• Asian-Australasian Journal of Animal Sciences
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• v.16 no.5
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• pp.639-643
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• 2003

The data on 283 Sahiwal cows, sired by 16 bulls, maintained at Cattle Breeding Farm of Nagpur Veterinary College and Dairy Farm of Agricultural College, Nagpur, were considered for the estimation of genetic parameters. Variance and covariance estimates of first lactation traits were obtained using restricted maximum likelihood technique (REML). When first lactation milk yield (FLMY), first lactation length (FLL) and average daily yield (ADY) traits were considered for REML analysis, the heritabilities were $0.184{\pm}0.146$, $0.132{\pm}0.131$ and $0.141{\pm}0.133$, respectively. While, genetic and phenotypic correlations between them were medium to high except phenotypic correlations between FLL and ADY (-0.025). REML procedure considering FLMY, age at first calving (AFC) and first service period (FSP) combination exhibits heritabilities as $0.274{\pm}0.173$, $0.506{\pm}0.233$ and $0.274{\pm}0.172$, respectively. Genetic correlations were $-0.120{\pm}0.376$, $0.225{\pm}0.423$ and $0.365{\pm}0.331$ between FLMY and AFC, FLMY and FSP, AFC and FSP, respectively. Phenotypic correlations were 0.057, 0.289 and 0.123, respectively. Considering all five traits REML combination heritabilities estimated were $0.238{\pm}0.162$, $0.160{\pm}0.139$, $0.136{\pm}0.132$, $0.409{\pm}0.209$ and $0.259{\pm}0.168$ for FLMY, FLL, ADY, AFC and FSP, respectively. The genetic correlations were positive except FLMY and AFC. The phenotypic correlations were also positive except FLL and ADY, ADY and FSP. Almost all estimates were associated with high standard error.

• Asian-Australasian Journal of Animal Sciences
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• v.7 no.3
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• pp.383-387
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• 1994

Six heifers each of $F_1$, $F_2$, $F_3$, $F_4$ H. Friesian $\times$ Sahjwal halfbreds and pure Sahiwal with overall average initial age and live weight of $315.6{\pm}134.8 days$ and $143.8{\pm}48.5kg$, respectively were used for this study. The under trial animals were fed according to N.R.C. (1978). The overall average age at maturity, first conception and first calving was $563.8{\pm}116.8$, $675.8{\pm}135.6$ and $956.8{\pm}149.8days$ with live weights as $302.2{\pm}58.6$, $342.6{\pm}41.5$ and $433.9{\pm}38.1kg$, respectively. The overall 305 days and total milk yield for 1st lactation was $2,729.0{\pm}669.3$ and $2,992.7{\pm}377.5litre$ while the FCM (at 4%) was $2,934.2{\pm}410.8litre$. The lactation length was $336.6{\pm}69.6days$. The fat and solids not fat contents were $4.5{\pm}0.2$ and $8.2{\pm}0.3percent$, respectively. The milk production in Sahiwal cows was significantly lower than crossbred cows. The services per conception were $2.5{\pm}1.3$. The overall per head per day consumption of DM, TCP and ME was $10.9{\pm}1.2kg$, $1,399{\pm}199gm$ and $22.6{\pm}2.4 M.Cal.$, respectively. The overall milk producing efficiency for the production of one litre of FCM was $1.12{\pm}0.15kg$ of DM, $142.2{\pm}17.76gm$ of TCP and $2.31{\pm}0.27M.Cal.$ of energy while the feeding cost was Rs. $1.46{\pm}0.22$ per litre of FCM produced. The feeding cost per litre FCM was significantly higher in Sahiwal and $F_2$ than in other genetic groups.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.3
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• pp.303-310
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• 2015

The study was conducted to analyze the genetic parameters of somatic cell score (SCS) of Holstein cows, which is an important indicator to udder health. Test-day records of somatic cell counts (SCC) of 305-day lactation design from first to fifth lactations were collected on Holsteins in Korea during 2000 to 2012. Records of animals within 18 to 42 months, 30 to 54 months, 42 to 66 months, 54 to 78 months, and 66 to 90 months of age at the first, second, third, fourth and fifth parities were analyzed, respectively. Somatic cell scores were calculated, and adjusted for lactation production stages by Wilmink's function. Lactation averages of SCS ($LSCS_1$ through $LSCS_5$) were derived by further adjustments of each test-day SCS for five age groups in particular lactations. Two datasets were prepared through restrictions on number of sires/herd and dams/herd, progenies/sire, and number of parities/cow to reduce data size and attain better relationships among animals. All LSCS traits were treated as individual trait and, analyzed through multiple-trait sire models and single trait animal models via VCE 6.0 software package. Herd-year was fitted as a random effect. Age at calving was regressed as a fixed covariate. The mean LSCS of five lactations were between 3.507 and 4.322 that corresponded to a SCC range between 71,000 and 125,000 cells/mL; with coefficient of variation from 28.2% to 29.9%. Heritability estimates from sire models were within the range of 0.10 to 0.16 for all LSCS. Heritability was the highest at lactation 2 from both datasets (0.14/0.16) and lowest at lactation 5 (0.11/0.10) using sire model. Heritabilities from single trait animal model analyses were slightly higher than sire models. Genetic correlations between LSCS traits were strong (0.62 to 0.99). Very strong associations (0.96 to 0.99) were present between successive records of later lactations. Phenotypic correlations were relatively weaker (<0.55). All correlations became weaker at distant lactations. The estimated breeding values (EBVs) of LSCS traits were somewhat similar over the years for a particular lactation, but increased with lactation number increment. The lowest EBV in first lactation indicated that selection for SCS (mastitis resistance) might be better with later lactation records. It is expected that results obtained from these multi-trait lactation model analyses, being the first large scale SCS data analysis in Korea, would create a good starting step for application of advanced statistical tools for future genomic studies focusing on selection for mastitis resistance in Holsteins of Korea.

• Animal Bioscience
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• v.35 no.7
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• pp.955-963
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• 2022

Objective: The aim of this study was to estimate genetic parameters for 305-day cumulative milk yield and components, growth, and reproductive traits in Guzerá cattle. Methods: The evaluated traits were 305-day first-lactation cumulative yields (kg) of milk (MY305), fat (FY305), protein (PY305), lactose (LY305), and total solids (SY305); age at first calving (AFC) in days; adjusted scrotal perimeter (cm) at the ages of 365 (SP365) and 450 (SP450) days; and adjusted body weight (kg) at the ages of 210 (W210), 365 (W365), and 450 (W450) days. The (co)variance components were estimated using the restricted maximum likelihood method for single-trait, bi-trait and tri-trait analyses. Contemporary groups and additive genetic effects were included in the general mixed model. Maternal genetic and permanent environmental effects were also included for W210. Results: The direct heritability estimates ranged from 0.16 (W210) to 0.32 (MY305). The maternal heritability estimate for W210 was 0.03. Genetic correlation estimates among milk production traits and growth traits ranged from 0.92 to 0.99 and from 0.92 to 0.99, respectively. For milk production and growth traits, the genetic correlations ranged from 0.33 to 0.56. The genetic correlations among AFC and all other traits were negative (-0.43 to -0.27). Scrotal perimeter traits and body weights showed genetic correlations ranging from 0.41 to 0.46, and scrotal perimeter and milk production traits showed genetic correlations ranging from 0.11 to 0.30. The phenotypic correlations were similar in direction (same sign) and lower than the corresponding genetic correlations. Conclusion: These results suggest the viability and potential of joint selection for dairy and beef traits in Guzerá cattle, taking into account reproductive traits.

• Korean Journal of Animal Reproduction
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• v.24 no.1
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• pp.19-33
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• 2000

Eighty dairy farms (38 farms responded) in Palmerston North area of New Zealand were questioned by mail questionnaire on: 1) general characteristics, 2) milk yield and feed supplementary, 3) reproductive efficiencies (12 questions) and 4) reproductive disorders (12 questions) from February to July, 1998. Among those 4 items, the reproductive efficiencies (3) and disorders (4), various diseases and culling rates were surveyed and analyzed for Korean dairy farmers (especially in Cheju island) and compared with New Zealand. The results are as follows: 1. Fifteen farms in 38 dairy farms relied entirely on artificial insemination, the rest of 23 dairy farms (60.5%) raised 5∼6 bulls to increase conception rate. The dairy farmers in Palmerston North used artificial insemination from Oct 4th to Dec 10th for 42.8 days, and then used bulls from that point to coming Jan 10th for 41.4 days. The submission rate within 3, 6 and 10 weeks following the initiation of AI season was 84.7, 93.9 and 97.9% respectively. 2. The average age of heifers at the first estrus, pregnancy and calving was 11.0, 18.0 and 24.7 months respectively, and an average 1.4 estrus cycles were required for conception. The intervals of estrus recurrence and the following conception after calving were 38 and 68 days respectively. 3. Among inseminated cows, calving, abortion and empty cow was 90.9, 1.6 and 7.4% respectively. Calving rate decreased according to increasing farm size, while the number of empty cows decreased. 4. Stillbirth, retained placenta and delivery abnormalities were 5.3, 3.7 and 5.5% respectively, not different depend on herd size. 5. The incidence of milk fever, grass tetany, and ketosis was 3.6, 3.0 and 1.0%, respectively. The delivery abnormality and mastitis treated with medicine were 3.1 and 6.7%, but decreased according to farm size. Lameness was 8.6% on average, but over 10% in farms which has more than 400 milking cows. 6. Among the culled cows (15.5% of the total), those culled due to an old age, lameness and other diseases were 2.9, 1.8 and 4.3% respectively and those culled due to low milk production, reproductive abnormality reduced with farm size. 7. Compared with the data collected in Korea, the reproductive efficiency was better, and lameness, metabolic problem and culling rate were higher in New Zealand

• Asian-Australasian Journal of Animal Sciences
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• v.30 no.11
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• pp.1550-1556
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• 2017

Objective: This study estimated the genetic parameters for productive and reproductive traits. Methods: The data included production and reproduction records of animals that have calved between 1979 and 2013. The genetic parameters were estimated using multivariate mixed models (DMU) package, fitting univariate and multivariate mixed models with average information restricted maximum likelihood algorithm. Results: The estimates of heritability for milk production traits from the first three lactation records were $0.03{\pm}0.03$ for lactation length (LL), $0.17{\pm}0.04$ for lactation milk yield (LMY), and $0.15{\pm}0.04$ for 305 days milk yield (305-d MY). For reproductive traits the heritability estimates were, $0.09{\pm}0.03$ for days open (DO), $0.11{\pm}0.04$ for calving interval (CI), and $0.47{\pm}0.06$ for age at first calving (AFC). The repeatability estimates for production traits were $0.12{\pm}0.02$, for LL, $0.39{\pm}0.02$ for LMY, and $0.25{\pm}0.02$ for 305-d MY. For reproductive traits the estimates of repeatability were $0.19{\pm}0.02$ for DO, and to $0.23{\pm}0.02$ for CI. The phenotypic correlations between production and reproduction traits ranged from $0.08{\pm}0.04$ for LL and AFC to $0.42{\pm}0.02$ for LL and DO. The genetic correlation among production traits were generally high (>0.7) and between reproductive traits the estimates ranged from $0.06{\pm}0.13$ for AFC and DO to $0.99{\pm}0.01$ between CI and DO. Genetic correlations of productive traits with reproductive traits were ranged from -0.02 to 0.99. Conclusion: The high heritability estimates observed for AFC indicated that reasonable genetic improvement for this trait might be possible through selection. The $h^2$ and r estimates for reproductive traits were slightly different from single versus multi-trait analyses of reproductive traits with production traits. As single-trait method is biased due to selection on milk yield, a multi-trait evaluation of fertility with milk yield is recommended.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.1
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• pp.14-22
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• 2019

Objective: The aim of the study was to find a possible association between the ${\beta}-$ and ${\kappa}-casein$ and ${\beta}-lactoglobulin$ genotypes and important milk physiochemical and technological characteristics such as acidity, alcohol stability, the contents of some minerals and the parameters of acid fermentation ability (FEA) in Czech Fleckvieh Cattle. Methods: Milk and blood samples were collected from 338 primiparous Czech Fleckvieh cows at the same stage of lactation. The genotypes of individual cows for ${\kappa}-casein$ (alleles A, B, and E) and ${\beta}-lactoglobulin$ (alleles A and B) were ascertained by polymerase chain reaction-restriction fragment length polymorphism, while their ${\beta}-casein$ (alleles $A^1$, $A^2$, $A^3$, and B) genotype was determined using melting curve genotyping analysis. The data collected were i) milk traits including active acidity (pH), titratable acidity (TA), alcohol stability (AS); calcium (Ca), phosphorus (P), sodium (Na), magnesium (Mg), and potassium (K) contents; and ii) yoghurt traits including active acidity (Y-pH), titratable acidity (Y-TA), and the counts of both Lactobacilli and Streptococci in 1 mL of yoghurt. A linear model was assumed with fixed effects of herd, year, and season of calving, an effect of the age of the cow at first calving and effects of the casein and lactoglobulin genotypes of ${\beta}-CN$ (${\beta}-casein$, CSN2), ${\kappa}-CN$ (${\kappa}-casein$, CSN3), and ${\beta}-LG$ (${\beta}-lactoglobulin$, LGB), or the three-way interaction between those genes. Results: The genetic polymorphisms were related to the milk TA, AS, content of P and Ca, Y-pH and Lactobacilli number in the fresh yoghurt. The CSN3 genotype was significantly associated with milk AS (p<0.05). The effect of the composite CSN2-CSN3-LGB genotype on the investigated traits mostly reflected the effects of the individual genes. It significantly influenced TA (p<0.01), Y-pH (p<0.05) and the log of the Lactobacilli count (p<0.05). Conclusion: Our findings indicate that the yoghurt fermentation test together with milk proteins genotyping could contribute to milk quality control and highlight new perspectives in dairy cattle selection.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.8
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• pp.1031-1040
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• 2011

The objective of this study was to characterize factors influencing genetic improvement of dairy cattle for milk production at farm level. Data were accumulated from 305-day milk yields and pedigree information from 1,921 first-lactation dairy cows that calved from 1990 to 2007 on 161 farms in Central Thailand. Variance components were estimated using average information restricted maximum likelihood procedures. Animal breeding values were predicted by an animal model that contained herd-year-season, calving age, and regression additive genetic group as fixed effects, and cow and residual as random effects. Estimated breeding values from cows that calved in a particular month were used to estimate genetic trends for each individual farm. Within-farm genetic trends (b, regression coefficient of farm milk production per month) were used to classify farms into 3 groups: i) farms with negative genetic trend (b<-0.5 kg/mo), ii) farms with no genetic trend (-0.5 kg/$mo{\leq}b{\leq}0.5$ kg/mo), and iii) farms with positive genetic trend (b>0.5 kg/mo). Questionnaires were used to gather information from individual farmers on educational background, herd characteristics, farm management, decision making practices, and opinion on dairy farming. Farmer's responses to the questionnaire were used to test the association between these factors and farm groups using Fisher's exact test. Estimated genetic trend for the complete population was $0.29{\pm}1.02$ kg/year for cows. At farm level, most farms (40%) had positive genetic trend ($0.63{\pm}4.67$ to $230.79{\pm}166.63$ kg/mo) followed by farms with negative genetic trend (35%; $-173.68{\pm}39.63$ to $-0.62{\pm}2.57$ kg/mo) and those with no genetic trend (25%; $-0.52{\pm}3.52$ to $0.55{\pm}2.68$ kg/mo). Except for educational background (p<0.05), all other factors were not significantly associated with farm group.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.9
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• pp.1340-1348
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• 2019

Objective: The objectives were to compare variance components, genetic parameters, prediction accuracies, and genomic-polygenic estimated breeding value (EBV) rankings for milk yield (MY) and fat yield (FY) in the Thai multibreed dairy population using five single nucleotide polymorphism (SNP) sets from GeneSeek GGP80K chip. Methods: The dataset contained monthly MY and FY of 8,361 first-lactation cows from 810 farms. Variance components, genetic parameters, and EBV for five SNP sets from the GeneSeek GGP80K chip were obtained using a 2-trait single-step average-information restricted maximum likelihood procedure. The SNP sets were the complete SNP set (all available SNP; SNP100), top 75% set (SNP75), top 50% set (SNP50), top 25% set (SNP25), and top 5% set (SNP5). The 2-trait models included herd-year-season, heterozygosity and age at first calving as fixed effects, and animal additive genetic and residual as random effects. Results: The estimates of additive genetic variances for MY and FY from SNP subsets were mostly higher than those of the complete set. The SNP25 MY and FY heritability estimates (0.276 and 0.183) were higher than those from SNP75 (0.265 and 0.168), SNP50 (0.275 and 0.179), SNP5 (0.231 and 0.169), and SNP100 (0.251and 0.159). The SNP25 EBV accuracies for MY and FY (39.76% and 33.82%) were higher than for SNP75 (35.01% and 32.60%), SNP50 (39.64% and 33.38%), SNP5 (38.61% and 29.70%), and SNP100 (34.43% and 31.61%). All rank correlations between SNP100 and SNP subsets were above 0.98 for both traits, except for SNP100 and SNP5 (0.93 for MY; 0.92 for FY). Conclusion: The high SNP25 estimates of genetic variances, heritabilities, EBV accuracies, and rank correlations between SNP100 and SNP25 for MY and FY indicated that genotyping animals with SNP25 dedicated chip would be a suitable to maintain genotyping costs low while speeding up genetic progress for MY and FY in the Thai dairy population.