scholarly journals Genetic Parameters of Milk Yield and Milk Fat Percentage Test Day Records of Iranian Holstein Cows

2005 ◽  
Vol 18 (9) ◽  
pp. 1231-1236 ◽  
Author(s):  
A. A. Shadparvar ◽  
M. S. Yazdanshenas
Author(s):  
T. V. Pidpala ◽  
Yu. S. Matashnyuk

Under the conditions of intensive technologies used in production of livestock products, the main selection feature that characterizes the economic feasibility of dairy farming and breeding value of animals is milk productivity. One of the factors that affects, not only the economy of production, but also the improvement of herds and breeds of cattle, is the use of highly productive cows. Therefore, the aim of our research was to assess the level of development of productive traits in Holstein cows under the conditions of intensive milk production technology, according to selection and genetic parameters. To conduct the study, a group of 1089 first-born cows was formed with the help of the Dairy Comp program and Microsoft Excel. The material for the research was the milk productivity of Holstein cows during the first three lactations. The level of development of selection traits in animals was determined by selection and genetic parameters. It was found that from the sample n = 1089 to the group of highly productive animals (“>10560”) were included 266 cows, and low-productive animals (“<8706”) – 249 first-borns. They had an average milk yield for the first lactation, 11439 kg of milk with a fat content of 3.96 % and 7737 kg of milk and 3.95 %, respectively. There is a difference in milk yield, milk fat and protein between the groups of cows “>10614” and “<8706”, but there is a general tendency towards changes in productivity with age. Based on the data of milk recurrence and the amount of milk fat of high-yielding cows (group “>10614”), it was found that higher values of the coefficient are characteristic of lactations I–III (rw= 0.105; rw= 0.135). As a result of comparative analysis it was found that low-yielding animals (group “<8706”) were characterized by higher recurrence rates for I–II and I–III lactation (rw = 0.345;rw = 0.316;rw = 0.320 and rw = 0.664;rw = 0.646;rw = 0.651, respectively). Higher rates of recurrence of traits of milk productivity are the characteristic of low-yielding cows (group “<8706”), i.e. they had more consistency of traits during different lactations and animals with a high level of productivity did not differ in age constancy. At a high level of milk yield in cows of group “>10614” appeared a negative correlation of low and medium level (r = -0.423). It was also found a negative correlation of low and medium level between milk yield and protein content in milk (r = -0.007… -0.332). At lower milk yields, there is no negative correlation between milk yield and fat content in milk. Thus, the existence of a negative correlation between milk yield and fat content in milk at a high level of animal productivity, and between milk yield and protein content in milk at both high and low levels of cow productivity was proved.


1990 ◽  
Vol 70 (2) ◽  
pp. 731-734 ◽  
Author(s):  
A. S. ATWAL ◽  
J. D. ERFLE

Large day-to-day variations in milk fat, particularly for the morning milkings, were observed in 36 Holstein cows. Changes in percent fat were gradual and produced wavelike patterns in a number of instances. Supplemental feeding of long hay had no effect on acetate/propionate ratio in rumen fluid, daily milk yield or weighted milk fat percentage. Key words: Dairy cows, milk, fat depression, hay


2019 ◽  
Author(s):  
Nuzul Widyas ◽  
Nada Mahfudhoh ◽  
Subiakti ◽  
Sigit Prastowo

2019 ◽  
Vol 99 (3) ◽  
pp. 521-531
Author(s):  
M. Duplessis ◽  
R. Lacroix ◽  
L. Fadul-Pacheco ◽  
D.M. Lefebvre ◽  
D. Pellerin

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.


2020 ◽  
Vol 87 (2) ◽  
pp. 220-225
Author(s):  
Navid Ghavi Hossein-Zadeh ◽  
Hassan Darmani Kuhi ◽  
James France ◽  
Secundino López

AbstractThe aim of the work reported here was to investigate the appropriateness of a sinusoidal function by applying it to model the cumulative lactation curves for milk yield and composition in primiparous Holstein cows, and to compare it with three conventional growth models (linear, Richards and Morgan). Data used in this study were 911 144 test-day records for milk, fat and protein yields, which were recorded on 834 dairy herds from 2000 to 2011 by the Animal Breeding Centre and Promotion of Animal Products of Iran. Each function was fitted to the test-day production records using appropriate procedures in SAS (PROC REG for the linear model and PROC NLIN for the Richards, Morgan and sinusoidal equations) and the parameters were estimated. The models were tested for goodness of fit using adjusted coefficient of determination $\lpar {R_{{\rm adj}}^2 } \rpar $, root mean square error (RMSE), Akaike's information criterion (AIC) and the Bayesian information criterion (BIC). $R_{{\rm adj}}^2 $ values were generally high (>0.999), implying suitable fits to the data, and showed little differences among the models for cumulative yields. The sinusoidal equation provided the lowest values of RMSE, AIC and BIC, and therefore the best fit to the lactation curve for cumulative milk, fat and protein yields. The linear model gave the poorest fit to the cumulative lactation curve for all production traits. The current results show that classical growth functions can be fitted accurately to cumulative lactation curves for production traits, but the new sinusoidal equation introduced herein, by providing best goodness of fit, can be considered a useful alternative to conventional models in dairy research.


2007 ◽  
Vol 2007 ◽  
pp. 185-185
Author(s):  
M. Bojarpour ◽  
A M Godarzi ◽  
N. Dabire

The non-NDF carbohydrates (NFC) are important sources of energy in the ration of high producing cows. The fibre must be proper quality and particle size to insure maximum DMI, optimal chewing activity, normal ruminal fermentation, and milk fat percentage. The NRC (1989) recommends 25 to 28% NDF in the rations of lactating cows; a minimum of 75% of the NDF should come from forages. These recommendations provide no adjustment for the physical effectiveness of the fibre, interactions among fibre sources and non fibre carbohydrates, or animal characteristics that may influence ration design. Few data are available to document the effect of the substitution of by-product NDF for forage NDF; our objective was to determine the effect of the substitution of alfalfa NDF from sugar beet on DMI, milk yield and composition, chewing activity, faecal and rumen pH, and apparent digestibility of DM.


2017 ◽  
Vol 57 (7) ◽  
pp. 1563
Author(s):  
H. N. Phuong ◽  
N. C. Friggens ◽  
O. Martin ◽  
P. Blavy ◽  
B. J. Hayes ◽  
...  

The present study determined the ability of a lifetime nutrient-partitioning model to simulate individual genetic potentials of Australian Holstein cows. The model was initially developed in France and has been shown to be able to accurately simulate performance of individual cows from various breeds. Generally, it assumes that the curves of cow performance differ only in terms of scaling, but the dynamic shape is universal. In other words, simulations of genetic variability in performance between cow genotypes can be performed using scaling parameters to simply scale the performance curves up or down. Validation of the model used performance data from 63 lactations of Australian Holstein cows offered lucerne cubes plus grain-based supplement. Individual cow records were used to derive genetic scaling parameters for each animal by calibrating the model to minimise root mean-square errors between observed and fitted values, cow by cow. The model was able to accurately fit the curves of bodyweight, milk fat concentration, milk protein concentration and milk lactose concentration with a high degree of accuracy (relative prediction errors <5%). Daily milk yield and weekly body condition score were satisfactorily predicted, although slight under-predictions of milk yield were identified during the last stage of lactation (relative prediction errors ≈11.1–15.6%). The prediction of feed intake was promising, with the value of relative prediction error of 18.1%. The results also suggest that the current recommendation of energy required for maintenance of pasture-based cows might be under-estimated. In conclusion, this model can be used to simulate genetic variability in the production potential of Australian cows. Thus, it can be used for simulation of consequences of future genetic-selection strategies on lifetime performance and efficiency of individual cows.


2008 ◽  
Vol 91 (1) ◽  
pp. 371-376 ◽  
Author(s):  
M. Cassandro ◽  
A. Comin ◽  
M. Ojala ◽  
R. Dal Zotto ◽  
M. De Marchi ◽  
...  

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