scholarly journals Bias of Calf Sex on Milk Yield and Fat Yield in Holstein Crossbreed Cows

Animals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 2536
Author(s):  
Radica Djedović ◽  
Dragan Stanojević ◽  
Vladan Bogdanović ◽  
Dušica Ostojić Andrić ◽  
Ljiljana Samolovac ◽  
...  
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Environmental Conditions ◽  
Software Package ◽  
Female Offspring ◽  
Male Offspring ◽  
Individual Factors ◽  
Yield Traits ◽  
Software License ◽  
Fat Yield

In order to examine the biased milk production depending on the sex of calves, data on calving and milk yield characteristics of 15,181 Holstein type cows in PK Belgrade, Serbia were analyzed. A total of 30,362 lactations that were realized in the period from 1985 to 2017 were analyzed. Data were prepared and analyzed using the SAS software package (SAS Institute Inc. Software License 9.3, 2012). The expression and variability of investigated traits were determined using the PROC MEANS procedure, while the effect of individual factors on milk yield traits was analyzed using the PROC GLM procedure. Obtained results deviate from the views of the Trivers–Willard (TW) hypothesis. The results indicate that mothers invest more in female offspring by producing a higher milk and fat yield in the first and second lactation compared to male offspring. This is especially emphasized under better environmental conditions. The highest milk yield (7788 kg) and fat yield (271 kg) in the second lactation were achieved in the combination with two consecutive female calves in the group of higher-than-average milk production farms, and lowest in the combination of two consecutive male calves (6783 kg for the MY and 243 kg for the FY), respectively.

The use of short-term secretion rate measurements for estimating the milk production of suckler cows

1978 ◽  
Vol 45 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Y. L. P. Le Du ◽  
R. D. Baker ◽  
J. M. Barker
Keyword(s):  
Dairy Cows ◽  
Milk Production ◽  
Milk Yield ◽  
Milk Composition ◽  
Secretion Rate ◽  
Short Term ◽  
Beef Cow ◽  
Total Milk ◽  
Fat Yield ◽  
Good Agreement

SummaryTwo experiments with dairy cows and one with suckler cows and their calves were conducted to examine the use of secretion rate measurements for estimating total milk production. In the first experiment both 4- and 6- h intervals between measurements gave similar estimates of total 7-d milk yield. The second experiment compared estimated and measured milk composition as well as yield. Milk and solids-not-fat yields were underestimated with dairy cows as a result of an extended milking interval before measurement. However, fat yield was overestimated, indicating that all residual milk was not removed at the first oxytocinaided milking. It was concluded that for the beef cow, previous interval effects would be eliminated by the frequency of calf suckling, but that residual milk effects might cause a 3–6% and a 16% overestimation of milk and fat yields respectively.In the third experiment, the milk yield of suckler cows was estimated from measurements of secretion rate and from changes in calf weight; good agreement was obtained provided there were at least 3 consecutive controlled sucklings.

Genetic Polymorphism in Prolactin Gene and its Effect on Test Day Milk Production Traits in Sahiwal Cattle

2021 ◽  
Author(s):  
T. Karuthadurai ◽  
A.K. Chakravarty ◽  
A. Kumaresan ◽  
D.N. Das ◽  
A. Sakthivel Selvan ◽  
...  
Keyword(s):  
Genetic Polymorphism ◽  
Milk Production ◽  
Milk Yield ◽  
Early Stage ◽  
Direct Sequencing ◽  
Production Performance ◽  
Prolactin Gene ◽  
Pcr Rflp ◽  
Sahiwal Cattle ◽  
Fat Yield

Background: The selection of genetically superior animals at an early stage of life, the molecular markers are used along with traditional selection. The study was carried out to identify the genetic polymorphism in the exon3 region of the Prolactin and enumerate its effect on milk production performance in Sahiwal cattle. Prolactin plays an imperative regulatory role in mammary gland development, milk emission and lactogenesis. Analysed the sequence of this gene to explore whether mutations in this sequence and it could be accountable for quantitative variations in milk production and its composition traits.Methods: Total DNA was isolated from the blood samples of 98 pedigreed Sahiwal population. Using PCR-RFLP method and direct sequencing, noticed a single-nucleotide polymorphism in exon3 region of the Prolactin gene in 156bp and also the effect of non- genetic factors on each trait was assessed by least-squares analysis for non-orthogonal data by a fixed model.Result: PCR-RFLP was done with RsaI restriction endonuclease for the identification of different genotypes. The frequency of G and A alleles of the Prolactin gene was evaluated as 0.575 and 0.425, whereas the frequencies of GG, GA and AA genotypes for the Prolactin gene were 0.45, 0.25 and 0.30, respectively. SNP (G55A) conferred an increase in test-day milk yield around 321.5g, in test day fat yield around 13.9g and in test day SNF yield increase was 19.4g, respectively. High correlation was perceived from test day (TD2) onwards between test day traits and lactation milk yield indicating that selection based on identified SNP in TD2 increased test day milk yield, fat yield and SNF yield by 1.1472 kg, 29.6gm and 45.4gm, respectively.

scholarly journals Associations between polymorphisms of SLC22A7, NGFR, ARNTL and PPP2R2B genes and Milk production traits in Chinese Holstein

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ruike Jia ◽  
Yihan Fu ◽  
Lingna Xu ◽  
Houcheng Li ◽  
Yanhua Li ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Milk Production ◽  
Association Analysis ◽  
Milk Yield ◽  
Production Traits ◽  
Milk Production Traits ◽  
Fat Percentage ◽  
Protein Percentage ◽  
Fat Yield ◽  
Percentage Protein

Abstract Background Our preliminary work confirmed that, SLC22A7 (solute carrier family 22 member 7), NGFR (nerve growth factor receptor), ARNTL (aryl hydrocarbon receptor nuclear translocator like) and PPP2R2B (protein phosphatase 2 regulatory subunit Bβ) genes were differentially expressed in dairy cows during different stages of lactation, and involved in the lipid metabolism through insulin, PI3K-Akt, MAPK, AMPK, mTOR, and PPAR signaling pathways, so we considered these four genes as the candidates affecting milk production traits. In this study, we detected polymorphisms of the four genes and verified their genetic effects on milk yield and composition traits in a Chinese Holstein cow population. Results By resequencing the whole coding region and part of the flanking region of SLC22A7, NGFR, ARNTL and PPP2R2B, we totally found 20 SNPs, of which five were located in SLC22A7, eight in NGFR, three in ARNTL, and four in PPP2R2B. Using Haploview4.2, we found three haplotype blocks including five SNPs in SLC22A7, eight in NGFR and three in ARNTL. Single-SNP association analysis showed that 19 out of 20 SNPs were significantly associated with at least one of milk yield, fat yield, fat percentage, protein yield or protein percentage in the first and second lactations (P < 0.05). Haplotype-based association analysis showed that the three haplotypes were significantly associated with at least one of milk yield, fat yield, fat percentage, protein yield or protein percentage (P < 0.05). Further, we used SOPMA software to predict a SNP, 19:g.37095131C > T in NGFR, changed the structure of NGFR protein. In addition, we used Jaspar software to found that four SNPs, 19:g.37113872C > G,19:g.37113157C > T, and 19:g.37112276C > T in NGFR and 15:g.39320936A > G in ARNTL, could change the transcription factor binding sites and might affect the expression of the corresponding genes. These five SNPs might be the potential functional mutations for milk production traits in dairy cattle. Conclusions In summary, we proved that SLC22A7, NGFR, ARNTL and PPP2R2B have significant genetic effects on milk production traits. The valuable SNPs can be used as candidate genetic markers for genomic selection of dairy cattle, and the effects of these SNPs on other traits need to be further verified.

The effects of abomasal infusions of glucose or casein on milk yield and on some blood constituents of dairy cows in early and late lactations

1984 ◽  
Vol 1984 ◽  
pp. 73-73
Author(s):  
L. Istasse ◽  
E.R. Ørskov
Keyword(s):  
Dairy Cows ◽  
Protein Content ◽  
Milk Production ◽  
Milk Yield ◽  
Blood Parameters ◽  
Protein Yield ◽  
Blood Constituents ◽  
Clear Cut ◽  
Abomasal Infusion ◽  
Fat Yield

Abomasal infusion of casein has been shown to increase the milk yield, fat yield, protein content and protein yield while no clear-cut effects were observed with infusion of glucose (Ørskov, Grubb and Kay, 1977 and confirmed by Oldham, Bines and MacRae, 1983). Little information is available about the changes in blood parameters with abomasal infusion of glucose or casein. The objective of the present experiments was to relate changes in milk production to blood parameters in dairy cows given casein or glucose as an infusion into the abomasum during early or late lactation.

The effect of physiological state, milk production traits and environmental conditions on milk fat globule size in cow's milk

2019 ◽  
Vol 86 (4) ◽  
pp. 454-460 ◽  
Author(s):  
Leonie Walter ◽  
Sue Finch ◽  
Brendan Cullen ◽  
Richard Fry ◽  
Amy Logan ◽  
...  
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Environmental Conditions ◽  
Milk Fat ◽  
Physiological State ◽  
Production Traits ◽  
Milk Production Traits ◽  
Milk Fat Globule ◽  
Fat Globule ◽  
On Farm

AbstractThis research was carried out to quantify the effects of a range of variables on milk fat globule (MFG) size for a herd of Holstein-Friesian cows managed through an automatic milking system with year-round calving. We hypothesised that the overall variation in average MFG size observed between individual animals of the same herd cannot sufficiently be explained by the magnitude of the effects of variables that could be manipulated on-farm. Hence, we aimed to conduct an extensive analysis of possible determinants of MFG size, including physiological characteristics (parity, days in milk, days pregnant, weight, age, rumination minutes, somatic cell count) and milk production traits (number of milkings, milk yield, fat yield, protein and fat content, fat-protein ratio) on the individual animal level; and environmental conditions (diet, weather, season) for the whole herd. Our results show that when analysed in isolation, many of the studied variables have a detectable effect on MFG size. However, analysis of their additive effects identified days in milk, parity and milk yield as the most important variables. In accordance with our hypothesis, the estimated effects of these variables, calculated using a multiple variable linear mixed model, do not sufficiently explain the overall variation between cows, ranging from 2.70 to 5.69 µm in average MFG size. We further show that environmental variables, such as sampling day (across seasons) or the proportion of pasture and silage in the diet, have limited effects on MFG size and that physiological differences outweigh the effects of milk production traits and environmental conditions. This presents further evidence that the selection of individual animals is more important than the adjustment of on-farm variables to control MFG size.

Significance of Environmental Influences on Average Daily Milk Traits of Sirohi Goats in Their Native Tract

2019 ◽  
Author(s):  
L. Gautam H.A. Waiz ◽  
R. K. Nagda
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Environmental Conditions ◽  
Genetic Factors ◽  
Genetic Correlations ◽  
Goat Milk ◽  
Least Square ◽  
Milk Traits ◽  
Daily Milk Yield ◽  
Daily Milk

Data on 3244 Sirohi kidding during 2004 to 2016 in farmer’s flocks under All India Co-ordinated Research Project on Goat Improvement (AICRP) project, Vallabhnagar, Udaipur were utilized to estimate the average daily milk (ADM) at different lactation months and subjected to least square analysis to study the effect of various non-genetic factors like cluster, periods of kidding, season of kidding, parity, type of birth and regression of dam’s weight. The overall least-squares means for ADM1, ADM2, ADM3, ADM4, ADM5 and overall ADM were 564.07±18.34, 671.92±15.17, 633.41±10.75, 508.93±8.01, 329.72±7.93 and 540.79±10.78 ml, respectively. Cluster and period wise variation were highly significant on all stages of average daily milk yields. The parity had statistically highly significant effect on average daily milk yields, in which seemed that milk yields increase as parity increase, thereafter declined slowly. The effect of type of kidding was non-significant on all stages of average daily milk yield under this study. The regression of dam’s weight at kidding was positive and highly significant (P£ 0.01) on all average daily milk yield. The heritability estimates for these traits ranged from 0.03 ± 0.01 (ADM4) to 0.19 ± 0.02 0.06 ± 002 (ADM1). The high estimates of genetic correlations of average milk yield of different periods with overall average daily milk yield. The phenotypic correlations were positive and low between ADM1 and ADM4­, ADM5 and medium between ADM1 and ADM4, ADM5. In order to augment goat milk production, goat keepers need to be focused on nutritional and others environmental conditions as it affect their flock.

The effects of wilting of grass prior to ensiling on the response to bacterial inoculation. 2. Intake and performance by dairy cattle over three harvests

1996 ◽  
Vol 62 (3) ◽  
pp. 419-429 ◽  
Author(s):  
D. C. Patterson ◽  
T. Yan ◽  
F. J. Gordon
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Period Change ◽  
Design Experiment ◽  
Bacterial Inoculation ◽  
Reduced Fat ◽  
Bean Meal ◽  
Fat Yield ◽  
And Performance ◽  
Soya Bean Meal

AbstractFour silages (unwilted with and without inoculant, and wilted with and without inoculant) were prepared from perennial ryegrass swards at each of three harvests over the growing season. The four silages from each of the first (primary growth), second (first regrowth) and third (second regrowth) harvests were offered ad libitum to 48 dairy cows during periods 2, 1 and 3 respectively, in a two (control and additive) × two (unwilted and wilted) × three (harvest (period)) change-over design experiment with 8-week experimental periods. The animals also received a concentrate supplement at 7·0, 6·1 and 5·2 kg dry matter (DM) per day in the first, second and third periods respectively. The supplement was based on barley, molasses and soya-bean meal.There were significant interactions between inoculation and wilting across the three harvests on silage DM intake (F<0-01), milk yield (P<0·05), and outputs of fat (P<0·01) and protein (P<0·05). Inoculation had no significant effects on silage intake and milk production across the unwilted and wilted silages. However, within the unwilted silages, inoculation significantly increased silage DM intake by 0·46 kg/day (P < 0·05) and fat yield by 0·032 kg/day (P<0·05). In contrast, within the wilted silages inoculation significantly reduced fat yield by 0·030 kg/day (P < 0·05). Wilting of grass prior to ensiling significantly increased silage DM intake by 0·73 kg/day (P <0·001), milk yield by 0·42 kg/day (P<0·05), fat yield by 0·053 kg/day (P < 0·001) and protein yield by 0·047 kg/day (P < 0·001) across the inoculant-treated and untreated silages. The effects however were mainly derived from the untreated silages as within the inoculant-treated materials the differences in silage intake and milk yield were not significant between unwilted and wilted treatments. The results of the current experiment indicate that wilting with no additive significantly improved silage intake and milk production, but otherwise the improvement was reduced with wilting following inoculation. Inoculation significantly increased silage intake and fat yield when used with the unwilted grass, but it significantly reduced fat yield when used with the wilted grass.

scholarly journals Genetic Effects of LPIN1 Polymorphisms on Milk Production Traits in Dairy Cattle

Genes ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 265 ◽  
Author(s):  
Bo Han ◽  
Yuwei Yuan ◽  
Ruobing Liang ◽  
Yanhua Li ◽  
Lin Liu ◽  
...  
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Genetic Effects ◽  
Production Traits ◽  
Milk Production Traits ◽  
Association Analyses ◽  
Protein Percentage ◽  
Haplotype Blocks ◽  
Flanking Region ◽  
Fat Yield

Our initial RNA sequencing work identified that lipin 1 (LPIN1) was differentially expressed during dry period, early lactation, and peak of lactation in dairy cows, and it was enriched into the fat metabolic Gene Ontology (GO) terms and pathways, thus we considered LPIN1 as the candidate gene for milk production traits. In this study, we detected the polymorphisms of LPIN1 and verified their genetic effects on milk yield and composition in a Chinese Holstein cow population. We found seven SNPs by re-sequencing the entire coding region and partial flanking region of LPIN1, including one in 5′ flanking region, four in exons, and two in 3′ flanking region. Of these, four SNPs, c.637T > C, c.708A > G, c.1521C > T, and c.1555A > C, in the exons were predicted to result in the amino acid replacements. With the Haploview 4.2, we found that seven SNPs in LPIN1 formed two haplotype blocks (D′ = 0.98–1.00). Single-SNP association analyses showed that SNPs were significantly associated with milk yield, fat yield, fat percentage, or protein yield in the first or second lactation (p = < 0.0001–0.0457), and only g.86049389C > T was strongly associated with protein percentage in both lactations (p = 0.0144 and 0.0237). The haplotype-based association analyses showed that the two haplotype blocks were significantly associated with milk yield, fat yield, protein yield, or protein percentage (p = < 0.0001–0.0383). By quantitative real-time PCR (qRT-PCR), we found that LPIN1 had relatively high expression in mammary gland and liver tissues. Furthermore, we predicted three SNPs, c.637T > C, c.708A > G, and c.1521C > T, using SOPMA software, changing the LPIN1 protein structure that might be potential functional mutations. In summary, we demonstrated the significant genetic effects of LPIN1 on milk production traits, and the identified SNPs could serve as genetic markers for dairy breeding.

Environmental and genetic causes of variation in production traits of Damascus goats. 2. Goat productivity

1984 ◽  
Vol 38 (1) ◽  
pp. 99-104 ◽  
Author(s):  
A. P. Mavrogenis ◽  
A. Constantinou ◽  
A. Louca
Keyword(s):  
Genetic Correlation ◽  
Milk Production ◽  
Milk Yield ◽  
Genetic Influences ◽  
Yield Traits ◽  
Production Traits ◽  
Quadratic Effect ◽  
Litter Weight ◽  
Lactation Length ◽  
Weaning Age

ABSTRACTData on 1474 lactation records obtained from 1972 to 1978 were used to study environmental and genetic influences on production characters in the Damascus goat. Year and month of kidding had a significant effect on 90- and 150-day milk production after weaning, lactation length and litter weight at weaning (P < 0·01), but no influence on litter weight at birth. Milk production after weaning was not related to litter weight at birth or at weaning. Age of goat at kidding had a significant quadratic effect on milk production, and litter weight at birth and at weaning. No such effects were found for lactation length.Estimates of heritability, from paternal half-sib correlations, for 90- and 150-day milk production were similar (0·29 (s.e. 0·14)). The genetic correlation between 90- and 150-day milk yield (0·92 (s.e. 0·03)) was high, indicating that part and whole lactation yields are influenced by the same genes. The phenotypic correlations among milk yield traits and lactation length were also high and positive.

Predicting milk responses to cereal-based supplements in grazing dairy cows

2017 ◽  
Vol 57 (4) ◽  
pp. 746 ◽  
Author(s):  
J. W. Heard ◽  
M. Hannah ◽  
C. K. M. Ho ◽  
E. Kennedy ◽  
P. T. Doyle ◽  
...  
Keyword(s):  
Dairy Cows ◽  
Milk Production ◽  
Milk Yield ◽  
Correction Factor ◽  
Bias Correction ◽  
Milk Fat ◽  
Milk Protein ◽  
Prediction Models ◽  
Dairy Industry ◽  
Fat Yield

The feeding of cereal-based supplements is common in the Australian dairy industry, as it allows cows to increase intakes of total dry matter (DM) and metabolisable energy (ME), while achieving greater stocking rates, greater pasture utilisation and greater milk production per hectare than occurs when cows are fed pasture-only diets. However, for this practice to be profitable, it is important to know how much extra milk, milk protein and milk fat are produced for each kilogram DM consumed. This is difficult to determine in such a complex biological system. We combined information from 24 concentrate-feeding experiments using meta-analysis techniques, so as to develop improved prediction models of the milk, milk protein and milk fat produced when cereal-based concentrates are fed to grazing, lactating dairy cows. Model terms, consistent with biological processes, linear, quadratic and factorial, were selected according to statistical significance. The models were then tested in two ways, namely, their goodness of fit to the data, and their ability to predict novel production data from a further six, unrelated, experiments. A sensitivity analysis was also undertaken to determine how sensitive these predictions are to changes in key inputs. The predictive model for milk yield was shown to very closely reflect milk yield (kg/cow.day) measured under the experimental conditions in unrelated experiments (r = 0.96), with very little bias (Lin’s bias correction factor = 0.98) and high concordance (Lin’s concordance coefficient = 0.95). Predictions generated by multiplying predicted milk protein concentration by predicted milk yield closely matched observed milk protein yield (kg/cow.day) (r = 0.96, Lin’s bias correction factor = 0.98, Lin’s concordance coefficient = 0.95), and predictions found by multiplying predicted milk fat concentration by predicted milk yield closely matched observed milk fat yield (kg/cow.day) (r = 0.94, Lin’s bias correction factor = 0.99, Lin’s concordance coefficient = 0.93). Factors included in the new models for milk, milk protein and milk fat yield reported here have been identified previously as elements that can influence milk production. The value to the dairy industry from being able to predict profitable amounts of concentrates to feed at various stages throughout lactation is considerable. For farmers and their advisers, being able to apply these models to estimate the immediate marginal milk protein and milk fat responses to supplementary feeds should lead to more robust, efficient and profitable milk production systems.

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