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S-1008 Project Summary
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      Project Number
      Title
      Duration
      Statement of Problem and Justification
      Related, Current, and Previous Work
      Objectives
      Methods
      Measurement of Progress and Results
      Milestones
      Outreach Plan
      Organization and Governance
      APPENDIX A: Projected Participation
      APPENDIX B: Projected Collaborators
      APPENDIX C: References

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S-1008 Annual Report

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Genetic progress in production has been strong and sustained for many years, but undesirable correlated responses continue to occur in fertility, health, and fitness traits. Dairy producers question the need for additional progress in productive traits without simultaneous improvement in these other areas.

The research outlined in this project will evaluate and develop methods to improve fertility, health, and fitness traits through selection and crossbreeding. The project includes a delivery method to implement findings – the lifetime economic merit indexes for sire selection published by USDA. If this research is not undertaken, farmers will continue to look for alternatives to improve fitness traits in dairy cattle that will involve unnecessary sacrifices in lifetime economic merit.

The research proposed is technically feasible. The proposal to implement national genetic evaluations for male and female fertility continues a portion of the previous S-284 project. Selection indexes to improve lifetime economic merit were also developed in the previous S- 284 project, providing another opportunity to build on an existing framework. Crossbreeding proposals have been reviewed and accepted by coworkers at cooperating institutions prior to this project. We limit the number of experimental animals at each station to a sustainable number that allows other work to continue.

Pooled effort in the crossbreeding project is essential to evaluate sufficient experimental animals for reliable results. Further, no one station could conduct an equally valuable project because of confounding of breed group and herd effects. Multi-state projects to advance genetic evaluation of health, fitness, and reproductive traits are necessary because of the magnitude of these problems in animal breeding. Finally, acceptance of revised national selection indexes by the industry is much more likely if endorsed by the leading research institutions in dairy cattle breeding.

We anticipate that national genetic evaluations for fertility traits will help equip the dairy industry to reverse a gradual decline in reproductive performance of high producing dairy animals. We hold the same expectation for health and fitness traits. Published results from carefully controlled crossbreeding trials will help producers make more informed decisions about the possible role of crossbreeding in their operations. Finally, continued development of national sire selection indexes for lifetime economic merit is essential for US dairy breeders to compete globally in the economic production of dairy products. Such efforts are also essential to insuring a plentiful supply of affordable dairy products for the US consumer.

The National Association of Animal Breeders, a trade organization of the US artificial insemination (AI) industry and one of several stakeholders for genetic research, identified the following research priorities in October 2001: High priority: “Fertility: Work toward development of a national genetic evaluation system for male and female fertility traits. Consider an adjustment for days open in the current lactation for milk, fat, and protein evaluations.” “Crossbreeding: Work toward an improved system for coding crossbred animals, and consider methods to incorporate records of crossbred progeny into the national genetic evaluation system.” Medium priority: “Calving ease: Refine the data editing and genetic evaluation systems for direct (sire of calf) calving ease, and develop a system for evaluation of maternal (sire of cow) calving ease.” The objectives of this project include these specific priorities. In addition, the objectives are structured to anticipate priorities of producers (our most important stakeholders) in the future.

Reproductive costs for dairy cattle are increasing (Lindhe and Philipsson, 1999; Lucy, 2001; Royal et al., 2000). Poor reproductive performance often leads to premature culling of dairy cows. Lucy, 2001, showed that calving interval increased from just fewer than 13.5 months in 1980 to more than 14.5 months in 2000 in 143 commercial herds. Number of inseminations per conception increased from 2.0 to more than 3.5 in the same time period. A UK study by Royal et al., 2000 showed that pregnancy rate to first insemination declined from 56% in the late 1970’s to 40% in the late 1990’s while percentage of cows with abnormal ovarian hormone patterns increased from 32 to 44%. A decline in fertility of 0.45% per year between 1975 and 1997 was reported in the United States (Butler and Smith, 1989; Beam and Butler, 1999). A study in progress (R. H. Miller, Personal communication, 2001), documents a substantial decline in first service conception rate from 53% to 45% from 1983 to 1999, with greatest declines in the most recent years.

Meanwhile, milk production per cow per lactation increased from 17,444 lbs to 25,013 lbs from 1978 to 1998 for the Holstein breed. Mean breeding values for milk of Holstein cows increased by 4,829 lbs during this period (http://aipl.arsusda.gov/main/data.html#gtrend). High producing cows are increasingly difficult to breed and are subject to higher health costs than cows of lower genetic merit for production (Cassell, 2001). Lucy (2001) implicated management systems with larger herd sizes and more cows per worker, administration of recombinant bST, and increased levels of inbreeding as contributing factors to declining reproductive performance.

Production recording and genetic evaluation systems for milk yield and composition have evolved well beyond those for reproductive performance in the US. One national evaluation system for male fertility encompasses about 25% of cows in the dairy records program (Clay and McDaniel, 2001, Weigel and Rekaya, 2000) but unlike Europe (Interbull, 1998), no female fertility evaluations are published in the US. The proposed research will lead to such a system.

Genetic evaluation programs for reproductive performance are available elsewhere (The Netherlands, France, Germany, and Italy (Interbull, 1998)). International demand for semen produced domestically (NAAB, 1999) is important to maintaining current progeny testing programs. Competitiveness of the US industry could be diminished if genetic evaluations for reproductive traits are not developed. More importantly, US dairy producers need to control reproductive costs to remain competitive in an increasingly global dairy market.

New genetic diagnostic techniques can identify individual loci with major effects in certain families. Research with QTL in dairy cattle has not emphasized calving-related traits such as perinatal mortality (PM) and birth weight. Some QTL for dystocia have been reported, but further investigation of their relationship with QTL for other calving-related traits is needed. The promise of this approach is that calving-related traits and fertility, due to their low heritability, are prime targets for refined techniques for selection of sires for the AI industry. This project will search for QTL associated with calving-related traits. Genetic evaluations for fertility and other reproductive traits developed in this project will enhance identification of QTL for these traits, also. Concurrently, techniques will be developed to enhance genetic evaluation of calving related traits by inclusion of maternal effects on calving difficulty.

Crossbreeding also provides an opportunity to increase the reproductive performance, health, and efficiency of cattle by incorporating favorable genes from numerous breeds, by removing inbreeding depression, and by capitalizing on gene interactions that cause heterosis. Pure Holsteins have exceeded all other breeds of dairy cattle for milk yield. Consequently, Holsteins have dominated among the dairy herds during the past quarter century in the US, comprising more than 90% of cows in dairy herds (Young, 1984). Recent shifts toward multiple component pricing have lessened the economic importance of the Holstein breed’s advantage in milk yield.

Intensive selection for higher yield has increased relationships among animals within breed and increased the rate of casual inbreeding. Even diligent mate assignment will ultimately fail to prevent inbreeding depression, as only a limited number of potential mates have desirable enough genotypes to be used. Crossbreeding eliminates inbreeding depression and permits the expression of heterosis. Both inbreeding depression (usually negative consequences) and heterosis (typically positive effects) affect calf survival, fertility, and growth and have important consequences on health and survival of mature cows.

Genetic evaluations of US dairy cattle are calculated within each breed. Cows with sires and dams of different breeds are excluded from USDA-DHIA evaluations unless identified as part of a breed association’s “grade-up” program, which leads to purebred status in future generations. This system avoids bias in genetic evaluations within breed but provides no genetic evaluations of crossbreds and no unbiased method of comparing animals across breeds. Crossbreds should be included in genetic evaluations because limited research suggests that they may be more profitable than the average of their purebred parents. NAAB identified an improved system of recording multi-breed ancestry of dairy cattle and commensurate genetic evaluations of crossbreds as priority research interests in the immediate future.

Many of the traits that affect profitability in crosses of modern dairy breeds have not been studied in designed experiments. Indeed, all crossbreeding research involving North American breeds and strains is very dated (McAllister, 2001) if it exists at all. Generation intervals in dairy cattle and resources required to conduct breeding trials limit the ability of individual stations to address this issue effectively.

Most dairy producers trust and use genetic evaluations, but some important traits that affect profit are not now used in selection indexes including reproductive, health and fitness traits of heifers and cows. Electronic data recording on farms provides more detailed accounting of individual cow incomes and expenses than was previously possible. Research to determine which traits to record and how to record them uniformly is needed before genetic theory can be put into action, either through selection or crossbreeding. A multi-state approach is needed to gain knowledge and reach consensus on proposed goals and indexes before seeking industry acceptance. For instance, consensus among participating scientists in the validity of Net Merit (a product of S- 284 research) during its development encouraged industry adoption and use by farmers.

The ultimate objective of this coordinated research is to develop the information required to establish and achieve more profitable breeding objectives for US dairy cattle. Regional research projects in dairy cattle breeding were vital to the genetic improvement documented earlier. Widely dispersed research herds allowed producers to witness the results and contribute many of the questions addressed. We anticipate similar results from a regional approach, particularly to crossbreeding. Knowledge acquired from this research will allow development of breeding objectives better suited for several segments of the industry, such as grazers or heifer growers.

Previous multi-state projects were successful in introducing new traits for genetic selection. For example, Wisconsin, Minnesota, Maryland, and others cooperated in S-284 and its predecessor S-251 to develop methods to evaluate the lactation averages of log-transformed somatic cell scores, both nationally and internationally (SCS) (Wiggans and Shook, 1987, Boettcher et al, 1992, Shook and Schutz, 1994, Schutz 1994, Mark et al, 2001). Today, dairy producers can record other traits using a variety of on-farm software to manage databases on their own computers (Boettcher, 2001, Etherington et al, 1995). Additional research is required to make optimal use of such data.

Several studies have found strong genetic or phenotypic associations between milk yield and reproduction (Nebel and McGilliard, 1993). Spalding et al., (1975) reported that days open in the highest milk yield quartile of cows within herds was 37 days longer and conception rate at first breeding was 20.5% lower than for cows in the lowest quartile. A North Carolina study showed conception rate averaged 57% in 1174 cows producing less than 16,000 lbs milk but only 17% in 241 cows producing more than 21,500 lbs (Washburn et al., 2000). Dematawewa and Berger, 1998 found genetic correlations of milk yield with days open and with number of breedings until conception of 0.63 and 0.44.

Estrus detection is becoming more difficult. A study of 17 commercial herds that used an electronic estrus monitoring system showed that 24% of cows exhibited estrus with low intensity and short duration (Dransfield et al., 1998). Emanuelson and Oltenacu (1998) found extended interval to first breeding and prolonged days open in herds with poorer estrus detection.

Large standard deviations for gestation length and few records with normal gestation length suggest poor data quality (Zhang and Shook, 2000). When all records were used, heritability of gestation length was 13.9%. Heritability consistently increased as higher data quality standards were applied. Standards based on the standard deviation of gestation length had a stronger influence than percent of normal gestation lengths on heritability (Zhang and Shook, 2001). Additional studies of the effect of data quality on other measures of reproductive performance are needed.

Energy balance in early lactation affects health and reproductive performance of dairy cattle. Veerkamp et al. (2000) found a genetic correlation between postpartum energy balance and interval to first luteal activity. Dechow et al. (2001) found heritabilities from 0.07 to 0.20 for serially collected body condition scores (BCS) with highest heritabilities for BCS scored near first service and pregnancy check. They reported negative relationships between BCS and milk yield, but favorable relationships of BCS with days to first service and services per conception. Beam and Butler (1999), concluded that positive relationships among changes in energy balance, peripheral IGF-1, and function of dominant follicles support IGF-1 and time of recovery from negative energy balance as metabolic modulators of postpartum ovarian activity. Simultaneous consideration of production, change in BCS, and reproduction or fertility is necessary to untangle the complex relationships among them. Additional work is needed in this area.

Dystocia has a major economic impact on productivity and profitability in dairy cattle. Predictions of genetic merit for calving difficulty have been available since 1976 (Berger, 1994). A threshold model (Foulley and Gianola, 1996; Harville and Mee, 1984) was implemented in 1988. Recent comprehensive analyses of perinatal mortality (PM) in herds in the Upper Midwest showed that the incidence of PM increased from 9.5% in 1985 to 13.2% in 1996 for virgin heifers and from 5.3 to 6.6% in multi-parous cows over the same time period (Meyer et al., 2001a). Replacement value of calves lost due to PM in the U.S. is about $125.3 million per year (Meyer et al., 2001a). Sufficient data exists to permit identification of sires whose daughters have a higher than average incidence for PM with the birth of their calves. Further research is needed to understand the mode of inheritance and interrelationships among all of the calvingrelated problems. Clearly, dystocia and PM are closely related traits.

Gestation length appears to be related to PM in dairy cattle. Meyer et al. (2000) found an incidence of PM of 41.6% at gestation lengths up to two standard deviations (sd = 7.5 d) below the mean (280d) for Holsteins. Calves born with short gestation lengths were expected to be smaller sized calves than those with longer gestation lengths. Therefore, Meyer et al. (2000) concluded that PM was not just caused by big calves. Also, incidence of PM decreased, from 41.6% at -15 to -13 d to 25.7% for gestation length -4 to 2 d from the mean.

Crossbreeding has not been an objective of regional dairy breeding projects in the United States for many years. Earlier studies with experimental herds indicated that crossbreds were at least as profitable as pure Holsteins at the University of Illinois (Touchberry, 1992) and the Canadian Department of Agriculture (McAllister et al., 1994). The classically designed crossbreeding study at Illinois was conducted from 1949 to 1969 and involved Holsteins and Guernseys (Touchberry, 1992). Crossbreds exceeded purebreds by 15% on the basis of income per lactation and by 11% on the basis of income per cow per year.

The Canadian study was conducted in five research herds during the 1970s and 1980s and involved hybrid male crosses in a closed-herd breeding system (McAllister et al., 1994). Additive and non-additive genetic effects on a) lifetime milk production, b) a comprehensive array of growth, health, and reproductive traits, and c) the composite influence of these traits on lifetime economic performance were measured. Heterosis was large (>20%) for lifetime performance and some crossbred groups exceeded pure Holsteins for the trait.

Crossbreeding of Holstein and Jersey is common in New Zealand, where crossbreds comprise nearly one quarter of milk-recorded cows (Harris, 2000). New Zealand field data of first lactation production of Holstein, Jersey, and crosses of the two breeds revealed a breed effect favoring Holstein, a maternal effect favoring Jersey, and heterosis of 6% for milk production and 7% for fat production (Ahlborn-Breier and Hohenboken, 1991). First-generation Holstein-Jersey crosses exceeded pure Holsteins for fat production. Subsequent studies (Harris, 2000) showed significant heterosis for live weight, reduction of days to first mating, and survival of lactating cows (heterosis of 21%), in addition to heterosis for the production traits. Rotational crossbreeding is profitable for commercial milk production in New Zealand (Lopez-Villalobos et al., 2000).

Crossbreeding is common in tropical climates of the world, where higher producing breeds from North America and Europe are less adapted to the environment than local breeds (McDowell, 1982). In some tropical environments, crossbreeding with local breeds is essential because of low quality feedstuffs and a heavy parasite and disease load. Obviously, comparison of crossbreds and purebreds depends heavily on the environment in which cows will be performing.

Methods for genetic evaluation of crossbred cows have been developed (Swan and Kinghorn, 1992; VanRaden and Sanders, 2001). A combined evaluation of purebreds and crossbreds may be preferable when cows of different breed composition are managed together in the same herds (Harris, 1994).

National selection indexes have been expanded over time to include more traits, beginning with only milk and fat in 1971 and then protein in 1977. Net Merit was introduced as a measure of profit in 1994 and included SCS and also productive life, which was shown by previous multistate research to have high economic value (VanRaden and Wiggans, 1995). In August 2000, a revised Net Merit index developed by project S-284 added breed association conformation traits (Rogers, 1993, VanRaden, 2000) and was well received. Also in August 2000, the Holstein Association revised its national index (TPITM) to include productive life and SCS.

Traits with low heritability but high economic value such as longevity, reproduction, and disease resistance are receiving increased attention in major dairy producing nations. Genetic evaluations for these traits will become more accurate and useful if AI organizations continue the recent trend of obtaining more progeny per bull tested (Norman et al, 2001). Producers expect more complete information. If not provided domestically, bulls with such information are increasingly available from other nations. The increase in number of gene sources and number of traits has created a growing need for scientific and computer-based approaches for selection and mating of dairy cattle. By considering a range of economic or environmental conditions (Kearney et al, 2001, Ravagnolo and Misztal, 2000, Weigel et al., 1997, Weigel and Rekaya, 2000), multiple genetic rankings better matched to the needs of individual producers can be provided.

A search of the CRIS system found only one crossbreeding project involving dairy cattle: Project NC06600 led by S. P. Washburn, who it is expected will collaborate on this project. The objective of that project is to investigate productive and reproductive efficiency of crossbred [Jersey X Holstein] dairy cattle in a pasture based system. Five multi-state projects were identified that have a reproduction component: NC-113: FY 97-02; Methods to Increase Reproductive Efficiency in Cattle, NC-209: FY 97-02; Genetic Improvement of Cattle Using Molecular Genetic Information, NE-161; FY 97-02; Association of Fertility with Temporal Changes in Ovarian Function of Domestic Ruminants, S-299; FY 00-05; Enhancing Production and Reproductive Performance of Heat Stressed Dairy Cattle, and SR-IEG-72; FY 97-02; Enhancing Reproductive Efficiency in Cattle. Only the NC-209 project involves a genetic approach to improving reproductive efficiency; all others involve physiological and nutritional approaches. In the NC-209 project only one group is focusing on reproduction; in that effort cDNA libraries from reproductive tissues are being developed – an activity that does not duplicate the work we propose. We understand that the NC-113 project is planning a revision for FY 02-07. Collaboration with that group is being planned as described in the methods for objective 1 below.

Objectives (top)

1. Develop selection tools to enhance reproduction and survival using field data. (Obj. 1)
2. Explore the impact of crossbreeding on the lifetime performance of cows. (Obj. 2)
3. Develop breeding goals and appropriate indexes for optimum improvement of health, survival, reproduction, and production. (Obj. 3)

Methods(top)

Our intent is to merge data and/or pursue joint publication of results whenever science would be better served by joint effort. Such efforts are clearly warranted a priori for some specific projects and are so noted. In other cases, it is not, at this point, clearly in the best interests of science to commit to joint analysis or publication of results. We have avoided pledges of joint publication of results in such cases. We feel that it is always in the best interests of science to share results from and critique work in progress and to discuss future plans to focus those efforts and avoid unnecessary duplication.

These studies will involve variations of mixed models including combinations of random effects for animal, mating sire, and permanent environment and fixed effects as appropriate for the trait evaluated and data used. Estimation of variance components for heritabilities and genetic correlations between traits demands detailed statistical procedures such as complete consideration of pedigree relationships between individual animals and proper consideration of underlying distributions. Details are omitted from this proposal, but appropriate procedures will be used throughout the project.

Estimation of heritabilities and genetic correlations are planned for 1) male and female reproductive performance; 2) calving-related traits; 3) relationships among traits in the production-disease-fertility complex. These estimates will serve as tools for the estimation of breeding values for reproduction and survival traits on individual animals. Gestation length is unaffected by herd management and may become the standard of quality of reproductive event data in DHI as coefficient of variation is only 2.1% (Kadarmideen and Coffey, 2001; Zhang and Shook, 2000; Zhang and Shook, 2001). WI will study the effect of data quality on estimates of heritability for days from calving to first breeding or conception and non-return rate.

Conception rate to first insemination requires regular veterinary pregnancy exams not regularly available in field data. A proxy used to evaluate male fertility (Clay and McDaniel, 2001) is non-return to subsequent insemination within 70 days after first insemination (NR70). NC will estimate male and female components of NR70 simultaneously in a direct and maternal effects model. WI will examine the extreme category problem in NR70 where a contemporary group class contains only successes or failures. Variance components and breeding values will be estimated using linear and threshold models (Luo et al., 2000; Weller et al., 1988; Weller et al., 1992). Also, WI will examine effects of including repeated inseminations on reliability of genetic evaluations for NR70.

Interval from calving to first breeding is among the most important practical and easily recorded measures of reproductive performance (Thaller, 1998). In recent years, producers have implemented new technologies for synchronizing ovulation or estrus (Lucy, 2001; Weigel, 2001). Decision rules will be developed to identify herds or cows within herds that have been subjected to estrus synchronization treatments. Genetic parameters will be estimated separately for synchronized and non-synchronized first inseminations in preparation for accounting for synchronized breedings in genetic evaluation (NC, WI).

Single trait genetic evaluation for days open will be developed using the standard animal model method and a joint analysis of days open with days to first breeding and NR70 will be developed (MD). A survival analysis (Grohn et al., 1997; Kachman, 1999) will be use by WI to account for prolonged calving intervals. The approach allows animals with both known (confirmed by subsequent calving) and unknown days open (censored) to be used.

Results from Objective 1 will be used to generate a recommendation for the Council on Dairy Cattle Breeding and the Animal Improvement Programs Laboratory on appropriate reproductive traits, genetic parameters, and analytical models for genetic analysis. We expect that a national program of genetic evaluation for female fertility will grow out of this project and be implemented by the USDA-AIPL for the first time during the course of the project. Results from this new program will be published on an ongoing basis for use by producers and the dairy genetics industry. Joint publications (WI, MD, NC, perhaps others) documenting findings and recommendations for popular press and a scientific review article are planned.

Calving-Related Traits

Five stations will study calving difficulty and perinatal mortality (IA, IL, MD, NE, NY). The current evaluation system for calving difficulty (a sire model only) will be extended to a sirematernal grand sire model. This approach simultaneously considers calving difficulty as a trait of the calf and the dam (MD, NY). NY and IA and perhaps other stations will share data and collaborate on model definition and analysis.

IA and NE will study genetic effects on PM, calving difficulty, gestation length, birth weight, twin calving, and retained placenta, using data from the dairy breeding research herds at Iowa State University (Dunklee et al., 1994) and the University of Nebraska. Breeding values of sires and maternal grandsires have been estimated for PM (Meyer et al., 2001b). A granddaughter design will be used to identify Quantitative Trait Loci (QTL) effects for this trait in cooperation with the US Cooperative Dairy DNA Repository. Association between marker alleles inherited from elite sires and PTA for calving-related traits will be tested using regression analysis (IA). A related objective is to search for QTL among sires with high or low breeding values for calf survival. Similar methods previously identified the embryonic lethal condition, deficiency of uridine monophosphate synthase or DUMPS (Shanks et al., 1992) (IL).

Production-Disease-Reproduction Complex

Several stations (NE, NY, TN, WI) will investigate the joint interaction of disease and fertility. NY will study ketosis, milk fever, fertility, and longevity data from the Swedish National Recording System. This approach is possible because of an influx of US Holstein genes into the Swedish cattle population. Genotype (sire) by environment (herd) interaction, inbreeding, and indicators of metabolic stress in early lactation (fat-protein ratio) will be investigated for their effects on disease and low fertility (NY). Heritabilities of and genetic/phenotypic correlations between BCS, BCS loss in early lactation (BCSL), and milk urea nitrogen (MUN) will be estimated as will relationships with reproductive traits, metabolic disease, and milk yield using data from DRMS Raleigh and Holstein Association (TN). Metabolic disease rates among sires’ daughters will be obtained from the Scandinavian health recording systems and from several large cooperating US herds (TN). TN and NY plan to combine data analysis and publication for some of these traits. The effects of BST on and relationships of BST to production and reproductive performance will be evaluated (NE).

Three large trials by reproductive physiologists outside of this project are currently underway to evaluate reproductive management protocols. Detailed measures of reproductive function (ovulation rate, anovulation rate, cystic ovaries, conception rate, pregnancy loss from 30 to 60 days after conception, pregnancy loss after 60 days, serum progesterone concentration at 5 days after GnRH treatment or 4 days after ovulation, milk yield, BCS, retained placenta, and metritis) will be collected in all three trials. These data will be merged with sire, service sire, and dam identification and the three trials will be combined in a single analysis to estimate genetic variances and co-variances and sire by treatment interaction (WI).

Objective 2. Explore the impact of crossbreeding on the lifetime performance of cows. (return to Objectives)

Research Herds

Two approaches to document the consequences of crossbreeding will be conducted using research herds: 1) comparison of Jersey sired daughters of Holstein cows with straightbred Holsteins, and 2) classically-designed crossbreeding experiments including both straightbred Holsteins and Jerseys as well as reciprocal crosses of those two breeds.

Two stations (MN, WI) will cross portions of existing Holstein herds with Jersey AI sires. MN will cross one half of two 100-cow Holstein herds to Jersey AI sires. WI will cross one third of a 240-cow Holstein herd to Jersey AI sires. The remainder of Holstein cows at each location will be mated to Holstein AI sires. Over a two year period, approximately 200 crossbred calves (half female) should be born to compare to 200 pure Holsteins calves at MN and approximately 160 crossbred calves should result to compare to 320 pure Holstein calves at WI. Replication in the three research herds, each with somewhat different management systems, permits greater confidence in comparisons of crossbreds to pure Holsteins. Holstein-Jersey crossbreds at MN and WI will be mated to AI sires from a third dairy breed to be selected in 2002 and 2003 (MN,WI). Successive generations of pure Holsteins will be mated to Holstein AI sires to provide a control population for crossbreds of varying percentages of breed composition.

Two other institutions will contribute some data for comparisons of Jersey-Holstein crosses to straightbred Holsteins, NC and MO. Both of these herds emphasize grazing. The extent of participation of these two stations is unknown at the time this document is prepared. NC is a full participating member of other objectives in this project and has an established crossbreeding program that will yield early data. MO expects first crossbred calves born in 2002, but the project was originally established for nutrition and reproductive comparisons.

Earliest results will be comparisons of the fertility of same and different breed matings followed by survival of fetuses, calving difficulty for Holstein cows bred to Holstein or Jersey sires, birth weight, mortality, and vigor of crossbred versus purebred calves (MN, WI). Crossbred and pure Holstein heifers will be compared for mortality, growth rates, age at first breeding, fertility, age at first calving, calving weight, and BCS. First lactation animals will be compared for milk, fat, protein, health, fertility, body weight, and body condition. Ultimately, breed groups will be compared for lifetime economic merit, but not during the duration of this project.

Four stations (KY, NC, TN, VA) will each initiate a classically designed crossbreeding study of Holsteins and Jerseys. Studies will begin with equal numbers of purebred Holsteins and Jerseys with half of each foundation group bred to sires of its own breed and half to sires of the other breed. The resulting offspring should be 25% pure Holstein, 25% pure Jersey, and 50% reciprocal crosses of the two breeds. The cooperating stations will coordinate sire selection, to facilitate pooling of data. This design with purebreds of both breeds and reciprocal crosses permits the accurate estimation of heterosis and the maternal effects of the two breeds.

At full implementation, (F1’s will be born in 2003, 2004, 2005, and 2006), the total number of females across the cooperating stations is expected to be around 90 Holsteins, and equal numbers of Jerseys, Holstein-Jersey crossbreds, and Jersey-Holstein crossbreds. Breed of service sires for mating of first-generation (F1) crossbreds has not been confirmed, but rotational matings to Holstein and Jersey sires is an option, as is the use of one or more additional breeds. As with MN and WI, purebred Holstein and Jerseys will be maintained as controls for comparisons to crossbred groups of varying breed composition.

All stations (KY, NC, TN, VA) will record all estrus, breeding, and calving dates; results of reproductive exams; condition of calf at birth; calving difficulty; date and age at death; reason for culling or death; birth weight, body weight at intervals during the lifetimes of animals, postpartum BCS of cows; milk, fat, and protein production; and all health events and treatments. Our intent is to merge data from all four stations for a single analysis and joint publication of results on as many traits as possible. VA and TN will monitor estrus behavior in heifers through the Heatwatch ™ heat detection system to measure estrus behavior as one of the early comparisons of pure and crossbred groups. As with MN and WI, data on lifetime economic merit of cows will not be available until after the end of this project. Plans for joint analysis of data may be revised if data collection is slowed at some stations.

Analysis of field data from cooperating dairy herds

Cooperating commercial herds will be used to estimate heterosis for traits measurable under commercial conditions (MN, WI). Seven large California dairies have crossbred to three European breeds – Normande, Montbeliarde, and Scandanavian Red – since the spring of 2000. These dairies began providing data electronically to MN in 2001, including AI inseminations, calving ease, stillbirths, mortality, and reason for death or disposal. Milk production traits and survival from DHI will be provided starting in April 2002 when the crossbreds and their purebred contemporaries begin to calve. WI will analyze data from 20 to 40 dairy herds in WI that are crossing Holstein and Jersey breeds, though other crosses will be examined if sufficient data are available. Other stations (KY, NC, TN, VA) will seek cooperating dairy herds in their respective states that are crossbreeding and could contribute field data. Historical data in cooperating herds might provide earlier results than will be possible with designed studies with institutional herds.

Genetic evaluation of data from crossbred cows

Two stations (MD, WI) will evaluate DHI field data from a very large number of dairy herds. MD will develop a more comprehensive system to record the breeds of ancestors in DHI records. Changes in the US national genetic evaluation system will enable data from crossbred cows to be included; however, methods must be developed to determine which breeds to assign to cows for genetic evaluation purposes. Only then can sufficient data be accumulated to estimate heterosis (MD). Genetic evaluations will be developed specific to the breed of the mate, which combines the estimate of heterosis with the estimate of genetic merit for each trait (MD). Heterosis must be considered in genetic evaluation models to avoid bias in genetic evaluations of parents.

Objective 3: Develop breeding goals and appropriate indexes for optimum improvement of health, survival, reproduction, and production. (return to Objectives)

Dairy herds will be improved for more traits than just high yield by refining current breeding goals and indexes (Cassell, 2001). This objective of the project will a) evaluate new or indicator traits that can be added to the merit breeding indexes, b) estimate economic values of the new traits, c) determine optimum indexes for specific target populations (heifers, grazing herds, bST herds, etc.), and d) update economic values and revise evaluation methods for existing traits. New traits such as reproductive traits, direct and maternal calving ease, stillbirths, and BCS may deserve direct selection. Clinical mastitis, electrical conductivity of milk, milk urea nitrogen, milking speed, disposition, and metabolic or other diseases are not recorded in quantity on commercial herds, but research herds or cooperator herds could establish the value of such information.

Very few individual cow records include such information as estimates of feed and salvage value, so correlations and relationships of measured traits with non-measured incomes and expenses must be assumed or estimated from research herds (Hansen, 2000). Feed intake data will be obtained from several very large commercial herds (NE). Many stations will cooperate in estimating correlated responses from ongoing selection experiments (IA, MN) or from field data (IN, TN, VA). Some other nations have more detailed recording of disease traits and management factors than the United States and economic values for these will be estimated with cooperation of Swedish researchers (NY).

The final form of widely adopted selection indexes requires at least as much effort through consensus and informal public discussion as through formal computations. All stations will cooperate in revising the Net Merit index: NC, WI, and NY will contribute to fertility traits, IA and MD to calving ease and stillbirths, TN, VA, and MN to BCS, feed costs, and salvage value, and NY to other diseases. All stations share the goal of allowing more of the data collected on farms to contribute to genetic rankings. The global nature of the dairy germplasm industry could quickly expand this objective from multi-state to multi-national.

Genotype by environment interactions will be measured by GA, IN, and NE to determine the genetic re-ranking that occurs under conditions of heat stress, grazing, or bST use. MD will monitor the different milk prices currently reflected in separate Net Merit, Cheese Merit, and Fluid Merit indexes. NY will estimate the effect of milk and energy balance (indirect indicators such as change in fat%, BCS, ratio of fat to protein percentage, etc.) on reproductive performance, health and longevity for several herd environments using US and international data sets. A joint publication between IN and MD on appropriate selection indexes for grazing herds is anticipated.

Consensus among researchers and stakeholders helps make indexes useful both in theory and in practice. The current multi-state regional project, S-284, helped develop consensus and simplify procedures used to calculate the version of Net Merit indexes in use today. Many traits may deserve selection, but too many trait evaluations could confuse producers. Thus, health trait information will be more likely to be used properly when combined into simple indexes that are accepted by producers. The overall goal of Objective 3 is to develop those simple indexes through good science and consensus.

Measurement of Progress and Results (top)

Outputs

Objective 1 will produce refereed journal publications containing heritabilities, genetic variances, and genetic and phenotypic correlations among the reproductive traits and of the reproductive traits with milk yield and nutritional status, and identification of optimal statistical models to be used in a national genetic evaluation program. The ultimate result of Objective 2 will contrast lifetime economic merit from conception to disposal of crossbreeding animals to purebreds. Time constraints limit outputs of this project to traits measured early in life. Refereed journal publications of estimates of heterosis for many traits and the general and specific combining abilities of breeds will assist dairy producers in assessing alternative mating systems to use in their herds. Some extension publications will also result from Objective 2. While Objective 3 will produce refereed journal articles, perhaps the most important publication from this objective will be modified selection indexes distributed as part of routine USDA genetic evaluations. Supporting extension publications will be written. Breeding goals of the industry will likely closely follow the goals suggested by Objective 3.

Outcomes or Projected Impacts

Studies of reproductive performance of dairy cattle are intended to produce a national genetic evaluation by the USDA–AIPL. This program will provide estimates of genetic merit for individual bulls and cows that will be distributed widely via print media and the internet. The breeding industry will use this information in genetic improvement programs.

Profitability of dairying might be enhanced by the use of non-additive genetic effects (heterosis) via crossbreeding to supplement the improvement of additive effects of individual genes, especially for traits that influence reproduction, mortality, and health and survival of cows. Results of this project should enlighten producers on whether to crossbreed, which breeds (domestic and foreign) to include and in what sequence to use those breeds. Incorporation of crossbred field data from DHI in genetic evaluations will help assess the merit of crossbreeding for years to come.

Multi-trait selection that considers dairy cow health and reproduction can avoid the deterioration of those traits that occurs with single-trait selection for high yield. Mating programs will let breeders benefit from heterosis and dominant genes, reduce losses from inbreeding, and raise performance for traits with nonlinear economic responses. The end result will be improved animal performance and profitability, improved competitiveness of the AI industry, and reduced cost of food for consumers. Thus, the primary benefits of this research will go to consumers who can expect continued high quality and affordable prices of dairy products. Principles of genetic improvement developed for dairy cattle may also apply to many other species.

Milestones (top)

Measurement of data quality will be an early milestone, but minimum standards for data quality will depend on the genetic parameter estimates that result from various quality standards, so there will necessarily be overlap in these aspects of the research. Genetic parameters are necessary for genetic evaluations. Development and delivery of routine genetic evaluations for reproduction and fertility traits of US cattle will be the ultimate end product.

Heterosis for fertility and reproductive traits influenced by the fertilization process or by the fetus will be measured first in the crossbreeding study. Next will follow differences of purebred versus crossbred calves for calving ease of their dams, birth weight, stillbirth, and calfhood growth and mortality. Next in sequence will be comparison of purebred and crossbred heifers and cows for their own calving ease; birth weight of their calves; age and body weight at first calving; postpartum complications; milk production traits and overall health. Finally, crossbreds and purebreds will be compared for survival in herds and total economic merit.

Selection indexes are reviewed and updated periodically. Once every five years, the United States and many other nations update the genetic bases used as reference points in computing genetic evaluations. This provides a good opportunity to schedule other major revisions. Selection indexes will be revised in February 2005 along with the base change or earlier as the need arises. New traits considered for inclusion must have preliminary evaluations completed by early 2004 to allow time for debate, education, and acceptance by research and industry groups.

Outreach Plan (top)

Results will be presented at scientific and professional meetings and published in scientific journals used by animal geneticists. Presentation of results to conferences and staff members of artificial breeding companies is expected. Articles will be written for publication in dairy farm magazines. While all project investigators carry out active outreach activities, six members hold formal university appointments in extension and will provide leadership in communicating these research results to producers and AI industry leaders through Extension publications.

Organization and Governance (top)

The regional technical committee plans and coordinates research and performs other functions specified in the “Guidelines for Multistate Research Activities.” The committee meets annually to summarize and critically evaluate progress, to discuss results, and to plan data analyses, activities, reports, and publications.

The technical committee is composed of a regional administrative adviser (no vote), a representative of Cooperative States Research, Education, and Extension Service (CSREES/USDA)(no vote), and a technical representative from each participating institution (one vote per institution). Participating stations are written into the regional project outline or have an approved addendum and have their participation documented in CRIS. Multiple investigators per institution are permitted on the committee, but vote is limited to one per participating station.

The executive committee consists of secretary, chair, and immediate past chair chosen by and from voting representatives of the technical committee. A secretary is elected annually for a oneyear term and succeeds to Chair and Past-Chair in the two subsequent years. The chair notifies the technical committee, in consultation with the executive committee and approval of the administrative adviser, of meetings, and presides over meetings. The secretary records minutes of meetings, prepares the annual report, and distributes the minutes and annual station reports of the project to the committee. The executive committee functions on behalf of the technical committee in interim actions.

The following members are appointed as coordinators for the objectives to serve for the term of the project: Objective 1—George Shook; Objective 2—Les Hansen; Objective 3—Paul VanRaden. Vacancies among these coordinators will be filled by appointment by the executive committee. The research scientists who have committed resources to this regional research project are identified in this proposal under “Projected participation”.

Appendix A: Projected Participants
Appendix B: Projected Collaborators
Appendix C: References

APPENDIX B(top)

Projected Collaborators

Collaborators have been contacted and agreed to provide data, cooperate in data collection, contribute to analysis and/or interpretation, or to share in authorship of published results.

APPENDIX C(top)

References

[† indicates publications resulting from project S-284 and its predecessor, S-251.]

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