An analysis of the effect of milk compositional standard on the profitability of Ontario dairy farms
Bibliographic record
Abstract
The purpose of this study is to examine the effect of the milk compositional standard on the profitability of Ontario dairy farms. In 2005, Dairy Farmers of Ontario implemented the solids-non-fat (SNF) to butterfat (BF) ratio standard in order to overcome the problem of structural surplus of skim milk powder. The study develops a theoretical model to describe the effects of this standard and component pricing on profitability. To test whether the standard has an effect on the profitability of Ontario dairy farms, a fixed effect regression model is estimated using rotating panel data from the Ontario Dairy Farm Accounting Project over the period 1996 to 2008. The results reveal that the SNF:BF ratio standard may not have a statistically significant influence on the profitability of the sample farms. An in-depth descriptive analysis of producers' responses to the SNF:BF ratio standard shows that the majority of the sample farms were operating below the target ratio. The SNF:BF ratio standard may be a non-binding constraint for the majority of the farms, and allows producers operating below the target ratio to trigger for higher ratios. For a smaller percentage of farms operating above the target ratio the standard may be a binding constraint, and these producers may respond to the standard by decreasing their ratios. In this context, the SNF:BF ratio standard may not act as a restrictive policy in controlling SNF production; rather, it may act as a preventive policy signalling producers not to produce SNF above the target ratio.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".