2017 Wisconsin Agribusiness Classic: Awards, Scholarships & Agriculture Research, Study notes of Plant pathology

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Proceedings of the 2017 Wisconsin

Agribusiness Classic

January 10-12, 2017

Exposition Hall, Alliant Energy Center

Madison, Wisconsin

Co-Sponsored by:

Wisconsin Agri-Business Association

____________

Cooperative Extension

University of Wisconsin-Extension

____________

College of Agricultural and Life Sciences

University of Wisconsin-Madison

_____________

Program Co-Chairs:

Tom Bressner Matthew D. Ruark Shawn P. Conley

Wisconsin Agri-Business Assoc. Department of Soil Science Department of Agronomy

Appreciation is expressed to the Wisconsin fertilizer industry for the support provided

through the Wisconsin Fertilizer Research Fund for research conducted by faculty within

the University of Wisconsin System.

THESE P ROCEEDINGS ARE AVAILABLE ONLINE IN A SEARCHABLE FORMAT AT:

http://www.soils.wisc.edu/extension/wcmc/

“University of Wisconsin-Extension, U.S. Department of Agriculture, Wisconsin counties cooperating and providing equal opportunities in employment and programming including Title XI requirements.”

Proceedings of the 2017 Wisconsin Agribusiness Classic – Page i

Proceedings of the 2017 Wisconsin Agribusiness Classic – Page ii

2016 Wisconsin Agribusiness Association

Distinguished Service Awards

Distinguished Organization Award

Badger State Ethanol

{For Exemplary Industry Professionalism}

Education Award

Vince Davis, BASF

{For Leadership & Commitment to Educational Excellence}

Outstanding Service to Industry

John Shutske, UW-Madison CALS and UW-Extension

{For Dedication & Support to WABA and Its Members}

Friend of WABA Award

Representative James Edming

{Wisconsin State Assembly}

Board Member Service Award

Kathy Dummer, Buck Country Grain

{For Full-Term Board of Director Service}

President’s Service Award

Scott Firlus, United Cooperative

{For Dedication, Service, & Leadership}

Proceedings of the 2017 Wisconsin Agribusiness Classic – Page iii

2016 – 2017

Scholarship Recipients

Wisconsin Agribusiness

Association Scholarships

Casey Hahn

UW-River Falls

Derek Potratz

UW-Stevens Point

Katelyn Greenwood

Southwest Wisconsin Tech

Justin Laporte

Chippewa Valley Tech

Brent Olson

Western Wisconsin Tech

Justine Engman

Cheyenne Zipp

North Central Tech

Matthew Kronschnabel

Maria Weber

UW-Madison

Wisconsin FFA Foundation

Jared Retzlaff

Erica Helmer

Beth Zimmer

Ashley Zimmerman

Mike Turner Memorial

Scholarship

Adam Meinert

Amber Francois

Fox Valley Technical College

Proceedings of the 2017 Wisconsin Agribusiness Classic – Page iv

TABLE OF CONTENTS

Papers are in the order of presentation at the conference. A paper may not be included in the proceedings for all presentations; blank pages are provided for note taking.

TITLE/AUTHORS PAGE

SOIL AND WATER M ANAGEMENT

Soil Erosion: How Much Is Occurring When and Where? Rick Cruse, Brian Gelder, David James, and Daryl Herzmann…………………………. 1

Connecting Soil and Nutrient Loss to Crop Production Francisco J. Arriaga……………………………………………………………. 3

Managing Silage Leachate and Runoff for Water Quality Becky Larson and Eric Cooley………………………………………………… 5

The Benefit of Gypsum for Crop Production in Wisconsin Francisco J. Arriaga and Richard P. Wolkowski ……………………………… 6

Why is Conserving Wisconsin Soil and Water Resources a Global Necessity Rick Cruse………………………………………………………………………. 7

M ODERN CROP M ANAGEMENT

Industry Roundtable on Herbicide Resistant Trait Pipeline in Soybean – PANEL Steven Snyder, Tim Trower, Nick Weidenbenner and Steve Langton …………… 8

Revamping Soybean Nutrient Uptake, Partitioning, and Removal Data on Modern High Yielding Genetics and Production Practices Adam P. Gaspar, Carrie A.M. Laboski, Seth L. Naeve, and Shawn P. Conley …………………………………………………………………………. 9

Are These Corn Yield Trends Real? Can We Count on Them? Joe Lauer…………………………………………………………………………. 10

Comparison of Soybean Yields in On-farm Trials vs. Small Plot Experiments Tristan Mueller…………………………………………………………………… 12

DISEASE M ANAGEMENT

Improving White Mold Management of Soybean in Wisconsin Jaime Willbur, Megan McCaghey, Scott Chapman, Mehdi Kabbage and Damon L. Smith…………………………………………………………… 13

Integrated Management of Stripe Rust of Wheat in Wisconsin Brian Mueller, Scott Chapman, Shawn Conley, and Damon Smith…………… 18

Proceedings of the 2017 Wisconsin Agribusiness Classic – Page v

TITLE/AUTHORS PAGE

2016 Wisconsin Crop Disease Update Anette Phibbs, Susan Lueloff, and Adrian Barta……………………………… 23

The Imperfect World of Disease Resistance Mehdi Kabbage…………………………………………………………………. 26

Lesion Nematodes – Pests of Corn, Soybeans, and Every Other Crop Grown in Wisconsin Ann MacGuidwin and Kanan Kutsuwa…………………………………………. 27

F EED M ANAGEMENT

Veterinary Feed Directive: A Veterinarian Perspective Katie J. Mrdutt……………………………….………………………………… 30

Sanitary Transportation Regulations Wayne Nighorn…………………………………………………………………. 37

Food Safety Modernization Act: What Must I Do to Comply? Wayne Nighorn…………………………………………………………………. 39

M ANURE AND F ERTILIZER

Use of Nitrification Inhibitors with Manure Carrie A.M. Laboski…………………………………………………………… 40

Fall Manure and Cover Crops: Who Wins, Who Loses Matt Ruark and Jaimie West………………………………………………..…. 42

Airborne Pathogens from Dairy Manure Aerial Irrigation and the Human Health Risk Mark A. Borchardt and Tucker R. Burch……………………………………… 46

Conclusions from the Manure Irrigation Work Group Becky Larson and Ken Genskow………………………………………………. 50

Fertilizer Market Update Kathy Mathers………………………………………………………………….. 52

F ORAGE CROPS

Managing Equipment during Harvest to Minimize Leaf and Yield Loss Dan Undersander………………………………………………………………. 53

Managing Foliar Fungicide Applications in Reduced Lignin Alfalfa Systems Damon Smith, Scott Chapman, and Brian Mueller…………………………… 56

Proceedings of the 2017 Wisconsin Agribusiness Classic – Page vi

TITLE/AUTHORS PAGE

GMO versus Non-GMO Low Lignin Traits: What’s the Difference? Yoana Newman…………………………………………………………………. 63

Is There a Yield Penalty to Low Lignin Alfalfa? Dan Undersander and Ken Albrecht……………………………………………. 65

Establishing Alfalfa in Corn Silage John H. Grabber, Mark J. Renz, Heathcliffe Riday, William R. Osterholz, Joseph G. Lauer, and Peter A. Vadas………………………………. 67

Maximizing Milk Production on Wisconsin Pastuares: Lessons from the Paddock Chelsea Zegler, Mark Renz, and Geoff Brink…………………………………. 68

WEED M ANAGENET

Don’t Follow Our Lead: Our Experiences with Off-Target Movement of Dicamba in Missouri Last Season? Kevin Bradley…………………………………………………………………… 71

Past and Current Status of Pigweed Distribution Throughout Wisconsin Mark J. Renz and Tracy Schilder……………………………………………….. 72

Herbicide-Resistant Common Waterhemp and Palmer Amaranth in Wisconsin Nathan Drewitz, Devin Hammer, Shawn Conley, and Dave Stoltenberg……………………………………………………………….. 75

Trying Not to Get Lost in the Weeds: Management of Waterhemp in Corn and Soybean Production Systems Kevin Bradley…………………………………………………………………… 79

Preventing Cover Crops from Becoming Your Next Weed Problem Daniel H. Smith………………………………………………………………….. 80

Palmer Amaranth: An Uninvited Guest to Conservation Plantings Meaghan J.B. Anderson and Bob Hartzler………………………………………. 82

AGRIBUSINESS M ARKETING

Cyber-Crime Trends: A State of the Union Mark Eich……………………………………………………………………….. 86

Millennials Talk about Millennials: What You Should Know about the Evolving Workforce – PANEL Kristen Faucon, Aaron Cole, and Anne Moore…………………………………. 87

Emergency/Crisis Management: An Ounce of Prevention Paul Rutledge……………………………………………………………………. 88

Proceedings of the 2017 Wisconsin Agribusiness Classic – Page vii

TITLE/AUTHORS PAGE

VEGETABLE CROPS

Vegetable Weed Management Update Jed Colquhoun…………………………………………………………………. 89

Sustainability in U.S. Specialty Crop Production Paul Mitchell and Deana Knuteson……………………………………………. 91

Nitrogen Use Efficiency in Modern Snap Bean Production Systems Matt Ruark and Jaimie West…………………………………………………… 97

Vegetable Disease Update Amanda Gevens……………………………………………………………….. 100

Neonicotinoid Insecticides and IPM in Processing Vegetables Russell L. Groves, Kathryn J. Prince, and Benjamin Z. Bradford…………….. 101

SOIL F ERTILITY AND NUTRIENT M ANAGEMENT

Phosphorus and Potassium Response in No-Till Corn and Soybean Production Carrie A.M. Laboski and Todd W. Andraski………………………………….. 102

Assessing the Quality of Polymer-Coated Urea Matt Ruark and Mack Naber…………………………………………………… 109

Nutrient Management – PANEL Joe Baeten, Sara Walling, and Judy Derricks…………………………………. 114

Nitrogen Cycling on Wisconsin Dairy Farms J. Mark Powell…………………………………………………………………. 115

I NSECT M ANAGEMENT

Wisconsin Insect Survey Results 2016 and Outlook for 2017 Krista L. Hamilton…………………………………………………………….. 117

Insecticidal Seed Treatment in Soybean Kelley J. Tilmon……………………………………………………………….. 121

Insect Management in Conventional Corn Bryan Jensen…………………………………………………………………… 122

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page viii

TITLE/AUTHORS PAGE

Recognizing and Protecting Insect Pollinators in the Agricultural Landscape PJ Leisch and Bryan Jensen…………………………………………………….. 124

Will You Cry (1F) Over Western Bean Cutworm? Kelley J. Tilmon………………………………………………………………… 128

GRAIN M ANAGEMENT

Grain Origination Challenges in Today’s Environment Scott Hansen……………………………………………………………………. 129

Proactive Maintenance for the Grain Industry Edward LaPreze………………………………………………………………… 130

EPA Air Emission Regulations for Grain Elevators Jennifer Hamill, Lisa Ashenbrenner Hunt, and Renee Lesjak Basheli…………. 131

SPRAY RIG O PERATOR REFRESHER

Understanding Spray Tank Contamination: Reducing Your Risk Daniel Heider…………………………………………………………………… 132

Spray Rig Technology and Features Tim Reid, Kent Syth, and Pete Jordan ………………………………………… 134

Most Common Violations of Applications Mark McCloskey……………………………………………………………….. 135

ECONOMICS AND TECHNOLOGY

Commodity Futures Outlook Brenda Boetel………………………………………………………………….. 136

Policy Update on Neonicotinoids, Pyrethroids, and Atrazine Paul Mitchell……………………………………………………………………. 137

Tools and Technology for Practitioners: Five Big Ag Trends John Shutske……………………………………………………………………. 138

On-Farm Traffic Optimization for Increased Efficiency Brian D. Luck………………………………………………………………….. 140

Proceedings of the 2017 Wisconsin Agribusiness Classic – Page ix

SOIL EROSION: HOW MUCH IS OCCURRING, WHEN, AND WHERE?

Rick Cruse1/, Brian Gelder1/, David James2/, and Daryl Herzmann1/

Introduction

Soil erosion and water runoff drive water quality degradation and are liabilities to crop

production, yet their magnitude is neither quantified nor inventoried for US agricultural

areas. This project’s goals are to: (1) estimate soil erosion and surface runoff across the Upper

Midwest as contributors to soil and water degradation and (2) inventory these quantities for the

next several years.

The newly released Daily Erosion Project (DEP) gives daily estimates of water runoff and sheet

and rill erosion for each of Iowa’s 1,647 HUC 12 agricultural watersheds (HUC 12 average area

is approximately 35 square miles). For each watershed, water runoff and soil erosion is recorded

over time, allowing for a spatial and temporal inventory of runoff and soil erosion for

identification of soil degraded areas as well as water quality impairment source areas. These

estimates are made publicly available on a daily basis from an open access interactive website.

This data, as well as all input data, is publically available through this website. We are currently

in the process of expanding the use of this tool from Iowa only to other states in the

Midwest. This includes all or parts of Minnesota, Missouri, Kansas, Nebraska, and

Wisconsin. Results for Iowa will be exemplified as work in Wisconsin is not yet complete.

Approach

The Daily Erosion Project is a next generation upgrade of the original Iowa Daily Erosion

Project (Cruse et al., 2006). DEP provides statistically robust, daily estimates of hillslope water

runoff, sheet and rill soil erosion and profiles soil water storage on agricultural fields in the

covered area. DEP takes advantage of recent technological advancements that enable a field

level modelling approach to produce estimates important for crop production, environmental

evaluations and policy analysis. High temporal and spatial resolution precipitation data required

to drive soil erosion and water runoff estimates came from a 2-minute, 1-kilometer square (about

0.4 square miles) NEXRAD rainfall product. Soil and crop management inputs were field-based

and determined from Landsat satellite imagery of land cover, LiDAR surface elevations, the

USDA NASS Cropland Data Layer, and the USDA Soil Survey Geographic database. These

_______________

1/ Richard Cruse, Professor, Department of Agronomy, Iowa State University, Ames, IA.

1/ Brian Gelder, Associate Scientist, Agricultural and Biosystems Engineering, Iowa State

University, Ames, IA

2/ David James, Geographic Information Specialist, National Laboratory for Agriculture and the

Environment, USDA/ARS

1/ Daryl Herzmann, Systems Analyst III, Department of Agronomy, Iowa State University,

Ames, IA

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 1

data, excluding tillage management practices, are available as the USDA ARS’ Agricultural

Conservation Planning Framework (ACPF; http://northcentralwater.org/acpf/) and are a critical

component of DEP. Soil erosion, water runoff and soil water content are estimated using the

process based WEPP model and publicly reported at the HUC 12 level, which coincides with

existing watershed monitoring data and multiple federal and state projects addressing soil and

water quality improvements. While daily public reporting is at the HUC 12 level, erosion, water

runoff, and soil water storage estimates are made for each agricultural subcatchment within each

HUC 12; these sub catchments average 200 acres in size. Depending on user needs and computer

power available, these estimates could be made at a much finer scale. Within the current project

structure, a statewide rainfall event resulted in over 200,000 hillslope water runoff and soil

erosion estimates.

Results

To illustrate the utility of DEP, hill slope soil erosion and water runoff losses for Iowa were

estimated for an eight year period beginning in 2007 based on archived input data (precipitation,

crops and tillage in each field, hill slope steepness and slope length, soil types…). The statewide

hill slope soil erosion estimates with DEP matched the USDA estimate published in the National

Resources Inventory (NRI) (5.7 tons/acre/year for DEP and 5.8 tons/acre/year for NRI). NRI

uses RUSLE, an empirically based model, as the basis for soil erosion estimates. However, DEP

estimates illustrate the wide range of soil erosion that occurred spatially and temporally during

this period, a critically important capability not offered by any other technology. DEP results

indicate that average annual statewide soil erosion ranged from 10.6 tons/acre in 2010 to 1.

ton/acre in 2012. Key findings show the greatest soil erosion rate estimates exceeded 50

tons/acre in multiple HUC 12 watersheds in 2010. A majority of Iowa experienced less than 1

ton/acre hill slope loss of soil in 2012, which was a drought year in the Midwest.

Soil erosion averages over large areas (a state) and over long time periods (such as occurs when

long term average precipitation is used over a broad area) have value for land use planning and

for trend analysis on a broad scale. The NRI

(http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/technical/nra/nri/ ) is a current tool, and

a well-respected tool. DEP adds to this value by not only identifying critical areas in need of

elevated attention, but it also inventories soil loss through time for all HUC 12 watersheds in the

state.

DEP results can be accessed at: https://dailyerosion.org/

References

Cruse, Richard, Dennis Flanagan, Jim Frankenberger, Brian Gelder, Daryl Herzmann, David

James, Witold Krajewski, Michal Kraszewski, John Laflen, Jean Opsomer, and Dennis Todey.

2006. Daily estimates of rainfall, water runoff, and soil erosion in Iowa. J. Soil Water Conserv.

61:191-198.

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CONNECTING SOIL AND NUTRIENT LOSS TO CROP PRODUCTION

Francisco J. Arriaga 1/

The 4R concept (right source, right rate, right time and right place) provides a useful

structure to achieve increased crop production, improved farm profitability, greater

environmental protection and better sustainability. However, crop nutrient management

should go beyond the 4Rs of fertilizer and manure stewardship. Other soil management

factors that affect crop productivity, farm profitability, the environment, and sustainability

should be considered when thinking about crop nutrient management. While fertilizer and

manure applications affect nutrient availability to crops short-term (e.g., current growing

season or following year), other soil management factors affect nutrient availability long-

term. More specifically, factors that affect crop residues after harvest and soil structure/

aggregation affect the availability of nutrients in future years. One such soil property is soil

organic matter content.

Organic matter in the soil has several important roles. One such role of organic matter is

helping the formation of soil aggregates which are indispensable for well-functioning soil

hydraulic properties. Greater levels of soil aggregation are associated with greater infil-

tration rates, plant water availability and drainage capacity (Hillel, 1998). However,

organic matter also helps increase the cation exchange capacity of a soil. The cation

exchange capacity of soil is often referred to as the store house of fertility. Soil particles

have a small negative charge, which helps retain positively charged plant nutrient ions.

Note that an ion is a chemical element or molecule with either a positive or negative

charge; a positively charged ion is also called a cation. Most plant nutrients exist as ions in

the water within the soil (Foth and Ellis, 1988). Plant roots uptake these ions that are

dissolved in the soil water, or soil solution. As crop roots take up these nutrient ions from

the soil solution, they are replaced by other ions that were stored near a soil particle thanks

to the cation exchange capacity of soil. The cation exchange capacity also prevents plant

nutrients in a cationic form from been lost out of the root zone by leaching.

As mentioned earlier, soil particles inherently have a negative charge. However, organic

matter can contribute significantly to the cation exchange capacity of soil and boost the

nutrient retention capacity of soil (Parfitt et al., 1995). In some soils it has been reported

that organic matter contributes between 30 to 60% of the cation exchange capacity of the

plough layer (Schnitzer, 1967). Therefore, avoiding reductions and increasing organic

matter content in soil helps increase the nutrient retention capacity of a soil. Further, plant

nutrients are released and made available for root uptake as organic matter decomposes in

soil.

__________________

1/ Assistant Professor and State Soil Specialist, Dept. of Soil Science, Univ. of Wisconsin-

Madison and Univ. of Wisconsin-Extension, 1525 Observatory Dr., Madison, WI 53706.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 3

There are several ways that organic matter content in soil can decrease, such as erosion,

fast oxidation from excessive tillage, and reductions in additions of organic materials to

soil (e.g., long-term reductions in crop residue inputs because of crop biomass harvest).

The impacts and implications of crop/soil management practices such as tillage and crop

residue handling from a crop nutrient perspective and fertilizer replacement value will be

discussed during this presentation.

References

Foth, H.D., and B.G. Ellis. 1988. Soil fertility. John Wiley & Sons, New York.

Hillel, D. 1998. Environmental soil physics. Academic Press, San Diego, CA.

Parfitt, R.L., D.J. Giltrap, and J.S. Whitton. 1995. Contribution of organic matter and clay

minerals to the cation exchange capacity of soils. Communications in Soil Science and

Plant Analysis 26:1343-1355.

Schnitzer, M. 1965. Contribution of organic matter to the cation exchange capacity of

soils. Nature 207:667-668.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 4

MANAGING SILAGE LEACHATE AND RUNOFF FOR WATER QUALITY

Becky Larson 1/^ and Eric Cooley 2/

SPACE PROVIDED FOR QUESTIONS OR NOTES

_______________________

1/ Assistant Professor, Biological Systems Engineering, Univ. of Wisconsin-Madison,

Madison, WI 53706.

2/ Co-Director, Discovery Farms, Univ. of Wisconsin-Extension.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 5

THE BENEFIT OF GYPSUM FOR CROP PRODUCTION IN WISCONSIN

Francisco J. Arriaga1/^ and Richard P. Wolkowski2/

Abstract

Gypsum is a mineral whose chemical structure consists of calcium sulfate with two water

molecules in its structure (CaSO 4 ⸳ 2H 2 O). This mineral has been used in agriculture as a

fertilizer for centuries, mainly as a source of calcium and sulfur. There are three main

sources of gypsum available today for agricultural use: mined, recycled wallboard, and

flue-gas desulfurization (FGD) gypsum. Chemically these sources are identical, with the

exception of recycled wallboard gypsum, which might contain pieces of paper within the

material. Currently there is considerable interest in FGD gypsum for agricultural use as it

is readily available. Flue-gas desulfurization gypsum is generated in air scrubbers

engineered to remove sulfur from exhaust gases in coal-burning electric power plants. This

type of gypsum typically has a smaller particle size than mined sources; thus it dissolves

and reacts more readily.

Several benefits are attributed to gypsum application to soil, other than supplying calcium

and sulfur to crops. It is said that gypsum applied to soil works as a soil conditioner that

improves soil structure, infiltration capacity, drainage properties, can improve nitrogen

utilization of some crops, and reduce aluminum toxicity of the profile of acid soils.

Further, FGD gypsum application to soil in specific has been proposed as a potential

practice to reduce nutrients losses such as phosphorus. Research conducted in Wisconsin

has mainly concentrated on the impact of FGD application to soil as an amendment and its

impact on crop productivity, soil properties and phosphorus losses. The most recent data

from research studies conducted in the State focusing on gypsum application to soil will be

presented.

________________

1/ Assistant Professor and State Soil Specialist, Dept. of Soil Science, Univ. of Wisconsin-

Madison and Univ. of Wisconsin-Extension, 1525 Observatory Dr., Madison, WI 53706.

2/ Senior Scientist (Emeritus), Dept. of Soil Science, 1525 Observatory Dr., Univ. of

Wisconsin-Madison, Madison, WI 53706.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 6

WHY CONSERVING WISCONSIN SOIL AND WATER RESOURCES

IS A GLOBAL NECESSITY

Rick Cruse1/

As the world population continues to grow, and the environmental uncertainty of a less stable

climate becomes more manifest, the importance of our soil resources will only increase. The

goal of this presentation is to synthesize the catalysts of soil degradation, to highlight the

interconnected nature of the social and economic causes of soil degradation, and articulate why

maintaining or improving Wisconsin’s soil and water resources is imperative. An expected three

billion people will enter the middle class in the next 20 years; this will lead to an increased

demand for meat, dairy products, and consequently grain. As populations rise so do the

economic incentives to convert farmland to other purposes. With the intensity and frequency of

droughts and flooding increasing, consumer confidence and the ability of crops to reach yield

goals are also threatened. In a time of uncertainty, conservation measures are often the first to be

sacrificed. In short, we are too often compromising our soil resources when we need them the

most.

1/ (^) Professor, Dept. of Agronomy, Iowa State University, Ames, IA.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 7

INDUSTRY ROUNDTABLE ON HERBICIDE RESISTANT

TRAIT PIPELINE IN SOYBEAN – PANEL

Steven Snyder 1/^ , Tim Trower 2/^ , Nick Weidenbenner 3/^ , and Steve Langton 4/

SPACE PROVIDED FOR QUESTIONS OR NOTES

___________________________

1/ Dow AgroSciences.

2/ Syngenta.

3/ Bayer Crop Science.

4/ Monsanto.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 8

REVAMPING SOYBEAN NUTRIENT UPTAKE, PARTITIONING, AND REMOVAL

DATA OF MODERN HIGH YIELDING GENETICS AND PRODUCTION PRACTICES

Adam P. Gaspar^1 , Carrie A.M. Laboski 2 , Seth L. Naeve^3 , and Shawn P. Conley^1

Abstract

Soybean [ Glycine max (L.) Merr.] nutrient uptake and partitioning models are primarily

built from work conducted in the early 1960s. Since the 1960s, yields have nearly doubled

to 47.5 bu acre-1^ in 2014 and soybean physiology has been altered with approximately one

more week of reproductive growth and greater harvest index’s for currently cultivated

varieties. These changes in soybean development along with new production practices

warrant re-evaluating soybean nutrient uptake, partitioning. This study’s objective was to

re-evaluate these factors across a wide yield range of 40 to 90 bu acre-1. Trials were con-

ducted at three locations (Arlington and Hancock, WI and St. Paul, MN) during 2014 and

2015. Plant samples were taken at the V4, R1, R4, R5.5, R6.5, and R8 growth stage and

partitioned into stems, petioles, leaves, pods, seeds, fallen leaves, and fallen petioles,

totaling about 7,000 samples annually. Results indicate that dry matter accumulation at

R6.5 was only 84% of the total and that as yield increased the harvest index by 0.2% per

bushel. Nutrient uptake for N, P 2 O 5 , and K 2 O was 227, 55, and 153 lb a-1^ , respectively and

crop removal was 188, 44, and 74 lbs. a-1^ , respectively at a yield level of 60 bu acre -1. Data

showed that the extended reproductive growth phase (~7 days), greater nutrient remobiliza-

tion efficiencies (>70%) and higher nutrient harvest index with increasing yields helped

contribute to higher yields without greatly increasing total nutrient uptake.

1 Grad Research Assistant and Professor, Dept. of Agronomy, Univ. of Wisconsin- Madison, 1575 Linden Dr., Madison, WI 53706. 2 Professor, Dept. of Soil Science, Univ. of Wisconsin-Madison, 1525 Observatory Dr., Madison, WI 53706. 3 Associate Professor, Dept. of Agronomy and Plant Genetics, Univ. of Minnesota, 1991 Upper Buford Circle, St. Paul, MN, 55108

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 9

ARE THESE CORN YIELD TRENDS REAL? CAN WE COUNT ON THEM?

Joe Lauer 1

The 2016 corn production year was the best on record in Wisconsin. On November 10,

2016, the Wisconsin Agricultural Statistics Service projected corn to be harvested from 3.

million acres with an average yield of 180 bushels per acre and total production of 558

million bushels. Final estimates will be released in January of 2017.

Since 1996, Wisconsin corn

yields have increased an average

of 1.7 bu/A per year (Figure 1).

The previous yield record was

set in 2015 when corn yielded

164 bushels per acre. The

increase of 16 bushels per acre

over the previous record year

represents a 10% jump. Only

five other times in Wisconsin's

history has corn yields increased

at comparable or better rates

(Figure 2).

Many people are asking what

1 Corn Agronomist, Univ. of Wisconsin, 1575 Linden Dr., Madison, WI 53706

Figure 2. Years of record corn yield (N= 29 of 151) and the percent increase over the previous record year. Source USDA-NASS.

Figure 1. Corn grain yield for Wisconsin since 1866. Source USDA-NASS.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 10

happened during 2016 to produce record yields? More importantly, why did corn yields

jump 10% over the previous record? Are corn hybrids that much better? What

management practices were different during 2016? Was it the weather? If one were to list

the top reasons for the bumper crop, 8 of the top 10 reasons would have to be weather

related. Improved hybrid genetics and management might also make the top 10.

Common characteristics between these record years include: (1) earlier than normal

planting, (2) adequate spring soil moisture, (3) mild moisture stress during early corn

development with soil moisture eventually replenished to normal levels, (4) corn

development was typically ahead of normal at some point during the growing season, (5)

fall killing frosts were at the end of September or during October, and (6) fall harvest

conditions were typically dry.

In most years, the majority of Wisconsin's corn acreage is planted past the optimum date.

On average, approximately 27% of the corn acreage is planted by May 10, 45% by May

15, 62% by May 20, and 77% by May 25. In numerous studies, the optimum planting date

for corn production in Wisconsin was found to be between May 1 for southern Wisconsin

and May 10 for northern Wisconsin. Shortly after the optimum date, corn yields decrease

0.3 to 0.5% per day which accelerates to 1.5 to 2.3% per day when corn is planted during

late May. In the record years, planting was reported to be earlier than normal with more of

the acreage planted around the optimum planting date.

In record years, inadequate soil moisture supplies were often reported during late May and

early June. Mild moisture stress, during early corn development, increases the allocation

of photosynthate to roots at the expense of shoots and leaves, thus, promoting deeper root

growth and increased soil exploration for water, minerals and other nutrients. As moisture

stress becomes more severe, total root weight can decrease. In all of these years, rainfall

replenished soil moisture supplies to normal or above normal levels by late June to early

July.

Will 2017 be another record year? A record year follows another record year or tie about

31% of the time. There is no reason why another record year could not take place in 2017.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 11

COMPARISON OF SOYBEAN YIELDS IN ON-FARM TRIALS

VS. SMALL PLOT EXPERIMENTS

Tristan Mueller 1/

Abstract

Performance of foliar fungicides can be evaluated in field-scale on-farm replicated strip

trials and in small-plot experiments. This presentation will present analyses of two datasets

from Iowa to compare yield and yield response variability to fungicide applications in on-

farm trials versus small-plot experiments. An estimate number of locations, replications

and years required to detect yield differences of interest will be covered. One dataset

includes 123 on-farm trials evaluating Headline (BASF) foliar fungicide on soybean

( Glycine max (L.) Merr) in 2008 and 2009 across Iowa by farmers working with the Iowa

Soybean Association On-Farm Network. The other dataset includes small-plot experiments

conducted by university researches to evaluate the same fungicide during the same

growing seasons at six Iowa State University Research and Demonstration Farms. On-farm

trials were harvested by farmers’ combines equipped with yield monitors and GPS and

small-plot experiments by small-plot combines. Variance component analysis was used to

quantify the random sources of yield variation contributed by location and blocks nested

within each location and conduct power analyses for multi-location trials. Disease ratings

were done in all small-plot trials. While yield responses in the two types of trials were

similar (about 125 kg ha-1^ ), the residual random yield variation in on-farm trials tended to

be smaller than that in small-plot trials but the random variation due to location effect was

larger in on-farm trials. The presentation will show examples of power curves showing the

numbers of trials, replications and years required to detect specific response, often <68 kg

ha-1^. The results also suggest about the different utility of two methods for evaluating

fungicides, specifically, the on-farm trials for answering the question “when, where and

how likely” a given fungicide works while small-plot trials for comparing multiple

chemistries at the same locations and quantifying the interactive effects of application

timing.

_______________________

1/ Director of Global Field Development, BioConsortia.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 12

IMPROVING WHITE MOLD MANAGEMENT OF SOYBEAN IN WISCONSIN

Jaime Willbur1/, Megan McCaghey2/, Scott Chapman3/, Medhi Kabbage4/, Damon L. Smith5/

Introduction

White mold (Sclerotinia stem rot) is caused by Sclerotinia sclerotiorum and consistently ranks in the top ten diseases plaguing global soybean crops (Wrather et al. , 2010). In 2009, United States soybean losses due to white mold reached almost 59 million bushels and cost growers a corresponding ~$560 million (Koenning & Wrather, 2010; Peltier et al. , 2012). Furthermore, according to a United Soybean Board report from 2011, white mold epidemics in the Great Lakes region alone were responsible for 94% of nationwide losses to the disease and cost regional growers ~$138 million (USDA-NASS 2015). White mold is infamously characterized by its challenging fungal promiscuity and longevity, and by the subsequently devastating crop losses; Wisconsin growers justifiably rank white mold management third in significance and concern.

Disease control is limited due to the lack of complete resistance in commercial cultivars (Peltier et al. 2012) and the often incomplete or limited success of chemical applications. Rigorous investigation of white mold resistant soybean germplasm for release to breeding programs would improve commercially available resistance. Additionally, improving our understanding of the complex timing and conditions surrounding white mold development would assist in providing effective fungicide recommendations. Product selection and application timing must both be considered for successful white mold management. Furthermore, risk assessment tools may be used to more accurately predict the timing of effective fungicide applications based on weather conditions, pathogen presence, and host architecture. An improved understanding of chemical control, development of resistant germplasm, and an optimized forecasting system would improve management strategies of white mold in soybean.

Research Objectives

1. Evaluate fungicide product efficacy and application timing for white mold control in Wisconsin.
2. Evaluate physiological resistance to white mold in soybean germplasm using a panel of representative S. sclerotiorum isolates.
3. Further investigate the roles of weather variables in the formation of apothecia in soybean crops. Use this information to develop and refine an improved advisory system for white mold in soybean.

1/ (^) Graduate Research Assistant, Department of Plant Pathology, 1630 Linden Drive, University of

Wisconsin-Madison, Madison, WI, 53706. 2/ (^) Graduate Research Assistant, Department of Plant Pathology, 1630 Linden Drive, University of

Wisconsin-Madison, Madison, WI, 53706. 3/ (^) Researcher, Departments of Plant Pathology and Entomology, 1630 Linden Drive, University

of Wisconsin-Madison, Madison, WI, 53706. 4/ (^) Assistant Professor, Department of Plant Pathology, 1630 Linden Drive, University of

Wisconsin-Madison, Madison, WI, 53706. 5/ (^) Assistant Professor and Extension Field Crops Pathologist, Department of Plant Pathology,

1630 Linden Drive, University of Wisconsin-Madison, Madison, WI, 53706.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 13

Methods and Results

Fungicide efficacy and timing In 2016, 15 fungicide applications (including a non-treated control) were evaluated for white mold control in Hancock, Wisconsin (Table 2). Small plots were established in agricultural research station fields with a previous history of white mold; plots were irrigated to promote disease development. Products were applied at either the R1, R3, or both R1 and R3 growth stages. The disease incidence and disease severity index (DSI) was determined at the R6 growth stage and yield data were collected at harvest. The best treatments tended to include Aproach at 9 fl oz applied at R1 and R3 or Endura at 8 oz applied at R1. A combination treatment of Priaxor at 4 fl oz and Endura at 6 oz applied at R1 also resulted in comparably low disease levels and high yields.

Additionally in 2016, 16 fungicide treatment timings (including a non-treated) were evaluated for white mold control at the Hancock Agricultural Research Station (Table 1). Aproach at 9 fl oz, Endura at 8 oz, and Proline at 5 fl oz were applied at the R1, R3, R4, or R5 growth stages. DSI and DI data were collected at the R6 growth stage and yield data were collected at harvest. The best treatments were those where fungicide was applied at the R1 to R3 growth stages (or a combination of R1 and R3 applications). Endura at 8 oz applied at the R3 growth stage and Aproach at 9 fl oz applied at both R1 and R3 resulted in the lowest disease levels and the highest yields.

These results are similar to findings from corresponding trials in Michigan and Iowa. These data, therefore, have been incorporated into extensive fungicide evaluations conducted in the North Central region over the past 8 years. Overall, 26 site-years were analyzed, including data from Illinois, Iowa, Michigan, and Wisconsin, to determine the most efficacious products and timings for soybean white mold management.

White mold-resistant germplasm Previously, resistant soybean germplasm was generated from crosses between highly resistant experimental lines (W04-1002 or AxN-1-55) and lines exhibiting good resistance to other diseases such as brown stem rot, soybean sudden death syndrome, and soybean cyst nematode. Over the last 3 years, germplasm lines have been rigorously evaluated in white mold nurseries under high disease pressure. In 2016, seven elite lines were selected and evaluated against seven other check lines or industry standard varieties. The trial was conducted at the Hancock Agricultural Research Station in small, irrigated plots. Disease (DSI and DI), lodging, and yield data, as well as oil and protein content, were collected and evaluated for all lines. Additionally, the seven elite lines were challenged with a panel of nine representative S. sclerotiorum isolates in greenhouse evaluations. Stem lesion development was monitored for 14 days post-inoculation and used to evaluate the durability of germplasm line resistance. Overall, greenhouse line performance against multiple isolates was evaluated against field performance of the same lines to determine the best resistant lines for release to breeding programs. Of particular interest, line 91-38 consistently performed well in greenhouse and field evaluations. In 2016, the line exhibited low disease levels (38.9 DSI, 14% DI), moderate yield (49.8 bu/a), minimal lodging (score of 1.0, upright), and high protein (38.6%) and oil (19.2%) content (relative to averages in the Great Lakes region). Line 91-38 has been selected for public release (2018 growing season) as a food- grade soybean variety.

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 14

Table 1. White mold ratings and yield of soybeans treated with various fungicides (2016).

Treatment and Rate/Acre (Crop Growth Stage at Application)

Disease Incidence (%)

Disease Severity Incidence (DSI) z

Yield (bu/a)

Aproach 9.0 fl oz (R1 + R3) 3.7 20.8 cdy^ 82.5 Endura 6 oz (R1) + Priaxor 4.0 fl oz (R3) 3.5 17.0 cd 81.9 Endura 8 oz (R1) - Positive Control 3.9 20.3 cd 79.2 Priaxor 4.0 fl oz (R1) + Endura 6.0 fl oz (R1) 3.0 17.2 cd 78.5 Domark 5 fl oz (R1) 6.2 33.6 abc 78.4 Domark 4 fl oz (R3)+ Topsin-M 0.75 lbs (R3) 3.0 21.4 cd 78.0 Priaxor 4.0 fl oz + Domark 4.0 fl oz (R1) 7.4 44.7 a 77.8 Endura 6 oz (R1) 3.6 18.9 cd 77.2 Domark 5 fl oz (R3) 6.9 30.3 abc 77.1 Topsin-M 0.75 lbs (R1) 2.6 16.1 cd 76.2 Non-treated control 6.9 32.2 abc 74.9 Domark 4 fl oz (R1)+ Topsin-M 0.75 lbs (R1) 7.1 35.3 abc 74.9 Cobra 6.0 fl oz (R1) + Endura 8.0 oz (R1) 2.7 13.6 bcd 72.9 Vida 0.5 fl oz + Domark 5 fl oz (R3) 1.4 7.8 d 72.1 Topsin-M 0.75 lbs (R3) 3.8 26.4 a-d 69.3 F - value 1.33 2.05 1.54 Pr>F 0.24 0.03 0.14 zSclerotinia stem rot DSI was generated by rating 30 arbitrarily selected plants in each plot and

scoring plants with on a 0-3 scale: 0 = no infection; 1 = infection on branches; 2 = infection on mainstem with little effect on pod fill; 3 = infection on mainstem resulting in death or poor pod fill. The scores of the 30 plants were totaled and divided by 0.9. yMeans followed by the same letter are not significantly different based on Fisher’s Least

Significant Difference (LSD; α=0.05)

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 15

Table 2. White mold ratings and yield of soybeans treated with various fungicides applied at different growth stages (2016). Treatment and Rate/Acre (Crop Growth Stage at Application)

DI (%) DSIz^ Yield (bu/a) Aproach 9.0 fl oz (R1+R3) [Standard Check] 10.2 dey^ 30.8 f 77.0 a Endura 8.0 oz (R3) 6.8 e 20.2 g 75.3 ab Aproach 9.0 fl oz (R3) 15.0 b-d 45.2 de 72.5 abc Endura 8.0 oz (R1) [Standard Check] 14.3 cd 37.1 ef 68.6 bcd Proline 5.0 fl oz (R4) 21.0 abc

abc

68.5 bcd

Proline 5.0 fl oz (R3) 15.9 bcd

cde

66.4 cde

Aproach 9.0 fl oz (R5) 20.0 ac 49.1 be 66.0 c-f Aproach 9.0 fl oz (R4) 25.3 ab 67.1 ab 62.9 d-g Endura 6.0 oz (V5) 22.5 abc

51.9 be 61.7 e-g

Aproach 9.0 fl oz (V5) 24.2 abc

bcd

61.6 e-g

Non-Treated Control 25.6 ab 62.5 a-d 61.0 e-g Endura 8.0 oz (R4) 32.1 a 77.0 a 60.8 e-g Endura 8.0 oz (R5) 30.1 a 64.5 abc

60.3 e-g

Proline 5.0 fl oz (R1) 25.2 ab 66.3 abc

59.7 fg

Proline 5.0 fl oz (R5) 25.3 ab 56.9 a-d 59.0 g Aproach 9.0 fl oz (R1) 33.0 a 68.2 ab 57.2 g F - value 4.97 8.63 6.11 Pr>F <0.01 <0.01 <0.01 zSclerotinia stem rot DSI was generated by rating 30 arbitrarily selected plants in each plot and

scoring plants with on a 0-3 scale: 0 = no infection; 1 = infection on branches; 2 = infection on mainstem with little effect on pod fill; 3 = infection on mainstem resulting in death or poor pod fill. The scores of the 30 plants were totaled and divided by 0.9. yMeans followed by the same letter are not significantly different based on Fisher’s Least

Significant Difference (LSD; α=0.05)

White mold advisory development Previously, a 3-variable model, considering site-specific (GPS referenced) air temperature, relative humidity, and wind speed, was developed to predict apothecial presence in soybean fields. In 2016, model validation was conducted at agricultural research stations in Wisconsin and Michigan. Small plots were scouted to monitor apothecial presence and rated to evaluate disease control. Additionally, apothecial presence and the resulting disease incidence was monitored in 20 Wisconsin grower fields to further evaluate model implementation. Grower field observations matched 89% of same day model predictions; furthermore, full-season model predictions explained 74% of overall disease observations. In addition to the development of a publically

Proceedings of the 2017 Wisconsin Agribusiness Classic - Page 16