Estimation of Repeatability of Calving Ease in Canadian Holsteins

Klassen, Dana; Cue, R.I.; Hayes, J. F.

doi:10.3168/jds.s0022-0302(90)78665-1

Cited by 12 publications

(15 citation statements)

References 19 publications

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“…Dystocia at the previous calving, sire DCD, and sire breed were found to be significant by Mee et al (2011). Klassen et al (1990) and Mee et al (2011) both identified twin calving events as increasing the risk of calving difficulty. Our results differ for some of the previously reported risk factors.…”

Section: Discussionmentioning

confidence: 99%

“…The variables found to be significant in the present study's modeling process agree with previous findings. Klassen et al (1990), Bureš et al (2008), and Mee et al (2011) all included some measure of age, either by parity or primiparity. These age effects are likely to reflect some of the increased calving difficulty associated with small pelvic measurements reported by Bureš et al (2008).…”

Section: Discussionmentioning

confidence: 99%

“…Binary models comparing assisted calving and UC events in various breeds of dairy and beef cows have been created using logistic regression (Johanson and Berger, 2003;Bureš et al, 2008;Mee et al, 2011). A study of Canadian Holstein calving events performed least squares analysis to model 4 levels of calving difficulty (Klassen et al, 1990). However, few studies have evaluated the predictive ability of calving difficulty models.…”

Section: Introductionmentioning

confidence: 99%

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A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

Fenlon¹,

O’Grady

Mee

et al. 2017

Journal of Dairy Science

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The aim of this study was to build and compare predictive models of calving difficulty in dairy heifers and cows for the purpose of decision support and simulation modeling. Models to predict 3 levels of calving difficulty (unassisted, slight assistance, and considerable or veterinary assistance) were created using 4 machine learning techniques: multinomial regression, decision trees, random forests, and neural networks. The data used were sourced from 2,076 calving records in 10 Irish dairy herds. In total, 19.9 and 5.9% of calving events required slight assistance and considerable or veterinary assistance, respectively. Variables related to parity, genetics, BCS, breed, previous calving, and reproductive events and the calf were included in the analysis. Based on a stepwise regression modeling process, the variables included in the models were the dam's direct and maternal calving difficulty predicted transmitting abilities (PTA), BCS at calving, parity; calving assistance or difficulty at the previous calving; proportion of Holstein breed; sire breed; sire direct calving difficulty PTA; twinning; and 2-way interactions between calving BCS and previous calving difficulty and the direct calving difficulty PTA of dam and sire. The models were built using bootstrapping procedures on 70% of the data set. The held-back 30% of the data was used to evaluate the predictive performance of the models in terms of discrimination and calibration. The decision tree and random forest models omitted the effect of twinning and included only subsets of sire breeds. Only multinomial regression and neural networks explicitly included the modeled interactions. Calving BCS, calving difficulty PTA, and previous calving assistance ranked as highly important variables for all 4 models. The area under the receiver operating characteristic curve (ranging from 0.64 to 0.79) indicates that all of the models had good overall discriminatory power. The neural network and multinomial regression models performed best, correctly classifying 75% of calving cases and showing superior calibration, with an average error in predicted probability of 3.7 and 4.5%, respectively. The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

Fenlon¹,

O’Grady

Mee

et al. 2017

Journal of Dairy Science

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“…A small but real tendency to repeat prior calving assistance score has been shown, but not prior calf viability at birth (Thompson and Rege, 1984). For example, repeatability rates for gestation length (17%), prenatal growth rate (15%), birth weight (16%) and dystocia are low (6% to 14%), but that of calf viability at birth is even lower (2%) (Thompson and Rege, 1984;Klassen et al, 1990;Casanova et al, 1999).…”

Section: Discussionmentioning

confidence: 99%

Prevalence of, and risk factors associated with, perinatal calf mortality in pasture-based Holstein-Friesian cows

Mee

Berry

Cromie

2008

Animal

115

104

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Recent publications indicate that the prevalence of perinatal mortality has increased in some dairy industries and an increased proportion of this loss is not associated with the traditional risk factors for perinatal mortality. The objectives of this study were to establish the prevalence of perinatal mortality (calf death within 24 h of calving) in Irish dairy herds and to determine the current significance of putative risk factors in pasture-based management systems. A total of 182 026 records of full-term calvings from Holstein-Friesian dams served by artificial insemination (AI) sires of seven breeds in herds of 20 calvings or more per year were available from the Irish national breeding database over 4 years (2002 to 2005). The prevalence of perinatal mortality was 4.29% (7.7% in primiparae and 3.5% in pluriparae). The likelihood of perinatal mortality increased between 2002 and 2005 and was greatest in June and in winter. There was an interaction (P , 0.001) between the effect of calving assistance and parity with the effect of dystocia on perinatal mortality being greater in primiparae. The odds of perinatal mortality were greater in male (OR 5 1.12; P , 0.001) and in twin calves (OR 5 5.70-13.36; P , 0.001) and in dams that had perinatal mortality at the previous calving (OR 5 4.21; P , 0.001). The logit of the probability of perinatal mortality increased by 0.099 per unit increase in sire predicted transmitting ability (PTA) for direct perinatal mortality. The probability of perinatal mortality increased at an increasing rate in primiparae as animals calved at a younger age relative to the median age at first calving. The only herd-level factor examined, herd size did not affect the odds of perinatal mortality. These data indicate that the prevalence of perinatal mortality in this cattle population is similar to that in other pasture-based dairy systems worldwide. The putative exposures and attributes traditionally associated with perinatal mortality were associated with perinatal mortality in this pasture-based dairy cow population. The practical implication of these results is that as many of the significant risk factors are largely not under management control (year of calving, month of calving, twin calving, primiparity, previous perinatal mortality and foetal gender), herd owners must focus on the significant determinants under their control (age at first calving, sire genetic merit for direct perinatal mortality and both the extent of calving supervision and the degree of assistance), in order to reduce the prevalence of perinatal mortality and improve perinatal welfare.

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“…1999). In Canadian Holsteins calvings were easiest in the third‐parity cows and later, the dystocia score increased moderately as the cow age increased (Klassen et al. 1990).…”

Section: Specific Factors Affecting Dystocia In Cattlementioning

confidence: 99%

Factors Affecting Dystocia in Cattle

Zaborski

Grzesiak

Szatkowska

et al. 2009

Reprod Domestic Animals

108

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The paper reviews the various factors affecting dystocia in cattle. It is based mainly on the recent studies found in the literature of the subject but refers occasionally to some older papers as well. The factors are grouped into four main categories: direct factors, phenotypic factors related to calf and cow, non-genetic and genetic factors. The first group includes malpresentations and uterine torsion. The second one includes: calf birth weight, multiple calvings, perinatal mortality, cow pelvic area, cow body weight and body condition at calving, gestation length. The non-genetic factors are: cow age and parity, year and season of calving, place of calving, maintenance practises, disorders, calf sex and nutrition. Other non-genetic factors are the level of hormones in the periparturient period, in vitro production of embryos and embryo cloning. Finally, the genotypes of cow, bull and calf, inbreeding, muscular hypertrophy, selection and quantitative trait loci form the fourth group of genetic factors.

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Estimation of Repeatability of Calving Ease in Canadian Holsteins

Cited by 12 publications

References 19 publications

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

Prevalence of, and risk factors associated with, perinatal calf mortality in pasture-based Holstein-Friesian cows

Factors Affecting Dystocia in Cattle

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