162 Raising your kids the right way: A survey of rearing practices in Canadian dairy goat farms
Bibliographic record
Abstract
Abstract Kid rearing is the foundation of goat milk production, yet little is known about how to raise replacement does efficiently to make healthy and productive dairy animals. This study aimed to identify the common rearing practices of Canadian commercial dairy goat farms (≥40 goats/farm), from birth to weaning, and to determine best management practices to improve herd performances. A survey was sent to dairy goat producers across Canada by post or email, and 104 respondents were selected for analysis. The 70-questions survey collected information regarding kidding management, care of the newborn, feeding in the preweaning period, housing, weaning and herd performances. Respondents were from Ontario (69%), Quebec (22%) and the Western provinces (9%). Farm sizes ranged from 42 to 2,500 (median: 190) goats, and most producers (64%) were relatively new to goat milk production (≤10 yrs). A large amount of variation in rearing practices was seen across farms. Ad libitum milk was offered on 55% of farms, and there was no consistency as to when concentrates, forages and water were first offered to kids. Weaning criteria was predominantly a mix of age and weight of the kid (36%), followed by age only (27%) and weight only (22%). Weaning age varied between 4.5 and 20 (median: 8) wks and weaning weight varied between 9 and 35 (median: 15) kg. Weaning methods ranged from abrupt (37%) to different progressive strategies (20% skipping milk feedings, 19% reducing milk quantity, and 10% diluting milk with water). This research provides the dairy goat industry with information concerning current common kid rearing practices used on Canadian goat farms, and the lack of consensus indicates that further research is necessary to determine and refine the best kid rearing practices for Canadian farms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".