Soybean Pest Management Update

David KeeDirector of Research, MSR&PC

12/12/19 Prairie Grains Update

MSR&PC FY19 Production Projects

• MN soybean funded 25 production projects in FY19• 18 PI’s, 37 Co-PI’s

• Trained multiple Post-Docs, graduate and undergraduate students

• Entities include UMN, NDSU and MN Wheat OFR team• Many communicating research results today

• Updates today• Bio-pesticide development• Soybean Gall Midge• Unified fungicide trial• Waterhemp control

Bio-pesticides and Seed Treatments for Control of

SCN and SDS

• Aim 1: Identify biologically derived nematicides and anti-fungal compounds

• Aim2 : Test combinations of high-performing biological agents against SCN and SDS

• Aim3: Test seed coating treatments and amended spore formulations:

Kathryn Bushley

SenyuChen

Christine Salomon

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% Nematode Toxicityi) Bioactivity guided fractionation

ii) Identification of active compound(s)

Greenhouse pot trials

Optimize seed treatments formulations

Aim 1: Identify biologically derived nematicides and anti-fungal compounds

SDS treatment alone SDS + 407B13.1 seed treatmentNo SDS control

Aim 2: Greenhouse trials of endophyte seed treatments against SDS

-20-10

0102030405060708090

H A I E D MediacontrolPe

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Fungal Strains

SupernatantAqueous phaseOrganic Phase

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DAD1 B, Sig=220,16 Ref=360,100 (C:\CHEM32\...UDI\CHRISTINE_ILYONECTRIA_KB 2019-08-09 12-19-22\E_216_25.D)

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DAD1 B, Sig=220,16 Ref=360,100 (C:\CHEM32\...UDI\CHRISTINE_ILYONECTRIA_KB 2019-08-09 12-19-22\E_413_17.D)

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DAD1 B, Sig=220,16 Ref=360,100 (C:\CHEM32\...HRISTINE_ILYONECTRIA_KB 2019-08-09 12-19-22\SM_14_15_11_1.D)

Novel compound

Activity guided fractionation HPLC and identification of novel compounds

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© 2019 Regents of the University of Minnesota. All rights reserved.

55Bruce Potter 2019

Soybean Gall Midge Update

Bruce Potter, IPM educator, Lamberton

Soybean Gall Midge Resseliella maxima

(University of Minnesota Extension Pictures)

(University of Nebraska Institute of Agriculture and Natural Resources (Aug – 2019))

Two Similar Species:

Resseliella maxima (SGM)

Karshomyia caulicola(WGM) – Sclerotina

Sclerotiorum(University of Minnesota Extension Pictures)

Confirmed Areas Effected in 2019

Rock Co, MN 2019 Symptoms Presence 1st -2nd stage 3rd stage All

Edge position1 < 0.0001 < 0.0001 < 0.0001 0.0003 < 0.0001Insecticide2 0.8343 0.3536 0.0125 0.1081 0.0198Postition X Insecticide 0.2099 0.0802 0.1024 0.5284 0.1881Prob > F 1 Rows 3-6 vs 7-10 (rows 4 ,5 and 8, 9 sampled)2 Hero @ 5 fl oz/acre applied at V1-2 timed to adult emergence in adjacent field

SGM larvae / stem

6.8 2.1 3.8 1.9 10.6 4.00.0

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Effect of field position on 1st generation SGMA

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5.4 3.5 3.3 2.4 8.7 6.00.0

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Larval age class and insecticide* application

Effect of insectide on 1st generation SGM

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* Hero @ 5 fl oz/acre applied at V1-V2. Four days after 1st adult emergence

Soybean gall midge adult emergence

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Cumulative SGM adult emergence

Rock Co. Site 2 Rock Co. Site 3

V2V4

V7/R1

V13/R5

Overwintering and two summer flights. Two complete and a partial 3rd generation on soybean.OW adult emergence period ~ 2 weeks, 25-30 days/ generation (generations overlap).

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© 2019 Regents of the University of Minnesota. All rights reserved. Bruce Potter 2019

Predators e.g. Ground beetles Coleoptera (Carabidae)

Parasites e.g. Parasitoid Wasps Hymenoptera (Platygastridae)

SGM BIOLOGICAL CONTROL?

Synopeas myles a parasitoid of the Swede midge Photo: Paul Abram

DNA of platygastrid wasps was detected In SGM larvae from Rock County, MN.Synopeas spp. are known parasitoids of Cecidomyiidae.

Ground beetles are common predators in soybean fields. They have been documented to feed on wheat midge larvae and may feed on SGM larvae and pupae in the soil.

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© 2019 Regents of the University of Minnesota. All rights reserved.

1414Bruce Potter 2019

Fungicide for soybeans

Bruce Potter, IPM educator, Lamberton

Effect of variety and fungicide on soybean yield and moisture (MN 2016-19 )

YIELD @13.5% moisture and 60 lb./bu.

Site 0.9902 a

Variety < 0.0001 **** < 0.0001 **** < 0.0001 **** 0.0003 **** 0.2398 0.4011Fungicide <0.0001 **** 0.0068 **** 0.0667 ** 0.0001 **** 0.8216 0.4061Variety * Fungicide 0.7450 0.2678 0.9199 0.7624 0.9576 0.4131Site * Variety 0.0001 ****Site * Fungicide 0.8602Site * Variety * Fungicide 0.9074% MOISTURE

Site 0.8192Variety < 0.0001 **** < 0.0001 **** < 0.0001 **** 0.0002 *** 0.0035 **** 0.7760Fungicide < 0.0001 **** < 0.0001 **** 0.0001 **** 0.1366 * 0.8326 0.6359Variety * Fungicide 0.0001 **** 0.1407 * 0.0153 *** 0.0583 ** 0.5199 0.1188 *Site * Variety 0.0001 ****Site * Fungicide 0.0001 ****Site * Variety * Fungicide 0.0211 ***aCombined site Analysis of Variance (ANOVA) based on normalized yields and moistures (plot/site mean)§ Due to unique varieties - Morris, and Crookston not included in combined sites

SourceProb > F

COMBINED§ LAMBERTON WASECA ROSEMOUNT MORRIS CROOKSTON

MORRIS CROOKSTONProb > F

SourceCOMBINED§ LAMBERTON WASECA ROSEMOUNT

© 2019 Regents of the University of Minnesota. All rights reserved. Bruce Potter 2019

© 2019 Regents of the University of Minnesota. All rights reserved. Bruce Potter 2019

13.1 13.9 13.910

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Fungicide effect on soybean moistureLamberton, Waseca, Rosemount 2019

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© 2019 Regents of the University of Minnesota. All rights reserved. Bruce Potter 2019

SOYBEAN Fungicide economics WC and NW MN 2016-19

Project Title: Improving Management of White Mold, SDS, and Root Disease in Minnesota

Dean Malvick, Department of Plant Pathology University of Minnesota, St. Paul. dmalvick@umn.edu

Project Objectives

1. Optimize Management of White Mold Goal A. Determine the efficacy of genetic resistance, fungicides, and plant population reduction for management of white mold Goal B. Determine the value of Contans for white mold management in on-farm trials. 2. Improve management and risk analysis for SDS. Goal A. Determine the effects of pathogen population size in soil and other common root

pathogens on development and management of SDS. 3. Technical transfer of disease management information via extension education programs, and support of soybean disease diagnostic needs that address important soybean disease problems in Minnesota.

Malvick Project OutcomesSoybean white mold trials over the past few years have indicated that foliar fungicide are most effective for white mold reduction and yield enhancement with R1- R3 application timing, with up to 35% yield increases. Only low levels of the disease developed in our trials in 2019, however, fungicides delayed maturity (more greening at R6.5 growth stage)

Malvick Project OutcomesResults suggest that soybean SDS is influenced by many factors including soil populations of the SDS fungal pathogen and soil moisture throughout the growing season.In addition, recent studies indicate that other fungi in the soil interact with the SDS pathogen and can increase or decrease SDS severity. This suggests that these other fungi should be considered when managing SDS.

Malvick Project Outcomes

The relatively high level and occurrence of frogeye leaf spot in southern MN (historically uncommon in MN) was a surprise this year. Malvick’s team diagnosed samples and confirmed that frogeye leaf spot was occurring in a number of fields across southern Minnesota. They also confirmed, with collaborators at the University of Kentucky, that the frogeye leaf spot pathogen in Dakota, Faribault, and Watonwan counties is resistant to the QoI(strobilurin) class of fungicides, which significantly impacts management options for this disease in Minnesota.

Presenter

Presentation Notes

Experimental design and execution by Next Gen Ag, LLC. Funded by Minnesota Soybean Research and Promotion Council.

Objectives:• Screen waterhemp control from 13 residual herbicides

at 0.25X and 0.50X micro-rates as pre-liminary for micro-rate program.

• Utilize data from pre-liminary to target a cost effective and efficacy effective micro-rate program for 2020.

• Screen soybean crop injury from 13 residual herbicides at 1X and 2X rates

2X0.50X 1X0.25X

Data/Conclusions:

Table 4. Soybean growth reduction and yield estimate at 1X and 2X rates.Product Active Ingredient June 19 July 4 October 18

1X Rate 2X Rate 1X Rate 2X Rate 1X Rate 2X Rate-------------------------%---------------------------- ------Bu/A------

FirstRate Cloransulam 0 0 15 0 40 52Prowl Pendimethalin 5 15 0 10 50 95Dimetric EXT Metribuzin 2.5 10 0 0 37 81Cadet Fluthiacet 0 0 0 0 53 87Flexstar Fomesafen 0 0 0 0 82 76Sharpen Saflufenacil 10 12.5 0 0 20 88Blanket Sulfentrazone 10 30 0 0 68 108Valor SX Flumioxazin 60 85 15 60 104 74Warrant Acetochlor 35 60 0 20 65 92Dual II Magnum S-metolachlor 5 22.5 0 0 20 67Outlook Dimethenamid-P 15 15 0 0 62 89Zidua Pyroxasulfone 5 20 0 0 66 86LSD (0.1) 10 11 28

■ Micro-rates screening was successful. Exponential rate increase ratings were graphed, trendline applied, and trendline equation determined will base combination treatments in 2020 from this data (Ask David Kee for Full Article Summary).

■ Soybean crop safety below. Yields were pod estimates, but data is proportional to itself. Valor SX caused the greatest injury at 2X rate compared to 1X rate (1X rate = single application max on label).

Objectives:• Evaluate combinations of soil residual herbicides

targeting giant ragweed control.• Secondarily, evaluate waterhemp control.

• Target economical treatments to compete with current recommendations.

• Exceed 90% giant ragweed control 60 days after treatment.

Data/Conclusions:■ Giant ragweed pressure was much lower than anticipated; giant ragweed is

best controlled in a DT soybean system by more than one residual mode of action (Ask David Kee for Full Article Summary).

■ Waterhemp data (below) was taken on a moderate infestation. FirstRateand Xtendimax were weak EPOST options. FirstRate+Xtendimax had antagonism.

■ Yield was unaffected by herbicide or weed pressure.Table 3. Waterhemp control in soybean in 2019.

App. Waterhemp Control SoybeanTreatmenta Rate Codeb B+15c B+30 B+45 B+60 Yield

oz/A* or fl oz/A -----------------%----------------- Bu/ASharpen+Zidua 1+2.5* A 100 100 98 99 65Sharpen+Zidua / FirstRate 1+2.5* / 0.3* A / B 100 98 95 100 62Sharpen+Zidua / Xtendimax 1+2.5* / 22 A / B 100 98 99 99 63Sharpen+Zidua / FirstRate+Xtendimax 1+2.5* / 0.3*+22 A / B 100 98 98 100 60Authority First 6* A 55 75 63 62 58Authority First / FirstRate 6* / 0.3* A / B 73 70 70 79 66Authority First / Xtendimax 6* / 22 A / B 100 96 98 99 59Authority First / FirstRate+Xtendimax 6* / 0.3*+22 A / B 100 90 93 93 65Xtendimax 22 A 0 0 20 4 62Xtendimax / FirstRate 22 / 0.3* A / B 5 18 23 15 58Xtendimax / Xtendimax 22 / 22 A / B 100 81 80 74 67Xtendimax / FirstRate+Xtendimax 22 / 0.3*+22 A / B 95 78 75 69 58FirstRate 0.3* B 43 53 35 26 63Xtendimax 22 B 100 73 85 77 53FirstRate+Xtendimax 0.3*+22 B 87 64 44 42 57Cobra+COC 10+24 B 100 95 99 99 52Flexstar 16 B 100 74 80 95 61Roundup Powermax+Class Act NG 64+2.5%v/v B 41 38 40 15 62Untreated Check - - 0 18 13 8 55

LSD (0.1) 24 24 24 18 NS

Questions? Contact infodkee@mnsoybean.comDavidKeeMN on twitter