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Wang A, Brito LF, Zhang H, Shi R, Zhu L, Liu D, Guo G, Wang Y. Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle. Front Genet 2022; 13:1031557. [PMID: 36531242 PMCID: PMC9757536 DOI: 10.3389/fgene.2022.1031557] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 11/14/2022] [Indexed: 09/12/2023] Open
Abstract
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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Affiliation(s)
- Ao Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Luiz F. Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, United States
| | - Hailiang Zhang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Rui Shi
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Lei Zhu
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
| | - Dengke Liu
- Hebei Sunlon Modern Agricultural Technology Co., Ltd., Dingzhou, China
| | - Gang Guo
- Beijing Sunlon Livestock Development Co., Ltd., Beijing, China
| | - Yachun Wang
- Key Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture of China, National Engineering Laboratory for Animal Breeding, College of Animal Science and Technology, China Agricultural University, Beijing, China
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Zamorano-Algandar R, Medrano JF, Thomas MG, Enns RM, Speidel SE, Sánchez-Castro MA, Luna-Nevárez G, Leyva-Corona JC, Luna-Nevárez P. Effect of calving season on the parameters and components of the lactation curve in Holstein dairy cows managed in a semi-desert climate. Trop Anim Health Prod 2022; 54:88. [DOI: 10.1007/s11250-022-03098-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 02/01/2022] [Indexed: 11/29/2022]
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Zhou X, Zhang J. Comparison and estimation of different linear and nonlinear lactation curve submodels in random regression analyses on dairy cattle. CANADIAN JOURNAL OF ANIMAL SCIENCE 2021. [DOI: 10.1139/cjas-2020-0085] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
In the random regression model (RRM) for milk yield, by replacing empirical lactation curves with the five-order Legendre polynomial to fit fixed groups, the RRM can be transformed to a hierarchical model that consisted of a RRM in the first hierarchy with Legendre polynomials as individuals’ lactation curves resolved by restricted maximum likelihood (REML) software, and a multivariate animal model for phenotypic regression coefficients in the second hierarchy resolved by DMU software. Some empirical lactation functions can be embedded into the RRM at the first hierarchy to well fit phenotypic lactation curve of the average observations across all animals. The functional relationship between each parameter and time can be described by a Legendre polynomial or an empirical curve usually called submodel, and according to three commonly used criteria, the optimal submodels were picked from linear and nonlinear submodels except for polynomials. The so-called hierarchical estimation for the RRMs in dairy cattle indicated that more biologically meaningful models were available to fit the lactation curves; moreover, with the same number of parameters, the empirical lactation curves (MIL1, MIL5, and MK1 for 3, 4, and 5 parameters, respectively) performed higher goodness of fit than Legendre polynomial when modelling individuals’ phenotypic lactation curves.
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Affiliation(s)
- Xiaojing Zhou
- Department of Information and Computing Science, Heilongjiang Bayi Agricultural University, Daqing 163319, People’s Republic of China
- Bioinformatics Research Laboratory, Heilongjiang Bayi Agricultural University, Daqing 163319, People’s Republic of China
| | - Jingyan Zhang
- College of Life Science and Biotechnology, Heilongjiang Bayi Agricultural University, Daqing 163319, People’s Republic of China
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Masía F, Lyons N, Piccardi M, Balzarini M, Hovey R, Garcia S. Modeling variability of the lactation curves of cows in automated milking systems. J Dairy Sci 2020; 103:8189-8196. [DOI: 10.3168/jds.2019-17962] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 04/10/2020] [Indexed: 02/03/2023]
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Migose S, van der Linden A, Bebe B, de Boer I, Oosting S. Accuracy of estimates of milk production per lactation from limited test-day and recall data collected at smallholder dairy farms. Livest Sci 2020. [DOI: 10.1016/j.livsci.2019.103911] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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