A Hedonic model of Canadian dairy farmer Holstein-semen purchases
Bibliographic record
Abstract
The dairy industry in Canada has undergone huge changes in the last few decades. While the average annual milk production per cow grew over three times by 2015 (average rose to 8.65 Hectolitres per and reached 9.5 Hectolitres in 2018) from 1995 levels (2.5 Hectolitres a year per cow on average), the number of farms across the country continues to shrink. One key element of change may concern the genetic makeup of the cow herds: Canadian farmers have succeeded in producing higher-yielding cows through their breeding choices. Moreover, the incorporation of genomics into the toolset of sire selection in 2008 brought new possibilities to attain genetic gains in cattle herds. Semen selection decisions are hence critical to dairy operations’ efficiency and productivity levels. Characterizing farmers’ preferences towards the different sire traits during sire selection can help describe the importance of particular traits in the industry and ultimately, continue to move the dairy sector towards sustainable efficient production. Canadian dairy farmers’ preferences for sire attributes before and after the increased use of genomic technology are studied to help understand producers’ breeding decision-making process. This research is aimed at evaluating trait importance in sire selection decisions and if a shift in trait valuation is observable with the use of genomics from 2008, when genomic tools became more widely used in Canada, to 2016. Following Richard and Jeffrey’s (1996) last analysis of dairy farmers’ valuation of sire traits in Canada, this study expands the application of econometric estimations on market transactions of Holstein semen to examine dairy farmers’ preferences for the different production and type traits. The hedonic price modeling performed in this study offers an update of Holstein sire trait valuation for the average Canadian dairy farmer over the course of eight years, those immediate to the introduction of genomics. A variety of econometric functional forms will be used to characterize demand for sire traits. These models will allow the industry to better understand the demand for specific traits, predict future trait demands and ensure that genomic analysis focuses on traits of significant interest to producers.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".