Survival analysis of sow longevity and lifetime reproductive performance – Review

Soltész, Angéla; Hunyadi, Ágnes Baginé; Kusza, Szilvia; Balogh, Péter

doi:10.34101/actaagrar/70/1821

Cited by 3 publications

(3 citation statements)

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“…In our study, the culling rate of sows from the largest litters was considerably lower after rearing litters one, two, three, and four compared to the sows from small litters. Soltész et al [43] have recognized that longevity should be treated comprehensively and so their study focused not only on the effect of genetic factors on this trait, but also on the management system in the reproductive sector, the culling system, the housing conditions, and the feeding program. A breeder, as reported by Engblom et al [28], when deciding to cull a sow pays attention mainly to the number of litters produced, her reproductive status, health status, and herd structure.…”

Section: Discussionmentioning

confidence: 99%

The Effect of the Size of the Litter in Which the Sow Was Born on Her Lifetime Productivity

Warda¹,

Rekiel

Blicharski³

et al. 2021

Animals

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Improvement of lowly heritable traits is difficult, efforts must be made to take full advantage of the available information sources to improve them. The objective of the study was to determine the effect of the size of the litter in which the sow was born on her lifetime reproductive performance. Data on 22,683 litters were used to analyse the lifetime reproductive performance of 5623 Polish Large White sows. The sows from small litters (≤9) were on average the oldest at first farrowing, had the shortest herd life, the smallest number of litters, and the smallest sized litters (p ≤ 0.01). A positive relationship was established between the mean number of offspring born per litter and size of the litter in which the sow was born (p ≤ 0.01). For a sow to produce at least seven piglets per 100 days of reproduction, gilts from litters of at least 12 piglets should be selected for breeding.

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Section: Discussionmentioning

confidence: 99%

The Effect of the Size of the Litter in Which the Sow Was Born on Her Lifetime Productivity

Warda¹,

Rekiel

Blicharski³

et al. 2021

Animals

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“…In animal science, the most obvious application possibility is to analyze longevity (Baginé Hunyadi et al, 2016a;Soltész et al, 2016;Török et al, 2021) where longevity is describing productivity and usually covers the period between first parturition and culling. Longevity has high economic importance as culling of the animals is involuntary and related to developing certain diseases or leg problems (Balogh et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Application possibilities of survival analysis for time-to-event data in animal science

Bárdos

Benedek

Kodak

et al. 2022

AAK

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The application efficiency of several statistical methods was tested based on an open field behavior test of mice. The examined trait was the duration time until the animals approached the experimenter’s hand. The available time was fixed in 300 seconds. There were monitored 80 mice belonging to two different species of the Mus genus in equal proportion. Besides, male and female and young and adult animals have also represented the evaluated groups in equal proportions. The data of the examined trait was analyzed with Generalized Linear Models, Kaplan-Meier survival curves, and with Cox Proportional hazard model. The applied statistical procedure provided completely discordant results. According to the GLM results none of the examined factors (species, sex, and age) had significant effects on the examined variable. On the contrary, all factors proved to be significant using a procedure based on the survival analysis. Kaplan-Meier survival curves indicated a higher proportion of individuals successfully approaching the experimenter’s hand in all of the compared groups representing different species sexes and ages, respectively. The estimated Cox regression coefficients were significant indicating the significant effects of the species, sex, and age on the investigated trait. Based on the estimated Hazard ratios the probability that in the next time unit a successful approach of the experimenter’s hand would occur is three times more likely for one species than the other, twice as much for males and the juveniles than for the females and for the adults. Based on the present study it could be concluded that the successful approach of the experimenter’s hand by the mice is clearly “time to event data” thus it is suitable to be analyzed with survival analysis procedures. It could be concluded that applying conventional GLM was not adequate because due to the lack of the successful approach and approach times the censored data should not be used and thus the sample size would largely be reduced.

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“…To investigate what is necessary for (farm-specific) prediction of survival traits to be viable, it may be worthwhile to predict on a different livestock species. Survival traits in sows are similar to survival traits in dairy cows: both traits combine animal-specific factors like production and fertility with farm-characteristics and farm management factors (Soltész et al, 2016;Moeller et al, 2019) and there is evidence that culling policies could be improved through modelling in both species (Bergman et al, 2018). Similar to dairy cows, existing models created to assist replacement policies do not include predictions of survival traits like number of parities reached (Plà, 2007;.…”

Section: Discussionmentioning

confidence: 99%

Predicting survival in dairy cattle using machine learning

Heide¹,

Maria²

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VAN DER HEIDE. (2020). Predicting survival in dairy cattle using machine learning. PhD thesis, Wageningen University, the Netherlands A lthough cows can live to be twenty years old, the average lifespan for a dairy cow is only five to six years. Improving the lifespan of dairy cows would have several benefits such as increasing farm profitability and reducing the environmental impact of milk production. However, the complexity of survival makes it difficult to improve this trait in practice. In this thesis, I proposed using phenotypic prediction of survival to select young cows for the dairy herd, improving survival through increased lifespan of selected cows and better heifer management. The aim of this thesis was to investigate if it was possible to predict survival phenotype accurately enough to be of use in selection. I investigated three different methods to predict survival: multiple logistic regression, random forest and naive Bayes. In chapters two to four of this thesis I predicted the survival trait "survival to second lactation" using all three aforementioned methods. In chapter five, I predicted the survival trait "number of parities reached" using only the random forest method. Random forest and naive Bayes proved the best methods for predicting survival to second lactation, although predictive performance overall was low. The correlations between predictions for individual cows were much lower than expected, which indicated that the models predicted individual cows differently. Therefore, in chapter four I investigated if combining the results into an ensemble could improve predictive performance. An ensemble using multiple logistic regression resulted in the largest increase in performance, although none of the explored ensemble methods improved performance consistently across datasets. I further investigated if there was a benefit in including genomic information or a farm-specific effect. In chapter two, I investigated the benefit of combining genomic and phenotypic information. Genomic breeding values especially improved the prediction of survival early in life, with breeding values for fertility and longevity remained informative even after first calving. In chapter five I tested several different methods to include a farm effect and described the advantages and disadvantages of the various approaches. The results of this thesis provide valuable insights in the challenges of predicting survival traits and the suitability of various (machine learning) methods for the prediction of survival in dairy cattle.

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Survival analysis of sow longevity and lifetime reproductive performance – Review

Cited by 3 publications

References 0 publications

The Effect of the Size of the Litter in Which the Sow Was Born on Her Lifetime Productivity

The Effect of the Size of the Litter in Which the Sow Was Born on Her Lifetime Productivity

Application possibilities of survival analysis for time-to-event data in animal science

Predicting survival in dairy cattle using machine learning

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