Dairy genetic improvement through artificial insemination, performance recording and genetic evaluation

2003 ◽  
Vol 83 (3) ◽  
pp. 385-392 ◽  
Author(s):  
B. J. Van Doormaal ◽  
G. J. Kistemaker
Keyword(s):  
Dairy Cattle ◽  
Artificial Insemination ◽  
Genetic Improvement ◽  
Genetic Evaluation ◽  
Production Traits ◽  
Genetic Progress ◽  
Genetic Gains ◽  
Holstein Breed ◽  
The Past ◽  
Genetic Evaluations

Artificial insemination (AI) of dairy cattle in Canada was started more than half a century ago and today it is estimated that at least 75% of all dairy cattle nationally are bred using this common reproductive technology. A Best Linear Unbiased Prediction sire model for estimating genetic evaluations for production traits was introduced in 1975. The combination of extensive use of AI with genetic evaluations for bulls and cows has resulted in significant phenotypic and genetic gains over the past 20 yr. In the Holstein breed, mature equivalent yields have increased by an average of 200 kg milk, 7.0 kg fat and 6.3 kg protein per year since 1980. Genetically, the relative emphasis realized for production traits versus overall type during the past 5 yr has followed the 60:40 breeding goal represented in the Lifetime Profit Index, which has increased at an average rate of 0.28 standard units per year. Examination of the generation interval in the Canadian Holstein breed, associated with each of the four pathways for genetic improvement, indicates a 46% increase in the rate of annual genetic gain today compared to 20 yr ago. The increased accuracy and intensity of selection associated with the use of AI and genetic evaluations have also contributed to the rates of phenotypic and genetic progress achieved over the years. In the future , AI will continue to be a critical component of the genetic gains possible in dairy cattle breeding but it will be complemented by other reproductive technologies aimed at further reducing generation intervals and increasing the accuracy and selection of intensity, especially on the female side. Key words: Dairy cattle, artificial insemination, genetic progress, genetic evaluation

ARE TODAY’S DAIRY CATTLE BREEDING PROGRAMS SUITABLE FOR TOMORROW’S PRODUCTION REQUIREMENTS?

1980 ◽  
Vol 60 (2) ◽  
pp. 253-264 ◽  
Author(s):  
A. J. McALLISTER
Keyword(s):  
Dairy Cattle ◽  
Genetic Improvement ◽  
Herd Size ◽  
Breeding Program ◽  
Genetic Trend ◽  
Genetic Progress ◽  
Breeding Programs ◽  
Holstein Breed ◽  
Dairy Cattle Breeding ◽  
National Production

In the last decade the dairy cattle population has declined to a level of 1.9 million cows in 1978 with about 56% of these cows bred AI and nearly 20% of the population enrolled in a supervised milk recording program. The decline in cow numbers has been accompanied by an increase in herd size and production per cow. The current breeding program of the dairy industry is a composite of breeding decisions made by AI organizations, breeders who produce young bulls for sampling and all dairymen who choose the sires and dams of their replacement heifers. Estimates of genetic trend from 1958–1975 for milk production in the national milk recorded herd range from 21 to 55 kg per year for the four dairy breeds with Holsteins being 41 kg per year. Both differential use of superior proven sires and improved genetic merit of young bulls entering AI studs contribute to this genetic improvement. Various national production and marketing alternatives were examined. Selection is a major breeding tool in establishing a breeding program to meet national production requirements for milk and milk products once the selection goal is defined. AI and young sire sampling programs will continue to be the primary vehicle for genetic improvement through selection regardless of the selection goal. The current resources of milk-recorded cows bred AI is not being fully utilized to achieve maximum genetic progress possible from young sire sampling indicate that the number of young bulls sampled annually in the Holstein breed could be tripled with the existing milk-recorded and AI bred dairy cow population. Expanded milk recording and AI breeding levels could increase the potential for even further genetic improvement. The potential impact of selection for other traits, crossbreeding and the use of embryo transfer of future breeding programs is highlighted.

Autoregressive repeatability model for genetic evaluation of longitudinal reproductive traits in dairy cattle

2020 ◽  
Vol 87 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Hugo T. Silva ◽  
Paulo S. Lopes ◽  
Claudio N. Costa ◽  
Fabyano F. Silva ◽  
Delvan A. Silva ◽  
...  
Keyword(s):  
Dairy Cattle ◽  
Environmental Effects ◽  
Rank Correlation ◽  
Reproductive Traits ◽  
Genetic Evaluation ◽  
Information Criteria ◽  
Ar Model ◽  
Genetic Progress ◽  
Data Set ◽  
Genetic Evaluations

AbstractWe investigated the efficiency of the autoregressive repeatability model (AR) for genetic evaluation of longitudinal reproductive traits in Portuguese Holstein cattle and compared the results with those from the conventional repeatability model (REP). The data set comprised records taken during the first four calving orders, corresponding to a total of 416, 766, 872 and 766 thousand records for interval between calving to first service, days open, calving interval and daughter pregnancy rate, respectively. Both models included fixed (month and age classes associated to each calving order) and random (herd-year-season, animal and permanent environmental) effects. For AR model, a first-order autoregressive (co)variance structure was fitted for the herd-year-season and permanent environmental effects. The AR outperformed the REP model, with lower Akaike Information Criteria, lower Mean Square Error and Akaike Weights close to unity. Rank correlations between estimated breeding values (EBV) with AR and REP models ranged from 0.95 to 0.97 for all studied reproductive traits, when the total bulls were considered. When considering only the top-100 selected bulls, the rank correlation ranged from 0.72 to 0.88. These results indicate that the re-ranking observed at the top level will provide more opportunities for selecting the best bulls. The EBV reliabilities provided by AR model was larger for all traits, but the magnitudes of the annual genetic progress were similar between two models. Overall, the proposed AR model was suitable for genetic evaluations of longitudinal reproductive traits in dairy cattle, outperforming the REP model.

Generation intervals in Canadian dairy cattle herds

2020 ◽  
Vol 100 (1) ◽  
pp. 175-183
Author(s):  
Bernard Ato Hagan ◽  
Roger Cue
Keyword(s):  
Environmental Factors ◽  
Dairy Cattle ◽  
Artificial Insemination ◽  
Dairy Farms ◽  
Path Selection ◽  
Housing System ◽  
Genetic Progress ◽  
Brown Swiss ◽  
Holstein Breed ◽  
Cattle Herds

Genetic evaluation records for the Canadian Holstein, Ayrshire, Jersey, and Brown Swiss bulls and cows born from 1950 and 1960, respectively, were used to study the generation intervals (L) along the four-path selection model. The objectives of the study were to determine the L in the four dairy cattle breeds and the effects of some environmental factors and variations among herds or artificial insemination (AI) studs on the L achieved. Total L of the four selection paths was reduced by 55% from 29.2 yr in 1980 to 13.2 yr in 2016 in the Holstein breed. Substantial reductions in total L were also observed in the Ayrshire, Jersey, and Brown Swiss populations between 1980 and 2016. Unlike progeny year of birth, housing system, agricultural region in Québec (region) — as well as their interaction — were not important on realised L of sires and dams used on Canadian dairy farms. There were significant variations among herds and AI studs on the age of sires and dams used for breeding. The considerable variations in realised L among herds and AI studs offer opportunities to increase the annual rate of genetic progress in the four Canadian dairy cattle populations.

scholarly journals Invited review: Reproductive and genomic technologies to optimize breeding strategies for genetic progress in dairy cattle

2018 ◽  
Vol 61 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Allison Fleming ◽  
Emhimad A. Abdalla ◽  
Christian Maltecca ◽  
Christine F. Baes
Keyword(s):  
Dairy Cattle ◽  
Production Traits ◽  
Milk Production Traits ◽  
Genetic Progress ◽  
Breeding Programs ◽  
The Past ◽  
Genomic Technologies ◽  
Technological Advances ◽  
Cattle Breeders ◽  
The Many

Abstract. Dairy cattle breeders have exploited technological advances that have emerged in the past in regards to reproduction and genomics. The implementation of such technologies in routine breeding programs has permitted genetic gains in traditional milk production traits as well as, more recently, in low-heritability traits like health and fertility. As demand for dairy products increases, it is important for dairy breeders to optimize the use of available technologies and to consider the many emerging technologies that are currently being investigated in various fields. Here we review a number of technologies that have helped shape dairy breeding programs in the past and present, along with those potentially forthcoming. These tools have materialized in the areas of reproduction, genotyping and sequencing, genetic modification, and epigenetics. Although many of these technologies bring encouraging opportunities for genetic improvement of dairy cattle populations, their applications and benefits need to be weighed with their impacts on economics, genetic diversity, and society.

scholarly journals The impact of genetic selection on greenhouse-gas emissions in Australian dairy cattle

2017 ◽  
Vol 57 (7) ◽  
pp. 1451 ◽  
Author(s):  
Jennie E. Pryce ◽  
Matthew J. Bell
Keyword(s):  
Dairy Cattle ◽  
Milk Production ◽  
Genetic Gain ◽  
Genetic Improvement ◽  
Genetic Selection ◽  
Ghg Emissions ◽  
Production Traits ◽  
Milk Production Traits ◽  
The Past ◽  
Milk Solids

In Australia, dairy cattle account for ~12% of the nation’s agricultural greenhouse-gas (GHG) emissions. Genetic selection has had a positive impact, reducing GHG emissions from dairy systems mainly due to increased production per cow, which has led to (1) requiring fewer cows to produce the same amount of milk and (2) lowering emissions per unit of milk produced (emission intensity). The objective of the present study was to evaluate the consequences of previous and current genetic-selection practices on carbon emissions, using realised and predicted responses to selection for key traits that are included in the Australian national breeding objective. A farm model was used to predict the carbon dioxide equivalent (CO2-eq) emissions per unit change of these traits, while holding all other traits constant. Estimates of the realised change in annual CO2-eq emissions per cow over the past decade were made by multiplying predicted CO2-eq emissions per unit change of each trait under selection by the realised rates of genetic gain in each of those traits. The total impact is estimated to be an increase of 55 kg CO2-eq/cow.year after 10 years of selection. The same approach was applied to future CO2-eq emissions, except predicted rates of genetic gain assumed to occur over the next decade through selection on the Balanced Performance Index (BPI) were used. For an increase of AU$100 in BPI (~10 years of genetic improvement), we predict that the increase of per cow emissions will be reduced to 37 kg CO2-eq/cow.year. Since milk-production traits are a large part of the breeding goal, the GHG emitted per unit of milk produced will reduce as a result of improvements in efficiency and dilution of emissions per litre of milk produced at a rate estimated to be 35.7 g CO2-eq/kg milk solids per year in the past decade and is predicted to reduce to 29.5 g CO2-eq/kg milk solids per year after a conservative 10-year improvement in BPI (AU$100). In fact, cow numbers have decreased over the past decade and production has increased; altogether, we estimate that the net impact has been a reduction of CO2-eq emissions of ~1.0% in total emissions from the dairy industry per year. Using two future scenarios of either keeping the number of cows or amount of product static, we predict that net GHG emissions will reduce by ~0.6%/year of total dairy emissions if milk production remains static, compared with 0.3%/year, if cow numbers remain the same and there is genetic improvement in milk-production traits.

scholarly journals Recent developments in cattle, pig, sheep and horse breeding - a review

2014 ◽  
Vol 83 (4) ◽  
pp. 327-340 ◽  
Author(s):  
Alena Svitáková ◽  
Jitka Schmidová ◽  
Petr Pešek ◽  
Alexandra Novotná
Keyword(s):  
Dairy Cattle ◽  
Performance Test ◽  
Evaluation Method ◽  
Genetic Evaluation ◽  
Random Regression ◽  
Farm Animals ◽  
Breeding Objectives ◽  
Recent Developments ◽  
Genetic Evaluations ◽  
Method Of Evaluation

The aim of this review was to summarize new genetic approaches and techniques in the breeding of cattle, pigs, sheep and horses. Often production and reproductive traits are treated separately in genetic evaluations, but advantages may accrue to their joint evaluation. A good example is the system in pig breeding. Simplified breeding objectives are generally no longer appropriate and consequently becoming increasingly complex. The goal of selection for improved animal performance is to increase the profit of the production system; therefore, economic selection indices are now used in most livestock breeding programmes. Recent developments in dairy cattle breeding have focused on the incorporation of molecular information into genetic evaluations and on increasing the importance of longevity and health in breeding objectives to maximize the change in profit. For a genetic evaluation of meat yield (beef, pig, sheep), several types of information can be used, including data from performance test stations, records from progeny tests and measurements taken at slaughter. The standard genetic evaluation method of evaluation of growth or milk production has been the multi-trait animal model, but a test-day model with random regression is becoming the new standard, in sheep as well. Reviews of molecular genetics and pedigree analyses for performance traits in horses are described. Genome – wide selection is becoming a world standard for dairy cattle, and for other farm animals it is under development.

scholarly journals Joint genetic analysis for dairy cattle performance across countries in sub-Saharan Africa

2020 ◽  
Vol 50 (4) ◽  
pp. 507-520
Author(s):  
O. Opoola ◽  
G. Banos ◽  
J.M.K. Ojango ◽  
R. Mrode ◽  
G. Simm ◽  
...  
Keyword(s):  
South Africa ◽  
Dairy Cattle ◽  
Genetic Gain ◽  
Genetic Evaluation ◽  
Sub Saharan Africa ◽  
Genetic Progress ◽  
Germplasm Exchange ◽  
Sub Saharan ◽  
Estimated Breeding Values ◽  
Seven Generations

This study assessed the feasibility of across-country genetic evaluation of dairy cattle in sub-Saharan Africa where data on livestock production are scarce. Genetic parameters were estimated for the 305-day milk yield in the first lactation and across five lactations, for age at first calving and for interval between first and second calving. Estimated breeding values of individual animals for these traits were calculated. There were records from 2 333, 25 208, and 5 929 Holstein cows in Kenya, South Africa, and Zimbabwe, and 898 and 65134 Jersey cows from Kenya and South Africa. Genetic gain from sire selection within and across countries. was predicted Genetic links between countries were determined from sires with daughters that had records in two or more countries, and from common ancestral sires across seven generations on both the maternal and paternal sides of the pedigree. Each country was treated as a trait in the across-country evaluation. The results showed that genetic variance and heritability were not always estimable within country, but were significantly different from zero in the across-country evaluation. In all three countries, there was greater genetic gain in all traits from an across-country genetic evaluation owing to greater accuracy of selection compared with within country. Kenya stood to benefit most from an across-country evaluation, followed by Zimbabwe, then South Africa. An across-country breeding programme using joint genetic evaluation would be feasible, provided that there were genetic links across countries, and would provide a platform for accelerated genetic progress through selection and germplasm exchange between sub-Saharan African countries.Keywords: across-country genetic evaluation, genetic connectedness, genetic progress

Optimum selection indexes for production traits of holstein/friesian cattle in britain

1989 ◽  
Vol 1989 ◽  
pp. 17-17
Author(s):  
J.P. Gibson
Keyword(s):  
Genetic Improvement ◽  
Selection Index ◽  
Linear Functions ◽  
Production Traits ◽  
Economic Optimization ◽  
Genetic Progress ◽  
Index Approach ◽  
Production Environments ◽  
Non Linear ◽  
Production Economic

The goal of livestock genetic improvement is maximun increase in the economic efficiency of production (economic merit). When several traits contribute to economic merit, optimum genetic improvement can often be achieved by use of a discriminant function of available information (known as a selection index) which maximises expected genetic progress in the aggregate genotype, economic merit. This approach assumes that economic merit is a linear function of genetically controlled outputs. Although this may not always be true, since genetic responses are usually relatively small (0.005 to 0.020 of the mean per year) any non-linear effects are second-order and can generally be ignored. Economic optimization procedures which match production environments to genotypes would generate effectively non-linear functions, such non-linearity will generally be small. Thus the selection index approach can be applied, provided that functions describing economic merit are based on previously optimized production environments.

52 Genomic selection in the beef industry: Current achievements and future directions

2019 ◽  
Vol 97 (Supplement_3) ◽  
pp. 54-55
Author(s):  
Daniel W Moser ◽  
Stephen P Miller ◽  
Kelli J Retallick ◽  
Duc Lu ◽  
Larry A Kuehn
Keyword(s):  
Beef Cattle ◽  
Genomic Selection ◽  
Large Scale ◽  
Genetic Evaluation ◽  
Fiscal Year ◽  
Genomic Testing ◽  
Routine Practice ◽  
The Past ◽  
Angus Cattle ◽  
Genetic Evaluations

Abstract In the past decade, genomic testing of beef cattle has evolved from applications in research to a routine practice for many beef cattle seedstock breeders. Testing for lethal genetic conditions or parentage was many breeders’ first experience with genomic testing. While the American Angus Association (AAA) began utilizing 384 SNP genotypes in genetic evaluations in 2009, the adoption of genotyping with higher density (~50,000 SNP) arrays by AAA in 2010 launched large-scale genotyping of Angus cattle for genetic evaluation. AAA transitioned from semi-annual to weekly genetic evaluations in 2010, and cost of genotyping decreased from $139 per animal in 2011, to $37 in 2017. In fiscal year 2018, AAA members genotyped over 160,000 animals for genetic evaluation, and as of April 2019, the AAA and Canadian Angus Association joint genetic evaluation includes over 635,000 genotyped animals. Now genotyping arrays with Angus-specific SNP content are used. The primary benefit to Angus breeders has been increased accuracy of genetic prediction for young animals, especially for traits with limited phenotypic information such as carcass traits, feed intake and mature cow size. Future benefits from genotyping include identification and selection against embryonic lethal alleles, better characterization of inbreeding, and selection tools for additional traits relevant to or measured in unique environments. Electronic sensors and other novel approaches may yield previously unmeasurable phenotypes for health and efficiency traits, which can be extended to wider populations for selection using genomics. New techniques such as DNA pooling and genotyping by sequencing may reduce costs enabling widespread testing in commercial cow-calf and cattle feeding enterprises. The application of genomic selection has clearly been a significant advancement in genetic selection in Angus cattle in the past ten years. This early adoption will expedite subsequent genomic tools at an increasing rate and will foster innovation.

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