Soil P fractions as affected by on-farm composts in a controlled incubation study
Bibliographic record
Abstract
Information on the different forms and availability of P following compost addition to soil may help to better manage manure in respect to plant growth and the environment. An experiment was conducted to investigate through a sequential extraction procedure the availability of P of fresh dairy manure and several on-farm compost-soil mixtures after a 13 wk incubation in glass jars at 35°C. Materials were mixed at a rate of 200 mg N kg-1 with an Arago sandy loam (Humo-Ferric Podzol), supplying from 64 to 301 mg P kg-1. Fresh dairy manure gave the highest net increase of resin-P and labile P fractions in terms of percentage of total P added, whereas poultry litter compost was the most efficient in increasing NaHCO3-inorganic P (-Pi). Among compost materials, poultry litter, vegetable residue and sheep manure increased labile P fraction the most. The contribution of the young dairy manure compost to this fraction was largely negative, and lower than those of fresh manure or partially and well-decomposed manure composts. A large part of added P was found in the moderately labile P fraction. The organic P (Po) fractions in the soil were less affected by manure or compost addition. This study indicated that the material P availability was reduced by composting, and was more affected by the origin of residue than by manure management. Key words: Composting, farm manure, soil P fractionation
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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) | 0.001 | 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".