The Association of Herd Milk Production and Management with a Return-Over-Feed Index in Ontario Dairy Herds
Bibliographic record
Abstract
Associations of herd milk production and management variables to a return-over-feed (ROF) herd profit index were examined among 95 dairy farms. The ROF index is derived from 2 important determinants of profit on dairy farms: milk income and feed cost. All producers were participants in the Dairy Herd Improvement (DHI) ROF program in Ontario, Canada during 2002. Nutrition, housing, health, and other management data were collected through a phone survey of herd managers. Herd milk production, milk component percentages, and somatic cell count data were obtained from the Ontario DHI database. The linear regression model accounting for significant variation in ROF with highest R2 (0.66) included standardized milk production, milk protein percentage, milk fat percentage, and use of monensin in lactating cow rations. A 1-kg increase in standardized milk production (kg/d per cow) or a 0.1 percentage unit increase in milk protein was associated with $0.35/d per cow or $0.26/d per cow increase, respectively, in the ROF of the dairy herd. However, a 0.1 percentage unit increase in milk fat was associated with a $0.10/d per cow decrease in ROF, probably because of a negative association of milk fat with milk yield. Use of monensin in lactating cow rations was associated with a $0.39/d per cow increase in ROF. In a separate model (R2 = 0.27) that examined management factors independent of production variables, herds using 3 times daily milking had a $1.25/d per cow higher ROF vs. herds using twice daily, whereas use of an Escherichia coli mastitis vaccine was associated with $0.59/d per cow higher ROF. Production-related variables accounted for more variation in the ROF index than management variables, and the latter, e.g., use of monensin, only marginally increased R2 of production-based regression models.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".