Trop Anim Health Prod (2014) 46:529–535 DOI 10.1007/s11250-013-0524-y
REGULAR ARTICLES
Genetic association between milk yield, stayability, and mastitis in Holstein cows under tropical conditions Natalia Irano & Annaiza Braga Bignardi & Lenira El Faro & Mário Luiz Santana Jr & Vera Lúcia Cardoso & Lucia Galvão Albuquerque
Accepted: 11 December 2013 / Published online: 29 December 2013 # Springer Science+Business Media Dordrecht 2014
Abstract The objective of this study was to estimate genetic parameters for milk yield, stayability, and the occurrence of clinical mastitis in Holstein cows, as well as studying the genetic relationship between them, in order to provide subsidies for the genetic evaluation of these traits. Records from 5,090 Holstein cows with calving varying from 1991 to 2010, were used in the analysis. Two standard multivariate analyses were carried out, one containing the trait of accumulated 305day milk yields in the first lactation (MY1), stayability (STAY) until the third lactation, and clinical mastitis (CM), as well as the other traits, considering accumulated 305-day milk yields (Y305), STAY, and CM, including the first three lactations as repeated measures for Y305 and CM. The covariance components were obtained by a Bayesian approach. The heritability estimates obtained by multivariate analysis with MY1 were 0.19, 0.28, and 0.13 for MY1, STAY, and CM, respectively, whereas using the multivariate analysis with N. Irano (*) : A. B. Bignardi : L. G. Albuquerque Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Via de acesso Paulo Donato Castellane s/n, Prédio 2, CEP: 14884-900 Jaboticabal, São Paulo, Brazil e-mail: [email protected] L. El Faro : V. L. Cardoso Agência Paulista de Tecnologia dos Agronegócios—APTA, Pólo Regional Centro Leste, Ribeirão Preto, São Paulo CEP 14030-670, Brazil A. B. Bignardi : M. L. Santana Jr Grupo de Melhoramento Animal de Mato Grosso (GMAT), Instituto de Ciências Agrárias e Tecnológicas, Universidade Federal de Mato Grosso, MT-270, Km 06, CEP 78735-901 Rondonópolis, Mato Grosso, Brazil L. G. Albuquerque Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq) and Instituto Nacional de Ciência e Tecnologia—Ciência Animal (INCT—CA), Viçosa, Minas Gerais CEP 36570-000, Brazil
the Y305, the estimates were 0.19, 0.31, and 0.14, respectively. The genetic correlations between MY1 and STAY, MY1 and CM, and STAY and CM, respectively, were 0.38, 0.12, and −0.49. The genetic correlations between Y305 and STAY, Y305 and CM, and STAY and CM, respectively, were 0.66, −0.25, and −0.52. Keywords Genetic correlation . Heritability . Repeatability . Selection
Introduction Milk yield is the most important trait in breeding programs for dairy cattle in Brazil. However, studies need to investigate the association of this trait with other production, reproduction, and conformation traits and how these traits behave when selection is performed for higher milk yield. Over the past decade, a strong selection pressure to increase production has resulted in high levels of production in the world but has also led to the deterioration in functional traits such as udder health and fertility (Pérez-Cabal et al. 2009). Stayability in the herd and the occurrence of mastitis are economically important traits that are included in dairy cattle breeding programs in countries such as Canada, USA, Mexico, Germany, the Netherlands, Italy, Sweden, South Africa, Japan, and Australia. Hudson and Van Vleck (1981) defined stayability in the herd as the probability of a cow to remain productive until a specific age, given the opportunity to reach this age. This trait can be recorded continuously for each female, measured as the number of days the cow will stay in the herd until a certain age or as a discrete distribution in which a value 0 is attributed to cows that did not stay in the herd and value 1 to cows that stay until a certain age. According to Silva et al. (2003), the inclusion of stayability in genetic evaluation programs would permit breeders to select animals
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that produce female offspring that are more likely to stay in the herd for a longer period of time, thus reducing production costs. According to Galeazzi et al. (2010), greater longevity of a cow is desired since it permits voluntary culling, i.e., the culling of animals based on production, instead of involuntary culling. In addition, since the cost of raising a heifer is high, this cost can be diluted by increasing the time the animal stays in the herd. The authors also suggested that selection of animals with a greater ability to stay in the herd would result in the indirect selection for better fertility, since pregnancy does not occur in the absence of estrus. As a consequence, the cow does not produce milk, resulting in animals with low production levels that are economically unfeasible. Good udder health is fundamental for higher production and longevity of animals, reduction of costs, and improvement of production quality. Consequently, strategies designed to improve these traits are essential for breeding programs (Sewalem et al. 2006) due to the economic importance of these traits. Studies have shown that udder health problems such as mastitis are strongly associated with stayability and are the main reason for culling dairy cattle (Heringstad et al. 2003; Holtsmark et al. 2008; Ahlman et al. 2011). According to Rupp and Boichard (1999), selection for increased milk yield in the absence of simultaneous selection for mastitis resistance will increase susceptibility to mastitis. The inclusion of mastitis resistance in breeding programs is becoming increasingly more important because of the effect of this trait on the profitability of the production system and on animal well-being, in addition to the increased consumer demand for healthy and natural products (Koeck et al. 2010). Although knowledge of genetic parameters for stayability and occurrence of mastitis, as well as their genetic associations with important production traits, is fundamental for the successful implementation of these traits into selection objectives for dairy cattle and for the prediction of expected genetic gain, few studies have investigated these traits in dairy cattle in Brazil. In this respect, investigation of the role of these traits in animal production under tropical conditions is important, especially when considering the low adaptability of Holstein cattle to these environments. In view of the above considerations, the objective of the present study was to estimate genetic parameters for milk yield, cow stayability in the herd, and occurrence of clinical mastitis in Holstein cows and to evaluate genetic associations between these traits that would permit their use in genetic evaluations.
Material and methods Records of 10,335 lactations of 5,090 Holstein cows, which had calved between 1991 and 2010, were analyzed. The
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animals belonged to the Holstein herd of Agropecuária Agrindus S.A., municipality of Descalvado, São Paulo, Brazil (21°54′14″ south latitude and 47°37′10″ west longitude, altitude of 679 m). The following traits were analyzed in this study: a 305-day cumulative milk yield in the first lactation (MY1), cow stayability in the herd (STAY) until the third lactation, a 305-day cumulative milk yield (Y305), and the occurrence of clinical mastitis (CM). First- to third-parity cows were maintained in the analyses. True stayability (defined as the aptitude of a cow to delay voluntary culling) was defined as a binary trait, with 0 indicating failure and 1 indicating success. Success was attributed to cows in their third or subsequent lactation, given at least one previous lactation record, and failure was attributed to cows that did not meet these requirements. For CM, a value of 0 was attributed to cows that did not present mastitis and a value of 1 to cows that had least one event of mastitis during lactation. The contemporary groups for MY1, Y305, and CM were defined by year–season of calving, whereas the contemporary group for STAY was defined by year–season of birth. Two similar seasons were established for calving and birth: season 1, April to September, and season 2, October to March. A restriction was that each contemporary group should contain at least 10 animals. For MY1 and Y305, animals with records outside 3.5 standard deviations from the mean of the contemporary group were discarded. As proposed by Harville and Mee (1984), for STAYand CM contemporary groups in which all scores were the same, i.e., groups without variability were eliminated. Table 1 shows the structure of the data set. Two multiple-trait analyses were conducted, one including MY1, STAY, and CM and another including Y305, STAY, and CM. Both analyses were performed under an animal model considering the first three lactations as repeated measures for Y305 and CM. For MY1, Y305, and CM, additive genetic, permanent environmental (except for MY1), and temporary environmental effects were included as random effects and contemporary group and the covariate age of cow at calving (linear and quadratic effects) as fixed effects. For STAY, the model included additive genetic and temporary environmental effects as random effects and contemporary group as a fixed effect. The matrix representation of the general model used is as follows: y ¼ Xβ þ Za þ Wpe þ e; where y=the vector of observations and liabilities for categorical traits, β=the vector of systematic effects, a=the vector of random direct additive genetic effects of each animal, pe=the vector of random effects of the permanent environment, and e=the vector of residual random effects. X, Z and W are incidence matrices that relate the records to the systematic
Trop Anim Health Prod (2014) 46:529–535 Table 1 Data structure for a 305day milk yield in the first lactation (MY1), a 305-day cumulative milk yield (Y305), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM)
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Data
MY1
Y305
STAY
CM
Number of female cows with records Number of sires Number of dams Number of female cows in the pedigree file Number of sires in the pedigree file
4,900 329 3,047 10,750 1,830
5,048 352 3,109 10,827 1,835
4,223 335 2,592 10,137 1,843
4,748 308 2,940 10,663 1,825
Number of dams in the pedigree file Number of contemporary groups
6,336 32
6,355 32
5,994 31
6,271 45
effects and random direct additive genetic and permanent environmental effects, respectively. The covariance structure of the random effects can be described as follows: 2 3 2 3 G⊗A 0 0 a 5; I Nm σpe 0 Var4 pe 5 ¼ 4 0 e 0 0 R⊗I N
burn-in period of 25,000 iterations and a thinning interval of 25 iterations, for a total of 9,000 cycles. Heritability and repeatability were calculated using the following:
where G=(co)variance matrix of additive genetic effects, ⊗= Kronecker product, A=relationship matrix; σ2a =additive genetic variance σ2pe=permanent environmental variance; I=identity matrix; R=(co)variance matrix of residual effects, and N=number of animals with records. Regarding the structure of R, the residual variance was set to 1 for the binary trait (STAY and CM) because this parameter is not identifiable. STAY and CM were analyzed using the following threshold model:
where h2 is heritability, σ2a is the additive genetic variance, σ2p is the phenotypic variance, and σ2pe is the permanent environmental variance.
ni
f ðyi jl i Þ ¼ ∏1ðl i < t i Þ1ðyi ¼ 0Þ þ 1ðl i > t i Þ1ðyi ¼ 1Þ; i¼1
where yi is the ith phenotypic observation (categories 0 or 1); ni is the total number of data for the trait studied, li is the underlying liability of observation i; ti is the threshold that defines the response category for the trait. A probit model was used for STAY and CM, and normal distribution was assumed for MY1 and Y305: yjβ; a; pe; ReMVN Xβ þ Z a þ W pe ; R⊗I ; where R is the residual (co)variance matrix, ⊗ is the Kronecker product and I is an identity matrix of appropriate order. Analysis was performed with the THRGIBBS1F90 program (Misztal et al. 2002). An analysis that consisted of a single chain of 250,000 iterations was computed, with the final chain length, burn-in period, and thinning interval being defined on the basis of the criterion of Raftery and Lewis (1992). According to this criterion, a low serial correlation between cycles indicates convergence of the chain. The Bayesian Output Analysis (BOA) package of the R program (R Development Core Team 2008) was used for analysis. After confirmation of the convergence of the Gibbs chain, estimates of the posterior distribution were computed using a
h2 ¼
σ2a þ σ2pe σ2a ; R ¼ σ2p σ2p
Results and Discussion Mean Y305 increased from the first to the second lactation, followed by a decline in the third lactation (Table 2). The average of total milk yield was 9,215.40 kg, a value reflecting the high genetic standard of the herd, in addition to good management and feeding practices. The coefficients of variation for milk production ranged from 23.76 to 31.27 % and increased with increasing order of lactation. The percentage of cows that remained in the herd until the third lactation indicate that important culling occurred due to low milk production and reproductive or health problems in the first two lactations (Table 2). The age of the cows at the Table 2 Summary of the data structure, number of animals (N), mean, standard deviation (SD) and coefficient of variation (CV%) for a 305-day cumulative milk yield (Y305), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM) Trait Y305
first lactation second lactation third lactation
STAY CM
first lactation second lactation third lactation
N 4,900 3,455 1,980 N 4,223 N 4,331 3,209 1,875
Mean (kg) 9,001.3 9,489.7 9,401.7 Success (%) 48.3 Absent (%) 86.6 80.3 79.0
SD (kg) 2,138.3 2,395.2 2,746.0 Failure (%) 51.7 Present (%) 11.4 19.7 21.0
CV% 23.76 25.24 29.21
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first and third calving were 26 and 57 months, respectively. The success rate for STAY of 48.3 % estimated in the present study was lower than those observed for Holstein cows in the USA that survived up to the third lactation (53 %) (Tsuruta et al. 2005). On the other hand, this rate was slightly higher than those reported by Ahlman et al. (2011), who found survival rates to the third calving of 44 % and 45 % for Holstein and Swedish Red cattle, respectively. In Brazil, Queiroz et al. (2007) observed an average STAY at 48, 60, and 72 months of age of approximately 63 %, 53 %, and 48 %, respectively, for Caracu cattle. The frequency of CM was lower in the first lactation and increased in subsequent lactations when the cows reached peak milk production (Table 2). The percentages observed in the present study are similar to those reported for cattle outside Brazil, which range from 10.4 to 24.2 % (Carlén et al. 2004; Heringstad et al. 2005; Koivula et al. 2005; Zwald et al. 2006; Vallimont et al. 2009). In Table 3, the estimates of model 1are presented, considering the analysis of MY1 with STAY and CM. For CM, the additive genetic variance was higher than the permanent environmental variance. The phenotypic variances were 4,264,310 kg2, 1.386, and 1.300 for MY1, STAY, and CM, respectively. The heritability coefficient for MY1 estimated in this study (Table 3) indicates that genetic gain in this trait can be obtained by selection, but the value was lower than most coefficients reported in the literature for the first lactation, ranging from 0.24 to 0.30 (Rupp and Boichard 1999; Ferreira et al. 2003; Melo et al. 2005; Bignardi et al. 2008). The heritability for STAY was of moderate magnitude (Table 3) and suggests that a response to selection could be obtained if STAY was used as a selection criterion. This estimate was higher than those reported by Ahlman et al. (2011) who used a threshold animal model and reported
Table 3 Posterior mean, median, mode, standard deviation (SD), and the highest posterior density interval (95 % HPD) of additive genetic (σ2a ), permanent environmental (σ2pe), and temporary environmental variance (σ2e ); heritability (h2); and repeatability (R) for a 305-day cumulative milk yield in the first lactation (MY1), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM) obtained by a multiple-trait analysis
Parameter
estimates for survival until the third lactation of 0.20 and 0.24 for Holstein and Swedish Red cattle, respectively. In Brazil, Queiroz et al. (2007), using data from Caracu cattle and a single-trait threshold sire model, estimated heritabilities for STAY at 48, 60, and 72 months of age of 0.28, 0.27, and 0.23, respectively. In general, the heritabilities reported in the literature for STAY of Holstein cows are lower than those found in the present study, especially when linear models were used for analysis, with estimates ranging from 0.02 to 0.06 (Hudson and Van Vleck 1981; Short and Lawlor 1992; Vollema and Groen 1996; Posadas et al. 2004). The heritability estimate for CM was low (Table 3) and indicates that it is difficult to obtain genetic gain by selection. However, even if the genetic gain through selection is low, it does not mean that this trait is not important considering the impact of mastitis on milk production systems. Therefore, in addition to the improvement of environmental conditions, selection for more resistant animals should be performed. The magnitude of the heritability for CM was similar to that reported by Zwald et al. (2006) for first-lactation Holstein cows in the USA using a multiple-trait threshold model, with heritabilities of 0.12, 0.10, and 0.09 for the first, second, and third lactation, respectively. In contrast, Hinrichs et al. (2005), in Germany, and Vallimont et al. (2009), in the USA, estimated heritabilities for CM of 0.07 using a repeatability threshold animal model in a single-trait analysis. The heritability for Y305 estimated with the repeatability model was similar to the heritability for MY1 (0.19). The heritabilities for STAY and CM estimated by a multiple-trait analysis with Y305 were 0.31 and 0.14, respectively, i.e., closely similar to those obtained by a multiple-trait analysis with MY1. The repeatability coefficients for Y305 and CM (both analyses) were 0.38 and 0.23, respectively. These coefficients are of low magnitude, suggesting that a single measure
Mean
Median
Mode
SD
95 % HPD
795,410 3,468,900 0.19
787,900 3,467,000 0.19
780,200 3,448,300 0.18
135,390 121,280 0.03
532,300–1,059,000 3,234,000–3,708,000 0.14–0.22
0.384 1.002 0.28
0.378 1.002 0.27
0.346 1.007 0.26
0.083 0.015 0.07
0.229–0.547 0.973–1.030 0.19–0.35
0.166 0.133 1.001 0.13 0.23
0.164 0.131 1.001 0.13 0.23
0.162 0.125 1.001 0.13 0.22
0.036 0.046 0.014 0.03 0.08
0.096–0.234 0.050–0.232 0.974–1.030 0.09–0.16 0.13–0.31
MY1 σ2a σ2e h2 STAY σ2a σ2e h2 CM σ2a σ2pe σ2e h2 R
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of an animal may not provide good indications about its production capacity or the occurrence of disease in the next lactations. Thus, data from more than one lactation are necessary to decide whether to keep or to cull a cow. Lower repeatability estimates for CM ranging from 0.10 to 0.18 have been reported by Hinrichs et al. (2005, 2011) and Vallimont et al. (2009). The genetic correlations between the production traits (MY1 and Y305) and STAY were of moderate to high magnitude (Tables 4 and 5), indicating that the higher the genetic potential of a cow for milk production, the greater its chance to stay in the herd for a longer period of time. The genetic correlation between MY1 and STAY was similar to that reported by Posadas et al. (2008). The authors estimated a genetic correlation of 0.38 between first-lactation milk production and STAY at 48 months of age for Holstein cows in Mexico. However, greater genetic correlations between firstlactation milk production and STAY at 60 months (0.65) and 54 months (0.48) have been reported by Hudson and Van Vleck (1981) and Short and Lawlor (1992) for Holstein cows. The low genetic correlation between MY1 and CM (Table 4) suggests that selection for MY1 has little influence on mastitis resistance. This finding differs from those reported in most studies in which the correlations between first-lactation milk production and mastitis were higher. Carlén et al. (2004), studying Swedish Holstein cows, estimated a genetic correlation of 0.32 between first-lactation milk production and clinical mastitis. The genetic correlation between Y305 and CM was negative and of low magnitude (Table 5), suggesting that selection for Y305 would reduce CM or vice versa, i.e., animals with lower production would be more susceptible to mastitis. This estimate differs from those reported in the literature that Table 4 Posterior mean, median, mode, standard deviation (SD) and the highest posterior density interval (95 % HPD) of additive genetic (ra), temporary environmental (re) and phenotypic (rp) correlations between a 305-day cumulative milk yield in the first lactation (MY1), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM) Parameter MY1–STAY ra re rp MY1–CM ra re rp STAY–CM ra re rp
Mean
Median
Mode
SD
95 % HPD
0.38 0.00 0.09
0.38 0.00 0.09
0.38 0.00 0.09
0.09 0.00 0.01
0.21 – 0.55 0.00 – 0.00 0.05 – 0.13
0.12 0.00 0.02
0.12 0.00 0.02
0.12 0.00 0.02
0.11 0.00 0.02
−0.09 – 0.33 0.00 – 0.00 −0.02 – 0.05
−0.49 0.00 −0.09
−0.50 0.00 −0.09
−0.50 0.00 −0.09
0.11 0.00 0.02
−0.71 – −0.28 0.00 – 0.00 −0.13 – −0.05
Table 5 Posterior mean, median, mode, standard deviation (SD) and the highest posterior density interval (95 % HPD) of additive genetic (ra), permanent environmental (rpe), temporary environmental (re) and phenotypic (rp) correlations between a 305-day cumulative milk yield (Y305), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM) Parameter Y305–STAY ra re rp Y305–CM ra rpe re rp STAY–CM ra re rp
Mean
Median
Mode
SD
95 % HPD
0.66 0.00 0.16
0.66 0.00 0.16
0.66 0.00 0.16
0.06 0.00 0.02
0.53 – 0.78 0.00 – 0.00 0.13 – 0.19
−0.25
−0.25
−0.25
0.12
−0.46 – −0.01
0.15 0.02 −0.01
0.15 0.02 −0.01
0.15 0.02 −0.01
0.04 0.01 0.01
−0.15 – −0.75 −0.01 – 0.04 −0.04 – 0.02
−0.52 0.00 −0.10
−0.53 0.00 −0.05
−0.53 0.00 −0.05
0.10 0.00 0.02
−0.74 – −0.33 0.00–0.00 −0.15 – −0.06
has generally indicated unfavorable associations ranging from 0.21 to 0.66 (Carlén et al. 2004; Koivula et al. 2005; Negussie et al. 2008; Koeck et al. 2010). The moderate negative genetic correlations between STAY and CM (Tables 4 and 5) obtained with the two analyses indicate that cows that are more susceptible to mastitis present a lower chance of staying in the herd until the third lactation. Few studies have investigated the genetic correlation between STAY and CM. The present results agree with those reported by Nielsen et al. (1999) who observed a genetic correlation of −0.52 between survival until the end of the second lactation and mastitis resistance in Holstein cows. Heringstad et al. (2003), studying Norwegian Red cows, estimated genetic correlations of 0.48 and 0.53 between clinical mastitis and culling at 120 and 300 days after the first calving (the opposite of stayability in the herd), respectively. Holtsmark et al. (2008) reported a moderate positive genetic correlation (0.36) between culling in the first lactation and clinical mastitis also for Norwegian Red cows. All temporary environmental and phenotypic correlations were close to zero (Tables 4 and 5). The permanent environmental correlation between Y305 and CM was of low magnitude (Table 5), suggesting that the measures used in this herd to treat or prevent mastitis were adequate and caused no permanent damage to the mammary tissue of the animals.
Conclusion Direct selection for first-lactation 305-day cumulative milk yield may indirectly improve cow stayability in the herd until
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the third lactation but has little influence on the occurrence of clinical mastitis. The use of a 305-day cumulative milk yield as a selection criterion would also indirectly improve cow stayability in the herd until the third lactation and would even reduce the occurrence of clinical mastitis, although this effect would be small. In this respect, the use of a first-lactation 305day cumulative milk yield as a selection objective, which can be measured at the beginning of the animal's productive life, would reduce the generation interval and consequently increase genetic progress. A selection index combining all traits in such a way as to increase economic return with the use of improved animals would be the most appropriate tool in this case. Acknowledgments This work was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes), Brazil. The authors thanks the Agropecuária Agrindus S.A. for providing the data. Conflict of interest The authors declare that they have no conflict of interest.
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Trop Anim Health Prod (2014) 46:529–535 Holtsmark, M., Heringstad, B., Madsen, P. and Ødegård, J., 2008. Genetic relationship between culling, milk production, fertility, and health traits in Norwegian Red cows, Journal of Dairy Science, 91, 4006–4012 Hudson, G.F.S. and Van Vleck, L.D., 1981. Relations between production and stayability in Holstein cattle, Journal of Dairy Science, 64, 2246–2250 Koeck, A., Heringstad, B., Egger-Danner, C., Fuerst, C., Winter, P. and Fuerst-Waltl, B., 2010. Genetic analysis of clinical mastitis and somatic cell count traits in Austrian Fleckvieh cows, Journal of Dairy Science, 93, 5987–5995 Koivula, M., Mäntysaari, E.A., Negussie, E. and Serenius, T., 2005. Genetic and phenotypic relationships among milk yield and somatic cell count before and after clinical mastitis, Journal of Dairy Science, 88, 827–833 Melo, C.M.R., Packer, I.U., Costa, C.N. and Machado, P.F., 2005. Parâmetros genéticos para as produções de leite no dia do controle e da primeira lactação de vacas da raça Holandesa, Revista Brasileira de Zootecnia, 34, 796–806 Misztal, I., Tsuruta, S., Strabel, T., Auvray, B., Druet, T. and Lee, D.H., 2002. BLUPF90 and related programs (BGF90), In: CD-ROM communication, Proceedings of the 7th WORLD CONGRESS ON GENETICS APPLIED TO LIVESTOCK PRODUCTION, Montpellier, 2002, 7–28 Negussie, E., Strandén, I. and Mäntysaari, E.A., 2008. Genetic association of clinical mastitis with test-day somatic cell score and milk yield during first lactation of Finnish Ayrshire cows, Journal of Dairy Science, 91, 1189–1197 Nielsen, U.S., Pedersen, G.A., Pedersen, J. and Jensen, J., 1999. Genetic variation in disease trait and their relationships with survival in Danish dairy cattle, Wageningen: Institute of Animal Sciences, 1999, 170–177 (Bulletin, 21) Pérez-Cabal, M.A., Campos, G., Vazquez, A.I., Gianola, D., Rosa, G.J.M., Weigel, K.A. and Alenda, R., 2009. Genetic evaluation of susceptibility to clinical mastitis in Spanish Holstein cows, Journal of Dairy Science, 92, 3472–3480 Posadas, M.V., López, F.J.R. and Valdenegro, H.H.M., 2004. Estimación de parámetros genéticos para características de longevidad y producción de leche en ganado Holstein en México, Interciencia 29, 52–56 Posadas, M.V., Valdenegro, H.H.M. and López, F.J.R., 2008. Parámetros genéticos para características de conformación, habilidad de permanencia y producción de leche en ganado Holstein en México, Técnica Pecuária México, 46, 235–248 Queiroz, S.A., Figueiredo, G., Silva, J.A.II V., Espasandin, A.C., Meirelles, S.L. and Oliveira, J.A., 2007. Estimativa de parâmetros genéticos da habilidade de permanência aos 48, 60 e 72 meses de idade em vacas da raça Caracu, Revista Brasileira de Zootecnia, 36, 1316–323 R Development Core Team, 2008. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2008) Raftery, A.E. and Lewis, S.M., 1992. How many iterations in the Gibbs sampler? In: Bernardo, J.M., Berger, J.O., David, A.P. and Smith, A.F.M. (Eds.), Bayesian Statistics, 4. Clarendon Press, Oxford, 763–773 Rupp, R. and Boichard, D., 1999. Genetic Parameters for clinical mastitis, somatic cell score, production, udder type traits, and milking ease in first lactation Holsteins, Journal of Dairy Science, 82, 2198–2204 Sewalem, A., Miglior, F., Kistemaker, G.J. and Van Doormaal, B.J., 2006. Analysis of the relationship between somatic cell score and functional longevity in Canadian dairy cattle, Journal of Dairy Science, 89, 3609–3614 Short, T.H. and Lawlor, T.J., 1992. Genetic parameters of conformation traits, milk yield, and herd life in Holstein, Journal of Dairy Science, 75, 1987–1998
Trop Anim Health Prod (2014) 46:529–535 Silva, J.A.II V., Eler, J.P., Ferraz, J.B.S. and Oliveira, H.N., 2003. Análise genética da habilidade de permanência em fêmeas da raça Nelore, Revista Brasileira de Zootecnia, 32, 598–604 Tsuruta, S., Misztal, I. and Lawlor, T.J., 2005. Changing definition of productive life in US Holstein: effect on genetic correlations, Journal of Dairy Science, 88, 1156-1165 Vallimont, J.E., Dechow, C.D., Sattler, C.G. and Clay, J.S., 2009. Heritability estimates associated with alternative definitions of
535 mastitis and correlations with somatic cell score and yield, Journal of Dairy Science, 92, 3402–3410 Vollema, A.R. and Groen, A.F., 1996. Genetic parameters of longevity traits of an upgrading population of dairy cattle, Journal of Dairy Science, 79, 2261–2267 Zwald, N.R., Weigel, K.A., Chang, Y.M., Welper, R.D. and Clay, J.S., 2006. Genetic analysis of clinical mastitis data from on-farm management software using threshold models, Journal of Dairy Science, 89, 330–336
REGULAR ARTICLES
Genetic association between milk yield, stayability, and mastitis in Holstein cows under tropical conditions Natalia Irano & Annaiza Braga Bignardi & Lenira El Faro & Mário Luiz Santana Jr & Vera Lúcia Cardoso & Lucia Galvão Albuquerque
Accepted: 11 December 2013 / Published online: 29 December 2013 # Springer Science+Business Media Dordrecht 2014
Abstract The objective of this study was to estimate genetic parameters for milk yield, stayability, and the occurrence of clinical mastitis in Holstein cows, as well as studying the genetic relationship between them, in order to provide subsidies for the genetic evaluation of these traits. Records from 5,090 Holstein cows with calving varying from 1991 to 2010, were used in the analysis. Two standard multivariate analyses were carried out, one containing the trait of accumulated 305day milk yields in the first lactation (MY1), stayability (STAY) until the third lactation, and clinical mastitis (CM), as well as the other traits, considering accumulated 305-day milk yields (Y305), STAY, and CM, including the first three lactations as repeated measures for Y305 and CM. The covariance components were obtained by a Bayesian approach. The heritability estimates obtained by multivariate analysis with MY1 were 0.19, 0.28, and 0.13 for MY1, STAY, and CM, respectively, whereas using the multivariate analysis with N. Irano (*) : A. B. Bignardi : L. G. Albuquerque Departamento de Zootecnia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Via de acesso Paulo Donato Castellane s/n, Prédio 2, CEP: 14884-900 Jaboticabal, São Paulo, Brazil e-mail: [email protected] L. El Faro : V. L. Cardoso Agência Paulista de Tecnologia dos Agronegócios—APTA, Pólo Regional Centro Leste, Ribeirão Preto, São Paulo CEP 14030-670, Brazil A. B. Bignardi : M. L. Santana Jr Grupo de Melhoramento Animal de Mato Grosso (GMAT), Instituto de Ciências Agrárias e Tecnológicas, Universidade Federal de Mato Grosso, MT-270, Km 06, CEP 78735-901 Rondonópolis, Mato Grosso, Brazil L. G. Albuquerque Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq) and Instituto Nacional de Ciência e Tecnologia—Ciência Animal (INCT—CA), Viçosa, Minas Gerais CEP 36570-000, Brazil
the Y305, the estimates were 0.19, 0.31, and 0.14, respectively. The genetic correlations between MY1 and STAY, MY1 and CM, and STAY and CM, respectively, were 0.38, 0.12, and −0.49. The genetic correlations between Y305 and STAY, Y305 and CM, and STAY and CM, respectively, were 0.66, −0.25, and −0.52. Keywords Genetic correlation . Heritability . Repeatability . Selection
Introduction Milk yield is the most important trait in breeding programs for dairy cattle in Brazil. However, studies need to investigate the association of this trait with other production, reproduction, and conformation traits and how these traits behave when selection is performed for higher milk yield. Over the past decade, a strong selection pressure to increase production has resulted in high levels of production in the world but has also led to the deterioration in functional traits such as udder health and fertility (Pérez-Cabal et al. 2009). Stayability in the herd and the occurrence of mastitis are economically important traits that are included in dairy cattle breeding programs in countries such as Canada, USA, Mexico, Germany, the Netherlands, Italy, Sweden, South Africa, Japan, and Australia. Hudson and Van Vleck (1981) defined stayability in the herd as the probability of a cow to remain productive until a specific age, given the opportunity to reach this age. This trait can be recorded continuously for each female, measured as the number of days the cow will stay in the herd until a certain age or as a discrete distribution in which a value 0 is attributed to cows that did not stay in the herd and value 1 to cows that stay until a certain age. According to Silva et al. (2003), the inclusion of stayability in genetic evaluation programs would permit breeders to select animals
530
that produce female offspring that are more likely to stay in the herd for a longer period of time, thus reducing production costs. According to Galeazzi et al. (2010), greater longevity of a cow is desired since it permits voluntary culling, i.e., the culling of animals based on production, instead of involuntary culling. In addition, since the cost of raising a heifer is high, this cost can be diluted by increasing the time the animal stays in the herd. The authors also suggested that selection of animals with a greater ability to stay in the herd would result in the indirect selection for better fertility, since pregnancy does not occur in the absence of estrus. As a consequence, the cow does not produce milk, resulting in animals with low production levels that are economically unfeasible. Good udder health is fundamental for higher production and longevity of animals, reduction of costs, and improvement of production quality. Consequently, strategies designed to improve these traits are essential for breeding programs (Sewalem et al. 2006) due to the economic importance of these traits. Studies have shown that udder health problems such as mastitis are strongly associated with stayability and are the main reason for culling dairy cattle (Heringstad et al. 2003; Holtsmark et al. 2008; Ahlman et al. 2011). According to Rupp and Boichard (1999), selection for increased milk yield in the absence of simultaneous selection for mastitis resistance will increase susceptibility to mastitis. The inclusion of mastitis resistance in breeding programs is becoming increasingly more important because of the effect of this trait on the profitability of the production system and on animal well-being, in addition to the increased consumer demand for healthy and natural products (Koeck et al. 2010). Although knowledge of genetic parameters for stayability and occurrence of mastitis, as well as their genetic associations with important production traits, is fundamental for the successful implementation of these traits into selection objectives for dairy cattle and for the prediction of expected genetic gain, few studies have investigated these traits in dairy cattle in Brazil. In this respect, investigation of the role of these traits in animal production under tropical conditions is important, especially when considering the low adaptability of Holstein cattle to these environments. In view of the above considerations, the objective of the present study was to estimate genetic parameters for milk yield, cow stayability in the herd, and occurrence of clinical mastitis in Holstein cows and to evaluate genetic associations between these traits that would permit their use in genetic evaluations.
Material and methods Records of 10,335 lactations of 5,090 Holstein cows, which had calved between 1991 and 2010, were analyzed. The
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animals belonged to the Holstein herd of Agropecuária Agrindus S.A., municipality of Descalvado, São Paulo, Brazil (21°54′14″ south latitude and 47°37′10″ west longitude, altitude of 679 m). The following traits were analyzed in this study: a 305-day cumulative milk yield in the first lactation (MY1), cow stayability in the herd (STAY) until the third lactation, a 305-day cumulative milk yield (Y305), and the occurrence of clinical mastitis (CM). First- to third-parity cows were maintained in the analyses. True stayability (defined as the aptitude of a cow to delay voluntary culling) was defined as a binary trait, with 0 indicating failure and 1 indicating success. Success was attributed to cows in their third or subsequent lactation, given at least one previous lactation record, and failure was attributed to cows that did not meet these requirements. For CM, a value of 0 was attributed to cows that did not present mastitis and a value of 1 to cows that had least one event of mastitis during lactation. The contemporary groups for MY1, Y305, and CM were defined by year–season of calving, whereas the contemporary group for STAY was defined by year–season of birth. Two similar seasons were established for calving and birth: season 1, April to September, and season 2, October to March. A restriction was that each contemporary group should contain at least 10 animals. For MY1 and Y305, animals with records outside 3.5 standard deviations from the mean of the contemporary group were discarded. As proposed by Harville and Mee (1984), for STAYand CM contemporary groups in which all scores were the same, i.e., groups without variability were eliminated. Table 1 shows the structure of the data set. Two multiple-trait analyses were conducted, one including MY1, STAY, and CM and another including Y305, STAY, and CM. Both analyses were performed under an animal model considering the first three lactations as repeated measures for Y305 and CM. For MY1, Y305, and CM, additive genetic, permanent environmental (except for MY1), and temporary environmental effects were included as random effects and contemporary group and the covariate age of cow at calving (linear and quadratic effects) as fixed effects. For STAY, the model included additive genetic and temporary environmental effects as random effects and contemporary group as a fixed effect. The matrix representation of the general model used is as follows: y ¼ Xβ þ Za þ Wpe þ e; where y=the vector of observations and liabilities for categorical traits, β=the vector of systematic effects, a=the vector of random direct additive genetic effects of each animal, pe=the vector of random effects of the permanent environment, and e=the vector of residual random effects. X, Z and W are incidence matrices that relate the records to the systematic
Trop Anim Health Prod (2014) 46:529–535 Table 1 Data structure for a 305day milk yield in the first lactation (MY1), a 305-day cumulative milk yield (Y305), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM)
531
Data
MY1
Y305
STAY
CM
Number of female cows with records Number of sires Number of dams Number of female cows in the pedigree file Number of sires in the pedigree file
4,900 329 3,047 10,750 1,830
5,048 352 3,109 10,827 1,835
4,223 335 2,592 10,137 1,843
4,748 308 2,940 10,663 1,825
Number of dams in the pedigree file Number of contemporary groups
6,336 32
6,355 32
5,994 31
6,271 45
effects and random direct additive genetic and permanent environmental effects, respectively. The covariance structure of the random effects can be described as follows: 2 3 2 3 G⊗A 0 0 a 5; I Nm σpe 0 Var4 pe 5 ¼ 4 0 e 0 0 R⊗I N
burn-in period of 25,000 iterations and a thinning interval of 25 iterations, for a total of 9,000 cycles. Heritability and repeatability were calculated using the following:
where G=(co)variance matrix of additive genetic effects, ⊗= Kronecker product, A=relationship matrix; σ2a =additive genetic variance σ2pe=permanent environmental variance; I=identity matrix; R=(co)variance matrix of residual effects, and N=number of animals with records. Regarding the structure of R, the residual variance was set to 1 for the binary trait (STAY and CM) because this parameter is not identifiable. STAY and CM were analyzed using the following threshold model:
where h2 is heritability, σ2a is the additive genetic variance, σ2p is the phenotypic variance, and σ2pe is the permanent environmental variance.
ni
f ðyi jl i Þ ¼ ∏1ðl i < t i Þ1ðyi ¼ 0Þ þ 1ðl i > t i Þ1ðyi ¼ 1Þ; i¼1
where yi is the ith phenotypic observation (categories 0 or 1); ni is the total number of data for the trait studied, li is the underlying liability of observation i; ti is the threshold that defines the response category for the trait. A probit model was used for STAY and CM, and normal distribution was assumed for MY1 and Y305: yjβ; a; pe; ReMVN Xβ þ Z a þ W pe ; R⊗I ; where R is the residual (co)variance matrix, ⊗ is the Kronecker product and I is an identity matrix of appropriate order. Analysis was performed with the THRGIBBS1F90 program (Misztal et al. 2002). An analysis that consisted of a single chain of 250,000 iterations was computed, with the final chain length, burn-in period, and thinning interval being defined on the basis of the criterion of Raftery and Lewis (1992). According to this criterion, a low serial correlation between cycles indicates convergence of the chain. The Bayesian Output Analysis (BOA) package of the R program (R Development Core Team 2008) was used for analysis. After confirmation of the convergence of the Gibbs chain, estimates of the posterior distribution were computed using a
h2 ¼
σ2a þ σ2pe σ2a ; R ¼ σ2p σ2p
Results and Discussion Mean Y305 increased from the first to the second lactation, followed by a decline in the third lactation (Table 2). The average of total milk yield was 9,215.40 kg, a value reflecting the high genetic standard of the herd, in addition to good management and feeding practices. The coefficients of variation for milk production ranged from 23.76 to 31.27 % and increased with increasing order of lactation. The percentage of cows that remained in the herd until the third lactation indicate that important culling occurred due to low milk production and reproductive or health problems in the first two lactations (Table 2). The age of the cows at the Table 2 Summary of the data structure, number of animals (N), mean, standard deviation (SD) and coefficient of variation (CV%) for a 305-day cumulative milk yield (Y305), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM) Trait Y305
first lactation second lactation third lactation
STAY CM
first lactation second lactation third lactation
N 4,900 3,455 1,980 N 4,223 N 4,331 3,209 1,875
Mean (kg) 9,001.3 9,489.7 9,401.7 Success (%) 48.3 Absent (%) 86.6 80.3 79.0
SD (kg) 2,138.3 2,395.2 2,746.0 Failure (%) 51.7 Present (%) 11.4 19.7 21.0
CV% 23.76 25.24 29.21
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first and third calving were 26 and 57 months, respectively. The success rate for STAY of 48.3 % estimated in the present study was lower than those observed for Holstein cows in the USA that survived up to the third lactation (53 %) (Tsuruta et al. 2005). On the other hand, this rate was slightly higher than those reported by Ahlman et al. (2011), who found survival rates to the third calving of 44 % and 45 % for Holstein and Swedish Red cattle, respectively. In Brazil, Queiroz et al. (2007) observed an average STAY at 48, 60, and 72 months of age of approximately 63 %, 53 %, and 48 %, respectively, for Caracu cattle. The frequency of CM was lower in the first lactation and increased in subsequent lactations when the cows reached peak milk production (Table 2). The percentages observed in the present study are similar to those reported for cattle outside Brazil, which range from 10.4 to 24.2 % (Carlén et al. 2004; Heringstad et al. 2005; Koivula et al. 2005; Zwald et al. 2006; Vallimont et al. 2009). In Table 3, the estimates of model 1are presented, considering the analysis of MY1 with STAY and CM. For CM, the additive genetic variance was higher than the permanent environmental variance. The phenotypic variances were 4,264,310 kg2, 1.386, and 1.300 for MY1, STAY, and CM, respectively. The heritability coefficient for MY1 estimated in this study (Table 3) indicates that genetic gain in this trait can be obtained by selection, but the value was lower than most coefficients reported in the literature for the first lactation, ranging from 0.24 to 0.30 (Rupp and Boichard 1999; Ferreira et al. 2003; Melo et al. 2005; Bignardi et al. 2008). The heritability for STAY was of moderate magnitude (Table 3) and suggests that a response to selection could be obtained if STAY was used as a selection criterion. This estimate was higher than those reported by Ahlman et al. (2011) who used a threshold animal model and reported
Table 3 Posterior mean, median, mode, standard deviation (SD), and the highest posterior density interval (95 % HPD) of additive genetic (σ2a ), permanent environmental (σ2pe), and temporary environmental variance (σ2e ); heritability (h2); and repeatability (R) for a 305-day cumulative milk yield in the first lactation (MY1), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM) obtained by a multiple-trait analysis
Parameter
estimates for survival until the third lactation of 0.20 and 0.24 for Holstein and Swedish Red cattle, respectively. In Brazil, Queiroz et al. (2007), using data from Caracu cattle and a single-trait threshold sire model, estimated heritabilities for STAY at 48, 60, and 72 months of age of 0.28, 0.27, and 0.23, respectively. In general, the heritabilities reported in the literature for STAY of Holstein cows are lower than those found in the present study, especially when linear models were used for analysis, with estimates ranging from 0.02 to 0.06 (Hudson and Van Vleck 1981; Short and Lawlor 1992; Vollema and Groen 1996; Posadas et al. 2004). The heritability estimate for CM was low (Table 3) and indicates that it is difficult to obtain genetic gain by selection. However, even if the genetic gain through selection is low, it does not mean that this trait is not important considering the impact of mastitis on milk production systems. Therefore, in addition to the improvement of environmental conditions, selection for more resistant animals should be performed. The magnitude of the heritability for CM was similar to that reported by Zwald et al. (2006) for first-lactation Holstein cows in the USA using a multiple-trait threshold model, with heritabilities of 0.12, 0.10, and 0.09 for the first, second, and third lactation, respectively. In contrast, Hinrichs et al. (2005), in Germany, and Vallimont et al. (2009), in the USA, estimated heritabilities for CM of 0.07 using a repeatability threshold animal model in a single-trait analysis. The heritability for Y305 estimated with the repeatability model was similar to the heritability for MY1 (0.19). The heritabilities for STAY and CM estimated by a multiple-trait analysis with Y305 were 0.31 and 0.14, respectively, i.e., closely similar to those obtained by a multiple-trait analysis with MY1. The repeatability coefficients for Y305 and CM (both analyses) were 0.38 and 0.23, respectively. These coefficients are of low magnitude, suggesting that a single measure
Mean
Median
Mode
SD
95 % HPD
795,410 3,468,900 0.19
787,900 3,467,000 0.19
780,200 3,448,300 0.18
135,390 121,280 0.03
532,300–1,059,000 3,234,000–3,708,000 0.14–0.22
0.384 1.002 0.28
0.378 1.002 0.27
0.346 1.007 0.26
0.083 0.015 0.07
0.229–0.547 0.973–1.030 0.19–0.35
0.166 0.133 1.001 0.13 0.23
0.164 0.131 1.001 0.13 0.23
0.162 0.125 1.001 0.13 0.22
0.036 0.046 0.014 0.03 0.08
0.096–0.234 0.050–0.232 0.974–1.030 0.09–0.16 0.13–0.31
MY1 σ2a σ2e h2 STAY σ2a σ2e h2 CM σ2a σ2pe σ2e h2 R
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of an animal may not provide good indications about its production capacity or the occurrence of disease in the next lactations. Thus, data from more than one lactation are necessary to decide whether to keep or to cull a cow. Lower repeatability estimates for CM ranging from 0.10 to 0.18 have been reported by Hinrichs et al. (2005, 2011) and Vallimont et al. (2009). The genetic correlations between the production traits (MY1 and Y305) and STAY were of moderate to high magnitude (Tables 4 and 5), indicating that the higher the genetic potential of a cow for milk production, the greater its chance to stay in the herd for a longer period of time. The genetic correlation between MY1 and STAY was similar to that reported by Posadas et al. (2008). The authors estimated a genetic correlation of 0.38 between first-lactation milk production and STAY at 48 months of age for Holstein cows in Mexico. However, greater genetic correlations between firstlactation milk production and STAY at 60 months (0.65) and 54 months (0.48) have been reported by Hudson and Van Vleck (1981) and Short and Lawlor (1992) for Holstein cows. The low genetic correlation between MY1 and CM (Table 4) suggests that selection for MY1 has little influence on mastitis resistance. This finding differs from those reported in most studies in which the correlations between first-lactation milk production and mastitis were higher. Carlén et al. (2004), studying Swedish Holstein cows, estimated a genetic correlation of 0.32 between first-lactation milk production and clinical mastitis. The genetic correlation between Y305 and CM was negative and of low magnitude (Table 5), suggesting that selection for Y305 would reduce CM or vice versa, i.e., animals with lower production would be more susceptible to mastitis. This estimate differs from those reported in the literature that Table 4 Posterior mean, median, mode, standard deviation (SD) and the highest posterior density interval (95 % HPD) of additive genetic (ra), temporary environmental (re) and phenotypic (rp) correlations between a 305-day cumulative milk yield in the first lactation (MY1), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM) Parameter MY1–STAY ra re rp MY1–CM ra re rp STAY–CM ra re rp
Mean
Median
Mode
SD
95 % HPD
0.38 0.00 0.09
0.38 0.00 0.09
0.38 0.00 0.09
0.09 0.00 0.01
0.21 – 0.55 0.00 – 0.00 0.05 – 0.13
0.12 0.00 0.02
0.12 0.00 0.02
0.12 0.00 0.02
0.11 0.00 0.02
−0.09 – 0.33 0.00 – 0.00 −0.02 – 0.05
−0.49 0.00 −0.09
−0.50 0.00 −0.09
−0.50 0.00 −0.09
0.11 0.00 0.02
−0.71 – −0.28 0.00 – 0.00 −0.13 – −0.05
Table 5 Posterior mean, median, mode, standard deviation (SD) and the highest posterior density interval (95 % HPD) of additive genetic (ra), permanent environmental (rpe), temporary environmental (re) and phenotypic (rp) correlations between a 305-day cumulative milk yield (Y305), cow stayability in the herd (STAY), and the occurrence of clinical mastitis (CM) Parameter Y305–STAY ra re rp Y305–CM ra rpe re rp STAY–CM ra re rp
Mean
Median
Mode
SD
95 % HPD
0.66 0.00 0.16
0.66 0.00 0.16
0.66 0.00 0.16
0.06 0.00 0.02
0.53 – 0.78 0.00 – 0.00 0.13 – 0.19
−0.25
−0.25
−0.25
0.12
−0.46 – −0.01
0.15 0.02 −0.01
0.15 0.02 −0.01
0.15 0.02 −0.01
0.04 0.01 0.01
−0.15 – −0.75 −0.01 – 0.04 −0.04 – 0.02
−0.52 0.00 −0.10
−0.53 0.00 −0.05
−0.53 0.00 −0.05
0.10 0.00 0.02
−0.74 – −0.33 0.00–0.00 −0.15 – −0.06
has generally indicated unfavorable associations ranging from 0.21 to 0.66 (Carlén et al. 2004; Koivula et al. 2005; Negussie et al. 2008; Koeck et al. 2010). The moderate negative genetic correlations between STAY and CM (Tables 4 and 5) obtained with the two analyses indicate that cows that are more susceptible to mastitis present a lower chance of staying in the herd until the third lactation. Few studies have investigated the genetic correlation between STAY and CM. The present results agree with those reported by Nielsen et al. (1999) who observed a genetic correlation of −0.52 between survival until the end of the second lactation and mastitis resistance in Holstein cows. Heringstad et al. (2003), studying Norwegian Red cows, estimated genetic correlations of 0.48 and 0.53 between clinical mastitis and culling at 120 and 300 days after the first calving (the opposite of stayability in the herd), respectively. Holtsmark et al. (2008) reported a moderate positive genetic correlation (0.36) between culling in the first lactation and clinical mastitis also for Norwegian Red cows. All temporary environmental and phenotypic correlations were close to zero (Tables 4 and 5). The permanent environmental correlation between Y305 and CM was of low magnitude (Table 5), suggesting that the measures used in this herd to treat or prevent mastitis were adequate and caused no permanent damage to the mammary tissue of the animals.
Conclusion Direct selection for first-lactation 305-day cumulative milk yield may indirectly improve cow stayability in the herd until
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the third lactation but has little influence on the occurrence of clinical mastitis. The use of a 305-day cumulative milk yield as a selection criterion would also indirectly improve cow stayability in the herd until the third lactation and would even reduce the occurrence of clinical mastitis, although this effect would be small. In this respect, the use of a first-lactation 305day cumulative milk yield as a selection objective, which can be measured at the beginning of the animal's productive life, would reduce the generation interval and consequently increase genetic progress. A selection index combining all traits in such a way as to increase economic return with the use of improved animals would be the most appropriate tool in this case. Acknowledgments This work was funded by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Capes), Brazil. The authors thanks the Agropecuária Agrindus S.A. for providing the data. Conflict of interest The authors declare that they have no conflict of interest.
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