The effects of sow grouping practices on production and mixing aggression
Bibliographic record
Abstract
As the Canadian swine industry transitions gestation housing from stalls to groups, it is important to understand the impact of different grouping practices on sow productivity and welfare. When sows are housed in groups, a social hierarchy is established through aggressive behaviour which can negatively impact production. Many producers are implementing dynamic groups and early mixing using precision feeding; however, there is potential for greater aggression and the consequences of this practice are not fully known.\nThis study compared the effects of three grouping treatments in gestation on sow productivity and aggression. Treatments included: Control (Con): sows housed in stalls for 35 days after insemination, then moved to static groups; Static (Sta): sows mixed into static groups 1-8 days after insemination; and Dynamic (Dyn): sows mixed into dynamic groups 1-8 days after insemination with monthly mixing (8-10 sows removed and replaced). Mixed parity sows and gilts were housed in groups of 25 per pen in three replicates per treatment. Body weight, body condition score and backfat thickness were recorded once at breeding and again when sows were moved to farrowing. Farrowing rate, litter characteristics and piglet birthweights were recorded. On the day of mixing, sow behaviour was video recorded for measurement of reciprocal and one-sided aggression. Skin lesions and lameness were scored before and after mixing, at ~day 63 of gestation, ~day 91 of gestation, and on the day of moving to farrowing. Hair samples were collected at 7- and 12-weeks post-insemination for cortisol analysis. Statistical analysis was performed in SAS 9.4 using mixed effects models and Chi-square analysis.\nGrouping practice did not have a significant effect on change in body weight, backfat thickness or body condition during gestation. Farrowing rates for Con, Dyn and Sta treatments were 81%, 88% and 62%, respectively (Chi sq p<0.001). There were no significant treatment differences for litter characteristics. At mixing, Sta sows had a higher frequency of reciprocal fighting in the first half hour (Chi sq p<0.001), than did Con or Dyn sows. However, during the 24 hrs following mixing, sows in the Con treatment received more lesions in total than did Sta or Dyn sows (means ±SEM: Con: 11.71 ±0.46; Dyn: 8.69 ±0.40; Sta: 9.09 ±0.41, p<0.01)). Lesion scores decreased significantly over time in all groups. Throughout gestation, Dyn sows had higher lesions overall and a higher incidence of lameness than either Con or Sta sows (p<0.001 and p=0.046, respectively). Although treatment had no effect on hair cortisol concentrations, parity group had a significant effect on concentrations at both timepoints with young sows having the highest concentration and mid parity sows the lowest (p=0.04, p<0.01, respectively).\nIn conclusion, Con and Sta sows appeared to be more aggressive at mixing while aggression in Dynamic groups appeared to be moderated due to the smaller number of unfamiliar sows introduced at each mixing event. Dyn sows had more lesions and increased lameness overall during gestation suggesting increased chronic aggression for dynamic sows, although the results were not severe enough to impact farrowing rate or litter quality. In conclusion, dynamic mixing may serve as a viable housing alternative for pork producers provided that the management strategies are implemented to mitigate the effects of ongoing aggression.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".