Development of a welfare assessment protocol and assessment of dairy cattle welfare in Haryana and Punjab states of Northern India
Bibliographic record
Abstract
Abstract The aim of this study was to develop an on-farm dairy cattle welfare assessment protocol at different-sized farms in two major commercial dairy farming states in India. For developing the protocol, the basic ‘Integrative Diagnostic System Welfare’ (IDSW) framework was modified to include three welfare components (animal housing and other facilities; feeds and feeding practices; and animal health, performance and behaviour) and 20 welfare indicators (ten resource- and ten animal-based). Each indicator was weighed on a value scale with an aggregate welfare score of 100. The protocol was tested for feasibility, validity and reliability using Cronbach's alpha and Guttman split-half coefficient. Using this protocol, welfare was assessed on 60 commercial farms in Punjab and 50 in Haryana, divided into three adult herd sizes: small (S < 20), medium (M = 21–50) and large (L > 50). Welfare scores in L (76.60 [± 1.70]) and M (68.40 [± 2.27]) sized herds in Punjab were higher than in S herds (60.80 [± 2.77]). In Haryana these were higher in L (68.1 [± 1.18]) than in S (60.50 [± 2.74]) and M (59.35 [± 2.17]) sized herds. The aggregate average welfare score was higher in Punjab (68.60 [± 1.49]) than in Haryana (62.65 [± 2.02]). Welfare at more than 75% of the farms in Punjab and more than 50% of those in Haryana was judged as ‘acceptable.’ Six welfare indicators in Punjab and eight in Haryana were most compromised. Four indicators (microclimate protection measures, availability of milking parlour, cow cleanliness and reproductive efficiency) were the most compromised indicators in both states. To improve dairy cattle welfare in these states we recommend an emphasis on improving housing and feeding conditions, especially at small and medium farms, along with heat stress amelioration measures and improving hygiene and reproductive efficiency at all 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 | 0.039 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".