# Genetic Evaluation of Milk Production Traits in the Serbian Saanen Goat Population

## TL;DR

This study compares two genetic models for evaluating milk production traits in Saanen goats in Serbia, finding that the more advanced Animal model provides more accurate breeding value estimates.

## Contribution

The study demonstrates the superiority of the BLUP-Animal model over the traditional Sire model for genetic evaluation of milk traits in Saanen goats.

## Key findings

- The Animal model provided more accurate breeding value estimates compared to the Sire model.
- Heritability estimates for traits like milk yield and composition were obtained using both models.
- Breeding values from the two models were closely correlated but the Animal model is more reliable for selection.

## Abstract

The estimation of breeding values in the goat population in Serbia is not systematically implemented, resulting in the genetic parameters of economically important milk traits remaining largely unknown. It is well established that the effectiveness of selection depends on the accuracy of heritability; therefore, it is essential to apply modern methods and models for estimating the heritability of milk production traits in goats. In such estimations, as well as in the estimation of breeding values for milk traits in goats, the Sire model is most commonly used in genetic evaluations at the national level. Although traditionally applied, the Sire model has limitations, as it does not fully utilize all available pedigree and performance data. In this study, milk production traits were analyzed in a population of Saanen goats using both the Sire model and the more advanced Animal model. The Animal model, which incorporates information from all known relatives, proved to be more accurate and informative in the estimation of breeding values. The results indicated strong correlations between the two models; however, the Animal model provided more accurate and reliable breeding value estimates, making it more suitable for practical use in breeding programs. These findings underscore the importance of applying more modern and advanced methods in the genetic evaluation of milk production traits in goats, particularly for economically significant traits such as milk yield and composition.

Within the framework of this study, a genetic evaluation of milk traits was conducted in the Saanen goat breed. The focus of the research was placed on the application of more advanced models for estimating heritability and breeding values of economically important milk traits. The study included 670 Saanen goats and a total of 2155 lactations between 2010 and 2021 on a single farm located in the Autonomous Province of Vojvodina. The milk production traits included total milk yield per lactation (TMY), milk fat yield (FY), protein yield (PY), and the content of milk fat (MF) and protein (PC). The fixed effects included in the Sire and Animal models were as follows: kidding season, type of kidding, year of kidding, and lactation number. The permanent environmental effect of the doe and the animal’s additive genetic effect were considered as random effects. In the Animal model, the estimated heritability values for the traits were: 0.2216 (TMY), 0.2564 (FY), 0.2556 (PY), 0.3977 (MF), 0.2864 (PC). The heritability estimates obtained using the sire model were slightly higher: 0.2742 for TMY, 0.3256 for FY, 0.3855 for PY, 0.3925 for MF, and 0.3502 for PC. The estimation of breeding values for bucks was performed using both the Sire model and the Animal model. Breeding values for the bucks derived from the two models showed a close relationship, with correlations ranging from 0.85 for TMY to 0.90 for PC. The results of this study confirm that the application of the BLUP-Animal model provides a more accurate estimation of breeding values and represents a reliable basis for the selection of the Saanen goat breed. The findings from this study provide a practical basis for enhancing breeding programs and developing an effective strategy for genetic improvement of milk production in the population.