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Record W7005800991

Risk Factors for Sow Mortality in Canadian Swine Herds

2022· dissertation· en· W7005800991 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldVeterinary
TopicAnimal Behavior and Welfare Studies
Canadian institutionsnot available
Fundersnot available
KeywordsCullingBiosecurityHerdRisk assessmentMortality rateAnimal husbandry
DOInot available

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.022
GPT teacher head0.242
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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