Integrated Crop Management Conference

Proceedings of the 29th Annual

Integrated Crop Management Conference November 29 – 30, 2017 Iowa State University Ames, Iowa

AEP 0302 – 2017



2 — 2017 Integrated Crop Management Conference - Iowa State University

Prepared by Brent A. Pringnitz, program coordinator

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Copyright © 2017 - Iowa State University

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2017 Integrated Crop Management Conference - Iowa State University — 3



Table of Contents Speaker contact information ......................................................................................................................6

Crop management 1. Weather outlook 2018 and understanding inversions .........................................................................9 ElwynnTaylor, professor and Extension climatologist, Agronomy, Iowa State University

2. Key management practices that explain soybean yield gaps across the North Central US ..................13 Shawn Conley, professor and State Soybean and Small Grains Specialist, University of Wisconsin, Madison, Madison, WI

3. Corn planting decisions: What’s changed and what’s the same?.........................................................21 Mark Licht, assistant professor, Agronomy and Extension cropping systems specialist, Iowa State University; Emily Wright, research associate, Agronomy, Iowa State University; Mitch Baum, graduate student, Agronomy, Iowa State University; Nick Upah, graduate student, Agronomy, Iowa State University

4. Water availability, root depths and 2017 crop yields .........................................................................25 Sotirios Archontoulis, assistant professor, Agronomy; Mark Licht, assistant professor, Agronomy; Mike Castellano, associate professor, Agronomy; Raziel Ordonez, postdoc research associate; Javed Iqbal, research associate, Rafael Martinez-Feria, graduate student, Pat Edmonds assistant scientist; Emily Wright, research associate; Mitch Baum, graduate student; Ashlyn Kessler, graduate student; Huber Isaiah, graduate student; Aaron Sassman, research associate; Matt Liebman, professor, Agronomy; Matt Helmers, professor, Ag and Biosytstems Engineering

5. Is there loss of corn dry matter in the field after maturity? ................................................................35 Mark Licht, assistant professor, Agronomy and Extension cropping systems specialist, Iowa State University; Charles Hurburgh, professor, Agricultural and Biosystems Engineering and professor in charge, Iowa Grain Quality Initiative, Iowa State University; Matthew Kots, industrial technology undergraduate intern, Agricultural and Biosystems Engineering, Iowa State University; Philip Blake, industrial technology undergraduate intern, Agricultural and Biosystems Engineering, Iowa State University; Mark Hanna, Extension agricultural engineer, Agricultural and Biosystems Engineering, Iowa State University

9. Searching for the bottom ..................................................................................................................41 Chad Hart, associate professor, Economics and Extension Economist, Iowa State University

10. Current legal issues impacting crop production ................................................................................45 Kristine A.Tidgren, attorney and assistant director, Center for Agricultural Law and Taxation, Iowa State University

Pest management 12. Genetically modified crops: Marvel or malady? .................................................................................51 Peter Goldsbrough, professor, Botany and Plant Pathology, Purdue University

13. The Iowa Pest Resistance Management Plan: A community-based approach to address pest resistance in Iowa .........................................................................................................57 Evan Sivesind, program manager, Entomology, Iowa State University

14. Back to the basics: Integrating weed biology into weed management plans .......................................63 Jared Goplen, Lisa Behnken, Ryan Miller, and Liz Stahl, Extension Educators, Crops, University of Minnesota Extension

4 — 2017 Integrated Crop Management Conference - Iowa State University 15. Dicamba injury and insurance ..........................................................................................................69 Ray Massey, Extension professor, Agricultural and Applied Economics, University of Missouri

16. Dicamba: Past, present, and future....................................................................................................71 Bob Hartzler, professor, and Extension weed management specialist, Agronomy, Iowa State University

17. Weed management update for 2018 and beyond: The more things change... ....................................77 Micheal D.K. Owen, University Professor and Extension weed management specialist, Agronomy, Iowa State University

18. Rootworm behavior and resistance in Bt cornfields ...........................................................................89 Joseph L. Spencer, Principal Research Scientist and Research Program Leader in Insect Behavior, Illinois Natural History Survey, University of Illinois; Sarah A. Hughson, Extension Specialist - Pesticide Safety Education Program, Crop Sciences, University of Illinois

19. Resistance management plan for soybean aphid ................................................................................95 Erin Hodgson, associate professor and Extension entomologist, Entomology, Iowa State University

20. Iowa monarch conservation, pest management and crop production ................................................97 Steven P. Bradbury, professor, Natural Resources Ecology and Management, Iowa State University; Tyler Grant, postdoctoral research associate, Natural Resources Ecology and Management, Iowa State University; Niranjana Krishnan, graduate student, Entomology, Iowa State University

21. Where do we stand with soybean cyst nematode, resistance, and seed treatments? .........................105 Gregory Tylka, professor, Plant Pathology and Microbiology, Iowa State University

22. Status of bacterial leaf streak of corn in the United States................................................................111 Kirk Broders, assistant professor, Bioagricultural Sciences and Pest Management, Colorado State University

23. Fungicide use on corn ....................................................................................................................117 Alison Robertson, professor and Extension crop plant pathologist, Plant Pathology and Microbiology, Iowa State University

24. Update on soybean diseases ............................................................................................................121 Daren Mueller, associate professor and Extension crop plant pathologist, Plant Pathology and Microbiology, Iowa State University

25. Disease risks associated with cover crops in corn and soybean production .....................................125 Alison Robertson, professor, Plant Pathology and Microbiology, Iowa State University; Tom Kaspar, research scientist, USDA-ARS, Iowa State University; Leonor Leandro, associate professor, Plant Pathology and Microbiology, Iowa State University; Daren Mueller, associate professor, Plant Pathology and Microbiology, Iowa State University; Jyotsna Acharya, research assistant, Plant Pathology and Microbiology, Iowa State University

Nutrient management 26. Plant nutrition science for sustaining public trust ...........................................................................129 Tom Bruulsema, vice president, Americas and Research, International Plant Nutrition Institute (IPNI)

27. Nitrogen management for corn .......................................................................................................137 Emerson Nafziger, professor, Crop Sciences, University of Illinois

28. View from the sky: Mapping corn nitrogen status at the watershed level .........................................143 Chris Wilkins, database administrator/GIS specialist, Iowa Soybean Association; Suzanne Fey, data analyst, Iowa Soybean Association; Brad Wirt, GIS specialist/analyst, Iowa Soybean Association; Peter Kyveryga, Director of Analytics, Iowa Soybean Association

29. Watch potassium management - It also affects corn response to nitrogen and soybean diseases ......153 Antonio P. Mallarino, professor, Agronomy, Iowa State University


30. Nitrogen dynamics with a rye cover crop ........................................................................................161 John E. Sawyer, professor, Agronomy, Iowa State University; Swetabh Patel, graduate assistant, Agronomy, Iowa State University; Jose Pantoja, former graduate assistant, Agronomy, Iowa State University; Daniel W. Barker, assistant scientist, Agronomy, Iowa State University; John P. Lundvall, research affiliate, Agronomy, Iowa State University

Soil and water management 31. Meeting the nitrate reduction goal: What will it take? .....................................................................169 Jamie Benning, Water Quality Program Manager, Iowa State University Extension and Outreach; Matt Helmers, professor and Extension agricultural engineer, Agriculture and Biosystems Engineering, Iowa State University; Mark Licht, assistant professor and Extension Cropping Systems Specialist, Agronomy, Iowa State University

32. The Iowa Nutrient Reduction Strategy farmer survey: Tracking knowledge, attitudes, and behaviors .................................................................................173 Laurie Nowatzke, measurement coordinator, Iowa Nutrient Reduction Strategy, Iowa State University

33. Impacts of 4R nitrogen management on drainage water quality ......................................................181 Matthew J. Helmers, professor, Agricultural and Biosystems Engineering, Iowa State University

34. Integrating strips of native prairie into rowcrop agriculture fields ...................................................185 Timothy Youngquist, agricultural specialist, Agronomy, Iowa State University

35. The science of cover crops in Iowa..................................................................................................189 Liz Juchems, events coordinator, Iowa Learning Farms; Stefan Gailans, research and field crops director, Practical Farmers of Iowa

36. What is soil health, how do we measure it, and why the emphasis on soil biology? ........................193 Marshall McDaniel, assistant professor in soil-plant interactions, Agronomy, Iowa State University

Professional development 37. Financial stress in Iowa farms: 2014 - 2016 ....................................................................................199 Alejandro Plastina, Extension economist, Iowa State University

38. Where are Iowa and US land values headed? ..................................................................................203 Wendong Zhang, assistant professor, Economics, Iowa State University

39. Recent developments in China and impacts on US agricultural trade ..............................................207 Wendong Zhang, assistant professor, Economics, Iowa State University

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Weather outlook 2018 and understanding inversions Elwynn Taylor, professor and Extension climatologist, Agronomy, Iowa State University

Introduction Colorado is known for mountains. The temperature at the city of Colorado Springs was 80 degrees, the sky was clear, the air almost still. We took our jackets and boarded the train to the summit of Pike’s Peak (we were aware that “Pike’s Peak the first” was in Iowa and General Pike actually reached the summit of the Iowa version of the peak). The elevation of downtown Colorado Springs is 6,035 feet above sea level and the summit of the peak is 14,115 feet above sea level. We did not need to be told that the temperature would be cooler at the summit; we could see the snow from town. The jackets were important as the temperature was 40 degrees when we stepped off the train to admire the view that inspired the song “America the Beautiful” by Katharine Lee Bates when she saw the fields of grain stretching to the Eastern horizon, from her position on the majestic mountain. The temperature of well mixed air decreases 5 degrees for every 1,000 of elevation unless you encounter cloud cover. In the clouds the well mixed air temperature decreases 3 degrees for every 1,000 feet of elevation (simply because some heat is released by the forming of clouds, it is known as latent heat of condensation). Washington State is known for “Apple production.” We stopped in the spacious basin of the Columbia River in south central Washington. The basin is an arid region (less than 10” of rain per year) that produces alfalfa, mint, asparagus, and other irrigated crops including apples and cherries. The latter two are produced on the hills and hillsides a few 10’s of feet above the basin floor. The reason: there is less frost on the hills. The frost nights of concern are early in the flower and fruit set time of year when over-night temperatures in the basin cool to below freezing in the fields of the basin and the temperature on the hill sides is often ten degrees warmer. This is not a well-mixed air situation that we observed on our trip up Pike’s Peak. Here the temperature is noticeably warmer as we walk up the side of the basin. This is the “inverse” of our experience on Pike’s Peak; here the air is colder at the bottom of the hill… this effect is called “Inversion.”

Figure 1. Pike’s Peak, Colorado summit is about 8000 feet above a public park (Garden of the Gods) near Colorado Springs, CO. The temperature decreases about five degrees per one thousand foot increase in elevation. Five degrees per 1000 feet is the temperature lapse rate for well mixed dry air.


Figure 2. Fruit farm on elevated land to reduce frost risk. Orchards are typically planted on higher land to take advantage of warmer air 60 to 200 feet above the valley bottom. Temperature inversions develop most nights in the farm lands near Willard, Utah.

Thermal inversion Inversions are common on clear sky nights when wind is minimal. Objects exposed to a clear sky cool as the object (soil, leaves, rocks, automobile windshields, roof of a structure, etc.) radiate “heat” according to the nature and temperature of the surface of the object. When illuminated by the sun the object warms according to the amount of thermal radiative energy from the sun that it absorbs. All objects radiate thermal energy according to the temperature and nature of the surface of the object. When the sky is clear and the sun has set, very little radiation is received from above because of the nature of the “cosmic cold of space” and the nature of the intervening layer of air between space and the surface of the object (or ground surface) of interest. Air in contact with the radiating object (plants, exposed soil, windshields, etc.) is cooled by contact with the radiating surface(s). Even a slight slope of the land allows “rivers of cooled air” to flow downslope. Golfers often report walking through a “cold spot” on a sloping fairway just after sunset on a still evening. The “cold spot” is a parcel of air that has been cooled by contact with a radiating surface (often grass or a tree), flowing “down slope” because the cooled air is more dense than the surrounding air layer. When a significant amount of cooled air has pooled (or developed) in an area an inversion is said to exist in that the temperature is least in the low areas and increases with elevation. Slope of the terrain is not necessary for an inversion to develop. The inversion associated with the dew upon the mowed grass differs only in magnitude from the inversion that “traps” air pollutants in an urban area to a depth of a few hundred feet. Inversions and air pollution: polluted air is often associated with thermal inversions. Comedy weather forecast on the “Tonight Show” for Los Angeles: The sun will rise a 7:00 AM, the SMOG will rise at 7:45 AM. Los Angeles, CA is positioned between mountain and sea such that inversion is the rule not the exception. The dense population made it an early example of severe air pollution. Many cities in the U.S. developed similar conditions as the human population increased locally. Strict standards for vehicles and industry have benefited the local environment. However, the Los Angeles air pollution is exceeded in numerous cities where population density is equally dense. The consistent inversion of the Cache Valley of Northern Utah developed a significant orchard agricultural economy because the consistent inversion offered an extended frost-free growing season. However, the growing cities of the valley have placed the valley in the top 10 USA regions for health hazard levels of air pollution.


Figure 3. Utah State University in the Cache Valley of Utah is under a shroud of polluted air. The prevalent inversion common to the region made possible a thriving orchard agricultural center, but the trapping of pollutants associated with increasing population has been damaging to agriculture and a health hazard to residents. The locality is one of the 10 most polluted atmospheres of the USA.



Key Management Practices That Explain Soybean Yield Gaps Across the North Central US Juan I. Rattalino Edreira, James E. Specht, Patricio Grassini Department of Agronomy and Horticulture, University of Nebraska-Lincoln Spyridon Mourtzinis, Shawn P. Conley, Adam C. Roth Department of Agronomy, University of Wisconsin-Madison Ignacio A. Ciampitti Department of Agronomy, Kansas State University Mark A. Licht Department of Agronomy, Iowa State University Hans Kandel, Jordan Stanley Department of Plant Sciences, North Dakota State University Peter M. Kyveryga Iowa Soybean Association Laura E. Lindsey Department of Horticulture and Crop Science, Ohio State University Daren S. Mueller Department of Plant Pathology and Microbiology, Iowa State University Seth L. Naeve Department of Agronomy and Plant Genetics, University of Minnesota Emerson Nafziger Department of Crop Sciences, University of Illinois Michael J. Staton Michigan State University Extension

Highlights X We developed a novel approach that combines producer survey data with a biophysical spatial framework for identifying causes of yield gaps over large agricultural areas with diversity in climate and soils. X The approach was applied to both rainfed and irrigated soybean in the North Central US region, and it was based on producer survey data on yield and management collected from 3,568 fields over two crop seasons. X The analysis indicated that the average regional yield potential was 71 bu ac-1 (rainfed) and 85 bu ac-1 (irrigated), with a respective yield gap of 22% and 13% of maximum yield potential. X Planting date, tillage, and in-season foliar fungicide and/or insecticide were identified as explanatory causes for yield variation, with planting date the most consistent management factor that influenced soybean yield. Introduction To date identification of causes of yield gaps (difference between maximum yield potential and measured yield in producer yields) has been restricted to small geographic areas. In this study, we developed a novel approach that combines producer-reported data and a spatial framework to identify explanatory causes of yield gap over large geographic regions with diversity of climate, soils, and water regimes (rainfed and irrigated). We focused on soybean in the North-Central United States region, which accounts for approximately one third of global soybean production, as a case study to provide a proof of concept on the proposed approach. The specific objectives of this project were to evaluate the proposed approach for its ability to: (1) benchmark producer soybean yields in relation to yield potential of their fields, (2) identify key management practices that explain yield gaps, and (3) explain the drivers for some of the observed (M)anagement × (E)nvironment interactions.

Producer data collection and quality control Data on soybean yield and management practices were collected over two crop seasons (2014 and 2015) from fields planted to soybean in 10 states in the North Central US region: Illinois (IL), Indiana (IN), Iowa (IA), Kansas (KS),

The North Central Soybean Research Program, a collaboration of 12 state soybean associations, invests soybean checkoff funds to improve yields and profitability via university research and extension.

1

14 — 2017 Integrated Crop Management Conference - Iowa State University Figure 1. Example of an actual survey form from a Nebraska soybean producer that provides information for three irrigated fields and one rainfed field planted to soybean in 2014 and 2015. This survey form was used to collect information from producer fields across 10 states in the North Central US region. Note that producer name is not shown and field location was hatched in order to keep personal information confidential.

Michigan (MI), Minnesota (MN), Ohio (OH), Nebraska (NE), North Dakota (ND), and Wisconsin (WI). Soybean producers provided data via returned surveys distributed by local crop consultants, Extension educators, soybean grower boards, and Natural Resources Districts (Figure 1). Briefly, producers were asked to report the range of average field yield across the fields planted to soybean in each year and water regime and to provide data for a number of fields that portray well that yield range. Requested data also included field location, average field yield, crop management (e.g., planting date, seeding rate, row spacing, cultivar, and tillage method), applied inputs (e.g., irrigation, fertilizer, lime, manure, and pesticides), and incidence of biotic and abiotic factors (e.g., insect pests, diseases, weeds, hail, waterlogging, and frost). Survey data were inputted into a digital database and screened to remove erroneous or missing data entries. We were interested in yield variation as related with management factors; hence, a few fields with extremely low yield due to incidence of unmanageable production site adversities (hail, waterlogging, wind, and frost) were excluded from the analyses. After quality control, the database contained data from a total of 3,216 fields planted to soybean in 2014 and 2015.

Producer data stratification based on soil-climate conditions A major challenge with this kind of data is how to cluster producer fields in order to identify management factors that consistently lead to higher yields for a given climate-soil combination. In the present study, surveyed fields were grouped based upon their climate and soil using the spatial framework developed for the central and eastern US by the Global Yield Gap Atlas (http:// www.yieldgap.org). This framework delineates regions [hereafter called technology extrapolation domains (TEDs)] based on four biophysical attributes 2


Figure 2. Map of the North Central US region showing nine technology extrapolation domains (TEDs) and meteorological stations (solid circles) selected for the present study. A coding system (from TED 1 to 9) is used to identify each TED (shown with a unique color) and its associated water regime (I: irrigated, R: rainfed). There were actually 10 TED-water regimes (denominated as just TEDs for simplicity) because rainfed and irrigated fields co-existed in TED 7 (7R and 7I, respectively). Top inset. Soybean harvested area in year 2015 (green area; USDA-NASS, 2016b) and location of the 3,216 surveyed soybean fields (red dots). Bottom inset. Location of North Central US region — 12 states within the conterminous US.

that govern crop yield and its inter-annual variability: (1) annual total growing degree-days, which, in large part, determines the length of crop growing season, (2) aridity index, which largely defines the degree of water limitation in rainfed cropping systems, (3) annual temperature seasonality, which differentiates between temperate and tropical climates, and (4) plant-available water holding capacity in the rootable soil depth, which determines the ability of the soil to supply water to support crop growth during rain-free periods. We selected TEDs that portrayed the diversity of climate, soils, and water regimes in the North Central US region (Figure 2). Six TEDs included only rainfed soybean fields (1R, 2R, 3R, 4R, 5R, and 6R) while two TEDs included only irrigated soybean fields (8I and 9I). One TED included both irrigated and rainfed soybean fields (7I and 7R). Because the impact of management factors on yield is influenced by water supply, we separated water regimes (WR; rainfed and irrigated) within the same TED. Hence, a total of 10 TED-WR combinations were eventually used in this study, which are referred hereafter as TEDs for simplicity (total of 10 TEDs). Selected TEDs included 38% of the surveyed fields (total of 1343 fields) and accounted for 25 and 45% of US rainfed and irrigated soybean area, respectively. Each individual TED contained ≥98 (rainfed) and ≥59 (irrigated) surveyed fields, with an average of 137 fields per TED.

Yield potential, average producer yield, and yield gaps Annual yield potential (Yp, yield potential of irrigated field) and water-limited yield potential (Yw, yield potential of rainfed fields) were estimated using measured daily weather data (including solar radiation, rainfall, and maximum and minimum air temperature) collected at 2–3 meteorological stations located within each TED, preferably in proximity to the areas with the highest density of surveyed fields. Yw and Yp were used as benchmarks for calculating yield 3

16 — 2017 Integrated Crop Management Conference - Iowa State University gap for rainfed (TEDs 1R, 2R, 3R, 4R, 5R, 6R, and 7R) and for irrigated TEDs (7I, 8I, and 9I). The yield gap was calculated as the difference between Yp (or Yw) and average producer yield and expressed as percentage of Yp (irrigated) or Yw (rainfed). Average Yw ranged from 48–80 bu ac-1, while Yp varied from 80–91 bu ac-1 across TEDs (Figure 3). TED 3R exhibited the lowest Yw due to lower seasonal precipitation in relation with other TEDs. In contrast, Yp was highest in TED 8I due to non-limiting water supply and high incident solar radiation. Upscaled to the entire North Central US region, Yw and Yp averaged 71 and 85 bu ac-1, respectively. Average producer yield was consistently lower than Yw (or Yp) across all TEDs (p < 0.01), and there was a large variation in average annual yield across TEDs, ranging from 39–73 bu ac-1. Yield gap, expressed as percentage of Yp (irrigated) or Yw (rainfed), tended to be larger in rainfed (range: 15–28%) than in irrigated TEDs (range: 11–16%). At the regional level, the rainfed yield gap averaged 22% in contrast to the irrigated yield gap of 13%.

Management practices explaining yield gap between high- and Figure 3. Yield potential for rainfed (Yw) and irrigated (Yp) soybean in each of the 10 TEDs in 2014 (14) and 2015 (15). Solid and empty portions of the bars represent the average producer yield and yield gap, respectively. Values on top of the bars indicate the (2-year) average yield gap, expressed as percentage of Yw (rainfed) or Yp (irrigated).

low-yield fields As a first approach to identify factors explaining yield gap, high-yield (HY) and low-yield (LY) field classes were identified based on their respective presence in the upper and lower terciles (top 1/3 versus bottom 1/3 of fields) of the field yield distribution within each TED. Analysis of management practices allowed identification of candidate factors explaining yield gap in each TED. Differences in planting date, tillage, in-season foliar fungicide and/or insecticide, drainage system, and soybean cultivar maturity group (MG) between highand low-yield fields were statistically significant in half or more of the 10 TEDs (p < 0.10). Planting date: The main explanatory factor Planting date had the most consistent impact on soybean yield (Figure 4), representing 28% of the total yield gap across TEDs (range: 2–56%). HY fields were sown, on average, 7 days earlier than LY fields in both irrigated and 4


Figure 4. Producer soybean yield plotted against planting date in 10 technology extrapolation domains (TED) in the NC USA region, including rainfed (A–G) and irrigated (G–I) production areas. Solid line corresponds to the fitted boundary function using quantile regression (percentile 90th). Separate boundaries were derived for rainfed (empty symbols) and irrigated (solid symbols) soybean fields in TED7. Slope of the fitted boundary function (b) is shown, with asterisks indicating significance at p < 0.1*, p < 0.05**, and p < 0.01*** for the null hypothesis of b = 0.

rainfed conditions. There was a strong planting date × TED interaction on yield as indicated by the wide range in yield penalty across TEDs, ranging from 0 to -0.5 bu ac-1 day-1 (Figure 4). Assessment of the observed TED x M interactions, in relation to weather dynamics during the growing season, revealed a relationship between yield response to planting date and the degree of water deficit during pod setting (R3–R5) phase (Figure 5). Yield penalty (or response) to planting date was negligible when water balance was -1.6 inches, ranging from 0.3–0.5 bu ac-1 day-1. The role of water balance in influencing the yield response to planting date was evident for TED 7, where irrigated and rainfed crops exhibited a six-fold difference (0.5 versus 0.1 bu ac-1 day-1, respectively) (Figure 4). In other words, these findings indicated that yield response to planting date diminished as the degree of water limitation in the pod-setting period of the production environment increases. It was notable that yield response to planting date delay exhibited much higher explanatory power with the degree of water deficit during pod setting phase 5

18 — 2017 Integrated Crop Management Conference - Iowa State University (r2 = 0.73, p < 0.01) relative to the other crop phases (early vegetative phase, late vegetative phase, and seed filling) or entire crop season (r2 < 0.38, p > 0.06).

Figure 5. Soybean yield penalty due to planting date delay as a function of water balance during the pod-setting (R3–R5) phase across 10 technology extrapolation domains (TEDs) including rainfed (yellow circles) and irrigated (blue circles) production environments (averaged over 2014–2015). Water balance was estimated as the difference between rainfall and simulated non-water limiting crop evapotranspiration and set at zero for irrigated crops. Parameters of the fitted linear-plateau model (solid line) and coefficient of determination (r2) are shown.

Tillage, fungicide and/or insecticide applications, drainage system, and soybean maturity groups Similarly to planting date, other management practices also exhibited a significant M x TED interaction (Figure 6). For this analysis, fields were categorized as either no-till or tilled, with the latter including chisel, disk, strip-till, ridge-till, vertical, field cultivator, and moldboard plow. We did not find evidence of notill fields outperforming yield of tilled fields in every TED; indeed, tilled fields yielded significantly more in half of the TEDs (2.3 bu ac-1; p = 0.02) (Figure 6). However, there may still be other functional reasons for producers to adopt no-till despite the observed yield penalty. For example, no-till can help control soil erosion and reduce irrigation water requirements. Indeed we found that, on average, total irrigation was 2.5 inches less in no-till versus tilled fields (p < 0.01). While there was an overall statistically positive impact of foliar fungicide and/ or insecticide (4.6 bu ac-1, p < 0.01) and artificial drainage (2.7 bu ac-1; p = 0.05) on soybean seed yield, the magnitude of these yield differences were not consistent across TEDs and not even significant in some of them (Figure 6). For example, average yield of fields treated with foliar fungicide and/or insecticide was 11.2 bu ac-1 higher in relation with untreated fields in TED 7R, but this yield difference was negligible (-0.9 bu ac-1) and not statistically significant in TED 6R. Likewise, artificially drained fields achieved statistically higher yields compared with fields without artificial drainage in only two of six TEDs. Although differences in variety MG between high- and low-yield fields were less than one unit, there was a consistent trend towards shorter MGs in the high-yield field tercile (top 1/3) in all TEDs, except for those located in the northern fringe of the North Central US region (3R and 4R). 6


Figure 6. Comparison of average producer soybean yield between groups of fields with different management practices across ten technology extrapolation domains (TEDs): (A) tillage (tilled versus no-till), (B) inseason foliar fungicide and/or insecticide (treated versus untreated fields), and (C) artificial drainage (fields with and without artificial drainage system). Star inside symbols indicate statistically significant difference for a given TED (t-test; p < 0.1). Asterisks indicate significance of the impact on yield with respect to the specified management factor (M), and its interaction with year (M × Y) or with TED (M x TED) as evaluated using F-test at p < 0.1(*), p < 0.05(**), and p < 0.01(***). Data from the two crop seasons were pooled for the analysis because M × Y influence on yield was not statistically significant. TEDs 7R, 7I, 8I, and 9I are not included in (C) because of the low number of fields with artificial drainage.

Other management factors with low influence on yield gap In contrast to the aforementioned variables, there were inconsistent (and generally small) differences between HY and LY fields in relation to row spacing, seeding rate, seed treatment, nutrient (N, P, K) fertilizer application, lime, and manure. Lack of statistically significant differences between management practices need to be interpreted with caution. For example, some practices might influence yield depending upon the level of another management practice [e.g., seed treatment in relation with planting date (Gaspar and Conley, 2015)]. Likewise, the benefit of other practices may only be realized in crop seasons with unfavorable weather, which was not the case in our study [e.g., narrow row spacing, no-till (Taylor, 1980; Wilhelm and Wortmann, 2004)]. Similarly, yield impact of some practices may be masked by other field variables not accounted here. For example, lack of yield differences between fields that received fertilizer application versus those that did not receive fertilizer might reflect producer tendency to apply fertilizer only in fields where soil nutrient status is inadequate as evaluated using soil nutrient tests. It may also reflect that many producers over-fertilized the previous corn crop expecting the subsequent soybean crop to benefit from the residual soil fertility. Finally, there are management practices that exhibited a very narrow range (e.g., MG) or inputs that are applied in amounts well above their optimums. For example, on-farm average soybean seeding rate ranged from 147,000 to 172,000 seeds ac-1 across TEDs. These densities are higher than the required plant density for maximum yields (100,000–145000 plants ac-1) (Grassini et al., 2015); hence, our analysis will not fully capture the influence of these management factors on soybean yield.

Final consideration Beside the identification of yield gap causes, another contribution of the present study is to provide a solid basis to assess what would be the extra crop production, at both local (TED) and regional (North Central US) levels, that would 7

20 — 2017 Integrated Crop Management Conference - Iowa State University result from complete producer adoption or fine-tuning of a given management practice. For example, the potential extra production derived from earlier soybean planting can be calculated based on the (1) specific yield response to planting date in each TED, (2) the degree to which the current average planting date differs from the optimal one, and (3) soybean harvested area in each TED. For example, a 2-week shift towards early soybean planting in TED 4R, from current average planting on May 17 to a hypothetical, yet realistic, May 3 planting, would result in 5.2 bu ac-1 yield increase and 18.5 million bu production increase, leading to a 10% and 0.7% increase in soybean production in TED 4 and North Central US region, respectively. This example illustrates the power of this approach for impact assessment to support policy and investment prioritization and for monitoring the impact of research and Extension programs.

Conclusion Soybean yield gap and its causes were assessed for the North Central US region using a novel approach that combines a spatial framework and producer self-reported data. The framework applied in this study explained the largest portion of the spatial variation in yield and management practices across the North Central US region. Soybean yield gap in the North Central US were relatively small, averaging 22% (rainfed) and 13% (irrigated) of the estimated yield potential. Planting date was the most consistent factor explaining yield variation within the same TED and year, with magnitude of yield response to planting delay dependent upon degree of water deficit during pod setting phase. Other practices also explained yield variation (tillage, and in-season foliar fungicide and/or insecticide, and artificial drainage), but the degree to which each of these practices influences yield depended upon TED. The combined use of producer data and a robust spatial framework that captured regional variation in weather and soils represents a cost-effective approach to identify causes of yield gap across large geographic regions, which, in turn, can help inform and strategize research and Extension programs at both local and regional levels. Acknowledgements

The authors would like to thank the North-Central Soybean Research Program, Nebraska Soybean Board, and Wisconsin Soybean Marketing Board for their support to this project, and the University of Nebraska-Lincoln Extension Educators, Nebraska Natural Resource Districts, and Iowa Soybean Association for helping collect the producer data. Finally, we would like to thank Lim Davy, Agustina Diale, Laurie Gerber, Clare Gietzel, Mariano Hernandez, Ngu Kah Hui, Caleb Novak, Juliana de Oliveira Hello, Matt Richmond, and Paige Wacker for inputting and cleaning the survey data. 8

References Gaspar A.P., Conley S.P. (2015) Responses of canopy reflectance, light interception, and soybean seed yield to replanting suboptimal stands. Crop Sci 55:377-385. DOI: 10.2135/ cropsci2014.03.0200. Grassini P., Specht J.E., Tollenaar M., Ciampitti I.A., Cassman K.G. (2015) High-yield maize–soybean cropping systems in the US Corn Belt, in: V. O. Sadras and D. F. Calderini (Eds.), Crop physiology. Applications for genetic improvement and agronomy, Oxford: Academic Press. pp. 15-42. Taylor H.M. (1980) Soybean growth and yield as affected by row spacing and by seasonal water supply. Agron J 72:543-547. DOI: 10.2134/agronj1980.00021962007200030032x. Wilhelm W.W., Wortmann C.S. (2004) Tillage and rotation interactions for corn and soybean grain yield as affected by precipitation and air temperature. Agron J 96:425-432. DOI: 10.2134/agronj2004.4250.

Originally published: Juan Ignacio Rattalino Edreira, S. Mourtzinis, S.P. Conley, A.C. Roth; I.A. Ciampitti, M. A. Licht, H. Kandel, P.M. Kyveryga, L.E. Lindsey, D.S. Mueller, S.L. Naeve, E. Nafziger, J.E. Specht, J. Stanley; M.J. Staton, P. Grassini. 2017. Assessing causes of yield gaps in agricultural areas with diversity in climate and soils. Agricultural and Forest Meteorology. http://dx.doi.org/10.1016/j.agrformet.2017.07.010


Corn planting decisions: What’s changed and what’s the same? Mark Licht, assistant professor, Agronomy and Extension cropping systems specialist, Iowa State University; Emily Wright, research associate, Agronomy, Iowa State University; Mitch Baum, graduate student, Agronomy, Iowa State University; Nick Upah, graduate student, Agronomy, Iowa State University Corn planting is arguably the most important field operation. Wide sweeping decisions from hybrid selection, row spacing, and seeding rates to planter adjustments, seed depth, and field conditions all come together the day planting occurs. It is a perfect storm where all these decisions align to achieve ideal stand establishment and set the stage for high yield potential. It’s been stated many times that ideal planting conditions are when soil moisture is conducive for planting without compaction and soil temperatures are 50˚F and rising. We have consistently heard that seed depth placement should be 1.75 to 2 inches deep and that planter maintenance and adjustments should be made to not only achieve desired planting depth but also ideal plant-to-plant spacing. Hybrid selection, planter row spacing, and seeding rate are decisions made ahead of time. These are decisions that come at great cost. Seed costs account for approximately 20% of the cost of production and machinery is a large investment realized over several years. Choosing hybrids that match cropping system practices, weather conditions, and soil environments is extremely important.

The old Row spacing and plant population have been the focus of many studies throughout the years in an effort to identify ways to increase maize yields and minimize production costs. Many studies have shown that there was yield increase going to 30-inch rows from 40-, 38-, or 36-inch rows. Research looking at row spacing less than 30-inches have had varying results. In some cases row spacing has had no effect on yield, whereas others have seen anywhere from a 2 to 7% increase in yield by narrowing row spacing from the more common 30-inch rows. Since 2000, plant densities in Iowa have increased approximately 300 plants per acre per year. However, genetics and environment have an influential role to play. For many years, seed companies have targeted hybrids for placement in certain field environments and seeding densities. University recommendations often place target corn seeding rates at 34,000 to 37,000 seeds per acre. Research over the years has indicated that a corn-planting window of April 11 to May 18 is ideal to achieve 95% yield potential. Ideal planting dates and windows vary greatly from year to year and geographic area of the state. Planting date alone does not determine yield potential; genetics and environmental factors have significant influence. Soil conditions at and following planting combined with planter adjustments influence time to emergence, early root development, and plant stand establishment that carry forward through the growing season. Hybrid maturity combined with heat unit are factors that can affect the length of the grain filling period and ultimately yield potential.

The new Since 2009, there have been ten site-years of research conducted in northwest Iowa looking at the interaction of row spacing and seeding rate. In an analysis of the ten site-years, 50% of the trials resulted in 20-inch row spacing yielding significantly greater than 30-inch row spacing (Figure 1). In the other siteyears, yields were not significantly different. Overall, there was not a penalty to the 20-inch row spacing.

22 — 2017 Integrated Crop Management Conference - Iowa State University There were no row spacing by seeding rate interactions across the 10 site-years, therefore, it would not be recommended to change seeding rates when switching to a 20-inch row spacing. Corn planting date and hybrid maturity selection are often considered to be linked, especially when late planting is forced because of spring weather conditions. A trial conducted at seven sites from 2014 through 2016 looked at yield impact from late planting as well as hybrid maturity selection. The critical date of planting before yield penalties were realized was May 8, May 12, and May 6 at the northern, central, and southern locations (Figure 2). This early to mid-May critical planting date represents a slight narrowing of the planting date window from previous recommendations. Longer season hybrids had greater yield potential when planted before the critical planting date. Hybrid maturity selection became less important when planting dates were delayed past mid-May. For late May and June planting dates short season hybrids were significantly higher yielding only 10% of the time and nominally higher 33% of the time. It should be noted that in northern Iowa, 44% of the time none of the hybrids reached physiological maturity when planted in late June. This is ultimately the risk of late planting: that adapted hybrids may not reach maturity before a fall killing frost.

Summary As a result of recent research, there is evidence that 20-inch corn row spacing does not result in lower yield potential compared to 30-inch row spacing. This is significant because it could open an opportunity for overall greater profitability when considering the entire cropping sequence. While 20-inch row spacing may be inconsistently increase corn yields, there would certainly be a yield benefit in soybean production years Timely planting and use of full season hybrid maturities is extremely critical to attaining high yield potential. This does not mean hybrid maturity selection should be shortened when planting after mid-May. Well-adapted hybrid maturities should be retained until planting gets delayed into late June. For late June planting dates shortening hybrid maturity or switching crop selection should be considered to avoid the risk of not achieving physiological maturity prior to fall frost.

Figure 1. Corn yield response for 20-inch and 30-inch row spacing at ten site-years in northwest Iowa from 2009 through 2016. The solid line is the 1:1 yield line.


Figure 2. Critical planting dates for northern, central, and southern Iowa across 21 site-years using four planting dates and three hybrid maturities, except in central Iowa were four hybrid maturities were used.

References Elmore, R. 2013. Corn planting FAQ. ICM News, Iowa State University Extension and Outreach, Ames, IA. Farham, D.E. 2001. Row Spacing, Plant Density, and Hybrid Effects on Corn Grain Yield and Moisture. Agron. J. 93:1049-1053. Johnson, G.A., T.R. Hoverstad, and R.E. Greenwald. 1998. Integrated weed management using narrow corn row width, herbicides, and cultivation. Agron. J. 90:40-46. Porter, P.M., D.R. Hicks, W.E. Lueschen, J.H. Ford, D.D. Warnes, and T.R. Hoverstad. 1997. Corn response to row width and plant density in the Northern Corn Belt. J. Prod. Agric. 10:293-300. USDA-NASS. 2017. Crop production 2016 summary. National Agricultural Statistics Service, United States Department of Agriculture, Washington, D.C. Widdicombe, W.D., and K.D. Thelen. 2002. Row width and plant density effects on corn grain production in the Northern Corn Belt. Agron. J. 94:1020-1023.



Water availability, root depths and 2017 crop yields Sotirios Archontoulis, assistant professor, Agronomy; Mark Licht, assistant professor, Agronomy; Mike Castellano, associate professor, Agronomy; Raziel Ordonez, postdoc research associate; Javed Iqbal, research associate, Rafael Martinez-Feria, graduate student, Pat Edmonds assistant scientist; Emily Wright, research associate; Mitch Baum, graduate student; Ashlyn Kessler, graduate student; Huber Isaiah, graduate student; Aaron Sassman, research associate; Matt Liebman, professor, Agronomy; Matt Helmers, professor, Ag and Biosytstems Engineering

Introduction During 2016 and 2017, June-July precipitation was below normal in many parts of Iowa creating midseason concerns about potential yield loss due to water stress. However, these concerns were not realized. In contrast, 2016 and 2017 crop yields over-performed yields obtained in many years with average or above average June-July precipitation. In Iowa, deep root systems, high soil water storage capacity, and shallow water tables are common explanations for high yields in years with below normal precipitation. How deep can roots grow? How much does groundwater contribute to the yields? To answer these questions and more, the Forecast and Assessment of Cropping sysTemS (FACTS) project was established in 2015 (Archontoulis and Licht, 2016). The FACTS project takes a systems approach to explain factors and mechanisms behind high or low yields while collecting ground-truth measurements from many trials around Iowa. Process-based modeling is used to integrate and extrapolate knowledge beyond study factors and weather-year (forecasts and scenario analysis). In this article, we report FACTS measured corn and soybean yield, root depth and shallow water tables across the state in 2017. Then, we use modeling to explain how root depth and shallow water table interacted to protect 2017 crop yields from mid-season drought.

Materials and methods Experimental locations and treatments In 2017, the FACTS project had 11 experimental locations with corn and soybean crops across Iowa. In these locations, 12 soybean and 33 corn treatments were studied. Treatments included different planting dates, drainage systems, nitrogen rates and row spacing x plant populations. Each treatment was replicated three times. All plots were fertilized with P, K and S according to ISU recommendations. Nitrogen application rates followed MRTN recommendations for continuous corn and corn-soybean rotation systems. Planting dates and cultivars were selected to represent common practices at each site.

Field measurements Field measurements were specifically designed to facilitate a systems level understanding – that is an understanding of how multiple system components interact to produce outcomes – and calibrate cropping systems models. Soil, crop and atmospheric measurements were made at high resolution (from 1 hour to 15 days). Soil nitrate and ammonium were sampled every two weeks at 0-30 cm depth and once per month at 30-60 cm depth from March to November. Volumetric soil moisture and temperature were measured hourly at 15 cm and 45 cm depth in each plot. Groundwater sensors measured depth to water table on an hourly basis at every site (n = 34 wells). Crops were sampled every 15 days destructively. In soybean, plants we measured the following parameters: plant height, number of plants, node number, pod number, leaf area, dry weights and carbon and nitrogen concentration of different plant tissues including green leaves, senesced leaves, stems, pods, seeds and seed number. In corn plants, we measured the following parameters: plant height, leaf area, dry weights and

26 — 2017 Integrated Crop Management Conference - Iowa State University concentrations of plant tissues that is green leaves, senesced leaves, stems, kernel, cob, husk and shank, and kernel number per ear. From these data, crop growth and N uptake rates were calculated. In addition, we measured root growth velocity in each harvest at two row positions per plot using soil cores (in row and between two rows; see Ordonez et al., 2018). When roots reached maximum depth, deep soil cores with a hydraulic probe were taken from each plot during the growing season (in-row and between rows; approx. end of July to mid of August). The depth of each core was 2.2 meter. From these cores, we calculated root dry mass, root length, specific root length, root N and C concentrations per soil layer. Finally combine yield estimates were taken from the center four or six rows per plot. Corn yield were normalized to 15% moisture and soybean yields to 13% moisture. Hourly weather data were recorded from automated weather stations located at the borders of every fields (IEM Mesonet). A 35-yr historical weather data per location was used to calculate 2017 deviations from climatology.

Figure 1. Cumulative difference between 2017 rain and GDD from long-term average climatology (35 year average).

Modeling We used the APSIM software platform, version 7.8 (Holzworth et al., 2014). Inputs to the model were: management, weather, soil profile information and cultivar characteristics. The majority of model inputs were derived from the above measurements and the model simulated crop yields, soil nitrate, soil water by layer with good accuracy (see https://crops.extension.iastate.edu/facts/ ). For more details about APSIM performance in Iowa we refer to recent publications (Martinezt-Feria et al., 2016; Puntel et al., 2016; Togliatti et al., 2017).

Results and discussion Weather conditions Crawfordsville was the driest site with up to a 10 inch water deficit from May 15th to September 15th 2017 (Figure 1). Sutherland was the second driest location followed by Ames. Northern locations were much cooler than central and southern locations (Figure 1). Until the middle of July, thermal time accumulation was +/- 50 GDD from the 35-year average at all locations. After that period, GDD accumulation slowed in the northern locations, which resulted in delays in crop maturity and harvest in


2017. From May 15th to September 15th, the central and southern locations accumulated approximately 150 MJ/m2 more radiation than average while the northern location accumulated approximately 50 MJ/m2 less radiation compared to the 35-year average (data not shown).

Figure 2. Combine harvested crop yields at FACTS experimental sites. N0, N150 and N300 indicate 0, 150 and 350 lbs N/ac treatments. N-high and N-low indicate low and high N inputs (no zero due to irrigation). All other locations received the ISU recommended N rate (http://cnrc.agron.iastate.edu/).

Crop yields Despite the 10 inch water deficit in Sutherland and Crawfordsville, corn and soybean yields were approximately 200 and 60 bu/ac, respectively (Figure 2). Planting date had a minor impact on corn and soybean yields. Nitrogen application rate had a significant effect on corn yields, with an economical optimum N rate between around 160 and 240 lbs/acre in Ames and Kanawha, respectively. The McNay site was unresponsive to nitrogen fertilization and in general that site scored the lowest yield because of soil constraints. Muscatine, a sandy soil with low water holding capacity scored the highest yield (268 bu/ ac). However, that was the only irrigated FACTS site (10 inches of irrigation water across 14 applications). From rainfed sites, Nashua scored the highest corn yield (240 bu/ac) and soybean yield (65 bu/ac). Interestingly, the no tile drainage treatment at the Crawfordsville site yielded 15 bu/ac more corn yield than the tile drained treatment in 2017. Historically the tile drainage plots yield more than the no-tile drainage

28 — 2017 Integrated Crop Management Conference - Iowa State University plots in this site (1 to 15%; Helmers et al., 2012; Schott et al., 2017). No differences were found in soybean yield between tile and no tile drainage in that site. Corn yield at the Lewis site was slightly below 200 bu/ac. We expected more than 250 bu/ac at that site given excellent weather conditions (Figure 1). Model analysis revealed that relatively low corn yield at that site cannot be attributed to biophysical factors, leaving management as a potential cause.

Figure 3. Thermal requirements for key root phenological events. LN = leaf number (corn) and NN = node number (soybean). Data recorded in 2016 across six sites and 20 treatments in Iowa (Ordonez et al. 2017).

Root depth Across all sites, the 2017 root front measurements revealed similar rates of growth (depth) as in 2016 (Ordonez et al., 2018). Both corn and soybean roots increased at about the same rate (0.3 inch/day until 4.7 corn leaf number and 2.4 soybean node number and about 1.1 inch/day thereafter). Corn and soybean roots reached the center of 30 inches rows at 6th corn leaf and 5th soybean node, respectively (Figure 3). The maximum depth was determined by cultivar maturity and depth to water table. Across 20 trials, the maximum root depth recorded in 2016 was 6 feet and was similar for both corn and soybean. In most cases, the maximum root depth was about 4.5 to 5.5 feet (Figure 4). Shallow water tables limited root growth to these depths; roots cannot grow in anaerobic conditions due to lack of oxygen.


Figure 4. Maximum root depth versus average water table in July (Ordonez et al., 2017).

For corn we also developed a simple regression model that can be used for rapid assessment of root growth in the field (for every new corn leaf, the root increases by 2.7 inches, Figure 5)

Figure 5. Corn root depth versus leaf number

Water table depth Figure 6 illustrates water table levels for three time periods in 2017: June 1st, July 15th and August 15th. In June, the water table was about 3.5 feet in central and northern locations and dropped to 7-8 feet by mid-August in northwest and central Iowa. Crop roots in central and northwest Iowa compensated for the 2017 precipitation deficit by taking up water from deep soil layers (Figure 1). In northeast and central sites (Figure 6) the contribution of groundwater to crop transpiration and crop yields was less compared to the central and northwest sites due to adequate precipitation (Figure 1). The contribution of water table

30 — 2017 Integrated Crop Management Conference - Iowa State University in southwest and southern sites was little as the depth to water table was consistently at 7 to 8 feet. In the southeast Iowa water table data are still in analysis but preliminary results indicated that the depth to water table was about 2.5 feet in June 1st, dropped to 4.6 feet by July 15th and to 7.6 feet by August 15th.

Figure 6. Measured depth in feet to groundwater table in different sites.

The impact of ground water table depth on crop yields has not received the appropriate attention over the past years. Our systems analysis via the FACTS project has showed that the depth to groundwater is a critical factor that explains a large amount of the inter-annual yield variability (see below). The water table determines the maximum root depth (Figure 4) as well as the distribution of the roots along the soil profile (data not shown). June is the critical month for root development in Iowa. Below average precipitation in June promotes the development of a deep root system to supply both water and nutrient to the crop. However, there is a limit on how much water can be supplied by groundwater per site. If the dry conditions continue during grain filling period (August) there will be a decline in yield potential.

Quantifying yield credits from shallow water tables According to the literature groundwater table can supply up to 75% of the evapotranspiration (Gao et al., 2017) and there is an optimum water table depth for maximizing yields per location (Florio et al., 2014). Because of the dynamic nature of water table and interactions with soil topography, precipitation and vegetation, quantitative assessments of water table contribution to grain yields are missing from the literature. We used the APSIM model and data from the FACTS project to provide a first assessment for Iowa. To do that we simulated crop yields with and without shallow water tables and calculated yield credits and penalties caused by shallow water tables. Figure 7 illustrates the preliminary results.


Figure 7. Model analysis of groundwater table yield credit and penalty in 2017 across six location (top panel) and long term impacts for Ames (bottom panels).

According to the model analysis, the water table contributed 90 bu/ac to corn yield in Sutherland, about 40 bu/ac corn yield in Ames and Crawfordsville, 25 bu/ac in Kelley (central Iowa) and 2 to 10 bu/ac in Lewis and Nashua respectively. In terms of percent values, the contribution of water table to the final observed 2017 corn yields ranged from 1 to 75% and it was site specific. The contribution of water table in soybean yields in 2017 was less compared to corn. It ranged from 1 to 14 bu/ac (Figure 7) or 0 to 21% of the final yields. In the long term (2000 to 2017; 18 years) the contribution of water table to grain yields was variable (Figure 7). In particular, for central Iowa, the contribution ranged from -15 bu/ac to + 100 bu/ac for corn and from -7 bu/ac to +19 bu/ac in soybean. On average across 18 years, 40 bu/ac/year of the obtained corn yield was due to water table credit and 5 bu/ac/yr of the obtained soybean yields was due to water table credit (Figure 7). In 12 out of the 18 years in central Iowa, the water table had a positive effect on yield and in the remaining six years had a neutral or a negative effect. In Sutherland the long term benefit of groundwater for corn was 65 bu/ac/yr (14 out of the 18 years showed a positive response) and in Crawfordsville the long term benefit of groundwater for corn was 13 bu/ac/yr (11 out of the 19 years showed a positive response).

32 — 2017 Integrated Crop Management Conference - Iowa State University The above model analysis was specifically designed to quantify yield penalties and credits associated with presence or not of shallow water tables in Iowa. Additional model analysis (data not shown) indicated that drainage systems eliminated the yield penalties caused by shallow water tables while maintained or further increased the yield credits. In the long term (18 years), the simulation analysis has shown that tile drainage systems provided a yield benefit and stability as compared to the undrained plots in most of the years with few exceptions like 2012 and 2017 years in which the undrained plots scored higher yields than the drainage plots. Overall, the modeling work suggests that the impact of groundwater table on crop yields is significant and more research should be directed towards managing groundwater tables to maximize yields and environmental impacts.

Conclusions • Deep roots and shallow water tables compensated for much of the precipitation deficit occurred in summer of 2017 in parts of Iowa.

•

The contribution of water table to final corn yields in 2017 ranged from 2 (southwest) to 90 bu/ac (northwest) in 2017.

•

A below normal precipitation in June is favorable for Iowa conditions for two reasons: a) rapid and unconstrained root growth (1.1 inch per day); b) soil inorganic nitrogen is at highest levels during that period and excess of water will stimulate N loss.

•

Quantifying water table dynamics across the state will greatly assist management decisions and predictability of crop yields and N losses

Acknowledgements Iowa Soybean Association, Iowa Nutrient Reduction Center, Plant Science Institute and the Department of Agronomy for funding parts of the FACTS project and graduate and undergraduate students of the Integrated Cropping Systems Lab (Archontoulis and Licht) and Sustainable Agroecosystems Lab (Castellano) for assistance with field and lab measurements.

References Archontoulis SV, Licht M, 2016. A web platform for Forecasting and Assessment of Cropping sysTems (FACTS). Integrated Crop Management Newsletter, Iowa State University, http://crops.extension. iastate.edu/facts/ Florio EL, Mercau JL, Jobbágy EG, Nosetto MD, 2014. Interactive effects of water-table depth, rainfall variation, and sowing date on maize production in the Western Pampas. Interact. Water Management 146, 75-83. Gao X, Huo Z, Qu X, Xu X, Huang G, Steenhuis TS, 2017. Modeling contribution of shallow groundwater to evapotranspiration and yield of maize in an arid area. Nature Scientific Reports, 7:43122, DOI: 10.1038/srep43122 Helmers, M., Christianson, R., Brenneman, G., Lockett, D., Pederson, C., 2012. Water table, drainage, and yield response to drainage water management in southeast Iowa. J. Soil Water Conserv. 67, 495501. Holzworth DP, Huth NI, deVoil PG, et al. APSIM – evolution towards a new generation of agricultural systems simulation. Environmental Modeling & Software, 62: 2014: 327–350.


Martinez-Feria R, Dietzel R, Liebman M, Helmers M, Archontoulis SV, 2016. Rye cover crop effects on maize: A systems analysis. Field Crops Research 196: 145–159. Ordonez R, Castellano M, Hatfield J, Helmers M, Licht M, Liebman M, Dietzel R, Martinez-Feria R, Iqbal J, Puntel L, Cordova C, Togliatti K, Wright E, Archontoulis SV, 2018. Corn and soybean root front velocity and maximum depth in Iowa, USA. Field Crops Research, 215: 122-131, http://www. sciencedirect.com/science/article/pii/S0378429017306962 Puntel LA, Sawyer J, Barker D, Dietzel R, Poffenbarger H, Castellano M, Moore K, Thorburn P, Archontoulis SV, 2016. Modeling Long Term Corn Yield Response to Nitrogen Rate and Crop Rotation. Front. Plant Sci. 7:1630. doi: 10.3389/fpls.2016.01630 Schott L, Lagzdins A, Daigh A, Craft K, Pederson C, Brenneman G, Helmers M, 2017. Drainage water management effects over five years on water tables, drainage, and yields in southeast Iowa. Journal of Soil and Water Conservation 72, 251–259. Togliatti K, Archontoulis SV, Dietzel R, VanLoocke A, 2017. How does inclusion of weather forecasting impact in-season crop model predictions? Field Crops Research 214: 261–272



Is there loss of corn dry matter in the field after maturity? Mark Licht, assistant professor, Agronomy and Extension cropping systems specialist, Iowa State University; Charles Hurburgh, professor, Agricultural and Biosystems Engineering and professor in charge, Iowa Grain Quality Initiative, Iowa State University; Matthew Kots, industrial technology undergraduate intern, Agricultural and Biosystems Engineering, Iowa State University; Philip Blake, industrial technology undergraduate intern, Agricultural and Biosystems Engineering, Iowa State University; Mark Hanna, Extension agricultural engineer, Agricultural and Biosystems Engineering, Iowa State University

Background Mystery yield loss, phantom yield loss, and now invisible yield loss – all are terms used for the concept that dry matter (yield) is being lost in-field between maturity (at about 28% moisture) and harvest (approximately 15% moisture). Popular press articles continue to indicate up to 1% dry matter loss per 1% grain moisture decrease. If this amount of dry matter loss occurs, it would be significant in marketing terms. Some believe that this loss is caused by seed respiration. From 1991 to 1994, grain dry matter was determined for three hybrids over the course of four years in Indiana (Nielsen et al.). This work had an average yearly overall dry matter loss of 0.9% to 1.1% in 3 of 4 years. The fourth year was excluded because the results were non-significant. Follow up studies were published by Elmore and Roeth (1999), Pordesimo et al. (2006), and Thomison et al. (2011). In Nebraska, Elmore and Roeth concluded that kernel dry matter weights were consistent across harvest dates even though there were slight differences between hybrids in 1996 and 1997. Pordesimo et al. conducted work in Illinois in 1995, showed that dry matter accumulation increased over the growing season, then plateaued over a 51-day sampling period; there was no decrease in dry matter following the plateau. Thomison et al. in Ohio investigated dry matter response to seeding rate, hybrid and harvest date. The results indicated that no yield reductions occurred between the October and November harvest dates. Yield losses between the November and December harvest dates were due to lower stalk strength and greater stalk lodging. Nielsen et al. offers seed respiration as a possible cause of dry matter loss. Respiration is a metabolic reaction for retrieving stored energy and carbon while using up oxygen and releasing carbon dioxide. In 1973, Knittle and Burris determined that seed respiration decreased dramatically from 35 days after silking to 80 days after silking. Seed respiration was non-significant from 80 to 95 days after silking. Additional studies in storage environments have reported 1% dry matter loss over 10 to 50 days of corn storage (after harvest) for 23-28% corn at 75-85 degrees F (Saul and Steele, 1966; Seitz et al., 1982). These losses were primarily due to storage fungi, not seed respiration. Dry matter losses would be much less in unharvested field conditions since average temperatures in the Corn Belt are 55-65 degrees F in late September and 5060 degrees F in early October. Delaying harvest in order for in-field grain drydown to occur results in decreased stalk integrity (i.e. greater stalk and root lodging) potentially causing more dropped ears. Farmers should weigh the cost of harvesting grain at 20-25% moisture versus waiting for in-field drydown to occur and potentially increased yield losses due to field losses other than grain dry matter loss. Timeliness losses at corn harvest were traditionally associated with dropped ears from a weakened ear shank, often from European corn borer damage. Each loss of a single ear in 1/100th acre (436 sq ft) is the equivalent of 1 bu/acre field loss. Losses due to corn borer damage were variable by year, but frequently

36 — 2017 Integrated Crop Management Conference - Iowa State University are estimated at 1/3 %/day loss for each day beyond mid-October (ASABE, 2014). Widespread use of corn with bacillus thuringiensis (BT) traits has greatly reduced this type of damage. Lodged stalks with ears close enough to the ground to escape gathering by the corn head are now the primary cause of preharvest loss in most fields (due to the combine not capturing the crop).

Study methodology Field procedure Ears were collected from a date of planting maturity trial at the Iowa State University research farms near Kanawha and Crawfordsville, IA. The plots were 50 feet long by 20 feet wide, in 4 replications of each hybrid at a location. Selected planting dates and hybrids are listed in Table 1. Ear collection was weekly starting at physiological maturity for six to eight weeks. The selection of ears for sample collection was to collect seven consecutive ears from the second or seventh row of the eight row plots, skip seven consecutive ears before collection of the next set of ear samples. The middle four rows (rows 3 through 6) were used for grain yield determination in the planting date study. Table 1. Dry-matter loss study field design 2016

2017

Location

Dates of Planting

Hybrid (Relative Maturity)

Dates of Planting

Hybrid (RelativeMaturity)

Kanawha

April 17 May 18

P9526AMX (95-d) P0407AMXT (104-d) P0987AMX (109-d)

April 17 May 9

P0157AM (101-d) P0589AM (105-d) P1197AM (111-d)

Crawfordsville

April 14 May 9

P0636AM (106-d) P1151AM (111-d) P1365AMX (113-d)

April 13 May 16

P0589AM (105-d) P1197AM (111-d) P1555CHR (115-d)

At each harvest date, 100 stalks (with ears) adjacent to the harvest area were evaluated. Each ear within one foot of the ground was assumed to equal one percentage point of preharvest loss due to stalk lodging. This design gave 672 samples as three hybrids x two planting dates x four replications x six sampling dates x two locations in 2016 plus three hybrids x two planting dates x four replications x eight sampling dates x two locations in 2017. The samples were hand harvested, bundled in groups of seven ears in strong plastic trash bags and shipped to Ames immediately after harvest. The additional sampling dates were added in 2017 because of the late harvest and slow dry down rates in the first few weeks.

Laboratory analysis procedure The samples were husked and refrigerated immediately on receipt. Two ears were picked randomly from each bag, hand shelled and tested for moisture in the GIPSA-approved UGMA moisture meters, Perten AM5200 and Dickey-john GAC2500. Three readings in each meter were averaged. This was the “harvest moisture” to be used for informational purposes in tracking maturity. This moisture was not used in dry matter balance calculations. The remaining 5 ears in each sample were dried with forced room air to below 20% moisture. The ears were weighed, then shelled after drying. Both cobs and kernels were collected and weighed, which provided a mass balance check of ears versus cob and kernels. Moisture was measured of cobs and kernels, which established the dry matter weights on a full ear basis. Finally, the kernels were cleaned with a Kice laboratory aspirator; this improved the operation of the seed counter by preventing partial seeds from counting.


A Seedburo seed counter was used to count 1000 kernels, which were then weighed. The dry matter content per seed was determined using the kernel moisture measured after shelling. Corn composition (protein, oil, starch and density) was determined with an Infratec 1241 analyzer calibrated at Iowa State University.

Results from 2016 The progression of harvest moisture rapidly declines over the first three sampling periods (Figure 1). By the second three sampling periods, grain moisture begins to stabilize. This showed a predictable trend starting at about 30% moisture after black layer, to a relatively dry 15-16% in the fourth week. The field dry down rates were very nearly the same for all locations. Kernel dry matter is inconsistent within planting date and hybrid groupings (Figure 2). This inconsistency for some combinations may indicate a slight increase in dry matter during moisture dry down while in other treatment combinations there is either a negative or no dry matter accumulation.

Results from 2017 (to date of proceedings) The harvest moisture declined over the harvest period, but not as rapidly as in 2016 (Figure 1, bottom panels). The northern Kanawha, Iowa location lost approximately 2.6 percentage points per week, while the southern Crawfordsville, Iowa location lost approximately 3.1 percentage points per week. This difference was probably due to the widely varying weather conditions during grain fill and dry down periods in 2017. As in 2016, there was some inconsistency in the kernel dry matter change during the dry down period (Figure 2, bottom panels). The trend over sampling weeks was generally either flat, especially after dry matter accumulation stabilized. P1197 at both locations and P1555 at Kanawha had dry matter accumulation increases in the first three to four sampling weeks.

Summary Corn dry matter loss in the field after maturity was studied at two locations over two years with replication and repeated ear sample collection. Kernel dry matter weight over progressive harvest dates showed no change through the dry down period where grain moisture went from over 30% down to 15%.

References ASABE Standards. 2014. EP497.7 Agricultural machinery management data. Am. Soc. of Agric. and Biol. Eng., St. Joseph, MI. Elmore, R.W., and W.F. Roeth. 1999. Corn kernel weight and grain yield stability during post-maturity drydown. J. Prod. Agric. 12:300-305. Knittle, K.H. and J.S. Burris. 1976. Effect of kernel maturation on subsequent seedling vigor in maize. Crop Sci. 16:851-855. Nielsen, R.L., G. Brown, K. Wuethrich, and A. Halter. 1996. Kernel dry weight loss during post-maturity drydown intervals in corn. Purdue University, West Lafayette, IN. (accessed 24 October 2016). Pordesimo, L.O., A.M Saxton, L.E. Paul, and R.C. Belm. 2006. Investigation into grain dry matter loss during field drying of corn. 2006. ASABE Annual Int. Meet., Portland, OR. 9-12 July 2006. Proc. Paper. No. 066203. Am. Soc. of Agric. and Biol. Eng., St. Joseph, MI.

38 — 2017 Integrated Crop Management Conference - Iowa State University Saul, R.A. and J.L. Steele. 1966. Why damaged shelled corn costs more to dry. Agric. Eng. 47:326-329. Seitz, L.M., D.B. Sauer, H.E. Mohr, and D.F.Aldis. 1982. Fungal growth and dry matter loss during bin storage of high-moisture corn. Cereal Chem. 59:9-14. Thomison P.R., R.W. Mullen, P.E. Lipps, T. Doerge, and A.B. Geyer. 2011. Corn response to harvest date as affected by plant population and hybrid. Agron. J. 103:1765-1772. doi: 10.2134/agronj2011.0147.

Figure 1. Corn grain harvest moisture content and grain test weight progression over the post physiological dry down period (September and October) for two planting dates and three hybrid maturities at Crawfordsville and Kanawha, Iowa in 2016 and 2017.


Figure 2. Corn kernel dry matter progression over the post physiological dry down period (September and October) for two planting dates at Crawfordsville and Kanawha, Iowa in 2016 and 2017.



Searching for the bottom Chad Hart, associate professor, Economics and Extension Economist, Iowa State University Looking back over the last ten years, crop agriculture has swung between two regimes. The years 2007 to 2012 were characterized by strong crop prices, driven in the beginning by record building demand and at the end by a drought. The years 2013 to 2017 are characterized by strong crop production, a consistent string of large harvests that have been more than enough to meet and exceed demand. The outlook for 2018, based on the information we have today, suggests that pattern will continue for at least another year. For corn, while the 2017 crop is smaller than its’ predecessor, it is still projected to be the 2nd largest crop ever. This continues the string of large corn crops, with the last five U.S. corn crops being the five largest ever. Corn usage over the past five years has been robust as well, but usage is running just short of supplies. Corn stocks have built and corn prices have retreated. Feed usage continues to grow with the general expansion in the livestock sector. Biofuel usage has entered an era of slower, but steadier, growth. Combined, these two sources of demand provide a solid 11 billion bushel usage base for the corn market. Food, seed, and industrial usage has been slowly building as well. The wildcard is the export picture. In general, international corn demand has been growing over the past five years. But with greater competition of other corn producers worldwide (Brazil, Argentina, Ukraine, etc.), export projections have slipped a bit for the current marketing year. For soybeans, 2017 is basically a continuation of 2016, with record acreage, production, and usage. But as with corn, supplies are slightly exceeding usage. Stocks are building and prices have retreated. Soybean’s demand structure has been relatively more supportive than corn’s, so in relative terms soybean prices have held up better. Domestic soybean crush usage has slowly grown, while international usage continues to build at a record pace. Pricing patterns over the past few years have returned to a typical historical pattern, with crop prices generally building through the spring and early summer, only to retreat later as we approach the harvest season. Figures 1-3 show that pattern for 2017 under a few different assumptions. Figure 1 shows estimated crop margins for 2017/18, based on futures prices throughout the year, ISU Extension projected production costs, trend yields, and historical average basis levels. Under those assumptions, estimated Iowa crop margins started out in positive territory, swooned a bit from March to June, hit their peaks in early July, and retreated significantly in August and early September. Recently, corn margins (and prices) have been slowly falling, while soybean margins (and prices) have rebounded. But with the harvest, more information has come in to help us firm up these estimates. We know yield estimates are running above trend, but basis levels are below historical average.

42 — 2017 Integrated Crop Management Conference - Iowa State University 100

$ per acre

75 50 25 0 -25

1/3/2017 1/17/2017 1/31/2017 2/14/2017 2/28/2017 3/14/2017 3/28/2017 4/11/2017 4/25/2017 5/9/2017 5/23/2017 6/6/2017 6/20/2017 7/4/2017 7/18/2017 8/1/2017 8/15/2017 8/29/2017 9/12/2017 9/26/2017 10/10/2017 10/24/2017

-50

Corn

Soy

Figure 1. Estimated 2017/18 crop margins, at trend yield and historical average basis.

Figure 2 contains the estimated crop margins at updating for the 2017 yields. The yield shift alone adds roughly $20-30 per acre to projected margins. This bump comes from the extra bushels to sell, but also the reduction in costs (on a per bushel basis). This year, the yield bump on margins is similar across the crops. And if that were the end of the story, soybean returns would be decent, while corn returns would be hovering around breakeven. But alas, that is not the end of the story.

125

$ per acre

100 75 50 25 0

1/3/2017 1/17/2017 1/31/2017 2/14/2017 2/28/2017 3/14/2017 3/28/2017 4/11/2017 4/25/2017 5/9/2017 5/23/2017 6/6/2017 6/20/2017 7/4/2017 7/18/2017 8/1/2017 8/15/2017 8/29/2017 9/12/2017 9/26/2017 10/10/2017 10/24/2017

-25

Corn

Soy

Figure 2. Estimated 2017/18 crop margins, at 2017 estimated yield and historical average basis.


The building of crop stocks has limited improvement in basis levels. Throughout most of this year, basis levels have run below historical averages and that continues today. Figure 3 factors in the impact of those weaker basis levels. The margin impacts of the weaker basis are more than enough to offset the extra bushels. And corn margins take a bigger hit than soybean margins. In total, soybean margins are hovering around breakeven, while corn margins have sunk to nearly the lowest levels for the year. That said, these graphs also remind us why we look at pre-harvest marketing and post-harvest storage for margin improvement. Both crops had positive pricing opportunities earlier in the year and seasonal pricing patterns would suggest more opportunities next spring and summer as well.

75

$ per acre

50 25 0 -25 -50

1/3/2017 1/17/2017 1/31/2017 2/14/2017 2/28/2017 3/14/2017 3/28/2017 4/11/2017 4/25/2017 5/9/2017 5/23/2017 6/6/2017 6/20/2017 7/4/2017 7/18/2017 8/1/2017 8/15/2017 8/29/2017 9/12/2017 9/26/2017 10/10/2017 10/24/2017

-75

Corn

Soy

Figure 3. Estimated 2017/18 crop margins, at 2017 estimated yield and current basis.

Looking forward to the 2018 crop year, crop futures are providing some support for improving returns in the coming year. Computing margins based on 2018 trend yields, historical average basis, and holding costs steady at 2017 levels, both 2018/19 corn and soybean projected margins are holding above breakeven. However, given the stocks, I would discount these projections by $30-50 per acre (the basis effect). That would basically put both crops around breakeven. And while that’s not an optimistic outlook, it’s better than we’ve had over the past few years. Last year at this time, this graph was showing $50 losses.


125 100

$ per acre

75 50 25 0 -25

1/3/2017 1/17/2017 1/31/2017 2/14/2017 2/28/2017 3/14/2017 3/28/2017 4/11/2017 4/25/2017 5/9/2017 5/23/2017 6/6/2017 6/20/2017 7/4/2017 7/18/2017 8/1/2017 8/15/2017 8/29/2017 9/12/2017 9/26/2017 10/10/2017 10/24/2017

-50

Corn

Soy

Figure 4. Estimated 2018/19 crop margins, at trend yield, historical average basis, and 2017 costs.

The dynamics for better margins are still holding in the markets. Usage continues to grow and traders have shown a willingness to ride price higher, as evidenced by the spring rallies over the past couple of years. As has been indicated before, the markets are hoping supplies slow down just enough to allow usage to catch up. Given the wider basis levels today, futures and options based marketing strategies offer better opportunities than cash positions currently. And while the basic storyline in the crop markets remains similar to previous years, the outlook is getting a little brighter.


Current legal issues impacting crop production Kristine A. Tidgren, attorney and assistant director, Center for Agricultural Law and Taxation, Iowa State University

Des Moines Water Works litigation and water quality update On March 16, 2015, the Des Moines Board of Water Works Trustees (DMWW) filed a federal Clean Water Act (CWA) lawsuit against the supervisors and drainage districts of three Iowa counties (Sac, Buena Vista, and Calhoun). The lawsuit, which was filed in the United States District Court for the Northern District of Iowa, alleged that the supervisors, in their capacity as trustees for the drainage districts. The lawsuit, among its many demands, asked the federal court to order the drainage districts to cease “all discharges of nitrate that are not authorized by an NPDES or state operating permit.” DMWW alleged that the concentration of nitrate in the Raccoon River, which is a primary source for DMWW’s raw water supply, has steadily increased since the 1970s. The lawsuit states that the nitrate removal system costs DMWW up to $7,000 per day to operate. The complaint set forth nine causes of action, each of which is summarized briefly:

Federal and state water quality laws The primary claim by DMWW was that discharges from drainage districts are “point sources” of nitrate pollution. As such, DMWW alleged that the drainage districts must comply with the federal CWA and the National Pollutant Discharge Elimination System (NPDES) permit program, which is administered by the Iowa Department of Natural Resources. The complaint asked the federal court to declare that the drainage districts had violated federal and state law and to enjoin them from all discharges of nitrate not authorized by an NPDES or state operating permit. DMWW sought civil penalties for each continuing day of violation.

Nuisance The complaint also asserted three separate nuisance claims: public, statutory, and private. The claims asserted that drainage districts—including all similarly situated districts—were public nuisances contributing to a single, indivisible harm to the public. The drainage districts, the suit contended, created a substantial and unreasonable interference with DMWW’s property right to withdraw high quality water from the Raccoon River. DMWW asked the court to order the districts to take all actions necessary to abate the nitrate pollution and to award DMWW damages.

Trespass DMWW contended that the districts’ discharge of nitrate was a substantial physical invasion of DMWW’s use and enjoyment of its property. DMWW asked the court to declare that DMWW has created a trespass and to award DMWW damages.

Negligence DMWW alleged that the supervisors were negligent in creating and maintaining the network of drainage facilities because harm to DMWW was a “reasonably foreseeable consequence” of the drainage districts’ “normal and intended operation.” Again, DMWW asked the court to order the districts to abate the nitrate pollution and pay damages to DMWW.

Constitutional claims In addition to its tort claims, DMWW asserted several constitutional claims, one that the drainage districts, as “political subdivisions” of Iowa, had taken DMWW’s property without just compensation by invading it with nitrate pollution. The other is that DMWW would be deprived of due process and equal protection under the United States Constitution if the court were to enforce Iowa law declaring that drainage districts

46 — 2017 Integrated Crop Management Conference - Iowa State University cannot be sued for money damages. DMWW sought a declaration that the districts are subject to a suit at law for damages in tort and other relief.

Injunctive relief The complaint asked the court to order the drainage districts to “take all steps reasonably necessary within a reasonable period of time to reduce the discharge of nitrate to the Raccoon River.” DMWW sought money damages, costs, attorney fees, and other relief “deemed just, equitable and proper.”

Case resolution Two years and one day after DMWW filed its controversial lawsuit against the drainage districts in three northwest Iowa counties, the federal court dismissed the action in its entirety. The merits of the case were never considered. The court dismissed the lawsuit after finding that—even if DMWW was able to prove an injury—the drainage districts would have no ability to redress (or remedy) that injury. In other words, the drainage districts were not the proper defendants for this Clean Water Act lawsuit. The Supreme Court of Iowa had long held that a drainage district is “merely an area of land, not an entity subject to a judgment for tort damages.” Iowa courts have allowed lawsuits against drainage districts only where the claims implicate a specific statutorily granted power or duty granted to the district. In other words, a court can compel a drainage district to fix damaged drainage tile. DMWW acknowledged Iowa law, but argued that it was outdated and inapplicable to the facts at hand. DMWW asserted that this was a “new day” and that the court should have applied a “new rule of liability and responsibility for drainage districts concerning pollution.” DMWW urged that “implied immunity has survived through repetition rather than critical analysis.” But the Iowa Supreme Court disagreed, ruling in response to a certified question addressed to it by the federal court, that Iowa drainage districts are immune from claims for damages or injunctive relief. The Court affirmed that such districts have a “limited, targeted role—to facilitate the drainage of farmland in order to make it more productive.” The Court declared that it is for the Iowa Legislature, not the courts, to change that result. The federal court found that this ruling applied equally to DMWW’s tort claims and Clean Water Act claims. In other words, the court found that even if DMWW were to prevail in its Clean Water Act claims against the districts, drainage districts would have no legal ability to redress DMWW’s alleged injuries. If a claim is not redressable, meaning that the party against whom the suit is brought cannot provide a remedy, a federal court has no jurisdiction to hear it. Consequently, the federal court dismissed the lawsuit for lack of standing. The federal court also found no merit to DMWW’s claims that its constitutional rights were violated. The court ruled that the immunity Iowa law affords to drainage districts does not violate the Equal Protection Clause or the Due Process Clause of the United States Constitution. The court noted that DMWW’s policy arguments are best directed to the Iowa Legislature. Finally, the court also fully agreed with the Iowa Supreme Court’s analysis of DMWW’s takings claim. “A public entity such as DMWW cannot assert a Fifth Amendment takings claim against another political subdivision of the state.” DMWW did not appeal the order. The lawsuit, although dismissed, brought increased attention to Iowa’s water quality issues. The Water Quality Initiative, implementing the Iowa Nutrient Reduction Strategy, began in 2013. State legislators have not yet created a comprehensive framework for funding water quality projects. Legislation proposed in 2016 and 2017 failed, largely due to budget constraints.


Liabilities and remedies for off-target herbicide injury As the reports of damages stemming from dicamba drift increase, questions swirl. Just what is the problem? Who’s responsible? What can be done to prevent future damage? While there are no clear answers to many of these questions, it may be useful to review the general legal principles that apply to herbicide drift and discuss how they apply to the current problem.

Regulatory framework Pesticide applicators and manufacturers are regulated by both state and federal law. Iowa Code chapter 206 governs the use of pesticides in Iowa. The definition of “pesticides” includes herbicides designed to control weeds. The law tasks the Iowa Department of Agriculture and Land Stewardship (IDALS) with licensing and regulating pesticide applicators. Even those applying pesticides to their own lands must obtain a license before applying restricted use pesticides. Commercial applicators must also submit evidence of financial responsibility. IDALS additionally regulates the distribution and sale of pesticides within the state, although federal law also applies. The Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA) authorizes the Environmental Protection Agency (EPA) to regulate the sale, use, and distribution of pesticides. The EPA is responsible to register new formulations of chemicals throughout the United States. States are allowed to implement more restrictive laws or regulations with respect to the use of pesticides. They can also pass laws consistent with FIFRA. They cannot, however, create labeling or packaging requirements “in addition to or different from” those imposed by FIFRA. Pesticide drift occurs when the chemicals land outside of the target area. When farmers suspect that their land has been damaged because of pesticide drift from a neighboring farm, they should consider two courses of action. First, they should file an “incident report” with IDALS’ Pesticide Bureau. Second, they should consider whether to pursue a private action to recover damages. Once an incident report is filed, IDALS will decide whether to open an investigation to determine if a pesticide was misused or if any state or federal law was violated. Farmers must file incident reports within 60 days of the date of damage and before 25 percent of the crop has been harvested. Depending upon the results of the investigation, IDALS may initiate enforcement actions, which can result in any of the following:

• • • • • • • •

Notice of Violation Official Notice Civil Penalty of commercial applicators of up to $500 Pesticide License Suspension or Revocation Referral to EPA for review and enforcement action License and/or certification suspension or revocation Product Stop Sale, Use or Removal Order Crop Embargo or Detainment

IDALS may find a violation, for example, if the applicator operated in a “faulty, careless or negligent manner” or “made a pesticide recommendation or application inconsistent with the labeling.” While IDALS can impose civil penalties, it does not have the authority to collect damages on behalf of private landowners. The enforcement action may provide helpful evidence to a landowner, but the landowner must initiate a private legal action to seek monetary damages against the parties responsible for the harm if the parties who caused the harm are not willing to pay for the damage. Crop insurance will not cover losses caused by pesticide drift.

48 — 2017 Integrated Crop Management Conference - Iowa State University Already in 2017, IDALS has received a record number of incident reports of crop damage stemming from pesticide misuse. About half of the 250 or so 2017 complaints are dicamba related. Although this number is not as high as that in some other states, it continues to climb.

Private lawsuits Farmers injured by pesticide drift may bring private legal actions to recover damages. Depending upon the facts, such lawsuits may be brought against the applicator, the manufacturer, or both. Several legal causes of action may be included in such lawsuits, including claims of negligence, nuisance, trespass, or strict liability, as well as allegations of statutory violations. The primary cause of action against an applicator is generally negligence. If the landowner can prove damage caused by the applicator’s failure to use reasonable care, the action will be successful. Negligence can be proven, for example, by showing that the applicator did not follow the directions on the label, use the pesticide in the manner for which it was intended, or apply the chemical in a careful manner, taking into account weather and other key factors. To prevail in a negligence action against the manufacturer, a party who has been damaged must, for example, show that the manufacturer did not use reasonable care in its marketing, labeling, or distribution of the product. Negligence claims can also include allegations of product defects and failure to warn, although manufacturers can sometimes be found responsible for a manufacturing defect under a strict liability standard, even in the absence of proof of negligence.

How does the law apply to the dicamba situation? Although dicamba has been around for decades, its use was restricted. Because of its volatility or tendency to spread uncontrollably beyond its targeted area, dicamba was not approved for post-emergent use. Because of the increasing resistance of many weeds to glyphosate (Roundup), however, manufacturers such as Monsanto, BASF, and DuPont have been working to reduce the volatility of dicamba, which is highly effective against difficult weeds. As part of their system, they developed genetically modified versions of soybeans and cotton that are dicamba tolerant. During the last year, EPA approved certain formulations of dicamba for use over the top of these dicamba resistant plants. These systems were marketed for use during the 2017 crop year. The implementation has been less than seamless. Thousands of acres of crops are damaged due to dicamba drift, and various parties are pointing fingers. In some cases, the manufacturers are asserting that the applicators have not followed the specific instructions given for the use of this chemical or that they are using unapproved formulations of dicamba. Applicators and their insurers allege that despite using reasonable care, dicamba drifted to neighboring fields or pastures, harming crops, fruit, and trees. Some scientists contend that dicamba is prone to vaporize or turn from a liquid to a gas during warm weather, even when the label instructions are carefully followed. This vapor, they contend, can insidiously travel several miles to harm non-dicamba-resistant crops, particularly soybeans. The farmers suffering loss are left with no easy remedy. Already, multiple lawsuits have been filed and many more are sure to follow. These include class actions, as well as individual lawsuits. Much like we have seen with the Syngenta litigation, these actions will likely drag on for years. The questions are complex and facts will continue to emerge. Further regulatory action will likely ensue. On October 13, EPA announced that it had reached an agreement with Monsanto, BASF and DuPont on measures to further minimize the potential for drift to damage neighboring crops from the use of dicamba formulations used to control weeds in genetically modified cotton and soybeans. Manufacturers agreed to make label changes that impose additional requirements for “over the top” use of dicamba products in 2018, including:

•

Classifying products as “restricted use,” permitting only certified applicators with special training, and those under their supervision, to apply them; dicamba-specific training for all certified


applicators to reinforce proper use;

•

Requiring farmers to maintain specific records regarding the use of these products to improve compliance with label restrictions;

•

Limiting applications to when maximum wind speeds are below 10 mph (from 15 mph) to reduce potential spray drift;

• • •

Reducing the times during the day when applications can occur; Including tank clean-out language to prevent cross contamination; and Enhancing susceptible crop language and record keeping with sensitive crop registries to increase awareness of risk to especially sensitive crops nearby.

Farmers damaged by pesticide drift should promptly contact IDALS at 515-281-8591 to initiate an investigation. They should also contact the manufacturer to report the injury. Monsanto has asked farmers to contact them at 1–844-RRXTEND immediately with reports of leaf cupping. They will send an agronomic specialist to evaluate the damaged field. Farmers facing damage should also consider hiring legal counsel to assist them in assessing their legal rights. In some cases, private settlements may be reached without resorting to a lawsuit. As the facts shake out, legal remedies may become more apparent. In the meantime, those with damage need to protect their rights by documenting and preserving evidence of their claims.

Current legal landscape for unmanned aerial vehicles On June 21, 2016, the FAA issued its long-awaited final rule, 14 CFR part 107 (Part 107), for integrating small unmanned aircraft systems (UAS) into the U.S. airspace. Part 107, which changed little from the proposed rule issued in February of 2015, paved the way for the widespread use of small commercial unmanned aircraft. The new rule, which was effective August 29, 2016, was good news for agriculture. The requirements of Part 107 are summarized as follows.

Remote pilot in command certification Part 107 establishes a new airman’s certificate called a “remote pilot airman certificate with a small UAS rating.” This is a change in name from the proposed “unmanned aircraft operator certificate with a small UAS rating.” The requirements, however, are very similar to those suggested in the proposed rule. To become a remote pilot, a person would have to:

•

Pass an initial aeronautical knowledge test at an FAA-approved knowledge testing center (there are 9 Iowa centers) or (1) hold a current sport pilot’s license, (2) complete a flight review in the past 24 months, and (3) complete a small UAS online training course

• • • •

To retain the certificate, the remote pilot would be required to pass a test every two years. Complete a Transportation Security Administration (TSA) vetting process Be at least 16 years old Be able to read, speak, write, and understand English (except in the case of certain medical impairments)

Aircraft and flight requirements Part 107 applies only to UAS weighing less than 55 pounds. Standard airworthiness certificate requirements continue to apply to larger unmanned aircraft. Similarly, aircraft meeting the definition of model aircraft are not subject to the Rule; however, all model aircraft must be flown in a safe manner. Violators are subject to FAA enforcement actions.

50 — 2017 Integrated Crop Management Conference - Iowa State University Part 107 eliminates the need for an airworthiness certificate for small UAS, but requires the pilot to conduct a pre-flight check to make sure that it’s in a condition for safe operation. The aircraft must also be registered by the FAA and properly marked, as announced last December. Part 107 requires that a person other than the certified remote pilot may operate the controls of the UAS, as long as he or she is under the direct supervision of the remote pilot. This means that the remote pilot is on the ground, ready and able to take the controls at any time. It also means that the remote pilot can supervise only one unlicensed operator at a time. Both the operator and the remote pilot must keep the aircraft within their visual line of sight at all times. This visual line of sight must be accomplished through unaided vision. However, glasses or contacts are allowed. An operator may use a visual observer who is in communication with the operator to “supplement situational awareness.” However, visual observers are not required. The operator cannot fly the aircraft over people not directly participating in the operation. Nor can the operator fly the aircraft inside of a covered structure. Of course, the operator must also remain clear of other aircraft. Remote pilots may operate their UAS in uncontrolled airspace without prior permission; however, if they wish to fly in a controlled airspace (such as near an airport), they must obtain prior permission from air traffic control. Although the proposed rule required daylight-only operations, Part 107 allows for “civil twilight” operations as well. This means that the aircraft may be flown 30 minutes before the official sunrise or 30 minutes after the official sunset, as long as appropriate anti-collision lighting is employed. Part 107 allows the remote pilot to fly the aircraft at a maximum altitude of 400 feet above ground, unless within 400 feet of a structure (and then not more than 400 feet above that structure). The operator may not exceed a groundspeed of 100 miles per hour. Part 107 requires remote pilots to report “flight accidents” to the FAA within 10 days if the following has occurred:

• •

Serious injury or loss of consciousness to any person OR Damage to property (other than the aircraft) if the cost is greater than $500 to repair or replace (whichever is lower)

Other operations The FAA notes in Part 107 that it the new rule was created to allow for the immediate integration of the “safest” types of flights into the airspace. The agency recognizes, however, that some operations that could be conducted in a safe manner are not authorized by Part 107. Consequently, those wishing to engage in such operations may apply for a “certificate of waiver” to deviate from certain restrictions, so long as the FAA administrator finds that the operation can be “safely conducted” outside of those parameters. The waiver process should allow flights, for example, outside of visual range in unpopulated areas for specific purposes such as crop scouting.

Registering a UAS Small unmanned aircraft weighing more than .55 pounds and less than 55 pounds that do not fly exclusively under the Special Rule for Model Aircraft, must be registered with the FAA and marked with a registration number, either by registering online or by using the legacy paper based registration process. Model aircraft (those not used for commercial purposes) are no longer subject to the registration requirement.


Genetically modified crops: Marvel or malady? Peter Goldsbrough, professor, Botany and Plant Pathology, Purdue University

Introduction The first transgenic plants were produced in 1983 by three different groups of scientists in the United States and Europe. The successful transfer of genes from other species into petunia and tobacco plants was a major technological achievement that led to important advances in our understanding of how plants grow and develop. This technology also led to the development of genetically modified (GM) agronomic crop plants that were planted by farmers for the first time in 1996. This technology has generated more controversy than any other advance in agriculture in the last twenty years. Further scientific advances made during the last two decades are being applied to plant breeding and crop agriculture. These include genome sequencing, the discovery of RNA interference, and revelations about the microbial communities that live on plants and in the soil. Surpassing all of these, however, are new gene editing technologies that have been developed in the last few years. These provide new approaches for genetic modification that have even more potential than transgenic methods to modify crop plants. All of our cultivated plants have been genetically modified compared to their wild ancestors and so “GM” is an inaccurate term to describe transgenic plants. However, GM is generally understood by the public to refer to plants that have been modified by the addition of genes from non-traditional sources. Consequently, I will refer to plants that contain genes introduced or modified using these biotechnology methods as GM plants. Here I will discuss the impact of the GM traits that are widely used in corn, soybeans and cotton, the potential applications for gene editing, and the importance of these technologies in order to meet the future demand for food.

Development and introduction of GM crop varieties The first step in developing a GM variety is to identify a gene that can alter a specific trait in the crop. The fundamental advantage that GM technology provides over other methods of crop improvement is that there is no restriction on the source of genes to alter the target trait. Conventional plant breeders are limited to germplasm that is sexually compatible with the crop. Biotechnology provides access to genes from any organism for crop improvement – a giant genetic buffet! Depending on the source of the gene, it may have to be modified in order to function properly in the host plant. There are a variety of methods available to transfer DNA into plant cells. Agrobacterium tumefaciens, a bacterium that naturally transfers DNA into plant cells in order to cause crown gall disease, is the method of choice because it tends to produce plants with simple DNA integration events. The first generation of GM corn and soybean varieties, however, were produced using particle bombardment with the gene gun. After characterization of many transgenic “events”, plants with the transgene inserted at different chromosomal positions in the genome, the trait must be transferred into varieties with elite agronomic performance using conventional breeding methods. The final step before commercial release of a new GM variety is to obtain regulatory approval from federal agencies such as USDA, EPA and FDA. For a crop that will be exported, approval should also be obtained from the countries that will import this crop. A figure of $100 million is widely quoted as the cost of research and development for a new GM trait. Prado et al. (2014) provide a review of this process. Herbicide tolerance and insect resistance are the two traits that have been most widely planted since 1996, both in the United States and in other countries that have approved GM crops. The genes for both of these traits were obtained from bacteria. The crops where these traits have been widely used include soybean, corn, cotton and canola. Glyphosate tolerant soybeans were adopted very rapidly, planted on more than 90% of the US soybean acreage within ten years of their introduction. Although GM traits in other crops

52 — 2017 Integrated Crop Management Conference - Iowa State University were adopted more slowly, more than 80% of the corn and cotton planted in the US now contains at least one GM trait.

Impact of GM crops A committee convened by the National Academy of Science recently completed an extensive review of GM crops. The report from this committee (National Academies 2016) provides a detailed and balanced review of the impact of GM crops. Several factors contributed to the rapid adoption of Roundup Ready soybeans in the US. The higher price of GM soybean seed was offset by lower costs for weed control, simpler and more effective management of weeds, greater flexibility in timing of herbicide application, and compatibility with reduced tillage practices. The first Roundup Ready soybean varieties did not have any yield advantage over conventional varieties. One of the contentious issues surrounding the introduction of herbicide tolerant crops has been their impact on herbicide use. There has, of course, been a dramatic increase in the use of Roundup, accompanied by a decline in the use of other herbicides. The amount of herbicide applied per acre has not declined, in part because the application rate (pounds per acre) for Roundup is higher compared to many other herbicides. However, Roundup is widely regarded as having a better environmental safety profile compared to the herbicides it has displaced. Widespread adoption of Roundup Ready crops resulted in over-reliance on glyphosate as the primary (and in some cases the only) herbicide used to manage weeds. This intense selection pressure has led to the emergence of several weed species with resistance to glyphosate. It is important to point out that herbicide resistant weeds predate the development of GM crops and have been a problem essentially since herbicides were first used. Unfortunately, Roundup Ready technology has been a victim of its own success. If more attention had been paid to basic principles of weed management, including rotation of herbicides with different modes of action, emergence of these problems could have been delayed, perhaps for decades. Insect resistant crops have utilized Bt toxin proteins derived from Bacillus thuringiensis, a bacterium that has been used as an organic insecticide for decades. Bt toxins are not broad-spectrum insecticides but instead target specific groups of insects and they must be ingested by the insect in order to be effective. Cotton and corn are the major US crops that have been genetically modified by the addition of Bt toxin genes. Bt maize provides protection against insects such as the European corn borer and corn rootworm. In cotton, Bt toxins are used to control bollworms and budworms. The introduction of Bt cotton has dramatically reduced the amount of insecticides used on this crop. This is not solely due to the use of Bt varieties; other changes in pest management have contributed to reduced use of insecticides on cotton. The Bt trait has also reduced the amount of insecticides used on corn. To slow down the development of resistance to Bt toxins in insects, most Bt crops now produce at least two different toxin proteins. Growers are also required to plant refuges of conventional varieties that do not express Bt toxins. Rare insects that develop resistance will likely mate with insects from the refuge and the progeny will still be susceptible to the Bt toxin as long as resistance in the insect is a recessive trait. While GM crops have impacted crop production practices, they have also contributed to the restructuring and consolidation of the seed and agricultural chemical industries. Dr. Philip Howard at Michigan State University has documented how, over the last twenty years, small seed companies in the United States have been acquired by the large ag chemical companies that have developed transgenic traits (Howard 2015). Control of intellectual property, the high costs for research and development of these traits and the established marketing channels provided by the smaller regional seed companies have been cited as important factors behind these changes. Consolidation continues with another round of mergers (Dow and DuPont, Monsanto and Bayer) and acquisitions (ChemChina and Syngenta) among the largest companies in the last year.


Gene editing technology Gene transfer technology provides access to new sources of genetic variation for crop improvement. However, there are several types of genetic modification that cannot be produced using these methods including complete inactivation of specific genes, precise changes in target genes, and insertion of genes at specific locations in the genome. These modifications can all be produced by gene editing. There are several different gene editing techniques but they all use the same three-step process: 1. Identifying a specific target DNA sequence in the genome 2. Cutting both strands of the DNA at the target sequence 3. Changing the DNA at the target during the repair process These steps are analogous to the FIND/CUT/EDIT tools in a word processing program like Word. Zinc finger nucleases (ZFNs) and TALENs (transcription activator-like effector nucleases) use custom-designed proteins to find the target sequence in the genome. The nuclease part of the protein cuts the DNA backbone at that sequence and the DNA repair mechanism changes the DNA sequence of the gene. In spite of the technical challenges involved in designing these bespoke proteins, both methods have been used to alter specific genes in crop plants. Development of the CRISPR/Cas9 system has simplified gene editing. In this two-component system Cas9 is the DNA-cutting nuclease and CRISPR (clustered regularly interspersed short palindromic repeat) refers to the component that guides the nuclease to its target. The nuclease uses a short “guide” RNA to find its target sequence in the genome and this RNA is much easier to design and produce compared to the ZFN and TALEN proteins. The simplicity of the CRISPR/Cas9 system has made it the method of choice for gene editing across all organisms. CRISPR/Cas9 is to gene editing what the iPhone was to the smartphone. Prior to the iPhone, smartphones were available but were not very easy to use. The iPhone led to widespread adoption of these devices and the development of apps that made the phones more useful. CRISPR/Cas9 has had a similar transformative effect by making gene editing technology accessible and catalyzing development of new applications that use the DNA targeting system. As with the technology in smartphones there has also been a contentious legal battle over ownership of the intellectual property around CRISPR/Cas9 gene editing technology.

Applications of gene editing in crop plants One of the first successful examples of gene editing in plants was achieved with ZFNs in maize where a gene involved in synthesis of phytate was inactivated and a herbicide tolerance trait added (Shukla et al. 2009). Here is a small selection of other traits that have been modified by gene editing techniques:

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Resistance of wheat to powdery mildew by inactivating genes that make plants susceptible to this pathogen (Wang et al. 2014)

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Improved drought tolerance in maize by modifying plant responses to hormones under stress conditions (Shi et al. 2016)

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Male sterility in maize (Svitashev et al. 2016)

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Seedless tomato fruit by silencing a gene involved in hormone signaling (Ueta et al. 2017)

Low-gluten wheat by inactivating genes that encode gliadins, the proteins that are responsible for some allergic reactions to wheat (Sanchez-Leon et al. 2017)

Most of these studies have been published within the last two years, evidence for the rapid emergence and adoption of this technology. Some of these trait modifications have also been produced by the same

54 — 2017 Integrated Crop Management Conference - Iowa State University transgenic methods used in the first generation of GM plants. Gene editing, however, offers more options for the type of DNA changes that can be produced as well as greater precision. It is likely that gene editing will become an integral part of many plant breeding programs by providing simpler methods to introduce useful alleles from exotic germplasm into elite varieties (Scheben et al. 2017). CRISPR/Cas9 has also been used in animals. Hornless dairy cattle have been produced by introducing a specific allele using TALENs. Pigs have been modified to alter disease resistance and fat metabolism, important traits for animal health and meat quality. Pigs have long been considered as a potential source of organs to transplant into humans. However, the pig genome contains several copies of a virus that could be reactivated in humans. CRISPR/Cas9 has been used to inactivate these viruses so they cannot infect human recipients. Additional changes to protein glycosylation will be needed before these pigs could be used as organ donors. There is tremendous interest in the medical applications of gene editing and significant ethical concerns about how it may be used in human reproduction.

Why we need GM technology The traits introduced in the first twenty years of GM technology, primarily herbicide tolerance and insect resistance, have been widely adopted by farmers in the United States and some other countries. Unfortunately, this technology has not been embraced by consumers and there are many reasons for this. These traits provide little or no tangible benefit for food consumers. Public opposition has dampened enthusiasm for and reduced investment in GM technology. One can argue that the controversy generated by GM technology among consumers has negated whatever benefits farmers may have enjoyed from these traits. That does not mean that we should abandon these technologies. We are still in the first phase of using gene transfer and gene editing techniques and we are just beginning to realize their potential. Recent research has demonstrated that fundamental processes critical to plant biology, including photosynthesis, plant architecture and disease resistance, can be modified with dramatic effects on plant growth and potentially on crop productivity. If we are going to meet the demand for increased food production from a global population of 9 or 10 billion people in the coming decades we will need every available tool including these GM techniques. It is informative to look at the data for US corn yields over the last 150 years to understand and appreciate the impact of research and technology on crop productivity (Nielsen 2017). There has been a steady increase in corn yield from 30 bushels per acre in 1935 to 175 bushels per acre in 2016, a remarkable feat. This has been achieved through improved genetics and production methods. Equally stunning are the data from 1865 to 1935 when corn yields were about 30 bushels per acre and did not change at all during that 70-year period. It was only after knowledge about plant genetics, nutrition and pathology were applied to variety development that corn yields started to increase. In order to avoid another era of stagnant yields, at a critical time for the planet, we simply have to use these new genetic technologies.

References Howard (2015) Intellectual property and consolidation in the seed industry. Crop Science 55, 1-7 National Academies of Sciences, Engineering, and Medicine (2016) Genetically Engineered Crops: Experiences and Prospects. Washington, DC: The National Academies Press. https://doi. org/10.17226/23395 Nielsen (2017) Historical Corn Grain Yields for the U.S. http://www.kingcorn.org/news/timeless/ YieldTrends.html Prado et al. (2014) Genetically engineered crops: from idea to product. Annual Review of Plant Biology 65, 769-790


Sanchez-Leon et al. (2017) Low-gluten, non-transgenic wheat engineered with CRISPR/Cas9. Plant Biotechnology Journal (in press) Scheben et al (2017) Towards CRISPR/Cas crops – bringing together genomics and genome editing. New Phytologist 216, 682-698 Shi et al. (2016) ARGOS8 variants generated by CRISPR-Cas9 improve maize grain yield under field drought stress conditions. Plant Biotechnology Journal Shukla et al. (2009) Precise genome modification in the crop species Zea mays using zinc-finger nucleases. Nature 459, 437-441. Svitashev et al. (2016) Genome editing in maize directed by CRISPR–Cas9 ribonucleoprotein complexes. Nature Communications 7, 13274 Ueta et al. (2017) Rapid breeding of parthenocarpic tomato plants using CRISPR/Cas9. Scientific Reports 7,507 Wang et al. (2014) Simultaneous editing of three homoeoalleles in hexaploid bread wheat confers heritable resistance to powdery mildew. Nature Biotechnology 32, 947-952.



The Iowa Pest Resistance Management Plan: A community-based approach to address pest resistance in Iowa Evan Sivesind, program manager, Entomology, Iowa State University

Introduction Pest resistance to chemical, genetic, and agronomic management practices is a widespread and increasing problem in Iowa. Evolution of pest resistance to current management technologies is occurring at a faster rate than new technologies are being developed. Though many resistance management practices (RMPs) are well described and validated, their adoption has been slow. This project aims to bridge the gap between knowledge and adoption by engaging all sectors of Iowa agriculture utilizing a community-based approach, a tactical imperative when dealing with mobile pests that cross field boundaries. By increasing adoption of RMPs, valuable pest management tools will be preserved and long-term farm profitability will be protected.

Background In January 2015, a meeting led by the Iowa Department of Agriculture and Land Stewardship (IDALS) and Iowa State University (ISU) College of Agriculture and Life Sciences (CALS) led to the call for the development of a statewide, voluntary pest resistance management plan, which would be coordinated by IDALS and involve participation from all sectors of Iowa agriculture. A framework for a plan was developed by a taskforce made up of representatives from Agribusiness Association of Iowa (AAI), Agricultural Biotechnology Stewardship Technical Committee (ABSTC), Iowa Corn Growers Association (ICGA), Iowa Soybean Association (ISA), the Iowa Chapter of the American Society of Farm Managers and Rural Appraisers (ASFMRA), Iowa Farm Bureau Federation (IFBF), Iowa Independent Crop Consultants Association, Iowa Institute for Cooperatives (IIC), Pesticide Resistance Action Committees (RACs), Practical Farmers of Iowa (PFI), and the Soil and Water Conservation Society. This framework (https://www.ipm. iastate.edu/files/iprmp/resistance-management-conceptual-framework.pdf), approved in December 2015, provided a structure for Version 1.0 of the Iowa Pest Resistance Management Plan (IPRMP). The first version of the IPRMP (https://www.ipm.iastate.edu/files/iprmp/iprmp.pdf) contains chapters regarding governance, state of the science, communication and outreach, and pilot projects, and was approved in December 2016.

Overview of the IPRMP The Iowa Pest Resistance Management Plan is a statewide effort to slow the development of pest resistance, to foster methods of early resistance detection, and to mitigate resistance when it arises. The IPRMP involves broad participation from all sectors of Iowa agriculture to promote voluntary adoption of RMPs. Through successful implementation of voluntary efforts, we hope to minimize the need for additional regulatory intervention. In order to maximize adoption of RMPs, the IPRMP needs to be based on the most current, up-to-date science while also acknowledging the socio-economic realities farmers are facing. By engaging communities and including all sectors of agriculture, cohesion and consistency can be improved, maximizing the likelihood for success. While there are similarities in the general principles of resistance management for weeds, insects, and pathogens, each pest complex possesses unique challenges. Differences between pests in biology, mobility, current resistance profile, and diversity of available management tactics all have to be taken into consideration. For mobile pests with the ability to cross field borders, cooperation between neighboring farmers will be needed to manage resistance effectively.

58 — 2017 Integrated Crop Management Conference - Iowa State University Communication and outreach Communication and outreach is key to this project’s success. Clear, consistent messaging from all stakeholders is crucial to raising awareness and increasing understanding of pest resistance and the factors that contribute to its development. Certified Crop Advisers (CCAs), independent crop consultants (ICCs), agriculture retailers and other agronomic and farm advisers are key collaborators in this effort. Partnering organizations, including commodity groups and coops, will utilize existing partnerships and networks to reach out to farmers and landowners about adopting resistance management practices on their farms. Knowledge will be disseminated through press releases, blog posts, field days, and local-community events. The IPRMP website, www.ProtectIowaCrops.org, serves as a central hub for news, progress, information, announcements, and other relevant resources.

Pilot projects Community-based projects are being implemented across the state of Iowa, each focused on an insect or weed resistance issue. Through discussions with a broad cross-section of stakeholders, a refined understanding of local perceptions, level of awareness, and current management practices is being gathered. As we identify barriers to implementation of recommended practices, solutions to overcoming these barriers can be developed. Barriers may include gaps in knowledge, time constraints, lack of necessary equipment, unavailability of necessary tools, and economic constraints, either real or perceived. Four projects are being developed—two address insect pests and two address weed issues. One project targets western corn rootworm resistance to Bt (Bacillus thuringiensis) traits in corn and takes place in north central and northeastern Iowa. A second project focuses on soybean aphid resistance to pyrethroids and will take place in northwest Iowa. The third project concerns herbicide-resistant waterhemp in central Iowa. The fourth project is focused on Palmer amaranth and other resistant weeds and is taking place in Harrison County in southwest Iowa. For each pilot project, we are assembling teams with representation from all sectors of agriculture, including farmers, crop advisers, commodity groups, agricultural retailers, seed dealers, lenders, university research and extension, and representatives from seed and chemical companies. Identifying and engaging key influencers is crucial in order to maximize visibility and credibility within communities. In addition to creating new connections within communities, tapping into existing networks is instrumental. Project plans are being developed from the “ground-up,” with extensive input and guidance from farmers and other local stakeholders. A broad cross-section of stakeholders is vital as each brings unique viewpoints and valuable insights into barriers to adoption of RMPs and potential solutions.

Insect resistance Western corn rootworm (WCR, Diabrotica virgifera virgifera Leconte) is a serious insect pest of corn in the North Central United States. Western corn rootworm has been managed using conventional insecticides and rotation to non-host crops. In 2003, corn hybrids genetically modified to produce toxins derived from the soil bacterium Bacillus thuringiensis were commercialized and rapidly adopted in subsequent years. In 2009, severe feeding injury by western corn rootworm was observed in fields planted to single trait Cry3Bb1 corn in Iowa; subsequent laboratory analyses confirmed the presence of Bt resistance (Gassmann et al., 2011). Since then, cross-resistance between three of four Bt toxins targeting underground pests (Cry3Bb1, mCry3A, and eCry3.1Ab) has been observed (Gassmann et al., 2014). Practices that favor the development of Bt-resistance include a history of continuous corn, repeated use of the same Bt-trait, and high western corn rootworm pressure. If a WCR population in a field develops Bt-resistance, the resistant population can then spread to neighboring fields through the movement of resistant adult rootworms. Northeast Iowa was chosen as the location for the WCR pilot project as continuous corn production is common, which contributes to high WCR populations and higher risk for Bt-resistance development.


In addition, there is an active Iowa Corn Growers Association membership, which helps facilitate community building and outreach. We are currently working with local stakeholders to ascertain current local management practices that may affect the development of Bt-resistance in WCR. Such practices include crop rotation, use of transgenic Bt-corn, rotation between Bt-traits, use of hybrids with pyramided traits, use of soil-applied insecticides, and extent of field scouting. As we continue to gather information regarding current management practices, barriers to adoption of RMPs can be identified. Reasons for differences between current and recommended management practices include misunderstanding of practices that lead to the development of resistance, a lack of necessary equipment to diversify management practices, socio-economic factors, and an overly optimistic view of the timeline for when new pest management technologies will become available. Soybean aphid, first detected in the United States in 2000, is the most important insect pest of soybeans in the North Central United States. Soybean aphid feeding can reduce photosynthetic rate, plant growth, pod number, seed number, seed weight, and seed oil concentration (Beckendorf et al. 2008; Macedo et al. 2003; Ragsdale et al. 2011). Foliar insecticides, including pyrethroids and organophosphates, are used to manage soybean aphid infestations. Failures of pyrethroid applications to control soybean aphid have been reported in Minnesota since 2015 (Hanson et al. 2017). In 2016, a pyrethroid failure was documented in a field in northwest Iowa (E. Hodgson, unpublished data). Resistance to pyrethroids would restrict tools available to manage soybean aphid and could increase input costs. The second pilot project addresses soybean aphid resistance to pyrethroids, and will take place in northwest Iowa. As pyrethroid resistance in soybean aphid is an emerging threat, this project is focused on educating farmers about the risk of pyrethroid resistance and adopting practices that limit the development and spread of insecticide resistant aphid populations. Recommendations are available that will effectively manage soybean aphid while reducing the likelihood of insecticide resistance developing (Hodgson et al., 2012). As populations fluctuate year-to-year and location-to-location, regular scouting is recommended and economically sound. Foliar insecticide applications can then be made based on populations reaching the economic threshold of 250 aphids per plant on 80% of plants with populations increasing. If multiple applications are required in a single season, rotating insecticide modes of action will reduce selection pressure of any single insecticide.

Weed resistance Globally, there are at least 485 unique cases (species x site of action) of herbicide resistance in 252 weed species. In Iowa, populations of at least 10 weed species have been documented with herbicide resistance, some with resistance to multiple herbicide groups (Heap, 2017). In Iowa, the most common herbicideresistant weeds are waterhemp, marestail, and giant ragweed (Owen, 2016). Waterhemp populations exhibiting multiple herbicide resistance are increasing (Owen, 2016), and populations with resistance to five herbicide groups have been documented (Owen et al., 2015). In addition to widespread resistance to group 2 (ALS inhibitors), group 5 (triazines), and group 9 (EPSP synthase inhibitors, i.e. glyphosate), resistance to group 14 (PPO inhibitors) and group 27 (HPPD inhibitors) is increasing and of considerable concern. Populations of marestail and giant ragweed resistant to group 2 and/or group 9 herbicides can be found in Iowa and neighboring states and further complicate management efforts (Heap, 2017; Owen, 2015). Weed management has been dominated by herbicides for many years. However, as there has not been a new herbicide site of action commercialized in 30 years, proper stewardship of currently available herbicides in the face of increased herbicide resistance is vitally important. In addition to utilizing herbicides with multiple effective sites of action, it is important to diversify weed management to include strategies beyond the use of herbicides. Norsworthy et al. (2012) recommended 12 best management practices (BMPs) for herbicide resistance management, including ”Use a diversified approach toward weed management focused on preventing weed seed production and reducing the number of weed seed in the

60 — 2017 Integrated Crop Management Conference - Iowa State University soil seedbank,” ”Use multiple herbicide mechanisms of action (MOAs) that are effective against the most troublesome weeds or those most prone to herbicide resistance,” and ”Use mechanical and biological management practices where appropriate”. Growers have adopted some of the recommended BMPs to varying degrees; unfortunately, adoption of many of these practices remains poor. From an optimistic viewpoint, this leaves many opportunities for improvement. The third pilot project addresses herbicide-resistance in waterhemp in central Iowa. Herbicide resistant waterhemp is common and widespread in central Iowa. Stakeholders with significant presence in the region include major seed companies, Iowa State University, farm management companies, several coops, and commodity groups. The first documented infestation of Palmer amaranth in Iowa occurred in 2013 in a field in Harrison County. Since then, Palmer has been found in conservation plantings and agricultural fields in at least 50 of Iowa’s 99 counties (Hartzler, 2017). Palmer amaranth is aggressive and competitive, and poses a significant threat to Iowa agriculture. Palmer amaranth has exhibited a propensity to develop herbicide resistance, with populations in the United States resistant to herbicide groups 2, 5, 9, 14, and 27 (Heap, 2017). A Harrison County pilot project is focused on Palmer amaranth and other problematic herbicide-resistant weed species. Slowing the spread of Palmer amaranth in Iowa is a high priority, and this corner of the state contains the longest established infestations of Palmer amaranth in the state. The Harrison County project is led by a dedicated farmer who provided leadership in the noxious weed effort and the 2016 educational forum.

Summary Pest resistance poses a serious threat to Iowa agriculture. We are at a crucial point in the resistance timeline for many weed and insect resistance issues. Despite strong evidence for adopting RMPs, adoption of many such practices has been stubbornly low. The importance of integrating social and economic sciences into resistance management efforts has been suggested by other authors (e.g. Ervin and Jussaume, 2014). This project aims to understand the barriers to grower adoption of such practices and develop strategies and incentives to overcome them. By increasing adoption of resistance management practices, we hope to slow the development of resistance, protect management technologies, and preserve long-term farm profitability. We would like to gratefully acknowledge funding support for the Iowa Pest Resistance Management Plan provided by Iowa Soybean Association, Iowa Corn Growers Association, Iowa Farm Bureau Federation, North Central IPM Center, and the Agricultural Biotechnology Stewardship Technical Committee.

References Beckendorf, E. A., Catangui, M. A., and W. E. Riedell. 2008. Soybean aphid feeding injury and soybean yield, yield components, and seed composition. Agron J 100: 237–246. Ervin, D., and R. Jussaume. 2014. Integrating social science into managing herbicide-resistant weeds and associated environmental impacts. Weed Sci 62: 1-12. Gassmann, A. J. 2011. Field-evolved resistance to Bt mazie by western corn rootworm. PloS One 6(7): 1-7. Gassmann, A. J., Petzold-Maxwell, J. L., Clifton, E. H., Dunbar, M. W., Hoffmann, A. M., Ingber, D. A., and R. S. Keweshan. 2014. Field-evolved resistance by western corn rootworm to multiple Bacillus thuringiensis toxins in transgenic maize. PNAS: 111(14): 5141-5146. Hanson, A. A., Menger-Anderson, J., Silverstein, C., Potter, J. D., MacRae, I. V., Hodgson, E. W., and R. L. Koch. 2017. Evidence for soybean aphid (Hemiptera: Aphidae) resistance to pyrethroid insecticides in the upper Midwestern United States. J Econ Entomol 110(5): 2235-2246.


Hartzler, R. 2017. Add Osceola County to the map – Increased vigilance needed. ICM News. Iowa State University Extension and Outreach. August 16, 2017. https://crops.extension.iastate.edu/ cropnews/2017/08/add-osceola-county-map-%E2%80%93-increased-vigilance-needed Heap, I. 2017. The international Survey of Herbicide Resistant Weeds. [Online]. Available: www. weedscience.com. Accessed: October 25, 2017. Hodgson, E. W., McCornack, B. P., Tilmon, K., and J. J. Knodel. 2012. Management recommendations for soybean aphid (Hempitera: Aphididae) in the United States. J Int Pest Man 3(1): 1-10. Macedo, T. B., Bastos, C. S., Higley, L. G., Ostlie, K. R., and S. Madhavan. 2003. Photosynthetic responses of soybean to soybean aphid (Homoptera: Aphididae) injury. 2003. J. Econ. Entomol 96: 188-193. Owen, M. D. K., Beckie, H. J., Leeson, J. Y., Norsworthy, J. K., and L. E. Steckel. 2015. Integrated pest management and weed management in the United States and Canada. Pest Man Sci 71: 357-376. Owen, M. D. K. 2016. Weed management for 2017 and beyond. Pages 85-92 in Proceedings of the 2016 Integrated Crop Management Conference. Ames, IA: Iowa State University. Norsworthy, J. K., Ward , S. M., Shaw , D. R., Llewellyn, R., Nichols, R. L., Webster, T. M., Bradley, K.W., Frisvold, G., Powles, S. B., Burgos, N. R., Witt, W., and M. Barrett. 2012. Reducing the risks of herbicide resistance: best management practices and recommendations. Weed Sci 60: 31–62. Ragsdale, D. W., Landis, D. A., Brodeur, J., Heimpel, G. E., and N. Desneux. 2011. Ecology and management of the soybean aphid in North America. Annu Rev of Entomol 56: 375-399.

Resources • www.protectiowacrops.org • https://www.ipm.iastate.edu/files/iprmp/iprmp.pdf • https://www.ipm.iastate.edu/files/iprmp/resistance-management-conceptual-framework.pdf



Back to the basics: Integrating weed biology into weed management plans Jared Goplen, Lisa Behnken, Ryan Miller, and Liz Stahl, Extension Educators, Crops, University of Minnesota Extension Herbicide-resistant giant ragweed and waterhemp continue to spread across the midwestern U.S. and are occurring more frequently in the same fields, making control with herbicides especially difficult. Weed biology is one of the most important factors to consider when developing a successful weed management program, with three main factors influencing success: 1) weed emergence patterns, 2) the weed seed bank, and 3) incorporating sound agronomic practices. Each weed species has unique characteristics that influence weed emergence patterns, weed seed bank degradation, growth rates, and efficacy of various herbicides. Knowing these characteristics when developing a weed management plan can improve weed control considerably and save time and money. Giant ragweed and waterhemp have drastically different emergence patterns, which adds difficultly in obtaining excellent weed control in a field with both weeds, especially with herbicides alone. This presentation will discuss specifics of how weed management plans can be improved by incorporating knowledge of weed biology to manage herbicide-resistant weeds, with a primary focus on improving control of giant ragweed and waterhemp.

Weed emergence patterns Weed emergence is driven by a number of factors that vary by weed species, and include temperature, light, nitrogen, and/or chilling period.

Giant ragweed Giant ragweed is one of the earliest emerging weeds, with an emergence period of only several weeks (Figure 1). Soybean planting date can have a significant impact on giant ragweed density when soybean are planted following preplant tillage (Figure 1). Trials conducted in southeastern Minnesota have shown that delaying soybean planting until mid-may or later can remove over half of the total giant ragweed that emerge during the season. Soybean yield potential is still over 94% of optimal with a mid-may planting date, according to long-term research conducted in Minnesota (Hicks and Naeve). A later planting date has the added benefit of a much smaller giant ragweed population to control postemergence (Figure 1). In contrast, soybean yield potential of an early-may planting date will be close to optimal assuming weeds are controlled; however, pre-plant tillage removes less than 8% of the total giant ragweed with preplant tillage prior to an early planting date.


Figure 1. Giant ragweed emergence pattern and potential soybean yield during spring and early summer. Soybean yield potential is based on long-term U of MN research data (Hicks and Naeve). Giant ragweed emergence data from Goplen et al. (2017a, b).

Pre-plant tillage can be an effective weed control tool, especially when planting is delayed. Flushes of earlyemerging weeds, such as giant ragweed, common lambsquarters, and winter annuals, can be taken out with pre-plant tillage as long as tillage is aggressive enough to destroy the weeds, and not just uproot and transplant them.

Waterhemp Waterhemp has an emergence pattern that contrasts with the emergence pattern of giant ragweed. Waterhemp emerges later in the season, typically emerging over an eight to ten week time period. This is why residual herbicides, or the layering of residual herbicides (e.g. an application at planting and then 30 days later) is recommended for control of waterhemp (Figure 2). Delaying the planting date and preplant tillage may not be an effective strategy to control waterhemp with preplant tillage as it is for giant ragweed, but if both weeds are found in the same field the delayed tillage and planting can remove the majority of giant ragweed, allowing postemergence weed control to more-so target waterhemp. A delayed planting date may also allow the residual herbicide applications to be made late enough that control is extended through the entire waterhemp emergence period.


Figure 2. Concept of layering soil residual herbicides (PRE/POST) to provide season-long control of waterhemp populations in soybean. The green distribution represents the waterhemp emergence pattern. Table 1. Waterhemp control and soybean yield with residual herbicides applied either PRE only or layered PRE/POST at Rochester, MN in 2016. The layered application was made approximately 30 days after planting. The waterhemp population was ALS inhibitor resistant. Means followed by different letters indicate a significant difference at P ≤ 0.10 and P ≤0.20 for waterhemp control and yield, respectively. Herbicide1 Dual II Magnum Dual II Magnum layered Outlook Outlook layered Warrant Warrant layered

Rate (Units ac-1)

Application timing

Waterhemp Control (%)3

Yield2 (Bu ac-1)

1.5 pt

PRE

76 b

45.7 bc

1.5 / 1.0 pt

PRE/POST

94 a

47.7 abc

18 fl oz

PRE

79 b

50.3 ab

14 fl oz / 10 fl oz

PRE/POST

95 a

51.8 ab

1.6 qt

PRE

79 b

42.2 c

1.6 qt / 1.6 qt

PRE/POST

91 a

52.9 a

FirstRate (cloransulam) (Group-2) was applied PRE to control other broadleaf weeds in all treatments. Soybeans harvested October 14, 2016. Yield is adjusted to 13% moisture. 3 Rating date was September 26, 2016. 1 2

Managing the weed seed bank Seed production of weeds can vary significantly by species. Giant ragweed, for example has been found to produce from 1,800 to 10,000 seeds/plant, while waterhemp can average over 350,000 seeds/plant. Although competition with other plants can reduce seed production per plant, dense weed populations have the potential to produce tremendous amounts of weed seed. Considering that weed seeds can remain viable in the soil for years, one year of poor control can lead to significant weed control challenges for years to come. Common lambsquarter is a long-term survivor in the weed seedbank, taking 78 years to deplete 99% of the seedbank (Sprague 2013). In contrast, research conducted in southeastern Minnesota demonstrated that the giant ragweed seedbank could be 97 percent depleted in two years (Goplen et al. 2017a). The waterhemp seedbank can be depleted by more than 99 percent in 4 years (Steckel et al. 2007). These

66 — 2017 Integrated Crop Management Conference - Iowa State University results show that populations of giant ragweed and waterhemp could be dramatically reduced if weed populations are managed intensively and seed production is prevented for 2 to 4 years, respectively. Roguing weed escapes prior to seed production can help prevent replenishment of the seedbank. Species vary in how long it takes to form viable seed. Research by Bell and Tranel (2010) found that waterhemp could form viable seed 7 to 12 days after pollination. Seeds may also still mature on pulled plants if the plant pollinated before pulling. Regardless, NOT running the combine through a weed patch will help limit the spreading of weed seeds throughout the field. Also, manage weeds along field edges to help prevent buildup of the weed seedbank.

Incorporate sound agronomic tactics Ensuring the crop is as competitive as possible (e.g. adequate fertility, planting population, and disease and pest control) and canopies as early as possible can help enhance weed control. Narrow rows, expanding crop rotations, and cover crops have the potential to aid in weed control as well. Cultivation is another effective tool, allowing you to remove weeds without setting back the canopy as some postemergence herbicides can, leading to faster canopy closure and a more competitive environment for weeds. Cultivation was evaluated in Minnesota in 2015 and 2016 to evaluate its control of herbicideresistant waterhemp. A preemergence application of Boundary (1.95 pt/acre) was followed by either Liberty (29 fl oz/acre) or mechanical cultivation. In 2016, final waterhemp control was significantly better with the Boundary/Cultivation treatment (98%) compared to the Boundary/Liberty program at 89 percent (Table 2). The soybean canopy also closed sooner where cultivation occurred, so waterhemp that emerged under the canopy in July after cultivation did not survive. No differences in yield were observed among the treatments. Similar results for weed control, canopy closure, and soybean yield were found in 2015. Table 2. Waterhemp control using cultivation and herbicides for weed control at Rochester, MN in 2016. Means followed by different letters indicate a significant difference at P ≤ 0.10. Weed Control Tactic Boundary fb Liberty Boundary fb cultivation

Rate (Units ac-1)

Application timing

Waterhemp control (%)

1.95pts / 29 fl oz

PRE/POST

89 a

1.95pts / cultivation

PRE/POST

98 b

Additional resources University of Minnesota Extension, Resistance Management website: http://www.extension.umn.edu/agriculture/weeds/resistance/

References Bell, M.S. and Tranel, P.J. 2010. Time requirement from pollination to seed maturity in waterhemp (Amaranthus tuberculatus). Weed Sci. 58:167-173. Hicks, D. and Naeve, S. 1999. The soybean growers field guide for evaluating crop damage and replant options. University of Minnesota Extension. Goplen, J.J., Sheaffer, C.C., Becker, R.L., Coulter, J.A., Breitenbach, F.R., Behnken, L.M., Johnson, G.A., and Gunsolus, J.L. (2017a). Seed bank depletion and emergence patterns of giant ragweed (Ambrosia trifida) in Minnesota cropping systems. Weed Sci. 65:52-60.


Goplen, J.J., Sheaffer, C.C., Becker, R.L., Coulter, J.A., Breitenbach, F.R., Behnken, L.M., and Gunsolus, J.L. (2017b) Giant Ragweed (Ambrosia trifida) Emergence Pattern Influenced by Spring Tillage Timing in Minnesota. Weed Technology (in-review) Goplen, J.J., Sheaffer, C.C., Becker, R.L., Coulter, J.A., Breitenbach, F.R., Behnken, L.M., Johnson, G.A., and Gunsolus, J.L. (2016). Giant ragweed (Ambrosia trifida) seed production and retention in soybean and field margins. Weed Technology 30:246-253. Sprague, C. (2013) Common lambsquarters management in soybeans. Take Action, United Soybean Board Steckel, L.E., Sprague, C.L., Stoller, E.W., Wax, L.M., and Simmons, F.W. 2007. Tillage, cropping system, and soil depth effects on common waterhemp (Amaranthus rudis) seed-bank persistence. Weed Sci. 55:235-239.



Dicamba injury and insurance Ray Massey, Extension professor, Agricultural and Applied Economics, University of Missouri Multiperil crop insurance, such as Revenue Protection and Yield Protection, and General Liability Insurance have both been involved in cases where 3rd party damage from herbicide applications occurs. The introduction of dicamba resistant soybeans and cotton has been a rough ride. In 2016, dicamba resistant (Xtend) soybeans were released and planted in some areas of the U.S. At that time, the EPA had not yet approved lower volatility formulations of dicamba. Farmers who sprayed dicamba on top of soybeans in 2016 did an off-label, or illegal, application. These applications of dicamba injured many acres and began a struggle to understand liability associated with herbicide injury by 3rd parties. The EPA approved formulations of dicamba for in-season (pre-emergence and post-emergence) application in time for the 2017 growing season. The approved formulations were sold under the names Eugenia, Xtendimax with VaporGrip Technology and Fexapan plus VaporGrip Technology. These formulations are less volatile than previous formulations of dicamba. Xtend soybeans were planted throughout the corn belt and on more acres than in 2016. And more acres experienced dicamba injury. This time the applications were not off-label, or presumably legal. The previous confusion about of liability expanded to include the question of who was responsible for off-target movement of a legal activity. The USDA Risk Management Agency (RMA) made clear that the crop insurance manual specifically mentions pesticide drift as “not-covered damage.” Reduced yields from pesticide injury cannot be claimed as an insured loss. The RMA did modify their manual to allow farmers to not include reduced yields from 3rd party pesticide applications in Actual Production History (APH) calculations. So if a farmer has herbicide injury from a neighbor and reports that injury to their crop insurer within 72 hours of noticing the injury, the yield from those acres does not have to be included in the farmer’s APH. The second type of insurance that comes into play is general liability insurance that most farmers carry. In 2016, general liability insurance companies could say that the post-emergent application of dicamba over soybeans was an illegal act. Illegal acts are not covered by general liability insurance. The insurance company was not responsible for paying for any losses. However, in 2017, spraying approved formulations of dicamba over the top of soybeans in-season was a permitted practice. General liability insurance now could insure the loss. But whether they are liable or not is not a simple decision. When an insurance company receives a complaint they generally 1) determine if the insured is covered for that specific loss, 2) assess the liability by fact gathering and 3) evaluate damage which will be used to determine any payments. Determining the cause of loss is a critical hurdle to clear. Possible causes of loss for 3rd party herbicide injury include spray tank contamination, drift (e.g. gust of wind quickly carrying spray to nearby field) or volatilization (weather conditions that lift a sprayed product off the target area and carry it to another area, perhaps several hours after application). Accidental tank contamination and accidental drift are usually covered losses and the general liability insurance company would likely cover the liability. It is less clear whether herbicide injury due to volatility is a covered loss. A claim denial record from an insurance company asked to pay indemnity on a dicamba off-target movement case specifically mentions their investigation did not “find any negligence” on the part of the applicator or the farmer. The product was sprayed according to label. The cause of loss “was caused by product failure versus negligence on your part.” The general liability insurance company claimed no responsibility for the herbicide injury. Presumably the product manufacturer (BASF for Eugenia, Monsanto

70 — 2017 Integrated Crop Management Conference - Iowa State University for Xtendimax or DuPont for FeXapan) would be liable for the injury. Monsanto has made it clear that they do not believe inversion is a problem with their product. Collecting for product failure will require a court challenge. If the insurance company does acknowledge the injury as one covered by an insurance policy they are likely to wait until harvest to determine the extent of the loss. They are seeking an objective estimate of reduced yield. This also is not always easy since more than just herbicide injury can affect the yield in a field or portion of a field. On October 13, 2017, the EPA changed the label for dicamba formulations so that they are now Restricted Use Pesticides. This may actually create another wrinkle in the general liability insurance question. Who is responsible for product movement may be less clear than before. Restricted Use Pesticides require application by a certified pesticide applicator or under the direct supervision of a certified applicator. The certified pesticide applicator must have special training and advanced knowledge. This training actually causes them to be held to a higher standard, at least by insurance companies. Not only are certified pesticide applicators responsible for following the label but they are also expected to use the latest science and experience to inform their decisions. If they have reason to believe that spraying according to the label may not prevent a problem, they are expected not to proceed with the activity. In short, applicators might be found liable even if they followed the label when applying dicamba. Businesses need to be aware of new developments and reevaluate their general liability insurance policy with their insurance agent. Some ideas for controlling liability include:

•

Farmers must know that their applicator is certified to apply the pesticide being used. Check the applicator’s pesticide applicator license. If farmers spray their own fields, take the training and get certification.

•

Review your business’s policies and procedures for spraying decisions. Written procedures show how you interpreted the label and that you took the decision seriously.

•

Review your application for general liability coverage insurance. If things have changed (e.g. hired an employee who may spray crops) since you applied for insurance, update your application.

•

If you spray your own fields, make sure you have a spray endorsement provision in your policy. If you are spraying fields for others make sure your spray endorsement covers commercial rather than just private activities.

Note: Ray Massey is not an attorney. Some of the material in this paper is legal in nature. The information is offered for educational purposes rather than as legal advice. Contact your attorney for proper legal advice about 3rd party pesticide injury.


Dicamba: Past, present, and future Bob Hartzler, professor, and Extension weed management specialist, Agronomy, Iowa State University Dicamba has been an important component of Iowa weed management systems for more than 50 years. The history of its use is somewhat unique in that its popularity has ebbed and flowed over time. The increase in herbicide resistant weeds combined with the introduction of dicamba-resistant soybean (Xtend) promises a large increase in dicamba use in both corn and soybean. This article will review the characteristics of dicamba that differentiate it from other herbicides, provide an overview of problems observed in 2017, and describe how risks can be minimized in 2018.

Introduction The discovery of 2,4-D and other phenoxy herbicides in the 1940’s initiated the ea of chemical weed management. These herbicides mimic the action of auxin, indoleacetic acid), and are frequently referred to as growth regulator herbicides, synthetic auxins, or Group 4 herbicides. They bind to the receptor for auxin, therefore initiating transcription of genes involved in cell growth. Whereas plants can closely regulate concentrations of auxin within cells, they lack this ability with the synthetic auxin herbicides. Presence of Group 4 herbicides in cells results in deregulation of numerous important processes, resulting in abnormal growth and/or plant death. Three distinct chemical families have been discovered that interfere with auxin activity (Table 1). Table 1. Chemical families that interfere with auxin activity (Group 4 herbicides). Chemical family

Active ingredient

Tradename

Phenoxy

2,4-D

Weedone, many others

2,4-DB

Butyrac, many others

MCPA

Mecoprop, many others

dicamba

Banvel, Clarity, Sterling Blue, Engenia, Xtendimax with VaporGrip Technology, many others

chloramben

Amiben

triclopyr

Garlon, Remedy Ultra, many others

aminopyralid

Milestone

clopyralid

Stinger, Transline

picloram

Tordon

aminocyclopyrachlor

Streamline

Benzoic acids

Carboxylic acids / Pyridines

Nearly all Group 4 herbicides selectively control broadleaves in grass crops. The exception is quinclorac which is used to control certain weedy grasses in rice and turf. There is a wide range in selectivity among the products, and they are commonly used in combination to provide a broader spectrum of weed control. A combination of 2,4-D and dicamba was the most popular postemergence program in Iowa corn production in the 1970’s and early 1980’s. The products vary widely in soil persistence, and hence, length of residual control. Generally, the phenoxy herbicides have the shortest half-lives of the Group 4 herbicides, whereas the pyridines are most persistent. An advantage of dicamba over 2,4-D for use in resistant soybean is its greater residual activity; however, the residual control provided by dicamba is less than half of most

72 — 2017 Integrated Crop Management Conference - Iowa State University other preemergence herbicides. Thus, the value of dicamba as a preemergence herbicide is limited for managing waterhemp and other weeds with prolonged emergence patterns.

Plant sensitivity Group 4 herbicides induce plant responses at lower fractions of use rates than most other herbicides. For example, it takes 1% of the glyphosate use rate (0.75 lb/A) to injure corn, whereas 0.005% of the dicamba rate (0.5 lb/A) can injure soybean (Figure 1). Due to this high activity, injury to sensitive plants outside of treated areas has been a problem since the introduction of Group 4 herbicides. Improvements in application technology have reduced, but not eliminated, problems with off-target movement of the Group 4 herbicides.

% of labeled use rate

1

0.8

0.6

0.4

0.2

0

Glyphosate/corn

Dicamba/soybean

2,4-D/cotton

2,4-D/grape

Figure 1. Fraction of labeled rate required to cause visible injury on susceptible species. Adapted from Bhatti et al. (1996), Ellis et al. (2003), Everitt and Keeling (2009), and Solomon and Bradley (2014).

Volatility Another distinguishing characteristic of dicamba and certain other Group 4 products is their relatively high vapor pressure. Herbicides with high vapor pressures may evaporate following application, resulting in off-target movement even when the applicator uses appropriate application practices. The combination of vapor loss and the high sensitivity of certain plant species to dicamba results in a higher risk of offtarget injury than with most other herbicides. The following factors influence the potential for dicamba volatilization following application.

Temperature The potential for dicamba to volatilize increases as temperature increases (Figure 2). A threshold of 85° F is frequently cited as the temperature where caution should be used when applying dicamba in the vicinity of sensitive vegetation.


50 R² = 0.98343

% soybean injury

40 30 20 10 0 59

68

77

86

Temperature ( F) Figure 2. Influence of temperature on soybean injury associated with dicamba volatilizing from corn leaves in growth chamber experiments. Adapted from Behrens and Lueschen, 1979.

Application surface The amount of dicamba that evaporates varies depending on the characteristic of the surface it lands upon. Behrens and Leuschen (1979) reported that approximately 35% more dicamba volatilized off corn and soybean leaves than from a silt loam soil. Thus, there is greater risk of volatilization with postemergence applications when significant herbicide is intercepted by the crop rather than the soil surface.

Formulation The vapor pressure of herbicides can be influenced by their formulation. Amine salts of 2,4-D have a sufficiently low vapor pressure that volatility is not an issue under typical application conditions. Thus, volatilization should not be a problem with the choline salt of 2,4-D present in Enlist products. Numerous formulations of dicamba have been introduced with the intention of reducing the risk of volatilization. The parent acid of dicamba has a much higher vapor pressure than the salts used in commercial formulations. Xtendimax with Vapor Grip Technology and Engenia reduce the likelihood of the dicamba salt disassociating to the parent acid compared to older formulations during and after application. Independent research has verified these formulations reduce volatilization compared to older dicamba formulations (e.g. Banvel, Clarity), but they do not eliminate these losses.

The 2017 Iowa experience The Iowa Department of Agriculture and Land Stewardship (IDALS) received 271 pesticide misuse complaints in 2017, a record number. This increase was largely due to 107 off-target injury complaints associated with dicamba applications. The number of formal complaints to IDALS is a small fraction of total problems associated with pesticide application. At the time this article was written IDALS had not released the breakdown on the percentage of complaints associated with contaminated spray equipment, particle drift, and volatilization. Most people involved in investigating dicamba complaints acknowledge that all three avenues of dicamba exposure were involved with off-target injury. Problems associated with contaminated spray equipment and particle drift can be minimized through better training and

74 — 2017 Integrated Crop Management Conference - Iowa State University improved decision making; however, risks associated with volatilization are not easily managed since vapor movement is determined by the environment following application rather than actions of the applicator.

Moving forward in 2018 There has been considerable debate on how to reduce off-target movement associated with dicamba use in soybean. The United States Environmental Protection Agency (EPA) introduced several important label changes for dicamba products registered for use on dicamba-resistant soybean. These products are now classified as Restricted Use Products (RUPs). This classification will require all users of the products to be certified applicators and maintain detailed records of all applications. In addition, applicators of the products will be required to complete dicamba-specific training prior to use. The maximum wind speed allowed for applications was reduced from 15 MPH to 10 MPH, and applications are limited to hours between sunrise and sunset. Label language regarding sprayer cleanout and susceptible crops has been expanded. These label changes are appropriate to improve recordkeeping and should reduce problems associated with particle drift and sprayer contamination, but they do not address the issue of off-target movement associated with dicamba volatilization. Independent research and field observations indicate that that the new formulations have not reduced dicamba volatility sufficiently to prevent movement of phytotoxic concentrations of dicamba outside of treated soybean fields. Dicamba has long been used postemergence in corn with what is considered an acceptable level of risk. The following factors differentiate postemergence use in soybean from that in corn: 1) The peak postemergence application timeframe in soybean is mid-June, whereas in corn it is mid-May. This results in higher temperatures at application, increasing the potential for volatilization. 2) Soybean typically are sprayed at stages with more canopy development than corn, resulting in soybean foliage intercepting a greater percentage of the herbicide. Since more dicamba volatilizes from leaves than soil, there is greater risk of volatility with postemergence applications in soybean than in corn. 3) Non-dicamba resistant soybean will be at developmental stages more prone to yield impacts when dicamba is applied postemergence in soybean than when used in corn. It is important to recognize that soybean are not the only sensitive plants in the Iowa landscape. While most plants are not as sensitive to dicamba as soybean, the increase in postemergence applications in soybean will lead to greater risk to non-agricultural plants (e.g. trees, gardens). The EPA held several teleconferences with academic weed scientists and state regulatory officials during the summer of 2017 to discuss problems with off-target dicamba injury. Most participants agreed limits to how late in the growing season applications could be made were needed to reduce these problems. Persons from the south preferred a date restriction due to a prolonged planting period, whereas persons from northern production regions believed limiting soybean applications to either preplant and preemergence applications would be easier to manage and more effective at limiting off-target injury. The changes in the dicamba labels introduced by EPA in October, 2017 fail to address risks associated with volatilization. Due to concerns regarding volatilization of dicamba, ISU Weed Science is recommending that dicamba only be used preplant or preemergence when used in dicamba-resistant soybean. While preemergence applications reduce the value of dicamba in managing herbicide-resistant waterhemp, in our opinion the risks associated with postemergence applications exceed the weed management benefits. Early postemergence applications in soybean have lower risks than applications made later in the season. However, relying on early post-applications puts applicators under pressure due to limited hours suitable for spraying. During a four-year period, half of the days during the last week of May had zero hours suitable for spraying when considering both wind speed and rain (Figure 3). While spraying conditions are more favorable in mid-June, average temperatures are higher, therefore increasing the potential for volatilization. The daily high temperature exceeded 85° F during 22 of the 28 days used in creating the


box plots. It is likely that many applications targeted for early in the growing season would be delayed due to weather, resulting in them being made in a time-frame with a much greater likelihood of volatilization losses due to higher temperatures and larger crop canopies. This is our rationale for recommending only preemergence applications in soybean.

Figure 3. Hours available to spray during daylight when average wind speed is greater than 3 MPH and less than 10 MPH. Data from four years at the ISU Research Farm near Boone Iowa was used, providing 28 data points per plot. The box represents 2nd and 3rd quartiles, the horizontal solid and dashed line within the box represent median and average hours per day, respectively. The whiskers represent the maximum and minimum values. ‘x’ represents an outlier data point. No rain only considers wind speed, whereas rain considered both wind speed and rainfall in determining available hours.

The rapid increase of multiple herbicide resistant biotypes in waterhemp and other weeds continues to complicate and increase the cost of weed management for Iowa farmers. It is widely recognized that new tools are needed to manage these weed problems. New herbicide options, such as those provided with new herbicide resistant crops (e.g. Xtend, Enlist, Balance GT) will provide some relief from these pressures, however, they require stewardship to be used safely and to sustain their effectiveness. Long-term solutions to herbicide resistance will require diversifying current weed management programs beyond simply modifying herbicide use pattern.

76 — 2017 Integrated Crop Management Conference - Iowa State University References Behrens, R. and W. E. Lueschen. 1979. Dicamba volatility. Weed Sci. 27:486-493. Bhatti, M.A. et al. 1996. Wine grape response to repeated exposure of selected sulfonylurea herbicides and 2,4-D. Weed Technol. 10:951-956. Ellis, J.M. et al. 2003. Rice and corn response to simulated drift of glyphosate and glufosinate. Weed Technol. 17:452-460. Everitt, J.D. and J.W. Keeling. 2009. Cotton growth and yield response to simulated 2,4-D and dicamba drift. Weed Technol. 23:503-506. Solomon, C.B. and K.W. Bradley. 2014. Influence of application timings and sublethal rates of synthetic auxin herbicides on soybean. Weed Technol. 454-464.


Weed management update for 2018 and beyond: The more things change… Micheal D.K. Owen, University Professor and Extension weed management specialist, Agronomy, Iowa State University

Introduction Again there are no new herbicides with novel mechanisms of action and none are anticipated in the near future. Weed management issues were very evident in 2017 and weed populations with evolved resistance(s) to herbicides continued to escalate statewide. Palmer amaranth populations have been identified in many new Iowa counties and it is likely that populations will eventually be identified in all Iowa counties. Weed management remains a major concern for Iowa agriculture and addressing these burgeoning problems requires greater diversity of tactics beyond herbicides. Most herbicide labels now include sections describing management of herbicide-resistant (HR) weeds. These sections describe various best management practices (BMPs) which are important for the diversification of weed management programs. Typically, there is a statement that suspected HR weed populations should be reported to the company for investigation. It is hopeful that the BMPs as suggested on most herbicide labels will gain traction and more growers will adopt more diverse tactics to manage weeds. New HR crops represent a continuation of herbicide-based weed management and evolved resistance to the concomitant herbicides may already be evident. With dicamba-tolerant soybeans, the new dicamba formulations represent challenges for managing off-target dicamba movement. Given the problems with dicamba movement attributable to particle drift during application and movement after application from volatilization, it is clear that adoption of the dicamba-based technology incurs greater risk than past herbicide technologies. The off-target movement of dicamba is complex and involves a number of factors, some that can be addressed with better application techniques. Factors such as nozzle type, boom height, application speed, wind speed, and direction can be addressed by applicators. Other factors such as the inherent chemical characteristics of dicamba, the high sensitivity of susceptible soybean cultivars and other non-target plants, the effects of rain, temperature, relative humidity, and inversions, not just the day of application but for several days following application, cannot be addressed by applicators and increase the risk of adopting the dicamba-based technology. The Environmental Protection Agency (EPA) added new regulatory action on the XtendiMax with VaporGrip Technology, Engenia, and FeXapan with VaporGrip Technology labels in an attempt to address the widespread off-target issues in 2017. These label changes include classifying these herbicides as Restricted Use Products (RUP) thus permitting only certified applicators with special training to better apply dicamba products. Dicamba-specific training will be required. Applicators will be required to keep extensive records for two years and these records must be made available to the Iowa Department of Agriculture and Land Stewardship, the USDA and EPA upon request. Applicators must also keep the receipts for dicamba purchases. Application parameters have been modified; applications can now be made from sunrise to sunset and wind speed during application is now restricted to 3-10 mph. Label language regarding sprayer cleanout and proximity of susceptible crops with regard to dicamba-treated fields have been expanded. We feel these label changes are appropriate, but are concerned that they do not address the issue of off-target movement due to volatilization. See the specific dicamba product labels for specific information about the changes. The need for different technologies to address the burgeoning problem of HR weeds must be considered

78 — 2017 Integrated Crop Management Conference - Iowa State University in relation to the risks associated with the technology. Preemergence applications of dicamba with dicamba-resistant soybean represent the least risky use strategy and is recommended. Early postemergence applications in May, when temperatures are typically relatively cooler, have greater risk than the soil applications. The greatest risk from dicamba-based weed management is postemergence applications in June and later. We do not recommend using the dicamba-based weed management at this time due to the greater risk of off-target movement. Regardless of pending changes in herbicides and crop traits, weed management diversification beyond herbicides must be considered in order to support the tools currently available to farmers. Iowa agriculture will not be able to resolve weed management issues by simply spraying herbicides. What follows is a summary of the limited changes in the industry for 2018; the information should not be considered all encompassing. What follows is a summary of the limited changes in the industry for 2018; the information should not be considered all encompassing.

Selected industry updates AMVAC Parazone 3SL herbicide (paraquat) is now available from AMVAC. Parazone 3SL is a HG22 and the formulation contains stenching (odor) and emetic materials. The label is similar to other paraquat herbicides.

BASF Engenia herbicide was approved for use in 2017. Engenia is a 5 lb ai/gal formulation of N,N-Bis (3aminopropyl) methylamine [BAPMA] salt of dicamba. The main label specifies uses in asparagus, CRP, corn, cotton, fallow cropland, farmstead turf (non-cropland) and sod farms, grass grown for seed, pasture, hay, rangeland, and farmstead (non-cropland), proso millet, small grain, sorghum, soybean and sugarcane. A supplemental label allows use of Engenia in dicamba-tolerant soybeans. Application can be made preplant, preemergence or postemergence. Maximum application rate (per application) is 12.8 oz/A. Combined applications per season may not exceed a maximum rate of 51.2 oz/A, e.g. two 12.8 oz/A applications to the soil (preplant and preemergence) and two 12.8 oz/A applications post. Approved nozzles, adjuvants and tank mix partners can be found at http://agproducts.basf.us/campaigns/engenia/tankmixselector. Zidua SC herbicide (HG 15) will be available for the 2018 growing season. The water-based suspension concentrate formulation contains 4.17 lbs. of pyroxasulfone per gallon. The label allows preplant surface, preplant incorporated, preemergence or post emergence applications in both corn and soybeans. Use rates in corn range from 1.75 to 6.50 oz/A and from 1.75 to 5.75 oz/A in soybean. Use rates are based on soil texture and application timing. FIFRA 24(c) Special Local Needs Label allows applications of Zidua herbicide for control of Palmer Amaranth in established federal Conservation Reserve Program (CRP) fields. The label specifies both preemergence and early postemergence applications. Application rate range is from 1.0 to 4.0 oz/A depending on soil texture and application timing. This label is for distribution and use only in the state of Iowa.

Dow Agroscience Corn hybrids with 2,4-D (HG4) resistance (Enlist corn) are globally deregulated for the 2018 crop season. Enlist corn will tolerate applications of conventional corn herbicides and carry herbicide tolerance to glyphosate and Enlist Duo and Enlist One herbicides. Enlist Duo (HG4 and 9) is registered and available for non-Enlist corn and soybean as preplant burndown


and preemergence (corn) and preplant burndown (soybeans). Enlist Duo is a premix of 2,4-D choline and glyphosate with Colex-D technology. Enlist Duo is also labeled for use on Enlist corn in 2018, but not available for postemergence use until the Enlist soybeans or E3 soybeans are approved by China. Enlist Duo has less potential for volatilization than other HG4 formulations but care should be taken to avoid conditions that may cause off-target movement. The Enlist Duo label describes appropriate nozzles for application, buffer requirements, and specific application techniques. Enlist Duo can be tank mixed with many surfactants and additives including AMS. Refer to http://www.enlist.com/en/approved-tank-mixes to confirm a given product or nozzle is approved. Herbicide-resistant weed management requirements are also included in the Enlist Duo label. Enlist One (HG4) is a registered herbicide for the control of annual and perennial weeds for use on non-Enlist corn and soybeans as a preplant burndown, preemergence (corn) and preplant burndown (soybeans). Enlist One is also labeled on Enlist corn in 2018, but not available for postemergence use until the Enlist soybeans or E3 Soybeans are approved by China. Enlist One herbicide contains 3.8 lb ai/gal of 2,4-D choline with Colex-D technology and was developed to give greater flexibility in herbicide tank mix partners to combat hard to control and herbicide-resistant weeds in Enlist corn and soybean. Enlist One has a use rate of 1.5 to 2.0 pints/A and can be applied to Enlist corn from preplant to V8 growth state or 30 inches tall as an over the top application. Corn taller than 30 inches and less than 48 inches requires the use of drop nozzles. The Enlist One label describes appropriate nozzles for application, buffer requirements, and specific application techniques. Enlist One has the ability to be tank mixed with various surfactants, additives, and herbicides: refer to www.enlisttankmix.com to confirm a given product or nozzle is approved. Herbicide-resistant weed management requirements are also included in the Enlist One label. Elevore (HG4) herbicide will be launched in 2018 for preplant burndown control of annual broadleaf weeds with an emphasis on winter annuals like horseweed/marestail, henbit, purple deadnettle and early spring annuals like common ragweed and common lambsquarters. Elevore contains 0.572 lbs of halauxifen acid per gallon and is applied at 1.0 oz/A 14 days prior to planting for corn and soybeans. Refer to http://client.dow.com/elevoretankmix to determine the herbicides available for tank mixtures with Elevore. Herbicide-resistant weed management requirements are also included in the Elevore label.

DuPont EverpreX (HG 15) is a 7.62 EC formulation of s-metolachlor that is labeled for corn and soybeans. This product can be applied in the fall, early preplant, preplant incorporated, preemergence and postemergence for residual weed control. Note that the postemergence application does not control emerged weeds so if weeds are present at the time of application, a tankmixture with products that provides control of emerged weeds is needed. EverpreX will provide control of annual grasses and some small-seeded annual broadleaf weeds. Revulin Q (HG 2 and 27) is a prepackage mixture of nicosulfuron (HG 2) and mesotrione (HG 27). Label changes for 2018 include the addition of COC and AMS for applications to popcorn and sweet corn, the addition of topramezone (HG 27) (e.g., Impact and Armezon) as a tank mix partner, and ability to apply Revulin Q aerially. Popcorn and sweet corn may now be replanted immediately after a Revulin Q application (see the Rotational Crops Guideline) and new sections describing cover crops and a field bioassay are included on the label. Cover crops can be planted into Revulin Q-treated fields as long as the cover crops are not grazed by livestock or harvested for forage or food. However, not all cover crops have been evaluated for sensitivity to Revulin Q so there may be a risk of injury to the cover crop. The field bioassay of the Revulin Q label describes how to assess the potential for cover crop injury from the herbicide.

Monsanto Harness MAX herbicide (HG 15 and 27) is a premixture of acetochlor (HG 15) and mesotrione (HG

80 — 2017 Integrated Crop Management Conference - Iowa State University 27) and available for preplant, preemergence and postemergence use in field corn, seed corn and yellow popcorn. Postemergence applications of Harness MAX can be made to corn up to 11 inches in height. Harness MAX will provide excellent control of annual small-seeded broadleaf and grass weeds as well as postemergence control of some large-seeded broadleaf weeds.

Nufarm Panther Pro (HG 5, 14 and 2) contains 3 lbs metribuzin, 0.67 lbs flumioxazin, and 0.56 lbs imazethapyr per gallon and the use rate on soybeans is 12 to 15 fluid oz/A. The amount of metribuzin (0.28 to 0.35 lb a.i./A) is a bit higher than many other premixtures with metribuzin and may provide soil residual as well as contact activity. Panther Pro is restricted from use on sand soils (regardless of O.M.) as well as sandy loams or loamy sands containing less than 2% organic matter. Panther Pro is labeled for burndown/fallow uses as well as soybean preplant and preemergence uses. Panther Pro is very effective on many annual broadleaf weeds such as common lambsquarters, waterhemp and others as well as foxtail species and other annual grass weeds. Panther Pro provides excellent burndown of many seedling broadleaf weeds less than 4 inches in height but the addition of glufosinate, glyphosate or paraquat will help with the control of emerged grasses. Either a crop oil concentrate or methylated seed oil which contains at least 15% emulsifiers and 80% oil or a non-ionic surfactant at 0.25% v/v, may be used when applying Panther Pro as part of a burndown program.

Syngenta There are numerous modifications on the labels of existing Syngenta proprietary products (i.e., typos for the emergency telephone number on the Acuron label). These changes pertinent for Iowa can be found on the Acuron Flexi, Acuron, Dual Magnum, Evik DF, Flexstar, Fusilade DX, Gramoxone SL, Halex GT, Reflex, and Sequence herbicide labels.

Valent Valor EZ is a 4 lb ai/gal liquid flumioxazin formulation (HG14) registered for use in corn and soybean. Valor EZ can be applied 7 to 30 days prior to corn planting and can be applied preplant and preemergence to soybeans. The preemergence application can be made up to 3 days after soybean planting. Valor EZ may also be used as part of a fall burndown program, however it is recommended that this product be tankmixed with 2,4-D or dicamba (HG4) or glyphosate (HG9) herbicides if weeds are present at the time of application. Refer to the Valor EZ label for all use rates and restrictions. There do not appear to be significant changes in the proprietary products from Bayer Crop Science, FMC and Winfield.

Iowa survey of herbicide-resistant weeds Weeds with evolved resistance to herbicides are widely distributed in Iowa. Currently there are 10 weed species identified with evolved resistance to herbicides (Table 1). It is important to recognize that reporting herbicide resistance is voluntary and may not reflect the actual situation, particularly for weed populations with multiple resistances. Waterhemp populations have been reported in Nebraska with evolved resistance to HG 4 herbicides and common sunflower populations have evolved resistance to HG 9. Iowa horseweed/ marestail populations have evolved resistance to HG 2 and 5, but this has not been reported to the International Survey of Herbicide Resistant Weeds (http://www.weedscience.com). Similarly, giant ragweed populations in Iowa have evolved resistance to HG 27. Importantly, the evolution of herbicide resistance continues to increase in Iowa and herbicide resistant weed population densities in specific fields are increasing, thus becoming an economic concern.


In 2010, the Iowa Soybean Association requested that the Iowa State University weed science program survey soybean fields in Iowa to gain a better understanding of the herbicide resistance problem. The task became much greater than originally proposed and has just recently been completed. Approximately 900 waterhemp populations were sampled in Iowa (Figure 1). While the information is dated, and likely underreports resistance to HG 14 and HG 27, the information provides a comprehensive picture of herbicide resistance in Iowa for waterhemp. Giant ragweed and horseweed/marestail populations sampled will be completed next but the number of fields with these weeds is considerably less than those with waterhemp. Table 1. Weed species with evolved herbicide resistance in Iowa.


Figure 1. Sample collection locations in Iowa, 2011-2013.

Methods used The original idea was to arbitrarily sample soybean fields that had weeds visible above the canopy in August and September. Approximately 300 soybean fields were sampled in 2011 and 2012. This approach increased the likelihood that the escaped weeds were resistant to herbicides but no information about the herbicide use history was collected. The GPS coordinates of the fields were recorded so return visits were possible. It was decided that a random selection of fields would provide more useful and predictive information about evolved herbicide resistance. Thus in 2013, a procedure was developed that provided the prediction of herbicide resistance at the 95% confidence interval in any Iowa soybean field. This procedure used the number of soybean acres reported in each county to determine how many fields should be sampled in order to provide an estimate of herbicide resistance. Collaboration with the Iowa State University Department of Statistics and the Geographic Information Systems laboratory resulted in identifying the GPS coordinates of 400 fields that would be visited. Using the same two inclusionary principles as the 2011 and 2012 collections (e.g., soybean field and weeds visible above the canopy), weeds were collected and screened to allow predictions of herbicide resistance at the 95% confidence interval. Approximately 900 waterhemp populations were sampled for the project (Figure 1). Several female waterhemp plants were collected from each field, GPS coordinates of the fields were recorded and samples were dried for a number of days. Seeds were threshed by hand, cleaned, and samples were wet-chilled for several weeks to break dormancy. Seed samples were then air-dried and seeds planted and grown in the greenhouse until plants were approximately 3-4 inches tall, at which time they were treated with a herbicide. Herbicide treatments included an HG 2 herbicide (Pursuit), an HG 5 herbicide


(atrazine), an HG 9 herbicide (Roundup), an HG14 herbicide (Cobra), and an HG 27 herbicide (Callisto). All herbicides were applied at label rates with adjuvants included as suggested in the herbicide labels. Nine waterhemp plants from each population were evaluated for control for each herbicide group and populations were evaluated for all five herbicide groups. Herbicide response was visually assessed on a scale of 0-100 where 0 response indicated no herbicide affect and 100 indicated plant death. Populations were determined to be resistant if the averaged herbicide response was 80% or less when compared to a waterhemp population known to be sensitive to all herbicide groups.

Results The herbicide groups included in the survey were chosen as they represent the most commonly used herbicides in Iowa. The levels of herbicide resistance found were surprisingly high for the five herbicide groups evaluated. However, given the years these herbicides have been used in Iowa, often in both corn and soybean, and the inevitability of evolved herbicide resistance, perhaps it is not that surprising. It should be recognized that the waterhemp populations in most fields that fulfilled the inclusionary principles were relatively low in population density and often represented scattered patches and individual plants. Given the ability of waterhemp to produce high seed numbers, it is possible that the population density may increase quickly in these fields unless appropriate management tactics are adopted. Resistance to ALS inhibitor herbicides (HG 2) in waterhemp is widely distributed and represents virtually all fields in Iowa based on the 2013 evaluation (Figure 2). While one ALS herbicide was used in the screen (Pursuit), waterhemp demonstrates cross-resistance to all HG 2 herbicides regardless of application technique. Thus, HG 2 herbicides are not effective in managing waterhemp in Iowa. While waterhemp populations may not be homozygous for the resistance trait, the sensitive waterhemp in these populations is likely a minor component, given the historic use of HG 2 herbicides.

Figure 2. Evolved resistance in waterhemp to ALS herbicides (HG 2) in Iowa 2011-2013.

Atrazine was used as the representative HG 5 herbicide. There is some difference in opinion whether waterhemp with evolved resistance to atrazine behaves similarly to other HG 5 herbicides such at an asymmetric triazine such as metribuzin. Preliminary research suggests that these HG 5 herbicides may

84 — 2017 Integrated Crop Management Conference - Iowa State University still be effective to control waterhemp with evolved resistance to the symmetric triazine herbicides (e.g., atrazine) (Owen, data unreported). Given the continued use of atrazine for many decades, it is not surprising that evolved resistance in waterhemp is so widely distributed in Iowa (Figure 3). Historically, HG 5 herbicide was target site-based and incurred a fitness penalty to the resistant populations (LeBaron 1991). However, with the changes in atrazine usage, application rates have declined considerably which, considering the fitness penalty, should result in the decline of HG 5 biotypes. However, reduced herbicide rates facilitate the evolution of herbicide resistance and metabolic HG 5 resistance has been reported in waterhemp (Gressel 2011; Huffman et al. 2015). Based on the 2013 waterhemp collection, at the 95% confidence interval 97% of the fields in Iowa have detectable resistance to HG 5.

Figure 3. Evolved resistance in waterhemp to PSII herbicides (HG 5) in Iowa 2011-2013.

The adoption of crop cultivars with genetically-engineered tolerance to glyphosate in the mid-1990s was arguably the most important change in agriculture since the introduction of the moldboard plow. Despite the public concerns about technology, the benefits outweigh the risks (Duke and Powles 2008). However, the evolution of resistance to glyphosate has also changed agriculture and has benefited from the resurgence of the importance of weed management (Chatham et al. 2015). Glyphosate has been used on most of the Iowa corn and soybean acres for more than a decade and the inevitable evolution of glyphosate resistance is wide-spread and is predicted to be in 98% of the fields in Iowa (Figure 4). The occurrence of glyphosate resistance increased from 2011-2013 and it is unlikely that glyphosate resistance in waterhemp will decline even if other technologies are adopted.


Figure 4. Evolved resistance in waterhemp to glyphosate (HG 9) in Iowa 2011-2013.

The evolution of HG 14 resistance did not change during the course of this survey (Figure 5). However, given the increased importance of HG 14 herbicides to control glyphosate-resistant waterhemp, it is likely that these data greatly underestimate the occurrence of HG 14 resistance in waterhemp (Thinglum et al. 2011). This herbicide group is seen as the only selective postemergence option in soybean weed control despite the phytotoxicity these herbicides cause. The use of HG 14 herbicides as soil-applied treatments has also increased. The increased use of HG 14 herbicides will result in more waterhemp populations with resistance. (Wuerffel et al. 2015)

Figure 5. Evolved resistance in waterhemp to PPO inhibitor herbicides (HG 14) in Iowa 2011-2013.

The last new herbicide mechanism of action commercially introduced was the HG 27 herbicides almost 30 years ago. These herbicides have been widely used in corn, and as a result evolved resistance to HG

86 — 2017 Integrated Crop Management Conference - Iowa State University 27 herbicides is widely distributed in Iowa (Figure 6). It is suggested that these data underestimate the occurrence of HG 27 resistance given the increased use of these products since the survey ended. Resistance to the HG 27 herbicides is reported to be due to metabolism (Huffman et al. 2015). Recent research conducted at Iowa State University has verified that HG 27 is dominant or semi-dominant and polygenic with the number of genes involved with the metabolic resistance increasing with the herbicide rate (Kohlhase, unreported). The polygenic nature of HG 27 resistance may make management in the field more difficult. Further, the frequency of HG 27 resistance brings into question how effective the anticipated HG 27 resistance in soybean cultivars will be at supporting weed management.

Figure 6. Evolved resistance in waterhemp to HPPD inhibitor herbicides (HG 27) in Iowa 2011-2013.

A problem with herbicide resistance in waterhemp is there does not appear to be a fitness penalty associated with the resistance (Wu et al. 2017). As a result, the resistance trait is likely to be conserved even if the herbicide is not used. New evolved resistances will be added to previously evolved resistance (Patzoldt et al. 2005). Given the dearth of new herbicide mechanisms of action, multiple resistances in waterhemp dramatically increased the difficulty of management. Multiple herbicide resistances in Iowa waterhemp populations is the norm (Figure 7). Waterhemp populations with resistance to three herbicide groups increased over the course of this study, and in 2013 69% of the waterhemp populations demonstrated three-way resistance. This estimate is correct at the 95% confidence interval. Not surprising is the observation that the most common three-way resistance are for HG 2, HG 5, and HG 9, the most commonly used herbicide groups. Resistance to four herbicide groups and five herbicide groups (all the herbicide groups used in the screen) did not change over the course of the study with four-way resistance more commonly detected than five-way resistance. Management of multiple herbicide resistant waterhemp is a significant challenge for farmers.


Figure 7. Evolved multiple resistance in waterhemp to in Iowa 2011-2013.

Conclusion Regardless of pending changes in herbicides and crop traits, weed management diversification beyond herbicides must be considered in order to support the tools currently available to farmers. Iowa agriculture will not be able to resolve weed management issues by simply spraying herbicides. The new dicamba formulations must be used cautiously and with considerable attention to detail. While dicamba provides broadleaf weed control in dicamba-tolerant soybean cultivars, there is also risk of off-target movement. With regards to herbicide-resistant weeds, it is important to understand that most of the fields from which waterhemp populations were collected were transitioning from sensitive to resistant and the population density of waterhemp found was likely lower than the level that would be recognized by a farmer and cause a major concern. Nevertheless, the levels of HR detected suggests that unless remediation is initiated, wide resistance to herbicides in Iowa waterhemp populations will likely increase. Despite farmers’ desires to have available a new herbicide, it is impossible to spray the problem of herbicide resistance in waterhemp away. The only solution is the judicious use of herbicides and adoption of greater diversity of weed management tactics (Owen 2016; Owen et al. 2015).

The path forward The need for better weed management continues to be a critical concern for Iowa agriculture. However, despite the widespread occurrence of herbicide resistance in waterhemp, giant ragweed, and horseweed/ marestail, most fields in Iowa are in a position to allow continued effective weed control if farmers will diversify their tactics. The thoughtful choice of herbicides with still-effective mechanisms of action is critically important, however, given that most waterhemp populations have evolved multiple resistances, knowing which herbicide mechanisms of action are still effective is a major challenge. The drought of new herbicides in the developmental pipeline continues to be a major problem and the long-term forecast for new herbicides with novel mechanisms of action is not bright. While new herbicide-resistant crop cultivars are available, the herbicides for those cultivars already have resistant weed populations. Clearly, issues in

88 — 2017 Integrated Crop Management Conference - Iowa State University weed management continue to be increasingly complex, and there are no simple and convenient answers despite what product marketing might suggest. The problems of off-target dicamba represents a major problem for agriculture and there does not appear to be a clear answer to these issues.

References Chatham, L.A., Wu, C., Riggins, C.W., Hager, A.G., Young, B.G., Roskamp, G.K., Tranel, P.J. 2015. EPSPS Gene Amplification is Present in the Majority of Glyphosate-Resistant Illinois Waterhemp (Amaranthus tuberculatus) Populations. Weed Technology 29: 48-55. Duke, S.O., Powles, S.B. 2008 Glyphosate: a once-in-a-century herbicide. Pest Manag Sci 64: 319-325. Gressel, J. 2011 Low pesticide rates may hasten the evolution of resistance by increasing mutation frequencies. Pest Management Science 67: 253-257. Heap, I. 2017. The international survey of herbicide resistant weeds. Available at www.weedscience.com. Accessed October 9, 2017. Huffman, J., Hausman, N.E., Hagar, A.G., Riechers, D.E., Tranel, P.J. 2015. Genetics and Inheritance of Nontarget-Site Resistances to Atrazine and Mesotrione in a Waterhemp (Amaranthus tuberculatus) Population from Illinois. Weed Science 63: 799-809. LeBaron, H.M. 1991. Distribution and seriousness of herbicide-resistant weed infestations worldwide. Pages 27-44 in J. C. Caseley, G. W. Cussons, and R. K. Atkin, eds. Herbicide resistance in weeds and crops. Oxford: Butterworth-Heinemann. Owen, M.D.K. 2016. Diverse approaches to herbicide-resistant weed management. Weed Science 64: 570584. Owen, M.D.K., Beckie, H.J., Leeson, J.Y., Norsworthy, J.K., Steckel, L.E. 2015. Integrated pest management and weed management in the United States and Canada. Pest Manag Sci 71: 357-376. Patzoldt, W.L., Tranel, P.J., Hagar, A.G. 2005. A waterhemp (Amaranthus tuberculatus) biotype with multiple resistance across three herbicide sites of action. Weed Science 53: 30-36. Thinglum, K.A., Riggins, C.W., Davis, A.S., Bradley, K.W., Al-Khatib, K., Tranel, P.J. 2011. Wide distribution of the waterhemp (Amaranthus tuberculatus) G210 PPX2 mutation which confers resistance to PPO-inhbiting herbicides. Weed Science 59: 22-27. Tranel, P.J., Wright, T.R. 2002. Resistance of weeds to ALS-inhibiting herbicides: what have we learned? Weed Science 50: 700-712. Wu, C., Davis, A.S., Tranel, P.J. 2017. Limited fitness costs of herbicide-resistance traits in Amaranthus tuberculatus facilitate resistance evolution. Pest Manag Sci. (in press). Wuerffel, R.J., Young, J.M., Tranel, P.J., Young, B.G. 2015. Soil-Residual Protoporphyrinogen Oxidase– Inhibiting Herbicides Influence the Frequency of Associated Resistance in Waterhemp (Amaranthus tuberculatus). Weed Science 63: 529-538.


Rootworm behavior and resistance in Bt cornfields Joseph L. Spencer, Principal Research Scientist and Research Program Leader in Insect Behavior, Illinois Natural History Survey, University of Illinois; Sarah A. Hughson, Extension Specialist - Pesticide Safety Education Program, Crop Sciences, University of Illinois

Introduction The western corn rootworm (Diabrotica virgifera virgifera LeConte, WCR) is the most economically important U.S. corn pest (Gray et al. 2009). Together with the northern corn rootworm (Diabrotica barberi Smith and Lawrence, NCR), it was estimated that these pests annually cost producers $1 billion in yield losses and control costs (Metcalf 1986). Today the cost is thought to be much greater (Dun et al. 2010; Tinsley et al. 2013). Corn rootworm life cycles are closely tied to that of corn (Levine and Oloumi-Sadeghi 1991). WCR eggs hatch in late May or early June. The tiny newly-emerged “neonate” larvae use the CO2 emissions from growing corn roots to locate nearby hosts. Larval root-feeding causes most of the yield losses and costs associated with this species, while the adults feed on corn foliage, tassels, silks, and kernels (Branson and Krysan 1981, Moser and Vidal 2005). Adult emergence begins in late June. Mate-seeking males locate newly-emerged females and mate in cornfields. Within a few days of mating, a portion of the females disperse from the field where they emerged; they may be carried long distances on prevailing winds to distant cornfields. The remaining females feed and within a week or two begin laying the first of several 100’s of eggs in the soil. Egg-laying females use cracks and crevices to locate moist, protected locations. Most eggs overwinter, or diapause, in the top 10 inches of soil.

Managing corn rootworms A strong egg-laying fidelity to cornfields and the larval dependence on corn roots led to the original recommendation of annual crop rotation with a non-host crop (like soybean) as a corn rootworm management tactic (Forbes 1883, Gillette 1912). Corn rootworm larvae emerging in soybean fields will starve and die. Crop rotation remains one of the most effective methods of rootworm control in the Corn Belt and elsewhere, with some notable exceptions. Their long history of field-evolved resistance is a key reason why rootworms are such a threatening pest (Gray et al. 2009). Where crop rotation could not be practiced, growers relied on soil-applied and broadcast insecticides to control the root-feeding larvae and egg-laying adults beginning in the late 1940s. From the late 1950s to the present, rootworms have evolved resistance to multiple classes of insecticides. Most recently, WCR resistance to the pyrethroid insecticide, bifenthrin, was documented in Western Nebraska and Kansas (Pereira et al. 2015). In the mid-1980s, the great efficacy of crop rotation against WCR lead to its undoing. By 1995, the corn and soybean rotation had selected for females with reduced egg-laying fidelity to cornfields. The result was a pest that laid eggs everywhere, including the soybean fields where corn would grow the following year. Some Illinois and Indiana growers experienced 50% yield loss in corn after soybeans in 1995 (Levine et al. 2002). Decades earlier, the NCR had evolved resistance to crop rotation by delaying the normal time of egg hatch by prolonging egg diapause during the winter (Chiang 1965). Rather than hatching the next summer, a portion of the eggs remained in diapause for two winters (or more) and hatched when corn was again planted in the field. The evolution of WCR behavioral resistance to crop rotation resulted in expanded use of soil-applied insecticide on millions of rotated corn acres (Levine et al. 2002). The alarming escalation of soil-applied insecticide use was one of the motivating factors driving interest in rootworm management alternatives—like Bt corn.

90 — 2017 Integrated Crop Management Conference - Iowa State University Bt corn hybrids for rootworm management Commercial sales of Bt corn hybrids expressing Bt toxins that were protected from corn rootworm larval injury were first approved by the U.S. EPA in 2003 (EPA 2003). Additional Bt toxins have since been registered and commercialized in other Bt corn hybrids (Tabashnik and Carrière 2017). In-plant expression of toxic proteins from the soil microbe Bacillus thuringiensis (Bt) provided targeted control of corn rootworm larvae with an efficacy equivalent to that of soil insecticides, but without the human health risks and environmental concerns associated with broad-spectrum insecticides (Rice 2004). The threat of rootworm resistance to Bt technology factored into discussions of how best to deploy Bt corn to delay the inevitable evolution of resistance (Tabashnik and Gould 2012). Data on WCR biology and behavior from the published literature were used in computer models to evaluate the durability of Bt corn under a variety of deployment scenarios. Rapid evolution of insect resistance to Bt toxin(s) would be slowed by requiring that a contiguous block or broad strips of each Bt cornfield were planted with a “refuge” of non-Bt corn (initially 20% of total field area). Refuges of non-Bt corn would provide places where rootworms with susceptibility to Bt toxins could survive. The Bt-susceptible refuge rootworm beetles were expected to disperse into nearby Bt corn where they would mate with any rare, potentially resistant beetles that had survived on Bt plants (Tabashnik and Gould 2012). Because Bt-susceptible refuge beetles would vastly outnumber any Bt-resistant survivors, it would be highly unlikely that pairs of Bt-resistant beetles would ever mate, and thus few resistant offspring would be produced. Planting refuges was a key element of the Insect Resistance Management (IRM) plan designed to delay the evolution of rootworm resistance to Bt corn. Other expectations about rootworm biology/behavior in Bt cornfields (e.g., resistance alleles were initially rare in rootworm populations) and characteristics of the Bt plants (e.g., Bt toxin levels in plant roots would kill a very high proportion of larvae) were also integral to the plan’s success. In addition, growers were expected to adhere to refuge requirements and plant the required percentages of refuge acres.

Studying WCR biology in Bt cornfields with refuges In 2010, a three-year study began to document the abundance, behavior, and biology of WCR adults in large field plots of refuge and Bt corn at the University of Illinois (Hughson 2017, Hughson and Spencer 2015). The goal was to learn whether WCR beetles from refuges really would emerge, move into Bt corn and mate, as expected. Four configurations of refuge and Bt corn were used study the impact of different refuge designs on WCR biology and behavior. Refuge was deployed in contiguous blocks (i.e. Bt cornfields with 20% and 5% structured refuge blocks on one side of a field), as a seed blend where 5% of seeds in a bag of Bt seed were non-Bt refuge seeds (i.e. 5% seed blend refuge) and as a no-refuge control (i.e. 0% refuge). Nearly 35,000 individual beetles and almost 900 pairs of mating beetles were collected. Dissection and analyses revealed unexpected details of WCR abundance, movement patterns and reproductive biology. The reality of WCR behavior was very different from the optimistic expectations of the rootworm IRM plan, especially regarding beetle movement. Before corn pollination began, an average of 17 to 25% of adults left refuges for Bt corn each day. However, once corn began pollinating and female emergence peaked in the refuges, movement of WCR of both sexes nearly stopped. Though moving beetles traveled at substantial rates (up to 31 m/day), they represented just 3 to 10% of the population during and after pollination. Mate-seeking refuge males also did not live up to IRM expectations; they did not rapidly disperse into Bt corn. Instead, they stayed in refuge blocks where newly emerged, unmated females were already nearby and abundant. The normally delayed emergence of WCR females versus males (called protandry) was further delayed for beetles that developed on Bt plants. Thus, many refuge males emerged weeks before the females with whom they were intended to mate in Bt corn. Even if those refuge males had moved into


Bt corn, they would have been too old to mate by the time females emerged. This made mating between potentially-resistant individuals from Bt corn more likely than expected. The distribution of mating activity mirrored overall beetle abundance; both were concentrated in blocks of refuge corn. In fields with separate refuge blocks of refuge and Bt corn, there were few (ca. 9%) matings between partners from both sides of the fields, a.k.a. “mixed matings”. When these desired pairings occurred, they were found within a few rows of the interface between refuge and Bt corn areas. Where refuge plants were growing among Bt plants (i.e. seed blends), the distribution of mating pairs across fields was uniform. If poor dispersal of mate-seeking beetle into Bt corn was the problem with block refuges, integrating refuge plants into Bt corn should improve population mixing. However, analyses of beetles from those mating pairs revealed that regardless of whether the 5% of refuge plants were randomly scattered among Bt corn plants or deployed as a single refuge block on one side of a field, the percentage of mixed-matings between refuge and Bt beetles did not differ. Use of a seed blend to integrate refuge plants in to the Bt corn did not promote mixed-matings as anticipated. Movement and feeding data suggest that concentrations of beetles may persist in the near vicinity of isolated refuge plants in seed blends. Increasing the percentage of refuge plants in seed blends may be needed to put more refuge beetles much closer to Bt plants. These analyses indicated that expectations for rootworm behavior in Bt cornfields were wrong at nearly every turn. Unfulfilled assumptions were compounded by other failings. Critically, corn plants did not express Bt toxins at doses high enough to kill the expected proportion of WCR larvae in Bt corn and rootworm populations had higher than expected initial levels of Bt resistance. Another critical “man-made” problem was poor compliance with refuge planting. Only 75 to 80% of growers complied with rootworm refuge requirements (Jaffee 2010, Gray 2011). As a result, even before the susceptible beetles had a chance to “do their part” to ensure well-mixed populations of mixed-mating beetles, a myriad of problems with the plan had already compromised chances for its success.

Field-evolved Bt resistance Given the problems with refuge function, it is perhaps not surprising that field-evolved WCR resistance to Bt corn was first documented in Iowa during 2009 (Gassmann et al. 2011). Since then, rootworm resistance to multiple Bt toxins has been found across the U.S. Corn Belt. Beginning in 2012, Illinois WCR populations with resistance or significantly reduced susceptibility to multiple Bt toxins were documented from counties across the state. During 2013, broad areas in Livingston, Kankakee, and Ford counties (i.e. in east- and north-central Illinois) experienced severe injury in rotated Bt corn due to Bt-resistance in rotation-resistant WCR populations. Heightened awareness of Illinois Bt resistance, combined with greater adoption of pyramided Bt hybrids have likely prevented subsequent “surprises”. The role of extremely wet conditions during 2015 was also important; rootworm populations were reduced to historically low levels. They continue to slowly rebuild; however, abundance is still below levels likely to inflict economic injury in most areas. In spite of low population years, bioassays show that Illinois’ WCR beetles retain high levels of resistance to some Bt toxins. There is evidence of reduced susceptibility to all commercialized Bt toxins that have been used against corn rootworms (Tabashnik and Carrière 2017). In the coming year, a completely new mode of action (MOA) for rootworm management will be commercialized (Moar et al. 2017). Based on RNA interference (RNAi), the new MOA will kill larvae by interfering with expression of critical rootworm-specific genes. It will be pyramided with two Bt MOAs and deployed as a 5% seed blend (or with a 20% block refuge in cotton-growing areas) (EPA 2017). Hopefully, the lessons that rootworms have taught us will be applied to promote the long-term durability of RNAi corn hybrids for rootworm control.

92 — 2017 Integrated Crop Management Conference - Iowa State University References Branson, T.F., and J.L. Krysan. 1981. Feeding and oviposition behavior and life cycle strategies of Diabrotica: An evolutionary view with implications for pest management. Forum: Environ. Entomol. 10: 826-831. Chiang, H.C. 1965. Survival of northern corn rootworm eggs through one and two winters. J. Econ. Entomol. 58: 470-472. Dun, Z., P.D. Mitchell, and M. Agosti. 2010. Estimating Diabrotica virgifera virgifera damage functions with field trial data: Applying an unbalanced nested error component model. J. Appl. Entomol. 134: 409-419. Environmental Protection Agency. 2003. New corn pest control approved by EPA can lead to reduced pesticide use: Non-chemical alternative to conventional insecticides for control of corn rootworm. Online. Pesticides: Regulating Pesticides. Feb. 25, press release.https://yosemite.epa.gov/opa/ admpress.nsf/b1ab9f485b098972852562e7004dc686/b7a583e5ad62660c85256cd800729c3c?Op enDocument. Environmental Protection Agency. 2017. Registration decision for commercial use corn products containing the DvSnf7 dsRNA plant-incorporated protectant (Event 87411). Posted 6/14/2017. Docket ID:EPA-HQ-OPP-2014-0293-0407. Forbes, S.A. 1883. The corn root-worm. (Diabrotica longicornis, Say) Order Coleoptera. Family Chrysomelidae. Ill. State Entomol. Annu. Rep. 12: 10-31. Gassmann, A.J., J.L. Petzold-Maxwell, R.S. Keweshan, and M.W. Dunbar. 2011. Field-evolved resistance to Bt maize by western corn rootworm. PLoS ONE 6: e22629. DOI: 10.1371/journal.pone.0022629. Gillette, C.P. 1912. Diabrotica virgifera Lec. as a corn root-worm. J. Econ. Entomol. 5: 364-366. Gray M.E. 2011. Relevance of traditional Integrated Pest Management (IPM) strategies for commercial corn producers in a transgenic agroecosystem: A bygone era? J. of Agricultural and Food Chemistry 59: 5852-558. DOI:10.1021/jf102673s. Gray, M.E., T.W. Sappington, N.J. Miller, J. Moeser, and M.O. Bohn. 2009. Adaptation and invasiveness of western corn rootworm: Intensifying research on a worsening pest. Annu. Rev. Entomol. 54: 303321. Hughson, S.A. 2017. The movement behavior and reproductive ecology of western corn rootworm beetles (Coleoptera: Chrysomeliae) in Bt cornfields with structured and seed blend refuges. Ph.D. Dissertation. University of Illinois at Urbana-Champaign. 170 pp. Hughson, S.A., and J.L. Spencer. 2015. Emergence and abundance of western corn rootworm (Coleoptera: Chrysomelidae) in Bt cornfields with structured and seed blend refuges. J. Econ. Entomol. 108: 114-125. DOI: 10.1093/jee/tou029. Jaffe, G. 2009. Complacency on the farm: significant noncompliance with EPA’s refuge requirements threatens the future effectiveness of genetically engineered pest-protected corn. Center for Science in the Public Interest. Washington, D.C. 19 pp. www.cspinet.org. Levine, E. and H. Oloumi-Sadeghi. 1991. Management of diabroticite rootworms in corn. Ann. Rev. Entomol. 36: 229-255. Levine, E., J.L. Spencer, S.A. Isard, D.W. Onstad, and M.E. Gray. 2002. Adaptation of the western corn rootworm, Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae) to crop rotation: Evolution of a new strain in response to a cultural management practice. Am. Entomol. 48: 94107.


Metcalf, R.L. 1986. Foreword. In J. L. Krysan, and T. A. Miller (eds.), Methods for the study of pest Diabrotica, pg. vii - xv. Springer, New York, NY. Moar, W., C. Khajura, M. Pleau, O. Ilagan, M. Chen, C.J. Jiang, P. Price, B. McNulty, T. Clark, and G. Head. 2017. Cry3Bb1-resistant western corn rootworm, Diabrotica virgifera virgifera (LeConte) does not exhibit cross-resistance to DvSnf7 dsRNA. PLoS ONE 12(1): e0169175. https://doi.org/10.1371/ journal.pone.0169175 Moeser, J., and S. Vidal. 2005. Nutritional resources used by the invasive maize pest Diabrotica virgifera virgifera in its new South-east-European distribution range. Entomol. Exp. Appl. 144: 55-63. Pereira, A.E., H. Wang, S.N. Zukoff, L.J. Meinke, B.W. French and B.D. Siegfried. 2015. Evidence of fieldevolved resistance to bifenthrin in western corn rootworm (Diabrotica virgifera virgifera LeConte) populations in western Nebraska and Kansas. PLoS ONE 10(11): e0142299. doi:10.1371/journal. pone.0142299 Rice, M.E. 2004. Transgenic rootworm corn: assessing potential agronomic, economic, and environmental benefits. Plant Health Prog. DOI: 10.1094/PHP-2004-0301-01-RV Tabashnik, B.E. and Y. Carrière. 2017. Surge in insect resistance to transgenic crops and prospects for sustainability. Nature Biotechnology. 35(10): 926-935. Tabashnik, B.E. and F. Gould. 2012. Delaying corn rootworm resistance to Bt corn. J. Econ. Entomol. 105:767-776. Tabashnik, B.E., J.B.J. Van Rensburg and Y. Carrière. 2009. Field-evolved insect resistance to Bt crops: definition, theory, and data. J. Econ. Entomol. 102: 2011–2025. Tinsley, N.A., R.E. Estes, and M.E. Gray. 2013. Validation of a nested error component model to estimate damage caused by corn rootworm larvae. J. Appl. Entomol. 137: 161-169.



Resistance management plan for soybean aphid Erin Hodgson, associate professor and Extension entomologist, Entomology, Iowa State University

Introduction Soybean aphid remains the most important soybean insect pest in Iowa, and management over the last fifteen years has primarily relied on foliar insecticides. The economic injury level was defined by Ragsdale et al. (2007), and is approximately 675 aphids per plant or 5,560 cumulative aphid days. From that multistate research, a conservative economic threshold was developed to protect yield: 250 aphids per plant with 80% of the plants infested through the seed set plant growth stage (R5.5). The odds of making a profitable treatment decision are increased with regular scouting and applications made after exceeding the economic threshold (Johnson et al. 2009, Hodgson et al. 2012). The economic threshold is validated annually at Iowa State University (http://bit.ly/2erObl7) and is recommended regardless of fluctuating market values (Koch et al. 2016).

Insecticide resistance With any pest and pesticide interaction, exposures will eventually lead to resistance developing in the population. Insecticide resistance is common with aphids (http://www.irac-online.org/pests/), which are asexual, multigenerational pests in many crops. Since 2015, farmers in Minnesota experienced pyrethroid insecticides failures to control soybean aphid. Using a vial assay, entomologists in Minnesota confirmed soybean aphid resistance to bifenthrin and lambda-cyhalothrin. Pyrethroid failures were also documented in North Dakota and South Dakota in 2017 (Figure 1).

Figure 1. Counties with pyrethroid performance issues in 2017. Image created by Bruce Potter, University of Minnesota.

In 2016, one commercial field in northwest Iowa was confirmed to be resistant to pyrethroid. A combination of integrated pest management (IPM) and insect resistance management (IRM) tactics are needed to manage soybean aphid and prolong existing and emerging insecticide efficacy options.

96 — 2017 Integrated Crop Management Conference - Iowa State University Management recommendations Population fluctuations between locations and years are typical soybean aphid dynamics for Iowa. Regular scouting and timely use of foliar insecticides is still a reliable management tactic; however, recent changes in insecticide efficacy will make future management more complicated. My recommendation for sustainable soybean aphid management in Iowa is to:

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Consider using host plant resistant varieties if soybean aphid populations are persistent and the genetic traits are appropriate for the area. The use of a single resistant gene will result in lower cumulative aphid exposure and the use of a resistant pyramid (i.e., two or more genes) will greatly reduce the likelihood of needing foliar insecticides.

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Plant early if the field is in an area with persistent soybean aphid populations.

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Use a product labeled for soybean aphid, and use high volume and pressure so that droplets make contact with aphids on the undersides of leaves. Check aphid populations three days after application to assess product efficacy.

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Alternate the mode of action if soybean aphid populations need to be treated twice in a single growing season (e.g., organophosphates and pyrethroids).

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Understand that late-season accumulation of aphids, particularly after R5, may not impact yield like it does in early reproductive growth. A foliar insecticide applied after seed set may not be an economically profitable choice.

Scout for soybean aphid, especially during R1–R5, and use a foliar insecticide if aphids exceed the economic threshold of 250 per plant. Take note of natural enemies and other potential plant pests in addition to soybean aphid.

Before assuming insecticide resistance development in the field, rule out other possible factors, such as: misapplication of the product (incorrect rate, poor coverage, etc.), unfavorable weather conditions around the time of application (wind, rain, temperature), and pest recolonization. The overwintering and migratory behavior of soybean aphid is not fully understood. The magnitude of pyrethroid resistance for soybean aphid in the north central region is also not well characterized yet. In other words, the aphids that colonize soybean can come from different overwintering sites each year and the populations will have a range of susceptibly to insecticides.

References Hodgson, E. W., B. P. McCornack, K. Tilmon, and J. J. Knodel. 2012. Management recommendations for soybean aphid (Hemiptera: Aphididae) in the United States. Journal of Integrated Pest Management 3: 1-10. Johnson, K. D., M. E. O’Neal, D. W. Ragsdale, B. E. Potter, C. D. DiFonzo, S. M. Swinton, and E. W. Hodgson. 2009. Probability of cost-effective management of soybean aphid (Hemiptera: Aphididae) in North America. Journal of Economic Entomology 102: 2101-2108. Koch, R., B. Potter, P. Glogoza, E. Hodgson, C. Krupke, J. Tooker, C. DiFonzo, A. Michel, K. Tilmon, T. Prochaska, J. Knodel, R. Wright, T. Hunt, B. Jensen, A. Varenhorst, B. McCornack, K. Estes, and J. Spencer. 2016. Biology and economics of recommendations for insecticide-based management of soybean aphid. Plant Health Progress 17: 265-269. Ragsdale, D. W., B. P. McCornack, R. C. Venette, B. D. Potter, I. V. MacRae, E. W. Hodgson, M. E. O’Neal, K. D. Johnson, R. J. O’Neil, C. D. DiFonzo, T. E. Hunt, P. A. Glogoza, and E. M. Cullen. 2007. Economic threshold for soybean aphid. Journal of Economic Entomology 100: 1258-1267.


Iowa monarch conservation, pest management and crop production Steven P. Bradbury, professor, Natural Resources Ecology and Management, Iowa State University; Tyler Grant, postdoctoral research associate, Natural Resources Ecology and Management, Iowa State University; Niranjana Krishnan, graduate student, Entomology, Iowa State University

Introduction The monarch butterfly population has experienced an 80% decline in North America over the past two decades (Brower et al., 2012; Pleasants and Oberhauser, 2013; Jepsen et al., 2015). The three to four hectares of occupied overwintering forest in 2016 and 2015 was well below a target of six hectares needed to support a resilient population and reduce the risk of quasi-extinction (loss of the North American migration) in the next 10 to 20 years (Semmens et al., 2016). In response to a petition to evaluate the status of the species, the U.S. Fish and Wildlife Service (USFWS) is evaluating listing the monarch as a threatened species under the Endangered Species Act (USFWS, 2014 a, b). Under a court-supervised schedule, the USFWS must propose a listing decision in June 2019, which underscores the urgency of establishing viable, voluntary, state-based monarch conservation programs to provide USFWS a credible rationale to not list the species. If the monarch butterfly is listed, it could lead to significant regulatory and management burdens for farmers and livestock producers. Increasing summer reproductive success in the North Central United States has been identified as a high priority for monarch conservation (Oberhauser et al. 2017; Flockhart et al. 2015). Up to 58% of the monarchs overwintering in Mexico originate in Iowa and neighboring states in the North Central U.S. (Flockhart et al. 2017). Monarch butterflies oviposit only on milkweed species (mainly Asclepias spp.) and primarily on common milkweed (Asclepias syriaca) in the North Central states (Malcolm et al. 1993). To increase monarch populations to levels that would reduce the probability of quasi-extinction by 50% over 20 years, Thogmartin et al. (2017) estimated that 1.3 to 1.8 billion additional milkweed stems need to be added to the North Central U.S. landscape. The current amount of common milkweed stems in the North Central U.S. is estimated to be approximately 1.3 billion, the majority of which is in publically owned grasslands, land enrolled in conservation programs, such as the United States Department of Agriculture’s Conservation Reserve Program (CRP), crop field borders, road rights-of-way, urban and suburban land cover, and miscellaneous non-agricultural habitat (Pleasants and Oberhauser 2013; Thogmartin et al. 2017). Analyses by Thogmartin et al. (2017) indicate that adding 1.3 to 1.8 billion new milkweed stems in the North Central U.S. will require an ‘all hands on deck’ approach across all land cover/land use categories. Habitat conservation goals can only be achieved through significant adoption of habitat restoration in privately owned land in agricultural landscapes. Establishment of milkweed and forage plants in rural roadsides; marginal crop land; portions of existing CRP land, pastures and grassland; and grassy areas bordering crop fields, will create a patchwork of habitat within agricultural landscapes dominated by corn and soybean production.

Overview of monarch conservation efforts in Iowa In 2015, in response to the decline of the monarch butterfly, Iowa commodity and livestock organizations, pesticide technology providers, utility companies, city and county conservation organizations, Iowa colleges and universities, the Iowa Department of Agriculture and Land Stewardship, Iowa Department of Natural Resources and the College of Agriculture and Life Sciences at Iowa State University formed the Iowa Monarch Conservation Consortium (https://monarch.ent.iastate.edu). The consortium currently

98 — 2017 Integrated Crop Management Conference - Iowa State University includes 42 participating organizations, with USFWS and USDA serving as ex officio members. The mission of the Iowa Monarch Conservation Consortium is to enhance monarch butterfly reproduction and survival in Iowa through collaborative and coordinated efforts of farmers, private citizens and their organizations through research, education and direct action. The consortium’s outreach and extension efforts draw upon all the member organizations to ensure the broad delivery of practical, science-based information on monarch butterfly conservation practices for Iowa’s landscapes. Habitat improvements in rural landscapes are being designed to target underutilized areas that do not conflict with agricultural production, are sufficient in scale to support improved monarch breeding success, and strive to complement other conservation programs. In this regard, monarch habitat will be beneficial for pollinators and other wildlife species and can be ‘stacked’ with other conservation practices designed to reduce soil erosion and minimize nutrient runoff. The consortium published Version 1.0 of Iowa Monarch Conservation Strategy in February 2017 (https://monarch.ent.iastate.edu/files/file/ iowa-monarch-conservation-strategy.pdf). Version 2.0 of the strategy is under development and will include Iowa’s habitat goals, which will be integrated with neighboring North Central States’ habitat targets (see: http://www.mafwa.org/?page_id=2347). The research arm of the Iowa Monarch Conservation Consortium is based on the campus of Iowa State University and includes University and USDA scientists, technicians, graduate and undergraduate students. Below is a synopsis of ongoing demonstration and research activities.

Habitat establishment practices A seed mix has been developed that includes three milkweed species and several nectar-producing forbs that benefit butterflies and bees (Appelgate et al. 2017). Grass species included in the mix are mostly short to medium height in order to reduce competition with forbs. All species are native to Iowa and should thrive in well-drained and moderately-drained soils. This seed mix has been used to establish monarch habitat at 46 demonstration sites. Thirteen demonstration sites are associated with establishment of approved edge-of-field practices in the Iowa Nutrient Reduction Strategy, including six saturated buffers sites and seven bioreactors sites. The remaining sites are in underutilized grass areas (21 sites) and land near hog confinements (12 sites). Supporting experiments are ongoing to develop methods to establish milkweed into land dominated by smooth brome grass, Bromus inermis. Research completed at Iowa State University (Pocius et al. 2017 a, b) indicate a diversity of milkweed species including: Asclepias exaltata (Poke Milkweed), A. hirtella (Tall Green Milkweed), A. incarnata (Swamp Milkweed), A. speciosa (Showy Milkweed), A. sullivantii (Prairie Milkweed), A. syriaca (Common Milkweed), A. tuberosa (Butterfly Milkweed), A. verticillata (Whorled Milkweed), and Cynanchum laeve (Honeyvine Milkweed) could benefit monarchs in their summer range. Ultimately, monarch seed mixes and planting guidelines will provide farmers and landowners practical and reliable methods to expand the benefits of existing groundcover to promote monarch caterpillar development and provide nectar sources for monarch butterfly adults. Pollinators and other wildlife species also will benefit from the increased diversity of plant species.

Monarch population modeling Research is needed to better understand how habitat patch size, composition, and spatial arrangements (Oberhauser et al. 2017; Pleasants and Oberhauser, 2013; Zalucki et al. 2001, 2002; Zalucki and Lammers 2010; Zalucki et al. 2016) influence monarch survival and breeding success. This understanding provides a foundation for evaluating how different habitat patch distributions influence monarch population growth rates, which in turn supports conservation planning. Modeling is a useful tool for investigating how monarch butterfly reproductive performance, larval development, and population growth respond to


habitat plant composition and quality, including milkweed stem density and spatial arrangement; habitat patch size; and habitat fragmentation patterns within a spatially explicit landscape. Previous modeling efforts in artificial landscapes have identified monarch perceptual range and habitat attractiveness, which influences the probability a butterfly will choose a given habitat patch, as important data inputs (Zalucki et al. 2016). Modeling efforts to date (Zalucki et al. 2016, Zalucki and Lammers, 2010) have also identified the important role individual, lone milkweed plants and small low-density patches of milkweed play in increasing the number of monarch eggs laid on a landscape. To further evaluate and advance understanding of how female monarchs utilize breeding habitat in Iowa, we have developed a model that uses spatially explicit landscapes that reflect current habitat conditions and spatial patterns (Grant et al., 2017a). The model can incorporate results from on-going studies that are refining estimates of monarch perceptual range and flight patterns (Fisher et al., 2017a, b).

Estimating risk of herbicides and insecticides Monarch conservation benefits and risks associated with restoring habitat near conventional crop fields, due to potential pesticide exposure, are uncertain. The National Resources Conservation Service (NRCS) Monarch Butterfly Wildlife Habitat Evaluation Guide (2016) discourages placement of monarch breeding habitat within 38m (125ft) of crop fields that are treated with herbicides or insecticides; a risk-based rationale for the buffer size is not available. Employing a buffer of this size would result in a significant area of land unavailable for the establishment of breeding habitat. In Story County, Iowa, a 38m buffer around corn and soybean fields would eliminate approximately 84% of rural roadside habitat and 38% of grassland, Conservation Reserve Program (CRP) land, pastures, railroad rights-of-way, riparian corridors and wetlands. Based on possible pesticide use patterns, do the potential risks from pesticide exposure reduce the benefits of establishing habitat in close proximity to crop fields?

Herbicides The effect of nonlethal herbicide injury to milkweed on monarch oviposition and larval development has not been investigated. Utilization of forbs by pollinators was reduced by exposure to low concentrations of herbicides (Bohnenblust et al. 2016), largely due to fewer floral resources. The mechanism of toxicity for growth-regulator herbicides may be due to the stimulation of abscisic acid and ethylene production in affected plants (Grossman 2009). At sublethal doses, the growth-regulator herbicides cause responses such as epinasty and malformed leaves. Since a high percentage of milkweed occurs adjacent to crop fields, there is a need for an off-target risk assessment of herbicides on the value of milkweed to monarchs. Such an assessment is particularly necessary for evaluating the value of new habitat patches. Common milkweed persists in crop fields due to perennial rootstocks that are resilient to most herbicides; post-emergence herbicides can cause significant damage to shoots present at the time of application. Hartzler and Lizotte-Hall (Department of Agronomy, ISU) are undertaking dose response studies to determine the effect of fomesafen, a herbicide commonly used on soybeans, on biomass production of common milkweed. Field experiments are determining how herbicide injury influences utilization of common milkweed by adult monarchs. Fomesafen is being used to evaluate effects of in-field herbicide use, whereas dicamba is being used to study effects of off-field exposure.

Insecticides Insecticides for managing insect corn and soybean pests include organophosphate, pyrethroid, neonicotinoid, and anthranilic diamide (Hodgson and VanNostrand G 2016; University of Tennessee 2016; DuPont 2010). Monarch larvae in existing and newly established milkweed patches near crop fields could be exposed to insecticide spray drift following applications to manage soybean aphid (Aphis glycines Matsumura) between mid-July and mid-September (Iowa State University 2016), which coincides with estimated peak larval abundance of the 2nd and 3rd monarch generations in Iowa (Pleasants 2015; Prysby

100 — 2017 Integrated Crop Management Conference - Iowa State University and Oberhauser 2014; Nail et al. 2015). With expanding use of cover crops in Iowa, potential economic injury to corn from true armyworm (Mythimna unipuncta Haworth) has been observed, with foliar insecticide applications documented between mid-May and late June (Dunbar et al 2015), which overlaps with the 1st and 2nd generation larvae in Iowa. Monitoring studies designed to systematically document insecticide levels on milkweeds following foliar application are not available; however, models used by the U.S. EPA, such as AgDRIFT (USEPA 2017), indicate that spray drift exposure to milkweed up to 38m or more downwind is likely. Monarch larvae could also be exposed to insecticides through ingestion of milkweed. Corn and soybean are typically planted with neonicotinoid-treated seed (Douglas and Tooker 2015), including 70% of soybean acres in Iowa (Hodgson et al. 2016). Chlorantraniliprole also is entering the market as a corn seed treatment option (Corn and Soybean Digest 2015). Imidacloprid, clothianidin, and thiamethoxam have moved into Iowa streams (Hladik et al 2014), presumably due to subsurface flow (Hladik et al 2017), which raises concerns that plants downslope of cropped fields could absorb neonicotinoids systemically. Several studies (Botias et al. 2016, 2015; David et al. 2016; Krupke et al. 2012; Long and Krupke 2016; Paola and Kaplan 2015; Pecenka and Lundgren 2015) indicate a variety of non-crop plants, including milkweed, in the margins of fields previously sowed with neonicotinoid-treated seeds can have detectable levels of imidacloprid, clothianidin, and thiamethoxam in leaves, pollen and nectar; although the frequency of detections and concentrations are highly variable. There is a paucity of monarch toxicity data to interpret the significance of insecticide exposures due to spray drift or systemic uptake by milkweed. Consequently we are examining the toxicity of six representative insecticides: beta-cyfluthrin (pyrethroid, foliar uses); chlorpyrifos (organophosphate, foliar); imidacloprid (neonicotinoid, seed treatment and foliar); clothianidin (neonicotinoid, seed treatment); thiamethoxam (neonicotinoid, seed treatment and foliar); and chlorantraniliprole (anthranilic diamide, seed treatment and foliar) on monarch larvae, eggs, pupae and adults (Krishnan et al., 2017 a, b, c). Larvae, eggs and chrysalides are being treated topically to mimic exposure to a spray drift plume. Larvae and adults are also being exposed orally to mimic insecticide exposure from milkweed leaves (larvae - systemic uptake and spray drift deposition) and nectar of flowering plants (adults - systemic uptake), respectively. Dose response curves for mortality and growth/development are being compared to exposure levels obtained from spray drift models, on-going residue surveys, and existing ecological risk assessments and open literature studies. These analyses provide the means to assess risks at the habitat patch scale.

Predicting landscape-scale risk of insecticide exposure to monarch populations Using instar-specific, 96-hour mortality dose-response curves for beta-cyfluthrin, chlorpyrifos, imidacloprid, thiamethoxam and chlorantraniliprole and estimates of exposure using EPA’s AgDRIFT model, we have quantified spray drift risks at distances up to 38m downwind from treated fields following simulated aerial and ground applications (Krishnan et al 2017 a, b, c). In general, predicted mortality rates downwind were highest for chlorantraniliprole and beta-cyfluthrin and lowest for thiamethoxam and imidacloprid. To assess monarch population responses at a landscape scale we created a GIS layer that delineates areas within 38m of crop fields in Story County, Iowa. This layer was incorporated into the model described previously. A projection model is used to estimate survival from eggs to adults. Estimates of adult recruitment over a 10-year period are derived under three scenarios: a) no new milkweed planted within 38m of crop fields; b) milkweed patches placed within 38m of crop fields, with no insecticide exposure; and c) milkweed patches placed within 38m of crop fields with spray drift exposure. Different frequencies of economically-significant pest-pressure and associated foliar applications using typical wind direction and speed are used in the simulations (Bradbury et al. 2017; Grant et al 2017b). In future studies, the projection model will incorporate larval mortality rates based on systemic insecticide uptake in milkweed downslope of fields planted with treated corn or soybean seeds. Estimates of landscape-scale adult recruitment under varying spatial-temporal pest management scenarios provide the means to evaluate monarch conservation costs and benefits of establishing habitat in areas potentially exposed to insecticides.


Summary The USFWS is committed to make a decision regarding whether to list the monarch butterfly as a threatened species by June of 2019. On-going research is enhancing the scientific basis for supporting monarch conservation practices by elucidating relationships between landscape-level habitat patterns and monarch movement, survival, reproduction, and development in the context of pest management practices. Coordinated research, extension and outreach provides a foundation for advancing and implementing a proactive, science-based conservation program that will enhance North American recovery of the monarch while maintaining efficient agriculture and livestock production.

Acknowledgments Members of the Iowa State University monarch research team include: C. Abel, S. Appelgate, K. Bidne, R. Bitzer, T. Blader, S. Blodgett, D. Debinski, K. Fisher, T. Grant, R. Hartzler, R. Hellmich, N. Krishnan, S. Lizotte-Hall, J. Pleasants, V. Pocius, J. Pohl, D. Schweitzer, and N. Shryock. Special thanks to J. Appelhans, H. Arthur, J. Chavez-Martinez, K. Claus, W. Douglas, A. Euken, C. Haggard, , S. Hilby, B. Hoey, E. Kenyon, A. Kerker, N. Oppedal, J. Pfeiffer, R. Nylin and M. Van Loon, for technical help. The research and demonstration efforts summarized here are supported by two NRCS Conservation Innovation Grants (69-6114-15-006; 69-3A75-16-006), grants from the Iowa Soybean Association, Iowa Pork Producers Association, and the Garden Club of America, as well as the Iowa Monarch Conservation Consortium and the College of Agriculture and Life Sciences, Iowa State University.

References Appelgate, S., Blodgett, S., Bradbury, S., Debinski, D., Hartzler, R., Pleasants, J., Schweitzer, D. & Hellmich, R. (2017) Monarch Seed Mix: High Diversity. Iowa State Universtiy Extension and Outreach. ENT 0047. Bohnenblust E.W., Vaudo A.D., Egan J.F., et al (2016) Effects of the herbicide dicamba on nontarget plants and pollinator visitation. Environmental Toxicology and Chemistry 35:144–175. Botias C., David A., Hill E.M., Goulson D. (2016) Contamination of wild plants near neonicotinoid seedtreated crops, and implications for non-target insects. Sci Total Environ 566–567:269–278. Botías, C. D, A., Horwood J., Abdul-Sada A., et al (2015) Neonicotinoid residues in wildflowers, a potentialroute of chronic exposure for bees. Environ Sci Technol 49:12731–12740. Bradbury S.P., Grant, T.J., Krishnan, N. (2017) Landscape Scale Estimates of Monarch Butterfly (Danaus plexippus) Population Responses to Insecticide Exposure in an Iowa Agroecosystem. Society of Environmental Toxicology and Chemistry North America 38th Annual Meeting. November 12 -16, Minneapolis, MN. Brower L.P., Taylor O.R., Williams E.H., et al (2012) Decline of monarch butterflies overwintering in Mexico: Is the migratory phenomenn at risk? Insect Conserv Divers 5:95–100. Corn and Soybean Digest (2015) New seed treatment, high-tech corn coming to market. David A., Botías C., Abdul-Sada A., et al (2016) Widespread contamination of wildflower and bee-collected pollen with complex mixtures of neonicotinoids and fungicides commonly applied to crops. Environ Int 88:169–178. Douglas M.R., Tooker J.F. (2015) Large-scale deployment of seed treatments has driven rapid increase in use of neonicotinoid insecticides and preemptive pest management in U.S. field crops. Environ Sci

102 — 2017 Integrated Crop Management Conference - Iowa State University Technol 49:5088–2097. Dunbar M.W., O’Neal M.E., Gassmann A.J. (2016) Increased risk of insect injury to corn following rye cover crop. J Econ Entomol 109:1697–1697. DuPont (2010) Supplemental labeling. DupontTM CoragenR Insect Control. For foliar insect control on artichoke, asparagus, corn (field, pop, sweet), legume vegetables, okra, strawberry, sugarcane, tobacco & tuberous and corm vegetables. R-1070-02810 01-26-10. Du Pont Nemours Company, Crop Prot Wilmington, DE 4pp. Fisher, K.E., Adelman, J., and Bradbury, S.P. (2017a) Estimating perceptional range of the monarch butterfly (Danaus plexippus) with an automated radio telemetry system. Oral presentation. Annual meeting of the Entomological Society of America. November 5 – 8, 2017, Denver, CO. Fisher, K.E., Adelman, J., and Bradbury, S.P. (2017b) Testing methods for tracking monarch butterfly movement with radio telemetry. Poster presentation. Annual Meeting of the Ecological Society of America. August 6 – 11, 2017. Portland, OR. Flockhart D.T., Brower L.P., Ramirez M.I., et al (2017) Regional climate on the breeding grounds predicts variation in the natal origin of monarch butterflies overwintering in Mexico over 38 years. Glob Chang Biol 1–12. doi: 10.1111/gcb.13589 Flockhart D.T.T., Pichancourt J.B., Norris D.R., Martin T.G. (2015) Unravelling the annual cycle in a migratory animal: Breeding-season habitat loss drives population declines of monarch butterflies. J Anim Ecol 84:155–165. doi: 10.1111/1365-2656.12253 Grant T., Parry, H., R., Zalucki, M.J., and Bradbury, S.P. (2017a) Predicting monarch butterfly movement and egg laying with a spatially-explicit agent-based model: The role of monarch perceptual range and spatial memory. Ecological Modelling (Submitted). Grant, T.J., Krishnan, N. and Bradbury, S.P. (2017b) Spatially-explicit estimates of monarch butterfly (Danaus plexippus) population responses to insecticide spray drift exposure in north central agroecosystems. Poster presentation. Annual meeting of the Entomological Society of America. November 5 – 8, 2017, Denver, CO. Grossman K. (2009) Auxin herbicides: Current status of mechanism and mode of action. Pest Manag Sci 66:113–120. Hodgson E.W., Kemis M., Geisinger B. (2012) Assessment of Iowa soybean growers for insect pest management practices. J Extenstion 50:4RIB6. http://www.joe.org/joe/2012august/pdf/JOE_ v50_4rb6.pdf Hodgson E.W., VanNostrand G. (2016) Soybean aphid efficacy screening program, 2015. Arthropod Manag Tests Section F:1–3. Iowa State University (2016) Soybean aphid efficacy evaluations 2016-2005. http://www.ent.iastate.edu/ soybeanresearch/content/extension. Jepsen S., Schweitzer D.F., Young B., et al (2015) Conservation status and ecology of the monarch butterfly in the United States. http://www.natureserve.org/sites/default/files/natureserve-xerces_monarchs_ usfs-final.pdf. Krishnan, N., Bidne, K., Hellmich, R., Coats, J. and Bradbury, S.P. (2017a) Risk assessment of insecticides commonly used in corn and soybean production on monarch butterfly (Danaus plexippus) larvae. Society of Environmental Toxicology and Chemistry North America 38th Annual Meeting. November 12 -16, Minneapolis, MN.


Krishnan, N., Bidne, K., Hellmich, R., Coats, J. and Bradbury, S.P. (2017b) Risk assessment of foliar insecticides commonly used in corn and soybean production on monarch butterfly (Danaus plexippus) larvae. Oral presentation. Annual meeting of the Entomological Society of America. November 5 – 8, 2017, Denver, CO. Krishnan, N., Bidne, K., Hellmich, R., Coats, J. and Bradbury, S.P. (2017c) Risk assessment of foliar insecticides commonly used in corn and soybean production on monarch butterfly (Danaus plexippus) larvae. Poster presentation. 254th American Chemical Society National Meeting. August 20-24, 2017. Washington, DC. Krupke C.H., Hunt G.J., Eitzer B.D., et al (2012) Multiple routes of pesticide exposure for honey bees living near agricultural fields. PLoS One. 7(1) doi: http://doi.org/10.1371/journal.pone.0029268. Long E.Y., Krupke C.H. (2016) Non-cultivated plants present a season-long route of pesticide exposure for honey bees. Nat Communiations 7:11629. Malcolm, S.B., Cockrell, B.J., and Brower, L.P. 1993. Spring recolonization of eastern North America by the monarch butterfly: successive brood or single sweep migration? In S. B. Malcolm, & M. P. Zalucki (Eds.), Biology and Conservation of the Monarch Butterfly (pp. 253-267). Natural History Museum of Los Angeles County; Science Series, 38. Nail K.R., Stenoien C., Oberhauser K.S. (2015) Immature monarch survival: effects of site characteristics, density, and time. Ann Entomol Soc Am 108:680–690. doi: 10.1093/aesa/sav047. NRCS (2016) Monarch Butterfly Wildlife Habitat Evaluation Guide and Decision Support Tool: Midwest Edition 1.1. https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/plantsanimals/ pollinate/?cid=nrcseprd402207. Oberhauser K., Wiederholt R., Diffendorfer J.E., et al (2017) A trans-national monarch butterfly population model and implications for regional conservation priorities. Ecol Entomol 51–60. doi: 10.1111/ een.12351. Paola O.A., Kaplan I. (2015) Non-target effects of agrochemicals on milkweed and monarch butterflies. 63rd Annu. Meet. Entomol. Soc. Am. Nov. 15-18, Minneapolis, MN. Pecenka J.R., Lundgren J.G. (2015) Non-target effects of clothianidin on monarch butterflies. Sci Nat. doi: 10.1007/s00114-015-1270-y Pleasants J.M., Oberhauser K.S. (2013) Milkweed loss in agricultural fields because of herbicide use: Effect on the monarch butterfly population. Insect Conserv Divers 6:135–144. doi: 10.1111/j.17524598.2012.00196.x Pleasants J.M. (2015) Monarch Butterflies and Agriculture. In: Oberhauser KS, Nail KR, Altizer S (eds) Monarchs in a Changing World. Cornell Univeristy Press, Ithaca, pp 169–178. Pocius, V.M., Debinski, D.M., Bidne, K.G., Hellmich, R.L., & Hunter, F.K. (2017a). Performance of early instar monarch butterflies (Danaus plexippus L.) on nine milkweed species native to Iowa. The Journal of the Lepidopterists’ Society, 71(3), 153-161. Pocius, V.M., Debinski, D.M., Pleasants, J.M., Bidne, K.G., Hellmich, R.L., & Brower, L.P. (2017b). Milkweed matters: Monarch butterfly (Lepidoptera: Nymphalidae) survival and development on nine midwestern milkweed species. Environmental Entomology. doi.org/10.1093/ee/nvx137. Prysby M.D., Oberhauser K.S. (2004) Temporal and geographic variation in monarch densities: Citizen scientists document monarch population patterns. In: Monarch butterfly Biology and Conservation. pp 9–20.

104 — 2017 Integrated Crop Management Conference - Iowa State University Semmens B.X., Semmens D.J., Thogmartin W.E., et al (2016) Quasi-extinction risk and population targets for the eastern, migratory population of monarch butterflies (Danaus plexippus). Sci Rep 6:1–7. doi: 10.1038/srep23265. Thogmartin W.E., López-Hoffman L., Rohweder J., et al (2017) Restoring monarch butterfly habitat in the Midwestern US: “all hands on deck.” Environ Res Lett 12:74005. doi: 10.1088/1748-9326/aa7637 University of Tennessee Extension (2016) Insect control recommendations for field crops. https://extension. tennessee.edu/publications/Pages/default.aspx. USEPA (2017) Atmospheric Models. https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/ models-pesticide-risk-assessment#atmospheric. USFWS (2014a) Monarch Butterfly Listing Petition. http://www.biologicaldiversity.org/species/ invertebrates/pdfs/Monarch_ESA_Petition.pdf USFWS (2014b) Monarch Bufferfly, Status Review. http://www.regulations.gov/#!documentDetail;D=FWSR3-ES-2014-0056-0001. Zalucki M.P., Parry H.R., Zalucki J.M. (2016) Movement and egg laying in Monarchs: To move or not to move, that is the equation. Austral Ecol 41:154–167. doi: 10.1111/aec.12285 Zalucki M.P., Lammers J.H. (2010) Dispersal and egg shortfall in Monarch butterflies: What happens when the matrix is cleaned up? Ecol Entomol 35:84–91. doi: 10.1111/j.1365-2311.2009.01160.x Zalucki M.P., Clarke A.R., Malcolm S.B. (2002) Ecology and behavior of first instar larval Lepidoptera. Annu Rev Entomol 47:361–393. Zalucki M.P., Malcolm S.B., Paine T.D., et al (2001) It’s the first bites that count: Survival of first-instar monarchs on milkweeds. Austral Ecol 26:547–555. doi: 10.1046/j.1442-9993.2001.01132.x


Where do we stand with soybean cyst nematode, resistance, and seed treatments? Gregory Tylka, professor, Plant Pathology and Microbiology, Iowa State University The soybean cyst nematode (SCN), Heterodera glycines, is a major yield-limiting factor of soybean in the United States and Canada (table 1). One thing contributing to the large amount of damage caused by SCN is its widespread distribution. The nematode has been found in every soybean-producing state in the United States except West Virginia and in all Iowa counties (figure 1) (Tylka and Marett 2017). And results of repeated random surveys of Iowa done in the 1990s (Workneh et al. 1999) and in the mid 2000s and in 2017 (Tylka unpublished) indicate SCN is present in 60% to 70% or more of the fields in Iowa. Table 1. Five most destructive soybean diseases and most recent estimated annual yield losses in the northern United States (IL, IN, IA, KS, MI, MN, NE, ND, OH, PA, SD, WI) and Ontario, Canada, from 2011 to 2014. Yield loss values are expressed in thousands of bushels. (Source: Allen et al. 2017) 2011

2012

2013

2014

Rank

Disease

Loss

Disease

Loss

Disease

Loss

Disease

Loss

1

SCN

90,525

SCN

118,697

SCN

112,394

SCN

108,008

2

Seedling diseases

46,847

Charcoal rot

59,481

Seedling diseases

43,672

Seedling diseases

60,305

3

Phytophthora

33,180

Phytophthora

23,950

Charcoal rot

31,865

SDS

46,815

4

Charcoal rot

29,403

Seedling diseases

23,642

Phytophthora

29,134

white mold

40,709

5

SDS

22,835

SDS

21,831

SDS

20,391

Phytophthora

32,864

SCN = soybean cyst nematode; SDS = sudden death syndrome; Phytophthora = Phytophthora root and stem rot; Seedling diseases = diseases caused by Fusarium, Phomopsis, Pythium, and/or Rhizoctonia species; white mold = Sclerotinia stem rot.

Managing with resistant soybean varieties Resistant soybean varieties have been a very effective and economical means of managing SCN for several decades. The resistant varieties allow relatively low SCN reproduction and produce profitable yields compared to susceptible varieties (Tylka et al. 2016). And the cost of seed of resistant soybean varieties is no more than seed of susceptible (non-resistant) varieties. Unfortunately, a great majority (97%) of the SCN-resistant soybean varieties available for Iowa have contained resistance genes from a single breeding line or source of resistance, called PI 88788 (McCarville et al. 2017, Tylka and Mullaney 2017). A similar situation exists with SCN-resistant varieties in many other states in the north central United States.


Figure 1. Known distribution of the soybean cyst nematode in the United States and Canada as of 2017 (Source: Tylka and Marett. 2017)

Growing soybean varieties with the same SCN resistance genes year after year is akin to using a single pesticide active ingredient on populations of insects, fungi, or weeds year after year. Eventually, the pest population can build up resistance to the pesticide or resistance genes. And such is the case with SCN populations in Iowa and PI 88788 SCN resistance genes. Throughout the 1990s, resistant varieties with the PI 88788 source of resistance were effective, holding Iowa SCN populations to less than 10% reproduction. But beginning in the early 2000s, reproduction of Iowa SCN populations began increasing on varieties with PI 88788 resistance, and levels of 50% or greater reproduction now are not uncommon (McCarville et al. 2017). Farmers are advised to grow soybean varieties with different sources of resistance and also to grow different soybean varieties with the common PI 88788 source of resistance to delay the build-up of resistancebreaking populations of SCN. Farmers also should have fields sampled to determine and monitor the levels or population densities of SCN present in the soil.


Managing with nematode-protectant seed treatments Nematode-protectant seed treatments are a relatively new management option for SCN. The first products that became available, in the 2000s, were Avicta, N-Hibit, and Votivo. Several more products were released in subsequent years, and currently there are seven choices (table 2), with more new products likely to come in future years. Table 2. Characteristics of currently available nematode-protectant seed treatments. (Source: Bissonnette and Tylka 2017)

ppns = plant-parasitic nematodes; SCN = soybean cyst nematode; RKN = root-knot nematode; reniform = reniform nematode; lesion = root-lesion nematode.

An interesting aspect of nematode-protectant seed treatments is that each product has a different active ingredient and mode of action. Some products have chemicals and others have biological organisms as active ingredients. Many, but not all, of the seed treatments have direct effects on SCN. Some products are very specific for SCN, such as Clariva, whereas some have activity against several species of plant-parasitic nematodes. Most of the nematode-protectant seed treatments are not sold as stand-alone products, rather are offered bundled on top of seed insecticides and fungicides. Use of nematode-protectant seed treatments may reduce SCN reproduction, may increase soybean yields, may have both effects, or may have no effect (figure 2). The results obtained when using these seed treatments undoubtedly will vary among the different products and likely also will vary among growing seasons and, perhaps, among soil environments and other yet-to-be-identified factors.


Figure 2. Possible effects of nematode-protectant seed treatments on soybean yields and SCN population densities. Results will vary among seed treatment products and possibly across environments and soil conditions.

Conclusion Because of the high reproductive abilities of SCN on soybeans and its very effective long-term survival in the soil in the absence of host plants, successful long-term management requires coordinated use of all available management tactics, which include growing nonhost crops, growing resistant soybean varieties, and using nematode-protectant seed treatments.

References Allen, T.A. and 42 co-authors. 2017. Soybean yield loss estimates due to diseases in the United States and Ontario, Canada, from 2010 to 2014. Plant Health Progress 18:19-27. dx.doi.org/10.1094/PHPRS-16-0066 Bissonnette, K.M. and Tylka, G.L. 2017. Seed treatments for soybean cyst nematode. Extension Publication CROP 3142. Iowa State University, Ames. https://store.extension.iastate.edu/product/15311 McCarville, M.C., Marett, C.C., Mullaney, M.P., Gebhart, G.D., and Tylka, G.L. 2017. Increase in soybean cyst nematode virulence and reproduction on resistant soybean varieties in Iowa from 2001 to 2015 and its effects on soybean yields. Plant Health Progress 146-155. dx.doi.org/10.1094/PHPRS-16-0062 Tylka, G.L., Gebhart, G.D., Marett, C.C., and Mullaney, M.P. 2016. Evaluation of soybean varieties resistant to soybean cyst nematode in Iowa – 2016. Extension Publication IPM 52. Iowa State University, Ames. https://store.extension.iastate.edu/product/2429 Tylka, G.L. and Marett, C.C. 2017. Known distribution of the soybean cyst nematode, Heterodera glycines, in the United States and Canada, 1954 to 2017. Plant Health Progress 18:167-168. dx.doi. org/10.1094/PHP-05-17-0031-BR


Tylka, G.L. and Mullaney, M.P. 2017. Soybean cyst nematode-resistant soybeans for Iowa. Extension Publication PM 1649. Iowa State University Extension, Ames. https://store.extension.iastate.edu/ product/5154 Workneh, F., Tylka, G.L., Yang, X.B., Faghihi, J., and Ferris, J.M. 1999. Regional assessment of soybean brown stem rot, Phytophthora sojae, and Heterodera glycines using area-frame sampling: Prevalence and effects of tillage. Phytopathology 89:204-211.



Status of bacterial leaf streak of corn in the United States Kirk Broders, assistant professor, Bioagricultural Sciences and Pest Management, Colorado State University The bacterial pathogen Xanthomonas vasicola pv vasculorum (Xvv), which causes the disease bacterial leaf streak (BLS), was officially reported on maize in the U.S. in 2016 (Korus et al. 2017). This represents the first report of this disease in North American and is the only report of the disease anywhere in the world outside of South Africa. The disease was observed in 51 counties in Nebraska as well as 6 counties in eastern Colorado and 16 counties in western Kansas. The disease has continued to expand in 2016 reaching epidemic proportion in regions of Colorado, Kansas and Nebraska with several fields reporting disease incidence levels above 90% and disease severity reaching greater than 50% of leaf area infected. This level of disease will likely have an impact on yield. The disease was also recently identified from maize fields in Iowa, Illinois, Oklahoma, South Dakota and Texas. The rapid penetration of Xvv into the U.S. maize production region combined with a lack of management methods has created a critical and urgent need for research and engagement with affected producers and allied industry.

Figure 1. Symptoms of bacterial leaf streak caused by Xvv early in the season on lower leaves (upper left) and later in the season on the upper third of the plant (lower left, right)

Xvv causes bacterial leaf streak (BLS), which is primarily a foliar disease of corn. Growers in southwestern Nebraska first began to notice symptoms of BLS as early as 2014. The disease was observed to be the most severe in semi-arid regions, such as western Nebraska, western Kansas and eastern Colorado, under centerpivot irrigation and in continuous corn production systems. Early symptoms began to appear at the V4 growth stage on lower leaves (Figure 1). It is likely the bacteria are splashed by rain or irrigation water onto these lower leaves. As the season progresses, the disease continues to move throughout the field and into

112 — 2017 Integrated Crop Management Conference - Iowa State University the upper canopy, likely due to recurrent overhead irrigation or rain. Significant long-distance dispersal has also been observed in fields where no early or mid-season infections were observed, but after strong thunderstorms, severe symptoms were observed in the upper canopy (Figure 1). The combination of warm weather with periodic overhead irrigation and ample residue for survival of Xvv has likely amplified the outbreak of BLS in this region as the climatic and production conditions appear optimal for Xvv infection and reproduction. The only information pertaining to this disease on maize has come from work done in South Africa, which primarily investigated host range on other African crops, such as sugarcane and banana. We therefore have very limited information on how this pathogen infects it’s host, what plant tissue(s) it is capable of infecting, how the pathogen survives the winters, where initial inoculum comes from at the beginning of each crop season, how the bacteria spreads from plant to plant and long distance, what climatic variables favor disease development and spread, how many other plant species Xvv is capable of infecting or using as alternate hosts, and if this bacteria will be able to persist and thrive in all corn growing regions of the U.S.

Effort to develop new epidemiological models for Xanthomonas vasicola pv. vasculorum Since Xvv was first reported in the U.S. researchers at CSU, University of Nebraska and Iowa State University have been leading an effort to describe the disease (Korus et al. 2017), survey the extent and distribution of the disease, develop early detection methods (Lang et al. 2017), assess severity of the disease, and provide outreach programs designed to educate growers (Robertson et al. 2017). Through these efforts, we have established that the most significant levels of BLS have been observed in the region encompassing northeastern Colorado, southeastern Nebraska and northwestern Kansas, which is where first reports of the disease were made in 2014 and 2015 (Figure 2). During the 2016 production year, a significant eastward spread of the disease was observed as reports of BLS were confirmed in Iowa, Illinois, Oklahoma, South Dakota and Texas (Figure 2).

Figure 2. Distribution of BLS during 2015 and 2016 based on confirmed identification of Xvv from infected leaf tissue. Not all counties within the 2016 radius had positive confirmations of Xvv, but fall within the range of the most east, west, north and south locations with positive identifications, and therefore represent the potential distribution of BLS

In addition to monitoring the spread of this disease, work in the lab of Dr. Broders has focused on understanding the evolutionary history of Xvv and how it may have arrived to the US. BLS was first described in 1949 on corn in South Africa (Dyer 1949), but prior to 2016 it had not been documented


in the USA (Korus et al. 2017). Due to the importance of corn in the USA, and the implications of the emergence and spread of a new disease, accurate identification of the causal agent and determination of its relationship to the strains from South Africa are of critical importance to the corn industry. The only other report of BLS of corn outside of South Africa and the U.S. is Argentina (Plazas et al. In Review). Dr. Broders is collaborating with researchers at the Universidad Católica de Córdoba in Cordoba, Argentina, regarding symptoms on maize they believed to be BLS. Bacteria were isolated from symptomatic leaves and then shipped to the Broders lab, where seven of the bacterial isolates were determined to be Xvv. While the official report of the disease in Argentina is relatively recent, the symptoms of BLS were first observed in 2010 in Cordoba province and have since spread to the other nine provinces where maize is grown (Plazas et al. In Review). In order to understand the evolutionary history of maize Xvv isolates, we sequenced the genomes of 23 isolates recovered from maize in the U.S., Argentina and South Africa. We then compared these to isolates recovered from sugarcane, sorghum and Tripsicum laxum, a wild relative of maize. We found that Xvv isolated from maize in Argentina, South Africa and the US forms a distinct genetic group from isolates infecting sugarcane and T. laxum (Figure 3). The preliminary analysis indicated there is greater diversity among isolates of Xvv from Argentina than isolates from South Africa and the U.S. (Figure 3). This would suggest Xvv has been present in Argentina longer than previously thought. It also seems likely that the strain of Xvv introduced into the US is more similar to isolates from Argentina than South Africa. However, greater sampling density of Xvv in South Africa is currently in progress order to provide a more definitive answer on where strains from the US originated

Figure 3. Whole genome phylogeny of X. vasicola isolates recovered from sorghum, T. laxum, Sugar cane and Maize. Isolates from Argentina, South Africa and US are highlighted in blue, red and green, respectively.

114 — 2017 Integrated Crop Management Conference - Iowa State University The maize growing regions where BLS is present in Argentina, South Africa and the U.S. share several similarities. BLS seems to be most prevalent in semi-arid to dry subhumid production regions in Argentina, South Africa and the United States often located at elevations near 3000 feet above sea level and between 28-40° from the equator (Figure 4). These regions experience similar growing conditions with hot, dry summers with occasionally intense thunderstorms. We have several hypotheses that may explain this phenomenon. First, semi-arid maize production often requires some type of irrigation, most frequently overhead irrigation. We believe the combination of warm temperatures and frequent overhead irrigation creates an ideal environment for this disease to reach epidemic levels. This has certainly been the case in the U.S. where the most significant levels of disease have been observed in eastern Colorado and western Nebraska where the majority of the corn is under center-pivot irrigation and the climate is more arid. In the case of Argentina, much of the corn is grown without irrigation, but summers are hot and the area is prone to thunderstorms that produce strong winds and hail, which may be important for disease spread. Xvv may be very well-adapted to thrive in these hot, dry climates where periodic wind, rain and irrigation allow it to spread.

Figure 4. Global distribution of confirmed Xvv infections of maize causing BLS (red circles) in relation to global maize production and distribution of semi-arid and dry subhumid production regions

There are few effective methods for management of Xanthomonas in the field. Little is known about the effective management of BLS in maize. To date, there is no chemical control commercially available


for BLS. While cultural control measures are likely to offer some degree of control of BLS by decreasing the amount of primary inoculum present, further study is required. Preliminary data suggests that varietal resistance exists, indicating management may be possible through host resistance. Because of the limited amount of information available for BLS control, we must look to related crop species to examine which management methods might be successful for Xvv. For bacterial leaf streak of sorghum which is caused by X. v. pv. holcicola, resistant varieties are the main method of disease control, followed by cultural methods including rotation and weed control (Janse 2005). Currently little is known about host resistance to BLS. BLS can be severe on grain corn, sweet corn and popcorn, indicating there is susceptibility in all types of maize germplasm and maize varieties need to be evaluated and improved for BLS so that susceptible germplasm is not released. In order to query host resistance, it is necessary to evaluate populations to demonstrate that sufficient phenotypic variation exists in order to provide the raw material to breed for resistance. Once phenotypic variation is shown to exist, alleles for resistance and susceptibility can be identified. After identification of resistant alleles, favorable alleles need to be incorporated into breeding programs and detrimental alleles purged. The overall goal of this presentation is to provide a better understanding of the basic disease ecology and epidemiology of Xvv, and explain to growers how this information will be used to develop new decision support tools, resistant varieties and extension material to assist growers in managing this disease.

References Dyer RA. Botanical surveys and control of plant diseases. Farming in South Africa. Annu Rep Dep Agric South Afr. 1949;275: 119–121. Janse, J. D. (2005). Phytobacteriology: principles and practice, Cabi. Korus K, Lang J, Adesmoye AO, et al. (2017) First report of Xanthomonas vasicola causing bacterial leaf streak on corn in the United States. Plant Disease, 101, 1030. Lang J, DuCharme E, Ibarra-Caballero J, et al. (2017) Detection and characterization of Xanthomonas vasicola pv. vasculorum (Cobb 1894) comb. nov. causing bacterial leaf streak of corn in the United States. Phytopathology, In Press. Plaza MC, De Rossi RL, Brucher E, Guerra FA, Vilaro M., Guerra GD, Wu G., Ortiz-Castro MC, Broders K. (2017) First report of Xanthomonas vasicola pv. vasculorum causing bacterial leaf streak of maize (Zea mays L.) in Argentina Robertson A, Broders K, Jackson TA, et al. (2017) Bacterial Leaf Streak. Crop Protection Network, CPN2008. http://cropprotectionnetwork.org/corn/cpn-2008-bacterial-leaf-streak/.



Fungicide use on corn Alison Robertson, professor and Extension crop plant pathologist, Plant Pathology and Microbiology, Iowa State University

Introduction Over the past decade, the use of foliar fungicides on hybrid corn has become more commonplace. Reasons for this include increased foliar disease, new fungicides becoming available, and promoted plant health benefits. Both the public and private sector have done small plot and on-farm field trials evaluating the effect of foliar fungicides on disease severity and yield. At Iowa State University (ISU), the Robertson Lab has conducted small plot trials at the ISU Research Farms around the state for the past several years. These trials serve to (i) compare products, and (ii) the timing of application on disease severity and yield. In addition to these efficacy trials, the lab has also evaluated the effect of fungicides on standability of corn.

Small plot fungicide efficacy trials (2010-2017) Small plot trials were established at the following ISU Research Farms: Northwest Research Farm (NWRF), Sutherland; Northern Research Farm (NRF), Kanawha; Northeast Research Farm (NERF), Nashua; Armstrong Research Farm (SWRF), Lewis; Southeast Research Farm (SERF), Crawfordsville; and the Agricultural Engineering and Agronomy Farm (AEA), Boone. The respective farm managers chose the hybrids. Farm staff was responsible for field preparation, fungicide application and harvest. A randomized complete block design with 4 to 6 replications was used. Fungicides were applied at growth stage V5V6, growth stage VT-R1, or at both growth stage V5-V6 and VT-R1. In 2017, applications at growth stage V12 were evaluated. Only commercial products were evaluated each year, and for each product, fungicide rate and application timing were consistent with company recommendations. Foliar disease was visually assessed in each year at growth stage R5. The percent of canopy diseased in each plot was estimated. Yield and grain moisture were collected for each plot. Weather conditions in each growing season varied considerably, and consequently disease prevalence and severity also varied. Moreover, hybrid genetics played a role in disease development. The 2010 growing season was extremely wet and Goss’s wilt was the most prevalent disease in Iowa. Low northern corn leaf blight and common rust severity were observed in the trials. The 2012 and 2013 growing seasons were hot and dry conditions and foliar disease severity in the trials was very low. Cooler and wetter than usual conditions in 2014 and 2015, together widespread use of susceptible germplasm, resulted in an epidemic of northern corn leaf blight (NCLB) across Iowa. Up to to 30 percent of the canopy was blighted at R5 in some of the fungicide trials. More normal precipitation and temperatures occurred in 2011 and 2016. Grey leaf spot, common rust and northern leaf blight were all present in these years although severity was low (less than 10 percent). The 2017 growing season was hot and humid with little precipitation through vegetative and early reproductive growth throughout most of Iowa; the northern and northeastern part of the state received frequent and above normal precipitation. Grey leaf spot (GLS) was prevalent at all locations. In general, fungicide applications at growth stage V5-V6 had little effect on disease severity. Applications of fungicide at VT-R1 reduced disease. A benefit of an application of fungicide at V5-V6 plus VT-R1 on disease was detected in only one year at only one location (2014; at NERF), where severe northern corn leaf was present. In 2017, an application of fungicide at V12 reduced GLS more than an application at R1 at 4 of the 6 locations. Mean yield response to a fungicide application varied by year and application timing (Table 1). In general,

118 — 2017 Integrated Crop Management Conference - Iowa State University yield response was greater an application of a fungicide at VT-R1. In 2014 and 2015, the mean yield response with an application of fungicide at V5-V6 plus VT-R1 was greater than an application made at VTR1 alone. This was likely due to severe northern leaf blight that developed in the trials. Table 1. Mean yield response (bu/acre) of corn to a foliar fungicide application at either growth stage V5-V6, VT-R1 or both at 4 to 6 locations in Iowa from 2010-2016. N = number of data points for each mean. Fungicide application

2010

2011

2012

2013

2014

2015

2016

V5-V6

-0.7 (N=14)

1.4 (N=18)

4.5 (N=37)

-3.5 (N=30)

1.9 (N=20)

-0.7 (N=30)

-1.0 (N=20)

VT-R2

-1.6 (N=29)

1.2 (N=36)

1.2 (N=68)

2.1 (N=60)

8.2 (N=31)

5.8 (N=29)

2.3 (N=30)

V5-V6 + VT-R1

3.3 (N=14)

4.0 (N=18)

4.5 (N=26)

-2.4 (N=36)

5.8 (N=22)

6.6 (N=30)

4.3 (N=16)

Small plot versus on-farm trials The yield response data of fungicide applications applied at VT-R1 in small plot trials done by the Robertson Lab at outlying ISU research farms were compared with on-farm trial data generated by the Iowa Soybean Association (ISA) On-Farm Network from 2010 through 2016. Only data that compared a fungicide application applied at VT-R1 and an untreated control in the ISA data were downloaded from On-Farm Network® database (www.iasoybeans.com/onlinedb/). Data were comparable in all years except for 2010 and 2014 (Figure 1). In 2010, the mean yield response of trials done by the On-Farm Network® was over 6 bu/acre while no yield response was seen in the ISU small plot trials. In 2014, the yield response in the ISU trials was over 8 bu/acre compared to 1 bu/acre in the On-Farm Network® data. Severe northern corn leaf blight (NCLB) occurred on hybrids that were ranked low for resistance to NCLB in the ISU trials and this likely contributed to the greater yield response. No data on disease severity was available for the On-Farm Network® trials. 10

Mean yield repsonse (bu/A)

8

On-Farm Network®

ISU

6 4 2 0 2010

2011

2012

2013

2014

2015

2016

-2 -4

Figure 1. Comparison of the yield response of corn to an application of a fungicide at tasseling in small plot trials versus on-farm trials.


Effect of fungicide on standability Some farmers have observed improved standability when the crop has been sprayed with a fungicide. The objective of these trials was to collect replicated data to evaluate this observation. Trials were established at the following outlying ISU Research Farms: NWRF, NRF, NERF, SWRF, SERF and AEA in 2016 and 2017. A 2 x 5 factorial in a randomized complete block design with 4 replications was used. Plots were either sprayed with a fungicide at tasseling or not sprayed. The plots were harvested at weekly intervals over 5 weeks starting at approximately 20-23% moisture. Foliar disease severity was assessed at dent. Immediately prior to harvest, the push test was done on 100 consecutive plants and the percent of plants lodged calculated. In 2016, no fungicide by harvest date interaction was detected. In general, percent lodging increased as harvest date got later. Percent lodging was lower in the fungicide treated plots compared to the plots where no fungicide was applied (Figure 2).

Figure 2. Comparison of lodging between plots sprayed with a fungicide and those left unsprayed at four ISU research farms in 2016.

It is unlikely that the fungicide directly reduced stalk rot disease in these trials. We propose that maintaining the health of the canopy after physiological maturity contributes to photosynthates being stored in the stalk. Consequently, cellular senescence caused by carbohydrate deficiency is delayed and therefore decay of the stalks by stalk rot fungi (Dodds, 1980) Despite the positive effect of fungicides on standability, ISU still recommends that fungicides be only used to manage foliar disease. Resistance to fungicide products used in corn production in Iowa has been reported from the southern United States and it is in our best interest to use these tools judiciously to ensure their effectiveness.

References Dodds, J. 1980. The role of plant stresses in development of corn stalk rots. Plant Disease 64:533-537.



Update on soybean diseases Daren Mueller, associate professor and Extension crop plant pathologist, Plath Pathology and Microbiology, Iowa State University Although parts of Iowa experienced dry conditions during the 2017 growing season, several important diseases were observed. Early season diseases were not common, but by late July prevalence increased. White mold was the most damaging disease in the northern part of the state. There was also limited, late development of sudden death syndrome and pod and stem blight (Diaporthe). Tobacco ringspot was found across much of Iowa very late in the season. This presentation will focus on these specific diseases and provide management updates for soybean diseases in general.

White mold White mold has been severe in parts of the state for the past three years, with some fields experiencing substantial yield loss. In order to help inform white mold management, the latest fungicide trial results will be presented. Information from regional research coordinated by Damon Smith at the University of Wisconsin-Madison will also be discussed.

Figure 1. White mold (Sclerotinia sclerotiorum)

122 — 2017 Integrated Crop Management Conference - Iowa State University Sudden death syndrome (SDS) Sudden death syndrome (SDS) was much less severe in 2017 compared to previous years. It was also observed later in the season. Several SDS management strategies were evaluated in 2017, and updates on these studies will be presented.

Figure 2. Sudden death syndrome (Fusarium virguliforme)

Diaporthe (Diaporthe spp.) An increase in the amount of “top dieback” of soybeans was observed in August. Historically, this problem has been related to infection by Diaporthe, SCN and potassium deficiency, although further research is needed to determine firm conclusions.

Other diseases and problems Late in the season, many fields experienced issues with plants that stayed green when they should have reached maturity. Many of these fields tested positive for tobacco ringspot virus. We will give an overview of this relatively uncommon disease and other problems seen during the 2017 season.

Figure 3. Tobacco ringspot virus. (source: Kevin Black)


Additional crop protection resources Twitter Campaign A Twitter campaign to encourage farmers and agronomists to use social media for disease tracking and information collection purposes continued to be successful in its second year of use. Contributors are asked to include the name of the disease (or what they suspect it is), their county, state and depending on the crop, the Twitter handles @corndisease and @soydisease. The goal of the social media postings is to help crop protection specialists track specific diseases and as they show up in the U.S.

Crop Protection Network Another tool that continues to expand is the Crop Protection Network (www.cropprotectionnetwork.org). Information on several corn and soybean diseases has been added to the website, with further resources in development. This regional collaboration of pest management professionals works together to create resources that can be used in Iowa and across the United States.



Disease risks associated with cover crops in corn and soybean production Alison Robertson, professor, Plant Pathology and Microbiology, Iowa State University; Tom Kaspar, research scientist, USDA-ARS, Iowa State University; Leonor Leandro, associate professor, Plant Pathology and Microbiology, Iowa State University; Daren Mueller, associate professor, Plant Pathology and Microbiology, Iowa State University; Jyotsna Acharya, research assistant, Plant Pathology and Microbiology, Iowa State University

Introduction Cover crops have numerous environmental benefits, for example reducing erosion, improving infiltration, mitigating nutrient loading in surface waters, and improving soil health (Kaspar et al 2001, Kaspar and Singer 2011, Schnepf and Cox 2006). Still, some farmers are reluctant to introduce cover crops into their production systems. In a 2016 Cover Crop Survey, approximately 30 percent of the respondents stated that “increased disease potential” was a minor or major challenge to using cover crops on their farm (SARE, 2016). Of the respondents, 20 percent had been growing cover crops for 2-3 years, 30% for 4-5 years, and 19% for more than 10 years. The goal of our research is to understand how cover crop may affect disease potential in the following cash crop and thereby recommend actions that may be taken to mitigate disease risk.

Effect of cover crops on corn diseases Seedling diseases Cover crops, especially grass cover crops, can be hosts of the same pathogens that infect corn seedlings. Cover crops also can serve as a ‘green bridge’ for pathogens by maintaining pathogen populations over the winter between harvest and planting of cash crops when pathogen numbers normally decline (Smiley et al 1992; Acharya et al 2017). Recently Bakker et al (2016) demonstrated that the dying roots of winter rye cover crops hosted high populations of corn seedling pathogens. Acharya et al (2017) went on to show that corn following a winter rye cover crop that had been terminated with herbicide within 10 days of planting had greater seedling disease than corn following a winter fallow or longer termination intervals (Table 1). In one of the two years of this field study, lower corn yields could be partly explained by reduced stands and poor plant vigor caused by soil-borne pathogens (Acharya et al 2017; Bakker et al 2017). However, because no N fertilizer was applied until 32 days after corn planting in that year for sampling reasons and a cereal rye cover crop takes up a lot of nitrogen, it was assumed that nitrogen availability also affected yield. Acharya et al (2017) also found that Pythium species were more often associated with corn seedling disease and reduced stands after a cover crop of winter rye, than Fusarium or Rhizoctonia.

126 — 2017 Integrated Crop Management Conference - Iowa State University Table 1. Effect of rye termination date before planting (DBP) corn on corn seedling root rot, the incidence of Pythium and Fusarium species recovered from rotted corn roots, the number of barren corn plants per acre and corn yield in 2015. Root rot incidence (%)

Pythium incidence (%)

Fusarium incidence (%)

Barren plants/A

Yield (bu/A)

No rye, check

8.3 b

2.8 c

61.1

1,170 c

224.5 a

Rye, spray 21 DBP

25.0 b

19.4 b

69.4

1,170 c

209.7 b

Rye, spray 14 DBP

25.0 b

13.9 bc

47.2

2,356 c

208.2 b

Rye, spray 10 DBP

80.6 a

38.9 a

75.0

6,486 b

200.7 bc

Rye, spray 3 DBP

80.6 a

19.4 b

77.8

6,787 b

191.8 cd

Rye, spray 1 DAP

83.3 a

25.0 b

50.0

12,691 a

182.9 d

P-value