Elsevier

Livestock Science

Volume 213, July 2018, Pages 54-60
Livestock Science

Heterogeneity of variance for milk, fat and protein yield in small cattle populations: The Rendena breed as a case study

https://doi.org/10.1016/j.livsci.2018.05.002Get rights and content

Highlights

  • A small dual purpose breed was taken into account in the study.

  • Different productive groups of herds were identified via cluster analyses.

  • Heritability values was greater in high productive than in medium productive farms.

  • Genetic correlations between productive groups were all high and positive for all yield traits.

Abstract

The aim of this work was to study the possible heterogeneity of variance for productive traits in the small Rendena cattle population. The herds were divided into two productive levels (medium and high) based on average milk yield recorded in each farm. A total of 171,104 test-day records of milk, fat and protein yields belonging to 10,430 cows were used to estimate genetic parameters among groups in separated analysis that accounted for primiparous cows only or for up to the third lactation animals (whole dataset). The (co)variance components were greater in high than in medium productive levels for both milk, fat and protein yields, both in the primiparous dataset and in the whole dataset. Heritability for all yields traits in the medium productive level was lower (0.160, 0.134 and 0.137, resp.) than in the high productive level (0.292, 0.230 and 0.234) and on average greater when primiparous cows were analysed alone (from 0.025 to 0.045 in medium and high productive group, resp.). However, the genetic correlations between productive groups resulted greater than 0.965 for all productive traits and in both datasets analysed. The rank correlation between EBVs of bulls that had daughters in both groups was 0.99, but a significant deviation from the theoretical frequency expected in medium and high productive groups was observed in the number of top cows. This may be related to the heterogeneity of variance. This study suggests the need for a correction method for the heterogeneous variance in the small cattle breed used as a case study, particularly in the selection of best cows that are more susceptible to biases in EBVs.

Introduction

Small local breeds have gained increasing interest in the maintenance of genetic diversity within the existing animal genetic resources (Ollivier and Foulley, 2005). The survival of the small local populations is mainly attributable to their spread in marginal areas and to factors related to traditional rural culture (Gandini and Villa, 2003). Native cattle breeds, especially dual purpose breeds, have lower productions than cosmopolitan breeds, and they are, therefore, economically less competitive. However, they have maintained important secondary characteristics of hardiness and adaptability to the environment, longevity, good fertility, and resistance both to disease and to stress (Biscarini et al., 2015). The breeding goal in many small local populations is to maximize genetic gain trying to limit of the annual inbreeding rate that, according to Woolliams (1994), should be less than 1% per generation. Moreover, selection goals of these small populations are also based on the maintenance of a good compromise between the improvement of production performance (both milk and meat) and the preservation of the functional traits that distinguish them as compared to cosmopolitan breeds (Biscarini et al., 2015).

From a practical point of view, selection in small populations must take into account some technical problems linked to the population structure. Due to their adaptability to different environments, these populations may have different breeding areas, herd size, farming systems and feeding strategies, as well as production levels. In these situations, an effective genetic improvement must consider both the small population size and the heterogeneity of breeding environments that may differ largely, causing heterogeneity of variance between environments and/or farms (Brotherstone and Hill, 1986, Weigel et al., 1993, Ibanez et al., 1996, Costa, 1999, Fuerst-Waltl et al., 2013). Numerous studies have actually reported genetic, environmental and residual variances and, consequently, the heritability of production traits heterogeneous between environments and farms (Hill et al., 1983; De Veer and Van Vleck, 1987; Boldman and Freeman, 1990, Dong and Mao, 1990). According to some authors, there is a positive connection between the increase in production levels and the increase in variance components and heritability. For this reason, to cluster the herds on the mean yields could be considered to be an effective way of defining environments that can potentially show heterogeneity of variance, as a number of authors reported (De Veer and Van Vleck, 1987, Boldman and Freeman, 1990, Dong and Mao, 1990). Falconer (1952) observed that when the same trait is expressed in two different environments, the genotype-environment interaction may be exhibited in terms of a genetic correlation. Robertson (1959) suggested a genetic correlation of 0.80 between the same trait in different environments as a threshold limit to detect the occurrence of re-ranking and therefore eventually suggesting a possible presence of variance heterogeneity. In actual fact, ignoring heterogeneous variances reduces the accuracy of the estimated breeding values (EBV), resulting in inappropriate selective choices that could affect the genetic progress for a given trait (Hill, 1984, Vinson, 1987). To mitigate the problem of heterogeneous variance, different procedures have been suggested and implemented. Among the suggested methods, the adjustment of production data before genetic evaluation has been proposed by Wiggans and VanRaden (1991). Alternatively, other methods that consider models accounting directly for the effect of the heterogeneity of variance have been put forward (Foulley et al., 1990, Meuwissen et al., 1996, Robert-Granié et al., 1999).

Considering that the productive environments can be different both within and between breeds, detailed studies on the heterogeneity of environments could be useful for a correct breeding plan to be adopted for any small cattle population. In the context of the Italian local breeds, the Rendena is a small dual purpose cattle breed selected for milk and meat productions (Mazza et al., 2014). This breed has been raised both in mountain and in plain with similar numbers of heads in both the environments. There, the management of farms and the feeding strategies are different and associated with the area's resources; in the mountain, for example, the farming system is traditional (i.e., tied animals fed hay and concentrate) and based on the practice of the alpine pasture for the whole herds during the summer season. However, in the plain, the rearing system of Rendena cattle is more intensive (i.e, feeding system mainly based on the use of corn silage), with summer alpine pasture that is common for young cattle only. Moreover, bull sires and bull dams selected annually seem to come from a narrow group of farms, and breeders who complain about this problem are well known to the breeders’ association that is a charge of the selection program. Therefore, an investigation into possible between-herd variance heterogeneity due to the small population size could be useful for the Rendena cattle breed. It could also be of general interest to other small dual purpose breeds under selection.

The objectives of this study were to estimate variance components, heritability and the genetic correlations for milk, fat and protein yields between different environments defined for the Rendena breed. Rank correlations between EBV of bulls simultaneously estimated in different environments and the probability for the goodness of fit in the distribution of top cows from the different environments were also analysed as possible consequences of the correlation between environments and of the variance heterogeneity problem.

Section snippets

Subject of the study

The Rendena breed is a small cattle population accounting for about 4,000 registered cows in the herd book, reared in the North-east of Italy (i.e., Trentino Alto Adige and Veneto regions). Rendena cattle is characterized by small to medium body size, good fertility, i.e., an open period of about 116 days on average, and good longevity, amounting to approximately 3.3 lactations per cow (ANARE, 2016). The main characteristic of the breed is its rusticity, namely its ability to adapt itself to

Results

Table 2 shows some descriptive statistics of the considered milk yield traits divided by parity (up to the 3rd lactation) and belonging to the two productive levels identified in the whole dataset of Rendena breed. The mean daily milk, fat and protein yield increased with the lactation number both in medium (i.e., +1.9 mean kg for milk, and +0.06 mean kg for fat and protein comparing 2nd and 3rd parity cows with primiparous) and in high (i.e., +3.3 mean kg for milk and 0.10 mean kg for fat and

Discussion

Grouping the herds by production level was a simple way of investigating the possible effects of heterogeneity of variance in the genetic evaluation of production traits (Boldman and Freeman, 1990). In the present study, the clustering due to the productive level of farms was then analysed in order to describe the management practices in Rendena breed. In this work, genetic, permanent environment and residual variances estimated in medium and high productive levels were lower and higher,

Conclusions

The results of this study revealed differences in variance components and heritability for milk, fat and protein yields between medium and high productive herds in the Rendena breed. These differences resulted basically not affected by the use of different datasets containing up to three lactations or the primiparous cows, only. Variance components and heritability values increased as the production increased, and consequently, the selection would focus on animals from high producing herds

Conflict of interest

None. We declare that we have no financial or personal relationships with other people ororganizations in Italy countries who could inappropriately influence our work. Additionally, ourwork is focused only on quantitative genetics and the data provided by a national breedersorganisation. Thus, the data were not used for any commercial purposes.

Acknowledgments

The authors are grateful to the Italian Breeders Association of Rendena cattle (ANARE), Trento, Italy, for providing data and to the two anonymous Reviewers for the previous suggestions and for the careful work performed on our manuscript.

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