Duration: 10/01/2008 to 09/30/2013

Administrative Advisor(s):

NIFA Reps:

Non-Technical Summary

Statement of Issues and Justification

Over the past century, dairy cows have been largely bred and selected for milk production traits (milk yield, fat and protein content) and conformation. The application of genetic/selection tools to the US dairy population has resulted in tremendous genetic improvement in milk yield, an estimated change in genetic merit for milk production of 7,122 pounds per lactation in Holsteins from birth years 1957 through 2004 (http://aipl.arsusda.gov/eval/summary/trend.cfm). Similar genetic improvement has been documented for Ayrshire (4,372), Brown Swiss (5,546), Guernsey (6,011), Jersey (6,630) and Milking Shorthorn (4,253) breeds. The genetic improvement has been responsible for over 50% of the total improvement observed in milk yield. It is reasonable to assume that advanced management practices (i.e., housing, veterinary care, improved nutrient supply, feed mixing and delivery, etc.) are responsible for the remainder.
However, emphases on selection and management for higher yields have negatively affected reproductive and survival traits of different breeds in general and the Holstein breed in particular. Decline in conception rates, fertility and survival and incremental increase in number of services, number of days open and susceptibility to diseases, induce higher maintenance and management costs for the already struggling dairy farmers. Specific evidence shows an undesirable decrease in genetic merit for daughter pregnancy rate of from 2.8 to 7% for these dairy breeds and an increase of from .05 to .2 in somatic cell score. Average inbreeding in 2006 was 5.3% for US Holsteins and 7.1% for US Jerseys and has increased in the last 10 years at an annual rate of .14% and .20%, respectively in these breeds. Even though adjustments for inbreeding are considered in national animal model genetic evaluations (at least partially through previous efforts of this regional project), increases in average inbreeding for these breeds cannot continue without substantial inbreeding depression for cow fertility and health.

The current S-1008 project focuses on selection/crossbreeding as genetic avenues to improve dairy cattle performance. The proposed project will continue that focus but with emphasis on field data on reproduction, morbidity, mortality, somatic cell score, and body condition score to develop new selection/management tools and criteria to enhance reproduction and survival in dairy herds.

Dairy producers are turning to crossbreeding as a solution to health and fertility problems with purebreds and in response to increased market emphasis on milk components. The economic justification for crossbreeding has not been well established under modern management systems or with several breeds of cows that have experienced dramatic genetic change since crossbreeding trials of 30-40 years ago. Crossbreeding trials, currently underway in S-1008, are evaluating calving ease, heifer growth, reproduction, immune function, and reproduction, survival, health and lactation yields of lactating cows. These trials will provide the data to test the lifetime economic value of crossbred dairy cows against purebreds, primarily Holstein, under both research herd and commercial conditions and allow determination of the optimum combination of additive genetic merit and non-additive genetic merit in improving lifetime economic performance.

Multi-trait breeding goals and genetic indexes allow producers to combine traits of high economic importance into sound breeding practices. This is an evolving process as critical new traits become available for inclusion in indexes. Prior and current work of the S-1008 project has contributed significantly to the Lifetime Net Merit Dollar (LNM$) index, a measure of lifetime profit, published on US dairy cows and bulls by the Animal Improvement Programs Laboratory (AIPL) of USDA. Most recent enhancements of this index came from collaboration of AIPL and other S-1008 cooperators through revision of economic weights and credits for productive life and inclusion of genetic evaluations for stillbirths. More accurate measures of overall genetic merit will be developed and released to dairy cattle breeders, and incomes and expenses associated with new traits will be estimated. Changes in economic values of existing traits will be monitored to reexamine the hypothesis that current selection policies should continue. Future research will identify new/indicator traits that should be added to national selection indexes.
Of particular interest will be performance and economics of crossbred animals in regard to fitness and health traits. Resulting indexes will enable producers and industry personnel to make intelligent use of genetic evaluations on an expanding array of traits and large numbers of bulls and cows available for selection. A major objective of continued regional research collaboration is to give more detailed definition of lifetime economic performance of dairy cattle and ways to use the indexes to bring about change in the traits encompassed by it.

Today's commercial dairy farmers are seeking to improve the health/fertility/survival of their cattle through genetic selection. Systems for genetic evaluation of length of productive life, daughter pregnancy rate, somatic cell score, and direct and maternal components of calving ease and stillbirths have been implemented, and development of systems for genetic evaluation of resistance to specific infectious diseases and metabolic disorders is underway. As such, within-breed selection for health/fertility/survival is now relatively straightforward. However, development of systems for across-breed selection is lagging due to lack of information regarding breed means and heterosis parameters for many key health and fertility traits, particularly for European dairy breeds. The adoption of crossbreeding at an increasing rate apparently seeks to improve these health and fitness traits. At present, selection indices provided to US dairy farms are based on relative net income over opportunity costs (RNIOC), which reflect net lifetime income per animal. Indices based on economic efficiency, which reflects net return per dollar invested, may be more appropriate for genetic improvement programs that involve a combination of purebred selection and crossbreeding. The tools developed within the proposed project will allow objective, profit-based comparisons of alternative purebred selection (with attention to avoidance of inbreeding) and crossbreeding programs or combinations thereof. As a result of the proposed project, farmers, extension agents, and breeding advisors will be able to make informed decisions regarding the expected short and long-term performance, risk and net profit of their genetic programs from the protocols recommended.

A search of the CRIS system found only selection and crossbreeding projects involving dairy cattle that were already a part of S-1008. Two multi-state projects were identified that have a reproduction component: NC-1038: FY 07-12 Methods to Increase Reproductive Efficiency in Cattle; S-1023: FY 05-10 Enhancing Production and Reproduction Performance of Heat-Stressed Cattle. Collaboration with NC-1038 is planned as described in the methods of the proposed project for Objective 1. Genetic and environmental factors influencing death rates and sub-optimal reproduction. The genetic element of a project on mastitis resistance NE-1009 Mastitis Resistance to Enhance Dairy Food Safety focuses on the possible genomic identification of genetic variation with potential genetic associations with mastitis and related traits.

Major Accomplishments of S-1008:
The S-1008 research group was extremely productive during the most recent 5-year period, with 102 peer-reviewed publications (an average of 7.3 per station) that were directly related to one or more of the project objectives. Many of these were the result of collaborative projects involving multiple stations or agencies, while the remainders were complementary projects that were carried out at individual stations but supported the common objectives of our regional project. It is important to recognize the close collaborations between university researchers in the S-1008 group and government laboratories, breed associations, milk recording agencies, and breeding companies, as these relationships were critical in ensuring implementation and practical application of the research carried out in the present project. Accomplishments corresponding to each of the three project objectives are described below.

Objective 1: Develop selection tools to enhance reproduction and survival using field data.

Our work in Objective 1 focused on three key topics: development of selection tools for improvement of fertility and calving performance, refinement of selection tools for improvement of dairy cow longevity, and investigation of opportunities for genetic evaluation of clinical mastitis and other early postpartum diseases and disorders.

The decline in fertility of lactating dairy cows over the past forty years has been extremely costly to US farmers. Female fertility is a complex trait that is influenced by many management and environmental factors, but despite low heritability there exists significant genetic variation between sire families. VanRaden et al. (2004) described the implementation of a national genetic evaluation system for daughter pregnancy rate, a measure of female fertility. This development allowed US dairy farmers, for the first time, to select AI bulls proven to transmit genes leading to enhanced reproductive performance. Refinement of this process continues at the present time.

Recording, editing, and analysis of fertility data poses many unique challenges, as noted by Weigel (2004), who discussed the opportunities and pitfalls associated with selection for improved male and female fertility. Oseni et al. (2003, 2004a) evaluated seasonal differences in days open in US Holsteins and examined the influence of data editing criteria on genetic parameter estimates for days open and pregnancy rate. Differential utilization of reproductive management tools between farms is a key challenge, and Goodling et al. (2005) assessed the impact of hormonal synchronization programs on genetic parameters for female fertility. Caraviello et al. (2006a, 2006b) characterized the reproductive management practices used on large, modern commercial dairy farms and sought to identify management and environmental factors that were associated with poor reproductive performance using machine learning algorithms. The binary nature of fertility data, as well as the censoring of fertility records for cows that have not achieved pregnancy, create additional challenges in genetic evaluation of reproductive traits. Averill et al. (2004, 2006) developed methodology for genetic evaluation of male and female fertility for traits that are measured as a series of binary responses (e.g., success or failure in each of a series of insemination events). Gonzalez-Recio et al. (2005, 2006) assessed the impact of censoring of records. Chang et al. (2007) developed an ordinal censored threshold model in which pregnancy status was assessed in each of a series of 21-day opportunity periods, commencing at the end of the voluntary waiting period (which was also estimated from the data).

Calving difficulties (also known as dystocia events) and stillbirths lead to considerable economic losses on US dairy farms, and these traits are particularly problematic in the Holstein breed. Wiggans et al. (2003) and Van Tassell et al. (2003) described estimation of genetic parameters and implementation of a national genetic evaluation system for calving ease that allowed selection of Holstein sires for both the direct and maternal components of dystocia. Johanson and Berger (2003) assessed the potential of birth weight as a predictor of dystocia and stillbirths. Cole et al. (2007a, 2007b) described estimation of genetic parameters for direct and maternal stillbirth rate and implementation of national genetic evaluation system that allowed US dairy farmers identify sire families that transmit superior calving ability.
Genetic evaluations for length of productive life have been available since 1994 but, like female fertility, recording and analysis of productive life data poses many unique challenges. Caraviello et al. (2004a, 2004b) investigated the potential for improving genetic evaluations for dairy cow longevity through the use of Weibull proportional hazards models, which can accommodate time-dependent covariates and censored longevity records of cows that are still alive at the time of evaluation. Weigel et al. (2003) investigated factors associated with culling in expanding dairy herds in Wisconsin, and Hare et al. (2006a, 2006b) examined trends in dairy cow survival on US farms, as well as trends in age at first calving and calving interval. Norman et al. (2007a) studied the influence of milk production and fitness traits on the likelihood of culling of Holstein cows during the first three lactations.

Management practices and culling policies can change over time, and Tsuruta et al. (2004) noted that estimated genetic correlations between milk yield, body size, udder traits, and productive life of Holsteins had changed over time. Tsuruta et al. (2005) also noted that changes in the definition of productive life, in particular restrictions on length of the lactation, could lead to differences in estimated correlation parameters. Subsequently, VanRaden et al. (2006) and Dematawewa et al. (2007) described procedures for modeling extended lactation records and incorporating months in milk beyond 305 days postpartum into national genetic evaluations for length of productive life.

Genetic evaluations for lactation average somatic cell score, an indicator of mastitis susceptibility, have also been available since 1994. Additional improvement could come from direct selection for resistance to clinical mastitis, which is typically recorded in a binary manner. Nash et al. (2003) estimated genetic parameters for clinical mastitis and assessed relationships with other traits, including somatic cell score, udder conformation, productive life, and protein yield. Zwald et al. (2006) used a multiple-trait threshold model to estimate genetic correlations between liability to clinical mastitis in different lactations and at differing times within the lactation.
Additional opportunities may exist for recording and genetic evaluation of other health, fertility, and fitness traits that contribute to overall farm profitability. Norberg et al. (2004) estimated genetic parameters for electrical conductivity of test-day milk samples and also assessed the usefulness of electrical conductivity as an indicator of mastitis susceptibility (Norberg et al. (2006).

Zwald et al. (2004a, 2004b) documented the possibilities for genetic evaluation of susceptibility to key health disorders, including mastitis, ketosis, lameness, displaced abomasums, and metritis, using farmer-recorded incidence data from on-farm herd management software programs. Differences in fitness may be associated with genetic variation in production-related traits such as lactation persistency (Cole and Van Raden, 2006; Appuhamy et al., 2007), maturity rate (Norman et al., 2005, 2007b), or milking speed (Zwald et al., 2005, Wiggans et al., 2007) and on which genetic evaluations may be developed. In addition, Gonda et al. (2006) reported differences in susceptibility to Mycobacterium avium ssp. paratuberculosis (i.e., Johnes disease) between sire families within the Holstein breed.

Objective 2: Explore the impact of crossbreeding on lifetime performance of cows.
Dairy cattle breeding programs have traditionally focused on within-breed selection, which can lead to considerable improvement of fertility, calving ability, length of productive life, and other fitness traits over time. However, interest in dairy crossbreeding, which can provide more rapid improvement in such traits, has increased significantly in recent years. As such, our work in Objective 2 focused on evaluation of the performance of crossbred cattle on commercial farms, as well as the creation of crossbred populations in experimental herds.

Using data from commercial dairy herds in California, Heins et al. (2006a, 2006b, 2006c) evaluated fertility, survival, milk production, calving difficulty, and stillbirth rate in Holstein cows, as compared with Normande x Holstein, Montbeliarde x Holstein, and Scandinavian Red x Holstein cows. More recently, Dechow et al. (2007) assessed the lactation performance, udder health, and fertility of Holsteins, Brown Swiss, and Holstein x Brown Swiss crosses on commercial farms. This work culminated in the implementation of a national, multi-breed genetic evaluation system for genetic evaluation of dairy cattle, as described by VanRaden et al. (2007). This system will allow US dairy farmers to practice both within- and across-breed selection for key production and fitness traits and will allow the creation of optimal dairy crossbreeding programs.

In addition to analyses of field data, several universities initiated the development of crossbred resource populations in which novel phenotypes, such as detailed measures of health, fertility, milk composition, and feed efficiency can be assessed. These projects, at the University of Kentucky, the University of Minnesota, Virginia Tech, North Carolina State University and the University of Wisconsin, are ongoing and will be featured in numerous publications in the coming years. Preliminary results from several studies have been published, including an assessment by Kasimanickam et al. (2007) of factors in dairy sire semen that are associated with differences in fertility among Holstein and Jersey sires in the Virginia  Kentucky crossbreeding project, as well as an assessment of differences in conception rate, calving performance, and calf health and survival in Holstein x (Holstein x Jersey) crossbred calves, relative to their pure Holstein contemporaries, in the Wisconsin project by Maltecca et al. (2006).

The Virginia-Kentucky-North Carolina State University project is a major collaborative regional effort which has ties to the Minnesota and Wisconsin projects. Joint analyses and publication of results are planned.

Objective 3: Develop breeding goals and appropriate indexes for optimum improvement of health, survival, reproduction, and production.
Implementation of national genetic evaluation systems for fertility, calving ease, stillbirth rate, somatic cell score, and productive life, as well as implementation of a multi-breed genetic evaluation system, are critical steps in creating significant and permanent improvement in the fitness of US dairy cattle. However, such tools are of little practical value unless they can be incorporated into properly conceived economic indices, and selection programs based on these indices must also consider maintenance of genetic diversity. Furthermore, investigation of the manner in which the expression or economic value of such traits differs between herd management systems is necessary to ensure that genetic and management improvements go hand-in-hand. Work on these and related topics comprised our contributions in Objective 3, as noted below.
VanRaden (2004) reviewed development of the USDA-ARS Animal Improvement Programs Laboratorys Lifetime Net Merit index, which is the primary total merit index used by US dairy farmers. A key development in the formulation of economic indices in the past 5 years has been revision of our views regarding selection for dairy form. Dechow et al. (2002, 2004a) noted that body condition scores were associated with differences in milk yield and reproductive performance. Dechow et al. (2004b) computed genetic correlations between body condition score, dairy form scores, and health traits using data from two countries, whereas Dechow et al. (2003, 2004b) assessed correlations between body condition score, dairy form, and other conformation traits and estimated genetic parameters for body condition score and dairy form at various ages and stages of the lactation.

Heat stress leads to significant economic losses on US dairy farms, particularly in the Southeast. Ravagnolo and Misztal (2002a, 2002b) examined the impact of heat stress on non-return rate in Holstein cattle using temperature-humidity index data from nearby weather stations. Later, Oseni et al. (2004) estimated genetic parameters for days open in models that allowed differential losses in performance between sire families due to heat stress. More recently, Bohmanova et al. (2007) investigated possibilities for using temperature-humidity index data to account for differential losses in milk yield between sire families due to heat stress. Similarly, genotype by environment interactions between confinement and pasture-based systems could lead to selection of certain sire families for performance in specific environments, a possibility that was investigated by Boettcher et al. (2003), Kearney et al. (2004a, 2004b), and Fahey et al. (2007).

Lastly, several recent studies addressed inbreeding depression and maintenance of genetic diversity. Cassell et al. (2003a) quantified the importance of complete pedigree information when evaluating the impact of inbreeding on dairy cow performance, whereas Cassell et al. (2003b) measured the impact of maternal and fetal inbreeding depression on female fertility traits. Caraviello et al. (2003) examined the impact of inbreeding on dairy cow longevity in Jersey cattle using a proportional hazards model. Vallejo et al. (2003) assessed genetic diversity and linkage disequlibrium in US Holstein cattle. More recently, Adamec et al. (2006) assessed the impact of inbreeding on dystocia and stillbirth rate, whereas VanRaden and Miller (2006) investigated the impact of dominance, inbreeding, and inherited defects on embryonic loss in dairy cattle. Gulisija et al. (2007) examined the impact of inbreeding on the performance of Jersey cows using nonparametric methods.

Summary:
In summary, the last 5 years marked a watershed period with respect to the use of genetic selection and crossbreeding to improve the health, fertility, and survival of US dairy cattle. Work of this committee contributed directly to the development and implementation of a multi-breed genetic evaluation system that can be used by US dairy farmers who wish to improve fitness traits through crossbreeding. Likewise, the work of this committee contributed directly to the development and implementation of national genetic evaluation systems for daughter pregnancy rate, maternal calving ease, and direct and maternal stillbirth rate, as well as a significant improvement in the genetic evaluation system for length of productive life. Furthermore, major modifications to Lifetime Net Merit, the economic index use by most US dairy farmers for routine sire selection decisions, have their roots in the work of this research group. Based on this work, US dairy farmers now have a vast array of genetic tools at their disposal for improving the health, fertility, and survival of their cattle.

Related, Current and Previous Work

Selection tools for reproduction and survival:
A dramatic increase in cow death rates (7% - Tennessee Dairy Herd improvement Association (DHIA), 10% -Florida DHIA, 8.1 % - New York, Stone et al., 2006) and an increase in cows sold for disease and injury has taken place primarily over the last decade. Diseases other than reproductive diseases or mastitis are probably primarily related to metabolism or infection. Recent work by Dechow et al. (2004a ) has shown that high dairy form and low body condition have an undesirable relationship to disease incidence. This work suggests that metabolic diseases are an important part of the disease complex in modern dairy cattle because dairy form and body condition score reflect energy storage. Further research in this area may elucidate genetic options for reducing disease and death rates.

High death loss is the most costly dairy production problem faced by many dairy producers. Many herd and management risk factors for cow death are currently unknown. Lower mortality rates in higher producing compared to lower producing herds has been reported (Smith, et al. 2000, Young (2002). Beyond management differences, it is less certain whether genetic differences among cows predispose some to premature death. Daughter groups may differ in death rates. If so, bulls could potentially be summarized for daughter death rates and then used to improve disease resistance and cow survival. Also, current genetic evaluations like productive life; daughter calving ease and dairy form may be useful predictors of death rates in daughter groups.

Milk, protein, and fat yield are economically important traits that have been improved by selection in dairy populations (USDA-AIPL, http://aipl.arsusda.gov/index.htm) over the past few decades. But, in US populations, we have had limited selection emphasis directed to improving health/reproduction (Interbull, http://www-interbull.slu.se/). In addition, the genetic antagonism between milk yield and health traits (Mäntysaari et al., 1991; Simianer et al., 1991) and also between milk yield/reproduction has resulted in a decline in performance in most fitness traits. Some selection tools like linear type traits associated with the udder and somatic cell scores have helped to improve udder health (Rogers, 1994; Shook, 2006) and some relatively new tools like daughter pregnancy rate, maternal calving ease and productive life hold promise for improving other fitness traits in our dairy populations. However, metabolic health and general disease resistance may not be improved quickly or easily by our current selection procedures and many dairy producers believe that this decline in metabolic health has led to increased death losses.

Direct selection for cow health and favorable daughter disease resistance occurs in Scandinavian countries with extensive mandatory national disease recording systems (Heringstad et al., 2003; Heringstad et al., 2007; Philipsson, and Lindhé. 2003). US dairy producers currently cannot select bulls based on direct disease data due to lack of organized and standardized recording of diseases. However, cow health data can and is being recorded with on-farm herd management software and can now be used to generate genetic evaluations for cow health (Zwald et al., 2004a). Selection to reduce metabolic disorders might also be enhanced by incorporating energy balance indicators into selection programs (Coffey et al., 2003). Automated technology, which records daily milk yield, bodyweight and body condition scores, could enable selection for body energy traits.

Bovine somatotropin (bST) has long been recognized for increasing milk yield in lactating cows. Since 1994, a marketed form of bST has been used in the dairy industry. Several research projects have studied the effect of bST on welfare of treated cows, including whether an increase in production would be accompanied by an increase of occurrence of diseases, udder problems, mastitis, reproductive problems (Moallem et al., 1997, Cole et al., 1992) or increased culling risk (Chilliard et al., 1998, Dohoo et al. 1998) and reduced body condition scores (Moallem, et al. 2000). In a research performed on Jersey cows, Eppard et al. (1996) noticed fewer clinical mastitis occurrences in treated subjects, no difference in milk fever incidence and prolonged, but no occurrence, of ketosis. Moreira et al. (2000) concluded that pre-synchronized treated cows had higher first-service pregnancy rates with timed artificial insemination. Cole et al. (1992) also reported more frequent abortion with cows treated at high doses of bST, lower conception rates in first year lactating cows and higher clinical mastitis occurrences. The Univ. of Nebraska-Lincoln, using data are collected from commercial Holstein dairy farms across the US, has an ongoing project to estimate the effect of bST on culling rates due to the diseases previously mentioned as well as on the rates of calving difficulty.

Crossbreeding and components of lifetime performance:
A recent survey (Weigel and Barlass, 2003) indicated dairy farmers currently practicing crossbreeding did so to improve fertility, calving ease, longevity, and milk components. However, a question remains Will favorable heterosis and breed additive genetic merit for these traits overcome the advantage of the Holstein breed in yield per lactation under prevalent US production systems? Results from trials currently underway in research herds will help answer this question. Data from commercial herds are growing in quantity and quality and will complement results from research herds and enable comparisons of breeds and crosses not included in research herds.

The practice of crossbreeding in dairy cattle is increasing in the US as evidenced by the flow of production records into the national genetic evaluation system. VanRaden, et al. (2006) reported sire identified F1 cows at 1.3% of first parity records submitted for genetic evaluations in 2005 along with 0.3% from records of third breed crosses or backcrosses. Producers may view sire identification as more useful in crossbreds now that genetic evaluations for them are being calculated.

The most important questions pertaining to crossbreeding require evaluation of entire herds and management systems over time. Lopez-Villalobos, et al. (2000) compared profitability of mating systems under New Zealand conditions in a deterministic model. Crossbred herds were superior to purebred herds under the economic assumptions made. Heterosis for survival has important implications to the relative economic merit of purebred versus crossbred systems. New Zealand conditions may affect the validity of these findings to US conditions, but the methodology employed would be applicable to work of this sort in the US.
McAllister (2002) reported evidence in the North American literature for direct, maternal, heterosis and cytoplasmic maternal effects and a finding of 15 to 20% heterosis for lifetime traits in a Canadian study. Important heterosis for lifetime traits would be expected based on findings of substantial inbreeding depression for several lifetime performance traits including economic merit by Smith et al. (1998). McAllister noted a shortage of information on multiple-generation lifetime performance on a variety of purebreds and crossbreds under US conditions which is particularly relevant given the interest in European breeds (Scandinavian Red, Montebeliarde, and Normande) in crossbreeding and the paucity of data on current Ayrshire, Brown Swiss and Jersey pure breeds and crossbreds.

VanRaden and Sanders (2003) reported heterosis (3-4% for single lactation yield, 1.2% for productive life) and breed differences between purebred and crossbred cows from US dairy breeds. Specific heterosis was largest for Holstein sires used on Jersey dams. The authors reported that Jersey X Holsteins and Brown Swiss X Holstein crosses were predicted to have higher Net Merit $ values than average Holsteins. However, selection intensity favored Holstein sires due to a larger population. The Net Merit index used then did not include fertility, calving ease or calf mortality. Thus, any advantages of crossbred animals relative to purebreds in those traits were not considered in the results.

Research results from recent US studies comparing crossbred to purebred animals are available on traits expressed early in life. Cassell, et al. (2005) compared purebred Holsteins and Jerseys to reciprocal crosses of those two breeds in a controlled, diallel crossing scheme at university herds in Virginia and Kentucky. Births weights were highest for purebred Holsteins (HH, 38.5 kg), intermediate for crossbred calves born to Holsteins dams than for crossbred calves born to Jersey dams in the reciprocal crosses (JH 31.3 versus HJ 29.4 kg) and lowest for purebred Jersey calves (JJ, 22.4 kg). The same bulls were sires of purebred and crossbred animals in this trial. Dystocia scores were significantly greater for HH and HJ versus JH and JJ calves. Mortality did not differ by breed group of calf. This preliminary study found no significant heterosis for birth weight, dystocia, or stillbirths, but significant additive and maternal effects for all three traits. Maternal effects showed lower dystocia scores and reduced mortality for calves born to purebred Holstein dams compared to purebred Jersey dams.

Heins, et al (2006b) reported dystocia and stillbirth results from crossbreeding Holstein dams to Normande, Montbeliarde, and Scandinavian Red bulls in seven commercial herds in California. Calves out of Holstein dams and sired by Scandinavian Red and Brown Swiss bulls experienced considerably less calving difficulty (5.5% and 12.5%) than calves sired by Holstein bulls (16.4% difficult births). Stillbirths were much lower for calves sired by Scandinavian Red bulls with 7.7% stillbirths versus 15.1% for calves sired by Holstein bulls. Breed of dam effects were also examined in this study using Brown Swiss, Montbeliarde and Scandinavian Red bulls on purebred Holsteins and crossbred dams. All breed combinations of crossbred cows had significantly less calving difficulty than Holstein dams, with 3.7 to 11.6% difficult births versus 17.7% for Holstein dams. Results showed that calving difficulty and stillbirths could be reduced by using other than Holstein bulls on Holstein dams and that resulting crossbred dams would have less calving problems than purebred Holsteins.

Heins, et al. (2006a) compared 305-d actual first lactation production of cows calving in the previously referenced study. Pure Holsteins were superior to all of the crossbred groups for milk production. However, Holsteins and Scandinavian Red/Holsteins crosses were not significantly different for fat production. For fat plus protein Holsteins (651 kg) were significantly higher than Normande/Holstein (596 kg) and Montbeliarde/Holstein (627 kg). Pure Holsteins and Scandinavian Red/Holstein crosses (637 kg) did not differ in fat plus protein. Yields were not adjusted for days open in the current lactation.
Williams et al. (2006) reported on postpartum cyclicity based on milk progesterone for 46 Holsteins, 50 Jerseys, and 54 crossbred cows of various combinations of those breeds (average = 54.4% Holstein) in a pasture-based system. Jerseys and crossbred cows were similar but Holstein cows had lower percentages of cows cyclic at 30 d , 60 d and 90 d postpartum. By 90 d into a seasonal breeding program, only 59% of Holsteins had conceived compared to 84% of Jerseys and 84% of crossbred cows.

The interest in crossbreeding and increasing prevalence of data from crossbred animals for genetic evaluations suggest a need to include crossbred animals in genetic evaluations. VanRaden and Tooker (2006) reported new programs to include crossbred animals in routine genetic evaluations in the US which have been implemented (VanRaden, et al. 2007). Genetic differences among breeds were similar to previous estimates not including crossbreds and correlations between proofs for high reliability bulls were very high (>0.999). Advantages of including crossbreds in genetic evaluations include genetic estimates for crossbred cows themselves, increased contemporary group size, additional relatives for purebreds, and estimates of breed differences. Implementation of this system testifies to the increased importance of crossbreeding to the dairy industry in the US.

Selection indexes and the inclusion of fertility, fitness and other management traits:
Selection indexes for dairy cattle have been updated routinely and expanded over time to include more traits (Shook, 2006; VanRaden, 2004). From the first U.S. national index in 1971 with only milk and fat yields, protein yield was added in 1977. The net merit index, introduced in 1994, included productive life to measure longevity and somatic cell score to indicate mastitis resistance. Multi-State project S-284 developed a revised net merit index in 2000 that added direct emphasis on conformation traits. Daughter pregnancy rate as a measure of cow fertility and also calving ease were added in 2003. In 2006, net merit was revised again to include stillbirth rate (Cole et al., 2006) and a new measure of productive life (VanRaden et al., 2006). Economic values of other traits in the index were updated at each revision. Pearson and Miller (1981) anticipated many of these changes.

Traits with low heritability but high economic value such as fertility, calving traits, and disease resistance received early emphasis in Scandinavia (Philipsson and Lindhe, 2003) and now increasing attention in other dairy nations. The increasing number of traits and number of potential sources of genetic material has created a growing need for genetic evaluations and scientific and computer-based approaches for selection and mating of dairy animals. By considering a range of economic or environmental conditions (Kearney et al., 2004a; Weigel et al., 1997; Zwald et al., 2003), multiple genetic rankings can be provided to better match the needs of individual producers.

Fertility traits are evaluated in many nations, but definitions and methods vary greatly. With global breeding programs, traits and trait definitions must become more standard. While daughter pregnancy rate as an evaluation of female fertility has been adopted in U.S. selection programs, U.S. exports could increase if U.S. fertility evaluations more closely matched those provided in other countries. Male fertility genetic evaluations were first computed by DRMS in 1986 (Clay and McDaniel, 2001) and since 2006 by AIPL. The emphasis given to male fertility compared to female fertility and other trait evaluations when selecting semen for artificial-insemination use has not been documented. Rogers (1990) indicated that differences in fertility greatly affect the value of semen purchases.

Health and management traits increase in importance as wages rise but milk prices remain low. Differences in expenses of cows selected for high milk yield vs. control cows have increased over time (Hansen, 2000). Several health traits have sufficient incidence and heritability to deserve inclusion in selection programs (Zwald et al., 2004b).

Economic measures such as return on investment, discounted net present value, adjustment for opportunity cost, and annual income divided by annual cost can provide more accurate rankings than lifetime profit, both for within-breed comparisons (Cassell et al., 2001) and across-breed comparisons (Shook and Combs, 2003). Early U.S. indexes focused on gross income, whereas more recent indexes featured net income and lifetime profit. Future indexes will focus on measures of efficiency so that breeders will obtain a high return from every dollar they invest in genetic improvement.

Development of selection and crossbreeding protocols:
Estimates of breed differences and heterosis for milk, fat, protein, productive life, somatic cell score, and daughter pregnancy rate for the traditional US dairy breeds have been computed by AIPL using DHI data from mixed herds and crossbred cows (VanRaden and Sanders, 2003; VanRaden and Tooker, 2006). AIPL has also computed breed differences for Holsteins, Brown Swiss, and Jerseys for calving ease (Cole et al., 2005, Van Raden, et al. 2007). Minnesota has provided estimates of the mean performance of Holsteins and first-generation crosses of Holsteins with Normande, Montbeliarde, and Scandinavian Red crosses in commercial herds in California for milk, fat, protein, survival, calving ease, stillbirths, and fertility (Heins et al., 2006a; Heins et al., 2006b, Heins et al., 2006c). In addition, Minnesota has provided estimates of breed differences for Holsteins, Jerseys, and first-generation Holstein x Jersey crosses for yield, survival, and fertility in experimental herds that utilize both confinement and rotational grazing. Wisconsin and Virginia Tech have provided estimates of breed differences for Holstein and Holstein-Jersey backcrosses and reciprocal Holstein-Jersey crosses, respectively, for growth rates of calves and heifers.

Information regarding breed differences and heterosis parameters is needed for all traits comprising Lifetime Net Merit. In addition, information is needed regarding rearing costs, age at first calving, maturity rate, salvage value, bull calf value, maintenance costs, and male fertility. As such, a key step in our collaborative work under this objective will be to ascertain estimates of these parameters through on-campus experiments, analysis of field data, and review of the literature.

Given estimates of breed differences and heterosis, tools such as deterministic or stochastic simulation can be used to project the yield, fertility, health, and survival of offspring in the first and later generations. Alternative milk pricing systems (e.g., fluid market, cheese yield market) must be considered, as well as alternative herd management strategies and environmental stressors (e.g., rotational grazing, organic farming, heat stress). Risk must be considered as well, because knowledge regarding breed differences, particularly European and Scandinavian, and heterosis parameters for many traits is limited. Furthermore, economic parameters such as milk prices, component differentials, somatic cell count premiums, and salvage prices may change over time. Programs such as @RISK or CRYSTAL BALL can account for variation in response due to lack of precision in key input parameters, and as such these programs should be considered in the present study. This work will lead to set of guidelines for purebred and crossbred selection, most likely in the form of a web-based tool or Excel spreadsheet that can accurately predict the outcome of alternative breeding programs. These alternatives will consider purebred selection (including methods for inbreeding avoidance), 2-breed rotations, 3-breed rotations, 4-breed rotations, and backcrosses involving the traditional US breeds (Ayrshire, Brown Swiss, Guernsey, Holstein, Jersey, and Milking Shorthorn) and key European breeds (Danish Red, Finnish Ayrshire, Montbeliarde, Normande, Norwegian Red, and Swedish Red).

Objectives

1. Develop selection tools to enhance reproduction and survival using field data
   
2. Evaluate the biological and economic impact of crossbreeding on lifetime performance of dairy cattle
   
3. Develop breeding goals and appropriate indexes for optimum biological and economic improvement of health, reproduction, survival, and production of dairy cattle [BREEDING GOALS AND INDEXES]
   
4. Develop and recommend selection and crossbreeding protocols of optimum economic utility for adoption by US dairy farmers
   

Methods

Objective 1: Develop selection tools to enhance reproduction and survival using field data. (AIPL, GA, IA, IL, IN, MN, NC, NE, PA, WI) Reproduction, Fertility and Health Measurements of various aspects of reproduction and fertility will be undertaken in related studies at seven participating stations of the project (AIPL, GA, IA, IN, MN, NC, WI) to examine their potential use as selection tools. AIPL plans a major revision of the female fertility evaluation during the next 5 years. Some refinements of the existing stillbirth genetic evaluation model may be needed. Research on gestation length and abortions, and the potential to use secondary termination codes will be pursued. Beginning with days open, more complete cow fertility data has been collected, which should allow separate analysis of these traits. Use of all services could increase the reliability of male fertility evaluations while additional traits or inclusion of more sources of data could lead to improvements in female fertility evaluations. Collaborative efforts with other stations gathering and analyzing fertility data will likely contribute to these enhanced genetic evaluations for fertility. GA (University of Georgia) plans to refine the analysis of service records to allow inclusion of censored data. Censoring considered will be of at least three types: due to incomplete data recording, due to sickness, and due to lack of opportunity. IA (Iowa State University) will characterize incidences and trends for reproductive events in the high and average selection lines from 1968 to present. Random regression techniques will be used to study growth and development of heifers and cows in the selection project. Similarly, MN (University of Minnesota) will work closely with approximately 12 cooperating dairies in Minnesota. All data for reproduction, morbidity, and mortality, as well as health treatments will be recorded by animal (heifers and cows) with a uniform software program across the 12 cooperating dairies. The data for these traits should provide valuable information to develop selection tools within breeds. WI (UW-Madison) will continue its project on genetic analysis of health and fertility data from commercial farms. They will continue to build a database of mastitis, ketosis, metritis, displaced abomasums and lameness events using data from on-farm software programs. Their intent is to update the database and predict sire breeding values for the aforementioned traits, as well as gestation length and twinning rate, such that these can be used in association studies with genotypes of Holstein bulls that will be generated in the USDA-ARS Bovine Functional Genomics Laboratory single nucleotide polymorphism (SNP) project. IN (Purdue University) will examine the impact of estrus synchronization protocols on oocyte quality, and sire effects will be considered. Data will be obtained from herds utilizing Heatwatch estrus monitoring system and participating in production recording to determine genetic control of components of estrus expression. NC (NC State University) will conclude examination of determining if maternal lineages for up to 5 generations could be selected for superior fertility and have genetic influence through the selection of bull dams. Inbreeding and Survival IL (University of Illinois) will publish work from its collaborative work with Iowa on bimodal distributions of progeny survival, impacts of selection and inbreeding on survival, and prepotency of inbred AI bulls. NE (UN-Lincoln) plans for next year involve investigating the effect of inbreeding on reproduction traits. Additional research on the effect of bST administration on culling, SCS and days open will also be investigated. Body Measurements, Health and Production IN (Purdue University) plans to explore automatically collected reticular temperatures of cows as a potential indicator of general events of health, morbidity or estrus. Work continues on assessment of automatically collected body condition scores of dairy cattle. Economic cost and benefit analysis of these intervention technologies will be assessed and potential for evaluation as genetic traits will be considered. In a complementary study PA (Penn State University) will analyze daily body weight and milk production using random regression models. Disease incidence will be regressed on genetic evaluations for body weight to approximate genetic correlations between disease and daily body weight change. Mastitis will be analyzed over test day intervals and in multiple trait models with yield and SCS. Dairy herd survey results will be combined with culling and pedigree data from DRMS to determine what herd management, environmental and genetic factors place cows at risk of dying on Pennsylvania dairy farms. Objective 2: Evaluate the biological and economic impact of crossbreeding on lifetime performance of dairy cattle (AIPL, FL, KY, MN, NC, PA, TN, VA, WI) Two major collaborative efforts will take place in this objective: 1) KY (Univ. of Kentucky), VA (Virginia Tech), and NC will merge data from their common experimental design and use of sires to evaluate purebreds and crossbreds for economic measures of lifetime performance enabling contrasts of lifetime production, reproduction, health, and survival and estimation of additive, maternal, and heterosis effects for all lifetime traits. KY and VA have entered cooperative research agreements with Holstein and Jersey breed societies to score crossbred animals jointly with classification of purebreds. Holstein appraisers will evaluate Holsteins and crossbreds while Jersey appraisers will evaluate Jerseys and crossbreds. Scores on both systems for crossbred animals will enable system comparisons for physical traits defined similarly for Holsteins and Jerseys. 2) KY, MN, NC, VA, and WI will combine performance data on purebred Holsteins, Jerseys, and crosses to evaluate performance for all traits common to the three trials (i.e. KY, VA, NC; MN; WI) involved. Candidate traits include gestation lengths, birth characteristics, age at puberty, age at first freshening, reproductive performance, production, and somatic cell counts by lactation and lifetime characteristics. These stations will also be collecting blood samples for potential future use in molecular approaches to examining traits of interest among Jerseys, Holsteins, and various crosses among those two breeds. AIPL introduced an all-breed evaluation system in May, 2007 in which all-breed evaluations for yield, productive life, somatic cell score and daughter pregnancy rate were converted to traditional within-breed genetic bases. Inclusion of lifetime net merit in all-breed evaluations, will require research to insure that main traits differing across breeds are well estimated and properly credited. Cooperation with other stations will be essential to verify national assumptions or investigate other traits and data sources. Investigation of specific breed combination heterosis and recombination losses are planned to improve utilization of crossbreeding for commercial milk production. MN plans to continue collection of data from seven California dairies with crosses of Holsteins with Montbeliarde, Normande, Scandinavian Reds, and Brown Swiss for final analysis of production, reproduction, and survival. California costs and returns will be used to compare pure Holsteins to crosses. Comparison of pure Holsteins with three-breed crosses of Montbeliarde/(Jersey/Holstein) and Jersey/(Montbeliarde/Holstein) will continue in two MN research herds for production, fertility, survival, calving difficulty, stillbirths, and health events. MN will institute a new field study with 12 large dairies in Minnesota enrolled in DHI and using Dairy Comp 305 software. At each dairy, 30 to 50% of cows will remain pure Holsteins, but remaining cows will be crosses from a three-breed rotational mating system using Holstein, Montbeliarde, and Swedish Red sires. Collaboration with FL will examine economics of dairy performance of purebred and crossbred cattle. PA will evaluate the biological and economic impact of crossbreeding of Brown Swiss and Holstein crosses on lifetime performance traits under commercial conditions using records kept in on-farm computing systems. Backcrosses will be available to enable estimation of recombination losses. TN (University of Tennessee) plans to evaluate crosses of Swedish Red sires on Holstein, Jersey and crossbred cows in the Southeastern US using data from 8 commercial herds in Tennessee. TN plans to cooperate with University of Wisconsin and evaluate crosses of Norwegian Red bulls on Holstein and Holstein cross cows in the US. Crossbreds in at least 30 herds will be compared with Holstein sired herdmates for health, reproduction, survival, production and overall economic criteria. VA will compare unique phenotypes of Holsteins, Jerseys, and their reciprocal crosses in a diallel-crossbreeding scheme under research herd conditions. Feed intake data will be collected on first lactation cows in each of the four breed groups during the first 300 d of lactation and combined with feed analysis, body condition scores, and production, reproduction and daily body weight data to estimate lactation curves for energy balance. Return to normal reproductive cyclicity will be evaluated in first and second lactation animals and growth parameters from birth through maturity at 36 mos. will also be obtained. WI began creation of a three-quarter Holstein cross line in 2003 in the Arlington and Madison Campus herds with the objective of QTL detection. The mating strategy will continue until 300 crossbred females are generated and will allow detection of a completely linked, additive QTL that explains 5 to 10% of phenotypic variance using a single marker. The search will be for direct or maternal components of health, fertility, longevity, calving ability, physical conformation, milk yield and composition in a ¾ Holsteins/ ¼ Jersey backcross population. WI will conduct stochastic simulation to predict short- and long-term outcomes of alternative crossbreeding systems or two and three breeds, including risk analysis for robustness regarding breed effects and heterosis estimates. Objective 3: Develop breeding goals and appropriate indexes for optimum biological and economic improvement of health, reproduction, survival, and production of dairy cattle (AIPL, FL, IA, IL, IN, MN, NC, TN, VA, WI) The format for describing Methods under this objective will follow a slightly different format. The first part of the section will describe collaborations of a number of stations with AIPL in developing various breeding goals and appropriate indexes without identifying specific stations. The different methods to be pursued will be mediated through AIPL. In the latter part of the section other methods of efforts of particular stations will be described separately. AIPL and Collaborating Stations Selection goals to most rapidly improve dairy farm profit will be determined by monitoring prices of inputs and outputs. The lifetime net merit economic index will be calculated by an improved profit function using updated incomes and expenses associated with each trait available for selection. Alternative rankings for return on investment and for discounted net present value may better account for differences in investment required per cow and in timing of returns. Costs associated with health and management variables such as disease incidence, milking speed, and disposition will be examined to determine if national genetic evaluations for those traits are needed. Revision of the net merit index will be a cooperative effort, with contributions from many university research groups and collaborators in the multi-state project S-1008. Suggested revisions to the national index will be provided in advance to research and industry groups to promote debate, provide education, and encourage acceptance of official changes in selection programs. Multiple rankings for different markets may be needed to better match breeding stock with each herds management or environment. Optimum indexes for specific target populations (heifers, grazing herds, herds in hot climates, herds using bovine somatotrophin, different breeds, different milk prices, etc.) will be determined and could be provided routinely if sufficiently different than currently published indexes. Multiple indexes may be needed if producers face different economic conditions, if profit is a nonlinear function of the traits, if genetic effects are non-additive, or if genotype and environment interact. Separate cow fertility traits measuring ability to cycle and ability to conceive when inseminated could provide more progress than the current combined evaluation for daughter pregnancy rate, and economic values will be compared. Health trait data are becoming more widely available because of on-farm computers used in managing large herds. Genetic evaluations for several health traits may help to avoid the declines in dairy cow health and reproduction that would occur with single-trait selection for high yield. Data for traits such as clinical mastitis, electrical conductivity of milk, milk urea nitrogen, milking speed, disposition, and metabolic or other diseases are not yet collected nationally, but examination of research herd, cooperator herd, or regional data sets could demonstrate the potential value of acquiring this information, and subsequently developing genetic evaluation for these. Milking speed and disposition data are collected, evaluated, and displayed by most U.S. artificial-insemination companies using different scales and methods. National rankings for milking speed are only available in the U.S. for Brown Swiss. During the next five years, methods to collect more uniform, national data for milking speed and disposition in the U.S. could be justified if the economic values of these are sufficiently high. Cow mortality may be heritable and could provide additional information on economic efficiency for national indexes. Inclusion of genetic evaluation information on mortality into national indexes will depend on many factors including: genetic variation for mortality and the genetic relationships among mortality, disease, reproduction, and production. TN and AIPL will cooperate to examine the impact of cow mortality on national indexes. Economic values of traits included in lifetime net merit may depend on correlated responses for traits not measured nationally. AIPL will cooperate with VA to determine the extent that relative weights in the merit indexes need to change as fixed costs, variable costs, and net income change. Several stations will cooperate in the estimation of correlated responses from ongoing selection experiments in research herds (IA, IN, MN, and VA). Research on the value of fertility, health, mastitis, body condition score, feed costs, salvage value, and management traits will be conducted jointly with FL, GA, MN, NC, PA, TN, VA, and WI in addition to independent studies from these stations. As the number of traits in the index increases, the ability to accurately measure each traits value may decrease because of correlations among the included traits. To overcome this potential loss of precision, more precise economic modeling is needed. AIPL, IL, MN, and WI will compare alternative income / cost ratios to the current selection index for lifetime profit. Because results are used directly in ongoing industry selection programs, proposed revisions of lifetime net merit will be presented to the Council on Dairy Cattle Breeding for review and approval or possible modification. Objective 4: Develop and recommend selection and crossbreeding protocols of optimum economic utility for adoption by US dairy farmers (AIPL, IN, KY, MN, NC, TN, VA, WI) AIPL plans to refine estimates of breed differences and heterosis based on the national milk-recording (Dairy Herd Improvement (DHI)) database. Estimates are currently available for milk, fat, protein, somatic cell score (SCS), productive life (PL), and daughter pregnancy rate (DPR). However, information regarding breed effects and heterosis for other components of Lifetime Net Merit (LNM$), such as udder composite, feet and legs composite, body size composite, direct and maternal stillbirths, and direct and maternal calving ease, is lacking at the present time. The introduction of an all-breed model in May, 2007 should lead to more positive interaction between herd owners, DHI, and USDA on the subject of crossbreeding (VanRaden et al., 2006). During the next 5 years, many questions are expected on multiple breed selection and mating along with increasing requests for expert advice, software, or web tools. AIPL hopes to cooperate with university researchers and with AI companies to assist producers who choose to exploit heterosis. Questions that deserve further research are whether selection within a synthetic composite could provide more long-term progress than rotational crossing of purebreds and whether inbreeding is still a limiting factor if more of the commercial population is crossbred. More formal cooperation among researchers may be needed as the new evaluations become official, so that recommended breeding programs will be more credible, useful, up to date, and accepted. IL will initiate assessment of production per day of life as a potentially new lifetime measurement for genetic evaluation. IN plans to consider the issue of genetic selection for organic dairy farms and rotational graziers. In addition, outreach efforts will focus on technologies for automatic monitoring of traits such as body condition score or body temperature that have historically been difficult or costly to measure on a routine basis. Such automated monitoring could allow earlier detection and treatment of infectious diseases and metabolic disorders, which would be advantageous for organic farms that can't use antibiotics. KY plans to work on recommended breeding programs that have to do with crossbreeding programs alone or in combination with selection programs. Deterministic modeling of alternative crossbreeding programs involving numerous dairy breeds based on estimates of additive, maternal and heterosis from the crossbreeding trials underway in S-1008 and across-breed genetic evaluation results from AIPL will be considered. In addition, modeling the expected gains from pureline selection combined with rotational crossbreeding will be considered, as will the formation of a composite population to be developed by using selection and crossbreeding together. MN plans to place major emphasis on gauging, evaluating, and educating dairy producers and undergraduate students on levels of inbreeding in the Holstein, Scandinavian Red, Montbeliarde, Jersey, Brown Swiss, Normande, and Fleckvieh breeds. The consequences of inbreeding depression will be determined, and optimum mating systems for rotational crossbreeding systems will be evaluated, with special emphasis on 3-breed rotational crossbreeding systems compared with purebreeding, and crossbreeding systems for specific herd management conditions will be considered. NC plans to consider how selection indices and recommended crossbreeding programs might differ for producers opting for seasonal breeding and calving. Furthermore, sire rankings may differ for organic versus conventional herds, a form of genotype by environmental interaction. Indices will be based on net present value of costs and returns discounted to the date of first insemination of the dam or date of birth of the heifer calf. TN plans to work with AIPL, IN, KY, MN, NC, and VA to develop crossbreeding protocols and mating schemes involving various breeds to produce the most economically efficient dairy cattle for commercial milk production under the various major dairy production schemes used in North America. VA plans to provide information from their crossbreeding project, including data from reciprocal crosses of the Holstein and Jersey breeds, as well as data from three-way crosses involving the Brown Swiss or Swedish Red breeds (Cassell et al., 2005). Estimates of lifetime net economic merit will be provided for each group, although data regarding survival to various ages/disease incidence/reproductive performance/production will be available at an earlier age. These data will be used to help design dairy producer crossbreeding programs. WI plans to evaluate components of net lifetime profit per animal, including calving performance, fertility, growth, health, survival, production, and milk composition, in backcross ¾ Holstein x ¼ Jersey animals and pure Holstein controls (sired by AI young sires). In addition, data from crossbreeding on commercial farms, involving the Holstein, Jersey, Brown Swiss, Swedish Red, and Norwegian Red breeds, will be compiled. The issue of RNIOC versus economic efficiency will be considered. Traditionally, selection indices for RNIOC, evaluate net profit per animal (or perhaps per stall). However, indices based on economic efficiency, measured as net return per dollar invested, may be more appropriate for comparison of breeds that differ in body size, milk composition, milk production, housing requirements, and milking times.

Measurement of Progress and Results

Outputs

• Objective 1: Develop selection tools to enhance reproduction and survival using field data.1. AIPL-revision of the female fertility evaluation. 2. GA-Analysis of service records using censored data methods. 3. IL-Calculation of genetic evaluations of production per day of life as a lifetime measurement. 4. IN- Evaluation of automatically collected reticular temperatures of cows as indicators of health, estrus or morbidity events and estrus monitoring records as genetic indicators of estrus expression. 5. NC-Assessment of maternal influences on female fertility. 6. PA- Evaluation of daily body weight and its change on possible genetic association with metabolic diseases that predispose cows to premature death. 7. WI-Evaluation of the usefulness of on-farm computer databases of disease events for the genetic analysis of health and fertility.
• Objective 2: Evaluate the biological and economic impact of crossbreeding on lifetime performance of dairy cattle. 1. KY, VA and NC-Evaluation of Holsteins, Jerseys and their reciprocal crosses for physical conformation and economic measures of lifetime performance and VA for their comparison for feed intake, and related traits to estimate lactation curves of energy balance. 2. PA, MN and TN- Evaluation of Brown Swiss, Jersey, Montbeliarde and Swedish Red crossbreds for reproduction, lactation yields and survival. 3. WI  Assessment of direct and maternal components of health, fertility, longevity, calving ability, physical conformation, milk yield and composition in a ¾ Holstein/ ¼ Jersey backcross population.
• Objective 3: Develop breeding goals and appropriate indexes for optimum biological and economic improvement of health, reproduction, survival, and production of dairy cattle. 1. AIPL and various participating station collaborators: A. Development of an improved profit function to enhance the calculation of the lifetime net merit index and assessment of genetic evaluations of discounted net present value as an alternative economic measure. B. Evaluation of multiple genetic rankings for different markets (e.g. heifers, grazing, different breeds, different milk markets). C. Expansion of genetic evaluation of reproduction to other cow fertility traits in addition to daughter pregnancy rate. D. Evaluation of usefulness in genetic evaluations of health traits recorded via on-farm computers and other data such as milking speed and other non-traditional traits from research herds, cooperator herds and regional data sets. 2. AIPL and VA- Evaluation of appropriate economic values of traits included in lifetime net merit with regard to changes in fixed costs, variable costs and net income. 3. IA, IN, MN, VA- Assessment of correlated response from ongoing selection experiments or field data. 4. MN, NC, PA, TN-Assessment of the value of fertility, health, mastitis, body condition, feeds costs, etc. for inclusion in breeding goals or indexes. 5. AIPL, IL, MN, WI- complete precise economic modeling to compare alternative income/ cost ratios to the current selection index for lifetime profit.
• Objective 4: Develop and recommend selection and crossbreeding protocols of optimum economic utility for adoption by US dairy farmers. 1. AIPL and other participating project cooperators-Assessment of alternative breeding strategies and mating schemes to exploit additive and non-additive genetic variation to produce the most economically efficient dairy cattle for commercial milk production. 2. IN- Assess genetic selection options for graziers and organic producers and NC to assess selection and crossbreeding programs for seasonal grazing herds. 3. KY-Model expected gains from various alternatives combining selection and crossbreeding, including formation of a composite. 4. MN- Develop educational programs regarding inbreeding and its consequences. 5. VA, WI- Evaluation of relative net income over opportunity cost (RNIOC) as a trait for selection. 6. AIPL, WI- Evaluate real time blending of single nucl

Outcomes or Projected Impacts

• Objective 1: Develop selection tools to enhance reproduction and survival using field data. 1. Identification of genetically superior cows and bulls for female fertility through improved genetic evaluations of female fertility which include maternal influences on fertility. 2. Greater use of on-farm computer recorded data for assessment of genetic influences on health, reproduction, lifetime performance and survival. 3. Enhanced ability to select for lifetime performance due to improved genetic evaluations for it. 4. On-farm mortality rates established and genetic and environmental stressors leading to premature death identified.
• Objective 2: Evaluate the biological and economic impact of crossbreeding on lifetime performance of dairy cattle. 1. Enable dairy producers to strategically choose breeds and breed combinations to optimize the use of crossbreeding to maximize dairy performance. 2. Facilitate the use of crossbreeding to optimize lifetime performance of dairy cattle.
• Objective 3: Develop breeding goals and appropriate indexes for optimum biological and economic improvement of health, reproduction, survival, and production of dairy cattle. 1. Improve the economic efficiency of dairy production through selection on an improved lifetime net merit index based on an improved profit function. 2. Improved animal well-being enhanced by the ability to select for improved fertility, reproduction and health. 3. Enable dairy producers to make customized sire selection decisions through genetic rankings appropriate for different markets and production systems.
• Objective 4: Develop and recommend selection and crossbreeding protocols of optimum economic utility for adoption by US dairy farmers. 1. Provide optimum breeding systems which combine selection and crossbreeding and permit the formation of composite populations, if desired. 2. Production of the most economically efficient dairy cattle for commercial milk production through breeding strategies and mating systems. 3. Reduction of inbreeding and its consequences. 4.Provision of genetic options for graziers and organic producers. 5. Enhanced genetic evaluations which combine SNP genetic predictions and estimates of genetic merit from traditional field data.

Milestones

(2008): Objective 1: Develop selection tools to enhance reproduction and survival using field data. Assessment of maternal influences on female fertility Evaluation of automatically collected reticular temperatures of cows as indicators of health, estrus or morbidity events Analysis of service records using censored data methods Objective 2: Evaluate the biological and economic impact of crossbreeding on lifetime performance of dairy cattle The assessment of direct and maternal components of health, fertility, longevity, calving ability, physical conformation, milk yield and composition will be completed in a ¾ Holstein/ ¼ Jersey backcross population. Objective 3: Develop breeding goals and appropriate indexes for optimum biological and economic improvement of health, reproduction, survival, and production of dairy cattle An improved profit function will be developed and genetic evaluations for net present value will be evaluated as an alternative economic measure The genetic evaluation of female fertility traits will be expanded Correlated responses will assessed in ongoing selection experiments Objective 4: Develop and recommend selection and crossbreeding protocols of optimum economic utility for adoption by US dairy farmers Develop educational programs regarding inbreeding and its consequences

(2009): Objective 1: Develop selection tools to enhance reproduction and survival using field data. Calculation of genetic evaluations of production per day of life as a lifetime measurement. Evaluation of estrus monitoring records as genetic indicators of estrus expression. Evaluation of daily body weight and its change on possible genetic association with diseases leading to premature death. Objective 2: Evaluate the biological and economic impact of crossbreeding on lifetime performance of dairy cattle The assessment of direct and maternal components of health, fertility, longevity, calving ability, physical conformation, milk yield and composition will be completed in a ¾ Holstein/ ¼ Jersey backcross population. Objective 3: Develop breeding goals and appropriate indexes for optimum biological and economic improvement of health, reproduction, survival, and production of dairy cattle The value of fertility, health, mastitis, body condition, feed costs, etc. for inclusion in breeding goals and indexes will be assessed. The economic value of traits in the lifetime net merit index will be evaluated, particularly in regard to changes in fixed cost, variable costs and net income. Objective 4: Develop and recommend selection and crossbreeding protocols of optimum economic utility for adoption by US dairy farmers Model expected gains from various alternatives combining selection and crossbreeding, including a composite Evaluate real time blending of SNP based genetic predictions and genetic merit estimates from traditional field data

(2010): Objective 1: Develop selection tools to enhance reproduction and survival using field data. Evaluation of the usefulness of on-farm computer databases of disease events for the genetic analysis of health and fertility. Objective 2: Evaluate the biological and economic impact of crossbreeding on lifetime performance of dairy cattle Evaluation of Holsteins, Jerseys and their reciprocal crosses for physical conformation and economic measures of lifetime performance will be completed as will their comparison for feed intake, and related traits to estimate lactation curves of energy balance. Objective 3: Develop breeding goals and appropriate indexes for optimum biological and economic improvement of health, reproduction, survival, and production of dairy cattle Evaluate multiple genetic rankings for different markets (e.g. heifers, grazing, different breeds, different milk markets) Evaluate usefulness in genetic evaluations of health traits recorded via on-farm computers and other data such as milking speed and othe

Projected Participation

View Appendix E: Participation

Outreach Plan

Objective 4 of this project outlines several aspects of an outreach plan for developing and recommending selection and crossbreeding protocols of optimum economic utility for adoption by US dairy farmers. Key elements of this plan are:
1) With the introduction of an all-breed model in May, 2007AIPL now plans to refine estimates of breed differences and heterosis based on the national milk-recording (Dairy Herd Improvement (DHI)) database and will seek to obtain information regarding breed effects and heterosis for components of Lifetime Net Merit Dollars (LNM$) not currently available such as udder composite, feet and legs composite, body size composite, direct and maternal stillbirths, and direct and maternal calving ease. Results will be made available on the AIPL website (http://www.aipl.arsusda.gov). AIPL hopes to cooperate with university researchers and with AI companies to assist producers who choose to exploit heterosis and respond to the many questions expected on multiple breed selection and mating system along with increasing requests for expert advice, software, or web tools.


2) IN plans to consider the issue of genetic selection for organic dairy farms and rotational graziers and make recommendations. In addition, outreach efforts will focus on technologies for automatic monitoring of traits such as body condition score or body temperature that have historically been difficult or costly to measure on a routine basis. Such automated monitoring could allow earlier detection and treatment of infectious diseases and metabolic disorders, which would be advantageous for organic farms that can't use antibiotics.


3) KY plans to work on recommended breeding programs that have to do with crossbreeding programs alone or in combination with selection programs. Deterministic modeling of alternative crossbreeding programs involving numerous dairy breeds based on estimates of additive, maternal and heterosis from the crossbreeding trials underway in S-1008 and across-breed genetic evaluation results from AIPL will be the basis for recommendations. In addition, modeling the expected gains from pureline selection combined with rotational crossbreeding will be considered, as will the formation of a composite population to be developed by using selection and crossbreeding together.


4) MN plans to place major emphasis on gauging, evaluating, and educating dairy producers and undergraduate students on levels of inbreeding in the Holstein, Scandinavian Red, Montbeliarde, Jersey, Brown Swiss, Normande, and Fleckvieh breeds. The consequences of inbreeding depression will be determined, and optimum mating systems for rotational crossbreeding systems will be evaluated, with special emphasis on 3-breed rotational crossbreeding systems compared with purebreeding, and crossbreeding systems for specific herd management conditions will be considered.

5) NC plans to consider how selection indices and recommended crossbreeding programs might differ for producers opting for seasonal breeding and calving. Furthermore, sire rankings may differ for organic versus conventional herds, a form of genotype by environmental interaction. Indices will be based on net present value of costs and returns discounted to the date of first insemination of the dam or date of birth of the heifer calf.

6) TN plans to work with AIPL, IN, KY, MN, NC, and VA to develop crossbreeding protocols and mating schemes involving various breeds to produce the most economically efficient dairy cattle for commercial milk production under the various major dairy production schemes used in North America.

7) VA plans to provide information from their crossbreeding project, including data from reciprocal crosses of the Holstein and Jersey breeds, as well as data from three-way crosses involving the Brown Swiss or Swedish Red breeds (Cassell et al., 2005). Estimates of lifetime net economic merit will be provided for each group, although data regarding survival to various ages/disease incidence/reproductive performance/production will be available at an earlier age. These data will be used to help design dairy producer crossbreeding programs.

8) WI plans to evaluate components of net lifetime profit per animal, including calving performance, fertility, growth, health, survival, production, and milk composition, in backcross ¾ Holstein x ¼ Jersey animals and pure Holstein controls (sired by AI young sires). In addition, data from crossbreeding on commercial farms, involving the Holstein, Jersey, Brown Swiss, Swedish Red, and Norwegian Red breeds, will be compiled. The issue of RNIOC versus economic efficiency will be considered. Traditionally, selection indices for RNIOC, evaluate net profit per animal (or perhaps per stall). However, indices based on economic efficiency, measured as net return per dollar invested, may be more appropriate for comparison of breeds that differ in body size, milk composition, milk production, housing requirements, and milking times.

Organization/Governance

The project elects a chair each year and secretary. The secretary serves one year in that capacity and becomes the chair the following year. Each participating station has a representative on the project committee.

Officers of the project for 2007-2008 are:
Chair: Kent Weigel, University of Wisconsin
Secretary: Tony Seykora, University of Minnesota
Administrative Advisor: Nancy Cox (KY)
CSREES Rep: Muquarrab Qureshi

Participating Stations:
Animal Improvement Programs Laboratory, USDA
University of Florida
University of Georgia
University of Illinois
Iowa State University
University of Kentucky
University of Minnesota
University of Nebraska
North Carolina State University
Pennsylvania State University
Purdue University
University of Tennessee
Virginia Polytechnic Institute and State University
University of Wisconsin

Literature Cited

Adamec, V., B. G. Cassell, E. P. Smith, and R. E. Pearson. 2006. Effects of inbreeding in the dam on dystocia and stillbirths in US Holsteins. J Dairy Sci. 89: 307-314.


Appuhamy, J. A. D. R. N., B. G. Cassell, C. D. Dechow, and J. B. Cole. 2007. Phenotypic relationships of common health disorders in dairy cows to lactation persistency estimated from daily milk weights. J Dairy Sci. 90: 4424-4434.

Averill, T. A., R. Rekaya, and K. Weigel. 2004. Genetic analysis of male and female fertility using longitudinal binary data. J Dairy Sci. 87: 3947-3952.

Averill, T. A., R. Rekaya, and K. Weigel. 2006. Random regression models for male and female fertility evaluation using longitudinal binary data. J Dairy Sci. 89: 3681-3689.

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: 383-404.

Bohmanova, J., I. Misztal, and J. B. Cole. 2007. Temperature-humidity indices as indicators of milk production losses due to heat stress. J Dairy Sci. 90: 1947-1956.

BST Fact Sheet POSILAC, Bovine Somatotropine (BST) by Monsanto URL http://vm.cfsan.fda.gov/~ear/CORbST.html

Caraviello, D. Z., K. A. Weigel, and D. Gianola. 2003. Analysis of the relationship between type traits, inbreeding, and functional survival in Jersey cattle using a Weibull proportional hazards model. J Dairy Sci. 86: 2984-2989.

Caraviello, D. Z., K. A. Weigel, and D. Gianola. 2004a. Comparison between a Weibull proportional hazards model and a linear model for predicting the genetic merit of US Jersey sires for daughter longevity. J Dairy Sci. 87: 1469-1476.

Caraviello, D. Z., K. A. Weigel, and D. Gianola. 2004b. Prediction of longevity breeding values for US Holstein sires using survival analysis methodology. J Dairy Sci. 87: 3518-3525.

Caraviello, D. Z., K. A. Weigel, M. Craven, D. Gianola, N. B. Cook, K. V. Nordlund, P. M. Fricke, and M. C. Wiltbank. 2006a. Analysis of reproductive performance of lactating cows on large dairy farms using machine learning algorithms. J Dairy Sci. 89: 4703-4722.

Caraviello, D. Z., K. A. Weigel, P. M. Fricke, M. C. Wiltbank, M. J. Florent, N. B. Cook, K. V. Nordlund, N. R. Zwald, and C. L. Rawson. 2006b. Survey of management practices on reproductive performance of dairy cattle on large US commercial farms. J Dairy Sci. 89: 4723-4735.

Cassell, B. G., V. Adamec, and R. E. Pearson. 2003a. Effect of incomplete pedigrees on estimates of inbreeding and inbreeding depression for days to first service and summit milk yield in Holsteins and Jerseys. J Dairy Sci. 86: 2967-2976.

Cassell, B. G., V. Adamec, and R. E. Pearson. 2003b. Maternal and fetal inbreeding depression for 70-day nonreturn and calving rate in Holsteins and Jerseys. J Dairy Sci. 86: 2977-29.

Cassell, B.G., S. M. Jobst, M. L. McGilliard, and R. E. Pearson 2001. Evaluating sire selection practices using lifetime net income functions. J. Dairy Sci. 2002 85: 3492-3502.

Cassell, B., A. McAllister, R. Nebel, S. Franklin, K. Getzewich, J. Ware, J. Cornwell, and R. Pearson. 2005. Birth weights, mortality, and dystocia in Holsteins, Jerseys, and their reciprocal crosses in the Virginia Tech and Kentucky crossbreeding project. J. Dairy Sci. (Suppl. 1):92.


Chang, Y. M., O. González-Recio, K. A. Weigel, and P. M. Fricke. 2007. Genetic analysis of the twenty-one-day pregnancy rate in US Holsteins using an ordinal censored threshold model with unknown voluntary waiting period. J Dairy Sci. 90: 1987-1997.

Chilliard Y., J.J.Colleau, C. Disenhaus, C. Lerondelle, C. Mouchet , and A. Paris. 1998. « L hormone de croissance recombinante : intérêts et risques potentiels de son utilisation pour la production laitière bovine », INRA Prod. Animale, 11(1), p. 15-32.


Clay, J.S. and B.T. McDaniel. 2001. Computing mating bull fertility from DHI nonreturn data. J. Dairy Sci. 84: 1238-1245.

Coffey, M.P., G. Simm, W.G. Hill and S. Brotherstone. 2003. Genetic evaluation of dairy bulls for daughter energy balance profiles using linear type scores and body condition score analyzed using random regression. J Dairy Sci. 86:2005-2212.

Cole, J.B., R.C. Goodling, Jr., G.R. Wiggans and P.M. VanRaden, 2005. Genetic evaluation of calving ease for Brown Swiss, Jersey, and Holstein bulls from purebred and crossbred calvings. J. Dairy Sci. 88:1529-1539.

Cole, J. B., and P. M. VanRaden. 2006. Genetic evaluation and best prediction of lactation persistency. J Dairy Sci. 89: 2722-2728.

Cole W.J., P.J. Eppard, B.G. Boysen, K.S. Madsen, R.H. Sorbet, M.A. Miller, R.L Hintz, T.C. White, W.E Ribelin, B.G. Hammond, R.J. Collier and G. M. Lanza.
1992. Response of dairy cows to high doses of a sustained-release bovine somatotropin administered during two lactations. 2. Health and reproduction. J. Dairy Sci. 75:111-123.

Cole, J. B., G. R. Wiggans, and P. M. VanRaden. 2007a. Genetic evaluation of stillbirth in United States Holsteins using a sire-maternal grandsire threshold model. J Dairy Sci. 90: 2480-2488.

Cole, J. B., G. R. Wiggans, P. M. VanRaden, and R. H. Miller. 2007b. Stillbirth (co)variance components for a sire-maternal grandsire threshold model and development of a calving ability index for sire selection. J Dairy Sci. 90: 2489-2496.


Dechow, C. D., G. W. Rogers, J. B. Cooper, M. I. Phelps, and A. L. Mosholder. 2007. Milk, fat and protein yield, somatic cell score and days open among Holstein, Brown Swiss and their crosses. J Dairy Sci, 90 : 3542-3549.


Dechow, C. D., G. W. Rogers, U. Sander-Nielsen, L. Klei, T. J. Lawlor, J.S. Clay, A.E. Freeman, G., Abel-Azim, A. Kuck and S. Schnell. 2004a. Correlations among body condition score from various sources, dairy form, and cow health from the US and Denmark. J. Dairy Sci. 87:3526-3533.

Dechow, C. D., G. W. Rogers, L. Klei, and T. J. Lawlor. 2004b. Heritability and correlations for body condition score and dairy form within and across lactation and age. J Dairy Sci 87: 717-728.

Dechow, C. D., G. W. Rogers, L. Klei, and T. J. Lawlor. 2003. Heritabilities and correlations among body condition score, dairy form and selected linear type traits. J Dairy Sci. 86: 2236-2242.

Dechow, C. D., G. W. Rogers, and J. S. Clay. 2002. Heritability and correlations among body condition score loss, body condition score, production and reproductive performance. J Dairy Sci. 85: 3062-3070.

Dematawewa, C. M. B., R. E. Pearson, and P. M. VanRaden. 2007. Modeling extended lactations of Holsteins. J Dairy Sci. 90: 3924-3936.


Dohoo R., Report of the Canadian Veterinary Medical Association Expert Panel on rBST. Health Canada, Nov. 1998. URL www.hcsg.ca/english/protection/rbst/animals/index.htm

Eppard P.J., J.J. Veenhuizen, W.J. Cole, P.G. Comens-Keller, G.F. Hartnell, R.L. Hintz, L. Munyakazi, P.K. Olsson, R.H. Sorbet, T.C. White, C.A. Baile, R.J. Collier, J.P. Goff and R.L. Horst. 1996. Effect of bovine somatotropin administered to peripaturient dairy cows on the incidence of metabolic disease. J. Dairy Sci. 79:2170-2181.

Fahey, A. G., M. M. Schutz, D. L. Lofgren, A. P. Schinckel, and T. S. Stewart. 2007. Genotype by environment interaction for production traits while accounting for heteroscedasticity. J Dairy Sci. 90: 3889-3899.

Goodling, R. C., Jr., G. E. Shook, K. A. Weigel, and N. R. Zwald. 2005. The effect of synchronization on genetic parameters of reproductive traits in dairy cattle. J Dairy Sci. 88: 2217-2225.

Gonda, M. G., Y. M. Chang, G. E. Shook, M. T. Collins, and B. W. Kirkpatrick. 2006. Genetic variation of Mycobacterium avium ssp. paratuberculosis infection in US Holsteins. J Dairy Sci. 89: 1804-1812.


González-Recio, O., R. Alenda, Y. M. Chang, K. A. Weigel, and D. Pianola. 2006. Selection for female fertility using censored fertility traits and investigation of the relationship with milk production. J Dairy Sci. 89: 4438-4444.

González-Recio, O., Y. M. Chang, D. Gianola, and K. A. Weigel. 2005. Number of inseminations to conception in Holstein cows using censored records and time-dependent covariates. J Dairy Sci. 88: 3655 - 3662.

Gulisija, D., D. Gianola, and K. A. Weigel. 2007. Nonparametric analysis of the impact of inbreeding on production in Jersey cows. J Dairy Sci. 90: 493-500.

Hansen, L.B. 2000. Consequences of selection for milk yield from a geneticists viewpoint. J. Dairy Sci. 83: 1145-1150.


Hare, E., H.D. Norman and J.R.Wright. 2006a. Survival rates and productive life of dairy cattle in the United States. J. Dairy Sci. 89:3713-3720.

Hare, E., H. D. Norman, and J. R. Wright. 2006b. Trends in calving ages and calving intervals for dairy cattle breeds in the United States. J Dairy Sci. 89: 365-370.

Heins, B.J., L.B. Hansen, and A.J. Seykora. 2006a. Production of pure Holsteins versus crossbreds of Holstein with Normande, Montbeliarde, and Scandinavian Red. J. Dairy Sci. 89:2799-2804.


Heins, B.J., L.B. Hansen, and A.J. Seykora. 2006b. Calving difficulty and stillbirths of pure Holsteins versus crossbreds of Holstein with Normande, Montbeliarde, and Scandinavian Red. J. Dairy Sci. 89:2805-2810.

Heins, B.J., L.B. Hansen, and A.J. Seykora. 2006c. Fertility and survival of pure Holsteins versus crossbreds of Holstein with Normande, Montbeliarde, and Scandinavian Red. J. Dairy Sci. 89:4944-4951.


Heringstad, B., G. Klemetsdal and T. Steine. 2003. Selection responses for clinical mastitis and protein yield in two Norwegian dairy cattle selection experiments. J. Dairy Sci. 86:2990-2999.


Heringstad, B., G. Klemetsdal and T. Steine. 2007. Selection disease resistance in two selection experiments with Norwegian Red cows. J. Dairy Sci. 90:2419-2426.

History and How bST Works URL http://www.monsantodairy.com/about/history/index.html

Johanson, J. M., and P. J. Berger. 2003. Birth weight as a predictor of calving ease and perinatal mortality in Holstein cattle. J Dairy Sci. 86: 3745-3755.

Kasimanickam, R., V. Kasimanickam, C. D. Thatcher, R. L. Nebel, and B. G. Cassell. 2007. Relationships among lipid peroxidation, glutathione peroxidase, superoxide dismutase, sperm parameters, and competitive index in dairy bulls. Theriogenology. 67 : 1004-1012.

Kearney, J. F., M. M. Schutz, P. J. Boettcher, and K. A. Weigel. 2004a. Genotype x environment interaction for grazing versus confinement. I. Production traits. J Dairy Sci. 87: 501-509.


Kearney, J.F., M.M. Schutz, and P.J. Boettcher. 2004b. Genotype x environment interaction for grazing vs. confinement. II. Health and reproduction traits. J. Dairy Sci. 87: 510-516.


Lopez-Villalobos, N., D.J. Garrick, C.W. Holmes, H.T. Blair, and R.J. Spelman. 2000. Profitabilities of some mating systems for dairy herds in New Zealand. J. Dairy Sci. 83:144-153.


Maltecca, C., H. Khatib, V. R. Schutzkus, P. C. Hoffman, and K. A. Weigel. 2006. Changes in conception rate, calving performance, and calf health and survival from the use of crossbred Jersey x Holstein sires as mates for Holstein dams. J Dairy Sci. 89: 2747-2754.


Mäntysaari, E.A., Y.T. Grohn and R.L. Quaas. 1991. Clinical ketosis: Phenotypic and genetic correlations between occurrences and with milk yield. J. Dairy Sci. 74:3985-3993.


McAllister, A.J. 2002. Is crossbreeding the answer to questions of dairy breed utilization? J. Dairy Sci. 85:2352-2357.


Moallem, U., M. Kaim, Y. Folman, and D. Sklan 1997. Effects of somatotropin and dietary calcium soaps of fatty acids in early lactation on productive and reproductive performance of high yielding dairy cows. J. Dairy Sci. 80:2127-2136.


Moallem, U., M. Kaim, Y. Folman, and D. Sklan 2000. Effects of somatotropin and dietary calcium soaps of fatty acids in early lactation on milk production, dry matter intake, and energy balance in high yielding cows. J Dairy Sci. 83: 2085-2094.

Moreira F., Riscot ,A., Pires, M.F.A., Ambrose J.D., Drost M., and W.W. Thatcher . 2000. Use of Bovine Somatotropin in Lactating Dairy Cows Receiving Timed Artificial Insemination. J. Dairy Sci.. 83: 1237-1247.


Nash, D. L., G. W. Rogers, J. B. Cooper, G. L. Hargrove, and J. F. Keown. 2003. Heritability of intramammary infections at first parturition and relationships with sire transmitting abilities for somatic cell score, udder type traits, productive life, and protein yield. J Dairy Sci. 86: 2684-2695.


Norberg, E., G. W. Rogers, R. C. Goodling, J. B. Cooper, and P. Madsen. 2004. Genetic parameters for test-day electrical conductivity of milk for first-lactation cows from random regression models. J Dairy Sci. 87: 1917-1924.


Norberg, E., G. W. Rogers, J. Ødegård, J. B. Cooper, and P. Madsen. 2006. Short communication: Genetic correlation between test-day electrical conductivity of milk and mastitis. J Dairy Sci. 89: 779-781.


Norman, H. D., J. L. Hutchison, J. R. Wright, M. T. Kuhn, and T. J. Lawlor. 2007a. Selection on yield and fitness traits when culling Holsteins d uring the first three lactations. J Dairy Sci. 90: 1008 020.


Norman, H. D., J. R. Wright, R. L. Powell, and P. M. VanRaden. 2005. Impact of maturity rate of daughters on genetic ranking of Holstein bulls. J Dairy Sci. 88: 3337-3345.


Norman, H. D., J. R. Wright, R. L. Powell, P. M. VanRaden, F. Miglior, and G. de Jong. 2007b. Consistency of maturity rate for milk yield across countries and generations. J Dairy Sci. 90: 3937-3944.


Oseni, S., I. Misztal, S. Tsuruta, and R. Rekaya. 2004. Genetic components of days open under heat stress. J Dairy Sci. 87: 3022-3028.


Oseni, S., I. Misztal, S. Tsuruta, and R. Rekaya. 2003. Seasonality of days open in US Holsteins. J Dairy Sci. 86: 3718-3725.

Oseni, S., S. Tsuruta, I. Misztal, and R. Rekaya. 2004a. Genetic parameters for days open and pregnancy rates in US Holsteins using different editing criteria. J Dairy Sci. 87: 4327-4333.

Pearson, R.E. and R.H. Miller. 1981. Economic definition of total performance, breeding goals, and breeding values for dairy cattle. J. Dairy Sci. 64:857-869.


Philipsson, J., and B. Lindhé. 2003. Experiences of including reproduction and health traits in Scandinavian dairy cattle breeding programmes. Livest. Prod. Sci. 83:99-112.


Ravagnolo, O., I. Misztal. 2002a. Effect of heat stress on nonreturn rate in Holstein cows: genetic analyses. J Dairy Sci. 85: 3092-3100.

Ravagnolo, O., and I. Misztal. 2002b. Effect of heat stress on nonreturn rate in Holsteins: fixed-model analyses. J Dairy Sci. 85: 3101-3106.


Rogers, G.W. 1990. A utility function for ranking sires that considers production, linear type traits, semen cost, and risk. J. Dairy Sci. 73:532-538.


Rogers, G. W. 1994. Requirements and uses of evaluations for health and reproductive traits. Volume 17, pages 81-88 in Proceedings of the 5th World Congress on Genetics Applied to Livestock Production.


Shook, G.E. and D.K. Combs. 2003. Evaluating breed resources using economic models. Proc. Natl. Dairy Genetics Workshop, Raleigh, NC.


Shook, G.E. 2006. Major advances in determining appropriate selection goals. J. Dairy Sci. 89:1349-1361.


Simianer, H., H. Solbu and L.R. Schaeffer. 1991. Estimated genetic correlations between disease and yield traits in dairy cattle. J. Dairy Sci. 74:4358-4365.


Smith, L.A., B.G. Cassell, and R.E. Pearson. 1998. The effects of inbreeding on the lifetime performance of dairy cattle. J. Dairy Sci. 81:2729-2737.


Smith, J.W., L.O. Ely and A.M. Chapa. 2000. Effect of region, herd size and milk production on reasons cows leave the herd. J. Dairy Sci. 83:2908-2987.


Stone, W.C., C.L. Guard and D.A. Button. 2006. Why do cows die? Hoards Dairyman, February 25, Vol. 151(no. 4):117.

Tsuruta, S., I. Misztal, and T. J. Lawlor. 2005. Changing definition of productive life in US Holsteins: Effect on genetic correlations. J Dairy Sci. 88: 1156-1165.

Tsuruta, S., I. Misztal, and T. J. Lawlor. 2004. Genetic correlations among production, body size, udder, and productive life traits over time in Holsteins. J Dairy Sci. 87: 1457-1468.


Vallejo, R. L., Y. L. Li, G. W. Rogers, and M. S. Ashwell. 2003. Genetic diversity and background linkage disequilibrium in the North American Holstein cattle population. J Dairy Sci. 86: 4137-4147.


VanRaden, P.M., and A.H. Sanders. 2003. Economic merit of crossbred and purebred US Dairy Cattle. J. Dairy Sci. 86:1036-1044.


VanRaden, P.M. 2004. Invited review: Selection on net merit to improve lifetime profit. J. Dairy Sci. 87:3125-3131.


VanRaden, P.M., C.M.B. Dematawewa, R.E. Pearson, and M.E. Tooker. 2006. Productive life including all lactations and longer lactations with diminishing credits. J. Dairy Sci. 89(8):3213-3220.


VanRaden, P. M. Tooker, J. Cole, G. Wiggans, and J. Megonigal. 2006. Genetic evaluations for mixed breed populations. J. Dairy Sci. 89(Suppl. 1):98.

VanRaden, P. M., and R. H. Miller. 2006. Effects of nonadditive genetic interactions, inbreeding, and recessive defects on embryo and fetal loss by seventy days. J Dairy Sci. 89: 2716-2721.


VanRaden, P. M., A. H. Sanders, M. E. Tooker, R. H. Miller, H. D. Norman, M. T. Kuhn, and G. R. Wiggans. 2004. Development of a national genetic evaluation for cow fertility. J Dairy Sci. 87: 2285-2292.

VanRaden, P. M., M. E. Tooker, J. B. Cole, G. R. Wiggans, and J. H. Megonigal, Jr. 2007. Genetic evaluations for mixed-breed populations. J Dairy Sci. 90: 2434-2441.

VanRaden, P.M. and Tooker, M.E. 2006. Revised estimates of lifetime net merit for dairy breeds and breed crosses. J. Dairy Sci. 89(Suppl. 1):246-247.

VanRaden, PM., M.E. Tooker, G.R. Wiggans, J.B. Cole and J.H. Megonigal, Jr. 2007. All-breed evaluation. (http://aipl.arsusda.gov/reference/all-breed-2007.htm).

Van Tassell, C. P., G. R. Wiggans, and I. Misztal. 2003. Implementation of a sire-maternal grandsire model for evaluation of calving ease in the United States. J Dairy Sci. 86: 3366-3373.


Weigel, D.J., B.G. Cassell, and R.E. Pearson. 1997. Prediction of transmitting abilities for productive life and lifetime profitability from production, somatic cell count, and type traits in milk markets for fluid milk and cheese. J. Dairy Sci. 80: 1398-1405.


Weigel, K.A., and K.A. Barlass. 2003. Results of a producer survey regarding crossbreeding on US dairy farms. J. Dairy Sci. 86:4148-4154.


Weigel, K. A., R. W. Palmer, and D. Z. Caraviello. 2003. Investigation of factors affecting voluntary and involuntary culling in expanding dairy herds in Wisconsin using survival analysis. J Dairy Sci. 86: 1482-1486.

Weigel, K. A.. Improving the reproductive efficiency of dairy cattle through genetic selection. 2004. J Dairy Sci. 87: E86-92.

Wiggans, G. R., and R. C. Goodling, Jr. 2005. Accounting for pregnancy diagnosis in predicting days open. J Dairy Sci. 88: 1873-1877.


Wiggans, G. R., I. Misztal, and C. P. Van Tassell. Calving ease (co)variance components for a sire-maternal grandsire threshold model. 2003. J Dairy Sci. 86: 1845-1848.


Wiggans, G. R., L. L. M. Thornton, R. R. Neitzel, and N. Gengler. 2006. Genetic parameters and evaluation of rear legs (rear view) for Brown Swiss and Guernseys. J Dairy Sci. 89: 4895-4900.


Wiggans, G. R., L. L. M. Thornton, R. R. Neitzel, and N. Gengler. 2007. Short communication: genetic evaluation of milking speed for Brown Swiss dairy cattle in the United States. J Dairy Sci. 90: 1021-1023.


Williams, C. M., S. P. Washburn, A. N. Elias, and C. S. Whisnant. 2006. Breed differences in postpartum cyclicity of pasture-based dairy cows. J. Dairy Sci. 89: (Suppl 1): 129 (Abstr.).


Young, A. 2002. Death loss of lactating cows A major problem. Accessed Aug. 8, 2005. Available at: http://extension.usu.edu/files/agpubs/deathloss.pdf.


Zwald, N.R., K.A. Weigel, W.F. Fikse, and R. Rekaya. 2003. Identification of factors that cause genotype by environment interaction between herds of Holstein cattle in seventeen countries. J. Dairy Sci. 86: 1009-1018.


Zwald, N. R., K.A. Weigel, Y.M. Chang, R.D. Welper and J. S. Clay. 2004a Genetic selection for health traits using producer-recorded data. I. Incidence rates, heritability estimates, and sire breeding values. J. Dairy Sci. 87:4287-4294.


Zwald, N.R., K.A. Weigel, Y.M. Chang, R.D. Welper, and J.S. Clay. 2004b. Genetic selection for health traits using producer-recorded data. II. Genetic correlations, disease probabilities, and relationships with existing traits. J. Dairy Sci. 87: 4295-4302.


Zwald, N. R., K. A. Weigel, Y. M. Chang, R. D. Welper, and J. S. Clay. 2005. Genetic evaluation of dairy sires for milking duration using electronically recorded milking times of their daughters. J Dairy Sci. 88: 1192-1198.


Zwald, N. R., K. A. Weigel, Y. M. Chang, R. D. Welper, and J. S. Clay. 2006. Genetic analysis of clinical mastitis data from on-farm management software using threshold models. J Dairy Sci. 89: 330-336.

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