Genetic parameters for test-day milk yield, lactation persistency, and fertility in low-, medium-, and high-production systems in Kenya

Abstract

Genetic parameters for test-day milk yield, lactation persistency, and age at first calving (as a fertility trait) were estimated for the first 4 lactations in multiplebreed dairy cows in low-, medium-, and high-production systems in Kenya. Data included 223,285 test-day milk yield records from 11,450 cows calving from 1990 to 2015 in 148 herds. A multivariate random regression model was used to estimate variance and covariance components. The fixed effects in the model included herd, year, and test month, and age as a covariate. The lactation profile over days in milk (DIM) was fitted as a cubic smoothing spline. Random effects included herd, year, and test month interaction effects, genetic group effects, and additive genetic and permanent environmental effects modeled with a cubic Legendre polynomial function. The residual variance was heterogeneous with 11 classes. Consequently, the variance components were varied over the lactation and with the production system. The estimated heritability for milk yield was lower in the low-production system (0.04-0.48) than in the medium-(0.22-0.59) and high-production (0.21-0 60) systems. The genetic correlations estimated between different DIM within lactations decreased as the time interval increased, becoming negative between the ends of the lactations in the low-and medium-production systems. Low (0.05) to medium (0.60) genetic correlations were estimated among first lactation test-day milk yields across the 3 production systems. Genetic correlations between the first lactation test-day milk yield and age at first calving ranged from 0.27 to 0.49, 0 to 0.81, and −0.08 to 0.27 in the low-, medium-, and high-production systems, respectively. Medium to high heritabilities (0.17-0.44) were estimated for persistency, with moderate to high (0.30-0.87) genetic correlations between 305-d milk yield and persistency. This indicates that genetic improvement in persistency would lead to increased milk yield. The low to medium genetic correlations between test-day milk yield between production systems indicate that sires may be re-ranked between production systems. Therefore, we conclude that sires should be selected based on a genetic evaluation within the target production system.

Genomic evaluation of milk yield in a smallholder crossbred dairy production system in India

“…A very similar result was found for similar smallholder crossbred cattle in Kenya [8]. For purebred and crosses between Bos taurus dairy breeds in Kenya, Wahinya et al [19] also reported a lack of a typical lactation curve but it occurred at substantially higher production levels. This raises the question: to what extent is the response of the shape of the lactation curve to a lower input environment a function of the genotype of cow?…”

Genetic Parameter Estimation and Genome-Wide Association Study-Based Loci Identification of Milk-Related Traits in Chinese Holstein

“…Furthermore, the trend and range of additive genetic variations of MY during lactation (Figure 1A) were consistent with those of Japanese Holstein cows (Togashi et al, 2008). Many studies have shown that the cows with high MY have greater genetic variances than low-yield cows (Togashi et al, 2008;Wahinya et al, 2020). These higher additive variances indicate that the cows are more genetically diverse (Wahinya et al, 2020).…”

“…The additive genetic variances, permanent environmental variances, and the heritabilities of five milkrelated traits changing during lactation were assessed in this study (Figure 1), which revealed that the genes or the expression of genes that control these milk-related traits might differ along the lactation trajectory of cows (Wahinya et al, 2020). The scope of the additive genetic variances of the MY in this study (Figure 1A) was higher than that in cows in North Carolina and Portugal (Silvestre et al, 2005), but was in the same range as the cows in medium-and high-production systems in Kenya (Wahinya et al, 2020). Furthermore, the trend and range of additive genetic variations of MY during lactation (Figure 1A) were consistent with those of Japanese Holstein cows (Togashi et al, 2008).…”

“…A test-day model (TDM) can account for the persistence and the test-day effect on individual phenotypes, making it a powerful method to improve the reliability of EBV estimation (Schaeffer et al, 2000). The additive genetic variances, permanent environmental variances, and the heritabilities of five milkrelated traits changing during lactation were assessed in this study (Figure 1), which revealed that the genes or the expression of genes that control these milk-related traits might differ along the lactation trajectory of cows (Wahinya et al, 2020). The scope of the additive genetic variances of the MY in this study (Figure 1A) was higher than that in cows in North Carolina and Portugal (Silvestre et al, 2005), but was in the same range as the cows in medium-and high-production systems in Kenya (Wahinya et al, 2020).…”

“…The random regression test-day model has been widely applied to the genetic evaluation of traits that are measured repeatedly at different time points, as it simulates the environmental and genetic effects along the lactation trajectory, and provides higher accuracy (with a 6%-8% increase) than genetic evaluation based on the lactation model (using full or extended 305-day lactation records) in cows (Li et al, 2020). In addition, it is convenient to infer the total variance components of performance during the entire lactation period based on the variance components estimated from test-day records, without requiring individuals to have a record every day of the lactation period (Wahinya et al, 2020). Due to the goodness-of-fit and the low correlation among parameters, Legendre polynomials (LP)-especially the high-order polynomials-have generally been used to model the lactation curve of cows (Silvestre et al, 2006;Bignardi et al, 2009a).…”