variety testing theory and practice - soybean...

Variety TestingTheory and Practice

Brian Diers

University of Illinois

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

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

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.

Yield testing is key to crop improvement

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Akaike Information Criterion (AIC)

indicates that the two segment

model most probable.

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1920 1940 1960 1980 2000 2020

USA

Average Africa

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Yields in Africa are equal to the USA in the 1930’s

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


Region 3: Perry, New Berlin & Urbana


Region 4: Belleville & St. Peter


Region 5: Elkville & Harrisburg


http://vt.cropsci.illinois.edu/soybean16/2016 CV Entries.xls

http://vt.cropsci.illinois.edu/soybean16/2016 LL entries.xls

http://vt.cropsci.illinois.edu/soybean16/2016 RR entries.xls

http://vt.cropsci.illinois.edu/soybean16/2016 Region 1 CV Final.xlsx

http://vt.cropsci.illinois.edu/soybean16/2016 Region 1 LL Final.xlsx

http://vt.cropsci.illinois.edu/soybean16/2016 Region 1 RR Final.xlsx













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

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)

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.

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

5 m

1 m

1 m 0.5 m

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

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

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.

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

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

Flower color

• Why score? Quality control

• When score? At flowering

• Flowers are either purple or white in soybean varieties (plots can be mixed).

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)

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

Date of R8 (Pod maturity)

• Why score? Determine adaptation


• 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

Green stem disorderNormal maturity

Maturity scoring can be obscured by green stem disorder

Lodging• Why score? Measure the average of how upright

the plants are in the plot


• Score plots on a 1-5 scale (use 0.5 increments)

• Score when the plots are mature

1 2 3 4 5

Lodging

What scores would you give these plots?

Plant height• Why score? Measure

adaptability of varieties


• 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

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

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

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

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

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

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.

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.

Questions?

University of

Illinois

Soybean breeding

Chicago

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USDA National Agricultural Statistics Service

Soybean Area and Production in the USA

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On-Farm Soybean Yield Gains USALinear 23 kg ha-1 year-1

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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.

• 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

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B

II

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IV

(1000 bu = 27.216 tons)

Soybean Maturity Groups

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

• 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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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

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1 9 6 4M G I I I

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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 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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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

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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 )

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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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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 )

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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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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

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.

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.

• 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



Stability Analysis

Year of Release

Stab

ility

Co

effi

cien

tNo change in stability across years



Stability Analysis

Year of Release

Stab

ility

Co

effi

cien

t

New cultivars less stable

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

• 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?


Cu

ltiv

ar Y

ield

New cultivars

Old cultivars

• 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?


Cu

ltiv

ar Y

ield

New cultivars

Old cultivars

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

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

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

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.

Modeling YieldLisa Ainsworth USDA-ARS

Changes in Parameters Over Time

Interception

Conversion

Harv. index

Koester, R.P. et al. J. Experimental Botany.

doi:10.1093/jxb/eru187.

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

variety testing theory and practice - soybean...

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