Pig production systems and related effects on pre-slaughter animal welfare and meat quality
Bibliographic record
Abstract
Pre-slaughter handling practices, such as fasting, transport, mixing and human interventions affect the welfare of pigs and carcase and meat quality individually and cumulatively. Behavioural and physiological studies conducted during the pre-slaughter period revealed that producer-controlled factors at the farm, such as housing system, previous handling experience, genetics, gender, nutrition and slaughter weight can have an impact on pigs’ ease of handling and sensitivity to stress, which result in loss of profits for the pork chain due to transport losses, reduced carcase value due to lesions and bruises and meat quality defects. Research has shown that pigs originating from enriched housing conditions, not over-selected for lean deposition and trained to be handled are easier to handle and more resilient to the effects of physical stress prior to slaughter. However, the effects on meat quality are not clear. The production of entire males and immunocastrates can be a valid alternative to surgical castration, provided specific practices are applied to limit aggressiveness in mixed group situations and the risk of bruised carcases. Recommendations for the transport and handling of heavier slaughter pigs must be adapted to improve ease of handling and reduce transport losses, aggressiveness and fatigue-related meat quality defects. The response of pigs to pre-slaughter physical stress and feed deprivation can be affected by ractopamine dietary supplementation, feed composition and feeding regime. The objective of this paper is to overview the effects of on-farm producer-controlled factors on pigs’ response to pre-slaughter handling and meat quality, and environmental, social and economic sustainability.HIGHLIGHTSOn-farm factors impact pig lossesOn-farm factors impact ease of handlingOn-farm factors impact food safety; effects on meat quality are unclear
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".