Influence of Estrus on the Milk Characteristics and Mid-Infrared Spectra of Dairy Cows

Milk produced by dairy cows is a complex combination of many components. However, at present, changes in only a few milk components (e.g., fat, protein, and lactose) during the estrus cycle in dairy cows have been documented. Mid-infrared (MIR) spectroscopy is a worldwide method routinely used for milk analysis, as MIR spectra reflect the global composition of milk. Therefore, this study aimed to investigate the changes in milk MIR spectra and milk production traits (fat, protein, lactose, urea, total solids (TS), and solid not fat (SnF)) due to estrus. Cows that were successfully inseminated, leading to conception, were included. Cows confirmed to be pregnant were considered to be in estrus at the day of insemination (day 0). A general linear mixed model, which included the random effect of cows, the fixed classification effects of parity number, days in relation to estrus, as well as the interaction between parity number and days in relation to estrus, was applied to investigate the changes in milk production traits and 1060 milk infrared wavenumbers, ranging from 925 to 5011 cm−1, of 371 records from 162 Holstein cows on the days before (day −3, day −2, and day −1) and on the day of estrus (day 0). The days in relation to estrus had a significant effect on fat, protein, urea, TS, and SnF, whose contents increased from day −3 to day 0. Lactose did not seem to be significantly influenced by the occurrence of estrus. The days in relation to estrus had significant effects on the majority of the wavenumbers. Besides, we found that some of the wavenumbers in the water absorption regions were significantly changed on the days before and on the day of estrus. This suggests that these wavenumbers may contain useful information. In conclusion, the changes in the milk composition due to estrus can be observed through the analysis of the milk MIR spectrum. Further analyses are warranted to more deeply explore the potential use of milk MIR spectra in the detection of estrus.

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Using quantile regression for fitting lactation curve in dairy cows

Journal of Dairy Research ◽

10.1017/s0022029919000013 ◽

2019 ◽

Vol 86 (1) ◽

pp. 19-24

Author(s):

Hossein Naeemipour Younesi ◽

Mohammad Mahdi Shariati ◽

Saeed Zerehdaran ◽

Mehdi Jabbari Nooghabi ◽

Peter Løvendahl

Keyword(s):

Quantile Regression ◽

Dairy Cows ◽

Milk Production ◽

Milk Yield ◽

Information Criterion ◽

Production Traits ◽

Milk Production Traits ◽

Fat Percentage ◽

Protein Percentage ◽

Breeding Center

AbstractThe main objective of this study was to compare the performance of different ‘nonlinear quantile regression’ models evaluated at theτth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali & Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali & Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali & Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution.

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Relationship between udder and teat conformation and milk yield performance in dairy cows pre- and post-milking

Canadian Journal of Animal Science ◽

10.4141/a06-074 ◽

2007 ◽

Vol 87 (3) ◽

pp. 285-289 ◽

Cited By ~ 1

Author(s):

Nilufer Sabuncuoglu ◽

Omer Coban

Keyword(s):

Dairy Cows ◽

Milk Production ◽

Milk Yield ◽

Relative Reduction ◽

Production Traits ◽

Milk Production Traits ◽

Simple Method ◽

Absolute Reduction ◽

Daily Milk Yield ◽

Daily Milk

The objectives of this study were to determine the relationship between udder floor area (UFA) and milk production traits in dairy cows, pre- and post-milking, as well as to examine the usefulness of a simple method of measuring UFA. The absolute reduction (AR, cm2) and relative reduction (RR, %) in UFA post-milking relative to pre-milking was calculated. Absolute reduction was influenced by breed and lactation stage, but not by parity of the cows (P < 0.05). There was no effect of breed and stage of lactation on the RR of UFA. Moderate correlations were observed between absolute reduction of UFA and daily milk yield (r2 = 0.422), lactation milk yield (r2 = 0.426), and absolute fat yield (r2 = 0.515) (P < 0.01 and P < 0.05). Moderate and weak correlations were found between RR of UFA and daily milk yield (r2 = 0.335) (P < 0.01), AR of the udder area and the 305-d lactation milk yield (r2 = 0.326) (P < 0.10). It was concluded that measuring UFA using the method described may be useful for the selection of dairy cattle mammary conformation traits. Key words: Dairy cow, udder and teat conformation, milk production traits, measurement technique

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Co-segregation of quantitative trait loci (QTL) for milk production traits and length of productive life with QTL for left-sided displacement of the abomasum in German Holstein dairy cows

Livestock Science ◽

10.1016/j.livsci.2011.03.001 ◽

2011 ◽

Vol 140 (1-3) ◽

pp. 149-154 ◽

Cited By ~ 2

Author(s):

S. Mömke ◽

W. Brade ◽

O. Distl

Keyword(s):

Quantitative Trait Loci ◽

Dairy Cows ◽

Milk Production ◽

Quantitative Trait ◽

Production Traits ◽

Milk Production Traits ◽

Productive Life ◽

Trait Loci ◽

Holstein Dairy Cows

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Association of polymorphism in the alpha-1-antitrypsin gene with milk production traits in Holstein dairy cows

South African Journal of Animal Science ◽

10.4314/sajas.v44i2.8 ◽

2014 ◽

Vol 44 (2) ◽

pp. 155 ◽

Cited By ~ 2

Author(s):

M Heihavand-Kheiripour ◽

A H Mahdavi ◽

H R Rahmani ◽

M Soltani-Ghombavani ◽

M A Edriss

Keyword(s):

Dairy Cows ◽

Milk Production ◽

Production Traits ◽

Milk Production Traits ◽

Alpha 1 Antitrypsin ◽

Holstein Dairy Cows

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Lactation curves of Sarda breed goats estimated with test day models

Journal of Dairy Research ◽

10.1017/s0022029905001366 ◽

2005 ◽

Vol 72 (4) ◽

pp. 470-475 ◽

Cited By ~ 11

Author(s):

Nicolò PP Macciotta ◽

Pancrazio Fresi ◽

Graziano Usai ◽

Aldo Cappio-Borlino

Keyword(s):

Milk Production ◽

Milk Yield ◽

Linear Models ◽

Morphological Structure ◽

Production Traits ◽

Mixed Linear Models ◽

Milk Production Traits ◽

Milk Traits ◽

Lactation Stage ◽

Parity Number

Test day records of milk yield (38765), fat and protein contents (11357) of Sarda goats (the most numerous Italian goat breed) were analysed with mixed linear models in order to estimate the effects of test date (month and year of kidding for fat and protein contents) parity, number of kids born, altitude of location of flocks (<200 m asl, 200–500 m asl, >500 m asl), flocks within altitude and lactation stage (eight days-in-milk intervals of 30 d each) on milk production. All factors considered in the models affected milk traits significantly. Milk yield was lower in first parity goats than in higher parities whereas fat and protein contents showed an opposite trend. Goats with two kids at parturition had a higher milk yield than goats with one kid and tended to have lower fat and protein percentages. Repeatability between test days within lactation was 0·34, 0·17 and 0·45 for milk yield, fat content and protein content, respectively. Lactation curves of goats farmed at different altitudes were clearly separated, especially for milk yield. Results of the present study highlight differences in milk production traits among the three subpopulations that have been previously identified within the Sarda breed on the basis of the morphological structure of animals and altitude of location of flocks.

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Genetic parameters of milk production traits in response to a short once-daily milking period in crossbred Holstein × Normande dairy cows

Journal of Dairy Science ◽

10.3168/jds.2017-13173 ◽

2018 ◽

Vol 101 (3) ◽

pp. 2235-2247 ◽

Cited By ~ 2

Author(s):

C. Charton ◽

J. Guinard-Flament ◽

R. Lefebvre ◽

S. Barbey ◽

Y. Gallard ◽

...

Keyword(s):

Dairy Cows ◽

Milk Production ◽

Genetic Parameters ◽

Production Traits ◽

Milk Production Traits ◽

Once Daily

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Genetic and phenotypic parameters for yield, food intake and efficiency of dairy cows fed ad libitum 1. Estimates for ‘total’ lactation measures and their relationship with live-weight traits

Animal Science ◽

10.1017/s0003356100013003 ◽

1991 ◽

Vol 52 (3) ◽

pp. 435-444 ◽

Cited By ~ 30

Author(s):

P. Persaud ◽

G. Simm ◽

W. G. Hlll

Keyword(s):

Food Intake ◽

Dairy Cows ◽

Milk Production ◽

Canonical Transformation ◽

Genetic Correlations ◽

Live Weight ◽

Production Traits ◽

Milk Production Traits ◽

Pooled Data ◽

Ad Libitum

ABSTRACTRecords on milk yield, fat plus protein yield, food intake, food efficiency, calving live weight and mean live weight, up to 26 and 38 weeks of lactation, were obtained from dairy cows, fed ad libitum, in the Edinburgh School of Agriculture's Langhill herd. The data were divided into first and later lactations and restricted maximum likelihood analyses carried out on heifer, cow and pooled data, fitting an animal model, with repeat lactations as an additional random effect. Univariate analyses were done after canonical transformation of heifer data and approximate canonical transformation of cow and pooled data. Heritability estimates for food efficiency and food intake, from pooled data, were 0·13 (s.e. 0·09) and 0·37 (s.e. 0·11) for 26-week and 0·13 (s.e. 0·12) and 0·52 (s.e. 0·14) for 38-week lactation periods, respectively. Over the same periods, estimates for milk yield were 0·20 (s.e. 0·08) and 0·20 (s.e. 0·11), respectively. Estimates from the analyses of cow and heifer data separately were higher, as were their standard errors. Genetic correlations between milk production traits and efficiency, from the pooled data analysis, ranged from 0·44 to 0·61 and those between milk production traits and food intake from 0·32 to 0·74. Genetic correlations between live-weight traits and efficiency ranged from −0·81 to −;0·99, and those between food intake and live-weight traits from 0·28 to 0·46. The results indicate that when selection is on yield, the correlated responses in efficiency may be smaller under ad libitum feeding, compared with published values where cows were given food according to yield. Including live weight in the selection criterion may give higher responses in efficiency compared with selection on yield alone. In MOET nucleus schemes it may be worthwhile to include food intake or efficiency directly in the selection criteria.

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Genetic Analysis of Milk Production Traits and Mid-Infrared Spectra in Chinese Holstein Population

Animals ◽

10.3390/ani10010139 ◽

2020 ◽

Vol 10 (1) ◽

pp. 139

Author(s):

Chao Du ◽

Liangkang Nan ◽

Lei Yan ◽

Qiuyue Bu ◽

Xiaoli Ren ◽

...

Keyword(s):

Milk Production ◽

Genetic Factors ◽

Genetic Correlations ◽

Production Traits ◽

Milk Production Traits ◽

Fat Percentage ◽

Mid Infrared ◽

Lactation Stage ◽

Calving Season ◽

Holstein Population

Milk composition always serves as an indicator for the cow’s health status and body condition. Some non-genetic factors such as parity, days in milk (DIM), and calving season, which obviously affect milk performance, therefore, need to be considered in dairy farm management. However, only a few milk compositions are used in the current animal selection programs. The mid-infrared (MIR) spectroscopy can reflect the global composition of milk, but this information is currently underused. The objectives of this study were to detect the effect of some non-genetic factors on milk production traits as well as 1060 individual spectral points covering from 925.92 cm−1 to 5011.54 cm−1, estimate heritabilities of milk production traits and MIR spectral wavenumbers, and explore the genetic correlations between milk production traits and 1060 individual spectral points in a Chinese Holstein population. The mixed models procedure of SAS software was used to test the non-genetic factors. Single-trait animal models were used to estimate heritabilities and bivariate animal models were used to estimate genetic correlations using the package of ASReml in R software. The results showed that herd, parity, calving season, and lactation stage had significant effects on the percentages of protein and lactose, whereas herd and lactation stage had significant effects on fat percentage. Moreover, the herd showed a significant effect on all of the 1060 individual wavenumbers, whereas lactation stage, parity, and calving season had significant effect on most of the wavenumbers of the lactose-region (925 cm−1 to 1200 cm−1), protein-region (1240 cm−1 to 1600 cm−1), and fat-regions (1680 cm−1 to 1770 cm−1 and 2800 cm−1 to 3015 cm−1). The estimated heritabilities for protein percentage (PP), fat percentage (FP), and lactose percentage (LP) were 0.08, 0.05, and 0.09, respectively. Further, the milk spectrum was heritable but low for most individual points. Heritabilities of 1060 individual spectral points were 0.04 on average, ranging from 0 to 0.11. In particular, heritabilities for wavenumbers of spectral regions related to water absorption were very low and even null, and heritabilities for wavenumbers of specific MIR regions associated with fat-I, fat-II, protein, and lactose were 0.04, 0.06, 0.05, and 0.06 on average, respectively. The genetic correlations between PP and FP, PP and LP, FP, and LP were 0.78, −0.29, and −0.14, respectively. In addition, PP, FP, and LP shared the similar patterns of genetic correlations with the spectral wavenumbers. The genetic correlations between milk production traits and spectral regions related to important milk components varied from weak to very strong (0.01 to 0.94, and −0.01 to −0.96). The current study could be used as a management tool for dairy farms and also provides a further understanding of the genetic background of milk MIR spectra.

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