• Food Science of Animal Resources
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• v.25 no.3
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• pp.328-332
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• 2005

The effect of k-casein (k-CN) variant on milk production traits (milk yield, fat yield, protein yield, fat percentage and protein percentage) was estimated for 568 Holstein cows in the first lactation. The k-CN valiant were determined by PCR-RFLP (restriction fragment length polymorphism) technique at the DNA level. Single trait linear model was used for the statistical analysis of the data. Result of this study indicated that k-CN variant affected significantly milk yield (P<0.05) and protein yield (P<0.01). Animals with the BB variant produced 622kg milk more and had protein yield higher by 32kg compared with animals with the AA variant No associations between the k-CN variants and other milk production trait were found. Therefore, milk and protein yield may be improved through milk protein typing by increasing the frequencies of k-CN B variant in dairy cattle population. In cheese making, it will be also preferable to have milk with the B variant of k-CN, which gives higher yield having a better quality than the A variant milk.

• Food Science of Animal Resources
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• v.26 no.1
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• pp.121-126
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• 2006

Major milk proteins have considerable variane which comes from substitution and deletions in their amino arid sequences. Variants in genes that code for milk proteins, such as ${\beta}$-lactoglobulin (${\beta}-LG$) have been established as genetic markers for milk production and milk protein composition in dairy cattle. The effect of ${\beta}-LG$ variant on milk production traits, such as milk yield. fat yield, protein yield, fat percentage and protein percentage, was estimated for 482 Holstein cows in the first lactation. The ${\beta}-LG$ variants were determined by PCR-RFLP technique at the DNA level. Single trait linear model was used for the statistical analysis of the data. Results of this study indicated that ${\beta}-LG$ variants affected significantly protein yield (p<0.05) and fat percentage (p<0.05). Animals with the AA variant produced 31kg of milk protein more than animals with the BB variant. On the contrary, cows with the BB variant had fat percentage higher by 0.35 and 0.32% compared with cows with the AA and AB variants, respectively. No associations between the ${\beta}-LG$ variants and milk yield, protein percentage and fat yield were found Therefore, milk production traits could be improved through ${\beta}-LG$ typing by increasing the frequency of A variant for protein yield or the frequency of B variant for fat content in Holstein dairy cattle population.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.9
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• pp.1285-1290
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• 2003

The present study was carried out to investigate the effect of test day milk yield, test day evening milk yield, parity, stage of lactation and body weight on milk urea and milk protein concentration. A total of 319 milk samples was collected from buffaloes over four month's period and subjected to urea and protein analysis. Milk urea concentration (mg/dl) was significantly (p<0.01) increased with increasing test day milk yield. The lowest value ($57.03{\pm}1.13$) was observed in the milk yield group ${\leq}4.5kg/day$ and the highest value ($64.15{\pm}1.13$) in the group 7.7-10.7 kg/day. However, test day evening milk yield had no significant effect on milk urea concentration. Milk protein did not vary significantly with the test day milk yield as well as test day evening milk yield. A clear decreasing trend of milk urea concentration (mg/dl) was found with the increasing parity. The highest MU concentration ($64.03{\pm}1.14$) was found in the first parity and the lowest ($55.67{\pm}1.22$) was found in the sixth and above parity. Whereas, stage of lactation had no effect on milk urea concentration. Moreover, parity and stage of lactation did not have any significant effect on milk protein concentration. Body weight (kg) was also found negatively (p<0.05) related with urea content (mg/dl) in milk. The highest mean MU concentration ($64.34{\pm}0.88$) was found when body weight was between 532 and 598 kg and lower mean values ($59.24{\pm}0.94$ and $59.33{\pm}1.23$) were observed in 599 to 665 kg and ${\geq}666kg$ group. Body weight also had significant (p<0.05) effect on milk protein content. The highest milk protein content (%) was found in ${\geq}666kg$ group and the lowest in <531 kg group. In conclusion, for proper interpretation of milk urea values to monitor protein nutrition status of the buffaloes parity, milk yield and body weight should be considered.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.12
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• pp.1813-1821
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• 2002

The aim of this study was to identify useful secondary traits for estimating genetic ability of milk production traits. We investigated the value of using plasma metabolites concentrations. Two hundred and nineteen cattle out of 271 had only milk production traits records (G1), 33 had only metabolites records (G2), and 19 had both milk production traits and metabolites records (G3). Fifty two calves with metabolites records (G2 and G3) were born from 1992 to 1997. Forty three calves (29 females, 14 males) were used from 10 to 90 d of age and the others (3 females, 6 males) from 10 to 60 d of age. A total of 566 records of milk yield, fat yield and protein yield for 240 to 305 d on 238 heads (G1 and G2) were collected The collected blood samples were divided into three age groups: AG1, 10 to 30 d; AG2, 40 to 60 d; and AG3, 70 to 90 d. Heritabilities of milk yield, fat yield and protein yield were $0.45{\pm}0.04$, $0.50{\pm}0.04$ and $0.38{\pm}0.04$, respectively. Heritability of plasma glucose concentration at AG1 was $0.45{\pm}0.08$. Genetic correlations between plasma glucose concentration and milk yield, fat yield and protein yield were -$0.35{\pm}0.28$, $0.64{\pm}0.24$ and $0.36{\pm}0.35$, respectively. When the plasma glucose concentration at AG1 was used to estimate genetic ability of these milk production traits, reliability of milk yield of animals without milk record increased 8.2%, fat yield increased 24.2% and protein yield increased 9.5%. Heritability of plasma total cholesterol concentration at AG3 was $0.83{\pm}0.04$. Genetic correlation between plasma total cholesterol concentration and milk yield, fat yield and protein yield were $0.58{\pm}0.21$, $0.42{\pm}0.20$ and $0.45{\pm}0.22$, respectively. When the plasma total cholesterol concentration at AG3 was using to estimate genetic ability of these milk production traits, reliability of milk yield of animals without milk record increased 19.0%, fat yield increased 9.6%, and protein yield increased 13.5%. The annual genetic gain is in proportion to the reliability of selection. These results show that the plasma metabolite concentrations would be useful for improvement of genetic ability for milk production traits in the genetic improvement in herd of cows, where half of the animals selected are from a herd without its own milk record.

• Korean Journal of Organic Agriculture
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• v.24 no.4
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• pp.957-967
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• 2016

The present study was conducted to investigate effect of dietary protected amino acid on milk yield and composition in dairy cow using meta-analysis. Total 21 research papers were employed in analysis, and mixed model was used for the analysis of effects. Effect of protected methionine (PM) and combination of protected methionine and lysine (PML) were investigated under two different levels of dietary crude protein (CP, <18% and >18%). For performance of dairy cow, milk yield, milk composition including milk fat and protein content and yield and 4% FCM (fat corrected milk) production were used for analysis. In case of milk yield, a trend of increment was found at PM supplementation at low CP (P=0.055). However, the effect of PM at high CP was detected as not significant (P>0.05). In case of milk protein, inclusion of PM at low CP showed significant decrement (P<0.05). However, there was no significant effect of MP on milk protein at high CP (P>0.05). Supplementation of MP at high CP level showed significant increment of milk fat (P<0.05). MP supplementation represented significant increment of 4% FCM production (P<0.05) regardless of dietary CP levels. Effects of PML on milk yield and composition at both of low and high dietary CP were not significant in this study. However, it seem to be that there was a possible positive effect of MPL application at high dietary CP on performance of dairy cow.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.8
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• pp.1159-1163
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• 2006

This experiment was performed to examine the influences of high ambient temperature on milk production, nutrient digestibility, energy and protein sufficiency ratio, and plasma metabolites concentration in lactating cows. In a $2{\times}2$ crossover design, four multiparous lactating Holstein cows were maintained in a chamber under treatment of constant moderate ($18^{\circ}C$) ambient temperature (MT) or high ($28^{\circ}C$) ambient temperatures (HT). The DMI and milk protein yield were significantly lower in HT (p<0.05). The milk yield, milk lactose yield, and milk SNF yield tended to be lower in HT (p<0.10). No statistical differences for 4% fat-corrected milk and milk fat yield were observed. Rectal temperatures were significantly higher in HT than MT (p<0.05). The apparent DM, OM, ether extract, CF, and ash digestibility did not differ between treatments. On the other hand, the apparent CP digestibility was increased significantly (p<0.05) and nitrogen free extract tended to increase (p<0.10) in HT. The sufficiency ratio of ME and DCP intake for each requirement tended to be lower in HT than in MT (p<0.10). Concentrations of total protein (TP), albumin, and urea nitrogen in plasma did not differ between treatments. Plasma 3-methylhistidine (3MH) concentration as a marker of myofibrillar protein degradation tended to be higher in HT (p<0.15). In conclusion, high ambient temperature was associated with a lower energy and protein sufficiency ratio, and decreased milk protein production, even though the body protein mobilization tended to be higher.

• Animal Bioscience
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• v.34 no.9
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• pp.1466-1478
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• 2021

Objective: Ruminants are completely dependent on their microbiota for rumen fermentation, feed digestion, and consequently, their metabolism for productivity. This study aimed to evaluate the rumen bacteria of lactating yaks with different milk protein yields, using high-throughput sequencing technology, in order to understand the influence of these bacteria on milk production. Methods: Yaks with similar high milk protein yield (high milk yield and high milk protein content, HH; n = 12) and low milk protein yield (low milk yield and low milk protein content, LL; n = 12) were randomly selected from 57 mid-lactation yaks. Ruminal contents were collected using an oral stomach tube from the 24 yaks selected. High-throughput sequencing of bacterial 16S rRNA gene was used. Results: Ruminal ammonia N, total volatile fatty acids, acetate, propionate, and isobutyrate concentrations were found to be higher in HH than LL yaks. Community richness (Chao 1 index) and diversity indices (Shannon index) of rumen microbiota were higher in LL than HH yaks. Relative abundances of the Bacteroidetes and Tenericutes phyla in the rumen fluid were significantly increased in HH than LL yaks, but significantly decreased for Firmicutes. Relative abundances of the Succiniclasticum, Butyrivibrio 2, Prevotella 1, and Prevotellaceae UCG-001 genera in the rumen fluid of HH yaks was significantly increased, but significantly decreased for Christensenellaceae R-7 group and Coprococcus 1. Principal coordinates analysis on unweighted UniFrac distances revealed that the bacterial community structure of rumen differed between yaks with high and low milk protein yields. Furthermore, rumen microbiota were functionally enriched in relation to transporters, ABC transporters, ribosome, and urine metabolism, and also significantly altered in HH and LL yaks. Conclusion: We observed significant differences in the composition, diversity, fermentation product concentrations, and function of ruminal microorganisms between yaks with high and low milk protein yields, suggesting the potential influence of rumen microbiota on milk protein yield in yaks. A deeper understanding of this process may allow future modulation of the rumen microbiome for improved agricultural yield through bacterial community design.

• Korean Journal of Veterinary Research
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• v.41 no.2
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• pp.243-252
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• 2001

Milk constituents and somatic cell count (SCC) were analysed for 4,059 milk samples from 178 dairy farms from April to December in 1999. Correlations among each milk constituents, milk urea (MU) concentration and SCC in association with lactation stage, milk yield and parity, and balancing status of nutrients' supplies were analysed, and the results are summarized as follows; Averages of milk fat percent, total solids percent and milk yield were $3.72{\pm}0.91%$, $12.50{\pm}1.31%$ and $23.80{\pm}8.54kg$, respectively, whereas those were significantly lower during the summer season. In contrast, average of MU concentration was $0.0361{\pm}0.0006%$ which was significantly higher during the summer season. With milk yield, concentrations of fat, protein and SCC in milk decreased but concentrations of lactose and urea in milk and body condition score (BCS) were not altered. Concentrations of fat, protein, lactose, total solids, SNF, and urea in milk were significantly affected by stage of lactation ($P{\leq}0.0001$) but SCC was not changed. Parity of dairy cows had a significant effect on concentrations of fat ($P{\leq}0.02$), lactose ($P{\leq}0.0001$), total solids ($P{\leq}0.002$), and SNF ($P{\leq}0.0001$) in milk and milk yield ($P{\leq}0.0005$) but did not change concentrations of urea and protein in milk. Somatic cell count had significant positive correlationship with percentages of fat, protein and total solids ($P{\leq}0.0001$), respectively, but had negative correlationship with percentages of urea and lactose in milk and milk yield ($P{\leq}0.0001$). Milk urea concentration was negatively correlated with concentrations of protein, fat, total solids, and SNF in milk and milk yield ($P{\leq}0.0001$) and, according to regression analysis using milk urea concentration and SCC, following equation was obtained; $Y(MU)=3.688{\times}10^{-2}-4.04{\times}10^{-7}{\times}X(SCC{\times}1,000)(r^2=0.0038$, $P{\leq}0.0001$). We studied balance between protein and energy supplies to dairy cows in each farm based upon milk urea and protein concentrations, and results showed that 137 of total 178 farms fed cows unbalanced amounts of dietary protein and energy.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.6
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• pp.857-864
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• 2006

The aim of this study was to apply the Cornell net carbohydrate and protein system (CNCPS) in subtropical Taiwan. This was done by means of 3 trials, viz, in situ, lactation and metabolic trials, the latter using the urinary purine derivatives (UPD) to estimate the ruminal microbial yield. Dietary treatments were formulated according to different nutrient requirement systems including, (1) a control NRC78 group on NRC (1978), (2) a NRC88 group on NRC (1988), and (3) a CNCPS group on Cornell Net carbohydrate and protein system model. Results from the lactation trial showed that DM intake (DMI) was higher (p<0.05) in the NRC78 than the other treatment groups. The treatments did not significantly influence milk yield, but milk yield after covariance adjustment for DMI was higher in the CNCPS group (p<0.05). The FCM, milk fat content and yield were greater in both the NRC78 and the NRC88 group over the CNCPS group (p<0.05). The treatments did not significantly influence the DMI adjusted FCM. The solid-non-fat and milk protein contents were higher in the CNCPS group (p<0.05) with or without DMI covariance adjustment. Lactating efficiency was higher in the CNCPS group (p<0.05) compared to the other groups. The significantly lowest milk urea-N (MUN) with better protein utilization efficiency in the CNCPS group (p<0.05) suggested that less N would be excreted into the environment. Cows in the CNCPS group excreted significantly more and the NRC88 group significantly less urinary purine derivatives (UPD) implying that more ruminal microbial protein was synthesized in the CNCPS over the NRC88 group. The CNCPS could become the most useful tool in predicting the trends in milk yield, microbial yield and MUN.

• Asian-Australasian Journal of Animal Sciences
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• v.11 no.3
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• pp.311-317
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• 1998

A total of 1033 Brown Swiss and 610 Canadienne cows were phenotyped for the genetic variants ${\alpha}_{s1}$-casein, ${\beta}$-casein, ${\kappa}$-casein, ${\beta}$-lactoglobulin and ${\alpha}$-lactalbumin. In Brown Swiss, frequency distributions were: 97.3% B and 2.7% C variant of ${\alpha}_{s1}$-casein; 31.6% $A^1$, 51.8% $A^2$, 0.5% $A^3$ and 16.1% B variant of ${\beta}$-casein; 70.4% A, 29.3% B, and 0.3% C variant of ${\kappa}$-casein; 41.7% A and 58.3% B variant of ${\beta}$-lactoglobulin; and 100% B variant of ${\alpha}$-lactalbumin. Corresponding frequencies in Canadienne for those five milk proteins were: 98.6 and 1.4%;58.5, 33.5, 0.08 and 7.9%; 78.8, 21.1 and 0.1%, 42.4 and 57.6%; and 100%. Analysis of variance by least squares showed possible association between milk protein phenotypes and some lactational production traits. There were no significant association of phenotypes of ${\alpha}_{s1}$-casein, ${\beta}$-casein and ${\beta}$-lactoglobulin with milk yield, fat yield, protein yield, fat percentage and protein percentage in both breeds during the three lactations. In the Brown Swiss, ${\kappa}$-casein phenotype was associated with 305-day fat yield and protein yield during the first lactation. ${\kappa}$-Casein AB was associated with higher milk, fat and protein yield during the second lactation. During the third lactation, ${\beta}$-lactoglobulin AA in Canadienne cows was associated with higher protein content in the milk (3.70%) when compared to phenotypes AB (3.54%) and BB (3.64%).