4 Deregressed proof (DRP) - genotyped animals Females: Own phenotypic deviation from parent average if animal should have the calculated EBV Bulls: Phenotypic deviation of daughters is used as a phenotype of the bull. Deviation from parent average of the bull if the bull should have the calculated EBV Each phenotype is only included once! 4...
5 Tested cows Females in reference group - information from 10 daughters of a bull with yield in 1. lactation Adjusted phenotype Not tested cows Adjusted phenotype Adjusted phenotype Adjusted phenotype 5...
6 DRP - use of pedigree information for tested animals Previous routine: Sire, MGS Now: Sire, Dam (Animal model) Advantage: DRP are closer to the true yield deviation of the animal 6...
7 DRP - use of pedigree information for tested animals Now Previously Pedigree: 0,5 + 0,25 = 0,75 + genetic group Pedigree: 0,5 + 0,25 + 0, ,0625 = 0, genetic group 7...
8 Calculation of BV Tested cows and proven bulls with DRP DRP = μ + Animal + e Type of output BV depends on pedigree: Genomic pedigree DGV (genomic value) Traditional pedigree EBV (traditional value) 8
9 Pedigree on the basis of SNP Genomic pedigree: 0-1!Bull!Full Sib Traditional pedigree: 0,5 9...
10 Calculation of BV Tested heifers and young bulls without DRP Missing DRP = μ + Animal + e Type of BV depends on used pedigree: Genomic pedigree DGV Traditional pedigree Pedigree index 10...
11 Blending (GEBV) Combines DGV and EBV DGV: Pedigree (genomic), performance (DRP) EBV: Pedigree (traditional), performance (DRP) Combination BUT much overlap of information New method better to avoid double counting makes it possible to include females in reference group 11...
12 Reference population January 2015 Reference population Bulls Cows Holstein a) (8400) RDC 8150 b) Jersey 2450 c) 8550 a) Includes proven bulls from NLD, FRA, DEU, ESP b) Includes proven bulls from NOR c) Includes proven bulls from USA 12
13 Genomic information gives higher reliability for candidates! Reliability for 4 latest birth years of proven bulls compared to reliability on pedigree information Example: Validation reliability on DGV 45% Validation reliability on pedigree 20% Extra reliability 25% 13
14 Extra reliability in addition to pedigree information for RDC Reference population Bulls Bulls and cows Yield 0,13 0,18 Growth 0,13 0,14 Fertility 0,14 0,14 Birth 0,18 0,18 Calving 0,02 0,02 Udder health 0,17 0,23 Other diseases 0,14 0,14 Frame 0,24 0,29 Feet&Legs 0,24 0,33 Udder 0,23 0,30 Milking speed 0,17 0,22 Temperament 0,18 0,21 Longevity 0,07 0,07 Claw health 0,11 0,11 14
15 Extra reliability in addition to pedigree information for HOL Reference population Bulls Bulls and cows Yield 0,33 0,35 Growth 0,24 0,24 Fertility 0,32 0,32 Birth 0,31 0,31 Calving 0,22 0,22 Udder health 0,39 0,41 Other diseases 0,06 0,06 Frame 0,36 0,36 Feet&Legs - - Udder 0,52 0,52 Milking speed 0, Temperament 0, Longevity 0, Claw health (0,00) (0,00) 15
16 Extra reliability in addition to pedigree information for JER Reference population Bulls Bulls and cows Yield 0,16 0,22 Fertility 0,17 0,17 Birth 0,00 0,00 Calving 0,00 0,00 Udder health 0,09 0,16 Other diseases 0,00 0,00 Frame 0,19 0,30 Feet&Legs 0,05 0,13 Udder 0,26 0,29 Milking speed 0,15 0,34 Temperament 0,00 0,00 Longevity 0,11 0,11 16
17 Difference in reliability between breeds Reliability for RDC and Jersey still 12-15% point lower than HOL but higher than before! Expect increase in reliability when more genotyped RDC and Jersey cows are included in reference populationen 17
18 Correlations - previously (May 2014) and now (since August 2014) RDC Jersey Reference Genotyped animals group: Young bulls and Cows Young bulls and Cows heifers heifers Without cows With cows
19 Genetic level Genetic level for young bulls and heifers has increased RDC: 4 index units for yield and NTM 0-2 index units for other traits with cows in the reference population 19
20 Blue = New, Red = previously 20
21 Summary What is improved? RDC/JER Previous routine New Pedigree Reference population Blending method Sire-Maternal grandsire Animal Model Important for candidates and cows only traits with strong trend Bulls Bulls and cows (8400 HOL), Developed by MTT (2010) Improved by MTT (2013/14) RDC, 8550 JER Better to avoid Double counting 21
22 Challenges Calculation of DRP Method sensitive for traits with low heritability when females are included in reference group or reference group is not uniform Causes instability in DGV Improved method is expected to be introduced in February 2015 Improvement of genomic prediction is a continuous process 22
23 Tested females per country and birth year Year Holstein RDC Jersey DNK FIN SWE DNK FIN SWE DNK FIN SWE Total HOL total : Last year: RDC total : Jersey total :
24 Level of genomic tested Holstein November 2014 Bulls with HB Bulls with out HB Females Born Number NTM Number NTM Number NTM , , , , , , , , , , , , , , , , , , , , ,2
25 Level of genomic tested RDC November 2014 Bulls with HB Bulls without HB Females Born Number NTM Number NTM Number NTM ,0 60 2, , , , , , , , , , , , , , , , , , , ,3
26 Level of genomic tested Jersey November 2014 Bulls with HB Bulls without HB Females Born Number NTM Number NTM Number NTM ,8 33-1, , , , , , , , , , , , , , , , , , ,8
27 Stated from practice First time a bull gets an EBV based on a progeny test it some times deviates/drops significantly from the GEBV but closes in on GEBV in subsequent runs and Top GEBV bulls tend to drop in level when daughters enter production
28 Current thresholds going from GEBV to EBV Trait Reliability EBV Yield, Beef 60% Type traits, temperament, milkability 15 daughters Longivity, calving traits direct 50% Mastitis, claw health, calving traits maternal, young stock survival 40% Fertility, other diseases 35% GEBV reliability are > than EBV reliability threshold
29 How well do the bulls meet expectations? HOL - Official GEBVs/EBVs for Yield Udder health NTM Depending on trait bulls Standard deviations of (EBV GEBV) Do the 1 st indices have larger STDVs
30 GEBV 1 year old genotype GEBV EBVref1 EBVref2 EBVref3 4-5 year old Genotype last evaluation before daughter information 5 year old daughter information 5 year old daughter information 5 year old daughter information
31 Expected results If a bull have GEBV NTM = 30 we do expect EBV NTM < 30! The regression βebv,gebv expected < 1 unless the bull has thousands of daughters. Why? Not Part-Whole, but to some degree independent data in the two evaluations. E(βEBV,GEBV) = r 2 EBV for independent tests
41 How well do the bulls meet expectations? RDC & JER changed genomic model in AUG14. Changed to AM and cows in reference GEBVs calculated for 4 birth years of progeny tested bulls. Bulls own EBVs and daughters EBVs not in reference population.
42 RDC - NTM βebv,gebv a little low Tend to overestimate high GEBV Bulls
43 JER - NTM
44 Conclusion Results generally looks as expected. Average EBV lower as not Part-Whole. βebv,gebv for RDC NTM looks a little low. Quite different population structure. Best GEBV group get best avg. EBV. Action Keep focus that top bulls get expected results
45 Conclusion Lower threshold r 2 EBV than GEBV r 2 Larger STDV for (EBVref1-GEBV) HOL yield Actions NAV will increase thresholds for r 2 EBV breedwise depending on GEBV r 2 Combine daughter information and genomic information (Part-Whole)
46 r2 of mean EBV 5-8 Genomic tested bulls has same expected change in EBV as one progeny tested bull 1,00 0,90 0,80 0,70 0,60 0,50 0, No genomic bulls in calculated mean EBV Focus less on single bulls and more on a group of bulls (5-8)
47 Implemented GEBV in 2014 Date February 2014 March 2014 May 2014 July/August 2014 August 2014 November 2014 Comment Adjustment Holstein longevity US Jersey bulls included in ref population Publication age decreased from 17 month to 10 month Cows in reference populations and revised blending method applied for RDC and Jersey. Results from GMACE routine evaluation published Cows in reference populations and revised blending method applied for Holstein yield traits
48 Implementation plan GEBV 2015 Date February 2015 Spring 2015 Spring 2015 Comment Cows in reference populations and revised blending method applied for Holstein remaining traits All type traits at each evaluation GEBV for females Official GEBV reliabilities Spring/summer 2015 Young stock survival 2015 Weekly evaluations 2015 Cows in ref for more traits fertility, claw health, longevity
49 More frequent genomic prediction Aim GEBV for bull calves available at a younger age than today 1. Efficient registration of animal and collection of DNA (farmer and VG) 2. More frequent and faster genotyping (GenoSkan) 3. More frequent genomic prediction (NAV) Room for improvement in all 3 steps!
50 Steps in routine genomic prediction Step 1. NAV pedigree file Information from all three countries only new animals needed 2. Check of pedigree Has to be solved in the future pedigree assignment program 3. Genotypes from GenoSkan 4. Imputing (FImpute) Check of pedigree done here as well can handle that part until step 2 is solved 5. GBLUP/SNP BLUP 6. Blending 7. Publication
51 More frequent genomic prediction Requirement for weekly evaluations More automatic Establish a simple warning/error detecting system Maybe need for more computer capacity or SNP BLUP (require GMACE test run sept 2015)
52 Work ongoing Kevin Byskov and Martha Bo Almskou in cooperation with Timmy Gregers Madsen, AU built stepwise a system up. Aim to introduce more frequent evaluation during 2015
53 Applied research focus 2015 One step (MTT) Simultaneously use of phenotypes and genotypes in evaluation More optimal use of data Use of extra SNPs added on LD (AU) Include information that some SNPs carry more information than other SNPs
54 (G)MACE MACE use national EBVs based on phenotypes (G) GMACE use national GEBVs based on genotypes 58
55 MACE versus GMACE Breeding values from MACE and GMACE are expressed on the same scale and comparable on each country scale All bulls included in GMACE are expressed on all participating countries scale also scales for countries only participating in MACE.
56 What has to be published from GMACE? All countries participating in MACE have to publish: International EBVs for all Interbull traits Interbull EBVs can be EBVs from MACE or GEBV from GMACE
57 Which countries participate in GMACE? December routine Australia, Belgium, Canada (not Semex bulls), Germany, DFS, France, England, Italy, Holland, Poland
58 Groups of countries (e.g. NLD, FRA, DEU and DFS) exchange genotypes for young AI bulls A bull with a genotype in more countries get an GEBV with the same reliability as national bulls on all country scales within the exchange group. (equivalent to a situation that a bull has daughters in all countries no conversion by use of genetic correlation) and A slightly higher reliability on 3rd country scales than if information from GMACE is coming from one country only.
59 Y-index - MACE and GMACE MACE bulls born after 2007 GMACE bulls born >2010 Australia 97,5 106,0 Canada 101,1 110,9 (without Semex) Germany 101,3 111,4 DFS 104,7 111,3 France 104,3 109,2 England 101,1 109,6 Italia 97,4 107,2 Holland 103,9 111,6
60 Udder health - MACE and GMACE MACE bulls born after 2007 GMACE bulls born >2010 Australia 96,9 - Canada 94,4 109,4 (without Semex) Germany 95,8 110,1 DFS 101,5 110,8 France 95,1 110,7 England 96,6 109,3 Italia 95,7 108,2 Holland 97,0 110,4
61 Interbull GMACE Focus on top lists Proportion of top bulls primary depending on average and number of tested bulls. Results see NAV interbull search tool
What could the pig sector learn from the cattle sector. Nordic breeding evaluation in dairy cattle Gert Pedersen Aamand Nordic Cattle Genetic Evaluation 25 February 2016 Seminar/Workshop on Genomic selection
Nordic NTM promotion NAV workshop 10th January 2013 Anders Fogh, Elina Paakala and Emma Carlén ddd History of NTM Nordic countries have unique history of total merit indices Nationally: Total merit index
Establishment of a Single National Selection Index for Canada Brian Van Doormaal, Gerrit Kistemaker and Filippo Miglior Canadian Dairy Network, Guelph, Ontario, Canada Introduction Ten years ago, in 1991,
Single-step genomic evaluation for fertility in Nordic Red dairy cattle Kaarina Matilainen, Minna Koivula, Ismo Strandén, ert P. Aamand and Esa A. Mäntysaari Background Female fertility genetic evaluations
Abstract Managing genetic groups in single-step genomic evaluations applied on female fertility traits in Nordic Red Dairy cattle K. Matilainen 1, M. Koivula 1, I. Strandén 1, G.P. Aamand 2 and E.A. Mäntysaari
19. WORLD SIMMENTAL FLECKVIEH CONGRESS The robust Fleckvieh cow breeding for fitness and health Dr. Christa Egger Danner, Dr. Christian Fürst und Dr. Hermann Schwarzenbacher, ZuchtData, Vienna, Austria
J. Anim. Breed. Genet. ISSN 0931-2668 ORIGINAL ARTICLE Strategy for applying genome-wide selection in dairy cattle L.R. Schaeffer Department of Animal and Poultry Science, Centre for Genetic Improvement
Operation of a genomic selection service in Ireland Mary McCarthy, Karl O Connell & Francis Kearney Irish Cattle Breeding Federation (ICBF), Highfield House, Bandon, Co. Cork, Ireland Abstract Since the
Use of data from electronic milking meters and perspective in use of other objective measures Anders Fogh Knowledge Center for Agriculture, Agro Food Park 15, 8200 Aarhus N, Denmark Gert Pedersen Aamand
BLUP and Genomic Selection Alison Van Eenennaam Cooperative Extension Specialist Animal Biotechnology and Genomics University of California, Davis, USA firstname.lastname@example.org http://animalscience.ucdavis.edu/animalbiotech/
Big Data, Science and Cow Improvement: The Power of Information! Brian Van Doormaal, Canadian Dairy Network (CDN) Building a Sustainable Dairy Industry, DFC Symposium November 7-8, 2017, Ottawa Our Product
Simulation study on heterogeneous variance adjustment for observations with different measurement error variance Pitkänen, T. Mäntysaari, E. A., Nielsen, U. S., Aamand, G. P., Madsen, P. and Lidauer, M.
R R Bull Selection Strategies Using Genomic Estimated Breeding Values Larry Schaeffer CGIL, University of Guelph ICAR-Interbull Meeting Niagara Falls, NY June 8, 2008 Understanding Cancer and Related Topics
Section 4 23 by Noirin McHugh & Mark McGee Introduction Carefully identifying better animals and breeding them with other superior animals will gradually improve the genetics of a herd. Enhanced genetics
J. Dairy Sci. 94 :4700 4707 doi: 10.3168/jds.2010-3765 American Dairy Science Association, 2011. Open access under CC BY-NC-ND license. Reliabilities of genomic prediction using combined reference data
Genetic Analysis of Cow Survival in the Israeli Dairy Cattle Population Petek Settar 1 and Joel I. Weller Institute of Animal Sciences A. R. O., The Volcani Center, Bet Dagan 50250, Israel Abstract The
Genetics of dairy production E-learning course from ESA Charlotte DEZETTER ZBO101R11550 Table of contents I - Genetics of dairy production 3 1. Learning objectives... 3 2. Review of Mendelian genetics...
Genomic selection +Sexed semen +Precision farming = New deal for dairy farmers? Laurent Journaux General secretary of France Génétique Elevage SIA souvenir Introduction After a long period of stability
technical bulletin Enhancements to Angus BREEDPLAN A number of enhancements have been implemented in the December 2017 Angus BREEDPLAN analysis. These enhancements are part of the ongoing maintenance and
Fundamentals of Genomic Selection Jack Dekkers Animal Breeding & Genetics Department of Animal Science Iowa State University Past and Current Selection Strategies selection Black box of Genes Quantitative
TSB Collaborative Research: Utilising i sequence data and genomics to improve novel carcass traits in beef cattle Dr Mike Coffey SAC Animal Breeding Team 1 Why are we doing this project? 1 BRITISH LIMOUSIN
Cork Marts Kevin Downing Introduction Euro-Star Index What are Euro-Stars about? How do you use the Euro-Star Index? What is Reliability? What is Genomics? Are 5-Star animals more profitable? Beef Data
The powerful new genomic selection tool Built By Angus Genetics Inc. For Angus. by angus. Angus GS sets the standard for genetic testing in Angus cattle. More power More accuracy More value As a new genomic
Simulation study on heterogeneous variance adjustment for observations with different measurement error variance Pitkänen, T. Mäntysaari, E. A., Nielsen, U. S., Aamand, G. P., Madsen, P. and Lidauer, M.
INTERBULL BULLETIN NO. 48. Berlin, Germany, May 20 21, 2014 GMACE Pilot #4: Adjuting the National Reliability Input Data P. G. Sullivan 1 and J. H. Jakoben 2 1 Canadian Dairy Network, Guelph, ON, Canada
IRISH CATTLE BREEDING FEDERATION & SHEEP IRELAND The new infrastructure for cattle and sheep breeding in Ireland. Brian Wickham Chief Executive ICBF & Sheep Ireland Irish Cattle Breeding Federation Soc.
Melding genomics and quantitative genetics in sheep breeding programs: opportunities and limits Ron Lewis Genetics Stakeholders Committee ASI Convention, Scottsdale, AZ Jan. 28, 2016 My talk Genomics road
A journey: opportunities & challenges of melding genomics into U.S. sheep breeding programs Presenter: Dr. Ron Lewis, Department of Animal Science, University of Nebraska-Lincoln Host/Moderator: Dr. Jay
IRISH CATTLE BREEDING FEDERATION Breeding for Profit from Beef Production ( ) Animal Evaluation Unit 1 Overview Overview of Irish beef industry ICBF, ICBF database & Animal Events What are genetic evaluations?
Improving fertility through management and genetics., Director of Research, Holstein USA Introduction It is a great pleasure to be giving this talk to an international group of enthusiast Holstein breeders
IRISH CATTLE BREEDING FEDERATION Information system technology for integrated animal identification, traceability and breeding the example of the Irish cattle sector. Brian Wickham (PhD) Chief Executive,
Revisiting the a posteriori granddaughter design M? +? M m + m?? G.R. 1 and J.I. Weller 2 1 Animal Genomics and Improvement Laboratory, ARS, USDA Beltsville, MD 20705-2350, USA 2 Institute of Animal Sciences,
58 Test-day models for South African dairy cattle for participation in international evaluations B.E. Mostert 1#, H.E. Theron 1, F.H.J. Kanfer 2 and E. van Marle-Köster 3 1 ARC-LBD, Private Bag X2, Irene
Experiences in the use of genomics in ruminants in Uruguay Ignacio Aguilar Instituto Nacional de Investigación Agropecuaria Uruguay ICAR 2016, Puerto Varas, Chile INIA Research Team Olga Ravagnolo Elly
Understanding Results CLARIFIDE is a DNA-marker-based technology that provides a comprehensive genetic evaluation of each animal. CLARIFIDE is a 3,000-marker panel (3K) that delivers as many as 30 production,
Breeding Program of the Bulgarian Murrah Buffalo Tzonka PEEVA *, Plamen NIKOLOV, Tania NIKOLOVA, Pencho PENCHEV and Yordanka ILIEVA Bulgarian National Association for Development of Buffalo Breeding, 3
Published June 25, 2015 Genetic evaluation using single-step genomic best linear unbiased predictor in American Angus 1 D. A. L. Lourenco,* 2 S. Tsuruta,* B. O. Fragomeni,* Y. Masuda,* I. Aguilar, A. Legarra,
Agriculture and Natural Resources FSA3068 Understanding and Using Expected Progeny Differences (EPDs) Brett Barham Associate Professor Animal Science Arkansas Is Our Campus Visit our web site at: http://www.uaex.edu
Fertility Factors Fertility Research: Genetic Factors that Affect Fertility By Heather Smith-Thomas With genomic sequencing technology, it is now possible to find genetic markers for various traits (good
FREQUENTLY ASKED QUESTIONS GENEMAX ADVANTAGE : THE SOLUTION FOR COMMERCIAL ANGUS HEIFER SELECTION August 2016 GENEMAX ADVANTAGE Q. What is GeneMax Advantage? A. GeneMax Advantage is a DNA test for prospective
BREEDPLAN EBVs The Traits Explained BREEDPLAN currently reports EBVs for a range of economically important traits. These traits include: Weight Fertility/Calving Carcase Other Birth Weight Scrotal Size
2017 Annual and Final Report on Cooperation on the Genetic Evaluation System (GES) Under the Nonfunded Cooperative Agreement (NFCA) 8042-31000-101-07 Between the Agricultural Research Service (ARS), U.S.
J. Dairy Sci. 100:10234 10250 https://doi.org/10.3168/jds.2017-12954 American Dairy Science Association, 2017. A 100-Year Review: Methods and impact of genetic selection in dairy cattle From daughter dam
1 Genetic Improvement of Functional Traits in Cattle Report from EU Concerted Action GIFT A.F. Groen Animal Breeding and Genetics Group, Wageningen Institute of Animal Sciences Wageningen University, P.O.
Dairy Cattle Backgrou d I for atio Dairying is another major Australian rural industry in which production significantly exceeds domestic requirements and Australia has emerged as one of the world s major
THE NEW INFRASTRUCTURE FOR BEEF CATTLE BREEDING IN IRELAND B Wickham 1, S Coughlan 1, A Cromie 1, M Burke 1, F Kearney 1, R Evans 1, T Pabiou 1, D Berry 2. 1 Irish Cattle Breeding Federation (www.icbf.com),
UNDERSTANDING & USING GENEMAX FOCUS TM GeneMax Focus is a genomic test created through collaboration between Angus Genetics Inc. (AGI), Certified Angus Beef (CAB) and Zoetis to help inform commercial Angus
Breeding for improved calving performance in Piemontese cattle - economic value. Andrea Albera¹, Paolo Carnier 2, and Ab F. Groen 3 ¹ANABORAPI, Strada Trinità 32/A, 12061 Carrù (CN), Italy. 2 Department
One Bar Eleven Ranch John E. Rouse Beef Improvement Center 29 th Annual BIC Bull Sale Monday April 13 th, 12:30 p.m. Selling Approximately 50 Yearling Bulls One Bar Eleven Ranch John E. Rouse Beef Improvement
Graduate Theses and Dissertations Graduate College 2014 Statistical analyses of milk traits in the New Zealand dairy cattle population Melanie Kate Hayr Iowa State University Follow this and additional
Now available exclusively through the International Brangus Breeders Association: Welcome to Igenity Brangus and the Power of Confident Selection - - - - - - - - - - - - - - - - - - - - - - - - - - - -
MCA/MSU Bull Evaluation Program 2016 Buyer Survey and Impact Report Daniel D. Buskirk*, Kevin S. Gould, and Daniel L. Grooms *Department of Animal Science Michigan State University Extension Department
BGP-Beef Genomics Program Beef genetics for accelerated economic growth presented by Thys Meyer 11 March 2014 Issue 1 Origin An initiative from South African Beef Breeders under the auspices of the South
Interpreting Holstein Association USA s Individual Prediction Report When you genomic test an animal through Holstein Association USA, you will receive a report with a variety of traits and their genomic
EVALUATION OF UDDER AND TEAT CHARACTERISTICS, CALF GROWTH, AND REPRODUCTION IN YOUNG Bos indicus Bos taurus COWS A Thesis by CODY JACK GLADNEY Submitted to the Office of Graduate Studies of Texas A&M University
Delivering Valued Genomic Products to Livestock Customers Sue DeNise, Ph.D. Sr. Director, Global Genetics R&D Veterinary Medicine Research and Development Predict The Future Now! 1 Who We Are u A Pfizer
The Data Which Breeders May Collect For BREEDPLAN Analysis Includes: Bull in Date Birth Date Birth Weight Calving Ease Calf Weights from 150days to 600days+ Scrotal Size Docility Scores Flight time (from
Proceedings, The Range Beef Cow Symposium XXIII December 3, 4 and 5, 2013 Rapid City, South Dakota Understanding and Applying Economically Relevant Traits (ERT) and Indices for the Commercial Cattle Rancher
Haplotypes, recessives and genetic codes explained This document is designed to help explain the current genetic codes which are found after the names of registered Holstein animals on pedigrees, web factsheets
Measurement error variance of testday observations from automatic milking systems Pitkänen, T., Mäntysaari, E. A., Nielsen, U. S., Aamand, G. P., Madsen, P. and Lidauer, M. H. Nordisk Avlsværdivurdering
Genetics of milk yield and fertility traits in Holstein-Friesian cattle on large-scale Kenyan farms J. M. Ojango and G. E. Pollott J ANIM SCI 2001, 79:1742-1750. The online version of this article, along
J. Dairy Sci. 98:6499 6509 http://dx.doi.org/10.3168/jds.2014-9192 American Dairy Science Association, 2015. Evaluation of genomic selection for replacement strategies using selection index theory M. P.
PROCEEDINGS OF THE SYMPOSIUM ON CONTINUOUS EVALUATION IN DAIRY CATTLE College Park, MD June 13, 1993 PROCEEDINGS OF THE SYMPOSIUM ON CONTINUOUS EVALUATION IN DAIRY CATTLE College Park, MD June 13, 1993
J. Dairy Sci. 91:4116 4128 doi:10.3168/jds.2008-1273 American Dairy Science Association, 2008. Invited Review: Crossbreeding in Dairy Cattle: A Danish Perspective M. K. Sørensen,* 1 E. Norberg,* J. Pedersen,
Veterinary World, EISSN: 2231-0916 Available at www.veterinaryworld.org/vol.7/november-2014/6.pdf RESEARCH ARTICLE Open Access Evaluation of efficiency of sire and animal in Holstein Friesian crossbred
' hoc. Aust. Soc. Anim. Prod. (1974) 10; 25 ORGANIZATION OF ON-FARM BREEDING PLANS FOR BEEF CATTLE R.G. BEILHARZ* Summary The steps for deriving an optimum breeding plan for any particular breeding situation
TECHNICAL BULLETIN November 2016 GENEMAX FOCUS - EVALUATION OF GROWTH & GRADE FOR COMMERCIAL USERS OF ANGUS GENETICS Zoetis Genetics 333 Portage Street Kalamazoo, MI 49007-4931 KEY POINTS GeneMax Focus
The development of breeding strategies for the large scale commercial dairy sector in Zimbabwe Ntombizakhe Mpofu (2002) ZaBelo Livestock Consultancy, P.O. Box 911, Bulawayo, Zimbabwe Background The Zimbabwean
J. Dairy Sci. 96 :3986 3993 http://dx.doi.org/ 10.3168/jds.2012-6133 American Dairy Science Association, 2013. Prediction of clinical mastitis outcomes within and between environments using whole-genome
PoultryTechnical LOHMANN TIERZUCHT LOHMANN TIERZUCHT GmbH will continue to conduct comprehensive performance testing and is continuously investing in extending the testing capacities as well as looking
AN165 Crossbreeding Systems in Beef Cattle 1 Gary R. Hansen 2 As selection for carcass quality has taken center stage in the beef cattle industry, cattle producers have adopted strategies that have decreased
W 471 Crossbreeding in Beef Cattle F. David Kirkpatrick, Professor Department of Animal Science Improving the productivity and efficiency of a commercial beef production operation through genetic methods
Approaches to Estimating Daily Yield from Single Milk Testing Schemes and Use of a.m.-p.m. Records in Test-Day Model Genetic Evaluation in Dairy Cattle Z. Liu, R. Reents, F. Reinhardt, and K. Kuwan United
What do I need to know about Genomically Enhanced EPD Values? Lisa A. Kriese Anderson Extension Animal Scientist Auburn University Take Home Message Genomically enhanced EPD values are used exactly like
J. Dairy Sci. 86:3756 3764 American Dairy Science Association, 2003. Estimation of Environmental Sensitivity of Genetic Merit for Milk Production Traits Using a Random Regression Model M. P. L. Calus and
CHARACTERIZATION OF HEREFORD AND TWO-BREED ROTATIONAL CROSSES OF HEREFORD W ANGUS AND SIMMENTAL CAllLE: CALF PRODUCTION THROUGH WEANING D. M. ~arshall', M. D. onf fore? and C. A. ~inke? Department of Animal
Gene Mapping of Reproduction Traits in Dairy Cattle Johanna Karolina Höglund Faculty of Veterinary Medicine and Animal Science Department of Animal Breeding and Genetics, Uppsala Faculty of Science and
A new international infrastructure for beef cattle breeding Brian W. Wickham* and João W. Dürr *Irish Cattle Breeding Federation, Highfield House, Shinagh, Bandon, Co. Cork, Ireland; and Interbull Centre,
Matching Cow Type to the Nutritional Environment Don D. Hargrove Animal Science Department University of Florida The goal in planning a management program for a commercial cow-calf operation is to maximize
A Basic Guide to BREEDPLAN EBVs INTERNATIONAL BEEF RECORDING SCHEME BREEDPLAN BREEDPLAN A General Introduction. 1 Comparing EBVs between Different Breeds...... 2 Interpreting BREEDPLAN EBVs. 3 BREEDPLAN
VISION To foster a collective, of Holstein Breed which will act as a producers to maximize and strategy for the for Canadian dairy Today s challenges 1 2 3 4 The fast-paced race to find animals with the
TECHNICAL BULLETIN December 2017 MORE INFORMED COMMERCIAL ANGUS REPLACEMENT HEIFER DECISIONS WITH GENEMAX ADVANTAGE Zoetis Genetics Angus Genetics Inc. 333 Portage Street 3201 Frederick Avenue Kalamazoo,
RECOLAD. Introduction to the «atelier 1» Genetic approaches to improve adaptation to climate change in livestock Ahmed El Beltagy (email@example.com and D. Laloë (firstname.lastname@example.org) Introduction
Navigates you towards a prosperous future Open source (i.e. license-free) operating system and database management. Internet enabled. Multi-user, multi-species and multi-lingual. Runs on low-cost servers.