Enhancing Quality Of Dystocia Data By Integration Into A National Dairy Cattle Production Database C. P. Van Tassell 1,2 and G. R. Wiggans 1 Animal Improvement.

Slides:



Advertisements
Similar presentations
Factors affecting milk ELISA scores of cows tested for Johne’s disease H. D. Norman 1, J. R. Wright 1 *, and T. M. Byrem 2 1 Animal Improvement Programs.
Advertisements

Impact of selection for increased daughter fertility on productive life and culling for reproduction H. D. Norman, J. R. Wright*, R. H. Miller Animal Improvement.
2006 J.B. Cole,* G.R. Wiggans, and P.M. VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
Breed composition of the United States dairy cattle herd R. L. Powell,* H. D. Norman, and J. L. Hutchison Animal Improvement Programs Laboratory, Agricultural.
2006 J.B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Genetic.
2005 George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD AIPL Projects.
2002 Curt Van Tassell Gene Evaluation and Mapping Laboratory and Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville,
India Emerging Markets Conference, May 2009 (1) Leigh Walton Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville,
WiggansARS Big Data Workshop – July 16, 2015 (1) George R. Wiggans Animal Genomics and Improvement Laboratory Agricultural Research Service, USDA Beltsville,
2001 ADSA annual meeting, July 2001 (1) Timeliness of progeny-testing through AI and percentage of bulls returned to service (abstract 1020) H.D. NORMAN,*
2007 J. B. Cole 1,*, P. M. VanRaden 1, J. R. O'Connell 3, C. P. Van Tassell 1,2, T. S. Sonstegard 2, R. D. Schnabel 4, J. F. Taylor 4, and G. R. Wiggans.
Wiggans, 2013Japanese Genomics Tour (1) Dr. George R. WiggansDr. H. Duane Norman Acting Research LeaderInterim Administrator Animal Improvement Programs.
George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD National Association.
2007 ADSA 2007 (1)H.D. Norman Effect of service sire and cow sire on gestation length H.D. Norman,* J.R. Wright, P.M. VanRaden, and J.B. Cole Animal Improvement.
NAAB Dairy Trade Mission (1) A.H. Sanders 2002 Ashley H. Sanders AIPL on the Web Accessing Data and aipl.arsusda.gov Animal Improvement Programs.
Comparison of Holstein service-sire fertility for heifer and cow breedings with conventional and sexed semen H. D. Norman*, J. L. Hutchison, and P. M.
C. P. Van Tassell, G. R. Wiggans, and L. L. M. Thornton Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville,
Norway (1) 2005 Status of Dairy Cattle Breeding in the United States Dr. H. Duane Norman Animal Improvement Programs Laboratory Agricultural Research Service,
Breed Composition Codes for Crossbred Dairy Cattle in the United States John B. Cole,* Melvin E. Tooker, Paul M. VanRaden, and Joel H. Megonigal, Jr. Animal.
AFGC Convention 2004 (1) 2004 Possibilities for Improving Dairy Cattle Performance Dr. H. Duane Norman Animal Improvement Programs Laboratory Agricultural.
REGRESSION MODEL y ijklm = BD i + b j A j + HYS k + b dstate D l + b sstate S l + b sd (S×SD m ) + b dherd F m + b sherd G m + e ijklm, y = ME milk yield,
John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD , USA The use and.
2007 Paul VanRaden, George Wiggans, Jeff O’Connell, John Cole, Animal Improvement Programs Laboratory Tad Sonstegard, and Curt Van Tassell Bovine Functional.
G. R. Wiggans and P. M. VanRaden Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
2002 George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD USDA Dairy Goat.
John B. Cole, Ph.D. Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD, USA The U.S. genetic.
Genetic correlations between first and later parity calving ease in a sire-maternal grandsire model G. R. Wiggans*, C. P. Van Tassell, J. B. Cole, and.
2007 Melvin Tooker Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD, USA
2003 Melvin Tooker, Paul VanRaden, Ashley Sanders, and George Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville,
2007 J.B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Genetic Evaluation.
Effect of temperature and humidity on gestation length H.D. Norman, J.R. Wright,* and J.B. Cole Animal Improvement Programs Laboratory, Agricultural Research.
Effects of dam’s dry period length on calf M. T. Kuhn,* J. L. Hutchison, and H. D. Norman Animal Improvement Programs Laboratory, Agricultural Research.
2007 Melvin Tooker Animal Improvement Programs Laboratory USDA Agricultural Research Service, Beltsville, MD, USA
Accuracy of reported births and calving dates of dairy cattle in the United States Poster 1705 ADSA 2001, Indiannapolis H. D. Norman *,1, J. L. Edwards,
G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2009 G.R. WiggansCouncil.
A.H. Sanders 2002 AIPL on the Web Accessing Data and aipl.arsusda.gov Ashley H. Sanders Ashley H. Sanders Animal Improvement Programs Laboratory.
John B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Best prediction.
G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2008 AIPL Centennial.
J. B. Cole 1,*, P. M. VanRaden 1, and C. M. B. Dematawewa 2 1 Animal Improvement Programs Laboratory, Agricultural Research Service, USDA, Beltsville,
J. B. Cole *, G. R. Wiggans, P. M. VanRaden, and R. H. Miller Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville,
Norman, 2014ICAR / Interbull annual meeting, Berlin, Germany, May 20, 2014 (1) Dr. H. Duane Norman Interim Administrator Council on Dairy Cattle Breeding.
H.D. Norman, J.R. Wright, and R.H. Miller Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD, USA
WiggansARS Big Data Computing Workshop (1) 2013 George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville,
Genetic and environmental factors that affect gestation length H. D. Norman, J. R. Wright, M. T. Kuhn, S. M. Hubbard,* and J. B. Cole Animal Improvement.
H.D. Norman* and J.L. Hutchison Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD , USA
2005 George R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD Reproduction.
2007 John B. Cole USDA Animal Improvement Programs Laboratory Beltsville, MD, USA 2008 Data Collection Ratings and Best Prediction.
H. Duane Norman Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD NDHIA 2009 meeting.
Multi-trait, multi-breed conception rate evaluations P. M. VanRaden 1, J. R. Wright 1 *, C. Sun 2, J. L. Hutchison 1 and M. E. Tooker 1 1 Animal Genomics.
Ashley H. Sanders and H. Duane Norman Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
2002 George R. Wiggans and Curt P. Van Tassell Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
G.R. Wiggans* 1, P.M. VanRaden 1, L.R. Bacheller 1, F.A. Ross, Jr. 1, M.E. Tooker 1, J.L. Hutchison 1, T.S. Sonstegard 2, and C.P. Van Tassell 1,2 1 Animal.
H.D. Norman* J.R. Wright, P.M. VanRaden, and M.T. Kuhn Animal Improvement Programs Laboratory Agricultural.
2004 P.M. VanRaden, M.E. Tooker*, and J.B. Cole Animal Improvement Programs Laboratory Agricultural Research Service, USDA, Beltsville, MD
Effects of dam’s dry period length on heifer development H. D. Norman and J. L. Hutchison* Animal Improvement Programs Laboratory, Agricultural Research.
2006 GEORGE R. WIGGANS Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, Maryland ,
2001 ADSA Indianapolis 2001 (1) Heterosis and Breed Differences for Yield and Somatic Cell Scores of US Dairy Cattle in the 1990’s. PAUL VANRADEN Animal.
G.R. Wiggans 1, T. A. Cooper 1 *, K.M. Olson 2 and P.M. VanRaden 1 1 Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville,
C.P. Van Tassell 1, * G.R. Wiggans 1, J.C. Philpot 1, and I. Misztal Animal Improvement Programs Laboratory Agricultural Research Service, USDA,
 The United States provided the most foreign sires of sons every year, as high as 86%.  Canada was second in most years.  Combined, North American contributed.
G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD Select Sires‘ Holstein.
G.R. Wiggans Animal Improvement Programs Laboratory Agricultural Research Service, USDA Beltsville, MD 2011 National Breeders.
MARTIN-LUTHER-UNIVERSITY HALLE-WITTENBERG Institute of Agricultural and Nutritional Sciences, Group Animal Breeding Genetic evaluations for birth weight:
CRI – Spanish update (1) 2010 Status of Dairy Cattle Breeding in the United States Dr. H. Duane Norman Animal Improvement Programs Laboratory Agricultural.
H.D. NORMAN,* R.L. POWELL, J.R. WRIGHT
Use of a threshold animal model to estimate calving ease and stillbirth (co)variance components for US Holsteins.
Where AIPL Fits In Agricultural Research Service (ARS) is the main research arm of USDA (8,000 employees with 2,000 scientists at >100 locations) Beltsville.
Percent of total breedings
Effectiveness of genetic evaluations in predicting daughter performance in individual herds H. D. Norman 1, J. R. Wright 1*, C. D. Dechow 2 and R. C.
Relationship of gestation length to stillbirth
Presentation transcript:

Enhancing Quality Of Dystocia Data By Integration Into A National Dairy Cattle Production Database C. P. Van Tassell 1,2 and G. R. Wiggans 1 Animal Improvement Programs Laboratory 1 and Gene Evaluation and Mapping Laboratory 2 Agricultural Research Service, United States Department of Agriculture Beltsville, MD , USA World Congress on Genetics Applied to Livestock Production Le Corum, Montpellier – France – August th August 20, Session 20: “Prediction of Breeding Values” No INTRODUCTION  AIPL first calculated national genetic evaluations for calving ease (CE) (dystocia).  The addition of maternal effects improves evaluations by addressing antagonism between direct and maternal genetic effects on dystocia.  Only 58% of the records submitted to AIPL include maternal grandsire (MGS) identification (ID).  Integrating the CE database with the production database was expected to improve data editing. MATERIALS and METHODS  Master file of dystocia data obtained by AIPL in 1999 is updated biannually with DHIA and AI cooperators’ data.  CE database includes two primary tables: CE records. Pedigree information supplemental to the production database.  Dystocia records are compared to the production database.  In the case of ambiguous or unknown dam ID a negative key is assigned to the dam. Allows retention of data. Preserves MGS ID.  Duplicate records were identified. Records with the same positive dam key and calving  6mo. Records with the same negative dam key and non-zero dam ID, in the same herd, and calving  6mo. Records with no dam ID, in the same herd, with the same calving date.  Editing of CE data is improved by accessing information from existing databases.  Rate of MGS ID increased via existing pedigree data.  Database supports complex queries and on-line access. RESULTS and DISCUSSION CE Database Source Data Input11,063,139 Updates373,318 Duplicates132,160 Rejects79,371 Accepted10,478,290 Accepted Records Non-Holstein62,390 Born <198089,058 Multiple birth52,996 Bull >15yr old78,749 Extracted data10,195,097 Distribution of CE scores Source Data Extracted Data CE Score Percent 1 – No Problem – Slight Problem – Needed Assistance – Considerable Force – Extreme Difficulty 1.5 Percent Distribution of CE Scores by Parity for Extracted Data  Rate of MGS ID increased from 58% to 73%.  Nearly 70% of the accepted dams were matched with the production database and 99% of these records included MGS ID.  Of dams with a negative key, only 13% included MGS ID.  Variation in the distribution of CE Score within herd was considerable.  The extraction process produce no appreciable change in the distribution of CE scores.  Percentage of male calves was 51.5 in both the accepted source data and the database extract.  The distribution of records across parity was similar in the accepted source data and the database extract.  First calvings were significantly more difficult, but little difference was seen between later parities. CONCLUSIONS Parity 1 st 2 nd ≥3 rd Accepted data Extracted data