Organic and Conventional Dairy Farm Comparisons in Ontario, Canada
Bibliographic record
Abstract
Organic dairy fanning systems were compared with conventional (defined here as the prevailing high-technology, high-intensity) dairy farming systems in the province of Ontario, Canada from both technical and economic standpoints, over a three-year period. Primary data were collected from organic farmers as part of the study, patterned on a data collection protocol for conventional dairy farmers extant since 1976. Comparability between the samples drawn from the two subpopulations with respect to farm size, climate, natural resource endowments, and emphasis on dairying ensured to the extent possible that differences in performance reflected differences in farming systems and management procedures. The study findings revealed a superior technical performance on conventional farms, as measured by milk yield per cow and milk shipments per ha of land and per person equivalent. In contrast, economic performance was found to be superior on organic farms, by virtue of the much lower costs of production for almost all material inputs, including dairy herd replacements and livestock feeds. The lower costs were rooted in an apparent bid by organic dairy fanners to be as self-sufficient as possible in plant nutrients, animal nutrients and replacement livestock. The majority of land on organic farms was devoted to feed crop production to support the dairy herd. Conventional dairy farmers tended to import large portions of their inputs across a broad range from crop seeds through synthetic chemical fertilizers and pesticides to feedstuffs and herd replacements. Much land on conventional farms was allocated to cash crop production. Consequently their costs of production and marketing were higher, more than offsetting the higher revenues generated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".