The document presents considerations for dairy producers regarding sire selection, focusing on genomic advancements and genetic progress in breeding. It discusses the evaluation of bulls across various traits, the importance of selection indices, and the matching of genetics with management systems. Key takeaways emphasize the use of good sires, the adoption of health evaluations, and the strategic matching of herd genetics to production and health goals.

1. Chad
Dechow
Penn State
University
SIRE SELECTION
CONSIDERATIONS FOR
DAIRY PRODUCERS

2. Adoption of genomics
Progress versus lag
Selection indexes
Breeding for healthier cows
OUTLINE

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. 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. 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. 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. UNFAVORABLE TREND AND LAG

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. 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. 0
0.5
1
1.5
2
2.5
3
3.5
Young Proven
2012
2015
DPR

11. 0
5
10
15
20
25
30
35
40
45
Young Proven
2012
2015
PROTEIN LBS

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. 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. 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. 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. REALISTIC WITH HIGH HERD
TURNOVERS?

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. 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. 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. 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. 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. 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. 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. 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. 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. Thanks for joining us today!
QUESTIONS?