Rearing goat kids away from their dams 1. A survey to understand rearing methods
Bibliographic record
Abstract
Despite an estimated global goat population of over one billion, little is known about methods being used to feed milk to artificially reared kids (reared away from their dams) and how kids are weaned from these systems. Quantifying and characterising current methods utilised on farms will enable future targeted research to investigate best practice methods for milk feeding and weaning of artificially reared kids. A recall-based survey that investigated on-farm kid-rearing practices (focusing on the milk feeding and weaning stages) was distributed via social media, and regional goat organisations across multiple countries. A total of 242 responses from 16 countries were collected and geographically grouped. Responses that could not be grouped sufficiently were removed (nine responses from eight countries). A total of 233 responses from eight countries (United States of America (USA) 72; United Kingdom (UK) 71; Australia 33; Canada 23; New Zealand 20; European Union (EU) 14), were analysed. Most farms (217; 93%) bred their own kids. The most common milk feeding method was bottle feeding, used on 135 farms (57.9%), followed by ad libitum feeding used by 72 (30.9%). A relationship between number of kids reared and feeding system was identified, χ2(3, N = 233) = 89.605, P < 0.001, with farms rearing > 100 kids more likely to feed milk ad libitum. A total of 170 farms (72.9%) were weaned based on a target age and 85 (36.4%) on a target weight, 53 (22.7%) used both and 45 (19.3%) neither. Target weaning ages and weights varied across countries; the median age was 84 days (interquartile range (IQR) 56–84), and the median weight was 16 kg (IQR 15–18). A difference was found between milk feeding systems for weaning method (Χ2(2, N = 232) = 63.797, P ≤ 0.001), with kids most likely to be abruptly weaned from ad libitum systems (or gradually weaned from bottle feeding). Abrupt weaning was used by 67 farms (28.8%), and gradual weaning was used by 165 (71.1%). Gradual weaning strategies included reducing milk quantity (150 farms; 93% of farms providing detail) and diluting milk (six farms; 4%). A total of 169 (72.5%) supplied enrichment that met the survey’s definition; items to climb on/hide in were most common, provided by 157 farms (92.8%). Findings suggest differing practices in smaller-scale bottle-fed versus larger-scale ad libitum milk systems, likely reflecting differing system needs. This highlights a requirement for welfare-focused research in kids reared artificially in order to identify and communicate best practices to ensure on-farm welfare is optimised within each system.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".