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Heterosis effects in a black and white dairy cattle population under different production environments
Livestock Science 131 (2010) 52–57
Contents lists available at ScienceDirect
Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i
Heterosis effects in a black and white dairy cattle population under different production environments M. Penasa a,⁎, M. De Marchi a, R. Dal Zotto a, G. de Jong b, G. Bittante a, M. Cassandro a a b
Department of Animal Science, University of Padova, Viale dell'Università 16, 35020, Legnaro, Italy NRS, Animal Evaluation Unit, P.O. Box 454, 6800 AL Arnhem, the Netherlands
a r t i c l e
i n f o
Article history: Received 16 October 2009 Received in revised form 22 February 2010 Accepted 22 February 2010 Keywords: Age at first calving Friesian strains Heterosis by environment interaction Production Somatic cell score
a b s t r a c t The effect of the environmental level of production (ENV) on the expression of heterosis for 305-day milk, fat, protein, and fat plus protein (FP) yields, lactation average somatic cell score (LSCS), and age at first calving (AFC) was investigated in first lactation Black and White dairy cows in the Netherlands, and officially enrolled in the Dutch herd-book. Holstein Friesian (HF), Dutch Friesian (DF), and first generation (F1) crosses obtained from the mating of HF sires and DF dams (HD) were involved in the study, and data from animals with a calving date between 1990 and 2000 were used. A total of 22,930 cows with production and AFC information distributed in 3549 herds and 11,055 cows with LSCS information distributed in 2071 herds, were available. Adjusted lactation yield of milk for each herd was obtained using a model that accounted for fixed effects of herd, year and month of calving, genotype, and AFC. The overall mean of all adjusted data was computed, and 3 ENV were defined on the basis of the overall mean ±0.5 standard deviations. Once ENV was defined, traits were analysed with a model that included fixed effects of ENV, herd nested within ENV, AFC (only production traits and LSCS), year and month of calving, genotype, and the interaction between ENV and genotype. Least squares means for the interaction effect were used to estimate heterosis and to evaluate its magnitude across ENV. Holstein Friesian achieved higher productions than DF. First generation crosses showed productions close to HF, especially in the low ENV. Estimates of heterosis for yield traits ranged from 2.4% (milk) in the high to 5.3% (fat) in the low ENV, and reduced with increasing ENV. Estimates for LSCS and AFC were low, with the exception of LSCS in the high ENV. Results suggest that the highest non-additive genetic effects for yield traits and LSCS were expressed in the most stressful ENV, i.e., the low one for production and the high one for LSCS. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Crossbreeding of dairy cattle in temperate climates has been widely investigated in the past (Pearson and McDowell, 1968; Turton, 1981; Touchberry, 1992), along with its effects on traits of economic relevance (e.g., López-Villalobos, 1998). Recently, the interest in the application of this mating system has been growing among dairy producers (McAllister, 2002; Weigel and Barlass, 2003; López-Villalobos et al., 2010), mainly because well-designed crossbreeding programs may ⁎ Corresponding author: Tel.: +39 049 8272629; fax: +39 049 8272633. E-mail address: [email protected] (M. Penasa). 1871-1413/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2010.02.027
lead the farmer to exploit desirable characteristics of the breeds or strains involved, and to take advantage of heterosis. Different breeds do not perform equally under different environments (Falconer and Mackay, 1996; Bryant et al., 2005), and recent studies have emphasized the existence of genotype by environment (G × E) interaction for production traits (Boettcher et al., 2003; Fikse et al., 2003), somatic cell score (SCS; Calus et al., 2006), and reproduction aspects (Boettcher et al., 2003; Kearney et al., 2004) in dairy cows. Environmental factors may also influence the level of heterosis in a crossbred population, i.e., there may be an interaction between heterosis and environment (H × E; Sheridan, 1981; Cunningham, 1982; Hill, 1982). Hence, the
M. Penasa et al. / Livestock Science 131 (2010) 52–57
assessment of crossbreeding parameters becomes more complicated. Barlow (1981), in a comprehensive review on the topic, reported that heterosis for most traits appears to be greater in stressful rather than in favourable conditions, and the nature of interactions depends on the species and trait studied. Bryant et al. (2007) analysed heterosis for milk, fat, and protein yields in different environments, with evidence that the environment affected the size of heterosis for these traits. However, literature on H × E interaction for production is not extensive, and is scarce for SCS and age at first calving (AFC). Aim of this study, therefore, was to investigate the effect of the environment on the expression of heterosis for milk, fat, protein, and fat plus protein (FP) yields, lactation average SCS (LSCS), and AFC using the Dutch Black and White dairy cattle population as case study.
2. Materials and methods
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2.2. Environmental level of production (ENV) Adjusted lactation yield of milk for each herd was obtained through the GLM procedure (SAS, 2007), using a model that accounted for herd, year and month of calving, genotype (HF, DF, and HD), and AFC as fixed effects. The overall mean of all adjusted yields of milk was computed, and 3 ENV were created according to the overall mean ± 0.5 standard deviations. On this basis, herds were classified into low (x ≤ 5980 kg), medium (5980 kg b x b 6780 kg), or high (x ≥ 6780 kg) ENV. 2.3. Statistical analysis Data were analysed using the GLM procedure (SAS, 2007) according to the following linear model: yijklmno = ENVi + Hj:i + CYk + MCl + Gm + ðENV*GÞim + AFCn + εijklmno ;
2.1. Data Data were obtained from the Dutch milk recording database provided by the Nederlands Rundvee Syndicaat (NRS). In the Netherlands, breed codes from each animal are stored on database as well as the proportion of genes from each breed. The proportion of genes is given in classes from 1 to 8, and each class represents 12.5% of genes (Harbers, 1997). Based on this information, Holstein Friesian (HF) and Dutch Friesian (DF) purebred cows, and first generation (F1) crosses from these two strains of Black and White dairy cattle were identified. In particular, only F1 crosses from HF sires × DF dams (HD) were used, because reciprocal crosses (DF sires × HF dams) were represented by a very low number of animals. Production data comprised 305-day milk, fat, and protein yields, and LSCS recorded on first lactation cows calving between 1990 and 2000. Cows were required to be herd-book registered and to have parents with known breed composition. Only lactations produced within the same herd were retained, as well as incomplete lactations of 100 days or more previously extrapolated to 305 days by NRS. For each cow, FP yield was calculated as sum of the corresponding fat and protein yields, and AFC was obtained as difference between calving date and birth date. Cows with AFC less than 520 days or greater than 1070 days were discarded from the dataset. In order to guarantee the contemporary presence of purebred and crossbred records within a farm, herd-year groups with at least one crossbred and one purebred cow were extracted. After editing, 22,930 cows with production and AFC information distributed in 3549 herds were available for subsequent statistical analyses. Collection of SCS data started during the 1990s in the Netherlands, thus there were many cows without SCS information. Lactation average SCS was calculated as follows: first, all records of individual somatic cell count (SCC) within the lactation were logtransformed as log2(SCC/1000); then, the mean of these second logs was computed. Only cows with 3 to 15 SCC measurements within the 305 days were kept. After editing, 11,055 cows with LSCS information distributed in 2071 herds were available for statistical investigation.
where yijklmno = 305-day milk, fat, protein, and FP yields, LSCS, or AFC; ENVi = fixed effect of the ith environmental level of production (i = low, medium, high); Hj:i = fixed effect of the jth herd (j = 1,…,3549 for yield traits and AFC; j = 1,…,2071 for LSCS) nested within the ith ENV; CYk = fixed effect of the kth year of calving (k = 1,…,11); MCl = fixed effect of the lth month of calving (l = 1,…,12); Gm = fixed effect of the mth genotype (m = HF, DF, HD); (ENV*G)im = fixed effect of the interaction between the ith ENV and the mth genotype; AFCn = fixed effect of the nth class of age at first calving (n = 1,…,8, with classes of 30 days, and the first and the last being open classes of b720 and N900 days, respectively); εijklmn = random residual ∼ N (0, σ2ε). The effect of AFC was included only in the analysis for production traits and LSCS. Least squares means of the interaction effect were used to estimate heterosis as [(LSMF1 − LSMAB)/(LSMAB )] × 100, where LSMF1 is LSM for F1 crosses (HD), and LSMAB is the average LSM for the purebred parents (HF and DF). Confidence intervals at the 95% level were used to test if heterosis effects were significantly different from zero. 3. Results All effects included in the model were significant (P b 0.05), with the exception of herd nested within ENV for milk yield, ENV and the interaction between genotype and ENV for AFC, and classes of AFC for LSCS (data not shown). Descriptive statistics for production traits and AFC are in Table 1, along with the number of animals for each breed type. The availability of DF animals was fairly low, particularly in the high ENV, whereas HF and HD accounted for the highest number of cows. Average differences of 1948, 71, 66, and 137 kg of milk, fat, protein, and FP yields, respectively, were observed between the high and the low ENV. Moreover, production evidenced a greater variation in the high than in the low ENV when expressed in kg but not as percentage of the mean. For AFC, a reduction of 9 days was observed in medium and high ENV compared with the low one. Descriptive statistics for LSCS are reported in Table 2. Decreasing values were found when changing from the low
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Table 1 Means (SD) of first lactation production traits and age at first calving (AFC), and number of herds and cows for each environmental level of production (ENV). ENV a
Trait b
Low Medium High
Herds (n)
Milk (kg)
Fat (kg)
Protein (kg)
FP (kg)
AFC (days)
5389 (907) 6307 (930) 7337 (1094)
243 (41) 280 (40) 314 (43)
185 (30) 217 (30) 251 (34)
428 (69) 498 (67) 565 (73)
806 (83) 797 (77) 797 (74)
1064 1457 1028
Cows c(n) Total
HF
DF
HD
6111 10,344 6475
1660 3947 3523
1322 1263 299
3129 5134 2653
a ENV = classification of herds based on their first lactation milk production adjusted for herd, year of calving, month of calving, genotype (HF, DF, and HD), and AFC (low: x ≤ 5980 kg, medium: 5980 kg b x b 6780 kg, high: x ≥ 6780 kg). b FP = fat plus protein yield; AFC = age at first calving. c HF = Holstein Friesian; DF = Dutch Friesian; HD = F1 crosses between HF sires and DF dams.
to the high ENV, with a reduction of 0.39 units. This trend is probably due to a dilution effect, i.e., somatic cells were ejected in a higher volume of milk. As expected, HF achieved higher milk, fat, protein, and FP yields than DF cows, whereas comparable estimates of LSCS were observed between the two strains (Fig. 1). First generation crosses showed productions close to HF, particularly in the low ENV, and LSCS slightly lower (favourable) than HF across ENV. For AFC, the lowest values were found for HF, with a reduction of 36 days compared to DF in the high ENV (Fig. 1). Heterosis estimates and 95% confidence intervals for milk yield traits, LSCS, and AFC are displayed in Fig. 2. Overall, all production traits revealed positive but decreasing levels of heterosis when changing from the low to the high ENV, with estimates ranging from 2.4% (milk yield in the high ENV) to 5.3% (fat yield in the low ENV). For LSCS, heterosis effects were negative (favourable) but significantly different from zero only in the high ENV, with a value close to 5%; this result should be interpreted with caution because of the fairly low number (n = 126) of DF cows available in the high ENV to obtain reliable estimates of heterosis for this trait. For AFC, heterosis effects were always very low and significantly different from zero in the medium and high ENV. 4. Discussion Despite the common origin, HF and DF can be considered as different strains during the period investigated (1990 to 2000). Average breeding values for cows born in 2000 showed that genetic differences between HF and DF for lactation milk, fat, and protein yields were 335, 9, and 10 kg, respectively, in favour of the former (de Jong, personal communication). Production statistics by NRS (2006) Table 2 Means (SD) of first lactation average somatic cell score (LSCS), and number of herds and cows for each environmental level of production (ENV). ENV a
LSCS (units)
Herds (n)
Low Medium High
6.22 (1.18) 5.96 (1.16) 5.83 (1.18)
615 823 633
Cows b(n) Total
HF
DF
HD
3047 4732 3276
1035 2113 1861
642 515 126
1370 2104 1289
a ENV = classification of herds based on their first lactation milk production adjusted for herd, year of calving, month of calving, genotype (HF, DF, and HD), and AFC (low: x ≤ 5980 kg, medium: 5980 kg b x b 6780 kg, high: x ≥ 6780 kg). b HF = Holstein Friesian; DF = Dutch Friesian; HD = F1 crosses between HF sires and DF dams.
reported phenotypic differences of 2252, 84, and 72 kg of milk, fat, and protein yields, respectively, between HF and DF (all parities). Hence, even if the American strain was substituting the original Dutch one in the studied period, it appears appropriate to consider HF and DF as two different strains on which the effect of different rearing conditions (production levels) can be investigated. Differences of milk, fat, protein, and FP yields, and AFC among breed types were greater within the high than within the low ENV (Fig. 1), indicating that they were not equally influenced by the environment and that a G × E interaction existed. Probably, HF cows were more sensitive to the less favourable conditions imposed by the low ENV, characterized by inadequate management in relation to their needs; this could have limited HF animals to fully exploit their genetic advantage for production. In a recent study, Bryant et al. (2007) observed a scaling effect for milk, fat, and protein yields comparing overseas HF and New Zealand Jersey breeds over nutritional environments; in particular, HF cows were more adapted to an intensive feeding system, i.e., an environment where nutritional requirements of this breed are better met. Trend of heterosis across ENV for milk yield traits (Fig. 2) can be explained if we consider that the low ENV is stressful for production because management does not meet the need of genetically highly producing cows such as HF. Thus, the highest heterosis values were expressed in the most stressful environment. Barlow (1981), summarizing the effects of H × E interaction in animals, debated that sub-optimal conditions let heterosis be better expressed. Heterosis effects for lactation yields found in literature are variable. Ahlborn-Breier and Hohenboken (1991) reported estimates of 6.1 and 7.2% for milk and fat yield, respectively, using data from primiparous HF, Jersey, and crossbred cows. McAllister (1986) found estimates of 3.7, 3.9, and 4.0% for milk, fat, and protein yield, respectively, using data from HF, Ayrshire, and crossbred primiparous cows. When calculated on the phenotypic mean, similar values were reported by Penasa et al. (2009) for 305-day productions in North American Holstein Friesian x Friesian crosses (parities 1 to 5) in Ireland. Heterosis estimates for 305-day milk yield traits using first lactation records were significant and approached 2.5% in a study conducted on HF× DF crosses by Van der Werf and de Boer (1989). In the present research, only first lactations were available for the analysis. However, the parity of cows seems to play an important role in the expression of heterosis as reported by several authors (Donald et al., 1977; McAllister, 1986; Touchberry, 1992; Dechow et al., 2007). Regarding H × E interaction, Bryant et al. (2007) estimated heterosis effects ranging between 5.0 and 9.5% for production traits in overseas
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Fig. 1. Least squares means and standard errors for (a) milk, (b) fat, (c) protein, and (d) fat plus protein (FP) yields, (e) age at first calving (AFC), and (f) lactation average somatic cell score (LSCS) for Holstein Friesian (HF, ◊), Dutch Friesian (DF, ♦), and F1 crosses (HD, ■) between HF sires and DF dams in each environmental level of production (ENV).
HF x New Zealand Jersey crosses, and trend of heterosis in relation to environmental level that was almost opposite compared to our study. However, the average level of production in Bryant et al. (2007) was much lower than in our research. Heterosis estimates close to zero for SCS were provided by VanRaden and Sanders (2003) in the United States. Cassell (2007) discussed that traits not much influenced by inbreeding depression are expected to show less non additive genetic effects, and SCS could be recognized as one of these traits. Nevertheless, Dechow et al. (2007) reported heterosis effects close to 8% (favourable) for SCS.
Heterosis for AFC is known to be low and it is important to bear in mind that this trait depends more on farmers' decisions than on physiological aspects. Touchberry (1992) reported heterosis effects of 1.1% for AFC. More recently, Dechow et al. (2007) observed estimates close to 2.1 and 3.5% (favourable), with values that varied across lactations and depending on the breed of sire. The present study was based on the data of pure HF and DF cows, and F1 crosses extracted from a population upgrading with HF and with various levels of heterozygosity. Harbers (1997) underlined the need of a correction for heterosis in a population with breed replacement and a multibreed genetic
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Fig. 2. Heterosis estimates and confidence intervals (P b 0.05) for F1 crosses (HD) between Holstein Friesian (HF) sires and Dutch Friesian (DF) dams for (a) milk, (b) fat, (c) protein, and (d) fat plus protein (FP) yields, (e) age at first calving (AFC), and (f) lactation average somatic cell score (LSCS) in each environmental level of production (ENV).
evaluation. However, our research suggests that not only heterosis should be taken into consideration, but also H×E interaction, especially if individuals have different levels of heterozygosity. Recently, Lidauer et al. (2006) proposed a random heterosis model to account for H×E interaction in genetic evaluation, and Su et al. (2009) proposed a reaction norm model to account for H×E interaction in situations in which heterosis changes gradually over an environmental gradient. 5. Conclusion Changes across ENV were found between HF and DF, particularly for production traits and AFC, and HF appeared to be better suited to intensive conditions. Estimates of heterosis varied across ENV, with the largest effects observed in the low
ENV for milk yield traits and in the high ENV for LSCS. The low ENV was the most stressful for production and the high ENV for LSCS; therefore, the highest heterosis estimates were expressed in the worst ENV for these traits. Because evidence of G×E interaction arose from this research, it can be argued that an advantage could derive from the exploitation of breed types positively interacting with the ENV in which they are producing. Also, the existence of H×E interaction suggests that further studies are needed to ascertain their impact on genetic evaluation of animals. Acknowledgment The authors want to thank the Nederlands Rundvee Syndicaat (NRS, the Netherlands) for providing data used in
M. Penasa et al. / Livestock Science 131 (2010) 52–57
this study, and the Superbrown Consortium of Bolzano and Trento (Italy), and Trento Province for the financial support. Scientific research funds (quota ex 60%-prot. 60A08-2099/08) are also acknowledged. Special thanks are extended to Nicolás López-Villalobos (Massey University, New Zealand) who contributed helpful ideas. The useful comments and suggestions provided by 2 anonymous reviewers are gratefully acknowledged.
References Ahlborn-Breier, G., Hohenboken, W.D., 1991. Additive and nonadditive genetic effects on milk production in dairy cattle: evidence for major individual heterosis. J. Dairy Sci. 74, 592–602. Barlow, R., 1981. Experimental evidence for interaction between heterosis and environment in animals. Anim. Breed. Abstr. 49, 715–737. Boettcher, P.J., Fatehi, J., Schutz, M.M., 2003. Genotype x environment interactions in conventional versus pasture-based dairies in Canada. J. Dairy Sci. 86, 383–389. Bryant, J.R., López-Villalobos, N., Holmes, C.W., Pryce, J.E., 2005. Simulation modelling of dairy cattle performance based on knowledge of genotype, environment, and genotype by environment interactions: current status. Agric. Syst. 86, 121–143. Bryant, J.R., López-Villalobos, N., Pryce, J.E., Holmes, C.W., Johnson, D.L., Garrick, D.J., 2007. Short communication: effect of environment on the expression of breed and heterosis effects for production traits. J. Dairy Sci. 90, 1548–1553. Calus, M.P.L., Janss, L.L.G., Veerkamp, R.F., 2006. Genotype by environment interaction for somatic cell score across bulk milk somatic cell count and days in milk. J. Dairy Sci. 89, 4846–4857. Cassell, B., 2007. Mechanisms of inbreeding depression and heterosis for profitable dairying. Proc. 4th Biennial W.E. Petersen Symposium. Univ. Minnesota, St. Paul, pp. 1–6. Cunningham, E.P., 1982. The genetic basis of heterosis. Proc. 2nd World Congr. Genet. Appl. Livest. Prod., Madrid, Spain, VI, pp. 190–205. Dechow, C.D., Rogers, G.W., Cooper, J.B., Phelps, M.I., Mosholder, A.L., 2007. Milk, fat, protein, somatic cell score, and days open among Holstein, Brown Swiss, and their crosses. J. Dairy Sci. 90, 3542–3549. Donald, H.P., Gibson, D., Russell, W.S., 1977. Estimations of heterosis in crossbred dairy cattle. Anim. Prod. 25, 193–208. Falconer, D.S., Mackay, T.F.C., 1996. Introduction to Quantitative Genetics, fourth ed. Longman Group, Essex, UK. Fikse, W.F., Rekaya, R., Weigel, K.A., 2003. Genotype × environment interaction for milk production in Guernsey cattle. J. Dairy Sci. 86, 1821–1827.
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Harbers, A.G.F., 1997. The usage of heterosis correction in a multiple breed genetic evaluation. Proc. Interbull Annu. Mtg., Vienna, Austria. Bull. No. 16. Interbull, Uppsala, Sweden, pp. 89–93. Hill, W.G., 1982. Dominance and epistasis as components of heterosis. Z. Tierz. Zuechtungsbiol. 99, 161–168. Kearney, J.F., Schutz, M.M., Boettcher, P.J., 2004. Genotype × environment interaction for grazing vs. confinement. II. Health and reproduction traits. J. Dairy Sci. 87, 510–516. Lidauer, M., Mäntysaari, E.A., Strandén, I., Pösö, J., Pedersen, J., Nielsen, U.S., Johansson, K., Eriksson, J.-Å., Madsen, P., Aamand, G.P., 2006. Random heterosis and recombination loss effects in a multibreed evaluation for Nordic Red dairy cattle. Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, MG, Brasil, Commun, p. 24-02. López-Villalobos, N., 1998. Effects of crossbreeding and selection on the productivity and profitability of the New Zealand dairy industry. PhD Thesis, Massey University, Palmerston North, New Zealand López-Villalobos, N., Penasa, M., Dal Zotto, R., Cassandro, M., Brade, W., Distl, O., Evans, R., Cromie, A., 2010. Calculation of a cow culling merit index including specific heterosis in a multibreed dairy population. Arch. Tierz. 53, 9–17. McAllister, A.J., 1986. The role of crossbreeding in breeding programs for intensive milk production in temperate climates. Proc. 3rd World Congr. Genet. Appl. Livest. Prod., Lincoln, NE, IX, pp. 47–61. McAllister, A.J., 2002. Is crossbreeding the answer to questions of dairy breed utilization?. J. Dairy Sci. 85, 2352–2357. NRS, 2006. http://www.hg.nl/. Pearson, L., McDowell, R.E., 1968. Crossbreeding of dairy cattle in temperate zones: a review of recent studies. Anim. Breed. Abstr. 36, 1–15. Penasa, M., López-Villalobos, N., Evans, R.D., Cromie, A.R., Dal Zotto, R., Cassandro, M., 2009. Crossbreeding effects on milk yield traits and calving interval in spring-calving dairy cows. Accepted for publication in. J. Anim. Breed. Genet. doi:10.1111/j.1439-0388.2009.00840.x. SAS® User's Guide, 2007. Version 9.1.0 Edition. SAS Inst., Inc., Cary, NC. Sheridan, A.K., 1981. Crossbreeding and heterosis. Anim. Breed. Abstr. 49, 131–144. Su, G., Madsen, P., Lund, M.S., 2009. Reaction norm model with unknown environmental covariate to analyze heterosis by environment interaction. J. Dairy Sci. 92, 2204–2213. Touchberry, R.W., 1992. Crossbreeding effects in dairy cattle: the Illinois experiment. J. Dairy Sci. 75, 640–667 1949 to 1969. Turton, J.D., 1981. Crossbreeding of dairy cattle—a selective review. Anim. Breed. Abstr. 49, 293–300. Van der Werf, J.H.J., de Boer, W., 1989. Estimation of genetic parameters in a crossbred population of black and white dairy cattle. J. Dairy Sci. 72, 2615–2623. VanRaden, P.M., Sanders, A.H., 2003. Economic merit of crossbred and purebred US dairy cattle. J. Dairy Sci. 86, 1036–1044. Weigel, K.A., Barlass, K.A., 2003. Results of a producer survey regarding crossbreeding on US dairy farms. J. Dairy Sci. 86, 4148–4154.
Contents lists available at ScienceDirect
Livestock Science j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / l i v s c i
Heterosis effects in a black and white dairy cattle population under different production environments M. Penasa a,⁎, M. De Marchi a, R. Dal Zotto a, G. de Jong b, G. Bittante a, M. Cassandro a a b
Department of Animal Science, University of Padova, Viale dell'Università 16, 35020, Legnaro, Italy NRS, Animal Evaluation Unit, P.O. Box 454, 6800 AL Arnhem, the Netherlands
a r t i c l e
i n f o
Article history: Received 16 October 2009 Received in revised form 22 February 2010 Accepted 22 February 2010 Keywords: Age at first calving Friesian strains Heterosis by environment interaction Production Somatic cell score
a b s t r a c t The effect of the environmental level of production (ENV) on the expression of heterosis for 305-day milk, fat, protein, and fat plus protein (FP) yields, lactation average somatic cell score (LSCS), and age at first calving (AFC) was investigated in first lactation Black and White dairy cows in the Netherlands, and officially enrolled in the Dutch herd-book. Holstein Friesian (HF), Dutch Friesian (DF), and first generation (F1) crosses obtained from the mating of HF sires and DF dams (HD) were involved in the study, and data from animals with a calving date between 1990 and 2000 were used. A total of 22,930 cows with production and AFC information distributed in 3549 herds and 11,055 cows with LSCS information distributed in 2071 herds, were available. Adjusted lactation yield of milk for each herd was obtained using a model that accounted for fixed effects of herd, year and month of calving, genotype, and AFC. The overall mean of all adjusted data was computed, and 3 ENV were defined on the basis of the overall mean ±0.5 standard deviations. Once ENV was defined, traits were analysed with a model that included fixed effects of ENV, herd nested within ENV, AFC (only production traits and LSCS), year and month of calving, genotype, and the interaction between ENV and genotype. Least squares means for the interaction effect were used to estimate heterosis and to evaluate its magnitude across ENV. Holstein Friesian achieved higher productions than DF. First generation crosses showed productions close to HF, especially in the low ENV. Estimates of heterosis for yield traits ranged from 2.4% (milk) in the high to 5.3% (fat) in the low ENV, and reduced with increasing ENV. Estimates for LSCS and AFC were low, with the exception of LSCS in the high ENV. Results suggest that the highest non-additive genetic effects for yield traits and LSCS were expressed in the most stressful ENV, i.e., the low one for production and the high one for LSCS. © 2010 Elsevier B.V. All rights reserved.
1. Introduction Crossbreeding of dairy cattle in temperate climates has been widely investigated in the past (Pearson and McDowell, 1968; Turton, 1981; Touchberry, 1992), along with its effects on traits of economic relevance (e.g., López-Villalobos, 1998). Recently, the interest in the application of this mating system has been growing among dairy producers (McAllister, 2002; Weigel and Barlass, 2003; López-Villalobos et al., 2010), mainly because well-designed crossbreeding programs may ⁎ Corresponding author: Tel.: +39 049 8272629; fax: +39 049 8272633. E-mail address: [email protected] (M. Penasa). 1871-1413/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.livsci.2010.02.027
lead the farmer to exploit desirable characteristics of the breeds or strains involved, and to take advantage of heterosis. Different breeds do not perform equally under different environments (Falconer and Mackay, 1996; Bryant et al., 2005), and recent studies have emphasized the existence of genotype by environment (G × E) interaction for production traits (Boettcher et al., 2003; Fikse et al., 2003), somatic cell score (SCS; Calus et al., 2006), and reproduction aspects (Boettcher et al., 2003; Kearney et al., 2004) in dairy cows. Environmental factors may also influence the level of heterosis in a crossbred population, i.e., there may be an interaction between heterosis and environment (H × E; Sheridan, 1981; Cunningham, 1982; Hill, 1982). Hence, the
M. Penasa et al. / Livestock Science 131 (2010) 52–57
assessment of crossbreeding parameters becomes more complicated. Barlow (1981), in a comprehensive review on the topic, reported that heterosis for most traits appears to be greater in stressful rather than in favourable conditions, and the nature of interactions depends on the species and trait studied. Bryant et al. (2007) analysed heterosis for milk, fat, and protein yields in different environments, with evidence that the environment affected the size of heterosis for these traits. However, literature on H × E interaction for production is not extensive, and is scarce for SCS and age at first calving (AFC). Aim of this study, therefore, was to investigate the effect of the environment on the expression of heterosis for milk, fat, protein, and fat plus protein (FP) yields, lactation average SCS (LSCS), and AFC using the Dutch Black and White dairy cattle population as case study.
2. Materials and methods
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2.2. Environmental level of production (ENV) Adjusted lactation yield of milk for each herd was obtained through the GLM procedure (SAS, 2007), using a model that accounted for herd, year and month of calving, genotype (HF, DF, and HD), and AFC as fixed effects. The overall mean of all adjusted yields of milk was computed, and 3 ENV were created according to the overall mean ± 0.5 standard deviations. On this basis, herds were classified into low (x ≤ 5980 kg), medium (5980 kg b x b 6780 kg), or high (x ≥ 6780 kg) ENV. 2.3. Statistical analysis Data were analysed using the GLM procedure (SAS, 2007) according to the following linear model: yijklmno = ENVi + Hj:i + CYk + MCl + Gm + ðENV*GÞim + AFCn + εijklmno ;
2.1. Data Data were obtained from the Dutch milk recording database provided by the Nederlands Rundvee Syndicaat (NRS). In the Netherlands, breed codes from each animal are stored on database as well as the proportion of genes from each breed. The proportion of genes is given in classes from 1 to 8, and each class represents 12.5% of genes (Harbers, 1997). Based on this information, Holstein Friesian (HF) and Dutch Friesian (DF) purebred cows, and first generation (F1) crosses from these two strains of Black and White dairy cattle were identified. In particular, only F1 crosses from HF sires × DF dams (HD) were used, because reciprocal crosses (DF sires × HF dams) were represented by a very low number of animals. Production data comprised 305-day milk, fat, and protein yields, and LSCS recorded on first lactation cows calving between 1990 and 2000. Cows were required to be herd-book registered and to have parents with known breed composition. Only lactations produced within the same herd were retained, as well as incomplete lactations of 100 days or more previously extrapolated to 305 days by NRS. For each cow, FP yield was calculated as sum of the corresponding fat and protein yields, and AFC was obtained as difference between calving date and birth date. Cows with AFC less than 520 days or greater than 1070 days were discarded from the dataset. In order to guarantee the contemporary presence of purebred and crossbred records within a farm, herd-year groups with at least one crossbred and one purebred cow were extracted. After editing, 22,930 cows with production and AFC information distributed in 3549 herds were available for subsequent statistical analyses. Collection of SCS data started during the 1990s in the Netherlands, thus there were many cows without SCS information. Lactation average SCS was calculated as follows: first, all records of individual somatic cell count (SCC) within the lactation were logtransformed as log2(SCC/1000); then, the mean of these second logs was computed. Only cows with 3 to 15 SCC measurements within the 305 days were kept. After editing, 11,055 cows with LSCS information distributed in 2071 herds were available for statistical investigation.
where yijklmno = 305-day milk, fat, protein, and FP yields, LSCS, or AFC; ENVi = fixed effect of the ith environmental level of production (i = low, medium, high); Hj:i = fixed effect of the jth herd (j = 1,…,3549 for yield traits and AFC; j = 1,…,2071 for LSCS) nested within the ith ENV; CYk = fixed effect of the kth year of calving (k = 1,…,11); MCl = fixed effect of the lth month of calving (l = 1,…,12); Gm = fixed effect of the mth genotype (m = HF, DF, HD); (ENV*G)im = fixed effect of the interaction between the ith ENV and the mth genotype; AFCn = fixed effect of the nth class of age at first calving (n = 1,…,8, with classes of 30 days, and the first and the last being open classes of b720 and N900 days, respectively); εijklmn = random residual ∼ N (0, σ2ε). The effect of AFC was included only in the analysis for production traits and LSCS. Least squares means of the interaction effect were used to estimate heterosis as [(LSMF1 − LSMAB)/(LSMAB )] × 100, where LSMF1 is LSM for F1 crosses (HD), and LSMAB is the average LSM for the purebred parents (HF and DF). Confidence intervals at the 95% level were used to test if heterosis effects were significantly different from zero. 3. Results All effects included in the model were significant (P b 0.05), with the exception of herd nested within ENV for milk yield, ENV and the interaction between genotype and ENV for AFC, and classes of AFC for LSCS (data not shown). Descriptive statistics for production traits and AFC are in Table 1, along with the number of animals for each breed type. The availability of DF animals was fairly low, particularly in the high ENV, whereas HF and HD accounted for the highest number of cows. Average differences of 1948, 71, 66, and 137 kg of milk, fat, protein, and FP yields, respectively, were observed between the high and the low ENV. Moreover, production evidenced a greater variation in the high than in the low ENV when expressed in kg but not as percentage of the mean. For AFC, a reduction of 9 days was observed in medium and high ENV compared with the low one. Descriptive statistics for LSCS are reported in Table 2. Decreasing values were found when changing from the low
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Table 1 Means (SD) of first lactation production traits and age at first calving (AFC), and number of herds and cows for each environmental level of production (ENV). ENV a
Trait b
Low Medium High
Herds (n)
Milk (kg)
Fat (kg)
Protein (kg)
FP (kg)
AFC (days)
5389 (907) 6307 (930) 7337 (1094)
243 (41) 280 (40) 314 (43)
185 (30) 217 (30) 251 (34)
428 (69) 498 (67) 565 (73)
806 (83) 797 (77) 797 (74)
1064 1457 1028
Cows c(n) Total
HF
DF
HD
6111 10,344 6475
1660 3947 3523
1322 1263 299
3129 5134 2653
a ENV = classification of herds based on their first lactation milk production adjusted for herd, year of calving, month of calving, genotype (HF, DF, and HD), and AFC (low: x ≤ 5980 kg, medium: 5980 kg b x b 6780 kg, high: x ≥ 6780 kg). b FP = fat plus protein yield; AFC = age at first calving. c HF = Holstein Friesian; DF = Dutch Friesian; HD = F1 crosses between HF sires and DF dams.
to the high ENV, with a reduction of 0.39 units. This trend is probably due to a dilution effect, i.e., somatic cells were ejected in a higher volume of milk. As expected, HF achieved higher milk, fat, protein, and FP yields than DF cows, whereas comparable estimates of LSCS were observed between the two strains (Fig. 1). First generation crosses showed productions close to HF, particularly in the low ENV, and LSCS slightly lower (favourable) than HF across ENV. For AFC, the lowest values were found for HF, with a reduction of 36 days compared to DF in the high ENV (Fig. 1). Heterosis estimates and 95% confidence intervals for milk yield traits, LSCS, and AFC are displayed in Fig. 2. Overall, all production traits revealed positive but decreasing levels of heterosis when changing from the low to the high ENV, with estimates ranging from 2.4% (milk yield in the high ENV) to 5.3% (fat yield in the low ENV). For LSCS, heterosis effects were negative (favourable) but significantly different from zero only in the high ENV, with a value close to 5%; this result should be interpreted with caution because of the fairly low number (n = 126) of DF cows available in the high ENV to obtain reliable estimates of heterosis for this trait. For AFC, heterosis effects were always very low and significantly different from zero in the medium and high ENV. 4. Discussion Despite the common origin, HF and DF can be considered as different strains during the period investigated (1990 to 2000). Average breeding values for cows born in 2000 showed that genetic differences between HF and DF for lactation milk, fat, and protein yields were 335, 9, and 10 kg, respectively, in favour of the former (de Jong, personal communication). Production statistics by NRS (2006) Table 2 Means (SD) of first lactation average somatic cell score (LSCS), and number of herds and cows for each environmental level of production (ENV). ENV a
LSCS (units)
Herds (n)
Low Medium High
6.22 (1.18) 5.96 (1.16) 5.83 (1.18)
615 823 633
Cows b(n) Total
HF
DF
HD
3047 4732 3276
1035 2113 1861
642 515 126
1370 2104 1289
a ENV = classification of herds based on their first lactation milk production adjusted for herd, year of calving, month of calving, genotype (HF, DF, and HD), and AFC (low: x ≤ 5980 kg, medium: 5980 kg b x b 6780 kg, high: x ≥ 6780 kg). b HF = Holstein Friesian; DF = Dutch Friesian; HD = F1 crosses between HF sires and DF dams.
reported phenotypic differences of 2252, 84, and 72 kg of milk, fat, and protein yields, respectively, between HF and DF (all parities). Hence, even if the American strain was substituting the original Dutch one in the studied period, it appears appropriate to consider HF and DF as two different strains on which the effect of different rearing conditions (production levels) can be investigated. Differences of milk, fat, protein, and FP yields, and AFC among breed types were greater within the high than within the low ENV (Fig. 1), indicating that they were not equally influenced by the environment and that a G × E interaction existed. Probably, HF cows were more sensitive to the less favourable conditions imposed by the low ENV, characterized by inadequate management in relation to their needs; this could have limited HF animals to fully exploit their genetic advantage for production. In a recent study, Bryant et al. (2007) observed a scaling effect for milk, fat, and protein yields comparing overseas HF and New Zealand Jersey breeds over nutritional environments; in particular, HF cows were more adapted to an intensive feeding system, i.e., an environment where nutritional requirements of this breed are better met. Trend of heterosis across ENV for milk yield traits (Fig. 2) can be explained if we consider that the low ENV is stressful for production because management does not meet the need of genetically highly producing cows such as HF. Thus, the highest heterosis values were expressed in the most stressful environment. Barlow (1981), summarizing the effects of H × E interaction in animals, debated that sub-optimal conditions let heterosis be better expressed. Heterosis effects for lactation yields found in literature are variable. Ahlborn-Breier and Hohenboken (1991) reported estimates of 6.1 and 7.2% for milk and fat yield, respectively, using data from primiparous HF, Jersey, and crossbred cows. McAllister (1986) found estimates of 3.7, 3.9, and 4.0% for milk, fat, and protein yield, respectively, using data from HF, Ayrshire, and crossbred primiparous cows. When calculated on the phenotypic mean, similar values were reported by Penasa et al. (2009) for 305-day productions in North American Holstein Friesian x Friesian crosses (parities 1 to 5) in Ireland. Heterosis estimates for 305-day milk yield traits using first lactation records were significant and approached 2.5% in a study conducted on HF× DF crosses by Van der Werf and de Boer (1989). In the present research, only first lactations were available for the analysis. However, the parity of cows seems to play an important role in the expression of heterosis as reported by several authors (Donald et al., 1977; McAllister, 1986; Touchberry, 1992; Dechow et al., 2007). Regarding H × E interaction, Bryant et al. (2007) estimated heterosis effects ranging between 5.0 and 9.5% for production traits in overseas
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Fig. 1. Least squares means and standard errors for (a) milk, (b) fat, (c) protein, and (d) fat plus protein (FP) yields, (e) age at first calving (AFC), and (f) lactation average somatic cell score (LSCS) for Holstein Friesian (HF, ◊), Dutch Friesian (DF, ♦), and F1 crosses (HD, ■) between HF sires and DF dams in each environmental level of production (ENV).
HF x New Zealand Jersey crosses, and trend of heterosis in relation to environmental level that was almost opposite compared to our study. However, the average level of production in Bryant et al. (2007) was much lower than in our research. Heterosis estimates close to zero for SCS were provided by VanRaden and Sanders (2003) in the United States. Cassell (2007) discussed that traits not much influenced by inbreeding depression are expected to show less non additive genetic effects, and SCS could be recognized as one of these traits. Nevertheless, Dechow et al. (2007) reported heterosis effects close to 8% (favourable) for SCS.
Heterosis for AFC is known to be low and it is important to bear in mind that this trait depends more on farmers' decisions than on physiological aspects. Touchberry (1992) reported heterosis effects of 1.1% for AFC. More recently, Dechow et al. (2007) observed estimates close to 2.1 and 3.5% (favourable), with values that varied across lactations and depending on the breed of sire. The present study was based on the data of pure HF and DF cows, and F1 crosses extracted from a population upgrading with HF and with various levels of heterozygosity. Harbers (1997) underlined the need of a correction for heterosis in a population with breed replacement and a multibreed genetic
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Fig. 2. Heterosis estimates and confidence intervals (P b 0.05) for F1 crosses (HD) between Holstein Friesian (HF) sires and Dutch Friesian (DF) dams for (a) milk, (b) fat, (c) protein, and (d) fat plus protein (FP) yields, (e) age at first calving (AFC), and (f) lactation average somatic cell score (LSCS) in each environmental level of production (ENV).
evaluation. However, our research suggests that not only heterosis should be taken into consideration, but also H×E interaction, especially if individuals have different levels of heterozygosity. Recently, Lidauer et al. (2006) proposed a random heterosis model to account for H×E interaction in genetic evaluation, and Su et al. (2009) proposed a reaction norm model to account for H×E interaction in situations in which heterosis changes gradually over an environmental gradient. 5. Conclusion Changes across ENV were found between HF and DF, particularly for production traits and AFC, and HF appeared to be better suited to intensive conditions. Estimates of heterosis varied across ENV, with the largest effects observed in the low
ENV for milk yield traits and in the high ENV for LSCS. The low ENV was the most stressful for production and the high ENV for LSCS; therefore, the highest heterosis estimates were expressed in the worst ENV for these traits. Because evidence of G×E interaction arose from this research, it can be argued that an advantage could derive from the exploitation of breed types positively interacting with the ENV in which they are producing. Also, the existence of H×E interaction suggests that further studies are needed to ascertain their impact on genetic evaluation of animals. Acknowledgment The authors want to thank the Nederlands Rundvee Syndicaat (NRS, the Netherlands) for providing data used in
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this study, and the Superbrown Consortium of Bolzano and Trento (Italy), and Trento Province for the financial support. Scientific research funds (quota ex 60%-prot. 60A08-2099/08) are also acknowledged. Special thanks are extended to Nicolás López-Villalobos (Massey University, New Zealand) who contributed helpful ideas. The useful comments and suggestions provided by 2 anonymous reviewers are gratefully acknowledged.
References Ahlborn-Breier, G., Hohenboken, W.D., 1991. Additive and nonadditive genetic effects on milk production in dairy cattle: evidence for major individual heterosis. J. Dairy Sci. 74, 592–602. Barlow, R., 1981. Experimental evidence for interaction between heterosis and environment in animals. Anim. Breed. Abstr. 49, 715–737. Boettcher, P.J., Fatehi, J., Schutz, M.M., 2003. Genotype x environment interactions in conventional versus pasture-based dairies in Canada. J. Dairy Sci. 86, 383–389. Bryant, J.R., López-Villalobos, N., Holmes, C.W., Pryce, J.E., 2005. Simulation modelling of dairy cattle performance based on knowledge of genotype, environment, and genotype by environment interactions: current status. Agric. Syst. 86, 121–143. Bryant, J.R., López-Villalobos, N., Pryce, J.E., Holmes, C.W., Johnson, D.L., Garrick, D.J., 2007. Short communication: effect of environment on the expression of breed and heterosis effects for production traits. J. Dairy Sci. 90, 1548–1553. Calus, M.P.L., Janss, L.L.G., Veerkamp, R.F., 2006. Genotype by environment interaction for somatic cell score across bulk milk somatic cell count and days in milk. J. Dairy Sci. 89, 4846–4857. Cassell, B., 2007. Mechanisms of inbreeding depression and heterosis for profitable dairying. Proc. 4th Biennial W.E. Petersen Symposium. Univ. Minnesota, St. Paul, pp. 1–6. Cunningham, E.P., 1982. The genetic basis of heterosis. Proc. 2nd World Congr. Genet. Appl. Livest. Prod., Madrid, Spain, VI, pp. 190–205. Dechow, C.D., Rogers, G.W., Cooper, J.B., Phelps, M.I., Mosholder, A.L., 2007. Milk, fat, protein, somatic cell score, and days open among Holstein, Brown Swiss, and their crosses. J. Dairy Sci. 90, 3542–3549. Donald, H.P., Gibson, D., Russell, W.S., 1977. Estimations of heterosis in crossbred dairy cattle. Anim. Prod. 25, 193–208. Falconer, D.S., Mackay, T.F.C., 1996. Introduction to Quantitative Genetics, fourth ed. Longman Group, Essex, UK. Fikse, W.F., Rekaya, R., Weigel, K.A., 2003. Genotype × environment interaction for milk production in Guernsey cattle. J. Dairy Sci. 86, 1821–1827.
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Harbers, A.G.F., 1997. The usage of heterosis correction in a multiple breed genetic evaluation. Proc. Interbull Annu. Mtg., Vienna, Austria. Bull. No. 16. Interbull, Uppsala, Sweden, pp. 89–93. Hill, W.G., 1982. Dominance and epistasis as components of heterosis. Z. Tierz. Zuechtungsbiol. 99, 161–168. Kearney, J.F., Schutz, M.M., Boettcher, P.J., 2004. Genotype × environment interaction for grazing vs. confinement. II. Health and reproduction traits. J. Dairy Sci. 87, 510–516. Lidauer, M., Mäntysaari, E.A., Strandén, I., Pösö, J., Pedersen, J., Nielsen, U.S., Johansson, K., Eriksson, J.-Å., Madsen, P., Aamand, G.P., 2006. Random heterosis and recombination loss effects in a multibreed evaluation for Nordic Red dairy cattle. Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, MG, Brasil, Commun, p. 24-02. López-Villalobos, N., 1998. Effects of crossbreeding and selection on the productivity and profitability of the New Zealand dairy industry. PhD Thesis, Massey University, Palmerston North, New Zealand López-Villalobos, N., Penasa, M., Dal Zotto, R., Cassandro, M., Brade, W., Distl, O., Evans, R., Cromie, A., 2010. Calculation of a cow culling merit index including specific heterosis in a multibreed dairy population. Arch. Tierz. 53, 9–17. McAllister, A.J., 1986. The role of crossbreeding in breeding programs for intensive milk production in temperate climates. Proc. 3rd World Congr. Genet. Appl. Livest. Prod., Lincoln, NE, IX, pp. 47–61. McAllister, A.J., 2002. Is crossbreeding the answer to questions of dairy breed utilization?. J. Dairy Sci. 85, 2352–2357. NRS, 2006. http://www.hg.nl/. Pearson, L., McDowell, R.E., 1968. Crossbreeding of dairy cattle in temperate zones: a review of recent studies. Anim. Breed. Abstr. 36, 1–15. Penasa, M., López-Villalobos, N., Evans, R.D., Cromie, A.R., Dal Zotto, R., Cassandro, M., 2009. Crossbreeding effects on milk yield traits and calving interval in spring-calving dairy cows. Accepted for publication in. J. Anim. Breed. Genet. doi:10.1111/j.1439-0388.2009.00840.x. SAS® User's Guide, 2007. Version 9.1.0 Edition. SAS Inst., Inc., Cary, NC. Sheridan, A.K., 1981. Crossbreeding and heterosis. Anim. Breed. Abstr. 49, 131–144. Su, G., Madsen, P., Lund, M.S., 2009. Reaction norm model with unknown environmental covariate to analyze heterosis by environment interaction. J. Dairy Sci. 92, 2204–2213. Touchberry, R.W., 1992. Crossbreeding effects in dairy cattle: the Illinois experiment. J. Dairy Sci. 75, 640–667 1949 to 1969. Turton, J.D., 1981. Crossbreeding of dairy cattle—a selective review. Anim. Breed. Abstr. 49, 293–300. Van der Werf, J.H.J., de Boer, W., 1989. Estimation of genetic parameters in a crossbred population of black and white dairy cattle. J. Dairy Sci. 72, 2615–2623. VanRaden, P.M., Sanders, A.H., 2003. Economic merit of crossbred and purebred US dairy cattle. J. Dairy Sci. 86, 1036–1044. Weigel, K.A., Barlass, K.A., 2003. Results of a producer survey regarding crossbreeding on US dairy farms. J. Dairy Sci. 86, 4148–4154.