Phosphorus availability and speciation in soils amended with upcycled dairy-waste nutrients
Bibliographic record
Abstract
The excessive application of dairy manure to soils to supply plant nutrients can result in increased offsite nutrient transport and degraded water quality. However, by concentrating nutrients from dairy-waste onto biochar or as biosolids, a viable alternative fertilizer can be produced that will benefit soil health, promote carbon sequestration, and decrease nutrient leaching into surface waters. In this study, a greenhouse experiment was conducted to assess soil phosphorus (P) speciation and barley plant growth in soils amended with dairy-waste treated biochar and fermented manure. Phosphorus characterization in the post-harvest soils was determined using selective extractions, 31 P-NMR and XANES spectroscopy, and isotopic tracing (P-δ 18 O). Selective extractions and NMR spectroscopy revealed that most of the P in the amended soils occurred as inorganic species (>85%). XANES spectroscopy further showed that the soil P occurred as either calcium-P minerals (54%–87%) or adsorbed P (0%–46%) species. Analysis by P-δ 18 O in water and sodium bicarbonate extractions of the treated soils showed that the water-extracted P pool is cycled much faster than the sodium bicarbonate extracted P. Although less than 10% of the total P in the dairy-derived bioproducts was extracted using water, plant productivity in the soils treated with these amendments was the same as that in soils treated with equivalent amounts of conventional nitrogen and P fertilizer. This suggests that dairy-derived bioproducts are good soil amendments to supply nutrients and limit leaching.
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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) | 0.001 | 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".