Genetic and environmental factors influencing mortality up to weaning of Bruna dels Pirineus beef calves in mountain areas. A survival analysis1

Abstract

Survival analysis techniques were used to analyze survival up to weaning of beef calves in the Pyrenean mountains areas of Catalonia, Spain. The Kaplan-Meier curve showed that the survival experience was not constant throughout the lactation period, as the mortality rate was more pronounced during the first month of life. The proportional hazards model analysis showed that several factors influenced the instantaneous mortality rate, with the herd-year effect having the strongest influence. Calves born in the first part of the breeding season, from September to February, had the lowest mortality risk (P < 0.001), showing that mortality risk increases as births accumulate. Calves from cows younger than 1,300 d of productive life had a higher risk of mortality (P < 0.05). Unassisted calvings presented the smallest risk of mortality, and mortality risk increased up to five times as birth became more difficult (P < 0.001). This risk also tended to increase slightly when calf birth weight was small (P < 0.10); for bigger calves, no increase of risk was detected, probably because calving difficulty was included in the model. These results suggest the need for improving the environment in the second part of the breeding period and paying more attention to births from younger cows. The survival curve fitted a parametric piecewise exponential function very well, with cut points at 16 and 32 d. The lower risk corresponded to the period of 33 to 180 d, the risk for the periods 17 to 32 d and 1 to 16 d being multiplied by 7 and 26, respectively. Confirming the robustness of the Cox model, the relative risks estimated for the different factors under this piecewise exponential model or a Weibull time-dependent model were similar to those reported above, as well as to those estimated under a frailty model, including the sire as a random effect. The modal estimates of sire variance under different baseline functions were close to 0.3, although the standard errors were very large. At weaning, the heritability estimate in the binary scale reached a value of only 0.037 because the survival at weaning was very high (96.9%) in this population. Nevertheless, in populations with a higher mortality, the inclusion of survival to weaning in the breeding objective might be justified. Overall, these results show that survival analysis is a powerful tool to analyze the mortality curve until weaning of beef calves.

Competing risk analyses of longevity in Duroc sows with a special emphasis on leg conformation

“…This peculiarity led to high censoring percentages when competing risk analyses were performed. More specifically, censoring percentage ranged between 81.09% (LP) and 93.19% (DE), these values being clearly higher than the censoring percentages reported in other studies that focused on sow longevity (Tarré s et al, 2006a and2006b) although similar (Casellas et al, 2004(Casellas et al, and 2007 or smaller (Tarré s et al, 2005) than the ones obtained in young pigs or other species. Note that the analytical power of survival analysis substantially depends on censoring percentage (Vukasinovic et al, 1999;Yazdi et al, 2002).…”

“…Survival functions are shown in Figure 1a. Note that data sets for competing risk analyses showed a high censoring percentage, similar to the values reported in other analyses (Casellas et al, 2004 andTarré s et al, 2005). Although this phenomenon implies a partial loss of information and reduced analytical power, the obtained estimates must be bias free if null correlation holds between culling causes (Allison, 1995).…”

“…For these 587 purebred Duroc sows, pedigree was extended up to four previous generations, including 31 sires with daughters with longevity data. Heritabilities for each specific cause of sow failure were calculated by formulas developed by Ducrocq (2001), Yazdi et al (2002) and Tarré s et al (2005). All computations were performed using the Survival Kit package (Ducrocq and Sö lkner, 1998).…”

“…Depending on the definition applied, these estimates provided respective heritabilities of 0.024 and 0.083 (Ducrocq, 2001), 0.008 and 0.017 (Yazdi et al, 2002), and 0.008 at 640 days and 0.017 at 645 days on the binary scale, respectively (Tarré s et al, 2005).…”

Comparison between linear and proportional hazard models for the analysis of age at first lambing in the Ripollesa breed

“…Note that both approaches provide similar estimates and departures must be partially attributable to the absence of systematic, permanent and genetic factors when computing the baseline hazard function under model PWPH3; the Kaplan-Meier estimate is computed from raw AFL data and all additional sources of variation are implicitly included. The need for this kind of additional modeling of the standard Weibull baseline function has been previously demonstrated on longevity (Tarrés et al, 2005;Casellas et al, 2008) and fertility data (Casellas and Bach, 2012), although this should not be viewed as a weakness of the survival analysis techniques but a remarkable advantage in terms of flexibility of the PWPH (Casellas, 2007) model. Systematic and genetic influences on AFL Although three systematic effects were included in the analytical model, only the season of birth of the ewe had relevant influences on the AFL under PWPH3 parameterization.…”