On-farm phosphorus budget: Model to predict yearly phosphorus contents in manure of dairy herds
Bibliographic record
Abstract
In order to reduce soil phosphorus (P) saturation, it is essential to assess the amount of manure P on cultivated soil. The purpose of this study was to estimate yearly herd manure P outputs from production and feeding records with a model based on replacement and dairy animals. The model predicts manure P based on P ingested by dairy cows (kg yr-1), P secreted in milk (kg yr-1), P in calf at birth (kg yr-1), and the number of first-lactation cows. The relationship between first-lactation cows and heifers was established; there were 1.3 ± 0.05 heifers for each first-lactation cow. Manure P from heifers was then obtained by fitting the model to manure P accumulated in concrete pits of 12 farms, measured over two 6-mo periods at 1 yr intervals. The model added 10.6 ± 4.6 kg of P for each first-lactation cow to predict the yearly P output of 1.3 heifers. Ratios between P harvested as feed and P predicted in manure were calculated in 1133 herds. High ratios were obtained in herds with less customized concentrate (P < 0.001), more harvested grain and silage (P < 0.001) on farm and larger size of herd (P < 0.001) with more milk (P < 0.001) and lower calf production (P < 0.001). Decreasing purchased customized concentrates and increasing the amount of silage fed to animals are valid options to reduce non-point-source P pollution. Key words: Dairy herds, manure, phosphorus, model, reproductive efficiency
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".