Genetic and environmental sources of variation of milk yield of Skopelos dairy goats

Small Ruminant Research 36 (2000) 1±5

Genetic and environmental sources of variation of milk yield of Skopelos dairy goats A. Kominakisa,*, E. Rogdakisa, Ch. Vasiloudisb, O. Liaskosc a

Department of Animal Breeding and Husbandry, Faculty of Animal Science, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece b Agricultural Development Service of Skopelos, Skopelos, Greece c Centre of Livestock Genetic Improvement, Karditsa, Greece Accepted 8 August 1999

Abstract Lactation records of the Skopelos dairy goat population were used to assess the importance of various environmental effects and to estimate genetic parameters for 90-day milk yield (9OMY) and total milk yield (TMY) on untransformed and on log scale. Average 9OMY, TMY and days of milking were 164.7, 239.2 kg and 187 days, respectively. Herd, production year, lambing month, birth type, lactation number, the herd by year, year by month, year by lactation number interactions and days of milking were found statistically signi®cant for TMY. Heritability estimates were 0.15 and 0.14 for 9OMY and TMY, respectively. Higher heritabilities of 0.24 and of 0.28 were estimated on the log scale for 90MY and TMY, respectively. Genetic and phenotypic correlations between 9OMY and total milk yield were found high, of 0.95 and 0.80, respectively. # 2000 Published by Elsevier Science B.V. All rights reserved. Keywords: Goat; Milk yield; Heritability; Correlation

1. Introduction In the group of Aegean islands called the Northern Sporades, east of the Magnisia continental region of Greece, a goat breed called the ``Skopelos goat'' (deriving its name from the island of Skopelos) has been known to have excellent productivity potential. According to the local authority of the agricultural development service of the island of Skopelos the breed numbers in its area of origin around 8,000± *

Corresponding author. Tel.: ‡30-1-5294403; fax: ‡30-1-5294442. E-mail address: [email protected] (A. Kominakis).

10,000 animals. The animals are small bodied (68 cm height at withers) but exceptionally heavy (56 kg for the adult females). The breed is characterized by smooth glossy hair coat with dominating red brownish coloration. Both males and females are horned (Pappas et al., 1992). The commercial average milk yield of the recorded does was 258 kg in 1991 (Pappas et al., 1992) with an average of 263 kg during years 1986±1992 (Rogdakis et al., 1996). Fat content of milk is reported exceptionally high (6.08%, Pappas et al., 1992; 5.6%, Rogdakis et al., 1996). Protein and lactose content is 4.0% and 4.6%, respectively. The proli®cacy is 1.4 kids per doe and year, the average birth weight of kids is 3.3 kg, while the kids' body

0921-4488/00/$ ± see front matter # 2000 Published by Elsevier Science B.V. All rights reserved. PII: S 0 9 2 1 - 4 4 8 8 ( 9 9 ) 0 0 1 0 5 - 4

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A. Kominakis et al. / Small Ruminant Research 36 (2000) 1±5

weight at the age of 59 days is 12.0 kg (Rogdakis et al., 1996). Reliable and speci®c population parameter estimates of heritabilities and genetic correlations of production traits are essential in predicting breeding values accurately as well as in developing ef®cient breeding schemes. For dairy goats, a number of genetic parameter estimates have been reported in the past for milk yield, based on paternal half-sib correlations (Bouillon and Ricordeau, 1975; Steine, 1976; Roenningen, 1980; Mavrogenis et al., 1984; Sullivan et al., 1986; Constantinou, 1989; Mavrogenis et al., 1989; Kala and Prakash, 1990) on dam-daughter regression (Constantinou et al., 1985; Rabasco et al., 1993) on the MINQUE method (Kennedy et al., 1982) or on the REML method (Boichard et al., 1989; Bishop et al., 1994; Andonov et al., 1998). The aim of this study was to assess the importance of various environmental factors and to estimate genetic parameters for milk yield in the Skopelos breed using an animal model on untransformed and transformed data. 2. Materials and methods 2.1. Data Lactation records (n ˆ 24,420) for milk yield from 4962 does recorded during years 1988±1997 were collected from the center of livestock genetic improvement located at Karditsa in Central Greece. Initial data edits involved dam number, herd code, production year, lambing month, lactation number, birth type (single, twin), days in milk, 90-day milk yield (90MY) and total milk yield (TMY). A total of 4990 records were excluded from the initial data set according to the following criteria: milk yield less than 110 kg or higher than 730 kg; days of milking less than 110. The records retained were 19,430 lactations from 4802 does (data set 1). Five month lambing classes, January, February, October, November and December, and six lactation number classes, 1±6, were built. A smaller data set (no. 2) with pedigree information was used for estimation of genetic parameters. This data included 1251 lactation records from 1029 does. The number of sires and dams with progeny records were 56 and 130, respectively. A statistically

signi®cant linear correlation was detected between herd means and herd phenotypic standard deviations (SD) for untransformed data (r ˆ 0.66, p < 0.001). Log transformation was employed in an attempt to adjust for heterogenous variance across herds. 2.2. Test of signi®cance of environmental effects The mathematical model for 90MY included: herd, year, month, birth type, lactation number and the herd by year, year by month and lactation by year interactions. Analysis of TMY included, apart from the above environmental effects, days of milking as a linear and quadratic covariate. The signi®cance of the various environmental effects was determined by least squares analysis of variance (SAS, 1996). 2.3. Parameter estimation Genetic parameters for 9OMY and TMY were estimated by bivariate analysis applying an animal model with animals' additive genetic effect as the only random effect. The (co)variance structure for this analysis can be described as P V…a† ˆ PA A V…e† ˆ E I; where a is the vector of the animals' direct effects, e P the vector of residual errors, A thePq  q matrix of additive genetic covariances …sAij †; E the matrix of error covariances …sEij †, A the numerator relationship matrix between animals, I the identity matrix with order of the number of records and * denotes the direct matrix product. All remaining covariances were assumed to be zero. The ®xed effects part of the model included the effects found statistically signi®cant for the two traits in the previous test. (Co)variance components as well as standard errors of estimates were estimated by a derivate-free algorithm using the DFREML computer program (Meyer, 1997). 3. Results 3.1. Descriptive statistics Estimates of means and CVs for 9OMY, TMY and days of milking for data set 1 and data 2 are

A. Kominakis et al. / Small Ruminant Research 36 (2000) 1±5 Table 1 Mean (x) and coef®cient of variation (CV%) for 90MY, TMY and days in milk in the Skopelos goat Trait 9OMY (kg) Set 1 Set 2 TMY (kg) Set 1 Set 2 Days of milking Set 1 Set 2

x (S.E.)

CV (%)

164.7  0.4 155.7  1.3

32.2 29.5

239.2  0.7 224.2  2.4

38.9 38.6

187  0.3 167  1.0

21.1 20.4

summarized in Table 1. 9OMY, TMY and days of milking were 164.7, 239.2 kg and 187 days for data set 1 and 155.7, 224.2 kg and 167 days for data set 2. Furthermore, log transformation of data resulted in a non-signi®cant correlation (r ˆ 0.09) between herd means and herd phenotypic SD.

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3.3. Genetic parameters Table 2 presents the variance components, heritabilities, genetic and phenotypic correlations for 9OMY and TMY. To facilitate a comparison on the linear and log scales, variance component estimates of log scale are multiplied by 105. The heritability estimates for untransformed data for 9OMY and TMY were 0.15 and 0.14, respectively. Log transformation of the data did not signi®cantly change the additive genetic variances for 9OMY but reduced that of TMY. Most evident was the reduction in residual and therefore in phenotypic variances. As a result, higher heritabilities of 0.24 and of 0.28, after log transformation, were estimated for 9OMY and TMY, respectively. Genetic correlation between 9OMY and milk yield (MY) was found to be 0.95 and 0.93 for untransformed and transformed records, respectively. Phenotypic correlation was estimated of 0.80 and 0.78 on the untransformed and on the log scale, respectively. 4. Discussion

3.2. Environmental effects Herd, production year, lambing month, birth type, lactation number and the herd by year, year by month and year by lactation number interactions were found to be statistically signi®cant for 9OMY and TMY in data set 1. In data set 2 all the main effects and only the herd by year interaction were signi®cant for 9OMY and TMY. Days of milking were found statistically signi®cant as a quadratic covariate for TMY in both data sets.

4.1. Environmental effects The importance of the various environmental effects on milk yield has been also examined in other studies. Mavrogenis et al. (1984) found that year of kidding, month of kidding, age of the goat and the year by month interaction had a signi®cant effect on milk yield of Damascus goats in Cyprus. In a later study, Mavrogenis et al. (1989) noted the importance of herd, season of kidding, season by year interaction and the

Table 2 Estimates of variance components, heritabilities, genetic and phenotypic correlations for 9OMY and TMYa 9OMY

s2a s2e s2p h2 (S.E.) rA12 (S.E.) rP12

TMY

NT (kg2)

LT (105  kg2)

NT (kg2)

LT (105  kg2)

107.12 597.66 704.78 0.15  0.04 0.95  0.13 0.80

102 322 424 0.24  0.04 0.93  0.13 0.78

185.08 1136.64 1321.72 0.14  0.04

117 299 416 0.28  0.04

a NT: non-transformed data; LT: log transformed data. s2a : additive genetic variance; s2e : error variance; s2p : phenotypic variance; h2: heritability; rA12 : genetic correlation; rP12 : phenotypic correlation.

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A. Kominakis et al. / Small Ruminant Research 36 (2000) 1±5

lactation number on milk yield of Damascus goats. Boichard et al. (1989) reported that month within year, age at kidding and birth type signi®cantly affected milk yield of Alpine and Saanen goats. Kala and Prakash (1990) found that year and season of lambing accounted for variation of milk yield in two Indian goat breeds while Rabasco et al. (1993) found that production year, lactation number and birth type but not herd, were signi®cant effects for milk yield in the Spanish Verata goats. 4.2. Parameter estimates Herd means were found not to be correlated to herd phenotypic SD after log transformation. Transforming records to algorithms was thus an appropriate method to equalizing variances across herds implying that heterogeneity of variance was just a scale effect. Estimated variances on the untransformed scale, expressed as a coef®cient of variation (CV), was 0.063 and 0.056 for genetic effects (sA/my) and 0.148 and 0.140 for residual effects (se/my) for 9OMY and TMY, respectively. CV on the log scale was 0.061 and 0.045 for genetic effects and 0.109 and 0.072 for residual effects of 9OMY and TMY, respectively. Thus, log transformation resulted in reduction of genetic but in proportionally higher decreases in residual variances. As a result higher heritability estimates for 9OMY and TMY were obtained when compared to the untransformed scale. Higher heritabilities for log yields have been also reported in dairy cattle (Hill et al., 1983; De Veer and Van Vleck, 1987). Genetic variance components are also found to be related to the mean although this relationship has not been consistent (Hill et al., 1983; Lofgren et al., 1985; Mirande and Van Vleck, 1985). Heritability estimates for TMY obtained from untransformed records were lower but those obtained after transformation were comparable to those reported in the literature. Early literature reviews of heritabilities of milk yield in dairy goats (Iloeje and Van Vleck, 1978; Shelton, 1978) reported estimates ranging from 0.16 to 0.60 with an unweighted mean value of 0.32. Later references reported heritabilities of milk yield in the range of 0.18±0.32 (Kennedy et al., 1982; Mavrogenis et al., 1984; Constantinou et al., 1985; Sullivan et al., 1986; Constantinou, 1989; Mavrogenis et al., 1989; Kala and Prakash, 1990;

Rabasco et al., 1993). Estimates by the REML method ranged from 0.29 to 0.31 (Boichard et al., 1989; Bishop et al., 1994; Andonov et al., 1998). Literature estimates of 9OMY were in the range of 0.29±0.52 (Mavrogenis et al., 1984, 1989; Constantinou et al., 1985; Constantinou, 1989). Genetic correlations between 9OMY and TMY were very high. These estimates are agreement with other studies (Mavrogenis et al., 1984, 1989; Constantinou et al., 1985; Constantinou, 1989). The present ®ndings suggest that genetic variability in milk yield is adequate for selection provided that herd variation has been accounted for. Selecting on the basis of part lactation records is probably as ef®cient, in view of the very high and positive genetic correlations between 9OMY and TMY. 9OMY as a trait to select for, appears to be more advantageous because it is standardized for lactation length, is obtained on a much shorter time interval and can be recorded with reduced costs than total milk yield. Attention, however, should be paid when selection is practiced on milk yield because of the negative genetic correlations between milk yield and fat content and milk yield and protein content (Bouillon and Ricordeau, 1975; Boichard et al., 1989; Kala and Prakash, 1990; Andonov et al., 1998). Selection on milk yield would result in loss of protein and fat contents with a negative impact on quality of various dairy products (goat cheese and yogurt). An answer to this problem might be selecting the breeding animals for fat yield or attempting to introduce independent culling levels for milk yield and milk contents (Kominakis et al., 1998). 5. Conclusion Employment of log transformation on milk yield records in an attempt to adjust for heterogenous herd variation resulted in higher heritability estimates for milk yield in Skopelos dairy goats. TMY and 90MY have similar heritabilities and are genetically highly correlated. Thus, the latter could be used as an alternative selection criterion. References Andonov, S., Kovac, M., Kompan, D., Dzabirski, V., 1998. Estimation of covariance components for test day production in dairy goat, Proceedings of 6th World Cong. Genet. Appl. Livest. Prod., 24, pp. 145±148

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