The Role of Pig Diseases in Structural Change in the Canadian Pig Industry
Bibliographic record
Abstract
Since their first discoveries in the early 1990s in Canada, pig diseases, especially porcine reproductive respiratory syndrome (PRRS) and porcine circovirus associated disease (PCVAD), have plagued the Canadian pig industry, and the problem became more severe with the onset of porcine epidemic diarrhea (PED) in 2014. In the meantime, the industry has also seen dramatic structural change with a decrease in pig farm numbers and an increase in total pig numbers. Using the census division (CD) level data obtained from the Census of Agriculture Questionnaires, we find not every CD across the country experienced the same type of structural change. Indeed, about 69% of the CDs in Canada went through decreases in both pig farm numbers and pig numbers over the period 1981 to 2016. This research examines how pig diseases (PRRS, PCVAD, and PED) have affected structural change in the Canadian pig industry at the individual census division level while controlling for the effect of other key economic explanatory variables over the period 1981 to 2016. Farm structure in our study is defined by farm size (i.e., average number of pigs per farm), and the impacts of various economic factors including the U.S. country of origin labelling on the industryâs structural change are empirically assessed using random effects generalized least squares models. The empirical results indicate pig diseases did affect the Canadian pig industryâs structure, and they have played a more significant role in the structure of farms in eastern Canada. Given that pig diseases had played a significant role in pig farming operations in some geographical regions in Canada, we further investigate how various factors including: 1) on-farm disease status; 2) management variables; 3) farmersâ knowledge about and attitudes towards various treatment methods play a role in pig farmersâ decisions regarding the uptake of preventive measures. In general, we find : 1) farmers who experienced disease outbreaks are more likely to implement more preventive measures than those haven no experience; 2) farm and farmer characteristics such as production type and operatorsâ age are important determinants of adoption decisions; and 3) the better the farmersâ knowledge about a particular practice, the stronger their biosecurity behavior.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".