Published in Holstein Journal
November, 1997
J.W. Wilton and Marc Lazenby
Centre for Genetic Improvement of Livestock
Animal & Poultry Science
University of Guelph
Research programs in the dairy industry are rapidly changing to a more business oriented style. Companies in all industries develop products to fill the ever-changing needs of their customers. The process of developing these new products makes use of new or improved techniques and methods that have been discovered through the research efforts of the company. These new techniques and methods are then used to make the development of the product more efficient.
Semen, live animals and embryos are the products sold by Canadian A.I. organizations and dairy cattle breeders. The process of developing these products uses many tools and systems that have been developed and refined over the years by livestock breeding researchers. Unlike many companies, which do all of their own research and development work, A.I. co-operatives and breeders obtain genetics and breeding research results from grants and contracts with research centres.
As with all businesses, the money spent on research and development by dairy breeding organizations is necessary for their continued survival. With inefficient product development a company would soon lag behind the competition. While it is often easy to see the financial returns to money invested in a tangible change, like a new machine or increased marketing, it is often difficult to see a direct return to an investment in research and development. This does not, however, decrease the realized economic return or its necessity.
Genetic improvement research aims to improve the efficiency of producing, evaluating and utilizing the best genetics in the breed. Past and future research keeps Canadians on the forefront of developing elite genetic products through efficient and accurate animal evaluation and effective breeding strategies. For example, research funded by the A.I. industry has determined the optimum numbers of sires to test based on Canada’s cow population and the number of daughters required for sufficiently high reliability. Other past research has resulted in the development and use of economic selection indexes, monthly and quarterly evaluations, international comparisons of sires, and nucleus herds.
| A Summary of 10 Years of Research: A Tribute to Dr. Charles Smith Dr. Charles Smith joined the Centre for Genetic Improvement of Livestock (CGIL) in January 1987 when he was appointed to the J.C. Rennie Chair in Animal Breeding Strategies funded by Semex and the Natural Sciences and Engineering Research Council of Canada (NSERC). Working with Dr. John Gibson their research examined a wide variety of applied problems in animal breeding.. Some of the findings from four main areas that can, and are, being applied to dairy cattle improvement are summarized below. The improvement of current breeding systems. In response to concerns about inbreeding from too few sires of sons it was determined that 10-15 effective sires of sons per generation maximized the net genetic response. Work in conjunction with Dr. Mike Lohuis showed that a dispersed open nucleus MOET breeding program could achieve close to maximum genetic response in dairy cattle. Genetic evaluation over populations and across countries offers new opportunities for additional selection and increased genetic improvement. The definition of breeding objectives and goals in improvement. Selection indexes of dairy cattle based on economic values of milk and milk components were developed. The trait ‘herd-life, adjusted for milk yield’ was determined to be a genetic trait worthy of selection. The value of new reproductive technologies in livestock improvement. It was determined that cloning offered rapid and large phenotypic gains in dairy cattle, however, the collection of oocytes from fetal ovaries did not increase genetic response. The exportation of embryos to developing countries to produce young bulls for artificial insemination was determined to be a genetically and economically sound practise. The value of other new biotechnologies in animal breeding. Methods were developed for use of molecular genetic information in genetic improvement. Projects to use molecular genetic tools to find genes controlling economically important traits in dairy cattle. This area is becoming increasingly important around the world and CGIL is expanding its efforts in this area. Sadly, Dr. Smith passed away on June 16, 1997, one week before a symposium was held in his honour at the American Dairy Science Association Annual Meeting held in Guelph. His influence and innovative work will continue to be with us for many years to come. |
Previous articles in the Holstein Journal have highlighted the effect of economic selection indexes (August 1997) and more frequent evaluations (September 1997). Future articles will explain the research behind international comparisons and nucleus herds. International comparisons, such as MACE, provide a direct comparison of sires that have been proven in various countries. "Nucleus herds" is the term used to describe the gathering of elite genetics
Nucleus herds, also known as nucleus breeding schemes, bring elite animals together in one system for a more accurate evaluation of the animals due to the absence of management differences and preferential treatment. There are many types of nucleus herds including open, closed or hybrid breeding schemes and centralized and dispersed nucleus herds. Open or closed refers to the source of new genetics. Open schemes select genetics from outside the herd where closed schemes select genetics (male and female) only from the nucleus herd. Hybrid schemes involve a combination of the two levels of outside selection. Centralized and dispersed nucleus herds refer to the location and grouping of the herd or herds. Nucleus herds, of some form, are currently operated by most of the major A.I. organizations around the world including Alta Genetics and Holland Genetics. The Regional Test Herd program currently being developed by Gencor is an example of a centralized open nucleus breeding scheme.
Very recent research in the genetic improvement of dairy cattle involves the Test Day Model that will improve the accuracy of production evaluations. Genetic markers, which someday could help buyers from A.I. organizations in their selection of bulls are being studied as are cow survival and health traits that aim to increase the profitability and efficiency of the next generation of dairy cattle.
Dr. Larry Schaeffer’s development of the Test Day Model contains great potential in the area of improving the accuracy and heritability of production proofs and making possible the evaluation of persistency of lactation. Testing is currently being done in co-operation with Canadian Dairy Network (CDN) to determine the benefits and effects of the Test Day Model on Canadian production proofs.
Dr. Mike Lohuis continues to be a world leader in his research on breeding strategies. Also, in association with Dr. John Gibson, he is continuing the work of Dr. Charles Smith (see sidebar) in examining the potential use of genetic markers to assist breeders and bull buyers with selection for economically important traits.
Dr. Paul Boettcher’s work is an example of the importance of producer input into research. His research deals with the survival and efficiency traits such as mastitis resistance, mobility, calving ease, feed intake and feed efficiency. Many of these research studies involve data collection from farms. Data for the mobility study is currently being collected on over 25 Ontario dairy farms from more than 3000 cows.
This research work being done by Larry, Mike and Paul is funded, primarily, through the Cattle Breeding Research Council (CBRC). The council is made up of producers, representatives from the Canadian A.I. organizations and staff from Holstein Canada and CDN. Dairy producers fund the research indirectly through the purchase of semen from Canadian A.I. Units and activities with Holstein Canada.
The important research topics of the future will be shaped by breeders and A.I. organizations in the search to develop the best genetic products as quickly as possible. Just as for companies in other industries, research must be a multi-pronged effort to build a stable and reliable future for the industry.