A survey of dairy goat kid-rearing practices on Canadian farms and their associations with self-reported farm performance
Bibliographic record
Abstract
Although future production of dairy goats is influenced by kid-rearing practices, little is known regarding which practices maximize kid growth, welfare, and future production success. The objectives of this survey study were to (1) identify common rearing practices of Canadian commercial dairy goat farms and evaluate their associations with 6 farm performance indicators and (2) determine if farms could be grouped by management style on the basis of the 6 performance indicators and compare rearing practices common across the different groups. A survey was sent by post or electronic media to reach dairy goat producers across Canada. The questionnaire contained 70 questions on the following areas of kid rearing: kidding management, care of newborn, colostrum management, milk and solid feeding in the preweaning period, health management, disbudding, housing conditions, weaning strategies, record keeping and growth monitoring, and farm performance data. Performance indicators, calculated on self-reported data, were 305-d milk production, preweaning mortality rate, diarrhea and respiratory disease prevalence, average daily gain from birth to weaning, herd milk production, and replacement rate. A total of 175 questionnaires were returned. After applying inclusion criteria, including herd size (≥40 goats) and completeness of surveys, 104 respondents from Ontario (n = 72, 69%), Québec (n = 23, 22%), and the Western provinces (n = 9, 9%) were retained for analysis, representing 29% of all Canadian producers. Farm sizes ranged from 42 to 2,500 (median = 190) goats. A large amount of variation in rearing practices and farm performance was found between farms. Colostrum and milk feeding management were found to be associated with all performance indicators except for kid respiratory disease prevalence, with timing of colostrum delivery and feeding method accounting for most the associations within each of the 2 areas. Replacement rate was mostly affected by whether or not kids were reared with their dam. Herds surveyed in the study could successfully be divided into 3 distinct groups (production-focused, longevity-focused, and low performance), representing different management styles on the basis of farm self-reported performance levels. Rearing practices found to be associated with higher farm performance could be targeted by advisory services to help improve management practices on Canadian dairy goat farms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".