Genetic Parameters Estimation of Milking Traits in Polish Holstein-Friesians Based on Automatic Milking System Data

Abstract

The automatic milking system (AMS) provides a large amount of information characterizing the course of each milking cow, which is not available in the conventional system. The aim of our study was to estimate heritability and genetic correlations for milk yield (MY), milking frequency (MF), and speed (MS) for 1713 Polish Holstein-Friesian primiparous cows milked in barns with an AMS. Daily heritability indicators estimated using second-order Legendre polynomials and Random Regression Models showed high variation during lactation, ranging 0.131–0.345 for MY, 0.153–0.322 for MF, and 0.336–0.493 for MS. The rates of genetic correlation between traits ranged: 0.561–0.929 for MY-MF, (−0.255)−0.090 for MF-MS, (−0.174)−0.020 for MY-MS. It is possible to carry out effective selection for milking speed, which provides an opportunity to increase the number of cows per milking robot, and thus increase the profitability of production in the herd. The results proved that selection for milk yield and daily milking frequency is also feasible. The research showed a high, positive genetic correlation between milking frequency and milk yield, which allows us to conclude that preferring breeding cows with a natural tendency to frequent visits to the milking robot should indirectly improve the genetic basis of milking.

Precision Livestock Farming Research: A Global Scientometric Review

“…In PLF, a large amount of monitoring data requires processing, and deep learning models can handle massive data, quickly and accurately detecting animal behaviors and anomalies, automatically diagnosing animal diseases, predicting potential diseases, and more [ 92 , 93 ]. The next term is “automatic milking”, the automatic milking system provides a detailed description of the milking process for each cow, recording many parameters of the milking process, which can help improve milk yield and quality [ 94 ]. The most recent bursts of keywords suggest the future direction of research.…”

Genetic parameters and genome-wide association for milk production traits and somatic cell score in different lactation stages of Shanghai Holstein population

“…To evaluate the genetic parameter of milk production traits, we collected the test-day records from the farms of Shanghai Bright Dairy and Food Co., Ltd. from primiparous cows born between 1995 and 2020 with the regular and standard performance of DHI. In total, there are 1,968,589 records for the first lactation of 305,031 cows from 260 farms with the following criteria ( Aerts et al, 2021 ; Mbuthia et al, 2021 ): 1) age at first calving between 19 and 37 months; 2) test day from 5 to 305 DIM, of which only 12% records out of the range; 3) milk yield of 1.0–65 kg, fat percentage of 0.5–8.5%, protein percentage of 0.5–7.5%, SCC less than 2 million cells per milliliter ( Yang et al, 2013 ); 4) a minimum of three test-day records were required for a cow observation to be included in the analysis ( Soumri et al, 2020 ), of which one was before DIM 45 ( Bignardi et al, 2009 ); 5) the calving date was required to be before December 2019 so that all cows had the opportunity to finish the complete first lactation. A summary of data set used in this analysis is given in Table 1 .…”

Comparison of Random Regression Models with Different Order Legendre Polynomials for Genetic Parameter Estimation on Race Completion Speed of Arabian Horses

“…The −2logL, Bayesian information criterion (BIC), Akaike information criterion (AIC), Log likelihood values and Residual Error Variance (RV) were used to compare the random regression models [ 18 , 22 , 27 , 28 ]. The fitting compatibility of the random regression models was also examined regarding the eigenvalues of the covariance matrices [ 29 ].…”