scholarly journals An investigation into heterogeneity of variance for milk and fat yields of Holstein cows in Brazilian herd environments

1999 ◽  
Vol 22 (3) ◽  
pp. 375-381 ◽  
Author(s):  
Claudio Napolis Costa

Heterogeneity of variance in Brazilian herd environments was studied using first-lactation 305-day mature equivalent (ME) milk and fat records of Holstein cows. Herds were divided into two categories, according to low or high herd-year phenotypic standard deviation for ME milk (HYSD). There were 330 sires with daughter records in both HYSD categories. Components of (co)variance, heritability, and genetic correlations for milk and fat yields were estimated using a sire model from bivariate analyses with a restricted maximum likelihood (REML) derivative-free algorithm. Sire and residual variances for milk yield in low HYSD herds were 79 and 57% of those obtained in high HYSD herd. For fat yield they were 67 and 60%, respectively. Heritabilities for milk and fat yields in low HYSD herds were larger (0.30 and 0.22) than in high HYSD herds (0.23 and 0.20). Genetic correlation between expression in low and high HYSD herds was 0.997 for milk yield and 0.985 for fat yield. Expected correlated response in low HYSD herds based on sires selected on half-sister information from high HYSD was 0.89 kg/kg for milk and 0.80 kg/kg for fat yield. Genetic evaluations in Brazil need to account for heterogeneity of variances to increase the accuracy of evaluations and the selection efficiency for milk and fat yields of Holstein cows. Selection response will be lower in low variance herds than in high variance herds because of reduced differences in daughter response and among breeding values of sires in low HYSD herds. Genetic investments in sire selection to improve production are more likely to be successful in high HYSD herds than in low HYSD Brazilian herds.

1968 ◽  
Vol 10 (4) ◽  
pp. 445-450 ◽  
Author(s):  
K. Christensen

The relationships among five traits in dairy cattle (milk yield, fat yield, protein, fat % and protein %) were examined on the original and on a logarithmic scale. The data comprised the records on 5333 Red Danish cows tested at the Danish Progeny Testing Stations during the years 1960–66. None of the heritabilities or the correlations among the variates were altered appreciably by transformation. For the five traits heritability estimates were 0·56, 0·80, 0·65, 0·64 and 0·56, respectively. Phenotypic and genetic correlations among milk yield, fat yield and protein yield were all very high, about 0·95. The correlations between fat % and protein % were about 0·6. The coefficient of variation of a variate proved to be a good approximation of the standard deviation of the transformed variate even for milk, fat and protein yield with coefficients of variation of 17–18%. It was concluded that little is likely to be gained by using index selection for fat yield and protein yield. However, about 90% of the response obtainable by direct selection for fat or protein yield could be obtained merely by selection for milk yield. A large correlated response for protein yield could be obtained by selecting for fat yield.


1999 ◽  
Vol 22 (3) ◽  
pp. 383-386 ◽  
Author(s):  
S.G. Machado ◽  
M.A.R. Freitas ◽  
C.H. Gadini

Data were obtained from 17,968 records from 2,130 first lactations of Holstein cows calving between 1988 and 1991. The subjects were daughters of 136 sires monitored by Brazilian Breeders Association, Animal Science Institute, Department of Agriculture, a branch of the State of São Paulo. Data were divided into 10 subsets based on the number of days in milk yield. Test day milk yields (M1 to M10) and 305-day milk yield (M305) were the traits studied. These traits were adjusted for several environmental effects: class of cow age at calving, interval from calving to first test day, and herd-year-season. Restricted maximum likelihood estimates of (co)variance components were obtained from one and two-traits analysis under a sire model. Estimates of heritabilities for M ranged from 0.04 to 0.32. The highest values were found in the second half of lactation (M5 to M7). Heritability estimate for M305 was 0.32. Genetic correlations between individual test days and M305 ranged from 0.78 to 1.00. Results suggested that test day milk yields, mainly in mid-lactation, can be used instead of 305-day milk yield in genetic evaluations, because estimates of these two-trait heritabilities are nearly alike. Moreover, early selection can reduce generation intervals.


2016 ◽  
Vol 52 ◽  
pp. 6-12 ◽  
Author(s):  
M. V. Gladiy ◽  
G. S. Kovalenko ◽  
S. V. Priyma ◽  
G. A. Holyosa ◽  
A. V. Tuchyk ◽  
...  

The main goal of dairy breeds selection should be improving breeding and productive qualities of animals under modern conditions. The majority of farms, using native breeds to produce milk, has created optimal conditions for keeping and feeding, selection and matching, growing of replacements etc. Further improvement of created native dairy breeds for economically useful traits occurs at total use of purebred Holstein bulls (semen) of foreign selection. In order to realistically assess milk productivity (milk yield, fat content in milk and fat yield) of Ukrainian Black-and-White and Red-and-White Dairy cows should be conducted a comparative analysis of Holstein cows under the same conditions of feeding and keeping. It was established that Ukrainian Red-and-White Dairy cows were characterized by the highest milk yields for 305 days of all lactations, taken into account, the among three investigated breeds. Their milk yield during the first lactation was 5933 kg of milk, during the second – 6393 kg, the third – 6391 kg and during higher lactation – 6650 kg. Ukrainian Black-and-White Dairy cows were second by milk yield (except for the second lactation), during the first lactation – 5932 kg of milk, the third – 6462 kg and higher – 6541 kg, and Holstein cows were third, during the first lactation – 5794 kg of milk, the second – 6381 kg, the third – 6335 kg and higher – 6469 kg. The fat content was almost the same and varied within 3.49-3.58% in milk of Ukrainian Red-and-White Dairy cattle, 3.50-3.60% in milk of Ukrainian Black-and-White Dairy cattle and 3.50-3.56% in Holsteins’ milk. The difference between the breeds was within 0.01-0.04%. All the investigated breeds had predominance in fat yield for three lactations over standards of these breeds: Ukrainian Red-and-White Dairy cows from 75.1 to 93.4 kg, Ukrainian Black-and-White Dairy cows – 75.1-89.0 kg respectively and Holstein cows – 41.9-60.2 kg. It was found different level of positive correlation between milk yield and fat yield in all the cases and high correlation (r = 0.604-0.921, P < 0.001) in five cases (41.7%) Negative correlation coefficients indicate that selection of animals to higher milk yield in the herd will decrease the second trait – fat content in milk. Positive and highly significant correlation between milk yield and fat yield indicates that selection of cows in the herd to higher milk yields will increase fat yield. It was revealed that bulls were among the factors impacted the milk productivity (milk yield, fat content, fat yield) of three investigated breeds. So, the force (η²x) of father’s impact on milk yield was15.4-47.9%, fat content – 22.0-43.4% and fat yield – 14.9-47.7% taking into account a lactation and a breed. The force of lines impact (η²x) was second; it was on milk yield 6.1-24.5%, fat content – 4.1-17.1 and fat yield – 5.8-23.5%. The force of breeds impact (η²x) was last; it was on milk yield 0.3-2.9%, fat content – 0.2-0.3% and fat yield – 0.6-2.7%. So, the comparative studies of milk productivity of Ukrainian Red-and-White and Black-and-White Dairy cattle with Holsteins indicate that under similar conditions of feeding and keeping, these native breeds can compete with Holstein cattle. The milk yield for 305 days of higher lactation was 6650 kg of milk in Ukrainian Red-and-White Dairy cows, 6541 kg in Ukrainian Black-and-White Dairy cows and 6469 kg in Holsteins. It was found the inverse correlation r = -0.025-0.316 between milk yield and fat content in milk in most cases. Selection and matching of animals in the herd should be carried out simultaneously on these traits. It was found positive repeatability of milk yields between the first and second, the third and higher lactations (rs = 0.036-0.741), indicating the reliability of forecasting increase in milk productivity during the next lactations in all herd. Bulls have the greatest impact (η²x) on milk productivity among the factors taken into account: milk yield – 15.4-47.9%, fat content in milk – 22.0-43.4% and fat yield – 14.9-47.7%.


2018 ◽  
Vol 58 (10) ◽  
pp. 1966
Author(s):  
Purna Kandel ◽  
Sylvie Vanderick ◽  
Marie-Laure Vanrobays ◽  
Hélène Soyeurt ◽  
Nicolas Gengler

Methane (CH4) emission is an important environmental trait in dairy cows. Breeding aiming to mitigate CH4 emissions require the estimation of genetic correlations with other economically important traits and the prediction of their selection response. In this study, test-day CH4 emissions were predicted from milk mid-infrared spectra of Holstein cows. Predicted CH4 emissions (PME) and log-transformed CH4 intensity (LMI) computed as the natural logarithm of PME divided by milk yield (MY). Genetic correlations of PME and LMI with traits used currently were approximated from correlations between estimated breeding values of sires. Values were for PME with MY 0.06, fat yield (FY) 0.09, protein yield (PY) 0.13, fertility 0.17; body condition score (BCS) –0.02; udder health (UDH) 0.22; and longevity 0.22. As expected by its definition, values were negative for LMI with production traits (MY –0.61; FY –0.15 and PY –0.40) and positive with fertility (0.36); BCS (0.20); UDH (0.08) and longevity (0.06). The genetic correlations of 33 type traits with PME ranged from –0.12 to 0.25 and for LMI ranged from –0.22 to 0.18. Without selecting PME and LMI (status quo) the relative genetic change through correlated responses of other traits were in PME by 2% and in LMI by –15%, but only due to the correlated response to MY. Results showed for PME that direct selection of this environmental trait would reduce milk carbon foot print but would also affect negatively fertility. Therefore, more profound changes in current indexes will be required than simply adding environmental traits as these traits also affect the expected progress of other traits.


2019 ◽  
Vol 32 (2) ◽  
pp. 100-106 ◽  
Author(s):  
Farzane Shokri-Sangari ◽  
Hadi Atashi ◽  
Mohammad Dadpasand ◽  
Fateme Saghanejad

Background: Lactation persistency influences cow health and reproduction and has an impact on the feed costs of dairy farms. Objective: To estimate (co)variance components and genetic parameters of 100- and 305-d milk yield, and lactation persistency in Holstein cows in Iran. Methods: Records collected from January 2000 to December 2012 by the Animal Breeding Center of Iran (Karaj, Iran) were used. The following four measures of lactation persistency were used: P21: Ratio of milk yield in the second 100-d in milk (DIM) divided by that of the first 100-d. P31: Ratios of milk yield in the third100-d divided by that of the first 100-d. PW: The persistency measure derived from the incomplete gamma function. PJ: The difference between milk yield in day 60th and 280th of lactation. Results: The estimated heritability of lactation persistency for the three first parities (first, second, and third lactation) ranged from 0.01 to 0.06, 0.02 to 0.10, and 0.01 to 0.12, respectively. Genetic correlations among lactation persistency measures for the three first parities ranged from 0.77 to 0.98, 0.65 to 0.98, and 0.58 to 0.98, respectively; while corresponding values for genetic correlations among lactation persistency with 305-d milk production ranged from 0.18 to 0.63, 0.32 to 0.75, and 0.41 to 0.71, respectively. The estimated repeatability for lactation persistency measures ranged from 0.06 to 0.20. Conclusion: The moderate positive genetic correlation between lactation persistency and 305-d milk yield indicates that selection for increasing milk yield can slightly improve lactation persistency.Key words: dairy cattle, heritability, lactation curve, milk yield, persistency, repeatability. ResumenAntecedentes: La persistencia de la lactancia tiene una gran influencia en la salud, la reproducción y los costos de alimentación de las granjas lecheras. Objetivo: Estimar los componentes de (co)varianza y los parámetros genéticos de la producción de leche a 100 y 305 d, asi como la persistencia de la lactancia en vacas Holstein en Irán. Métodos: Se utilizaron registros recopilados entre enero de 2000 y diciembre de 2012 por el Centro de cría de animales de Irán (Karaj, Irán). Se utilizaron las siguientes cuatro medidas de persistencia de la lactancia: P21: Proporción de producción de leche en los segundos 100-d en leche (DIM) dividida por la de los primeros 100-d. P31: Proporcion de producción de leche en los terceros 100-d dividido por el de los primeros 100-d. PW: medida de persistencia derivada de la función gamma incompleta. PJ: diferencia entre el rendimiento de leche en el 60 y el 280 día de lactancia. Resultados: La heredabilidad estimada de la persistencia de la lactancia para los tres primeros partos (primera, segunda y tercera lactancia) varió de 0,01 a 0,06; 0,02 a 0,10; y 0,01 a 0,12, respectivamente. Las correlaciones genéticas 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 genéticas entre la persistencia de la lactancia con la producción 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 varió de 0,06 a 0,20. Conclusión: La correlación genética positiva moderada entre la persistencia de la lactancia y la producción de leche a 305-d indica que la selección para aumentar la producción de leche puede mejorar ligeramente la persistencia de la lactancia.Palabras clave: curva de lactancia, ganado lechero, heredabilidad, persistencia, producción de leche, repetibilidad. ResumoAntecedentes: A persistência da lactação tem grande influência nos custos de saúde, reprodução e alimentação em fazendas leiteiras. Objetivo: Estimar os componentes da variância (co)variância e os parâmetros genéticos da produção de leite de 100 e 305 d e a persistência da lactação em vacas Holandesas no Irã. Métodos: Os dados utilizados foram registros coletados de janeiro de 2000 a dezembro de 2012 pelo Centro de Criação de Animais do Irã (Karaj, Irã). As seguintes quatro medidas de persistência de lactação foram utilizadas: P21: Razão da produção de leite no segundo 100-d em leite (DIM) dividido pelo primeiro 100-d. P31: Razões da produção de leite na terceira 100d dividida pela da primeira 100-d. PW: A medida de persistência derivada da função gama incompleta. PJ: A diferença entre a produção de leite no 60º e 280º dia de lactação. Resultados: A hereditariedade estimada da persistência da lactação para as três primeiras paridades (primeira, segunda e terceira lactação) variou de 0,01 a 0,06; 0,02 a 0,10; e 0,01 a 0,12, respectivamente. As correlações genéticas entre as medidas de persistência da lactação para as três primeiras paridades variaram de 0,77 a 0,98; 0,65 a 0,98; e 0,58 a 0,98, respectivamente; enquanto os valores correspondentes para correlações genéticas entre a persistência da lactação com produção de leite de 305d variaram de 0,18 a 0,63; 0,32 a 0,75; e 0,41 a 0,71, respectivamente. A repetibilidade estimada para medidas de persistência de lactação variou de 0,06 a 0,20. Conclusão: A correlação genética positiva moderada entre a persistência da lactação e a produção de leite de 305d indicou que a seleção para aumentar a produção de leite melhoraria ligeiramente a persistência da lactação.Palavras-chave: curva de lactação, gado de leite, hereditariedade, persistência, produção de leite, repetibilidade.


2021 ◽  
Vol 73 (6) ◽  
pp. 1371-1380
Author(s):  
O. Ermetin ◽  
B. Dağ

ABSTRACT In this study, milk yield, reproductive yield, and type traits of 533 Holstein cows in the first lactation raised in 54 farms were examined. In the three-year study, phenotypic (rP) and genetic (rG) correlations between type traits and milk yield were estimated based on the variance elements and heritability of the type traits of Holstein cows in the first lactation. Linear identification and scoring systems have been applied to classify the cows according to type traits. Heritability and correlations were estimated with ASREML models. The type traits included stature, angularity, rump width, hocks, rear udder height, central ligament, teat length, body capacity, feet and legs, udder composite and final score for genetic correlations with 305-day milk yield were estimated as -0.49, -0.14, -0.93, 0.35, 0.40, 0.11, -0.65, 0.70, 0.31, 0.54, and 0.70, for phenotypic correlations were estimated as 0.28, 0.28, 0.30, 0.21, 0.35, 0.39, -0.06, 0.46, 0.48, 0.56, and 0.58 respectively. Among the phenotypic correlations between the type traits, especially the phenotypic correlations between the final score and various type traits were found to be high and significant. The fact that these traits are in high correlation with other traits and milk yield may enable these to be used as indirect selection criteria in the selection for milk yield.


2020 ◽  
Vol 33 (1) ◽  
pp. 60-70
Author(s):  
Gabrieli S Romano ◽  
Luis Fernando B Pinto ◽  
Altair A Valloto ◽  
José-Augusto Horst ◽  
Victor B Pedrosa

Background: Somatic cell score is an important parameter to predict milk quality and health of cows. However, in countries like Brazil, this trait is still not selected on a large scale, and no genetic parameters are reported in the literature. Objective: To estimate the variance components and genetic parameters for somatic cell score, milk yield, fat yield, protein yield, fat percentage, and protein percentage in Holstein cows. Methods: Records from 56,718 animals were used to estimate variance components, heritability, and genetic correlations using a multi-trait animal model by the REML method. Results: The heritability estimates were 0.19 for somatic cell score, 0.22 for milk yield, 0.26 for fat yield, 0.18 for protein yield, 0.61 for fat percentage, and 0.65 for protein percentage. The estimates of genetic correlations among analyzed traits ranged from -0.50 to 0.82. Conclusion: The low heritability observed for somatic cell score indicates that selection for this trait should result in benefits related to animal health and milk quality, but only in the long term. The low correlation between productive traits and somatic cell score indicates that inclusion of somatic cell score in animal breeding programs does not interfere negatively with the genetic selection for milk yield or solids.Keywords: Holstein; genetic correlation; genetic parameters; heritability; mastitis; milk quality; milk yield; multi-trait model; somatic cell score; variance components.  Resumen Antecedentes: El conteo de células somáticas es un parámetro importante para predecir la calidad de la leche y la salud de las vacas. Sin embargo, en países como Brasil, esta característica aún no se selecciona a gran escala y no se reportan parámetros genéticos en la literatura. Objetivo: Estimar los componentes de varianza y parámetros genéticos para el conteo de células somáticas, producción de leche, producción de grasa, producción de proteína, porcentaje de grasa y porcentaje de proteína en vacas de la raza Holstein. Métodos: Se usaron registros de 56.718 animales para estimar los componentes de la varianza, heredabilidad y correlaciones genéticas usando un modelo animal multicaracterístico por medio del método REML. Resultados: Las estimaciones de heredabilidad fueron 0,19 para el conteo de células somáticas, 0,22 para la producción de leche, 0,26 para la producción de grasa, 0,18 para producción de proteína, 0,61 para el porcentaje de grasa y 0,65 para el porcentaje de proteína. Las estimaciones de correlación genética entre las características analizadas variaron entre -0,50 a 0,82. Conclusión: La baja heredabilidad encontrada para conteo de células somáticas demostró que la selección para esta característica podría resultar en beneficios para la salud animal y calidad de la leche, pero sólo a largo plazo. La baja correlación genética existente entre las características productivas y el conteo de células somáticas indica que la inclusión del conteo de células somáticas en programas de selección no interfiere negativamente en la selección genética para la producción de leche o sólidos.Palabras clave: calidad de leche; correlación genética; conteo de células somáticas; componentes de varianza; heredabilidad; Holstein; mastitis; modelo multicaracteristico; parametros geneticos; producción de leche; selección genetica. Resumo Antecedentes: O escore de células somáticas é um parâmetro importante para a predição da qualidade do leite, bem como para a saúde das vacas. No entanto, em alguns países como o Brasil, essa característica não é selecionada em larga escala e não há parâmetros genéticos disponíveis na literatura. Objetivo: Estimar os componentes de variância e parâmetros genéticos para o escore de células somáticas, produção de leite, produção de gordura, produção de proteína, porcentagem de gordura e porcentagem de proteína em vacas da raça Holandesa. Métodos: Foi utilizado um total de 56.718 animais para estimar os componentes de variância, herdabilidade e correlações genéticas, considerando-se o modelo animal multicaracterística por meio do método REML. Resultados: As estimativas de herdabilidade foram de 0,19 para o escore de células somáticas, 0,22 para a produção de leite, 0,26 para a produção de gordura, 0,18 para produção de proteína, 0,61 para a porcentagem de gordura e 0,65 para a porcentagem de proteína. As estimativas de correlação genética entre as características analisadas variaram entre -0,50 a 0,82. Conclusão: A baixa herdabilidade encontrada para o escore de células somáticas demonstrou que a seleção para esta característica poderá resultar em benefícios para a saúde animal e qualidade do leite, porém, somente a longo prazo. A baixa correlação genética existente entre as características produtivas e o escore de células somáticas demonstrou que a inclusão do escore de células somáticas em programas de seleção não causa interferência negativa na seleção genética para a produção de leite ou sólidos.Palavras-chave: componentes de variância; correlação genéticas; escore de células somáticas; herdabilidade; mastite; modelo multicaracterística; parâmetros genéticos; produção de leite; qualidade do leite; raça Holandesa; seleção genética.


2010 ◽  
Vol 55 (No. 3) ◽  
pp. 91-104 ◽  
Author(s):  
K. Yazgan ◽  
J. Makulska ◽  
A. Węglarz ◽  
E. Ptak ◽  
M. Gierdziewicz

The objective of this research was to examine heritabilities and genetic, phenotypic and permanent environmental relationships between milk dry matter (DM) and milk traits such as milk, fat, protein and lactose yields, milk urea nitrogen (MUN) and somatic cell score (SCS) in extended (to 395 days) lactations of Holstein cows from a big farm in Poland. The data set consisted of 78 059 test day records from the first, second and third lactations of 3 792 cows, daughters of 210 sires and 1 677 dams. Single- or two-trait random regression models were used with fixed effects of calving year, calving month, dry period and calving interval and random additive genetic and permanent environmental effects. The last two fixed effects were not included in the analysis of first lactation data. The highest values of heritabilities for all traits, except DM, were observed in the second lactation. First lactation heritabilities for all traits – except milk yield and SCS – were smaller than those in the third lactation. Lactose yield was highly heritable, with average h<SUP>2</SUP> equal to 0.25, 0.29 and 0.28 in lactations 1, 2 and 3, respectively. Heritability for DM was slightly lower than that for lactose (0.22, 0.26 and 0.28 for lactations 1, 2 and 3, respectively). In all lactations heritabilities for SCS were below 0.1. Genetic correlations between DM and milk yield (0.64–0.74) were lower than those between MUN and milk yield (0.67–0.79) as well as between lactose and milk yield (0.72–0.82). In general, DM was much more closely correlated with fat or protein yield (0.55–0.79) than with MUN or lactose (0.38–0.76). Only in the third lactation the correlation between DM and protein (0.72) was lower than between lactose and protein (0.76). For all lactations there were very high genetic correlations between DM and lactose (0.96–0.98) and high correlations between DM and MUN (0.63–0.83) and between lactose and MUN (0.70–0.85). The results suggest that further research is needed, focused on DM and its relationship with other traits in larger populations. &nbsp;


2018 ◽  
Vol 58 (10) ◽  
pp. 1779
Author(s):  
Purna Kandel ◽  
Sylvie Vanderick ◽  
Marie-Laure Vanrobays ◽  
Hélène Soyeurt ◽  
Nicolas Gengler

Methane (CH4) emission is an important environmental trait in dairy cows. Breeding aiming to mitigate CH4 emissions require the estimation of genetic correlations with other economically important traits and the prediction of their selection response. In this study, test-day CH4 emissions were predicted from milk mid-infrared spectra of Holstein cows. Predicted CH4 emissions (PME) and log-transformed CH4 intensity (LMI) computed as the natural logarithm of PME divided by milk yield (MY). Genetic correlations of PME and LMI with traits used currently were approximated from correlations between estimated breeding values of sires. Values were for PME with MY 0.06, fat yield (FY) 0.09, protein yield (PY) 0.13, fertility 0.17; body condition score (BCS) –0.02; udder health (UDH) 0.22; and longevity 0.22. As expected by its definition, values were negative for LMI with production traits (MY –0.61; FY –0.15 and PY –0.40) and positive with fertility (0.36); BCS (0.20); UDH (0.08) and longevity (0.06). The genetic correlations of 33 type traits with PME ranged from –0.12 to 0.25 and for LMI ranged from –0.22 to 0.18. Without selecting PME and LMI (status quo) the relative genetic change through correlated responses of other traits were in PME by 2% and in LMI by –15%, but only due to the correlated response to MY. Results showed for PME that direct selection of this environmental trait would reduce milk carbon foot print but would also affect negatively fertility. Therefore, more profound changes in current indexes will be required than simply adding environmental traits as these traits also affect the expected progress of other traits.


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