Genetic evaluation of lactation persistency and total milk yield in dairy goats

O H G B D Siqueira¹, R R Mota, H R Oliveira¹, D A S Duarte¹, L S Glória², M T Rodrigues¹ and F F Silva¹

University of Liège, Gembloux Agro-Bio Tech Faculty, TERRA Teaching and Research Center, B-5030 Gembloux, Belgium
rrmota@ulg.ac.be
¹ Department of Animal Science, Universidade Federal de Viçosa, Viçosa, Minas Gerais, Brazil
² Laboratory of Animal Science, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Centro de Ciências e Tecnologias Agropecuárias
Campos dos Goytacazes, Rio de Janeiro, Brazil

Abstract

Lactation persistency (LP) has been neglected over time in genetic evaluations of dairy goats. The main reason for this is the difficulty to infer about the lactation curve shape. However, some lactations models such as Wood seem to be appropriate to provide persistency estimates under biological viewpoints. The aim of this study was to fit the Wood lactation model as well as to calculate and evaluate LP as selection criteria in dairy goat breeding programs through genetic parameters estimates. A total of 23,265 first lactation test day milk yield observations from 900 animals were used. The Wood random regression model was primarily fitted to estimate the lactation curve parameters (a, b and c), and then LP and total milk yield (TMY). Posteriorly, a multi-trait animal model was fitted considering simultaneously LP and TMY.

The heritability estimates were 0.31 and 0.04 for TMY and LP, respectively. Based on the low LP heritability, selection based only on this trait might be inefficient. In conclusion, the results of this study suggests that selecting for high milk yields might result in high persistency since the genetic correlation between LP and TMY was moderate (0.39).

Keywords: genetic parameters, lactation curve, Wood model

Introduction

Lactation persistency (LP) has been neglected over time in dairy goats. There are few reports about LP in literature (Abdallah and McDaniel 2000). However, LP is economically important for milk production, since some advantages towards breeding programs have been reported (Dekkers et al 1996; Dekkers et al 1998). Animals with low LP might have high feed consumption, reproductive and health problems (Madsen 1975; Reents et al 1996; Grossman et al 1999; Harder et al 2006), which increase production costs.

Different definitions of LP have been reported, and the most common definition is the animal ability to maintain the milk yield after reaching the maximum daily milk yield (Cobuci et al 2004)”. Some methods to measure LP have already been reported (Sölkner and Fuchs 1987; Muir et al 2004; Cole and VanRaden 2006); however, the problem by using LP as selection criterion in animal breeding programs is the difficulty to infer about the lactation curve shape. In this context, the Wood model seems to be one of the most appropriate tools to estimate LP under biological viewpoints (Wood 1967).

Since genetic evaluations considering simultaneously total milk yield (TMY) and LP are scarce in dairy goats, the aim of this study was to fit the Wood lactation model as well as to calculate and evaluate LP and TMY as selection criteria for breeding programs through genetic parameters estimates under multi-trait approach.

Materials and methods

Data used in this current study included records of test day milk yield. After data consistency, a total of 23,265 first lactations from 900 Saanen and Alpine goats (including crosses) from the Universidade Federal de Viçosa, Brazil were used. The relationship matrix included a total of 1,283 animals with up to 3 previous generations for animals with phenotypic data.

The goats were kept in collective stalls under the free-stall system. They received feed composed of corn silage and hay, in addition to a concentrate mixture provided according to their nutritional requirements. The goats kidded between 2000 and 2014, and the milk yield records were obtained by mechanical milking. The animals were composed by Alpine and Saanen goats from three different genetic groups: group 1 (> 90 % Alpine), group 2 (> 90 % Saanen) and group 3 (< 90% Alpine or Saanen).

The lactation curve was estimated through the Wood model (Wood 1967) given by:

y _t = at ^b – exp ^ct

where, y_t is the test-day yield (Kg) at time t of lactation; a is a general scaling factor representing initial yield; b is the rate of increase to peak production; c is the rate of decline after peak production; and exp is the exponential term. Following the literature, we adopted the linearization of Wood model that can be described as follows:

ln (y_t) = ln(a)+ bln(t)-ct

A random regression model was fitted, considering as fixed effects, the covariate of age at first parity (range 317-1257 days), mean population, number of offspring (1 or 2), genetic group and contemporary groups (CG) defined by animals of the year and season (1 = April to September; 2 = October to March) of birth. Likewise, the additive genetic, permanent environment and residual (3 classes: 1-5 to 21, 2-22 to 56 and 3-57 to 280 days of lactation) efffects were fitted as random. The lactation persistency and total milk yield (LP and TMY) were then estimated for each animal by using a, b and c solutions. LP was obtained as functions of these mentioned parameters as

LP = c ^b-1

TMY of each animal was given by the sum the milk yield in each day. For the genetic evaluation, a multi-trait animal model was fitted considering as phenotypes LP and TMY records. The assumed animal model can be represented in matrix notation as follows:

y *= Zg + ɛ

where: y* are the LP and TMY observations; Z is the incidence matrix related to the additive genetic effects; g is the vector of additive genetic effect; and ε is the vector of random residuals. It was assumed that ɛ ~ N (0, σ²_ɛI) and g ~N (0, σ²_gA), being I the identity and A the pedigree-based relationship matrices. The Wombat software (Meyer 2007) was used to estimate the (co)variance components, genetic parameters and breeding values estimates for LP and TMY via Restricted Maximum Likelihood (REML) method.

The heritability (h²) and genetic correlation (r_g) were obtained, respectively, as follows:

where: σ²_g is the additive genetic variance; σ²_e is the residual variance; σ² _{g TMYLP} is the genetic covariance between TMY and LP; σ²_{g TMY} is the genetic variance of TMY and σ²_{g LP} is the genetic variance of the LP.

Finally, potential differences in re-rankings of animals for independent selection for TMY and LP were verified based on their estimated breeding values.

Results and discussion

The descriptive statistics (mean, minimum, maximum and standard deviations) of the coefficients for Wood parameters (a, b and c), LP and TMY are presented in Table 1. The means were 2.08 ± 0.71, 0.20 ± 0.14 and 0.005 ± 0.01 for a, b and c, respectively. In general, similar results were reported by Silva et al (2005) working with Saanen goats and using Bayesian inference. However, in this study, the parameter a was higher than the value reported by Silva et al (2005) for the lactation curve (2.08 vs. 1.13). The standard deviations for  LP, TMY, a, b and c were low and might indicate precise estimations.

The heritability estimates were 0.31 e 0.04 for TMY and LP, respectively. Guimarães et al (2006) have reported TMY heritability of 0.47 in Saanen and Alpine dairy goats. Heritability estimates for LP reported by Melo et al (2011) under different methodologies were equal to 0.07, 0.06 and 0.08; these values are slightly higher to the value estimated in our study (0.04). Higher heritability estimates for LP are desirable to further include this trait as a selection criterion in dairy goat breeding programs. Therefore, based on the low LP heritability, we can infer that there might be high influence of environmental effects on lactation curve shape. The selection based only on knowledge of the lactation curve shape, determined by its parameters (a, b and c ), might be inefficient.

The genetic correlation between LP and TMY was 0.39. This moderate and positive correlation between these traits indicates that selecting for high milk yields might result in high persistency. These results are higher (0.39 vs. 0.20) than those reported by Pesántez et al (2014) in Anglo Nubia x Creole.

The lactation curves from randomly selected animals are presented in Figure 1. In this figure, there are high and less persistent animals. It is noticeable that high persistent seemed to have more tenuous lactation curves than less persistent animals. Note that between these animals are high and less persistent and it is noticeable that high persistent animals presented more tenuous lactation curves than less persistent animals. Animals with low LP generally presented higher peaks of lactation and also steeper curves. Despite similar total milk yield among animals in Figure 1, animals with low persistency tended to end their lactation earlier. As previously mentioned, this might increase production costs, which is not desirable in a production system. According to Silva et al (2005), animals with higher lactation persistency are more productive and might be selected as parents of the next generations.

Figure 1. Lactation curves of animals randomly selected from the studied population

Finally, Table 2 presented the top 2.5% animals for TMY and LP. It can be seen that only two (2) animals (348 and 708) would be selected for both traits, if these traits were considered as independent selection criteria.

Conclusion

• Our results suggested that high milk yields might result in high persistency. However, the selection based only on knowledge of the lactation curve shape, determined by its parameters (a, b and c), might be inefficient.

References

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Received 3 April 2017; Accepted 11 May 2017; Published 2 July 2017

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