was applied alone or in combination with (NH4)2SO4, MgSO4 or (NH2)2CO. Ratios of applied P:N, P:Mg and P:S were 1:5, 1:4.5 and 1:6, respectively. Extraction and analysis of each 2-mm layer of the columns after incubation for 1, 2, 3, and 4 wk revealed that the addition of (NH4)2SO4 and MgSO4 with MCP significantly increased P diffusion whereas (NH2)2CO had little or no effect. The mechanisms of such effects were identified using a multi-ionic, mechanistic, diffusion model. According to model predictions, the dissolution of MCP was increased by more than twofold when mixed with (NH4)2SO4 and MgSO4, and by 1.2-fold when mixed with urea. The main difference between SO4 salts and urea in affecting P diffusion was the competition between the anion of the salt and P for precipitation with Ca. Sulphate competed strongly with P, reducing the precipitation of Ca phosphates. Application of urea increased soil pH initially, but eventually soil pH decreased with nitrification of NH4. Initial increase in pH to above 8.0 favoured precipitation of Ca phosphate, but the pH was not high enough to favour CaCO3 precipitation. The application of P fertilizers with fertilizers containing SO4 could be beneficial in calcareous soils due to enhancement of P solubility and mobility. Key words: Calcareous soils, phosphorus diffusion, precipitation, sulphate salts, transport model, urea
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".