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Enregistrement W6981325144

The effects of sow grouping practices on production and mixing aggression

2023· dissertation· en· W6981325144 sur OpenAlexfundaboutno aff

Notice bibliographique

RevueUniversity Library (University of Saskatchewan) · 2023
Typedissertation
Langueen
DomaineVeterinary
ThématiqueAnimal Behavior and Welfare Studies
Établissements canadiensnon disponible
Organismes subventionnairesSwine Innovation Porc
Mots-clésAggressionLitterGestationStatistical analysisProductivitySocial behaviourLameness
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,090
Score d'incertitude au seuil0,178

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,001
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,020
Tête enseignante GPT0,245
Écart entre enseignants0,226 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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