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* Universidad Autónoma de Tamaulipas, Cd. Victoria Tamaulipas, México 87030
Department of Animal Science, Cornell University, Ithaca NY 14853
Universidad Autónoma Metropolitana-Xochimilco Calzada del Hueso 1100, DF, México 04960
1 Corresponding author: ecienfue{at}uat.edu.mx
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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1,300 kg) and high- (
1,600 kg) opportunity Mexican herd environments and a low-opportunity ( 1,024 kg) US environment. Genetic variances for the average Mexican herd (all data) for AFC and CI were 65 and 85% as large as those obtained from half-sisters in the average US herd. Genetic correlations for first-lactation milk in the average US herd and AFC and CI in the average Mexican environment were unfavorable (0.18 and 0.10). Regression coefficients of AFC in Mexican environments on US genetic gain in milk ranged from 2 to 7 d/1,000 kg. However, the favorable predicted response in AFC from genetic gain in milk in Mexican environments, like those in average US herds, ranged from – 4 to – 7 d/1,000 kg (rg = – 0.20). This unequal AFC response may indicate genotype by environment interaction in fitness performance or differential breeding management of high and low yielding Mexican cows. The potential effects of age at first service of breeding females need to be disentangled to accurately assess genetic improvement needs for Mexican Holstein herds.
Key Words: genotype by environment interaction • milk yield • age at first calving • Mexico
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Although certain attention has been devoted to genotype by environment interaction in milk yield, unequal genetic relationships between milk and fertility traits across environments also have been suggested (Castillo-Juarez et al., 2000; Windig et al., 2005). Given the disturbance in genetic expression of milk production between herd opportunity groups in the US and Colombia (Stanton et al., 1991a,b), Brazil (Costa et al., 2000), Mediterranean Italy (Raffrenato et al., 2003), and Mexico (Stanton et al., 1991b; Cienfuegos-Rivas et al., 1999), heterogeneous genetic (co)variances should be expected in other traits. For example, genetic expression of age at first calving of Holstein cows (a composite of fertility and physical maturity) differed between Brazilian and Colombian herd environments (Cerón-Muñoz et al., 2004). Consequently, the main objective of this study was to evaluate the impact of US Holstein sires on the fertility performance of their Mexican daughters. To accomplish this, genetic parameters were estimated to determine the influence of US sires on age at first calving and first calving interval in Mexican herds.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Herd opportunity environments were defined by herd-year phenotypic standard deviation (HYSD) for first-lactation milk yield. As previously indicated, this criterion has been utilized to study differential responses in milk yield and in fertility (Castillo-Juarez et al., 2000; Cerón-Muñoz et al., 2004). The key assumption is that herds with greater expressed variation utilize more management inputs to provide greater opportunity for cows to express more fully their genetic potentials (Raffrenato et al., 2003). Consequently, 2 nonoverlapping opportunity classes were defined for Mexico (HYSD < 1,300 kg and HYSD > 1,600 kg) and a low opportunity class was defined for the United States (HYSD 1,024 kg). These arbitrary limits contained 50 and 22% of all herd-year subclasses (38 and 32% of records) for low- and high-opportunity Mexican environments, respectively. The low-opportunity US environment contained 38% of herd-year subclasses and 25% of all records.
Model and Analysis
Genetic parameters for first-lactation milk yield, AFC, and CI within and between countries were estimated using a multivariate linear mixed sire model with unequal design matrices and missing observations. In matrix notation
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where Y = vector of record observations; X = incidence matrix for the herd-year-season contemporary group effects; ß = vector of unknown herd-year-season of calving group fixed effects; Z = incidence matrix for sire effects; u = vector of unknown random sire effects; Q = incidence matrix that relates sires to their respective genetic groups (15 genetic groups were defined based on sire’s year of birth and origin); W = ZQ because sires are nested within genetic groups; g = vector of unknown genetic-group fixed effects; and e = vector of residual effects.
The joint distribution of u and e was assumed to be multivariate normal with mean zero and variance
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where G and R are the genetic and the residual (co)variance matrices, respectively.
Estimation of Genetic Parameters
Components of (Co)variance.
Variance components for first-lactation milk yield, AFC, and CI within and between countries were estimated by considering performance in each opportunity environment as a separate trait. Variance components were estimated by simultaneously considering 3 or 4 traits at a time including at least 2 fertility traits. For example, the variance components in G were estimated by considering first-lactation milk yield in the average US herd (all US data) and AFC from the average Mexican herd (all Mexican data), and from low and high opportunity Mexican environments. Therefore,
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where G0 = genetic (co)variance matrix between sire effects (ui) for the considered traits in different environments (
additive genetic variance). A = numerator relationship matrix among sires, and 
is the Kronecker product. Residual covariances among records in different environments were assumed equal to zero. Genetic covariances between all data in Mexico and low and high opportunity environments within the same multivariate analysis were restricted to zero because g23 and g24 are subsets of g22.
Estimates of (co)variance components and solutions for sire effects were obtained with REML methods (Boldman et al., 1995). The relationship matrix included sire of sire and maternal grandsire pathways. The initial values for sire and residual variance components were obtained from univariate solutions for traits analyzed for alternative opportunity environments. Convergence was assumed when the variance of the simplex values (– 2 log likelihood) was < 10– 8. A global maximum was considered to be achieved when 3 restarts produced convergence without a change in the first 3 decimal places of the F value (Boldman et al., 1995).
Genetic Correlation.
Product-moment genetic correlation coefficients between opportunity environments i and j were estimated by
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where 
ij = genetic covariance between milk in the ith environment and fertility trait (AFC or CI) in the jth environment; ii= genetic variance of milk in the ith environment, and jj = genetic variance of a fertility trait in the jth environment.
Bivariate analyses provided estimates of the variance of the simplex value (– 2 log likelihood) for testing genetic correlation coefficients against zero using the likelihood ratio test (Robert et al., 1995). The maximum likelihood function was obtained for 2 models, one without constraint (full model) and a reduced model with genetic correlation set to zero. The difference in twice the log likelihood was used to test the result by 
2 statistic with 1 degree of freedom.
Fertility Responses to Sire Selection for Milk
Expected changes in AFC and CI were estimated by regressing the EBV for AFC and CI in Mexico and the US on the EBV for milk in the United States.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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). Preliminary analyses of relationships between milk and fertility within Mexico and the US (Table 2) indicated poorer fertility (longer CI) and younger cows at first calving with sire selection for milk. This genetic correlation coefficient agreed with findings by Ojango and Pollott (2001) for Holstein cows in Kenya (rg= – 0.20) and by Moore et al. (1991) in Canada (rg= – 0.29).
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An important finding was that the genetic covariances between milk yield and AFC analyses within countries differed from analyses between countries: they were negative across Mexican environments (Table 2) but were positive between milk in US and AFC in Mexican herds (Table 3). This outcome implies inconsistent correlated responses to selection for milk between country production systems.
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. Favorable responses in AFC were predicted when selection is based on milk yield within country with a decrease of 3 to 7 d per 1,000 kg of genetic gain in milk. Thus, predicted AFC responses were similar when selection is based on within-country milk yield information.
Unfavorable CI responses were predicted when selection is based on milk yield within country with a 3-d increase/1,000 kg of genetic gain in milk in the average Mexican herd. In high-opportunity Mexican herds, CI may increase by 2 d/1,000 kg of genetic gain in milk yield. Similarly, a CI increase of 1 to 2 d/1,000 kg of genetic gain in milk may be expected in average- and low-opportunity US herds (Table 2).
Predicting Mexican Fertility Responses with US Information.
Predicted genetic responses in fertility of Mexican cows to US germplasm are shown in Table 3. For the average Mexican herd, AFC is expected to increase by about 7 d and CI by about 2 d/1,000 kg of genetic gain in first-lactation milk yield based on information from the average US herd. However, if selection were based on milking performance in low-opportunity US herds, a smaller AFC increase of about 4 d/1,000 kg of genetic gain in milk yield may be expected in Mexican herds. Similarly, CI may increase by 1 to 2 d/1,000 kg of genetic gain in milk yield.
This unequal AFC response to selection for milk production between countries may indicate genotype by environment interaction. Similar to milking ability, the increased age of Mexican cows at first calving may have resulted from their diminished growth expression compared with US half-sisters. These differences suggest a need for monitoring associations between these traits in Mexican production systems to facilitate genetic decision-making. Alternatively, these results could indicate differential breeding practices. Mexican farmers may have delayed breeding service for high-yielding daughters of high EBV sires to constrain semen cost by ensuring high pregnancy rates. Therefore, it will be important to disentangle the potential effect of age at first service of breeding females to accurately determine genetic improvement needs for alternative Mexican herds. Daughter pregnancy rate evaluations of US sires were not available when data for this study were obtained. A comparative assessment of daughter pregnancy rate across Mexican opportunity environments and the US should prove useful to dairy industries in Mexico and other Latin American countries.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSREFERENCES |
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Received for publication March 15, 2005. Accepted for publication February 1, 2006.
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REFERENCES |
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  R. Ruiz-Sanchez, R. W. Blake, H. M. A. Castro-Gamez, F. Sanchez, H. H. Montaldo, and H. Castillo-Juarez Short Communication: Changes in the Association Between Milk Yield and Age at First Calving in Holstein Cows with Herd Environment Level for Milk Yield J Dairy Sci, October 1, 2007; 90(10): 4830 - 4834. [Abstract] [Full Text] [PDF]
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