Genetic aspects of Wood's lactation curve parameters in Jersey crossbred cattle using Bayesian approach

The study was undertaken to estimate the genetic parameters of lactation curve parameters of Wood's function in Jersey crossbred cattle using the Bayesian approach. Data on 33,906 fortnightly test day milk yields of 1,718 lactation records of Jersey crossbred cows, maintained at the ICAR-National Dairy Research Institute in West Bengal, were collected over a period of 40 years. The lactation curve parameters including 'a' (initial milk yield after calving), 'b' (ascending slope up to peak yield) and 'c' (descending slope after peak yield) and lactation curve traits, peak yield (ymax), time of peak yield (tmax) and persistency of milk yield (P) of individual cow for each lactation were estimated using the incomplete gamma function (Wood's model) by fitting the Gauss-Newton algorithm as an iteration method using PROC NLIN procedure of SAS 9.3. Variance components and genetic parameters of lactation curve parameters/traits were estimated by a repeatability animal model using the Bayesian approach. Estimates of heritabilities were found to be 0.18 ± 0.05, 0.09 ± 0.03 and 0.11 ± 0.04 for parameters 'a', 'b' and 'c', respectively and 0.24 ± 0.05, 0.12 ± 0.04, and 0.15 ± 0.05 for ymax, tmax and P, respectively. Repeatability estimates were 0.31 ± 0.03, 0.21 ± 0.04 and 0.30 ± 0.04 for parameters 'a', 'b' and 'c' respectively and 0.39 ± 0.03, 0.24 ± 0.03 and 0.37 ± 0.03 for ymax, tmax and p, respectively. Genetic correlations among lactation curve parameters/traits ranged from -0.75 to 0.95. Existence of genetic correlations among lactation curve parameters/traits indicated substantial genetic and physiological relationships among lactation curve parameters/traits of Jersey crossbred cattle.

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

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 multiple-breed 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.

Different aspects of lactation persistency in dairy cows

Lactation persistency (cow’s ability to maintain milk production after reaching its peak) is a very important economic characteristic in the dairy cattle production system. Different definition and functions for describing and measuring of this trait were proposed by researchers. The random regression model using Legendre polynomial was one of the common and effective methodologies for evaluation of persistency in the last decade. Several factors affecting persistency such as different characteristics of lactation curve, environment factors, reproduction traits and health status of the dairy cow. Based on different studies the heritability of this trait was low to medium and negative or positive amount of genetic correlation between persistency and total milk yield in dairy cattle is attributed to persistency measures and method of data analysis. Persistency is related with low and later peak yield and selecting cows for peak yield will improve persistency and lactation curve traits. Analysis of relationships between persistency and other functional traits show signs that genetic improvement for persistency is possible and favorable. Different aspects and relationships of persistency with various lactation and other functional traits in dairy cows are reviewed in this article.