Risk Factors for Sow Mortality in Canadian Swine Herds
Notice bibliographique
Résumé
With increasing sow mortality and involuntary culling rates on commercial farms over the past two decades, more producers, researchers, and experts in swine health are looking into the causes and risks of sow mortality. In addition to increasing agricultural expenses and biosecurity risk for animals already in the herd, sow deaths also indicate compromised animal welfare. Understanding what is causing the high rates of sow mortality and culling in Canadian herds will provide a scientific basis for management recommendations to increase sow longevity, will benefit producers by reducing costs, and will help to support good animal welfare. The overall goal of this project was to identify the risk factors and causes of sow mortality in Canadian sow herds. Specific objectives included reviewing previous literature on sow mortality, examining causal factors related to housing, management, and genetics, and evaluating culling and removal reasons on Canadian commercial swine herds, with the aim to evaluate the risk factors of sow mortality. A simplified sow necropsy procedure was also developed to use on-farm for assessment of major causes of death. Chapter 2 of this study collected information regarding sow culling and mortality in Canadian swineherds from producers through an online survey. One hundred and four producers provided complete responses to 49 questions about sow management, culling and mortality factors. Data were analyzed in SAS 9.4 using Pearson’s correlation coefficients (r, PROC CORR) and regression analysis (PROC REG and PROC GLM). The survey results found that in 2019, the average sow replacement per year was 44% (range: 5% to 65%, SD= 9.88%), average sow mortality per year was 5.7% (range: 2% to 20%, SD= 3.9%), and average parity of the herd was 3.5 (range: 1.6 to 9, SD= 1.22). The two most common reasons for sow removal (i.e., sum of culling, euthanasia, and death) were ‘old age’ (72%) and ‘poor reproductive performance’ (61%). Average sow mortality (%) per year showed a significant positive correlation with herd size (P= 0.001) and number of barn staff (P <0.001), with the number of barn staff accounting for 17% of the variation in average sow mortality per year (P <0.001). It was found that 65% of the survey participants did not perform necropsies on dead sows. Survey participants from Quebec (QC) and Saskatchewan (SK) were then invited to participate in the next experiment (Chapter 3), which consisted of a farm visit with live sow observations and a necropsy demonstration (in SK). For chapter 3, thirteen farms were visited, with a total of 1,389 live sows observed (8 farms in QC: 945 sows, and five farms in SK: 444 sows). Live sows were evaluated based on animal-based measures (body condition score [BCS], lameness, and injury scores), and each farm’s annual sow mortality and parity were also recorded. The study results show that BCS 3 (ideal condition, where ribs, hips, and backbone can be palpated with firm pressure but cannot be observed visually) was the most prevalent in both provinces (47.9% in QC and 61.5% in SK). Of the 1,389 sows observed, 1,205 (88.8%) had no signs of lameness and 1,003 had no signs of injury (72.2%). Data from chapter 3 were analyzed in SAS 9.4 using a weighted Pearson’s chi-square statistic to determine relationships among variables. Sows in farrowing had the highest proportion of thin sows (BCS2) (20.9%) while sows in breeding had the highest prevalence of lameness score 1 (13.9%), and sows in stall gestation had the highest incidence of injury score 1 (30.8%) versus other stages. Saskatchewan in comparison to QC had a higher percentage of thin sows (24.8% vs 18.2%) and sows with injury score 1 (40.3% vs 14.7%). Conversely, Quebec had a higher proportion of lameness score 1 than SK (12.8% vs 3.8%), with sows showing signs of locomotor difficulties in at least one leg and reluctance to bear weight on the same leg. Herd size affected average sow mortality and parity. Larger herds (>1,000 sows) had a higher incidence of ‘high’ sow mortality rates (Avg. mort. >6%) than mid-size herds (40% vs 32.3%; P <0.03). Smaller herds (<500) had a higher proportion of old sows (parity >5) than larger herds (>1,000 sows) (27.6% vs 7% respectively; P <0.001). In addition to the sow observation in SK, a necropsy of four dead sows on different farms was performed to demonstrate the protocol developed for the study. In conclusion, the average mortality rate of the Canadian herds included in the survey was 5.7%, having a moderate average sow mortality rate according to the study. Herds with moderate (avg. mortality >4 – <6) and ‘high’ (avg. mortality >6) average sow mortality were represented by large herds (>1,000) which also showed the highest percentage of young sows (parity 0 – 2), conversely, smaller herds showed acceptable average sow mortality (avg. mortality <4) and higher average parity (>5).
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».