corn and soybean disease management - soy expo€¦ · i. soybean a) background on white mold b)...

Field Crops Pathology

Corn and Soybean Disease Management

Damon L. Smith, Ph.D.Field Crops Extension PathologistDepartment of Plant PathologyUniversity of Wisconsin-Madison


Outline I. Soybeana) Background on white moldb) Efforts to improve resistance

to white moldc) Best fungicides and

application timing for white mold control

d) Efforts to improve white mold prediction in soybean

II. Corna) New and Emerging Corn

Diseasesi. Bacterial leaf streakii. Tar Spot

b) Fungicides to manage mycotoxins in corn

White Mold Cycle (Soybean)


Yield Loss

0

1000

2000

3000

4000

5000

6000

7000

8000

0.0 0.2 0.4 0.6 0.8 1.0

Yiel

d (k

g/ha

)

Disease severity index (DIX)

𝒀𝒀𝒀𝒀𝒀𝒀𝒀𝒀𝒀𝒀 =𝒂𝒂

𝟏𝟏 + 𝒀𝒀− 𝒃𝒃+ 𝒄𝒄×𝑫𝑫𝑫𝑫𝑫𝑫𝟏𝟏𝟏𝟏𝟏𝟏

=𝟒𝟒𝟏𝟏𝟒𝟒𝟒𝟒.𝟕𝟕𝒌𝒌𝒌𝒌𝒉𝒉𝒂𝒂 𝒐𝒐𝒐𝒐 𝟒𝟒𝟏𝟏.𝟖𝟖 𝒃𝒃𝒃𝒃

𝒂𝒂

𝟏𝟏 + 𝒀𝒀− 𝟕𝟕.𝟏𝟏𝟏𝟏+ −𝟖𝟖.𝟏𝟏𝟏𝟏×𝑫𝑫𝑫𝑫𝑫𝑫𝟏𝟏𝟏𝟏𝟏𝟏

𝑷𝑷𝑷𝑷𝒀𝒀𝒃𝒃𝒀𝒀𝒐𝒐− 𝑹𝑹𝟐𝟐 = 𝟏𝟏.𝟐𝟐95𝒂𝒂 = 𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦𝑦 𝑝𝑝𝑝𝑝𝑝𝑝𝑦𝑦𝑝𝑝𝑝𝑝𝑦𝑦𝑝𝑝𝑦𝑦𝒃𝒃 𝑝𝑝𝑝𝑝𝑦𝑦 𝒄𝒄 = 𝑠𝑠𝑠𝑝𝑝𝑝𝑝𝑦𝑦 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑦𝑦𝑝𝑝𝑦𝑦𝑝𝑝𝑠𝑠

Willbur, J.F., Fall, M.L., Byrne, A.M., Chapman, S.A., Bradley, C.A., Chilvers, M.I., Kleczewski, N.M., Mueller, D.S., Malvick, D., Mitchell, P.D., Conley, S.P., and Smith, D.L. 2018. In prep.

Questions From Farmers• Is there genetic

resistance to white mold?

• What fungicides work for managing white mold?

• When should I spray for white mold?


ManagementFunction of:

• Field history• Mapping of field• Variety selection

• Need improved breeding material• Canopy row width and plant

population • Crop rotation• Chemical and/or biological

control• Incomplete control

• Application issues• Timing of application critical• Need better disease predictions Photo Credit: Daren Mueller, Iowa State University

Efforts to Improve White Mold Resistance in Soybean

Willbur, J. F., Ding, S., Marks, M. E., Lucas, H., Grau, C. R., Groves, C. L., Kabbage, M., and Smith, D. L. Comprehensive white mold screening of soybean germplasm requires multiple isolates of Sclerotinia sclerotiorum. 2017. Plant Disease.

McCaghey, M. and Willbur, J.F., Ranjan, A., Grau, C., Chapman, S., Diers, B., Groves, C., Kabbage, M., and Smith, D.L. 2017. Frontiers in Plant Science.

Variety Field Response to White Mold Can be Highly Variable

2017 SOUTHERN REGION GLYPHOSATE TOLERANT SOYBEAN TRIAL

2017 (Arlington)1 2017 Composition 2

Maturity Maturity Yield WM4 Lodging Protein Oil

Brand Entry Group Date 2 (bu/A) (%) (1-5) (%) (%)Dairyland DSR-1721/R2Y 1.7 16-Sep * 90 5 1.0 35.3 18.1 Asgrow AG2836 2.8 29-Sep * 90 1 1.0 37.4 17.0 Dairyland DSR-1870/R2Y 1.8 16-Sep * 88 13 1.0 35.8 18.4 Titan Pro TP-16X77 1.6 16-Sep * 86 9 1.0 34.5 19.0 Golden Harvest GH2230X Brand 2.2 20-Sep * 85 3 1.0 35.2 18.4 Legend Seeds LS 23X632N 2.3 27-Sep * 85 6 1.0 35.9 17.0 NK S21-W8X Brand 2.1 20-Sep * 85 10 1.0 35.5 18.0 Dyna-Gro S23XT78 2.3 18-Sep * 84 13 1.0 35.2 17.5 Dyna-Gro S24RY87 2.4 20-Sep * 83 8 1.0 34.5 19.1 Asgrow AG23X8 2.3 25-Sep * 83 12 1.0 36.3 17.7

2017 CENTRAL REGION GLYPHOSATE TOLERANT SOYBEAN TRIAL

2017 3-Test Average 2017 Yields 2017 Composition1

Maturity Maturity Yield Lodging Fond du Lac Galesville ---Hancock--- Protein Oil

Brand Entry Group Date 1 (bu/A) (1-5) (bu/A) (bu/A) (bu/A) WM2 (%) (%) (%)Dairyland DSR-1721/R2Y 1.7 22-Sep * 70 1.0 71 * 74 * 67 24 33.9 19.1 Dairyland DSR-1870/R2Y 1.8 18-Sep * 66 1.0 73 63 * 62 15 34.1 19.2 Golden Harvest GH2230X Brand 2.2 29-Sep * 65 1.0 74 62 * 60 20 33.7 19.5 Legend Seeds LS 23X632N 2.3 25-Sep * 67 1.0 71 67 * 62 13 35.4 17.3 NK S21-W8X Brand 2.1 25-Sep * 64 1.1 73 61 58 68 35.6 18.5 Dyna-Gro S23XT78 2.3 29-Sep 62 1.0 74 63 50 53 35.7 17.6 Dairyland DSR-1950/R2Y 1.9 18-Sep * 71 1.0 * 77 * 73 * 63 38 33.6 18.9 Golden Harvest GH1915X Brand 1.9 22-Sep * 70 1.0 * 79 * 69 * 64 18 33.3 19.4 Dairyland DSR-1120/R2Y 1.1 18-Sep * 69 1.0 73 * 69 * 65 19 34.1 19.2 Dyna-Gro S17RY67 1.7 18-Sep * 69 1.0 72 * 69 * 65 28 34.9 18.9


Consistent, Resistant Reactions in Controlled Environments Also Need to Be Used in Screening

Petiole Inoculations• Similar to procedure

by Otto-Hanson et al., 2011

• Place the fungus on a cut petiole

• Measure lesion over time

• Provides a measure of physiological stem resistance


Response to S. sclerotiorum Can Vary by Genotype in Controlled Environments

• Must screen germplasm using multiple isolates to reliably identify resistance

• 91-38 generally displays a ‘moderate’ reaction to most isolates in our collection; Yields well

Willbur, J. F., Ding, S., Marks, M. E., Lucas, H., Grau, C. R., Groves, C. L., Kabbage, M., and Smith, D. L. 2017. Comprehensive white mold screening of soybean germplasm requires multiple isolates of Sclerotinia sclerotiorum. Plant Disease.

91-145 (Resistant)

91-38 (Moderately Resistant)

Williams 82 (Susceptible)

Rela

tive

Dise

ase

Leve

l

Greenhouse Performance of Experimental Lines

fef

de decd cd bd bc bc

b

a

a

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250

300

350

400

450AU

DPC

Line

susceptible checks

McCaghey, M. and Willbur, J.F., Ranjan, A., Grau, C., Chapman, S., Diers, B., Groves, C., Kabbage, M., and Smith, D.L. 2017. Frontiers in Plant Science.

Field Performance of Experimental Lines

ee

de

cdb-d b-d b-d b-d bc

a-cab

a

0

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100

Dis

ease

Sev

erity

Inde

x

Line

released as ‘Dane’, a commercial food-grade varietysusceptible checksMcCaghey, M. and Willbur, J.F., Ranjan, A., Grau, C., Chapman, S., Diers, B., Groves, C., Kabbage, M., and Smith, D.L. 2017. Frontiers in Plant Science.


W16-9138 (Dane)A Food-Grade, Non-GMO Release with excellent White Mold Resistance!

• Seed shape: Spherical• Seed coat: Yellow• Seed Coat Luster: Dull• Seed Size: 18.1 g/100 seed(~2500 seeds/lb)• Hilum Color: Yellow*• Flower Color: white• Pod Color: Tan• Pubescence color: Gray• Plant Height: Medium• Habit: Semi-determinate• Maturity Group: 1.5*• Protein: 38%*• Oil: 17%

Seed of ‘Dane’ or 91-38 (W16-9138)

What is the Real Value of Resistance?

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60

*Variety and Treatment significantly affected DIX (P < 0.01 and P = 0.01)

whi

te m

old

Inde

x (%

)160,000 seeds per a

(395,360 seeds per ha)120,000 seeds per a

(296,520 seeds per ha)

52-82B (Resistant Soybean Line)

Dwight (Susceptible Cultivar)

What is the Real Value of Resistance?Yi

eld

(bu/

a)

160,000 seeds per a(395,360 seeds per ha)

120,000 seeds per a(296,520 seeds per ha)

52-82B (Resistant Soybean Line)

Dwight (Susceptible Cultivar)

*Variety and Treatment significantly affected Yield (P < 0.01 and P = 0.05)

0

20

40

60

80

0

20

40

60

80

• Endura (8 oz/a) @ R2 = $40/a

• Application fee = $7/a

• 160K seeding rate = 4.0 bu/a advantage to 52-82B

• Total Value ($10/bu) = $87/a

What are the Best Fungicides and application timings?

A Network Meta-Analysis

Willbur, J.F., Fall, M.L., Byrne, A.M., Chapman, S.A., Bradley, C.A., Chilvers, M.I., Kleczewski, N.M., Mueller, D.S., Malvick, D., Mitchell, P.D., Conley, S.P., and Smith, D.L. 2018. In prep.


2009-16 North Central White Mold Trials• Fungicide trials were conducted in Illinois, Iowa, Michigan, New Jersey, Minnesota, and

Wisconsin from 2009 to 2016 • A total of 25 site years (n = 2057)

• Different products, rates, and timings were evaluated using disease and yield • Focused on 10 common active ingredients and 7 common timings

• Also, used this data to evaluate the effects of disease severity index (DIX) on soybean yield (Discussed this earlier)

• DIX (%) = Disease incidence (%) x Disease severity of symptomatic plants (0-3) / 3• 0 = no disease; 1 = disease on lateral branches; 2 = disease on the main stem; 3 = disease on the main stem resulting in plant

death (Grau et al. 1982)


Meta-Analysis Evaluations• For each site-year, effect sizes were generated

and means calculated o k = 359 for active ingredientso k = 454 for application timing

• 𝐃𝐃𝐃𝐃𝐃𝐃 𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑𝐑 % = DIXtreated − DIXnon−treated

• 𝐘𝐘𝐑𝐑𝐑𝐑𝐘𝐘𝐑𝐑 𝐁𝐁𝐑𝐑𝐑𝐑𝐑𝐑𝐁𝐁𝐑𝐑𝐑𝐑 % = yieldtreatedyieldnon−treated

− 1 × 100

• Moderator effects (a.i. and timing) were analyzed for subsets of groups with k ≥ 10

# Active Ingredients FRAC

1 boscalid 72 boscalid +

fluxapyroxad/pyraclostrobin711

3 fluazinam 294 fluoxastrobin + flutriafol 3115 lactofen PPO6 picoxystrobin 117 prothioconazole 38 prothioconazole + trifloxystrobin 3119 tetraconazole 310 thiophanate-methyl 1

# Fungicide Timings

Growth Stage

1 V5 Late vegetative2 R1 Beginning flower3 R2 Full flower4 R3 Beginning pod5 R1+R2 Beginning + full flower6 R1+R3 Beginning flower + pod7 R5 Beginning seed

Common Active Ingredients Provide a Reduction in Disease Severity Index

gfg

e-g eg d-f ceb-d bc b-d

aba

-25

-20

-15

-10

-5

0

5

LAC

T

BO

SC

PIC

O

BO

SC+F

LUX+

PYR

A

FLU

A

PRO

T+TR

IF

PRO

T

THIM

FLU

O+F

LUT

TETR

CO

NTR

OL

Mea

n D

IX R

educ

tion

(%)

P < 0.01

*Mean Severity Index = 40% (as high as 90%)

Cobr

a

Endu

ra

Apro

ach

Prol

ine

+ St

rate

go

YLD

Most Active Ingredients Resulted in a Positive Yield Benefit

aab ac

a-db-d

cd d dde

ee

0

5

10

15

20

25

BO

SC+F

LUX+

PYR

A

BO

SC

PIC

O

PRO

T+TR

IF

FLU

A

FLU

O+F

LUT

THIM

PRO

T

LAC

T

TETR

CO

NTR

OL

Yiel

d B

enef

it (%

)P < 0.01

*Mean 3773 kg/ha (56 bu/a)

Evidence Suggests That Disease Pressure May Have a Marginal Influence on the Active Ingredient Effect of Yield Benefit

-20

-10

0

10

20

30

40

50

BO

SC+F

LUX+

PYR

A

BO

SC

PIC

O

LAC

T

FLU

A

PRO

T+TR

IF

FLU

O+F

LUT

PRO

T

THIM

TETR

CO

NTR

OL

Yiel

d B

enef

it (%

)Low Disease PressureHigh Disease Pressure

*Non-treated disease severity index <40% considered low pressure. DIXnt ≥ 40% considered high pressure.

χ2 test; P = 0.07


Timing of Fungicide Application Plays a Significant Role in Maximizing Disease Reduction

bc

cdb-e

de

e

bc

b

a

-20

-18

-16

-14

-12

-10

-8

-6

-4

-2

0

V5 R1

R2

R1+

R2

R1+

R3

R3

R5

CO

NTR

OL

DIX

Red

uctio

n (%

)

Growth Stage

P < 0.01


Fungicide Applications During the Major Flowering Period Result in the Best Yield Benefits

b-d

a-c

a-d

ab

a

bc

c

d0

5

10

15

20

25

V5 R1 R2 R1+R2 R1+R3 R3 R5 CONTROL

Yiel

d B

enef

it (%

)

Growth Stage

P < 0.01

Applications in High Pressure Situations have High Probabilities of ROI

0

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90

100Pr

obab

ility

of B

reak

ing

Even

(%)

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60

70

80

90

100

Boscalid (R1)

Picoxystrobin(R1+R3)Lactofen

$USD/kg ($/bu)

Low Pressure High Pressure

Efforts to Improve White Mold Prediction

Apothecial ModelingWillbur, J.F., Fall, M.L., Bloomingdale, C., Byrne, A.M., Chapman, S.A., Isard, S.A., Magarey, R.D., McCaghey, M., Mueller, B.D., Russo, J.M., Schlegel, J., Chilvers, M.I., Mueller, D.S., Kabbage, M., and Smith, D.L. 2018. Plant Disease. doi.org/10.1094/PDIS-04-17-0504-RE.

How Important Are Apothecia?

• Formation of apothecia critical for white mold in soybean

• Majority of infections in soybean occur due to ascospore release from apothecia within the field

Boland and Hall, 1988, Plant Pathology , 37:329-336Wegulo, Sun, Martinson, and Yang, Can. J. Plant Sci., 80:389-402


Data Collection• Developed standardized protocols

for intensive, multi-state apothecial and ascospore monitoring

• Scouted research trials for apothecia in Iowa, Michigan, and Wisconsin

• 9 site-years (n = 3,866)• Monitored ascospores using

Sclerotinia semi-selective media • Accessed high-resolution gridded

weather data and validated with an on-site Campbell weather station


Model for Non-Irrigated Fields

64

0.00

0.50

1.00

18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

30-d

ay m

ovin

g av

g of

m

ax w

ind

spee

d (m

/s)

Prob

abili

ty o

f apo

thec

ial

pres

ence

(0-1

)

30-day moving avg of max temp (°C)

<68°F

Logit (P) = -0.47*MaxAT – 1.01*MaxWS + 16.65


Model for Irrigated Fields

9092

9496

990.00

0.50

1.00

18 20 22 24 26 28 30 32 34 30-d

ay m

ovin

g av

g re

lativ

e hu

mid

ity (%

)

Prob

abili

ty o

f apo

thec

ial

pres

ence

(0-1

)

30-day moving avg of max temp (C)>86°F

Logit (P) = -2.38*Row + 0.65*MaxAT + 0.38*MaxRH – 52.65, row = 1 if 30-inch row spacing or 0 if 15-inch row spacing


Apothecial Risk Maps2016 2017

• Available through the integrated pest information platform for extension and education (iPiPE)

• Used to provide state-wide risk alerts for growers to aid in fungicide application timing• Compare disease epidemics over different years

Low riskModerate riskHigh risk


Model Validation Results (60 Farmer Fields – Multiple States)Accuracy (%)a Success (1) or

Failure (0) @ DI 5%Success (1) or

Failure (0) @ DI 10%

Action Threshold 30 35 40 30 35 40Research trials, 2017 (n=8) 83.3 83.3 83.3 83.3 83.3 83.3Commercial fields, 2016-17 (n=60) 63.3 76.7 73.3 68.3 81.7 78.3

Total 65.2 77.3 74.2 69.7 81.8 78.8aBinary model successes (1) or failures (0), for each model action threshold (30, 35, or 40%) using disease incidence thresholds of 5 or 10% (Tables 5.9 and 5.10), were used to determine model accuracy across all locations and years. The number of successes were divided by the total number of field validations and multiplied by 100.

Strip Trial, Marathon Co., WI

66% DI

39% DI

26% DI

27% DI

DI 12-33%

56 bu/a47.5

bu/a41

bu/a55.5bu/a

58bu/a


2017 Risk Probabilities Based on the Model, Marathon Co. Strip Trial

Prob

abili

ty o

f Apo

thec

ial P

rese

nce

High riskModerate riskLow riskActive

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

05/0

1/17

05/1

1/17

05/2

1/17

05/3

1/17

06/1

0/17

06/2

0/17

06/3

0/17

07/1

0/17

07/2

0/17

07/3

0/17

08/0

9/17

08/1

9/17

08/2

9/17

09/0

8/17

09/1

8/17

09/2

8/17

R17/14/17

R2.57/25/17

R3 8/1/17


iPhone and Android App Coming in 2018!• Uses same models as the iPiPE

version• Available for the U.S. and

Canada• Can be run in the field or at the

desk• Uses a combination of user

inputs and GPS-referenced weather information to provide a risk of white mold so you can make a spray decision

• Look for it in the Apple and Android Stores in the 2018 growing season!


The “Take-Home”-Integrated Management is Key to Success

1. Must have better resistance in commercial varieties –Use the most resistant variety you can

I. Look at variety trials to find best varietiesII. Breeding efforts must focus on stable physiological

resistance

2. A few fungicides offer some control; choose the right one and resist the ”silver bullet” temptation

I. Reduce the expectation (over-reliance) of fungicide performance

II. “Fungicide only makes a bad situation, less bad”III. Applications should be focused between R1 and

R3 growth stages IV. Don’t Forget Resistant Varieties!!

3. Use predictions models to improve the use and efficiency of fungicide products

I. Fungicide application timing is critical!II. Epidemic initiation and duration isn’t the same

each season!III. White mold fungicide App. for iPhone and Android

Coming Soon!

4. Must adjust cultural practices to manage white moldI. Wider row spacingII. Lower planting populations ?III. Don’t Forget Resistant Varieties!!


New and Emerging Corn Diseases

Bacterial Leaf Streak (BLS) of Corn

• Reported Nebraska July 2016• First time reported in the United

States• Reported in 8 additional states

• Symptoms observed in Nebraska for a few years prior.

• Also in South Africa corn• Causes gumming disease of

sugarcane worldwideKorus, K., Lang, J., Adesemoye, A., Block, C., Pal, N., Leach, J., and Jackson-Ziems, T. 2016. First report of Xanthomonas vasicola causing bacterial leaf streak of corn in the United States. Plant Dis . 101:1030.

Plantwise.org

Slide Courtesy of Tamra Jackson-Ziems, University of Nebraska-Lincoln


• Caused by Xanthomonas vasicolapv. vasculorum

• Other reported hosts:• Several palm and grass species• Coconut• Sorghum species• Grain sorghum• Johnson- and Sudan grass

Lang, J.M., E. DuCharme, J. Ibarra Caballero, E. Luna, T. Hartman, M. Ortiz-Castro, K. Korus, J. Rascoe, T.A. Jackson-Ziems, K. Broders, and J.E. Leach. 2017. Detection and characterization of Xanthomonas vasicola pv. vasculorum nov. causing bacterial leaf streak of corn in the United States. Phytopathology 11:1312-1321.


• U.S. Distribution• Nebraska - 60 counties• Kansas• Colorado• Iowa• Illinois• Texas• Minnesota• South Dakota• Oklahoma• Others?



Bacterial Leaf Streak Gray Leaf Spot

backlitbacklit


Tar Spot• Phyllachora maydis –

confirmed 2015• Monographella maydis – not

reported, when present as complex, yield loss is greater

• Highlands of Mexico and Central America

• Favored by cool conditions 60-68 F

• Appears to be a late season disease

• Possibly brought in on Tropical Storm Bill

Kiersten Wise, UKY

Martin Chilvers, Michigan State Univ

Tar spot of corn

Steve KoemanMartin Chilvers


What about fungicides to to manage mycotoxins?


Foliar Fungicides for Corn - Summary• Fungicide application in fields where foliar disease pressure is

high = high odds of recovering the fungicide cost in the current market

• Fungicide application in fields where foliar disease pressure is low = low odds of recovering the fungicide cost in the current market

• Scout before VT and make a decision about disease level –VT/R1 application is recommended if disease is becoming active

• Corn-on-corn rotation, planting late, irrigating, and using susceptible hybrids can increase chances of disease

Ear Rots


Mycotoxins• Toxic, metabolic by-products produced by fungi (molds) growing on grain,

feed, or food in the field or in storage• 400-500 known mycotoxins• Production of mycotoxins is highly dependent on

• Environment• Factors that may cause wounding on plants (e.g. hail, insect feeding)• Situations where resource demand is high or resources are limiting (e.g. plant

stress)• Kernel moisture >18-20% does favor growth of all ear molds (including

those that produce toxins)• Presence of mold on an ear DOES NOT EQUAL mycotoxins are present• Similarly, no mold DOES NOT EQUAL NO mycotoxins are present• Most important organisms = Fusarium spp., Penicillium oxalicum, Aspergillus

flavus

Corn Fungicide Update

Victor Limay-Rios (UG Ridgetown)Dave Hooker (UG Ridgetown)Art Schaafsma (UG Ridgetown)Albert Tenuta (OMAFRA Ridgetown)

High-clearance sprayer equipped with drop nozzles

Limay-Rios and Schaafsma (Ridgetown, 2011)

Fungicide GPA Nozzle % DON of UTC2011

% DON of UTC2010

. UTC . 100a 100aProline 5 Above 58bc 100aProline 10 Above 61bc 75bProline 20 Above 61bc 60abProline 10 Drop 58bc 65abProline 20 Drop 52c 70abProline 10 Above+Drop 66b 70abProline 20 Above+Drop 56bc 65a

Headline 10 Above 96a 150aQuilt 10 Above 93a 110a

2 locations3.5 ppm

3 locations1.0 ppmLimay-Rios, Schaafsma, Hooker, Ridgetown (2011)

Application technology and product for managing DON

0

2

4

6

8

10

VT Silking Silking+5d

Silking+11d

UTC

* *

Timing of Proline Application on DON2010-2011

DON (ppm)

2011

2010

Limay-Rios, Schaafsma, Hooker, Ridgetown (2011)


2017 Wisconsin Silage Corn Fungicide Trials• Arlington ARS - Arlington, Wisconsin• Small Plots (10 x 20 ft)• BMR Hybrid – P0956AMX• Seeding rate a bit low (35,000 per acre)• Fungicide apps of various products x application timings

(V6, R1, R1+5, R1+10)• Harvested with a small plot silage chopper• Sub-samples of silage taken for quality analysis

2017 Yield and Quality

46.0

47.0

48.0

49.0

50.0

51.0

52.0

0.0

2.0

4.0

6.0

8.0

10.0

12.0

TTN

DFD

(%)

Yiel

d (T

ons D

ry M

atte

r/a)

Yield (tons/a) TTNDFD (%)

Yield P = 0.16TTNDFD P = 0.47

cbc bc

bc

bc bcbc

abc

ab

a

0.0

1.0

2.0

3.0

4.0

5.0

6.0

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Exp 1 + Tilt 3FL OZ/A (R1)

Exp 2 (R1) Proline 5.7 FLOZ/A (R1)

Delaro 8.0 FLOZ/A (R1)

Proline 5.7 FLOZ/A

(R1+5days)

Healine AMP10.0 FL OZ/A

(R1)

Proline 5.7 FLOZ/A

(R1+10days)

Delaro 4.0 FLOZ/A (V6)

Quilt Xcel10.5 FL OZ/A

(R1)

Non-treatedControl

DON

(ppm

)

Ear r

ot (%

)

Ear rot (%) DON (ppm)

2017 Ear rot and DON Ear Rot P = 0.75DON P = 0.02

Products and Timings below this line resulted in 50% or more reduction in DON


Fungicide For Mycotoxin ControlThink about the Goal on Your Farm when using fungicide• Yield responses to fungicide application will be variable

• Response more reliable if you consider the disease potential

• Fungicide might be more useful for controlling mycotoxins in your operation

• Consider your application strategy• Might need to target silks for good mycotoxin control• Be careful using products with strobilurin ingredients at the silking stage

(May be similar response of DON in wheat systems)

AcknowledgementsLab Personnel• Jaime Willbur• Megan McCaghey• Cristina Zambrana-Echevarria• Brian Mueller• Chris Bloomingdale• Dr. Carol Groves• Dr. Craig Grau• Dr. Scott Chapman• Hannah Lucas• Maria Weber• Jourdan Blackwell• Theresa Blackwell• Kelsey Azzolino• Danielle Holtz• Miranda Young• Patrick Beuthe• Brenden Peppard• Nate Westrick

UW Collaborators• Dr. Paul Mitchell• Dr. Shawn Conley

UK Collaborator• Dr. Carl Bradley

ISU Collaborators• Dr. Daren Mueller• Brian Anderson• Rachel Kempker

MSU Collaborators• Dr. Martin Chilvers • Dr. Mamadou Fall• Adam Byrne

UD Collaborator• Dr. Nathan Kleckzewski

Penn State Collaborator• Dr. Scott Isard

IPM Center – NCSU & ZedX, Inc.• Dr. Roger Magarey• Joe Russo• Jay Schlegel


Damon Smith, Ph.D.Assistant Professor and Extension SpecialistField Crops Pathology

University of Wisconsin-MadisonDepartment of Plant Pathology1630 Linden DriveMadison, WI 53706-1598

Phone: 608-286-9706Twitter: @badgercropdoce-mail: [email protected]: http://fyi.uwex.edu/fieldcroppathology/

Questions?

mailto:[email protected]

http://fyi.uwex.edu/fieldcroppathology/

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