variety testing theory and practice - soybean...
TRANSCRIPT
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Variety TestingTheory and Practice
Brian Diers
University of Illinois
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Outline
• Why variety testing is important
• Site selection and experimental design
• Data collection• Traits, how to score
• Data analysis
• IP protection
• Soybean diseases
• Plant breeding
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University of Illinois Morrow PlotsOldest Experimental Field in the USA"The wealth of Illinois is in her soil, and herstrength lies in its intelligent development."-Andrew Sloan Draper, President,University of Illinois, 1894-1904
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Reasons for yield and agronomic testing
• Testing of experimental lines in breeding programs to identify those that should be advanced and released.
• Testing released varieties to identify those best adapted to field environments.
• Testing agronomic methods and inputs to identify ways to increase profitability.
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Yield testing is key to crop improvement
1 9 2 0 1 9 3 0 1 9 4 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0
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U S A S o y b e a n Y i e l d
Y e a r
Se
ed
Yie
ld (
bu
/ac
)
Se
ed
Yie
ld (
kg
/ha
)
1 9 8 3
Akaike Information Criterion (AIC)
indicates that the two segment
model most probable.
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0
500
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1920 1940 1960 1980 2000 2020
USA
Average Africa
Seed Y
ield
kg/h
a
Year
Yields in Africa are equal to the USA in the 1930’s
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University of Illinois Variety Testing
• 13 locations grouped into 5 regions.
LocationConventiona
l TrialsLiberty Trials
Roundup Trials
Entries Excel PDF Excel PDF Excel PDF
Region 1: Erie, Mt. Morris & DeKalb
Excel PDF Excel PDF Excel PDF
Region 2: Monmouth, Goodfield & Dwight
Excel PDF Excel PDF Excel PDF
Region 3: Perry, New Berlin & Urbana
Excel PDF Excel PDF Excel PDF
Region 4: Belleville & St. Peter
Excel PDF Excel PDF Excel PDF
Region 5: Elkville & Harrisburg
Excel PDF Excel PDF Excel PDF
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2016 Soybean Test Results
Region 2: Roundup Resistant
2 yr 3 yr
Regional Results Monmouth Goodfield Dwight Avg Avg Regional
Yield Maturity Lodging Height Yield Yield Yield Yield Yield Protein Oil
COMPANY NAME ST1 bu/a Date in bu/a bu/a bu/a bu/a bu/a @ 13% @ 13%
Roundup Resistant Early (MG 2.6-3.1)
Asgrow AG28X7 ACC 75.7 9/18 2.3 41.1 77.6 73.0 76.5 34.2 19.0
Asgrow AG30X6 ACC 79.1 9/21 2.5 45.7 84.6 74.9 77.9 34.5 19.3
Channel 2617R2X ACC 81.5 9/14 2.7 38.8 82.5 79.9 82.1 34.6 19.7
Channel 2817R2X ACC 76.1 9/20 2.8 48.6 80.1 74.3 74.1 35.9 19.5
Channel 3116R2X ACC 80.1 9/20 2.4 45.8 92.2 74.0 74.2 34.9 18.5
Dyna-Gro S31RY86 ACC 79.5 9/17 2.7 43.9 82.0 79.7 76.8 78.4 34.8 19.0
Great Lakes 3055NRX AST+ 80.9 9/21 2.7 49.2 84.2 77.9 80.6 35.6 19.1
Hisoy HS 26X60 CC 74.3 9/14 1.6 39.8 72.8 76.3 73.7 33.8 19.9
Hisoy HS 27X60 CC 81.4 9/17 1.7 42.4 90.0 74.8 79.6 34.7 19.2
Hisoy HS 28A42 ACC 78.9 9/17 2.3 43.1 76.1 80.3 80.4 78.0 77.1 34.6 20.1
Hisoy HS 28X50 ACC 76.0 9/17 1.7 39.8 76.2 74.8 76.9 35.6 18.7
Hisoy HS 29X60 CC 76.8 9/17 2.9 43.1 76.7 79.0 74.6 34.0 19.0
Hisoy HS 31X60 CC 82.7 9/20 2.1 42.4 89.7 80.9 77.6 34.4 19.1
Monier M2766RX RAN 74.4 9/15 1.8 41.1 70.6 74.0 78.5 35.8 18.6
Monier M2837R2 RAN 81.6 9/17 2.4 43.3 81.5 84.2 79.2 79.1 34.6 20.2
Monier M2947R2 RAN 75.8 9/16 2.0 43.3 77.5 72.4 77.4 33.5 20.1
Monier M3016RX RAN 81.7 9/22 2.4 47.9 86.0 77.4 81.8 35.6 19.0
Munson 8284R2Y INTS 81.8 9/20 2.0 43.4 86.6 78.4 80.3 80.3 78.5 34.4 20.2
Munson 8306R2Y INTS 76.6 9/15 2.2 43.9 73.2 79.7 77.0 74.5 33.9 19.9
Munson 9286RR2X INTS 76.0 9/15 1.6 41.5 77.9 73.9 76.2 35.7 18.6
Munson 9316RR2X INTS 79.6 9/21 2.7 49.1 83.3 77.5 78.1 35.6 19.0
Nutech 7279 GIA 78.4 9/14 1.7 42.0 86.1 75.4 73.8 33.2 20.9
Nutech 7307 GIA 78.4 9/22 1.9 46.9 86.2 74.7 74.3 34.6 19.9
Pfister 29R25 CC 80.0 9/17 2.5 42.7 81.5 78.0 80.7 34.8 20.0
Pfister 30R205 CC 79.7 9/21 2.8 41.2 85.2 76.2 77.8 35.6 18.9
PowerPlus 28H5 EEGI 81.5 9/13 1.9 40.8 85.9 79.9 78.6 80.5 76.9 33.4 21.7
PowerPlus 31W7 PRSLD 80.1 9/22 2.3 46.2 86.2 75.8 78.2 34.7 20.0
Renk RS276NX CMXO 80.2 9/15 1.7 40.3 82.8 74.9 82.8 35.6 18.7
Renk RS306NX CMXO 80.5 9/21 2.8 48.2 84.3 78.1 79.1 35.7 18.9
Renk RS316NR2 CMXO 76.0 9/20 2.5 43.8 82.8 72.2 73.1 34.6 19.0
Renk RS317NX CMXO 83.1 9/21 2.3 42.3 88.9 80.6 79.9 34.4 19.0
Roeschley 2957CRR2 CMXV 73.8 9/17 2.1 43.9 74.9 71.0 75.5 33.8 19.9
Steyer 3110XR SS 81.0 9/21 2.4 44.1 86.2 79.7 77.1 34.1 19.1
Stone 2RX2627 ACC 80.1 9/13 2.6 39.3 80.1 78.9 81.4 34.7 19.6
Stone 2RX2827 ACC 71.0 9/17 2.7 47.3 66.1 72.5 74.5 35.6 19.6
Stone 2RX3116 ACC 71.4 9/19 2.2 45.9 69.1 70.3 74.8 34.9 18.6
Sun Praire SP31RX6 ACC 82.5 9/22 2.0 44.1 86.5 78.6 82.5 34.2 19.1
AVERAGE 77.5 2.2 43.2 80.4 75.5 76.7 34.7 19.5
L.S.D. 25% LEVEL 3.6 0.4 1.3 4.5 3.0 3.4 0.2 0.3
COEFF. OF VAR. (%) 8.4 30.2 5.4 5.9 4.3 4.7 1.6 1.6
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Setting up variety tests
• Sites need to be predictive of farmer’s fields.
• Field sites should be productive.• Results from unproductive fields often do not
differentiate varieties as all varieties will have similar poor performance.
• Fields should be as uniform as possible.• Uniform for soil type and fertility
• As little slope as possible.
• Consistent lighting (no shade)
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Need to use an appropriate experimental design
• Experimental design makes it possible to know if differences observed are meaningful.
• In experiments we:• Randomize – Gives all treatments an equal
chance of receiving a treatment. Assures unbiased estimates of treatment means and experimental error.
• Replicate – Makes it possible to estimate an experimental error and a more precise measure of treatment effects.
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Plot size
• Plots in the experiment will be 5 meters x 2 meters• 4 rows wide
• 5 meters long
• 1 meter between ranges
• 0.5 meter row spacing
• 5 cm between plants
• 1 meter alley between ranges of plots
• Harvest middle 2 rows
0.5
m
5 m
1 m
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5 m
1 m
1 m 0.5 m
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Arrange the field so replications are as square as possible• Each range of plots is 6 m (5
m plot and 1 m alley).
• Individual plots are 2 m wide.
• Assuming 30 varieties, arrange replications 10 plots wide (20 meters) and 3 ranges deep (18 meters).
• Surround field with fill plots
21 22 23 24 25 26 27 28 29 30
20 19 18 17 16 15 14 13 12 11
1 2 3 4 5 6 7 8 9 10
Rep 1
Rep 2
Rep 3
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Arrange reps so field differences are across reps
• Maximize the differences in slope, soil type and fertility across reps.
• Differences between reps is removed by the rep effect in the analysis
• Differences within reps will be part of the error effect which reduces the ability to show genotypes are significantly different.
Rep 1 Rep 2 Rep 3
Slope
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Data collection
• Need to consider what traits are important.
• Just because you can measure or count it, doesn’t mean that you need to score it.
• What is the focus of the project?• Identify soybean varieties that are adapted and high
yielding in Malawi.
• Need to identify what traits need to be measured to determine this.
• Seeds/pod? Pods/plant? Biomass? Weight of nodules?
• Focus on measure a few traits well instead of many poorly.
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Data collection
• Plant emergence
• Flower color during flowering
• Pubescence color at maturity
• Flowering date
• Maturity date
• Plant lodging
• Plant height
• Seed yield
• Plant shattering
• 100 seed weight
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Plant emergence
• Why score? Determine plant stands to identify if seed vigor influences yield
• When score? After emergence is completed
• Count the number of plants in the two middle rows
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Flower color
• Why score? Quality control
• When score? At flowering
• Flowers are either purple or white in soybean varieties (plots can be mixed).
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Pubescence color• Why score? Quality control
• When score? At maturity
• Score as grey or tawny (plants can be light tawny but this is difficult to score) (plots can be mixed)
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Date of R1 (First open flower)
• Why score? Determine adaptation
• When score? At flowering
• Score the date when 50% of the plants in a plot have at least 1 open flower
• Score the plots about every three days
• Need to push the canopy over to see flowers
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Date of R8 (Pod maturity)
• Why score? Determine adaptation
• When score? At maturity
• Score the date when 85% of the pods in a plot have turned to their mature color
• Score the plots about every three days
• Be sure to focus on pod color, not stem color
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Green stem disorderNormal maturity
Maturity scoring can be obscured by green stem disorder
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Lodging• Why score? Measure the average of how upright
the plants are in the plot
• When score? At maturity
• Score plots on a 1-5 scale (use 0.5 increments)
• Score when the plots are mature
1 2 3 4 5
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Lodging
What scores would you give these plots?
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Plant height• Why score? Measure
adaptability of varieties
• When score? At maturity
• Measure the height of plants (in cm) from the soil surface to the top node of plants
• Score when the plots are rated mature
cm
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Seed yield
• Why score? Yield is critical for variety selection
• When score? After maturity and before shattering
• Harvest the middle 2 rows of the 4-row plots
• Thresh and winnow the grain
• Measure the weight of grain at least 5 days after harvest from each plot
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Plant shattering
• Why score? Farmers need to know how much varieties shatter.
• When score? 2 weeks after maturity
• Rate each plot on a 1 to 5 scale with 1=no shattering and 5=100% of the pods shattered
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100 seed weight
•Why score? Seed size is important to growers and processors
•When score? After seeds are dry
•Count and weigh 100 seed from each plot
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Data analysis
• Analyses will be done across environments
• Collaborators will submit data from each plot to project organizers (Diers, Chigeza, Klauser).
• The data from each environment and across environments will be subjected to analysis of variance so statistics such as least significant differences can be calculated.
• GxE analyses will be conducted to identify environments that varieties are similarly adapted.
• Data will be placed in a database that is being developed by the Syngenta Foundation
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2016 Soybean Test Results
Region 2: Roundup Resistant
2 yr 3 yr
Regional Results Monmouth Goodfield Dwight Avg Avg Regional
Yield Maturity Lodging Height Yield Yield Yield Yield Yield Protein Oil
COMPANY NAME ST1 bu/a Date in bu/a bu/a bu/a bu/a bu/a @ 13% @ 13%
Roundup Resistant Early (MG 2.6-3.1)
Asgrow AG28X7 ACC 75.7 9/18 2.3 41.1 77.6 73.0 76.5 34.2 19.0
Asgrow AG30X6 ACC 79.1 9/21 2.5 45.7 84.6 74.9 77.9 34.5 19.3
Channel 2617R2X ACC 81.5 9/14 2.7 38.8 82.5 79.9 82.1 34.6 19.7
Channel 2817R2X ACC 76.1 9/20 2.8 48.6 80.1 74.3 74.1 35.9 19.5
Channel 3116R2X ACC 80.1 9/20 2.4 45.8 92.2 74.0 74.2 34.9 18.5
Dyna-Gro S31RY86 ACC 79.5 9/17 2.7 43.9 82.0 79.7 76.8 78.4 34.8 19.0
Great Lakes 3055NRX AST+ 80.9 9/21 2.7 49.2 84.2 77.9 80.6 35.6 19.1
Hisoy HS 26X60 CC 74.3 9/14 1.6 39.8 72.8 76.3 73.7 33.8 19.9
Hisoy HS 27X60 CC 81.4 9/17 1.7 42.4 90.0 74.8 79.6 34.7 19.2
Hisoy HS 28A42 ACC 78.9 9/17 2.3 43.1 76.1 80.3 80.4 78.0 77.1 34.6 20.1
Hisoy HS 28X50 ACC 76.0 9/17 1.7 39.8 76.2 74.8 76.9 35.6 18.7
Hisoy HS 29X60 CC 76.8 9/17 2.9 43.1 76.7 79.0 74.6 34.0 19.0
Hisoy HS 31X60 CC 82.7 9/20 2.1 42.4 89.7 80.9 77.6 34.4 19.1
Monier M2766RX RAN 74.4 9/15 1.8 41.1 70.6 74.0 78.5 35.8 18.6
Monier M2837R2 RAN 81.6 9/17 2.4 43.3 81.5 84.2 79.2 79.1 34.6 20.2
Monier M2947R2 RAN 75.8 9/16 2.0 43.3 77.5 72.4 77.4 33.5 20.1
Monier M3016RX RAN 81.7 9/22 2.4 47.9 86.0 77.4 81.8 35.6 19.0
Munson 8284R2Y INTS 81.8 9/20 2.0 43.4 86.6 78.4 80.3 80.3 78.5 34.4 20.2
Munson 8306R2Y INTS 76.6 9/15 2.2 43.9 73.2 79.7 77.0 74.5 33.9 19.9
Munson 9286RR2X INTS 76.0 9/15 1.6 41.5 77.9 73.9 76.2 35.7 18.6
Munson 9316RR2X INTS 79.6 9/21 2.7 49.1 83.3 77.5 78.1 35.6 19.0
Nutech 7279 GIA 78.4 9/14 1.7 42.0 86.1 75.4 73.8 33.2 20.9
Nutech 7307 GIA 78.4 9/22 1.9 46.9 86.2 74.7 74.3 34.6 19.9
Pfister 29R25 CC 80.0 9/17 2.5 42.7 81.5 78.0 80.7 34.8 20.0
Pfister 30R205 CC 79.7 9/21 2.8 41.2 85.2 76.2 77.8 35.6 18.9
PowerPlus 28H5 EEGI 81.5 9/13 1.9 40.8 85.9 79.9 78.6 80.5 76.9 33.4 21.7
PowerPlus 31W7 PRSLD 80.1 9/22 2.3 46.2 86.2 75.8 78.2 34.7 20.0
Renk RS276NX CMXO 80.2 9/15 1.7 40.3 82.8 74.9 82.8 35.6 18.7
Renk RS306NX CMXO 80.5 9/21 2.8 48.2 84.3 78.1 79.1 35.7 18.9
Renk RS316NR2 CMXO 76.0 9/20 2.5 43.8 82.8 72.2 73.1 34.6 19.0
Renk RS317NX CMXO 83.1 9/21 2.3 42.3 88.9 80.6 79.9 34.4 19.0
Roeschley 2957CRR2 CMXV 73.8 9/17 2.1 43.9 74.9 71.0 75.5 33.8 19.9
Steyer 3110XR SS 81.0 9/21 2.4 44.1 86.2 79.7 77.1 34.1 19.1
Stone 2RX2627 ACC 80.1 9/13 2.6 39.3 80.1 78.9 81.4 34.7 19.6
Stone 2RX2827 ACC 71.0 9/17 2.7 47.3 66.1 72.5 74.5 35.6 19.6
Stone 2RX3116 ACC 71.4 9/19 2.2 45.9 69.1 70.3 74.8 34.9 18.6
Sun Praire SP31RX6 ACC 82.5 9/22 2.0 44.1 86.5 78.6 82.5 34.2 19.1
AVERAGE 77.5 2.2 43.2 80.4 75.5 76.7 34.7 19.5
L.S.D. 25% LEVEL 3.6 0.4 1.3 4.5 3.0 3.4 0.2 0.3
COEFF. OF VAR. (%) 8.4 30.2 5.4 5.9 4.3 4.7 1.6 1.6
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IP protection
• It is critical to protect the intellectual property of varieties in the tests.
• Breeders have large investments in variety development that should not be compromised.
• As an inbred crop, soybean can easily be stolen and propagated.
• If this happens, soybean developers may not bring new germplasm into Malawi.
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IP protection
• Variety names will be coded in the field and names will not be provided until the test is completed.
• Talk about the importance of IP protection at field days.
• Have attendees at field days sign an agreement that they will respect IP and not steal seed.
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Questions?
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University of
Illinois
Soybean breeding
Chicago
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1 9 2 0 1 9 3 0 1 9 4 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0 2 0 2 0
0
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
0
5
1 0
1 5
2 0
2 5
3 0
3 5
4 0
U S A S o y b e a n A r e a a n d P r o d u c t i o n
Y e a r
Ha
rv
es
ted
Ar
ea
(m
illi
on
ac
or
ha
)
Pr
od
uc
tio
n (
10
0 m
illi
on
t o
r b
u)
H a r v e s t e d A r e a > < P r o d u c t i o n
4
1 6
2 0
2 4
2 8
1 2
8
a c h a
0 . 1 4
0 . 5 4
0 . 6 8
0 . 4 1
0 . 2 7
t b u
0 . 9 5
. 7 1
USDA National Agricultural Statistics Service
Soybean Area and Production in the USA
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1 9 2 0 1 9 3 0 1 9 4 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0
1 0
2 0
3 0
4 0
5 0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
U S A S o y b e a n Y i e l d
Y e a r
Se
ed
Yie
ld (
bu
/ac
)
Se
ed
Yie
ld (
kg
/ha
)
On-Farm Soybean Yield Gains USALinear 23 kg ha-1 year-1
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1 9 2 0 1 9 3 0 1 9 4 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0
1 0
2 0
3 0
4 0
5 0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
U S A S o y b e a n Y i e l d
Y e a r
Se
ed
Yie
ld (
bu
/ac
)
Se
ed
Yie
ld (
kg
/ha
)
1 9 8 3
On-Farm Soybean Yield Gains USAPre-breakpoint 21 kg ha-1 year-1
Post-breakpoint 29 kg ha-1 year-1
Akaike Information Criterion (AIC)
indicates that the two segment
model most probable.
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• How much of the yield gain in the USA is the result in improved genetics?• Yield increases are the result
of improved genetics, agronomics, environmental changes, and their interactions.
• How have soybean plants been altered to achieve greater yields?
Genetic Gain Evaluation
1 9 2 0 1 9 3 0 1 9 4 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0
1 0
2 0
3 0
4 0
5 0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
U S A S o y b e a n Y i e l d
Y e a r
Se
ed
Yie
ld (
bu
/ac
)
Se
ed
Yie
ld (
kg
/ha
)
1 9 8 3
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B
II
III
IV
(1000 bu = 27.216 tons)
Soybean Maturity Groups
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Genetic Gain Study
• Collected sets of MG II, III and IV soybean cultivars from the 1920’s to present day.• Included modern commercial
cultivars from Syngenta, Monsanto and Pioneer.
• In 2010-2011 cultivars grown:• 15 MG II locations
• 13 MG III locations
• 14 MG IV locations
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• Collected sets of MG II, III and IV soybean cultivars from the 1920’s to present day.• Included modern commercial
cultivars from Syngenta, Monsanto and Pioneer.
• In 2010-2011 cultivars grown:• 15 MG II locations
• 13 MG III locations
• 14 MG IV locations
Genetic Gain Study
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• Collected sets of MG II, III and IV soybean cultivars from the 1920’s to present day.• Included modern commercial
cultivars from Syngenta, Monsanto and Pioneer.
• In 2010-2011 cultivars grown:• 15 MG II locations
• 13 MG III locations
• 14 MG IV locations
Genetic Gain Study
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• Collected sets of MG II, III and IV soybean cultivars from the 1920’s to present day.• Included modern commercial
cultivars from Syngenta, Monsanto and Pioneer.
• In 2010-2011 cultivars grown:• 15 MG II locations
• 13 MG III locations
• 14 MG IV locations
Genetic Gain Study
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1 9 2 0 1 9 3 0 1 9 4 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0
2 0
3 0
4 0
5 0
6 0
7 0
1 5 0 0
2 2 5 0
3 0 0 0
3 7 5 0
4 5 0 0
S o y b e a n G e n e t i c Y i e l d I m p r o v e m e n t
Y e a r
Se
ed
Yie
ld (
bu
/ac
)
Se
ed
Yie
ld (
kg
/ha
)
M G I I 1 9 6 8
1 9 6 4M G I I I
M G I V 1 9 7 1
Soybean Genetic Yield ImprovementMG II 23 kg ha-1 year-1 Post-breakpoint 31 kg ha-1 year-1
MG III 23 kg ha-1 year-1 Post-breakpoint 29 kg ha-1 year-1
MG IV 19 kg ha-1 year-1 Post-breakpoint 26 kg ha-1 year-1
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1 9 2 0 1 9 3 0 1 9 4 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0
2 0
3 0
4 0
5 0
6 0
7 0
1 5 0 0
2 2 5 0
3 0 0 0
3 7 5 0
4 5 0 0
S o y b e a n G e n e t i c Y i e l d I m p r o v e m e n t
Y e a r
Se
ed
Yie
ld (
bu
/ac
)
Se
ed
Yie
ld (
kg
/ha
)
M G I I 1 9 6 8
1 9 6 4M G I I I
M G I V 1 9 7 1
Soybean Genetic Yield ImprovementMG II 23 kg ha-1 year-1 Post-breakpoint 31 kg ha-1 year-1
MG III 23 kg ha-1 year-1 Post-breakpoint 29 kg ha-1 year-1
MG IV 19 kg ha-1 year-1 Post-breakpoint 26 kg ha-1 year-1
Are the current yield increases
consistent with breeding efforts?
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1 9 2 0 1 9 3 0 1 9 4 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0
2 0
3 0
4 0
5 0
6 0
7 0
1 5 0 0
2 2 5 0
3 0 0 0
3 7 5 0
4 5 0 0
S o y b e a n G e n e t i c Y i e l d I m p r o v e m e n t
Y e a r
Se
ed
Yie
ld (
bu
/ac
)
Se
ed
Yie
ld (
kg
/ha
)
M G I I 1 9 6 8
1 9 6 4M G I I I
M G I V 1 9 7 1
Soybean Genetic Yield ImprovementOn-farm improvement 23 kg ha-1 yr-1
Genetic improvement MG II 23 kg ha-1 yr-1, MG III 23 kg ha-1 yr-1, MG IV 19 kg ha-1 yr-1
1 9 2 0 1 9 3 0 1 9 4 0 1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0 2 0 1 0
1 0
2 0
3 0
4 0
5 0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
U S A S o y b e a n Y i e l d
Y e a r
Se
ed
Yie
ld (
bu
/ac
)
Se
ed
Yie
ld (
kg
/ha
)
1 9 8 3
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1 9 2 0 1 9 4 0 1 9 6 0 1 9 8 0 2 0 0 0 2 0 2 0
1 0
2 0
3 0
4 0
5 0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
S o y b e a n Y i e l d T r e n d s : U S A & B y M G )
Y e a r
Yie
ld (
bu
/ac
)
Yie
ld (
kg
/ha
)
M G I I & I I I
M G I V
U S A
M G I I , I I I , I V
On-Farm Soybean Yield GainsMG II & III 27 kg ha-1 year-1 / MG II, III & IV 25 kg ha-1 year-1 /
USA 23 kg ha-1 year-1 / MG IV 21 kg ha-1 year-1
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1 9 2 0 1 9 4 0 1 9 6 0 1 9 8 0 2 0 0 0 2 0 2 0
1 0
2 0
3 0
4 0
5 0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
S o y b e a n Y i e l d T r e n d s : U S A & B y M G )
Y e a r
Yie
ld (
bu
/ac
)
Yie
ld (
kg
/ha
)
M G I I & I I I
M G I V
U S A
M G I I , I I I , I V
On-Farm Soybean Yield GainsMG II & III 27 kg ha-1 year-1 / MG II, III & IV 25 kg ha-1 year-1 /
USA 23 kg ha-1 year-1 / MG IV 21 kg ha-1 year-1
Approximately 2/3 of yield
increases the result of
genetic improvement
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1 9 2 5 1 9 4 5 1 9 6 5 1 9 8 5 2 0 0 5
1 7 0
2 1 0
3 1 0
3 8 0
Y e a r o f C u l t i v a r R e l e a s e
Se
ed
Oil
& P
ro
te
in (
g k
g-
1)
M G I I M G I I I M G I V
Changes in Seed Protein and OilAcross MG Protein -0.2 g kg-1 yr-1 / Oil 0.1 g kg-1 yr-1
Protein
Oil
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Vegetative Growth Duration (Days from V1 to R1) with Early and Late Planting
Year of Cultivar Release
1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Vegeta
tive G
row
th D
ura
tion (
Days)
0
5
10
15
20
25
30
35
40
45
MGII - May PD; y = -0.045 (±0.01)x +121.7
MGII - June PD; y = -0.009 (±0.01)x +46.4
MGIII - May PD; y = -0.069 (±0.01)x+160.8
MGIII - June PD; y = -0.071 (±0.01)x+164.4
• New cultivars have a shorter vegetative period because they flower earlier than old cultivars.
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Year of Cultivar Release
1920 1930 1940 1950 1960 1970 1980 1990 2000 2010
Repro
duct
ive G
row
th D
ura
tion (
Days)
40
50
60
70
80
90
100
110
MGII - May PD; y = 0.080 (±0.04)x-82.2
MGII - June PD; y = 0.058(±0.03)x-43.9
MGIII - May PD; y = 0.197(±0.03)x-320.2
MGIII - June PD; y = 0.175 (±0.02)x-281.9
Reproductive Growth Duration (Days from R1 to R7) with Early and Late Planting
• New cultivars have a longer reproductive period than old cultivars.
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• Yield stability (Finlay and Wilkinson, 1963) calculated for each cultivar by regressing cultivar yield on environment yields.
• These stability coefficients were then regressed on the year of release.
Stability Analysis
Environment Mean Yield
Cu
ltiv
ar Y
ield
Cult A b = 1
Cult B b = 0
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• Yield stability (Finlay and Wilkinson, 1963) calculated for each cultivar by regressing cultivar yield on environment yields.
• These stability coefficients were then regressed on the year of release.
Stability Analysis
Year of Release
Stab
ility
Co
effi
cien
tNo change in stability across years
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• Yield stability (Finlay and Wilkinson, 1963) calculated for each cultivar by regressing cultivar yield on environment yields.
• These stability coefficients were then regressed on the year of release.
Stability Analysis
Year of Release
Stab
ility
Co
effi
cien
t
New cultivars less stable
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1 9 2 5 1 9 4 5 1 9 6 5 1 9 8 5 2 0 0 5
0 . 6
0 . 8
1 . 0
1 . 2
Y e a r o f C u l t i v a r R e l e a s e
YIe
ld S
ta
bil
ity
Co
ef
fic
ien
t M G I I
M G I I I
M G I V
Stability Analysis
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• Compared performance of 6 newest and 6 oldest cultivars across a range of environments
• New cultivars greater yielding in both poor and good environments
Why Are New Cultivars Less Stable?
Environment Mean Yield
Cu
ltiv
ar Y
ield
New cultivars
Old cultivars
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• Compared performance of 6 newest and 6 oldest cultivars across a range of environments
• New cultivars greater yielding only in good environments
Why Are New Cultivars Less Stable?
Environment Mean Yield
Cu
ltiv
ar Y
ield
New cultivars
Old cultivars
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1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
S i t e - Y e a r M e a n Y i e l d ( k g h a- 1
)
Cu
ltiv
ar
Yie
ld (
kg
ha
-1
)
M G I V
New Cultivars Outperformed Old Cultivars at High and Low Yield Environments
New cultivars
Old cultivars
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1 0 0 0 2 0 0 0 3 0 0 0 4 0 0 0
1 0 0 0
2 0 0 0
3 0 0 0
4 0 0 0
5 0 0 0
6 0 0 0
S i t e - Y e a r M e a n Y i e l d ( k g h a- 1
)
Cu
ltiv
ar
Yie
ld (
kg
ha
-1
)
M G I I
M G I I I
M G I V
New cultivars
Old cultivars
Yield of New and Old Cultivars at Each Environment
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Urbana 2010
Urbana 2011
Arthur 2011
Foliar Ratings (R6)
Bacterial diseases -0.82*** -0.32 -0.67***
Brown spot -0.58*** NS -0.65***
Downy mildew 0.58*** NS NS
Insect feeding 0.75*** NS NS
Root and stem ratings (R8)
Anthracnose -0.69*** NT NS
Cercospora blight -0.45** NT NS
Charcoal rot -0.38* NT NS
Pod and stem blight -0.65*** NT NS
Root health -0.74*** NT -0.41**
Correlations Between Year of Release and Disease Resistance - Glen Hartman
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Soybean Breeding
Make crosses
Develop experimental lines
Select the best lines
• Most of the yield improvements of varieties are the result of traditional breeding.
Slow and
steady wins
the race.
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Modeling YieldLisa Ainsworth USDA-ARS
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Changes in Parameters Over Time
Interception
Conversion
Harv. index
Koester, R.P. et al. J. Experimental Botany.
doi:10.1093/jxb/eru187.
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Breeding conclusions• Soybean yields are increasing the USA
• Increases in soybean production needed
• Much of the yield increases that have occurred is through the genetic improvement of varieties
• Yield improvement occurred together with changes in protein and oil concentration and greater light interception efficiency, conversion and harvest index