| Livestock Research for Rural Development 33 (3) 2021 | LRRD Search | LRRD Misssion | Guide for preparation of papers | LRRD Newsletter | Citation of this paper |
This study aimed to estimate genetic and phenotypic correlations for reproductive and milk production traits of pure Jersey dairy cows at Adea-Berga dairy farm. Data were collected for the last 33 years (1986-2019) for 4223 calvings and 3606 lactations. The genetic and phenotypic correlation values for reproductive and milk production traits were estimated using WOMBAT software through bivariate analysis. The genetic correlations between reproductive traits varied from -0.10±0.00 to 1.00±0.11 while the values of phenotypic correlation were ranging from 0.03±0.04 to 0.98±0.00. For milk production traits, the genetic correlations varied from 0.93±0.22 to 0.98±0.07 while the values of phenotypic correlation were ranging from -0.03±0.02 to 0.82±0.01. The direct genetic correlations between reproductive and milk production traits varied from -0.89 to 1.00 while the values of phenotypic correlation ranged from -0.00±0.04 to 0.46±0.02. The direct genetic correlations estimate for calving interval-days open, calving interval-number of service per conception, days open-number of service per conception, age at first service-age at first calving, age at first service-number of service per conception, age at first service-days open and age at first calving-number of service per conception were 1.00±0.11, 0.97±0.69, 0.95±0.81, 0.89±0.11, 0.89, 0.80 and 0.78, respectively. The positive direct genetic correlations among traits in the present study indicated that the selection of one trait might improve the other trait. Selection for age at first service and calving interval traits would be important to improve the productivity of pure Jersey cows.
Keywords: Ethiopia, genetics, lactation, pure breed, reproduction
Correlations are measures of the strength of the relationship between two variables. A high correlation value implies a strong relationship between variables and vice versa (Bourdon 2000). Correlations are important as an aid in the prediction of response to selection in one trait due to selection in another and are partitioned into phenotypic and genotypic correlations. The genetic correlation expresses the extent to which two characters are influenced by the same genes or by genes located in the same chromosome and it is important when selecting for net merit involving several traits. Estimates of genetic correlation between any pair of traits suggest that selection for one trait can lead to an indirect genetic response in the other trait (Edward et al 2013, Gebeyehu et al 2014).
The potential for genetic improvement of a trait largely depends upon genetic variation existing in the population (Zeleke 2019). The most common genetic parameters are heritability, repeatability, genetic and phenotypic correlation (Yibrah 2008).
Furthermore, the development of effective genetic improvement programs require advanced knowledge of the genetic variation of economically important reproductive and production traits and accurate estimates of genetic and phenotypic correlations of economically important traits (Solomon et al 2002, Juma and Alkass 2006). However, there is limited information on genetic and phenotypic correlations among reproductive and milk production traits of pure exotic Jersey breed under different dairy management systems in Ethiopia. Therefore, the objective of this study was to estimate the genetic and phenotypic correlation among reproductive and milk production traits for pure Jersey cows at Adea-Berga dairy research center.
This study was conducted at Adea-Berga Dairy Research Center which is found in West Shewa Zone of Oromia regional state of Ethiopia. Adea-Berga wet land is located around 70 km away to the North-West of Addis Ababa and 35 km to the North-West of Holeta (at 9o 16’ N latitude, 38 o 23’ E longitudes and altitude of 2500 m). Mean annual temperature and rainfall are 18 oC and 1225 mm, respectively (Direba et al 2015).
Adea-Berga dairy farm was established at Adea-Berga wetland in 1986 for commercial milk production under government state farm using 400 pure Jersey pregnant heifers and two sires for natural mating purpose (foundation stock) introduced from Denmark. The farm has a total area of 400 ha of land. The animal barn, office and residence were constructed on about 10 ha of land and the rest of land is being utilized for grazing and hay production. The farm was transferred to Holeta Agricultural Research Center for a genetic improvement research program since 2007.
The herds are managed separately depending on sex, age, pregnancy and lactation (dry or milking). The female calves were allowed to suckle their dam immediately after birth for about five days to receive colostrum and then separated from their dams and offered fresh milk twice a day for about 6 months. However, the male calves were weaned within 98 days. The milking was done twice a day at the equal intervals (in the morning and afternoon) and the milk produced by each cow was measured and recorded in a prepared format immediately after milking.
The data for this study was obtained from long-term records of pure Jersey breed that has been kept for dairy production in Adea-Berga dairy farm. The recorded data for the last 33 years (1986-2019), genetic parameters for reproductive and milk production data were used for this study. A pedigree is the set of known parent-offspring relationships in a population, often displayed as a family tree diagram. Identity number was sequenced by pedigree viewer software package (version 6.5) for arranging animals ID and pedigree identity in chronological orders and to clear any mistake in identity number. The pedigree data includes animal ID, dam and sire of a cow.
The traits included in this study were categorized into reproductive and milk production traits. The reproductive traits include age at first service (AFS), age at first calving (AFC), calving interval (CI), days open (DO) and number of service per conception (NSPC). The milk production traits include lactation milk yield (LMY), daily milk yield (DMY) and lactation length (LL).
The genetic and phenotypic correlations were estimated by using WOMBAT software (Meyer 2012) fitted an animal model using bivariate analysis. Fixed factors (animal group, year, season and parity) that have a significant effect were included in the model for estimation of genetic and phenotypic correlations. The representations of the animal models used to estimate genetic and phenotypic correlation for reproductive and milk production traits are as follow:
Model:Y = Xb + Zla + e
Where
Y is a vector of records/ observations for the traits of interest,
b is a vector of fixed effects (fixed effects which had a significant effect),
a is a vector of random individual direct additive genetic effects,
X is a matrix relating records to fixed effects,
Z1 is an incidence matrix for direct additive genetic effect,
e is a vector of random residual effect.
The genetic and phenotypic correlations were calculated by using the following formulas:
|
Where:
rg: genetic correlations,
rp: phenotypic correlations,
 |
Direct genetic and phenotypic correlations for reproductive and milk production traits were estimated from a bivariate animal model considering two traits at a time. The estimates of direct genetic and phenotypic correlations between five reproductive traits (AFS, AFC, CI, DO and NSPC) and three milk production traits (LMY, DMY and LL) are shown in Table 1. The direct genetic correlations among traits in the present study were higher than the corresponding phenotypic correlations except for some reproductive traits that have negative values in both genetic and phenotypic correlations. The direct genetic correlations only evaluate how both traits were influenced by a common set of genes of an animal while the phenotypic correlation evaluate the influence of both common genetic and environmental effects of two traits (Zeleke 2019). Except for AFS-DMY and DMY-LL, the phenotypic correlations between reproductive and milk production traits in this study were found to be positive.
The direct genetic correlations between reproductive traits in the present study ranged from negative to strongly positive. Negative direct genetic correlations (-0.10±00) was observed among AFS and CI. The negative direct genetic correlations of these traits showed that most of the genes affecting these traits are different or the values of these traits were the result of different gene actions. On the other hand, strong and positive direct genetic correlations among reproductive traits (1.00±0.11, 0.97±0.69, 0.95±0.81, 0.89±0.11, 0.89±0.10, 0.80±0.11 and 0.78±0.11 for CI-DO, CI-NSPC, DO-NSPC, AFS-AFC, AFS-NSPC, AFS-DO and AFC-NSPC, respectively) were observed in this study. Likewise, moderate direct genetic correlations for AFC-DO (0.38±0.51) and AFC-CI (0.30±0.61) were noted in this study.
Genetic correlations estimate among CI and DO (1.00±0.11) is comparable with the estimate (1.00) of Belay et al (2016) for Friesian x Fogera, and (Tadesse 2014) for Ethiopian Boran x Friesian (0.99±0.00) and for Boran cows (0.99±0.00). The genetic correlation between AFC and DO in this study is higher than the report of Yosef (2006) for Jersey cows (0.13±0.02). The direct genetic correlation (0.89±0.11) between AFS and AFC in this study is similar to the report of Aynalem et al (2009b) for Ethiopian Boran x Friesian (0.88±0.052). However, the current estimate is higher than the reports of Berhanu and Ashim (2014) for Ethiopian Boran x HF (0.10±0.20), but lower than the report of Belay et al (2016) for HF x Fogera (1.00).
The genetic correlations between reproductive and milk production traits were also examined in this study and there were closely associated with each other. Very strong genetic correlations were observed for AFS-LMY (1.00) and for AFS-LL (0.99±0.11). However, the direct genetic correlations for CI-DMY, DO-DMY, NSPC-LMY and NSPC-LL were found to be negative (-0.14±0.36, -0.04±0.30, -0.51±0.11 and -0.89±0.11, respectively). The direct genetic correlation for CI-LL (0.32±0.44) in this result is comparable with the report of Tadesse (2014) for Boran breed (0.42±0.09).
Generally, the positive direct genetic correlations among traits in the present study indicated that the selection of one trait might be important for the improvement of other traits. Besides, these high genetic correlation values are due to the phenomenon of a single gene affecting more than one trait and due to the occurrence of two or more loci that affect the same trait on the same chromosome (Bourdon 2014). However, traits that have shown negative direct genetic correlations in the present study indicate that as one trait increases, the other trait tends to decrease, which might be favourable or unfavourable depending on the combination of traits considered.
The phenotypic correlations between reproductive traits in the present study were positive and ranges from low (0.03±0.04) to high (0.98±0.002). The lower phenotypic correlations were noted for AFC-CI (0.03±0.04), AFC-DO, (0.07±0.04), AFS-CI (0.10±0.05) and AFS-DO (0.13±0.05). Moderate phenotypic correlations of NSPC with AFS, AFC, CI and DO (0.24±0.05, 0.29±0.04, 0.48±0.02 and 0.48±0.02, respectively) were observed in this study. Similarly, high phenotypic correlations for AFS-AFC (0.98±0.002) and CI-DO (0.89±0.004) were observed in this study. These results are comparable with the estimates (0.85) for AFC-AFS and 0.99 for CI-DO of HF x Fogera cattle noted by Belay et al (2016) and, the estimate (0.99) for CI-DO of Boran and Boran x Friesian reported by Tadesse (2014). The phenotypic correlation for AFC-DO (0.07±0.04) in this study is lower than the estimate of 0.13±0.02 for the Jersey breed (Yosef 2006).
Phenotypic correlations were also estimated for milk production traits. Phenotypic correlations among milk production traits were negative for DMY-LL (-0.03±0.02), moderate for DMY-LMY (0.49±0.01) and high for LMY-LL (0.82±0.01). The phenotypic correlation between LMY and LL is in agreement with the report of Tadesse (2014). Relative to the present study, Tadesse (2014) found a higher phenotypic correlation for DMY-LL (0.39) and DMY-LMY (0.86) for Boran cattle. The variation of the present study from other literature might be due to breed, the number of observations and analysis methods used.
The phenotypic correlations between reproductive and milk production traits were found to be low except for AFS and DMY (-0.00±0.04). The phenotypic correlations of LMY with CI, DO and NSPC and also the correlation of LL with CI, DO and NSPC were found to be moderate. The phenotypic correlation for LL-CI in the present study is in agreement with the finding of Tadesse (2014) for Ethiopian Boran x Friesian cattle. Phenotypic correlations between reproductive and milk production traits in the present study were lower than direct genetic correlation estimates. Both environmental and genetic effects could influence phenotypic correlations of the reproductive and milk production traits.
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Table 1. Estimates of genetic correlations (above diagonal) and phenotypic correlations (below diagonal) among reproductive and milk production traits for Jersey cows |
||||||||
|
Parameters |
AFS |
AFC |
CI |
DO |
NSPC |
LMY |
DMY |
LL |
|
AFS |
* |
0.89±0.11 |
-0.10±00 |
0.80 ±0.11 |
0.89 ±0.10 |
1.00 ±0.11 |
0.50 ±0.11 |
0.99 ± 0.11 |
|
AFC |
0.98±0.00 |
* |
0.30±0.61 |
0.38±0.51 |
0.78 ± 0.11 |
0.27±0.45 |
0.21±0.53 |
0.55±0.56 |
|
CI |
0.10±0.05 |
0.03±0.04 |
* |
1.00±0.11 |
0.97±0.69 |
0.10±0.32 |
-0.14±0.36 |
0.32±0.44 |
|
DO |
0.13±0.05 |
0.07±0.04 |
0.89±0.00 |
* |
0.95±0.81 |
0.19±0.25 |
-0.04±0.30 |
0.29±0.36 |
|
NSPC |
0.24±0.05 |
0.29±0.04 |
0.48±0.02 |
0.48±0.02 |
* |
-0.51±0.11 |
0.50±0.11 |
-0.89±0.11 |
|
LMY |
0.07±0.04 |
0.13±0.04 |
0.45±0.02 |
0.46±0.02 |
0.40±0.02 |
* |
0.98±0.06 |
0.98±0.07 |
|
DMY |
-0.00±0.04 |
0.05±0.03 |
0.11±0.02 |
0.12±0.02 |
0.20±0.04 |
0.49±0.01 |
* |
0.93±0.22 |
|
LL |
0.03±0.04 |
0.06±0.04 |
0.41±0.02 |
0.43±0.02 |
0.36±0.02 |
0.82±0.01 |
-0.03±0.02 |
* |
| AFS= age at first service, AFC= age at first calving, CI= calving interval, DO= days open, NSPC= number of services per conception, LMY= lactation milk yield, DMY= daily milk yield and LL lactation length | ||||||||
Strong and positive direct genetic correlation estimates were observed for calving interval-days open, calving interval-number of service per conception, days open-number of service per conception, age at first service-age at first calving, age at first service-number of service per conception, age at first service-days open and age at first calving-number of service per conception, respectively. The calving interval and age at first service were strongly linked to other reproductive traits. Thus, selection for age at first service and calving interval traits would be important to improve the productivity of pure Jersey cows. The positive direct genetic correlations among reproductive and milk production traits in the present study indicated that the selection of one trait might be important for the improvement of other traits. Thus, the correlations between reproductive and milk production traits should be considered during selection and in designing of dairy cattle genetic improvement program. The reproductive traits should be considered as preliminary selection criteria of heifers and selection should be done with caution.
The authors would like to thank Ethiopian Institute of Agricultural Research for financing this research work and Adea-Berga dairy Research Center for allowing us to exploit long term pure Jersey breed data.
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