Phosphorus Utilization and Environmental and Economic Implications of Reducing Phosphorus Pollution from Ontario Dairy Cows
Bibliographic record
Abstract
A major source of environmental pollution has been overfeeding P to dairy cows, caused by the "safety margins" added to diets in order not to compromise the health and production of animals. An extant whole-animal model was evaluated using an experiment conducted in Ontario to assess its applicability for predicting P excretion. The objective of the study was to use the model to estimate P excretion levels and the economic and environmental implications of implementing mitigating options by following recommendations from studies that have reported sufficient levels of P inclusion in the diet. Mean square prediction error and concordance coefficient analysis showed that the overall predictions were close to the mean and that there was only a slight underprediction of fecal P output by the model. The majority of the error was random, with only 8.9% coming from error caused by deviation from the regression line, and the model did not show a systematic trend of over- or underprediction. The model was then used to predict P excretion in Ontario by using diets commonly fed to dairy cows on Ontario farms. It is estimated that Ontario dairy farms produce 7 kt of P annually at current levels of P inclusion in the diet. Reducing P levels from the current 0.41% P of dry matter to 0.35% is estimated to save producers CAN $20/cow per year and the environment 1.3 kt/yr without impairing cow health or productivity. Additionally, the reductions might be from inorganic P sources added to the feed, which are more polluting than organic sources because of their water-soluble nature and liability to leaching and runoff.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".