JDS
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Interpretive Summary
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by König, S.
Right arrow Articles by Swalve, H. H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by König, S.
Right arrow Articles by Swalve, H. H.
J. Dairy Sci. 88:4087-4096
© American Dairy Science Association, 2005.

Genetic Relationships for Dairy Performance Between Large-Scale and Small-Scale Farm Conditions

S. König1, G. Dietl2, I. Raeder2 and H. H. Swalve3

1 Institute of Animal Breeding and Genetics, University of Göttingen, Albrecht-Thaer-Weg 3, D-37075 Göttingen, Germany
2 Research Institute for the Biology of Farm Animals, Wilhelm-Stahl-Allee 2, D-18196 Dummerstorf, Germany
3 Institute of Animal Breeding and Husbandry with Veterinary Clinic, University of Halle, D-06099 Halle (Saale), Germany

Corresponding author: Hermann H. Swalve; e-mail: swalve{at}landw.uni-halle.de.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Genotype by environment interaction can be detected via the estimation of genetic correlations between environments under an animal model based on data comprising genetic links between the strata. Genetic correlations were estimated for protein yield of Holstein cows within and across regions of Germany using REML under an animal model for lactation and test-day records. Subsets of the entire data were created, stratified by region or herd size within region, and comprised between 16,307 and 132,972 cows with first-lactation records. Substantial heterogeneity exists between regions in Western and Eastern Germany. In Western states, most farms are small, with typical herd sizes of 30 to 60 cows, whereas in Eastern states, mostly large herds with herd sizes of 500 to 2000 cows are common. The results show drastic differences for residual and permanent environmental variance components between Eastern and Western regions with increases of around 30% for Eastern regions. Additive genetic variances were of similar magnitude in both regions. Genetic correlations between Eastern and Western states were between 0.90 and 0.95 but dropped to 0.79 when data from an Eastern state were reduced to contain large herds only. The results indicate that differences in herd size account for more of the differences in genetic correlation than do geographic regional differences.

Key Words: genotype x environment interaction • large-scale farm • small-scale farm • milk production

Abbreviation key: EAST-1, EAST-2, WEST-1, WEST-2 = different data sets from Eastern and Western states within Germany


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
The phenomenon of a genotype x environment interaction is a classic topic in animal breeding which in recent years has received even more attention due to the availability of modern methods of its estimation like animal models including all relationships. Genotype x environment interaction is present when different genotypes are not equally affected by different environments (Falconer, 1960). This results in unequal differences between genotypes across environments or even in a reranking of genotypes for different environments.

Methodologically, 3 types of approaches can be differentiated when analyzing genotype x environment interaction: 1) the use of a model including genetic and environmental effects along with an interaction term, 2) considering the records of genotypes for a given trait as different traits according to their environment in a multiple trait model, and 3) the use of the reaction norm concept. In the reaction norm concept, the phenotype is described as a function of quantitative environmental parameters (Veerkamp and Goddard, 1998; Kolmodin et al., 2002). Fikse et al. (2003) concluded that from their study of Guernsey data from 4 countries, the multiple trait approach was highly suitable with respect to goodness of fit and model complexity. The multiple trait approach is a logical choice when the scale of the criteria to define environments is discrete rather than continuous.

The application of the multiple trait approach has been facilitated by the use of animal models including all relationships. An individual animal under this concept always supplies information on one trait only, i.e., environment, whereas records on the other traits are missing. The reports in the literature on the genotype x environment interaction problem in dairy cattle have been numerous in recent years. We attempted to summarize the recent literature in 2 tables. Tables 1Go and 2Go are limited to estimates of the genetic correlation between environments for production traits. Table 1Go displays estimates obtained in studies across countries whereas Table 2Go presents estimates from within-country analyses. Within-country analyses often use stratifications by means within contemporary group, but other approaches, for example, stratification by standard deviations, are found as well. Raffrenato et al. (2003) indicate that stratification by means is often problematic due to heterogeneity of variances and conclude that stratification by herd-standard-deviations would be more adequate.


View this table:
[in this window]
[in a new window]
  		Table 1. Genetic correlations for production traits between countries.
 

View this table:
[in this window]
[in a new window]
  		Table 2. Genetic correlations for production traits stratified by production levels within countries.
 
Most of the estimates of the genetic correlation between countries (Table 1Go) are above 0.80, some between 0.70 and 0.80, and only very few below 0.70. Among the low estimates, it is not surprising that these are exclusively found for pairs of countries with obvious severe differences in production systems and climates; for example, Canada vs. New Zealand, or United Kingdom vs. Kenya.

As expected, the within-country estimates of the genetic correlation between herd levels of production or regions (Table 2Go) are generally higher than those from across-country analyses.

In the global world of dairy cattle breeding, the widespread use of international genetic evaluations has raised the question whether an evaluation system that defines the production in each participating state as a separate trait is optimal. Weigel and Rekaya (2000) suggested that rather than using country borders, production systems should be defined across countries. This would enable a borderless clustering of herds into production systems, because even within a country, substantial differences in production systems may exist. This approach to international genetic evaluations has become known as borderless clustering and has been used in scientific analyses (Fikse et al., 2003; Zwald et al., 2003a,b; Maltecca et al., 2004) but not yet for routine genetic evaluations. From a global perspective and the results of Weigel and Rekaya (2000) and Zwald et al., 2003a,b), it can be summarized that a grouping of herds into a few production systems ignoring country borders may be advantageous. At the same time, this approach would help overcome the consequences of a significant genotype x environment interaction between extreme pairs of countries.

Since the unification of the 2 formerly separated German states in 1990, the German dairy cattle population is exhibiting substantial heterogeneity in housing and management conditions. In Western Germany, small farms with herd sizes of around 30 to 100 cows are prevalent, whereas in Eastern Germany, large-scale dairy farms with herd sizes of 500 to 2000 cows are common. Obvious environmental differences exist with respect to the use of free-stalls, feeding of TMR, and grouping systems within herd. Grouping within herd is a standard practice in the large herds of Eastern Germany, whereas under the small farm conditions of Western Germany, grouping is not warranted due to the small herd sizes. Furthermore, under small farm conditions, the use of stanchion barns and feeding individual feeds separately are still common. Consequently, Zwald et al. (2003a) found that the majority of farms in Western Germany could be clustered together with most herds from the Netherlands, whereas most Eastern German farms fell into a cluster of predominantly large farms jointly with farms from the Czech Republic, Hungary, Israel, and the Southwestern United States—regions where large-scale farm conditions are found. The estimated genetic correlation between these 2 clusters was 0.92. Zwald et al. (2003b) found genetic correlations between clusters of varying herd sizes that were as low as 0.78 between extremes.

The aim of the present study was to estimate the genetic correlation between environments, (i.e., the Western and Eastern states of Germany) under multiple trait models defined as lactation models as well as test-day models. The use of test-day models in this context has the advantage that more observations contribute to the estimates and that it is possible to evaluate the within-cow variance (Hayes et al., 2003).


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Two sources of data were available for analysis. Both sources comprised first-lactation protein yield records from cows calving between 1997 and 1999 from 4 regions within Germany. Two of these regions were identical to 2 states in Eastern Germany (EAST-1 and EAST-2), and the other 2 represented states of Western Germany (WEST-1 and WEST-2). The first data source comprised 305-d lactation records of registered cows in all regions, and the second data source consisted of test-day records from all cows under milk recording. The difference in the status of registry, however, is not important in Germany because most cows under milk recording are also registered. Therefore, the 2 sources of data should more be considered as a lactation record vs. a test-day data set rather than as 2 data sets differing in the status of registry. Table 3Go (lactation records) and Table 4Go (test-day records) have an overview of the 4 data sets corresponding to regions within each data source.


View this table:
[in this window]
[in a new window]
  		Table 3. Structure of the data containing lactation records by region and herd size within region.
 

View this table:
[in this window]
[in a new window]
  		Table 4. Structure of the data containing test-day records by region and herd size within region.
 
As can be seen from Tables 3Go and 4Go, subsets of the lactation and test-day data set were created to enable bivariate analyses including data from 2 regions (states) at a time. Furthermore, within the Eastern states, stratification according to size of herds was additionally introduced. Within EAST-1, subsets were created comprising a minimum of 100 or 150 first-lactation cows per herd-year. Another 2 subsets contained small (Western type) herds of herd-year size 15 to 60 cows for WEST-1 and EAST-1, respectively. Finally, within EAST-1, a subset was selected containing only small and large herds, thus taking out all intermediate herd-year sizes. For the lactation record data set, limits were 50 and 150 cows; for the test-day data set, 2 different lower limits of either 50 or 60 cows were used. The choice of these 2 rather similar lower limits was arbitrary and had the intention of examining an effect of selecting different lower limits.

For test-day data, cows were required to have at least 8 test days and a maximum of 14 test days per lactation. The average number of test days per cow, differing slightly between subsets, was around 9.8. The substantial differences in herd sizes between WEST and EAST data sets can clearly be seen from Table 4Go. For WEST-1 and WEST-2, the ratio of number of cows to number of herd test days is roughly 1:1, whereas for EAST-1, this ratio is around 5:1, and increases to 14:1 when only large herds were selected.

Estimation of variance components was done using bivariate animal models for REML and applying the packages MTDFREML (Boldman et al., 1995) and VCE 4.0 (Groeneveld, 1997). For lactation records, herd-year and season of calving were included in the model as fixed effects along with the linear covariables age at first calving and length of lactation. The genetic effect of the animal was the only random effect in this model besides the residual component. For test-day records, fixed effects were herd-test-day, year-season of calving, and age at first calving as a linear covariable. The shape of the lactation curve was accounted for by the use of 4 coefficients of regression according to Ali and Schaeffer (1987) in the form of a fixed regression test-day model. Besides the residual, this model thus contained the genetic effect of the animal as well as the permanent environmental effect associated with each cow as random effects. For both lactation records and test-day records, no variance component for the residual covariance (between environments) could be defined and thus was set to zero.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Tables 5Go and 6Go show the results with respect to additive genetic and residual variances and the resulting heritabilities. The results given in Tables 5Go and 6Go show that although additive genetic variances in WEST and EAST were on a similar level when the complete regions were analyzed, residual variances were remarkably different. However, a tendency of increased additive genetic variances from Eastern subsets compared with Western data could be observed under the test-day model (Table 6Go). When smaller herds in EAST-1 were excluded (lines 4 and 5 in Tables 5Go and 6Go), this did not substantially change the residual variance but an increase of additive genetic variances was detected. When comparing all estimates for Eastern data, it became evident that the magnitude of the residual variances at least partly was associated with the large herds in the data. Estimates of the residual variance for small herds in EAST-1 (last line of Table 5Go and last 2 lines of Table 6Go) were on an intermediate level between those for Western data and the full data from EAST-1. Estimates of variance components in the material used were sensitive to the choice of limits used to stratify herds. This can clearly be seen from the last 2 rows of Table 6Go that differ only by the limit for herd sizes of smaller herds. The difference in the upper limit for small herds (50 or 60 per herd-year) resulted in a substantial change of the additive genetic variance.


View this table:
[in this window]
[in a new window]
  		Table 5. Results from the estimation of variance components ({sigma}2A = additive genetic variance, {sigma}2E = residual variance) and resulting heritabilities [h2 (SE)] for 305-d protein yield (kg) in first lactations from bivariate analyses of entire regions (states) or subsets thereof with specific limits according to herd sizes.
 

View this table:
[in this window]
[in a new window]
  		Table 6. Results from the estimation of variance components (x105; {sigma}2A = additive genetic variance, {sigma}2E = residual variance) and resulting heritabilities [h2 (SE)] for test-day protein yield (kg/d) in first lactations from bivariate analyses of entire regions (states) or subsets thereof with specific limits according to herd sizes.
 
The results for estimates of the permanent environmental variances are in Table 7Go. Compared with the regions WEST-1 and WEST-2, variances of the permanent environmental effect for EAST-1 and EAST-2 increased by almost 30%. This was upheld as long as the large herds were included in the data from EAST-1, but a drastic decrease was observed when only herds of a herd-year size between 15 and 60 cows were included for both regions. In this case, permanent environmental variances were on a similar level for both regions (EAST-1 and WEST-1). For EAST-1, permanent environmental variances for small herds decreased remarkably compared with the full data set of EAST-1 (last 2 lines in Table 7Go) but did not approach the level of the WEST-1 or WEST-2 data.


View this table:
[in this window]
[in a new window]
  		Table 7. Results from the estimation of permanent environmental variance components (x105; {sigma}2PE = permanent environmental variance, {sigma}2P = phenotypic variance) and resulting ratios ({sigma}2PE/{sigma}2P, SE in parentheses) for test-day protein yield (kg/d) in first lactations from bivariate analyses of entire regions (states) or subsets thereof with specific limits according to herd sizes.
 
Estimates of the genetic correlation between environments are given in Tables 8Go and 9Go. For lactation records, it was computationally feasible to run estimations using MTDFREML, which offers the possibility of evaluating the likelihood under specific restrictions such as fixing the estimate of certain variance components. In this case, as proposed by Van Vleck et al. (2000), firstly a "normal" estimation run was carried out until convergence was achieved. Then, an additional evaluation of the likelihood was done by running another single iteration fixing the additive genetic covariance to a value resulting in a genetic correlation of 0.995. The difference of the two –2log likelihood values obtained under both procedures was compared in the form of a likelihood ratio test against the {chi}2 value for P ≤ 0.01 (6.63). This test then gives some indication on the relevance (its deviation from unity) of the correlation estimated under convergence. However, this may not be regarded as a proper test of significance because the "restricted" estimation run was not a REML estimation in the strict sense.


View this table:
[in this window]
[in a new window]
  		Table 8. Genetic correlations and standard errors along with the values for –2logL under convergence and under an additional single iteration evaluating the likelihood for a covariance resulting in a genetic correlation of 0.995 ("fixed") for 305-d protein yield in first lactations from bivariate analyses of entire regions (states) or subsets thereof with specific limits according to herd sizes.
 

View this table:
[in this window]
[in a new window]
  		Table 9. Genetic correlations and standard errors for test-day protein yield in first lactations from bivariate analyses of entire regions (states) or subsets thereof with specific limits according to herd sizes.
 
In Table 8Go, estimates of the genetic correlation were essentially unity for EAST x EAST and WEST x WEST (first 2 rows). The correlation decreased when only larger herds were included in EAST, which was also connected with a notable difference in the likelihood values (rows 4 and 5). When only small herds were included for WEST and EAST (row 6), the estimate was higher suggesting that the decrease given in rows 4 and 5 was due to the herd size. However, this was not supported by the estimate given in the last row of Table 8Go, which shows a unity correlation within EAST from including small vs. large herds. This last finding is the only important difference between the results from the lactation record vs. the test-day data set (Table 9Go). The last 2 rows in Table 9Go indicate a correlation lower than unity within the EAST data set; that is, when comparing small and large herds within EAST.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 REFERENCES
 
The results obtained in this study exhibit clear differences between small-scale farms in Western Germany and large-scale farms in Eastern Germany. In large farms, additional residual variance and permanent environmental variance are introduced, possibly resulting from grouping systems within herd that are not accounted for by the model of analysis. Unfortunately, to date, no results from genetic studies based on large herd data and including groups in the model are available.

The comparison of the results from the lactation model vs. results from the test-day model show that in large herds, not only the residual variance but also, quite drastically, the variance of the permanent environmental effect of the cow increases from smaller to larger herds. This may be taken as further indication that management practices in large herds introduce additional variation that is permanently associated with each cow, and underlines the importance of the introduction of test-day models in genetic evaluations.

A further finding is that the larger the farm, the more the additive genetic variance increases. A reason for this could be that within-herd correlations of genotype x management (e.g., as arising from feeding according to the genetic potential) are higher in large farms because all available management tools can be applied (which would not be feasible on a small farm). It can be speculated that one of these management tools is feeding of a TMR, which is not practical on a small farm but is readily implemented (possibly even stratified by groups) on a large-scale farm.

The main difference between dairy farms in Western and Eastern states in Germany is the size of the herds. We attempted to examine possible effects of this main difference by certain edits applied to the subsets from EAST and WEST. Editing the Eastern data to contain large herds only resulted in increases of residual and additive genetic variances. On the other hand, editing the Eastern data to include small herds only or when comparing small vs. large herds within EAST-1 showed that additive genetic variances as well as residual variances decreased although the latter ones did not reach the low limit as set by the Western data.

In genetic evaluations, this heterogeneity should be accounted for, as it is in the present genetic evaluation system in Germany. The precorrection that is applied, however, is limited to the phenotypic scale, because it harmonizes within-herd phenotypic variances without altering the heritability.

Despite the clear differences in the magnitude of individual variance components, there is only little evidence for the existence of a genotype x environmental interaction. Following Robertson (1959), genetic correlations should be below 0.80 to indicate an interaction. The results from the present study agree closely with other recent studies on the phenomenon of genotype x environment interaction in dairy cattle. Cromie (2000) analyzed data from Irish herds stratified into high and low concentrate input vs. US Holstein data. That study found correlations of 0.85 (US x Irish low) and 0.93 (US x Irish high). Castillo-Juarez et al. (2000), Weigel et al. (1999), and Boettcher et al. (2003) examining data within the United States or Canada, estimated correlations to be between 0.90 and unity. Using Guernsey data from 4 countries, Fikse et al. (2003) reported correlations between 0.80 and unity. Other studies dealing with data from regions with temperate and tropical or subtropical climates (e.g., Costa et al., 1998, 2000; Cienfuegos-Rivas et al., 1999) have found genetic correlations in the range of 0.60 to 1.0.

Finally, the results of the present study are in good agreement with the results from Zwald et al. (2003b) with respect to estimates between varying herd sizes, which tended to approach 0.80 between environments of extreme differences in herd size. The estimates of the present study are also in line with the results of Zwald et al. (2003a) that placed Western and Eastern regions within Germany into separate clusters.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
REFERENCES
 
The results of the present study suggest that important differences exist between the residual variance and permanent environmental variance components estimated from large-scale farm data compared with small farm data. The estimates of the genetic correlation between the 2 environments (large and small farms) do not provide a strong reason to change the evaluation system; for example, into a multiple trait system. However, the results of this study do support the contention that a borderless clustering of herds into production systems for international genetic evaluation systems as proposed by Weigel and Rekaya (2000) should be considered as an alternative to the current system.

Received for publication February 18, 2005. Accepted for publication June 24, 2005.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 


Albuquerque, L. G., G. Dimov, and J. F. Keown. 1995. Estimates using an animal model of covariances for yields of milk, fat, and protein for the first lactation of Holstein cows in California and New York. J. Dairy Sci. 78:1591–1596.[Abstract]

Ali, T. E., and L. R. Schaeffer. 1987. Accounting for covariances among test day milk yield in dairy cows. Can. J. Anim. Sci. 67:637–644.

Banos, G., and G. E. Shook. 1990. Genotype by environment interaction and genetic correlations among parities for somatic cell count and milk yield. J. Dairy Sci. 73:2563–2573.[Abstract]

Berry, D. P., F. Buckley, P. Dillon, R. D. Evans, M. Rath, and R. F. Veerkamp. 2003. Estimation of genotype x environment interactions, in a grass-based system, for milk yield, body condition score, and body weight using random regression models. Livest. Prod. Sci. 83:191–203.

Boettcher, P. J., J. Fatehi, and M. M. Schutz. 2003. Genotype x environment interactions in conventional versus pasture-based dairies in Canada. J. Dairy Sci. 86:383–389.[Abstract/Free Full Text]

Boldman, K. G., L. A. Kriese, and L. D. Van Vleck. 1995 A manual for use of MTDFREML; A set of programs to obtain estimates of variances and covariances. Department of Agriculture, Agricultural Research Service, Lincoln, NE.

Calus, M. P. L., A. F. Groen, and G. de Jong. 2002. Genotype x environment interaction for protein yield in Dutch dairy cattle as quantified by different models. J. Dairy Sci. 85:3115–3123.[Abstract/Free Full Text]

Carabano, M. J., L. D. Van Vleck, G. R. Wiggans, and R. Alenda. 1989. Estimation of genetic parameters for milk and fat yields of dairy cattle in Spain and the United States. J. Dairy Sci. 72:3013–3022.

Carabano, M. J., K. M. Wade, and L. D. Van Vleck. 1990. Genotype by environment interactions for milk and fat production across regions of the United States. J. Dairy Sci. 73:173–180.[Abstract]

Castillo-Juarez, H., P. A. Oltenacu, R. W. Blake, C. E. McCulloch, and E. G. Cienfuegos-Rivas. 2000. Effect of herd environment on the genetic and phenotypic relationships among milk yield, conception rate, and somatic cell score in Holstein cattle. J. Dairy Sci. 83:807–814.[Abstract]

Ceron-Munoz, M. F., H. Tonhati, C. N. Costa, D. Rojas-Sarmiento, and D. M. Echeverri. 2004a. Factors that cause genotype by environment interaction and use of a multiple-trait herd-cluster model for milk yield of Holstein cattle from Brazil and Colombia. J. Dairy Sci. 87:2687–2692.[Abstract/Free Full Text]

Ceron-Munoz, M. F., H. Tonhati, C. N. Costa, D. Rojas-Sarmiento, and C. Solarte-Portilla. 2004b. Variance heterogeneity for milk yield in Brazilian and Colombian Holstein herds. Livest. Research for Rural Development 16.

Charagu, P., and R. Peterson. 1998. Estimates of G x E effects for economic efficiency among daughters of Canadian and New Zealand sires in Canadian and New Zealand dairy herds. Interbull Bull. 17:105–109.

Cienfuegos-Rivas, E. G., P. A. Oltenacu, R. W. Blake, S. J. Schwager, H. Castillo-Juarez, and F. J. Ruiz. 1999. Interaction between milk yield of Holstein cows in Mexico and the United States. J. Dairy Sci. 82:2218–2223.[Abstract]

Costa, C. N., R. W. Blake, E. J. Pollak, and P. A. Oltenacu. 1998. Genetic relationships for milk and fat yields between Holstein populations in Brazil and the United States. Proc.6th World Congr. Genet. Appl. Livest. Prod., Armidale, Australia, Vol. 23:323–326.

Costa, C. N., R. W. Blake, E. J. Pollak, P. A. Oltenacu, R. L. Quaas, and S. R. Searle. 2000. Genetic analysis of Holstein cattle populations in Brazil and the United States. J. Dairy Sci. 83:2963–2974.[Abstract]

Cromie, A. R. 2000. Genotype by environment interaction—re-ranking across countries for production traits. 10th World Holstein Friesian Conference, Sydney, Australia. Holstein Australia, Victoria, Australia.

Cromie, A. R., D. L. Keller, F. J. Gordon, and M. Rath. 1998. Genotype by environment interaction for milk production traits in Holstein Friesian dairy cattle in Ireland. Interbull Bull. 17:100–104.

Falconer, D. R. 1960. Introduction to Quantitative Genetics. 1st ed. Oliver and Boyd, Edinburgh, UK.

Fikse, W. F., R. Rekaya, and K. A. Weigel. 2003. Genotype x environment interaction for milk production in Guernsey cattle. J. Dairy Sci. 86:1821–1827.[Abstract/Free Full Text]

Groeneveld, E. 1997. VCE4–User’s guide and reference manual. Institut für Tierzucht und Tierverhalten, FAL, Neustadt, Germany.

Hayes, B. J., M. Carrick, P. Bowman, and M. E. Goddard. 2003. Genotype x environment interaction for milk production of daughters of Australian dairy sires from test-day records. J. Dairy Sci. 86:3736–3744.[Abstract/Free Full Text]

Jara, A., D. Casanova, M. Elzo, and N. Barria. 2002. Covariance components for first lactation milk yield at three production and variability levels in Argentinean dairy cows. 7th World Congr. Genet. Appl. Livest. Prod. CD-ROM commun. no. 18–14.

Kolmodin, R., E. Strandberg, P. Madsen, J. Jensen, and H. Jorjani. 2002. Genotype by environment interaction in Nordic dairy cattle studied using reaction norms. Acta Agric. Scand. 52:11–24.

Kearney, J. F., M. M. Schutz, P. J. Boettcher, and K. A. Weigel. 2002. Genotype by environment interactions for US Holsteins in two production systems. 7th World Congr. Genet. Appl. Livest. Prod. CD-ROM commun. no. 13–09.

Maltecca, C., A. Bagnato, and K. A. Weigel. 2004. Comparison of international dairy sire evaluations from meta-analysis of national estimated breeding values and direct analysis of individual animal performance records. J. Dairy Sci. 87:2599–2605.[Abstract/Free Full Text]

Ojango, J. M. K., and G. E. Pollot. 2002. The relationship between Holstein bull breeding values for milk yield derived in both the UK and Kenya. Livest. Prod. Sci. 74:1–12.

Raffrenato, E., R. W. Blake, P. A. Oltenacu, J. Carvalheiro, and G. Licitra. 2003. Genotype by environment interaction for yield and somatic cell score with alternative environmental definitions. J. Dairy Sci. 86:2470–2479.[Abstract/Free Full Text]

Rekaya, R., K. A. Weigel, and D. Gianola. 2001. Application of a structural model for genetic covariances in international dairy sire evaluations. J. Dairy Sci. 84:1525–1530.[Abstract]

Rekaya, R., K. A. Weigel, and D. Gianola. 2003. Bayesian estimation of parameters of a structural model for genetic covariances between milk yield in five regions of the United States. J. Dairy Sci. 86:1837–1844.[Abstract/Free Full Text]

Robertson, A. 1959. The sampling variance of the genetic correlation coefficient. Biometrics 15:469–485.

Shahrbabak, M., D. A. Saghi, S. E. Ashtiani, and A. N. Javaremi. 2002. Adaptation of Holstein dairy cattle to Iranian environmental conditions. 7th World Congr. Genet. Appl. Livest. Prod. CD-ROM commun. no. 18–18.

Stanton, T. L., R. W. Blake, and R. L. Quaas. 1991. Genotype by environment interaction for Holstein milk yield in Colombia, Mexico and Puerto Rico. J. Dairy Sci. 74:1700–1714.[Abstract]

Van Vleck, L. D., K. A. Leymaster, and T. G. Jenkins. 2000. Genetic correlations for daily gain between ram and ewe lambs fed in feedlot conditions and ram lambs fed in Pinpointer units. J. Anim. Sci. 78:1155–1158.[Abstract/Free Full Text]

Veerkamp, R. F., and M. E. Goddard. 1998. Covariance functions across herd production levels for test day records on milk, fat, and protein yields. J. Dairy Sci. 81:1690–1701.[Abstract]

Weigel, K. A., T. Kriegl, and A. L. Pohlman. 1999. Genetic analysis of dairy cattle production traits in a management intensive rotational grazing environment. J. Dairy Sci. 82:191–195.[Abstract]

Weigel, K. A., and R. Rekaya. 2000. A multiple-trait herd cluster model for international dairy sire evaluation. J. Dairy Sci. 83:815–821.[Abstract]

Zwald, N. R., K. A. Weigel, W. F. Fikse, and R. Rekaya. 2003a. Application of a multiple-trait herd cluster model for genetic evaluation of dairy sires from seventeen countries. J. Dairy Sci. 86:376–382.[Abstract/Free Full Text]

Zwald, N. R., K. A. Weigel, W. F. Fikse, and R. Rekaya. 2003b. Identification of factors that cause genotype by environment interaction between herds of Holstein cattle in seventeen countries. J. Dairy Sci. 86:1009–1018.[Abstract/Free Full Text]


This article has been cited by other articles:


Home page
 J DAIRY SCIHome page
M. Haile-Mariam, M. J. Carrick, and M. E. Goddard
Genotype by Environment Interaction for Fertility, Survival, and Milk Production Traits in Australian Dairy Cattle
J Dairy Sci, December 1, 2008; 91(12): 4840 - 4853.
[Abstract] [Full Text] [PDF]

Home page
 J DAIRY SCIHome page
G. Freyer, S. Konig, B. Fischer, U. Bergfeld, and B. G. Cassell
Invited Review: Crossbreeding in Dairy Cattle From a German Perspective of the Past and Today
J Dairy Sci, October 1, 2008; 91(10): 3725 - 3743.
[Abstract] [Full Text] [PDF]

Home page
 J DAIRY SCIHome page
S. Konig, X. L. Wu, D. Gianola, B. Heringstad, and H. Simianer
Exploration of Relationships Between Claw Disorders and Milk Yield in Holstein Cows via Recursive Linear and Threshold Models
J Dairy Sci, January 1, 2008; 91(1): 395 - 406.
[Abstract] [Full Text] [PDF]

Home page
 J DAIRY SCIHome page
S. Konig, S. Lessner, and H. Simianer
Application of Controlling Instruments for Improvements in Cow Sire Selection
J Dairy Sci, April 1, 2007; 90(4): 1967 - 1980.
[Abstract] [Full Text] [PDF]

Home page
 J DAIRY SCIHome page
J. R. Bryant, N. Lopez-Villalobos, J. E. Pryce, C. W. Holmes, D. L. Johnson, and D. J. Garrick
Environmental Sensitivity in New Zealand Dairy Cattle
J Dairy Sci, March 1, 2007; 90(3): 1538 - 1547.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow Full Text (PDF)
Right arrow Interpretive Summary
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by König, S.
Right arrow Articles by Swalve, H. H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by König, S.
Right arrow Articles by Swalve, H. H.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS