Genetic Parameters For Milk Yield Research Articles

Evaluation of performance and estimation of genetic parameters for milk yield and reproductive traits in Awassi and Asaf sheep breeds and crosses in the Palestinian territory.

This study aimed to estimate genetic parameters, including heritability and repeatability, for milk yield and prolificacy in sheep. It included 3682 records from 1837 ewes across various breeds: Awassi, Assaf, and Awassi x Assaf crosses, two Awassi lines: Improved Awassi, AFEC Awassi. The study measured total milk yield (TMY), yield up to 120 days (TMY120), and yield up to 150 days (TMY150), alongside reproductive traits: litter size (LS), number of lambs born alive (NLBA), and lambing interval (LI). The analysis utilized a mixed model and the REML procedure to estimate genetic parameters. Results indicated that litter size (LS) had no significant impact on milk traits, whereas breed, location, ewe parity, and lambing season showed significant effects. Lactation length also significantly influenced TMY. For reproductive traits, treatment was significant for NLBA, with location associated with breed, parity, and season affecting all traits. Heritability estimates for TMY ranged from 0.00 in Awassi x Assaf crosses to 0.11 in Awassi, and for TMY120 and TMY150, from 0.00 in Awassi x Assaf crosses to 0.16 in Awassi. Estimates for LS and NLBA varied similarly. The heritability for LI was 0.03 in Awassi and zero in other breeds and crosses. The findings suggest that in the northern West Bank regions of Nablus and Jenin, Assaf or Awassi x Assaf breeds are preferable. The study underscores the importance of comprehensive performance and pedigree recording for effective sheep farm management.

Genetic parameters for milk yield and reproductive traits in Honduran Holstein cattle.

The aim of this study was to estimate the (co)variance components and genetic parameters for milk yield adjusted to 305d (MY305), calving-to-conception interval (CCI), number of services per conception (NSC) and calving interval (CI) of Honduran Holstein cows, by fitting a bivariate animal model using Maximum Restricted Likelihood procedures. Model included the fixed effects of calving number, the contemporary calving group (farm-season-year of calving and the cow age as covariate). The estimated means and standard deviations for MY, CCI, NSC and CI were, 5098.60 ± 1564.32kg, 168.27 ± 104.71 days, 2.46 ± 1.69 services, and 448.73 ± 109.16 days, respectively; and their estimated heritabilities were 0.21 ± 0.05, 0.03 ± 0.028, 0.02 ± 0.024 and 0.06 ± 0.04, respectively. The genetic correlations between MY305 and CCI, NSC and CI were positive and antagonist, with values of 0.64 ± 0.52, 0.99 ± 0.56, and 0.32 ± 0.24 respectively. Even though moderate to low heritability was estimated for MY305, systematic selection for milk yield, with a reduction in reproductive efficiency, if considered as the only selection criterion is important to be considered. By including reproductive traits and considering permanent environment effects into the breeding program, might yield a slow, but constant and permanent improvement over time.

Genetic-parameter estimation of milk yield in White Maritza sheep breed using different test day models.

The aims of this study were to estimate the genetic parameters of the test day milk yield (TDMY) of the White Maritza sheep breed population and to choose the most appropriate linear models for genetic-parameter estimation of test day milk yield. The White Maritza sheep breed is a multipurpose native sheep breed in Bulgaria. Test day milk yield data were collected from 1992 to 2015 (24years). Milk yield recordings were made in 18 flocks according to the AC method (official milk recording by ICAR regulations). The database includes 8768 test day milk yield records belonging to 987 ewes. The pedigree file includes 1937 animals. Nine test day models (TDMs) were formulated and tested for the estimation of the genetic parameters of milk yield. The first three models were repeatability models (REP models), the second three were random regression models (RRMs), and the last three models were also random regression models with an added Ali and Schaeffer regression to describe the lactation curve using first-, second- and third-order polynomials. The average TDMY was 764.47 mL. There were no significant differences in the values of heritability () calculated by the three REP models: REP1 0.355 0.060, REP2 0.344 0.047 and REP3 0.347 0.060. The same applied to the repeatability coefficients, which, for the three REP models, were 0.384 0.065, 0.376 0.051 and 0.378 0.065, respectively. Based on REP model 1, three models with random regression RRM1, RRM2 and RRM3 were constructed, which is associated with the use of first-, second- and third-order polynomials (for the random effects of both the animal and the permanent environment). The trajectories of calculated by the three RRMs were not similar and demonstrated some differences, both at the beginning and in the middle of the milking period. The RRM with third-order polynomials demonstrated more genetic diversity until the 165th day of lactation, but Akaike information criterion (AIC), Bayesian information criterion (BIC) and log-likelihood (LogL) estimates were higher. The regression models with first- and second-degree polynomials were insufficient to reveal genetic diversity to a higher degree than REP model 1. The trend in the trajectories of calculated by the three random regression models with Ali and Schaeffer regression models (ASRMs) was similar to that of random regression models without the Ali and Schaeffer regression incorporated. Although the noted advantages of the random regression models revealed, to a greater extent, the genetic diversity of test day milk yield, AIC, BIC and LogL estimates indicated that repeatability models achieved a better balance between complexity and fitness and a smaller prediction error compared to random regression models.

Random regression models to estimate genetic parameters for milk yield, fat, and protein contents in Tunisian Holsteins.

This study aimed to find the parsimonious random regression model (RRM) to evaluate the genetic potential for milk yield (MY), fat content (FC), and protein content (PC) in Tunisian Holstein cows. For this purpose, 551,139; 331,654; and 302,396 test day records for MY, FC, and PC were analysed using various RRMs with different Legendre polynomials (LP) orders on additive genetic (AG) and permanent environmental (PE) effects, and different types of residual variances (RV). The statistical analysis was performed in a Bayesian framework with Gibbs sampling, and the model performances were assessed, mainly, on the predictive ability criteria. The study found that the optimal model for evaluating these traits was an RRM with a third LP order and nine classes of heterogeneous RV. In addition, the study found that heritability estimates for MY, FC, and PC ranged from 0.11 to 0.22, 0.11 to 0.17, and 0.12 to 0.18, respectively, indicating that genetic improvement should be accompanied by improvements in the production environment. The study also suggested that new selection rules could be used to modify lactation curves by exploiting the canonical transformation of the random coefficient covariance (RC) matrix or by using the combination of slopes of individual lactation curves and expected daily breeding values.

Genetic evaluation to design a reference cow population for the Holstein breed in Tunisia: a first step toward genomic selection

Context There is a large gap between developed and developing countries in the field of genetic evaluation of livestock animals. Introduction of genomic evaluation procedures and methods could contribute to reducing this gap. Aims The goal of this study was to select a reference cow population for the Holstein breed in Tunisia. Specific objectives were to update adjustments for non-genetic factors affecting milk yield, estimate genetic parameters for milk yield, and predict cow breeding values. Methods A BLUP animal model was used for 11 175 lactations recorded between 2012 and 2017 from 6251 dairy cattle raised in 33 Holstein dairy herds from three types of herds or production sectors. A pedigree file of 16 211 males and females was included in the genetic evaluation. Multiplicative adjustment factors were computed for age and month of calving, using adjusted 305 days in milk. Key results Month of calving, age at calving and farm ownership were significant sources of variation for milk yield. Cows calving in autumn and early winter (September–January) yielded more milk than those calving in spring (February–May) by 430 kg, and summer (June–August) by 455 kg. Holstein cows in Tunisia reached their maximum milk yield during the fourth lactation. Average adjusted milk yield for days in milk, month and age of calving was 6621 ± 2883 kg. Heritability estimates of adjusted milk yield were 0.14 ± 0.02 for all seven lactations and 0.16 ± 0.03 for the three first lactations. Phenotypic correlations among lactations were all positive, ranging from 0.25 to 0.43. On the basis of two main traditional designs (extreme yield and top accuracy), 1000 cows were selected to form the Tunisian female reference population. Conclusion The first step of genomic evaluation has been realised by forming a reference population from cows selected for adjusted milk yield production, based on their predicted breeding values and accuracies, using a BLUP animal model. Implication In the absence of progeny testing and sufficient proven sires, a cow reference population could represent the alternative for implementing genomic selection in developing countries.

Changes in genetic parameters for milk yield and heat tolerance in the Thai Holstein crossbred dairy population under different heat stress levels and over time

The objectives of this study were to investigate changes in genetic parameters for milk yield (MY) and heat tolerance of the crossbred Thai Holstein Friesian population under different heat stress levels over time, and to investigate the threshold point of heat stress manifestation on milk production. Genetic parameters were estimated using single-step genomic REML (ssGREML) and traditional REML models. Data included 58,965 test-day MY records from 1999 to 2008 (old data) and 105,485 test-day MY records from 2009 to 2018 (recent data) from the first parity of 24,520 cows. The pedigree included 55,168 animals, of which 882 animals had genotypes. Variance components were estimated with the REMLF90 program using a repeatability model with random regressions on a function of temperature-humidity index (THI) for additive genetic and permanent environmental effects. Fixed effects included farm-calving season combination, breed group-months in milk combination, and age at first calving. Random effects included additive genetic (intercept and slope) effects, permanent environmental (intercept and slope) effects, and herd-month-year of test. The phenotypic mean for MY was 13.33 ± 4.39 kg/d in the old data, and 14.48 ± 4.40 kg/d in the recent data. Estimates over different THI levels for the intercept additive genetic variance using old data ranged from 2.61 to 2.77 and from 5.02 to 5.38 using recent data with the REML method. In ssGREML analyses (performed with recent data only) the estimates for the intercept additive genetic variance ranged from 4.71 to 5.05. Estimates for the slope additive genetic variance were close to zero in all cases, with the largest values (0.024-0.030) at the most extreme THI value (80). Using REML, the covariance between the intercept and the slope additive genetic effects (THI from 72 to 80) ranged from -0.001 to 0.019 with old data and from 0.027 to 0.060 with recent data. The same covariance ranged from 0.026 to 0.057 in ssGREML analyses. The covariance between the intercept and the slope permanent environmental effects ranged from -0.42 to -0.67 for all data and THI levels. Across THI levels, the genetic correlation between MY and heat tolerance varied from -0.06 to 0.13 with old data, from 0.16 to 0.30 with recent data in REML analyses, and from 0.15 to 0.30 in ssGREML analyses, suggesting that in the current population the top animals for MY are more resistant to heat stress. This was expected, because of the introduction of Bos indicus genes in the last years. Heritability estimates for MY ranged from 0.19 to 0.21 (old data) and from 0.33 to 0.40 (recent data) for REML analyses. Heritability estimates for MY using ssGREML ranged from 0.31 to 0.38. A decline in MY was found when the animals' breed composition had more than 97.3% of Holstein genetics, and it was greatest at THI 80. The heritability and genetic correlations observed in this study show that selection for MY is possible without a negative correlated response for heat tolerance. Although the inclusion of genomic information is expected to increase the accuracy of selection, more genotypes must be collected for successful application. Future research should address other production and fitness traits within the Thai Holstein population.

Genomic-polygenic evaluations using random regression models with Legendre polynomials and linear splines for milk yield and fat percentage in the Thai multibreed dairy cattle population

This research aimed to compare single-step random regression models with third-order Legendre polynomials (RRLP) and splines with four (RRSP4) and five (RRSP5) knots for milk yield (MY) and fat percentage (FP) genomic-polygenic evaluations in the Thai multibreed dairy population. Models were compared using estimates of variance components, genetic parameters, goodness of fit, genomic-polygenic EBV (GPEBV) accuracies, and animal rankings. The dataset included pedigree and monthly test-day records (69,029 for MY; 29,878 for FP) of 7,206 first-lactation cows from 761 farms, and genotypic records (74,144 actual and imputed SNP) from 2,661 animals. Variance components and genetic parameters for MY and FP were estimated using REML procedures. Models contained contemporary group (herd-year-season), calving age, heterozygosity, and population lactation curve regression coefficients as fixed effects. Random effects were animal additive genetic, permanent environment random regression coefficients, and residual. The population lactation curve, additive genetic, and permanent environment effects were fitted using regression coefficients of third-order Legendre polynomials for RRLP, splines of four knots for RRSP4, and five knots for RRSP5. The estimates of 305-day additive genetic variances (σ^a2) and heritabilities (h^2) were higher for RRLP (MY: σ^a2 = 279,893.2 kg2, h^2 = 0.27; FP: σ^a2 = 0.10%2, h^2 = 0.16) than for RRSP4 (MY: σ^a2 = 260,178.1 kg2, h^2 = 0.19; FP: σ^a2 = 0.08%2, h^2 = 0.11), and for RRSP5 (MY: σ^a2 = 266,198.0 kg2, h^2 = 0.20; FP: σ^a2 = 0.08%2, h^2 = 0.12). Similarly, RRLP yielded better goodness of fit and higher GPEBV accuracies than RRSP4 and RRSP5. The goodness of fit values for RRLP were 293,813 for -2 log-likelihood (-2logL), 293,855 for the Akaike's information criterion (AIC), and 293,915 for the Bayesian information criterion (BIC). The corresponding values for RRSP4 were 362,738 for -2logL, 362,888 for AIC, and 363,101 for BIC, and those for RRSP5 were 354,473 for -2logL, 354,699 for AIC, and 355,020 for BIC. Lastly, the GPEBV accuracies for RRLP were 49.3% for MY and 38.6% for FP, those for RRSP4 were 47.2% for MY and 37.8% for FP, and the ones for RRSP5 were 47.4% for MY, 37.5% for FP. Rank correlations between animal GPEBV from these three models were high ranging from 0.91 to 0.98 for MY and 0.88 to 0.98 for FY. Results indicated that GPEBV from RRLP should be preferred to GPEBV from RRSP4 and RRSP5 to increase selection responses for MY and FP in the Thai multibreed dairy population.

Genetic parameters for milk yield and quality traits of Brazilian Holstein cows as a function of temperature and humidity index.

Measurements of milk yield (MY), somatic cell score (SCS), percentage of fat (FP), protein (PP), lactose (LP), casein (CP) and percentage of palmitic (C16:0), stearic (C18:0), oleic (C18:1), total saturated (SFA), unsaturated (UFA), monounsaturated (MUFA) and polyunsaturated (PUFA) fatty acids in milk from 5,224 Holstein cows were evaluated as a function of a temperature and humidity index (THI). Legendre orthogonal polynomials from second to seventh order were tested. The best fit order for MY, PP and C18:0 was the third, whereas the second for all other traits. The heritability estimates decreased for MY (0.31 to 0.14), FP (0.28 to 0.16), LP (0.43 to 0.30), SCS (0.14 to 0.09), SFA (0.33 to 0.22) and C16:0 (0.31 to 0.26), whereas increased for CP (0.32 to 0.42), MUFA (0.08 to 0.13), UFA (0.07 to 0.11) and C18:1 (0.07 to 0.11) as the THI level increased. For PP, heritabilities (0.26 to 0.39) presented larger values in intermediate THI. For PUFA and C18:0, heritabilities were approximately constant (0.13 to 0.14 and 0.15, respectively). However, the greatest variations may have been the result of the limitations of Legendre polynomials at the extreme points of the curve, and the pattern of heritabilities curves was approximately constant for the evaluated traits. Spearman's rank correlations between breeding values in extreme THI levels were greater than 0.80 for all traits considering all animals, only cows and only bulls. When considering the top 1% and the top 50% animals (only cows, only bulls and all), Spearman correlations smaller than 0.70 were found, suggesting reranking of the animals. Although there was little variation in the variance components over THI, it is possible that there is no heat stress in the animals studied, because, on average, there was no great impact of the thermal load on the traits. One possible explanation is the use of herds with little climatic difference among herds, as well as the use of fans and sprinklers into the barns. However, the THI levels may be important factors in the selection process, as reranking of animals was verified.

Estimation of genetic parameters for milk yield using a random regression test-day model in first parity dairy cows under pasture-based systems of Los Ríos region in Chile

In dairy cattle, genetic selection for milk yield was generally based on 305 days lactation records that were calculated from available monthly test-day milk yield records. A test-day milk yield record, multiplied by the number of days between the current and following test-day, was the monthly milk yield and summed to all other monthly milk yields represented a 305 days lactation yield. Cows that for any reason did not complete their lactation got a 305 days yield via correction factors assuming a common lactation curve. Random regression models allow individual deviation from a common curve. The objective of this study was to estimate genetic parameters for daily milk yield using a random regression model (RRM) in Chilean dairy cows. A data set containing 97,683 monthly test-day records of 10,528 cows from 15 commercial dairy herds of Los Rios Region in southern Chile was used. Days in milk (DIM) were modelled using the fourth-order Legendre polynomials and the model also included, as fixed effects, contemporary group and cow age at test-day as a covariate. The average daily milk yield was 17.83 ± 5.25 kg. Average estimated heritability and repeatability from five to 305 DIM was 0.26 ± 0.02 and 0.61 ± 0.04, respectively. The heritability estimate varied from 0.23 to 0.31. Both parameters did not vary dramatically except after 270 DIM when repeatability increased while heritability decreased. Although the estimated genetic parameters did not seriously depart from the most recent results available in the Chilean literature, they are mathematically more precise for estimating the true parameters than those calculated using adjustment factors, suggesting that the model used could be the starting point to develop a genetic evaluation system for dairy cattle in Chile.

Genetic parameters for milk yield in imported Jersey and Jersey-Friesian cows using daily milk records in Sri Lanka.

ObjectiveThis study was conducted to estimate genetic parameters for milk yield traits using daily milk yield records from parlour data generated in an intensively managed commercial dairy farm with Jersey and Jersey-Friesian cows in Sri Lanka.MethodsGenetic parameters were estimated for first and second lactation predicted and realized 305-day milk yield using univariate animal models. Genetic parameters were also estimated for total milk yield for each 30-day intervals of the first lactation using univariate animal models and for daily milk yield using random regression models fitting second-order Legendre polynomials and assuming heterogeneous residual variances. Breeding values for predicted 305-day milk yield were estimated using an animal model.ResultsFor the first lactation, the heritability of predicted 305-day milk yield in Jersey cows (0.08±0.03) was higher than that of Jersey-Friesian cows (0.02±0.01). The second lactation heritability estimates were similar to that of first lactation. The repeatability of the daily milk records was 0.28±0.01 and the heritability ranged from 0.002±0.05 to 0.19±0.02 depending on day of milk. Pearson product-moment correlations between the bull estimated breeding values (EBVs) in Australia and bull EBVs in Sri Lanka for 305-day milk yield were 0.39 in Jersey cows and −0.35 in Jersey-Friesian cows.ConclusionThe heritabilities estimated for milk yield in Jersey and Jersey-Friesian cows in Sri Lanka were low, and were associated with low additive genetic variances for the traits. Sire differences in Australia were not expressed in the tropical low-country of Sri Lanka. Therefore, genetic progress achieved by importing genetic material from Australia can be expected to be slow. This emphasizes the need for a within-country evaluation of bulls to produce locally adapted dairy cows.

Genome-wide association study applied to type traits related to milk yield in water buffaloes (Bubalus bubalis)

This research aimed to estimate genetic parameters for milk yield and type traits [withers height (WH), croup height (CH), body length (BL), croup length (CL), iliac width (ILW), ischial width (ISW), and thoracic circumference] in Murrah buffaloes and to identify genomic regions related to type traits by applying a single-step genome-wide association study. Data used to estimate the genetic parameters consisted of 601 records of milk yield in the first lactation and the aforementioned type traits. For the single-step genome-wide association study, 322 samples genotyped with a 90K Axiom Buffalo Genotyping array (Thermo Fisher Scientific, Santa Clara, CA) were used. Bivariate analysis revealed that heritability for milk yield (kg) at 305 d was 0.31 ± 0.11, whereas it ranged from 0.22 ± 0.07 to 0.34 ± 0.09 for the studied conformation traits. Based on the percentages of genetic variance explained by windows of 10 markers, there were 16 genomic regions explaining more than 0.5% of the variance for WH, CH, BL, CL, ILW, ISW, and thoracic circumference. Between those regions, 4 were associated with more than 1 trait, suggesting pleiotropic roles for some genes of Bos taurus autosome (BTA) 12 on CL and WH, BTA13 on ISW and ILW, BTA23 on CH and BL, and BTA28 on ISW and BL. Most of these regions coincide with known quantitative trait loci for milk traits. Thus, further studies based on sequence data will help to validate the association of this region with type traits and likely identify the causal mutations.

Genetic parameters for milk yield and lactation persistency in the three first parities of Iranian Holstein cows

Antecedentes : La persistencia de la lactancia tiene una gran influencia en la salud, la reproduccion y los costos de alimentacion de las granjas lecheras. Objetivo : Estimar los componentes de (co)varianza y los parametros geneticos de la produccion de leche a 100 y 305 d, asi como la persistencia de la lactancia en vacas Holstein en Iran. Metodos : Se utilizaron registros recopilados entre enero de 2000 y diciembre de 2012 por el Centro de cria de animales de Iran (Karaj, Iran). Se utilizaron las siguientes cuatro medidas de persistencia de la lactancia: P 21 : Proporcion de produccion de leche en los segundos 100-d en leche (DIM) dividida por la de los primeros 100-d. P 31 : Proporcion de produccion de leche en los terceros 100-d dividido por el de los primeros 100-d. P W : medida de persistencia derivada de la funcion gamma incompleta. P J : diferencia entre el rendimiento de leche en el 60 y el 280 dia de lactancia. Resultados : La heredabilidad estimada de la persistencia de la lactancia para los tres primeros partos (primera, segunda y tercera lactancia) vario de 0,01 a 0,06; 0,02 a 0,10; y 0,01 a 0,12, respectivamente. Las correlaciones geneticas entre las medidas de persistencia de lactancia para los tres primeros partos variaron de 0,77 a 0,98; 0,65 a 0,98; y 0,58 a 0,98, respectivamente; mientras que los valores correspondientes para las correlaciones geneticas entre la persistencia de la lactancia con la produccion de leche a 305 d variaron de 0,18 a 0,63; 0,32 a 0,75; y 0,41 a 0,71, respectivamente. La repetibilidad estimada para las medidas de persistencia de la lactancia vario de 0,06 a 0,20. Conclusion : La correlacion genetica positiva moderada entre la persistencia de la lactancia y la produccion de leche a 305-d indica que la seleccion para aumentar la produccion de leche puede mejorar ligeramente la persistencia de la lactancia.

Use of Random Regression Test-Day Model to Estimate Genetic Parameters of Milk Yield in Holstein Cows

(Co) variance components and genetic parameters were estimated for milk yield of Iranian Holstein cows. A total number of 68,945 milk test-day records of first, second and third lactations of 8515 animals from 100 sires and 7743 dams originated from 34 herds collected during 2007 to 2009 by Iranian animal breeding center were used. The ASReml computer program was used to analyze the milk test-day records using the random regression procedure. Herd test date (HTD), milking times per day (milking frequency), number of lactations, year of birth, year of calving, age of animal at calving and days in milk (DIM) considered as fixed effects and additive genetic effects and animal permanent environmental effects were considered as the random effects. Additive genetic variance, animal permanent environment variance, residual variance, phenotypic variance, heritability and repeatability were estimated during different months of lactation between 5.7 - 19.6, 15.3 - 27.1, 31.4 - 17.2, 45.8 - 64.83, 0.1 - 0.32 and 0.4 - 0.6, respectively. Genetic correlation and phenotypic correlation were also estimated between months of lactation in range of -0.35 - 0.98 and 0.03 - 0.67, respectively. Genetic correlation and phenotypic correlation both showed the same changing pattern and they decreased as the interval between months of lactation increased.

Milk yield, fat yield and fat percentage associations in a Thai multibreed dairy population

Milk yield (MY), fat yield (FY) and fat percentage (FP) are important traits for dairy cattle selection and dairy farm profitability in Thailand. Most dairy cattle in Thailand are multibreed, comprising multiple breeds (three from eight breeds per animal). This multibreed composition of dairy animals has generated a large amount of variation in dairy traits among cows raised under farm, tropical environmental conditions across the country. Effective genetic evaluation and selection programs for dairy traits in this population require reliable variance components and genetic parameters estimated under the management, nutritional, health, and climatic conditions in Thai dairy farms. Thus, the objective of this study was to estimate genetic parameters for MY, FY and FP in a Thai dairy multibreed dairy cattle population using farm-collected information. The dataset consisted of pedigree and phenotypic data for MY, FY and FP from 6596 first lactation cows from 687 farms. The data were analyzed using a three-trait (MY, FY and FP), animal mixed model. Fixed effects were herd-year-season, Holstein fraction, heterozygosity and age at first calving. Random effects were animal and residual. An average, information-restricted, maximum likelihood procedure was used to estimate variance components, which in turn were used to compute heritabilities and genetic correlations. Means (SD) were 4315.43 kg (1112 kg) for MY, 157.41 kg (50.42 kg) for FY and 3.59% (0.56%) for FP. Heritability estimates were 0.22 ± 0.06 for MY, 0.17 ± 0.06 for FY and 0.24 ± 0.07 for FP. Genetic correlations were 0.47 ± 0.16 between MY and FY, −0.30 ± 0.20 between MY and FP, and 0.30 ± 0.21 between FY and FP. These estimates of genetic parameters indicated that Thai dairy producers would achieve reasonable amounts of genetic progress if they selected dairy animals based on MY and either FY or FP.

Genetic parameters for milk yield and reproduction traits in the Chilean Dairy Overo Colorado cattle breed

The objective of this study was to estimate genetic parameters for milk production and reproduction traits, with phenotypic and pedigree information from the Dairy Overo Colorado breed from southern Chile. Single and multi-trait mixed models were used to analyze the data. Analyses were performed using ASReml software. Heritability estimates for milk yield, fat yield, protein yield, fat content, protein content, age at first calving and calving interval, were 0.29, 0.34, 0.30, 0.56, 0.56, 0.01 and 0.002, respectively. Genetic correlations between yield traits were high and positive (≥ 0.81). Genetic correlations of milk yield with fat content (-0.32) and protein content (-0.30) were unfavorable. Genetic correlation estimates between age at first calving and yield traits were negative between -0.71 and -0.29. Phenotypic correlations between calving interval and milk yield traits were slightly positive (0.06-0.09), suggesting a weak relationship between reproduction and yield in this population. Genetic variation is considerable for milk production traits. Genetic improvement for age at first calving and calving interval may be slow, because small differences between animals and low reliabilities are expected for estimated breeding values for these traits due to low heritabilities. The design of an efficient breeding program for yield traits based on artificial insemination for this population is feasible using either progeny testing or genomic breeding value prediction for selecting sires.

Genetic and phenotypic associations of type traits and body condition score with dry matter intake, milk yield, and number of breedings in first lactation Canadian Holstein cows

The objective of the present study was to estimate genetic parameters of milk yield (MY), intake traits, type traits, body condition score (BCS), and number of breedings (NOB) in first lactation Canadian Holsteins with a focus on the possibility of using type traits as an indicator of feed intake. Data were obtained from the Canadian Dairy Network and Valacta. A mixed linear model was fitted under REML for the statistical analysis. The multivariate (five traits) model included the fixed effects of age at calving, stage of lactation, and herd-round-classifier for type traits; age at calving, stage of lactation, and herd–year–season of calving (HYS) for BCS; age at calving and HYS for MY, feed intake, and NOB. Animal and residual effects were fitted as random effects for all traits. Estimates of heritabilities for MY, dry matter intake (DMI), angularity, body depth, stature, dairy strength, final score, BCS, and NOB were 0.41, 0.13, 0.24, 0.30, 0.50, 0.30, 0.22, 0.20, and 0.02, respectively. Genetic correlations between type traits and DMI ranged from 0.16 to 0.60. Results indicate that type traits appear to have the potential to predict DMI as a combination/index of two or more traits.

Estimation of genetic parameters for milk traits in cows milked once- or twice-daily in New Zealand

The objective of the present study was to estimate genetic parameters for milk yields, average somatic cell score (SCS) and milk composition traits in dairy cows milked either once a day (OAD) or twice a day (TAD) in New Zealand. The data set comprised 124,620 and 194,631 lactation records from OAD and TAD populations, respectively, during the period 2008–2012. Overall, estimates of parameters were similar between milking frequencies (MF), although heritabilities of production traits tended to be greater in the TAD cows. Estimates of heritability in OAD and TAD were: 0.33 and 0.36 for milk yield; 0.21 and 0.26 for fat yield; 0.22 and 0.25 for protein yield; and 0.12 and 0.12 for SCS, respectively. Estimates of correlations were similar across MF, in particular the genetic correlation between milk yield and protein yield (0.84 for TAD and 0.85 for OAD). Estimates of genetic correlations between SCS and other traits tended to be close to zero in both populations. The results indicate that genetic progress can be lower in the OAD population due to lower phenotypic and genetic variances compared to the TAD population. However, a potential disadvantage is that evaluating both dairy populations together could lead to systematic inaccuracies and biases in the estimation of breeding values for the population milked OAD as future dams.

Phenotypic and genetic parameters for milk yield in traditional Nublang cattle (Bos indicus) of Bhutan

SummaryThe Nublang is a unique dual-purpose breed ofBos indicuscattle found in Bhutan. They have been crossed with Mithun (Bos frontalis) andBos taurusbreeds in an effort to improve milk production leading to a decline in the population of purebred Nublang. An alternative to crossbreeding would be the genetic improvement of milk yield within purebred Nublang, thus allowing conservation and sustainable utilization of the pure breed. It is important to measure the genetic variability (heritability) of milk yield to assess the potential for genetic improvement within Nublang. Therefore this study estimated phenotypic and genetic parameters of milk yield using 2 052 test day milk yields from 66 cows recorded from 1997 to 2013 in the National Nublang Farm, Tashiyangphu. The average daily milk yield (DMY) was 2.12 ± 0.7 litres (N 2 052, range: 0.3–5.0 L, CV: 34 percent). Parity, month in milk and year all had a significant effect on DMY (P< 0.05). The average lactation milk yield (LMY) was 519.2 ± 151 L (N: 261, range: 115–881.7, CV: 29.1 percent) and average lactation length was 239 days. LMY was significantly influenced by parity (P< 0.05) and season of calving. The heritability of DMY and LMY was 0.22 ± 0.16 and 0.13 ± 0.20, respectively. The repeatability estimates were 0.45 ± 0.05 and 0.49 ± 0.08 for DMY and LMY, respectively. Overall the heritability and repeatability estimates of milk yields indicate potential for genetic improvement of milk yield in Nublang cattle through selection. However, it is recommended that a larger dataset is generated to enable more precise estimates of genetic parameters.

English

In this study, the effects of varying order of Legendre polynomials (LP) for permanent environmental (Pe) variance structure on the estimates of genetic parameters for first-lactation milk yield were evaluated fitting random regression (RR) test-day animal models. The total data set included 6850 test-day milk yield records from 800 first-lactation Holstein Friesian cows that calved between 1997 and 2013 and the pedigree file with total of 1779 animals. Four different random regression models (RR1, RR2, RR3 and RR4) all with second order LP for additive genetic effects but with varying order (intercept, 1st, 2nd and 3rd, respectively) of LP for modelling the Pe variances structure were tested for estimation of variance components and corresponding genetic parameters for milk yield. Variance components were estimated by average information restricted maximum likelihood method. The performances of competing RR models in the estimation of variance components were compared using estimates of log-likelihoods and the size of residual variances. Results showed that the estimates of log-likelihoods were higher and residual variances were lower for models that fitted second (RR3) and third order (RR4) LP for Pe effects. Heritability (h2) and genetic correlations from RR3 and RR4 models ranged from 0.13 to 0.29 and 0.45 to 0.98, respectively. Models with lower order fits (RR1 and RR2) with either a constant or a linear term for Pe resulted in oscillatory trend for variance components and highly erratic h2 estimates ranged from 0.18 to 0.52. Genetic correlations from these models were also implausible biologically indicating that models with lower order fits for the Pe effects were not robust enough to accurately model the variance structure at different stages of lactation. It is therefore suggested that at least a second or higher order polynomial fits are needed to model the Pe variance structure for the accurate estimation of genetic parameters for milk yield in first-lactation Ethiopian Holsteins. Key words: Dairy cows, milk yield, random regression model, genetic parameters, test-day.

Random regression test day models to estimate genetic parameters for milk yield and milk components in Philippine dairy buffaloes.

Heritabilities and genetic correlations for milk production traits were estimated from first-parity test day records on 1022 Philippine dairy buffalo cows. Traits analysed included milk (MY), fat (FY) and protein (PY) yields, and fat (Fat%) and protein (Prot%) concentrations. Varying orders of Legendre polynomials (Leg(m)) as well as the Wilmink function (Wil) were used in random regression models. These various models were compared based on log likelihood, Akaike's information criterion, Bayesian information criterion and genetic variance estimates. Six residual variance classes were sufficient for MY, FY, PY and Fat%, while seven residual classes for Prot%. Multivariate analysis gave higher estimates of genetic variance and heritability compared with univariate analysis for all traits. Heritability estimates ranged from 0.25 to 0.44, 0.13 to 0.31 and 0.21 to 0.36 for MY, FY and PY, respectively. Wilmink's function was the better fitting function for additive genetic effects for all traits. It was also the preferred function for permanent environment effects for Fat% and Prot%, but for MY, FY and PY, the Legm was the appropriate function. Genetic correlations of MY with FY and PY were high and they were moderately negative with Fat% and Prot%. To prevent deterioration in Fat% and Prot% and improve milk quality, more weight should be applied to milk component traits.