Pork carcass composition, meat and belly qualities as influenced by feed efficiency selection in sire and dam lines
Bibliographic record
Abstract
With the increased demand for lean meat and the high feed cost, the ideal animal with low input costs and excellent output quality is desired. Therefore, the selection of desired animals is crucial and could be made through estimated breeding values for feed conversion ratio (EBV-FCR), which is the genetic value of an animal and an estimation of the animal’s potential for specific traits. Thus, feed efficiency (FE) can be maximized as well as growth performance and carcass composition can be enhanced by using EBV. Although improving the pig’s efficiency would be profitable, concerns have been expressed about pigs selected for HFE because they may produce pork of inferior quality; however, contradictory results have been shown to date. In the current study, one hundred boars slaughtered at approximately 115 kg of body weight were used to study the influence of genetic lines (dam line vs. sire line) and EBV-FCR (low-, intermediate- and high-efficient groups) within the Large White breed on carcass attributes, meat, and belly qualities. At 96 h post-mortem, left carcass sides were evaluated for backfat depth and thickness (on first rib, last rib, and last lumbar), loin depth and area, subjective muscle colour and marbling, then fabricated into primals, and finally dissected into fat, bone, and lean. Boneless loins and skin-on boneless bellies were obtained from the right carcass sides. Loin pork chops (2.5 cm thick) were used for Warner Bratzler shear force (WBSF), cooking traits, drip loss, pH, and objective colour evaluation. Belly dimension (length, width and thick) and belly firmness (subjective belly firmness score and belly-flop angle) were measured. Slaughter weight was included as a covariate in the model due to the slaughter weight difference among genetic lines. No difference in carcass weight was detected among genetic lines (P > 0.05); however, the sire line had a greater loin area and loin depth and thinner fat depth than the dam line (P < 0.05), which favoured higher lean and lower trimmed fat proportions in the sire line (P < 0.01). For quality, genetic lines expressed minimal colour changes and drip losses (P < 0.05), with no differences in pH, marbling level and cooking traits (P > 0.05). On the other hand, regardless of genetic line, high-efficient animals presented the greatest loin area, the deepest loin, the thinnest back fat on the last rib and lumbar level (P < 0.05), the highest lean yield and the lowest proportion of trimmed fat (P < 0.01) compared with other efficient groups. For meat quality, efficient groups did not differ in pH, marbling level, drip loss, objective, subjective colour score, cooking traits and WBSF (P > 0.05). Based on the advantageous performance observed in most carcass yield traits, high-efficient animals offer a favourable response in greater loin and leaner animals without decreased meat and belly quality traits.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".