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Chad
Dechow
Penn State
University
SIRE SELECTION
CONSIDERATIONS FOR
DAIRY PRODUCERS
Adoption of genomics
Progress versus lag
Selection indexes
Breeding for healthier cows
OUTLINE
NUMBER GENOTYPED
0
5,000
10,000
15,000
20,000
25,000
30,000
1-2009
6-2010
3-2011
12-2011
9-2012
6-2013
3-2014
12-2014
9-20...
GENERATION INTERVAL
0
1
2
3
4
5
6
7
8
2005 2006 2007 2008 2009 2010 2011 2012 2013
Agewhensonborn
Sire Dam
Norman et al., ...
GENOMIC EFFECT
Number of bulls entering AI has not
shifted dramatically
Used differently
Generation interval halved for...
GENETIC PROGRESS ASSUMPTION
IntervalGeneration
Dev.St.Genetic*IntensitySelection*yreliabilit
ΔG/Year 
-800
-600
-400
-200...
UNFAVORABLE TREND AND LAG
PROGRESS & LAG EXPECTATIONS
Genetic progress
Same for elite breeders and commercial
population
Lag depends on
Generati...
Young sires
Born 2008 to 2009
 0 daughters in 2012
 ≥100 daughters in
2015
DPR
 Top 25%
Protein lbs
 Top 10%
Proven...
0
0.5
1
1.5
2
2.5
3
3.5
Young Proven
2012
2015
DPR
0
5
10
15
20
25
30
35
40
45
Young Proven
2012
2015
PROTEIN LBS
We evaluate bulls for more than 40 traits!
Time consuming
Easy to lose focus on most important traits
Start with a sel...
Protein yield example
Protein price projection = $2.48/pound
Increased feed required = $0.90/pound
Health costs = $0.0...
Protein
20%
Fat
22%
Milk
1%
PL
19%
SCS
7%
Udder
8%
Feet/legs
3%
Body size
5%
Fertility
10%
Calving ability
5%
EMPHASIS IN ...
0%
20%
40%
60%
80%
100%
Holstein TPI Jersey JPI Brown Swiss
PPR
Lifetime Net
Merit
Emphasis
Protein Fat Fertility
Producti...
REALISTIC WITH HIGH HERD
TURNOVERS?
0
1
2
3
4
5
6
7
8
9
10
Died, High
Mortality Herds
Died, Low
Mortality Herds
60-d, High
Mortality Herds
60-d, Low
Mortality...
MASTITIS EXAMPLE
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
-2.5 -0.5 1.5 3.5
STAMastitis
Udder Depth
2.4 2.8 3.2 3.6
SCS
KETOSIS EXAMPLE
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
-2.5 -0.5 1.5 3.5
STAKetosis
Dairy Form
-5 -3 -1 1 3 5
PL
OPPORTUNITIES:
GENOMIC EVALUATION OF HEALTH?
Producer records from 1996 to 2012
132,066 (ketosis) to 274,890 (mastitis)
...
Breed for extremes, or optimal?
Be realistic about your
management system
PSU trial herds
Split into high/low for dry ...
SIRE REGRESSION COEFFICIENTS
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Milk Fat Protein
High DMR
Low DMR
(Dekleva, 2012)
GENETIC CORRELATIONS WITH YIELD
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
BW BCS
High DMR
Low DMR
Dekleva et al., 2012
Use good sires
Marketing?
 Use young sires
Commercial producers?
 Young and daughter proven are both good options
 H...
Start with a selection index
Match the genotype of your cows to your
management level
Herds that struggle with cow heal...
Thanks for joining us today!
QUESTIONS?
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Sire Selection Considerations for Dairy Producers

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Dr. Chad Dechow presented this material for a DAIReXNET webinar on February 1, 2016. To see the full recorded webinar, please visit our archive at http://bit.ly/1wb83YV

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Sire Selection Considerations for Dairy Producers

  1. 1. Chad Dechow Penn State University SIRE SELECTION CONSIDERATIONS FOR DAIRY PRODUCERS
  2. 2. Adoption of genomics Progress versus lag Selection indexes Breeding for healthier cows OUTLINE
  3. 3. NUMBER GENOTYPED 0 5,000 10,000 15,000 20,000 25,000 30,000 1-2009 6-2010 3-2011 12-2011 9-2012 6-2013 3-2014 12-2014 9-2015 Females 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 1-2009 6-2010 3-2011 12-2011 9-2012 6-2013 3-2014 12-2014 9-2015 Males Holstein Jersey CDCB; 2016
  4. 4. GENERATION INTERVAL 0 1 2 3 4 5 6 7 8 2005 2006 2007 2008 2009 2010 2011 2012 2013 Agewhensonborn Sire Dam Norman et al., 2014.
  5. 5. GENOMIC EFFECT Number of bulls entering AI has not shifted dramatically Used differently Generation interval halved for sires Dams also lower Accelerated rate of genetic progress Degree is uncertain
  6. 6. GENETIC PROGRESS ASSUMPTION IntervalGeneration Dev.St.Genetic*IntensitySelection*yreliabilit ΔG/Year  -800 -600 -400 -200 0 200 400 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 $ Birth Year Net Merit Sire BV Cow BV Long lag Short lag
  7. 7. UNFAVORABLE TREND AND LAG
  8. 8. PROGRESS & LAG EXPECTATIONS Genetic progress Same for elite breeders and commercial population Lag depends on Generation interval, Reliability, Selection after daughter proof, Rate of progress in the elite population Good bulls are good bulls! It is not essential that commercial herds use young sires Mature bulls make more semen than young bulls
  9. 9. Young sires Born 2008 to 2009  0 daughters in 2012  ≥100 daughters in 2015 DPR  Top 25% Protein lbs  Top 10% Proven Bulls Born 2000 to 2007  ≥100 daughters in 2012  ≥100 added by 2015 DPR  Top 10% Protein lbs  Top 5% 2012 V. 2015
  10. 10. 0 0.5 1 1.5 2 2.5 3 3.5 Young Proven 2012 2015 DPR
  11. 11. 0 5 10 15 20 25 30 35 40 45 Young Proven 2012 2015 PROTEIN LBS
  12. 12. We evaluate bulls for more than 40 traits! Time consuming Easy to lose focus on most important traits Start with a selection index HOW MANY TRAITS?
  13. 13. Protein yield example Protein price projection = $2.48/pound Increased feed required = $0.90/pound Health costs = $0.09/pound  [$2.48/pound - $0.90 additional feed - $0.09 additional health] * 2.78 lactations = $4.14/pound HOW IS AN INDEX VALUE DERIVED? "Parmigiano reggiano factory". CC BY- SA 3.0 via Wikimedia Commons
  14. 14. Protein 20% Fat 22% Milk 1% PL 19% SCS 7% Udder 8% Feet/legs 3% Body size 5% Fertility 10% Calving ability 5% EMPHASIS IN $NM
  15. 15. 0% 20% 40% 60% 80% 100% Holstein TPI Jersey JPI Brown Swiss PPR Lifetime Net Merit Emphasis Protein Fat Fertility Productive life Mastitis resistance Other INDEX COMPARISON
  16. 16. REALISTIC WITH HIGH HERD TURNOVERS?
  17. 17. 0 1 2 3 4 5 6 7 8 9 10 Died, High Mortality Herds Died, Low Mortality Herds 60-d, High Mortality Herds 60-d, Low Mortality Herds %died/culled Low PL High PL HERD ENVIRONMENT & PRODUCTIVE LIFE * * *P<0.05 Dechow et al., 2012 * *
  18. 18. MASTITIS EXAMPLE -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 -2.5 -0.5 1.5 3.5 STAMastitis Udder Depth 2.4 2.8 3.2 3.6 SCS
  19. 19. KETOSIS EXAMPLE -2 -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 -2.5 -0.5 1.5 3.5 STAKetosis Dairy Form -5 -3 -1 1 3 5 PL
  20. 20. OPPORTUNITIES: GENOMIC EVALUATION OF HEALTH? Producer records from 1996 to 2012 132,066 (ketosis) to 274,890 (mastitis) 0 10 20 30 40 50 Mastitis DA Ketosis Lameness Metritis Mean Reliability Cole et al., 2013
  21. 21. Breed for extremes, or optimal? Be realistic about your management system PSU trial herds Split into high/low for dry matter refusals  How much was left in front of the cow MATCH GENOTYPE TO ENVIRONMENT
  22. 22. SIRE REGRESSION COEFFICIENTS 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Milk Fat Protein High DMR Low DMR (Dekleva, 2012)
  23. 23. GENETIC CORRELATIONS WITH YIELD -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 0 BW BCS High DMR Low DMR Dekleva et al., 2012
  24. 24. Use good sires Marketing?  Use young sires Commercial producers?  Young and daughter proven are both good options  Head-to-head proof comparisons not recommended TAKE HOME MESSAGE
  25. 25. Start with a selection index Match the genotype of your cows to your management level Herds that struggle with cow health  Avoid HIGH dairy form  Use high PL sires  Look for new health evaluations  $Net Merit places more emphasis on productive life Herds maximizing production  Less emphasis on PL if cow health is not a concern TAKE HOME MESSAGE
  26. 26. Thanks for joining us today! QUESTIONS?

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