Homogeneity and heterogeneity of variance components for milk and protein yield at different cluster sizes in Iranian Holsteins

Ehsaninia, Jamshid; Hossein-Zadeh, Navid Ghavi; Shadparvar, Abdol Ahad

doi:10.1016/j.livsci.2016.04.020

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…A pedigree file was constructed for all animals in the dataset. Detailed description of pedigree file was reported in a previous study (Ehsaninia et al ., 2016). Descriptive statistics for test-day productive records over the first three parities are shown in Table 1.…”

Section: Methodsmentioning

confidence: 99%

Estimation of genetic variation for macro- and micro-environmental sensitivities of milk yield and composition in Holstein cows using double hierarchical generalized linear models

Ehsaninia

Hossein-Zadeh

Shadparvar

2019

Journal of Dairy Research

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The aim of this study was to estimate genetic parameters for environmental sensitivities in milk yield and composition of Iranian Holstein cows using the double hierarchical generalized linear model (DHGLM) method. Data set included test-day productive records of cows which were provided by the Animal Breeding Center and Promotion of Animal Products of Iran during 1983 to 2014. In the DHGLM method, a random regression model was fitted which included two parts of mean and residual variance. A random regression model (mean model) and a residual variance model were used to study the genetic variation of micro-environmental sensitivities. In order to consider macro-environmental sensitivities, DHGLM was extended using a reaction norm model, and a sire model was applied. Based on the mean model, additive genetic variances for the mean were 38.25 for milk yield, 0.23 for fat yield and 0.03 for protein yield in the first lactation, respectively. Based on the residual variance model, additive genetic variances for residual variance were 0.039 for milk yield, 0.030 for fat yield and 0.020 for protein yield in the first lactation, respectively. Estimates of genetic correlation between milk yield and macro- and micro-environmental sensitivities were 0.660 and 0.597 in the first lactation, respectively. The results of this study indicated that macro- and micro-environmental sensitivities were present for milk production traits of Iranian Holsteins. High genetic coefficient of variation for micro-environmental sensitivities indicated the possibility of reducing environmental variation and increase in uniformity via selection.

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Section: Methodsmentioning

confidence: 99%

Estimation of genetic variation for macro- and micro-environmental sensitivities of milk yield and composition in Holstein cows using double hierarchical generalized linear models

Ehsaninia

Hossein-Zadeh

Shadparvar

2019

Journal of Dairy Research

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“…In the case of livestock, resilience is defined as "the capacity of the livestock to maintain their level of production under environmental disturbances or to recover rapidly to the state existing before exposure to a disturbance" (Colditz and Hine, 2016;Berghof et al, 2018). Several concepts related to resilience have been discussed in many studies: robustness (De La Torre et al, 2015), tolerance (Bishop, 2012), environmental sensitivity (Ehsaninia et al, 2019(Ehsaninia et al, , 2020, and plasticity (Debat and David, 2001). Despite the wealth of research in humans (Feder et al, 2019), studies on resilience in livestock are still incipient and there is no clear distinction between these definitions in terms of similarities and differences and their research strategies.…”

Section: Resilience In Holstein Cattlementioning

confidence: 99%

Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle

et al. 2022

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Genetic selection for resilience is essential to improve the long-term sustainability of the dairy cattle industry, especially the ability of cows to maintain their level of production when exposed to environmental disturbances. Recording of daily milk yield provides an opportunity to develop resilience indicators based on milk losses and fluctuations in daily milk yield caused by environmental disturbances. In this context, our study aimed to explore milk loss traits and measures of variability in daily milk yield, including log-transformed standard deviation of milk deviations (Lnsd), lag-1 autocorrelation (Ra), and skewness of the deviations (Ske), as indicators of general resilience in dairy cows. The unperturbed dynamics of milk yield as well as milk loss were predicted using an iterative procedure of lactation curve modeling. Milk fluctuations were defined as a period of at least 10 successive days of negative deviations in which milk yield dropped at least once below 90% of the expected values. Genetic parameters of these indicators and their genetic correlation with economically important traits were estimated using single-trait and bivariate animal models and 8,935 lactations (after quality control) from 6,816 Chinese Holstein cows. In general, cows experienced an average of 3.73 environmental disturbances with a milk loss of 267 kg of milk per lactation. Each fluctuation lasted for 19.80 ± 11.46 days. Milk loss traits are heritable with heritability estimates ranging from 0.004 to 0.061. The heritabilities differed between Lnsd (0.135–0.250), Ra (0.008–0.058), and Ske (0.001–0.075), with the highest heritability estimate of 0.250 ± 0.020 for Lnsd when removing the first and last 10 days in milk in a lactation (Lnsd2). Based on moderate to high genetic correlations, lower Lnsd2 is associated with less milk losses, better reproductive performance, and lower disease incidence. These findings indicate that among the variables evaluated, Lnsd2 is the most promising indicator for breeding for improved resilience in Holstein cattle.

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Estimation of genetic parameters for micro-environmental sensitivities of production traits in Holstein cows using two-step method

2020

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Context The request for more uniform animal products, which is motivated chiefly by economic reasons, has enhanced the interest in decreasing variability of characters via selection. In the present dairy operation, breeding dairy cows which have strong resistance against environmental changes for main traits is very important. Aims The aim of this study was to estimate genetic parameters for heterogeneity of residual variance in milk yield and composition of Iranian Holstein cows. Methods The dataset included 305-day production records of cows which were provided by the Animal Breeding Center and Promotion of Animal Products of Iran between 1983 and 2014. In two-step method, univariate analyses were conducted to estimate variance components for 305-day production traits. Then, genetic variability of residual variances was estimated. Key results Estimates of heritability for micro-environmental sensitivities of milk, fat and protein yields in the first three lactations of Holstein cows were low and equal to 0.043, 0.028 and 0.039; 0.031, 0.019 and 0.024; 0.027, 0.016 and 0.019 respectively. Considerable genetic coefficient of variations of residual variance for above mentioned traits (0.261, 0.247 and 0.218; 0.221, 0.204 and 0.194; 0.219, 0.199 and 0.178 respectively) indicated significant additive genetic variation for micro-environmental sensitivities. Conclusions The results of this study indicate that micro-environmental sensitivities were present for milk production traits of Iranian Holsteins. High genetic coefficient of variation for micro-environmental sensitivities indicated the possibility of reducing environmental variation and increase in uniformity via selection. Implications Reduction of environmental sensitivities would increase the predicted performance of animals and decreased corresponding threats for dairy farmers.

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Homogeneity and heterogeneity of variance components for milk and protein yield at different cluster sizes in Iranian Holsteins

Cited by 3 publications

References 38 publications

Estimation of genetic variation for macro- and micro-environmental sensitivities of milk yield and composition in Holstein cows using double hierarchical generalized linear models

Estimation of genetic variation for macro- and micro-environmental sensitivities of milk yield and composition in Holstein cows using double hierarchical generalized linear models

Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle

Estimation of genetic parameters for micro-environmental sensitivities of production traits in Holstein cows using two-step method

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