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(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.

Estimates of genetic parameters are important in the design of animal breeding programs aimed to maximizing genetic gain [

A common approach to investigate genetic associations between test day yields is to consider every yield at each time period as a separate trait and then to estimate the genetic correlations between these traits. This approach has some disadvantages when large numbers of test day yields are considered. The biological interpretation of a large number of correlations is furthermore often difficult [

The aim of this study is to estimate the genetic parameters (additive genetic and permanent environmental (Co) variances) and heritability values for test day milk yields of Iranian Holstein cows using a random regression Test-Day model.

Data were provided by the Animal Breeding Center of Iran (ABCI, Tehran) and consisted of a total number of 68,945 milk test-day records of first, second and third lactations of Holstein cows that calved between 22 and 36 month of age during the time period from 2003 to 2009. The records were measured on 8515 animals originated from 100 sires and 7743 dams from 34 herds. More details of the data are presented in

Single trait random regression Test-Day model was applied to estimate the genetic parameters of milk yield of Iranian Holstein Cows in the first, second and third lactations. 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) were fitted in the model as fixed effects. Linear, quadratic and higher orders of regression were tested for effect of age at calving. Fixed polynomial regression with different order of fit was considered for DIM. For changing scale of days in milk from 5 to 305 day was standardized to the interval [−1, ∙∙∙, 1] [

The following model was used for analyzing the data:

Y i j = X b + ∑ m = 0 K − 1 δ m ∅ m i j + ∑ m = 0 K a − 1 α i m ∅ m i j + ∑ m = 0 K ( i d e ) − 1 β i m ∅ m i j + ε i j

where:

y_{ij} is the performance of i^{th} cow.

Number of Animals | 8515 |
---|---|

Number of Records | 68,945 |

Number of Sires | 100 |

Number of Dams | 7743 |

Number of Herds | 34 |

Group | Days in milking | Number of records |
---|---|---|

1 | 5 - 35 | 8076 |

2 | 35 - 65 | 8608 |

3 | 65 - 95 | 8514 |

4 | 95 - 125 | 8912 |

5 | 125 - 155 | 8393 |

6 | 155 - 185 | 7713 |

7 | 185 - 215 | 6528 |

8 | 215 - 245 | 5409 |

9 | 245 - 275 | 4157 |

10 | 275 - 305 | 2635 |

X is an incidence matrix for fixed effects.

b is the vector for fixed effects.

δ_{m} is coefficient i of a fixed regression on element i of the polynomials of all environments.

α_{im}, m^{th} degree fitting random regression for additive genetic effects for i^{th} animal.

β_{im}, is a permanent environmental effect of i^{th} animal.

Ø_{mij}, m^{th} degree of fit of j^{th} day for i^{t}^{h} animal.

k_{a} and k_{(ide)}, degree of fit for additive genetic and permanent environmental effects, respectively.

e_{ij} is the temporary or residual environmental random effects associated with y^{ij}.

Models with different order of Legendre polynomials were fitted for both the additive genetic effects and the animal permanent environmental effects. To choose the best order of fit for the random effects, the models were compared using Schwarz’s Beysian Information Criterion (BIC) [

The variance-covariance matrix for models was assumed to be:

var [ u p e ] = [ G ⊗ A P ⊗ I R ]

where:

G and P are the (co)variance matrices of the random regression coefficients for additive genetic and permanent environmental effects;

R is a diagonal matrix of residual variance;

A is the additive genetic relationship matrix among cows;

I is an identity matrix, and ⊗ is the Kronecker product.

The best order of fit for additive genetic effects and animal permanent environmental effects were estimated. The restricted maximum likelihood (REML) procedure, under an average information algorithm, was used to estimate the (co)variance components and corresponding genetic parameters applying ASReml computer program [

Milk yield was significantly affected by the fixed effects of 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) (p < 0.001) (

K = 4 was the best order of fit for the fixed regression of days in milk. Genetic analysis was started with K = 2 for both direct additive genetic and animal permanent environmental effects and completed with higher order of fitting up to K = 4. The best model was selected using BIC (

Fixed effects | Significant level | Degree of freedom | Level | LSM ± SE |
---|---|---|---|---|

Milking frequency per day | *** | 1 | 3 | 34.71 ± 0.33 |

4 | 36.85 ± 0.43 | |||

Lactation period | *** | 2 | 1 | 34.71 ± 0.33 |

2 | 34/2 ± 0.56 | |||

3 | 33.07 ± 0.69 | |||

Birth year | *** | 3 | 2003 | 34.71 ± 0.33 |

2004 | 35.69 ± 0.56 | |||

2005 | 36.11 ± 0.67 | |||

2006 | 37.11 ± 0.82 | |||

Calving year | *** | 2 | 2007 | 34.71 ± 0.33 |

2008 | 33.71 ± 0.54 | |||

2009 | 34.97 ± 0.66 | |||

Age | *** | 3 | 1 | 34.71 ± 0.33 |

2 | 40.81 ± 0.74 | |||

3 | 32.4 ± 0.51 | |||

4 | 34.02 ± 0.51 | |||

Days in milking | *** | 3 | 1 | 34.71 ± 0.33 |

2 | 28.06 ± 0.53 | |||

3 | 31.88 ± 0.42 | |||

4 | 37.42 ± 0.42 |

***: P < 0.001.

Order of fit | Np | LogL | BIC | |
---|---|---|---|---|

K_{a } | K_{ide}_{ } | |||

2 | 2 | 17 | 4292.67 | 8774.74 |

3 | 2 | 20 | 4026.56 | 8275.93 |

3 | 3 | 23 | 3929.33 | 8114.91 |

4 | 3 | 27 | 3846.15 | 7993.09^{a} |

4 | 4 | 31 | 3844.09 | 8033.54 |

^{a}Best model based on BIC.

Additive genetic variance showed an increasing rate from the first to the 9^{th} month of milking but it was suddenly decreased at the end of lactation period (^{2}) and maximum (18.16 kg^{2}) additive genetic variance were observed in the first and 9^{th} month of lactation, respectively. Generally, the rate of variance changing in the first half of the milking period was less than the second half of the lactation period. The results are in agreement with the results of [

Animal permanent environment variance had a gradual increasing rate from the first to the 8^{th} month of lactation period and then sharply increased for the later months (^{2}) and maximum (27.1 kg^{2}) animal permanent environment variance were observed in 2^{nd} and 10^{th} months of lactation period. References [

Residual variance had decreased up to the 6^{th} month of lactation period and then smoothly increased to the end of lactation period (^{2}) was observed in the first month of lactation. In a study by [

Phenotypic variance had a decreasing rate and reached to the minimum level (45.8 kg^{2}) at the 4^{th} month of lactation and then showed an increasing rate to the later months of lactation period (

Heritability of milk yield estimated between 0.1 to 0.32 and it was different among months of lactation. Minimum heritability (0.1) belongs to the first month of lactation. Heritability had a gradually increasing to 3^{rd} month of lactation followed by smooth increasing up to 5^{th} month of lactation and then reaching to the highest heritability (0.32) at the 8^{th} month of lactation through a sharp increasing rate, and finally, a slight decreasing to the 9^{th} and a sharper decrease in the last month of lactation (^{rd} month of lactation period. In another study by [

Repeatability for milk yield trait was estimated between 0.4 to 0.69 in the months of lactation period (

Genetic correlation between months of milking estimated between −0.35 to +0.98 (^{th} month) estimated between 0.8 to 0.11 that shows genetic correlation had decreased as the intervals between months of lactation increased. Negative genetic correlation shows cows with high performance in milk production at the beginning of lactation have low performance at the end of lactation period. Genetic correlation between consecutive months at the beginning of lactation was less than genetic correlation between consecutive months at the end of lactation period. Genetic correlation between first and second month of lactation was 0.8 and genetic correlation for the four latest months of lactation estimated between 0.96 to 0.98. These results are in agreement with those reported by [

Phenotypic correlation for the milk yield estimated between 0.03 to 0.67 during months of lactation (

Month of lactation | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
---|---|---|---|---|---|---|---|---|---|---|

1 | - | 0.39 | 0.36 | 0.32 | 0.27 | 0.22 | 0.17 | 0.12 | 0.07 | 0.03 |

2 | 0.8 | - | 0.48 | 0.46 | 0.41 | 0.34 | 0.26 | 0.19 | 0.12 | 0.07 |

3 | 0.6 | 0.94 | - | 0.55 | 0.52 | 0.45 | 0.37 | 0.28 | 0.2 | 0.14 |

4 | 0.44 | 0.83 | 0.95 | - | 0.58 | 0.54 | 0.47 | 0.39 | 0.31 | 0.23 |

5 | 0.28 | 0.62 | 0.79 | 0.93 | - | 0.6 | 0.55 | 0.5 | 0.42 | 0.33 |

6 | 0.11 | 0.33 | 0.53 | 0.74 | 0.93 | - | 0.62 | 0.6 | 0.53 | 0.44 |

7 | −0.03 | 0.08 | 0.27 | 0.52 | 0.79 | 0.95 | - | 0.65 | 0.61 | 0.53 |

8 | −0.14 | −0.09 | 0.09 | 0.35 | 0.65 | 0.88 | 0.98 | - | 0.67 | 0.61 |

9 | −0.24 | −0.2 | −0.01 | 0.25 | 0.56 | 0.81 | 0.94 | 0.98 | - | 0.66 |

10 | −0.35 | −0.25 | −0.04 | 0.21 | 0.5 | 0.73 | 0.86 | 0.92 | 0.96 | - |

This research highlighted that milk yield in Iranian Holstein cows significantly affected by milking frequency, number of lactations, year of birth, year of calving, age of animal at calving and days in milk (DIM). Also, we can conclude that additive genetic variance, animal permanent environment variance, phenotypic variance, heritability and repeatability can have different values during the lactation period. Nutrition, management, parturition stress and genotype by environment interaction in Iranian dairy cows may be the most probable factors that change the milk production curve. High magnitudes of genetic and phenotypic correlations between consecutive months of lactation indicated that similar factors (Management, Nutrition, …) with the same pattern can affect the milk production and as the interval increases between months of lactation, the effects of these factors differ from a month to another month in the lactation period.

We are grateful to the Iranian Animal Breeding Center for providing us the data.

Fazel, Y., Fozi, M.A., Esmailizadeh, A., Fazel, F., Niazi, A.M., Rahmati, S. and Qasimi, M.I. (2018) Use of Random Regression Test-Day Model to Estimate Genetic Parameters of Milk Yield in Holstein Cows. Open Journal of Animal Sciences, 8, 27-38. https://doi.org/10.4236/ojas.2018.81003