Contents |
Introduction
Genetic improvement programs for dairy cattle are typically carried out on a national or international basis. However, differences in management practices and/or climate conditions exist between countries, as well as between individual farms within a country. Differences in the economic value of traits can be accounted for via a different selection index for each country or each farm, but changes in sire rankings between countries, farms, or production systems that cause genotype by environment interactions for specific traits can be problematic. A common question that arises with respect to genotype by environment interactions, is:
“Can the same bulls provide genetic improvement in all management systems?”
Two types of genotype by environment interactions can occur. The first type is a change in rank. For example, suppose that sires are progeny tested in herds with one particular set of management conditions, but their semen is ultimately used in herds with a different type of management. Further, suppose that the performance of their offspring differs between management systems, such that cows from certain sire families perform better in one type of herd and poorer in the other. In this case, sire ranking lists developed from the progeny test herds will not be suitable for decision-making in the other commercial herds, and the wrong sires will sometimes be chosen.
The second type is a change in scale. For example, suppose that sires rank more or less the same in both environments, but differences between sires tend to be larger in one environment than the other. This can occur in cases where environmental challenges in one management system prevent the full expression of an animal’s genetic potential, such that the difference between offspring of superior sires and breed-average sires is smaller than one would observe under optimal management conditions. In this case, sire selection decisions will generally be correct in both environments, but the return on investment from a unit of semen will be lower in the less favorable environment.
The objective of this paper is to describe recent research at the University of Wisconsin and other key institutions that addresses the magnitude of genotype by environment interactions within – and between – countries, as well as the implications of these interactions on sire selection programs.
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Within-Country Differences in Herd Management
The most obvious distinction is between herds that practice intensive rotational grazing among paddocks and herds that utilize stored feed (e.g., a total mixed ration) in a confinement setting. The popularity of each of these management systems differs widely between countries, as discussed in the subsequent section, as well as between individual farms or regions within a country.
In this section, we’ll address differences between grazing and confinement operations within a country or region, because in this situation it is possible to focus on genotype by management system interactions without worrying about complicating factors such as climate, government policy (e.g., milk quota), and so on. Individual herds can fall anywhere along a broad continuum from “pure grazing” (livestock harvest 100 percent of the forage, with no supplemental feeding), to a mixture of grazing and supplemental grains or forages, to “total confinement” (livestock never leave the barn and eat only a total mixed ration). For our purposes, we will consider grazing to include herds in which animals harvest the majority of forage through frequent rotation among paddocks, and we will consider confinement to include herds in which animals typically consume stored feed and spend, at most, a brief exercise period “on grass.”
Two key concerns exist with respect to sire selection in grazing versus confinement herds. The first issue is whether different breeds and/or sire families are needed to provide animals that produce milk efficiently when they are provided with stored grains and forages versus when they are expected to subsist only on the forage they can harvest themselves. The second issue is whether certain breeds or sire familes have adequate reproductive performance to provide animals that can maintain an annual calving pattern and resist culling due to infertility in herds with a strict seasonal milk production schedule.
The first issue, re-ranking of sire families for individual traits, can be addressed by looking at genotype by environment interaction for milk (or milk solids) production between confinement and grazing operations. Two studies will be quoted herein: one in the United States and one in Ireland.
In the United States, Kearney et al. (2004) analyzed data from 393 herds in 12 eastern states that practiced intensive rotational grazing, as well as 432 “control” herds in 13 eastern states that relied on stored feed in a confinement setting. Mature equivalent 305 d milk, fat, and protein yields of cows in grazing and confinement herds were regressed on the predicted transmitting abilities (PTA) of their sires from the routine November 2000 U.S. Department of Agriculture (USDA) Sire Summary, as shown in Table 1.
| Trait | Grazing | Confinement |
| Milk | 0.78** | 0.99 |
| (0.03) | (0.03) | |
| Fat | 0.76** | 0.96 |
| (0.02) | (0.02) | |
| Protein | 0.78** | 0.98 |
| (0.02) | (0.03) | |
| **Significantly different from 1.00 (P < 0.01). | ||
As shown in Table 1, cows in the rotational grazing herds produced an additional 0.78 kg milk, 0.76 kg fat, and 0.78 kg protein per 1.0 kg change in sire PTA for milk, fat, and protein, respectively. Meanwhile, the estimated regression coefficient for cows in confinement herds did not deviate significantly from its expectation of 1.0, such that the genetic superiority “as advertised” in the sire PTA vaue was realized in the farmer’s bulk tank.
Next, Kearney et al. (2004) estimated the heritability of milk, fat, and protein yield in grazing and confinement herds, as well as the genetic correlation between the lactation yield of each trait in the two different environments. Results are shown in Table 2.
| Trait | Grazing h2 | Confinement h2 | Genetic Correlation |
| Milk | 0.19 | 0.20 | 0.89 |
| (0.02) | (0.02) | (0.03) | |
| Fat | 0.19 | 0.23 | 0.88 |
| (0.01) | (0.02) | (0.06) | |
| Protein | 0.17 | 0.20 | 0.91 |
| (0.01) | (0.01) | (0.03) |
As shown in Table 2, heritability estimates for milk, fat, and protein were slightly higher in the confinement herds than in the control herds. More importantly, estimated genetic correlations between the grazing and confinement herds were 0.89, 0.88, and 0.91 for milk, fat, and protein, respectively. These estimates were very similar to estimates of 0.92 and 0.89 for milk and fat yield, respectively, between grazing and confinement herds in Wisconsin, as reported by Weigel et al. (1999).
In Ireland, Cromie et al. (1998) classified herds into quartiles based on the quantity of concentrates fed per cow per year. Next, they computed sire PTA separately for the “high input” (top quartile) and “low input” (bottom quartile) environments. Regression of sire PTA from the low-input herds on sire PTA from the high-input herds yielded estimated coefficients of 0.65, 0.67, and 0.62 for milk, fat, and protein, respectively, indicating a substantial interaction of scale between these environments. Next, they estimated heritabilities for lactation milk, fat, and protein yield in the high-input and low-input environments, as well as the genetic correlations between environments. Results are shown in Table 3.
| Trait | Low Input h2 | High Input h2 | Genetic Correlation |
| Milk | 0.29 | 0.43 | 0.92 |
| (0.04) | (0.03) | (0.06) | |
| Fat | 0.32 | 0.32 | 0.89 |
| (0.04) | (0.02) | (0.06) | |
| Protein | 0.24 | 0.38 | 0.91 |
| (0.03) | (0.03) | (0.07) |
As in the study of Kearney et al. (2004), heritability estimates tended to be higher in the high-input herds than in the low-input herds. Estimated genetic correlations between performance in the high-input herds and the low-input herds were 0.92, 0.89, and 0.91, respectively. These estimates were remarkably similar to esimates from Kearney et al. (2004) and Weigel et al. (1999) based on grazing and confinement herds in the United States. These results once again indicate that lactation performace is a genetically similar trait in high-input and low-input environments, such that sire rankings will be similar in both types of herds, despite the presence of a scaling effect in low-input herds.
The second issue, the ability of animals to reproduce efficiently (particularly in seasonal herds), can be addressed by looking at the relative weight given to reproductive performance in the sire selection index used by a particular country or breeding company. In the United States, routine national genetic evaluations for daughter pregnancy rate (DPR) were introduced in February 2003. The chosen trait, DPR, is computed from the days open records of each sire’s daughters. Days open data are collected within the milk recording system and subsequently transformed to DPR, which reflects the percentage of eligible, open cows that become pregnant during a given 21-day time period. Although a nonlinear relationship exists between these traits, a 1 percent change in DPR is roughly equivalent to four days open.
While it is obvious that environmental conditions and herd management practices have a strong impact on female fertility, large genetic differences exist as well. In fact, the standard deviation of DPR evaluations of currently available U.S. Holstein bulls is 1.1percent, indicating a range of more than 6 percent in DPR or 24 days open between daughters of the best and worst sires. Thus, appropriate selection tools exist for producers, such as rotational graziers with seasonal calving patterns, who wish to make female fertility a high priority. In the national U.S. breeding goal (i.e., the Lifetime Net Merit index), DPR is given a relative weight of 7 percent, which is quite similar to the emphasis on female fertility in other leading dairy countries.
Between-Country Differences in Herd Management
Dairy cattle selection programs are global in scope, and many breeding companies have operations in several countries on different continents. Management practices, climate conditions, governmental policies, and other factors may differ between countries. Furthermore, the genetic level (i.e., genetic base) of the dairy cow population in each country is different, as are things such as trait definition, units of measurement, and expression of results.
For more than a decade, the International Bull Evaluation Service (Interbull) has routinely gathered the results of national sire evaluations in more than two dozen leading dairy countries and combined these into international sire evaluations using the multiple-trait across-country evaluation (MACE) procedure. All bulls that have been progeny tested in a participating country are included, and genetic relationships between bulls in different countries are used. The result is not a single, “world” ranking list, but rather a set of PTA or estimated breeding values (EBV) that are expressed on the base, scale, and units of each participating country. As such, the genetic data of foreign bulls are “translated” to the same genetic base, units, and scale of measurement as that of domestic bulls, and bulls rank differently in each participating country.
Although Interbull has access to only the national sire PTA or EBV from each country, our research at the University of Wisconsin has focused on analysis of actual daughter records from participating countries. Because these “raw” data are free from any modifications or biases that may occur during the within-country genetic evaluation process, they make a more suitable database for investigating genotype by environment interactions between countries. Maltecca et al. (2004) estimated genetic correlations between first lactation milk yield in 13 countries that routinely participate in the Interbull dairy sire evaluation system. Production in each country was considered a different trait; results are in Table 4.
| AUS | BEL | CAN | CZE | DEU | FIN | HUN | IRL | ISR | ITA | NLD | NZL | USA | ZAF | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AUS | 0.85 | 0.83 | 0.86 | 0.85 | 0.87 | 0.76 | 0.92 | 0.79 | 0.82 | 0.82 | 0.98 | 0.84 | 0.83 | |
| BEL | 0.94 | 0.86 | 0.90 | 0.85 | 0.80 | 0.82 | 0.90 | 0.91 | 0.92 | 0.84 | 0.96 | 0.86 | ||
| CAN | 0.83 | 0.92 | 0.87 | 0.86 | 0.84 | 0.86 | 0.94 | 0.96 | 0.88 | 0.94 | 0.86 | |||
| CZE | 0.87 | 0.80 | 0.78 | 0.81 | 0.81 | 0.82 | 0.89 | 0.82 | 0.85 | 0.90 | ||||
| DEU | 0.82 | 0.78 | 0.83 | 0.85 | 0.92 | 0.92 | 0.90 | 0.93 | 0.85 | |||||
| FIN | 0.79 | 0.88 | 0.86 | 0.85 | 0.83 | 0.84 | 0.88 | 0.80 | ||||||
| HUN | 0.76 | 0.82 | 0.83 | 0.84 | 0.77 | 0.84 | 0.80 | |||||||
| IRL | 0.84 | 0.83 | 0.85 | 0.93 | 0.84 | 0.80 | ||||||||
| ISR | 0.84 | 0.83 | 0.83 | 0.89 | 0.79 | |||||||||
| ITA | 0.92 | 0.86 | 0.94 | 0.84 | ||||||||||
| NLD | 0.88 | 0.93 | 0.86 | |||||||||||
| NZL | 0.90 | 0.91 | ||||||||||||
| USA | 0.90 | |||||||||||||
| ZAF |
As shown above, estimated genetic correlations between certain pairs of countries were less than 0.80, such as: Australia and Hungary, Australia and Israel, Czech Republic and Hungary, Finland and Hungary, Germany and Hungary, Hungary and Ireland, Hungary and New Zealand, or Israel and South Africa. On the other hand, estimates between other pairs of countries exceeded 0.95, such as: Australia and New Zealand, Belgium and the United States, or Canada and the Netherlands. These estimates tend to reflect the similarity, or lack thereof, between the lactation performance of daughters of sires in the predominant management system(s) of each country.
Subsequently, Maltecca et al. (2004) used cluster analysis to group herds into production systems, without regard to country borders. Various descriptive variables, such as herd size, peak milk production, days to peak milk production, and average daily temperature, were used to describe the management circumstances in each herd, and herds were then grouped across countries into four different categories or production systems, as shown in Table 5.
| Country | Cluster 1 | Cluster 2 | Cluster 3 | Cluster 4 |
|---|---|---|---|---|
| AUS | 3281 | 201 | 129 | 6040 |
| BEL | 103 | 5 | 57 | 2863 |
| CAN | 4461 | 149 | 1167 | 8670 |
| CZE | 202 | 232 | 42 | 14,421 |
| DEU | 6020 | 1338 | 9942 | 48,502 |
| FIN | 407 | 6 | 661 | 13,102 |
| HUN | 164 | 546 | 87 | 557 |
| IRL | 202 | 62 | 221 | 5968 |
| ISR | 850 | 162 | 67 | 24 |
| ITA | 5609 | 935 | 700 | 10,340 |
| NLD | 1588 | 334 | 4574 | 19,844 |
| NZL | 1284 | 251 | 15,959 | 956 |
| USA | 19,682 | 1408 | 2334 | 19,054 |
| ZAF | 271 | 126 | 375 | 514 |
As shown above, Cluster 1 was composed primarily of medium-sized herds from Australia, Canada, Italy, and the United States. Cluster 2 included mainly herds from Germany, Hungary, Italy, and the United States, and this cluster was characterized by large herd size. Cluster 3 was characterized by low-peak yield and short days to peak yield, and it consisted largely of herds in Australia, Germany, and New Zealand. Cluster 4 included mostly small herds, the majority of which were from the United States, Canada, Germany, and the Netherlands. Genetic correlations between clusters ranged from 0.86 to 0.92. Sire EBV calculated using the borderless herd cluster model (Weigel and Rekaya, 2000; Zwald and Weigel, 2003) were shown to be superior, in terms of predictive ability in the importing country, to the methodology currently used by the Interbull Centre (Maltecca et al., 2004).
Implications for Sire Selection
Genotype by environment interaction between herds and/or countries is a legitimate concern in dairy cattle breeding programs, as is heterogeneity of breeding goals. For a given trait, such as protein yield, research shows that relatively little re-ranking of sires occurs between countries or between management systems within a country. In general, sire families that produce superior offspring in one system will tend to produce superior offspring in another. However, scaling effects do exist, such that the realized genetic gain in a less favorable environment (e.g., strict rotational grazing) is less than can be achieved in a more favorable environment (e.g., a total mixed ration). These scaling effects can, in turn, reduce the net profit realized per unit of semen in a less favorable environment.
Heterogeneity of breeding goals is perhaps more important, particularly among herds that seek a uniform, seasonal calving pattern. In such herds, greater emphasis should be placed on female fertility traits, and most leading dairy countries now routinely evaluate one or more measures of reproductive performance.
Overall, the same factors will lead to “genetic success” in any environment: intense sire selection, accurate breeding value estimation, and proper definition of the economic goal. Global sire selection is relatively easy, due to Interbull, and producers can easily select from the best genetics available in more than two dozen leading dairy countries.
References
Cromie, A. R., D. L. Kelleher, F. J. Gordon, and J. J. Rath. 1998. Genotype by environment interaction for milk production traits in Holstein Friesian dairy cattle in Ireland. Interbull Bull. No. 17, pp. 100-104.
Kearney, J. F., M. M. Schutz, P. J. Boettcher, and K. A. Weigel. 2004. Genotype by environment interaction for grazing versus confinement. I. Production traits. J. Dairy Sci. 87:501-509.
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.
Weigel, K. A., T. Kriegl, and A. L. Pohlman. 1999. Genetic analysis of dairy cattle production traits under management intensive grazing conditions. J. Dairy Sci. 82:191.
Weigel, K. A., and R. Rekaya. 2000. A multiple-trait herd cluster model for international dairy sire evaluation. J. Dairy Sci. 83:815.
Zwald, N. R., and K. A. Weigel. 2003. Application of a multiple-trait herd cluster model for genetic evaluation of dairy sires from seventeen countries. J. Dairy Sci. 86:376-382.
Author Information
Kent A. Weigel
University of Wisconsin