Growing a Healthy Industryndsoybean.org/wp-content/uploads/2018/03/2016-NDSC... · 2018-03-12 · knichols@ndsoybean .org 2016 Research Committee Report North Dakota Soybean Council

2016 Research Update

Soybean Research

Growing a Healthy

Industry

1 North Dakota Soybean Council • 2016 Research Update

2016 Research Committee Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .2Increasing the Awareness of Soybean Cyst Nematode in North Dakota . . . . . . . . . . . .3 Principal Investigator: Dr. Samuel Markell, Plant Pathology, NDSU; Co-Investigators: Dr. Guiping Yan, Plant Pathology, NDSU and Dr. Ted Helms, Plant Sciences, NDSU

Molecular Detection of Soybean Cyst Nematode in North Dakota . . . . . . . . . . . . . . . . .4 Principal Investigator: Dr. Guiping Yan, Plant Pathology, NDSU; Co-Investigators: Dr. Samuel Markell and Dr. Berlin Nelson Jr., Plant Pathology, NDSU

Plant-Parasitic Nematodes on Soybeans and the Relationship With Soybean Cyst Nematode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .5 Principal Investigator: Dr. Guiping Yan, Plant Pathology, NDSU; Co-Investigators: Dr. Samuel Markell and Dr. Berlin Nelson Jr., Plant Pathology, NDSU

Expanding the Virulence Evaluation of Soybean Cyst Nematode in North Dakota .6 Principal Investigator: Dr. Guiping Yan, Plant Pathology, NDSU; Co-Investigators: Dr. Samuel Markell and Dr. Berlin Nelson Jr., Plant Pathology, NDSU

Impacts of Tillage System, Fertility and Crop Rotation on Rhizobium Populations and Nitrogen Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .7 Principal Investigator: Ezra Aberle, NDSU Carrington Research Extension Center

Impact of Previous crops on Soybean and Canola Yield . . . . . . . . . . . . . . . . . . . . . . . . . . . .8 Principal Investigator: Brian Jenks, NDSU North Central Research Extension Center; Co-Investigators: Dr. Nancy Ehlke, Agronomy and Plant Genetics, UMN; Dr. Mike Ostlie, NDSU Carrington Research Extension Center; Dr. Jasper Teboh, NDSU Carrington Research Extension Center; Bryan Hanson, NDSU Langdon Research Extension Center; Eric Eriksmoen, NDSU North Central Research Extension Center

Optimizing Irrigation, Row Spacing, Partial Host Resistance and Fungicides for Managing Sclerotinia in Soybeans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9 Principal Investigator: Dr. Michael Wunsch, NDSU Carrington Research Extension Center; Co-Investigators: Michael Schaefer, Billy Kraft and Suanne Kallis, NDSU Carrington Research Extension Center; Leonard Besemann and Heidi Eslinger, NDSU Robert Titus Research Farm; Dr. Venkat Chapara and Amanda Arens, NDSU Langdon Research Extension Center

Managing Sclerotinia in Soybeans with Contans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Principal Investigators: Dr. Michael Wunsch, Michael Schaefer, Suanne Kallis and Billy Kraft, NDSU Carrington Research Extension Center

Management of Soybean Root Diseases for Multiple Planting Dates and Environments of North Dakota in 2015: Langdon, Carrington and Minot . . . . . . . 11 Principal Investigators: Dr. Venkat Chapara, Dr. Pravin Gautam and Amanda Arens, NDSU Langdon Research Extension Center; Dr. Michael Wunsch, NDSU Carrington Research Extension Center; Eric Eriksmoen, NDSU North Central Research Extension Center

Visual Ratings for Iron-Deficiency Chlorosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Principal Investigator: Dr. Ted Helms, Plant Sciences, NDSU

Breeding of Glyphosate-Resistant Soybean Cultivars . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Principal Investigator: Dr. Ted Helms, Plant Sciences, NDSU; Co-Investigator: Dr. Berlin Nelson Jr., Plant Pathology, NDSU

Breeding Improved Non-GMO Cultivars and Germplasm . . . . . . . . . . . . . . . . . . . . . . . . 15 Principal Investigator: Dr. Ted Helms, Plant Sciences, NDSU; Co-Investigator: Dr. Berlin Nelson, Plant Pathology, NDSU

Digital Imaging Techniques to Detect and Rate Iron-Deficiency Chlorosis (IDC) in Soybeans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Principal Investigators: Oveis Hassanijalilian and John Nowatzki, Agricultural and Biosystems Engineering, NDSU; Co-Investigators: Dr. Hans Kandel and Dr. Ted Helms, Plant Sciences, NDSU; Dr. Sreekala G. Bajwa, Dr. Saravanan Sivarajan and Alimohammad Shirzadi, Agricultural and Biosystems Engineering, NDSU

Polymers Derived from Biodiesel Waste for Road-Dust Control . . . . . . . . . . . . . . . . . 17 Principal Investigator: James A. Bahr, Research and Creative Activity, NDSU

In-field Crop-Sensing Technology Applications for Soybean Production . . . . . . . . . 18 Principal Investigator: John Nowatzki, Agricultural and Biosystems Engineering, NDSU; Co-investigators: Dr. Sreekala G. Bajwa, Dr. Saravanan Sivarajan and M.M. Maharlooei, Agricultural and Biosystems Engineering, NDSU

From the Ground Up: How Salinity Gradients Damage Soybeans, Contribute to Arthropod Pest Infestations and Impact Soil-Nitrogen Reserves . . . . . . . . . . . . . . . . 19 Principal Investigators: Dr. Abbey Wick and Dr. Tom DeSutter, Soil Science, NDSU; Dr. Jason Harmon and Dr. Deirdre Prischmann-Voldseth, Entomology, NDSU; Co-Investigators: Kirsten Butcher, Soil Science, NDSU; Jackie Eichele, Entomology, NDSU and Chandra Langseth, Soil Science, NDSU

Commercial Evaluation of a Novel SoyBased Resin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Principal Investigators: Dr. Dilpreet S. Bajwa, Mechanical Engineering, NDSU and Dr. Dennis P. Wiesenborn, Agricultural and Biosystems Engineering, NSDU

Research and Extension Efforts at the Soil Health and Agriculture Research Extension (SHARE) Farm (Year 3) . . . . . . . . . . . . . . . . . . . . . . . . 21 Principal Investigators: Dr. Abbey Wick, Soil Science, NDSU; Dr. Frank Casey, Natural Resource Sciences, NDSU and Dr. David Ripplinger, Agribusiness and Applied Economics, NDSU; Co-Investigators: Chandra Langseth and Rebecca Schewe, Soil Science, NDSU

Effect of Soil Salinity on Soybeans’ Disease Resistance . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Principal Investigators: Dr. Berlin Nelson Jr., Plant Pathology, NDSU and Dr. Abbey Wick, Soil Science, NDSU

Control of Soybean Diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Principal Investigator: Dr. Berlin Nelson Jr., Plant Pathology, NDSU; Co-Investigator: Dr. Ted Helms, Plant Sciences, NDSU

Digital-Imaging Technique to Detect and Count Aphids . . . . . . . . . . . . . . . . . . . . . . . . . 24 Principal Investigator: Dr. Sreekala G. Bajwa, Agricultural and Biosystems Engineering, NDSU; Co-investigators: Dr. Jason Harmon, Entomology, NDSU; John Nowatzki, Dr. Saravanan Sivarajan and M.M. Maharlooei, Agricultural and Biosystems Engineering, NDSU

The Effectiveness of Unmanned Aircraft Systems (UAS) for Soybean Crop Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Principal Investigator: John Nowatzki, Agricultural and Biosystems Engineering, NDSU; Co-investigators: Dr. Samuel Markell, Plant Pathology, NDSU; Dr. Sreekala G. Bajwa and Dr. Ganesh Bora, Agricultural and Biosystems Engineering Dept., NDSU

Soil and Water Management for Soybean Production in Fargo Clay . . . . . . . . . . . . . 26 Principal Investigators: Dr. Amitava Chatterjee and Dr. Aaron Daigh, Soil Science, NDSU

Calibration of a Ureide Tissue Test for Soybeans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Principal Investigators: Dr. R. Jay Goos and Shiny Mathews, Soil Science, NDSU; Dr. Jasper Teboh, NDSU Carrington Research Extension Center; Shana Forster, North Central Research Extension Center, NDSU

Soybean Response to Various Management Inputs with Tile-Drained Conditions . . . 28 Principal Investigator: Dr. Hans Kandel, Plant Sciences, NDSU

Seeding Date, Cultivar and Location Influence on Soybeans’ Performance and Phenology in Eastern North Dakota . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Principal Investigator: Dr. Burton Johnson, Plant Sciences, NDSU

Impact of Drainage-Water Management and Subirrigation on Soil and Water Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Principal Investigators: Dr. Xinhua Jia and Dr. Thomas Scherer, Agricultural and Biosystems Engineering, NDSU

Optimizing the Soybean Plant Population for Early, Ideal and Late Planting Dates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Principal Investigator: Dr. Yvonne Lawley, Plant Science, University of Manitoba; Co-Investigator: Cassandra Tkachuk, Plant Science, University of Manitoba

Lease of JD 4520 Tractor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Dr. Ted Helms, Plant Sciences, NDSU

Evaluating the Effects of Soybean Plant Stand by Planting Date . . . . . . . . . . . . . . . . . 33 Principal Investigators: Dr. Mike Ostlie and Greg Endres, NDSU Carrington Research Extension Center and Cassandra Tkachuk, Plant Science, University of Manitoba

Improving the Soil Health and Productivity of Sodic Soils . . . . . . . . . . . . . . . . . . . . . . . 34 Principal Investigator: Dr. Tom DeSutter, Dr. Amitava Chatterjee and Dr. Abbey Wick, Soil Science, NDSU; Co-Investigator: Maria Breker, Soil Science, NDSU

Weed Management in Soybeans Contributes to a Field-Based Weed-Control Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Principal Investigators: Dr. Thomas J. Peters, Plant Sciences, NDSU; Andrew Lueck, NDSU and Dr. Rich Zollinger, Plant Sciences, NDSU

Maximizing Soil Warming and Health with Different Tillage Practices in a Corn-Soybean Rotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Principal Investigator: Dr. Aaron Daigh, Soil Science, NDSU; Co-Investigators: Jodi DeJong-Hughes, UMN Extension and Dr. Abbey Wick, Soil Science, NDSU

Impact of Selected Establishment Factors on Soybean Production: Row Spacing and Plant Canopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Principal Investigator: Greg Endres, NDSU Carrington Research Extension Center Co-Investigator: Dr. Mike Ostlie, NDSU Carrington Research Extension Center

Management of Soybean Aphids and Interaction with Soybean Cyst Nematode . . . . . 38 Principal Investigators: Dr. Janet Knodel and Dr. Samuel Markell, Plant Pathology, NDSU; Co-Investigators: Dr. Ted Helms, Plant Sciences, NDSU; Patrick Beauzay, Plant Pathology, NDSU; Jodi DeJong-Hughes, UMN Extension and Dr. Abbey Wick, Soil Science, NDSU

Determination and Validation of Transcriptomic and Metabolite Biomarkers for Dietary Soybean-Meal Utilization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 Bridget Owen, Executive Director, Soy Aquaculture Alliance

Utilizing Soybean Maturity Class and Planting-Date Evaluation to Improve Soybean Production . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Principal Investigator: Dr. Jasper Teboh, NDSU Carrington Research Extension Center; Co-Investigators: John Rickertsen, NDSU Hettinger Research Extension Center and Szilvia Zilahi-Sebess, NDSU Carrington Research Extension Center

Getting to the Root of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 Allie Arp, NCRSP Communications Liaison

Table of Contents

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Dear Valued Soybean Producers: The soybean checkoff’s mission is to maximize profit opportunities for all soybean farmers, and that starts in the field. Research is one of the most important efforts the North Dakota Soybean Council (NDSC) supports . We pride ourselves in investing in research that is scientifically sound and practical . NDSC’s research provides insight into new methods of crop management, which enables farmers to produce soybeans at an affordable price to the consumer . Soybean breeding, pest control, variety testing and disease management are among the many soy checkoff funded research projects being done in our state .Research also serves as a valuable connection between the checkoff and an individual farmer’s operation . Farmers and researchers who are familiar with pertinent soybean issues serve on NDSC’s Research Committee. This committee identifies

farmer priorities, sets NDSC’s research objectives and solicits studies to meet these priorities . Our Research Committee’s mission is to strategically invest research dollars to secure a more profitable future for North Dakota soy producers . New and continuing research for the fiscal year (FY) beginning July 1, 2015, and ending June 30, 2016, totaled $1,650,479 . Along with 12 other upper Midwest states, NDSC’s checkoff leverages about $200,000 in soybean research through the North Central Soybean Research Program . In FY 2016, research funding represented approximately 32 percent of NDSC’s budget . Because delivering research results to North Dakota soybean growers is a high priority, NDSC also uses soybean checkoff funds to develop educational programs and print material, like this Research Update, to help you make soybean production and management decisions . As always, we welcome your

input about the production challenges you are experiencing that could be addressed through research . Sincerely,

Troy Uglem NDSC Research Committee Chair tuglem@ndsoybean .org

Kendall Nichols NDSC Director of Research Programs knichols@ndsoybean .org

2016 Research Committee Report

North Dakota Soybean Council Research Committee

Troy Uglem, Northwood – ChairDerik Pulvermacher, CrosbyMatt Danuser, MarionAustin Langley, WarwickRick Albrecht, WimbledonJoe Ericson, WimbledonDavid Teigen RugbyBrent Kohls, MayvilleCraig Olson, Colfax – ND Soybean

Growers Association PresidentDr . Emmett Lampert, WimbledonDr . Seth Naeve, Research Consultant

University of Minnesota, St . Paul, Minnesota

Staff – Kendall Nichols, Director of Research

North Dakota Soybean Council directors Matt Danuser and Levi Taylor.


Increasing the Awareness of Soybean Cyst Nematode in North DakotaPrincipal Investigator: Dr. Samuel Markell, Plant Pathology, NDSU; Co-Investigators: Dr. Guiping Yan, Plant Pathology, NDSU and Dr. Ted Helms, Plant Sciences, NDSU Funded Project

$85,975

Figure 2. SCN Survey 2015 SE Counties

Figure 1. SCN Survey 2015

In 2015, the North Dakota Soybean Council (NDSC) funded an Extension and Research program designed to educate North Dakota soybean growers about the threat posed by Soybean Cyst Nematode (SCN) . The core of the extension program was a producer-based SCN sampling effort operated by the NDSU Extension Service . SCN is the most-important yield-limiting disease threat to soybean production in the United States . This parasitic worm lives and reproduces on soybean roots, severely stresses plants and spreads by anything that moves soil . In 2003, SCN was first identified in North Dakota and has been quietly spreading through the state . Growers have tools to manage SCN (such as resistant varieties and good crop-rotation strategies), but they are most effective if SCN is identified before the pathogen reaches epidemic levels in the soil . Unfortunately, SCN symptoms are notoriously absent, making detection by visually inspecting fields difficult. Commonly, spots in a field may take

a 15-30 percent yield loss before any above-ground symptoms appear, and when the symptoms are visible, they are expressed as the non-specific yellowing and stunting of plants . Consequently, soil sampling is the most effective way to detect SCN, and this method is the only way to determine egg levels in a field. To educate North Dakota soybean growers about SCN and to encourage them to sample the soil, the NDSC covered the lab-fee cost for up to 2,000 pre-labeled SCN sample bags . The pre-labeled sample bags were distributed to every NDSU County Extension office, at three SCN-specific field days, other NDSU field days and by cooperators in the private industry . Once the bags were sent to the laboratory, egg counts were determined, and the submitter received the results in the mail . Additionally, egg-level and geospatial data (All other information was kept

anonymous .) were used to generate maps for the SCN distribution in North Dakota . Some additional data from the cooperators were also used with the map creation . By January 1, 943 SCN sample bags were submitted from nearly everywhere soybeans are

grown in the state (Figure 1) . Over 25 percent of the submitted samples were positive, although some of them were “low-level positives” (gray boxes) and should be viewed with caution . However, high egg levels were found in many counties . The highest egg levels recorded in 2015 were in the southeastern part of the state where SCN has been present the longest (Figure 2) . This program was designed to directly benefit soybean growers in North Dakota by giving them information about SCN, the encouragement and opportunity, to sample for it . We thank growers who allowed us to hold field days in their fields along with the growers, county agents and cooperators who participated in the sampling program . In addition to the benefit of a free sample, you helped raise awareness in your region every time a positive sample appeared on the map .

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Molecular Detection of Soybean Cyst Nematode in North Dakota Principal Investigator: Dr. Guiping Yan, Plant Pathology, NDSU;Co-Investigators: Dr. Samuel Markell and Dr. Berlin Nelson Jr., Plant Pathology, NDSU Funded Project

$30,750

The soybean cyst nematode (SCN) Heterodera glycines is a major pathogen that poses a greater disease threat to soybean production in the United States than any other pathogen . Other cyst nematodes, including sugarbeet cyst nematode, clover cyst nematode and cereal cyst nematode, may be present in North Dakota fields. Unfortunately, these nematodes are highly similar to SCN and are hard to distinguish using the traditional method based on morphology . This identification technique is time consuming and requires high expertise in nematode taxonomy . Consequently, with a low SCN population density, detection is highly unreliable due to these factors which often lead to false-positive or false-negative results . Sensitive and accurate SCN detection

is extremely important because the nematode has high reproductive potential and can persist in the soil as a cyst without host plants for decades . In this project, our goal was to develop a molecular diagnostic assay to detect SCN in field soils with low population densities and to differentiate SCN from other closely related cyst-forming nematodes .Different numbers of SCN eggs or juveniles were added to a sterilized soil from which soil DNA was extracted . SCN was detected from the extracted soil DNA by PCR using a published, SCN-specific primer set. This set was

evaluated for the first time to directly detect SCN in the soil DNA extracts . The primers’ specificity was confirmed by testing 34 isolates of other nematode species, including 12 cyst species and 10 species commonly found in North Dakota soils . The PCR assay could detect from 1 SCN egg or juvenile added to 10 g of sterilized soil equivalent to about 20 eggs/100 cc soil . We used the developed assay to identify SCN in the North Dakota field soil samples. In

Summer 2015, a total of 35 soil samples from fields with a history of SCN were collected from five major soybean-producing counties. For each field-soil sample, 400 g were collected and divided in half for molecular detection and for traditional egg extraction and counting comparisons . To enable SCN detection at low densities (< 300 eggs/100 cc soil), several strategies were employed . First, the soil samples were ground before DNA extraction . Second, the DNA extraction using a commercial kit was coupled with two PCR-inhibitor removal techniques to eliminate any chemical compound that could hinder the PCR reaction . Third, a nested PCR was coupled to a primary PCR in order to enable SCN detection at as low as < 12 SCN/100 cc soil, including 3 samples where SCN was not detectable with the traditional method .The molecular assay provides a method to directly detect SCN in the soil and does not require the nematode extraction from the soil . Also, it is sensitive and efficient to detect SCN at low population densities . The assay could be used to separate SCN from other closely related cyst nematodes, useful for field screening or laboratory diagnostics . The assay will help improve the accuracy, reliability and rapidity of SCN-detection test results for farmers .

The molecular assay provides a method to

directly detect SCN in the soil and does not require the nematode extraction from the soil .

Identifying soybean cyst nematode under a microscope


Plant-Parasitic Nematodes on Soybeans and the Relationship With Soybean Cyst NematodePrincipal Investigator: Dr. Guiping Yan, Plant Pathology, NDSU;Co-Investigators: Dr. Samuel Markell and Dr. Berlin Nelson Jr., Plant Pathology, NDSU Funded Project

$28,810

Plant-parasitic nematodes are an important group of pests on many field crops. Soybean cyst nematode (SCN), root-lesion nematode, lance nematode and root-knot nematode are highly aggressive on soybeans and can cause significant yield suppression . However, the occurrence and distribution of plant-parasitic nematodes in North Dakota soybean fields were unknown except for SCN . The project’s objectives were to determine the incidence, abundance and distribution of plant-parasitic nematodes and to determine whether economically important nematode species other than SCN were present in North Dakota soybean fields. In addition, we tried to determine the relationship of SCN with other plant-parasitic nematodes in North Dakota’s major soybean-growing regions .

A nematode survey was conducted in 2015 to ascertain the occurrence and distribution of plant-parasitic nematodes in North Dakota soybean fields. We collected 155 soil samples from 14 counties . Soil samples were processed, and nematodes were extracted from all samples . Vermiform (motile) and cyst (sedentary) nematodes were extracted separately from each soil sample (100-cc soil) and were then identified and quantified. Of the 155 soil samples, 144 samples (93 percent) were infested with plant-parasitic nematodes . Nine groups (genera) of plant-parasitic nematodes were detected: SCN, pin, spiral, stunt, ring, root-lesion, dagger, lance and stubby-root nematodes . SCN was found in 59 percent of the surveyed fields, and spiral, stunt, pin and root-lesion nematodes occurred in more than

19 percent of the fields. SCN had the highest-mean population density, followed by spiral, pin, stunt, lesion, dagger, lance, ring, and stubby-root nematodes . Root-lesion and lance nematodes are important on soybeans . Moderate levels of root-lesion and lance nematodes as well as high populations of pin and spiral nematodes were found in some fields. These nematodes were

detected in North Dakota soybean fields, requiring more attention from researchers .Seven groups of nematodes were identified to species using DNA-based (cloning/sequencing, direct sequencing, species-specific PCR and real-time PCR) and morphological methods . Two unknown root-lesion nematode species were detected; importantly, these nematodes’ populations increased greatly on soybean roots from the preliminary greenhouse experiments . Four new species of other nematodes were identified. A first report about the new lance nematode on North Dakota soybeans is being prepared . This species was reported in the southeastern U .S . and was the most predominant lance nematode on soybeans in North Carolina . The new nematode species’ effects on soybean growth and yield need to be examined . A correlation analysis was performed to evaluate the relationship between SCN and other plant-parasitic nematodes . Poor or no correlations were observed between population densities for each group of the vermiform nematodes and SCN . This research provided baseline information about the incidence, density and distribution of plant-parasitic nematodes in North Dakota soybean fields as well as their relationship with SCN . Such information is important to help farmers be aware of nematode species and population levels in infested fields in order to make the best management strategies for controlling these nematode diseases to increase soybean yield . Culturing root-lesion nematodes in surface-sterilized

carrot disks.

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Expanding the Virulence Evaluation of Soybean Cyst Nematode in North DakotaPrincipal Investigator: Dr. Guiping Yan, Plant Pathology, NDSU;Co-Investigators: Dr. Samuel Markell and Dr. Berlin Nelson Jr., Plant Pathology, NDSU Funded Project

$41,800

Soybean cyst nematode (SCN) can cause major yield losses for soybean production . An extensive SCN population has already been found in North Dakota fields. Due to its great genetic diversity, this nematode easily adapts to new environments, overcomes resistance and spreads quickly . It is important to monitor the virulence changing of SCN populations across the state so that we are better equipped to recommend cultivars that are still resistant to SCN, thus preventing yield losses due to SCN . Our main objective was to characterize the virulence of 50 fields containing SCN populations via the HG-type testing bioassay . HG refers to Heterodera glycines, and the type indicates which sources of resistance the particular SCN population is able to overcome . For example, HG type 1 .2 .3 . refers to a strain of the nematode that is capable of reproducing on the sources of resistance: PI 54840, PI 88788 and PI90763 . Therefore, that strain is not controlled by the resistance genes in those three resistance sources .We collected soil samples from

155 soybean fields in 14 counties and then determined if the fields had positive SCN. Of the 155 fields sampled, 87 were infested with SCN eggs. Of the 87 positive fields, 45 fields with SCN from 210 to 21,540 eggs/100 cc soil were selected for HG-type testing . Thirty-six of these SCN populations were assayed using naturally infested soil . To conduct HG-type bioassays on nine field samples with low egg numbers, inoculum was first increased by incubating the populations on susceptible cultivars . Cysts were extracted and crushed to release eggs for inoculum (Figure 1) . To confirm our HG-type results, we repeated some of the bioassays up to four times . As a result, we performed a total of 63 bioassays in a growth chamber that was maintained at 27 °C (Figure 2) . Among the successful experiments, the most common HG types were HG type 0 (frequency rate: 43 percent) and HG type 7 (22 percent) . Other HG types included HG type 2 .5 .7 (8 percent), HG type 2 .7 (9 percent),

HG type 2 .5 (9 percent) and HG type 5 (9 percent) . The virulent type that has been detected in North Dakota is HG type 0. We found five new HG types . By repeating the bioassay, we confirmed the presence of HG type 2.5.7 in North Dakota fields. Our findings demonstrated that the new virulent strain (HG type 2 .5 .7) reproduced well on PI88788, the most widely used source of resistance in North Dakota. More fields will be surveyed in 2016 to confirm the virulence changing of SCN in North Dakota . Knowledge about virulence types should influence the choice of populations and HG types used for resistance assays of new soybean cultivars and breeding lines . This research is important to navigate the use of alternative resistance cultivars for farmers and new resistance sources that should be introduced to North Dakota for soybean-breeding programs to develop new resistant cultivars to combat these new, virulent types to increase soybean production .

Figure 1. SCN cysts extracted from soil in an infested North Dakota field and then crushed to release the eggs used for inoculum in a HG-type experiment.

Figure 2: HG-type bioassays. Each bioassay was incubated in the growth chamber at 27 °C for 30 days before white SCN females were extracted from soybean roots.


Impacts of Tillage System, Fertility and Crop Rotation on Rhizobium Populations and Nitrogen DynamicsPrincipal Investigator: Ezra Aberle, NDSU Carrington Research Extension Center Funded Project

$14,392

Soybeans require little nitrogen (N) because Rhizobium bacteria form nodules on roots and can fix N from the atmosphere for their own use and that of their host plant . However, these N fixing bacteria are added by growers as an inoculant to soybean and do not always persist in sufficient numbers to forgo reapplying Rhizobium inoculant each year at planting . We inoculated our soybean plants with Bradyrhizobium japonicum with the aim of determining what management factors have the potential to effect persistence of Rhizobium inoculant in soil and N fixation in soybean nodules. This study’s results will improve our understanding of how management of tillage, fertilizer and crop rotations affect Rhizobium persistence and N fixation in soybeans.With the dry 2015 growing season tillage system had an impact on soybean yield with no-till being significantly higher at 32.9bu/acre than minimum tillage at 30 .9 bu/acre which was significantly higher

than conventional tillage at 28 .5 bu/acre . This is due to more available moisture for plant growth with decreasing amounts of tillage . However, the no-till system had lower grain protein content than the tilled systems by 0 .4-0 .7 points but significantly higher oil content than the tilled systems by 0 .2 points . This is a common phenomenon when grain yields are higher as protein content is reduced when the total amount of nitrogen available is diluted over more bushels of grain . Therefore the oil content becomes higher in the lower protein grain . The lower previous fertility treatments of 0 and 50 lbs of nitrogen treatments had significantly lower grain protein content and higher oil content than the higher previous fertility treatments due to lower available residual nitrogen in the soil .We found that soil depth, crop rotation and fertilizer management affected the survivability of soybean Rhizobium (Figure 1) . Soybean Rhizobium numbers

were lower below the plow layer of the soil than the plow layer itself . This effect was observed because the inoculant was placed on the soybean seed that is planted within the plow layer and the Rhizobium have minimal motility . In addition, the plow layer contains greater organic matter and residues that provide habitat and energy for growth of the Rhizobium . At lower soil depths, applying manure at a low rate once every 4 years enables the inoculated bacteria to survive more effectively than applying a high rate of nitrogen to other crops grown in a rotation . We also found that these bacteria did not significantly increase after inoculation, but instead had the greatest populations after soybean harvest . Rhizobium populations were highest after the soybean in the rotation due to the large numbers of root nodules containing Rhizobium at harvest . Without a soybean host plant, Rhizobium decreased in each rotation point following soybean during the wheat, field pea and corn in the rotation until soybeans were once again planted . Overall, there was an average of 162 soybean nodulating bacteria per gram of soil in all treatments, which is within the recommended range to produce 63 to 100% nodulation of soybean plants . This research is important to North Dakota soybean growers who desire to maximize nitrogen fixation and reduce the significant cost of applying nitrogen fertilizers and inoculant . Our data indicate that addition of inoculant may not be necessary each year and is dependent upon the interaction of management practices and annual variations in climate .

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Figure 1. Soybean-nodulating bacteria numbers per gram of soil for the 0-15 cm soil depths and the 15-30 cm soil depths in a 4-year rotation. The red line indicates the recommended minimum amount of bacteria to have 63 to 100 percent nodulation efficiency.

8www.ndsoybean.org

Impact of Previous crops on Soybean and Canola YieldPrincipal Investigator: Brian Jenks, NDSU North Central Research Extension Center; Co-Investigators: Dr. Nancy Ehlke, Agronomy and Plant Genetics, UMN; Dr. Mike Ostlie, NDSU Carrington Research Extension Center; Dr. Jasper Teboh, NDSU Carrington Research Extension Center; Bryan Hanson, NDSU Langdon Research Extension Center; Eric Eriksmoen, NDSU North Central Research Extension Center Funded Project

$20,250

Some farmers want to include both soybean and canola in their crop rotation . Some research and farmer experience indicate that soybeans grow just as well on canola ground as on wheat ground . Because both crops are moderately susceptible to sclerotinia stem rot, some farmers have avoided planting them close together in a crop rotation . While not ideal from a sclerotinia standpoint, there is evidence that there may be an advantage with growing soybeans on canola ground or vice versa . Growers want to know if this synergism is real, and if so, is it great enough to offset the risk of growing two sclerotinia-susceptible crops back to back? This study is designed to test that theory in the Northern Plains where soybean and canola are commonly grown . If the hypothesis is true, then the main benefit would potentially be higher soybean yields following a profitable canola crop . The objectives were 1) to determine if soybean yield is greater following canola than wheat and 2) to determine if canola yield is greater following soybeans than wheat . These objectives were accomplished using a three-year crop sequence to evaluate soybean and canola

production grown back-to-back in a rotation with wheat . The four crop rotations from 2013-2014-2015 were 1) Wheat-Wheat-Soybean, 2) Wheat-Canola-Soybean, 3) Wheat-Wheat-Canola and 4) Wheat-Soybean-Canola . The agronomic data collected were crop yield, test weight, oil content, crop density and crop height . The results showed no significant difference in yield at any location between soybeans grown on wheat ground and soybeans grown on canola ground . Generally, there were no differences in soybean density,

height, test weight and oil content . At Carrington, the soybean height was greater for soybeans grown on wheat, but there was no height difference at Minot and Langdon . Similarly, there was no significant yield difference at any location for canola grown on soybean ground . Canola

yield, test weight, height, density and oil were similar whether canola was grown on wheat ground or soybean ground . Agronomic data collected were crop yield, test weight, oil content, crop density, and crop height . Based on one rotation cycle, we observed no advantage or disadvantage with growing soybeans on canola ground or with growing canola on soybean ground . The sclerotinia incidence was very low at all locations . This can be viewed as a positive situation for farmers who want to grow both soybeans and canola in a rotation with wheat . Based on this information, it appears that farmers can profitably grow soybeans and canola in a rotation . However, farmers must monitor for sclerotinia and make the necessary fungicide applications if the weather conditions favor sclerotinia . A second cycle of the study will be completed in 2016 .

The results showed no significant

difference in yield at any location between soybeans grown on wheat ground and soybeans grown on canola ground .

Soybean-Canola wheat rotation study at Langdon, ND.


The objective of this multi-year project is to develop agronomic recommendations that significantly improve the soybean yield and Sclerotinia control under high white-mold disease pressure . When conditions are favorable for white mold, current management strategies often do not confer satisfactory control of white mold (Sclerotinia) in soybeans . Field trials conducted in 2014 and 2015 suggest that the current recommendation to apply fungicides at the R1 growth stage (bloom initiation) may not be optimal when targeting Sclerotinia . Across 11 field trials conducted with multiple soybean varieties and diverse environments, delaying applications of the Endura fungicide from the R1 to R2 growth stage improved Sclerotinia disease control from 25

percent to 44 percent and increased the yield response to the fungicide application from 5 .3 bu/ac to 8 .1 bu/ac . Results were similar when Endura was applied at 5 .5 oz/ac and 8 .0 oz/ac . When soybeans were seeded to narrow rows (7 to 15 inches apart), applying Endura at the early R2 growth stage (80 to 98 percent of plants with an open blossom at one of the top two nodes) was optimal; when soybeans were seeded to intermediate and wide rows (21 and 28 inches apart), applying Endura at the full R2 growth stage (100 percent of plants with an open blossom at one of the top two nodes) was optimal . Research initiated in 2015 suggests that seeding soybeans in wide rows (28 inches apart) may not optimize the soybean yield even under high white-mold disease

pressure . Across eight soybean varieties evaluated in Carrington, five varieties evaluated in Oakes, and three varieties evaluated in Langdon at three seeding rates (132,000; 165,000; and 198,000 viable seeds per acre), soybeans seeded to wide (28-inch) rows consistently developed less white mold than soybeans seeded to narrow (7- and 14-inch) and intermediate (21-inch) rows . However, even under high white-mold disease pressure, yields were optimized when soybeans were seeded to rows that were 14 and 21 inches apart . If producers can tolerate slightly increased levels of sclerotia in the harvested grain, the results suggests that seeding soybeans in rows that were 14 and 21 inches apart might be optimal even when the risk of Sclerotinia is high . Results from 2015 also suggest

that row spacing may have a stronger influence on Sclerotinia disease pressure than seeding rate .Research on these research questions and related research questions is continuing in 2016, and revised recommendations cannot be developed until the multi-year testing is completed . However, findings from 2015 research suggest that it may be possible to significantly increase soybean yields under high white-mold pressure with modifications to the current recommendations .

Optimizing Irrigation, Row Spacing, Partial Host Resistance and Fungicides for Managing Sclerotinia in SoybeansPrincipal Investigator: Dr. Michael Wunsch, NDSU Carrington Research Extension Center; Co-Investigators: Michael Schaefer, Billy Kraft and Suanne Kallis, NDSU Carrington Research Extension Center; Leonard Besemann and Heidi Eslinger, NDSU Robert Titus Research Farm; Dr. Venkat Chapara and Amanda Arens, NDSU Langdon Research Extension Center Funded Project

$50,100

Carrington ND (2014)

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8 oz. / ac 5.5 oz. / ac

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10www.ndsoybean.org

Managing Sclerotinia in Soybeans with ContansPrincipal Investigators: Dr. Michael Wunsch, Michael Schaefer, Suanne Kallis and Billy Kraft, NDSU Carrington Research Extension Center Funded Project

$20,345

Due to its unique biology, Sclerotinia stem rot (white mold) is a particularly good target for biological control . Unlike most fungal plant pathogens, Sclerotinia sclerotiorum, the fungus that causes Sclerotinia stem rot, does not produce spores on diseased tissues . Instead, fungus spores are produced when sclerotia (fungal resting structures in the soil) germinate to produce tiny mushroom-like structures called apothecia . Biological control agents that degrade the soil’s fungal resting structures have the capability of significantly reducing Sclerotinia spore production and, thereby, reducing disease development .This multi-year project evaluated the efficacy of Contans WG, a commercial formulation of the biological control agent Coniothyrium minitans, for controlling Sclerotinia stem rot in soybeans . Application timing (fall versus spring) and rate (1 versus 2 lb/ac) were tested, and the efficacy of Contans was compared to a single application of the foliar fungicide, Endura (boscalid) . Contans was applied to the surface of a disked field and incorporated the same day with a shallow cultivation . Testing was conducted on a 34-acre field that was irrigated with a center pivot. In the first two years of this project, small plots were utilized, where each plot was separated by 60 ft of untreated (first year) or Contans-treated (second year) ground . In the last two years of this project, testing was conducted with 1 .16-acre plots; all treatments were imposed with commercial-scale equipment .

Across the four years that this project was conducted, Contans applications were consistently associated with reduced apothecia production (fruiting structures of the Sclerotinia fungus) but differences were often not statistically significant. Fall Contans applications generally performed better than spring applications . Yield responses to Contans applications were modest . In 2016, under low disease pressure, Contrans performed comparably to 5 .5 oz/ac of the foliar fungicide Endura; in 2015, under high disease pressure, Contrans was less effective than Endura . Rigorous assessment of Contans’ efficacy was hampered by spore movement of the Sclerotinia fungus between plots (first year), low disease pressure (second and fourth years) and high

variability in white-mold disease pressure across the research trial’s footprint (third and fourth years) . Studying the efficacy of a biological control agent such as Contans, which is expected to reduce the production of Sclerotinia fungus spores, is difficult due to movement of Sclerotinia spores between plots . Results from this multi-year research project strongly suggest that Contans applications can reduce apothecia production by the Sclerotinia fungus, but the impact of Contans applications on soybean yield under high white-mold disease pressure remains unclear .

Contans applications were consistently

associated with reduced apothecia production .

Dr. Michael Wunsch discusses white mold management at a field tour at the NDSU Carrington Research Extension Center.


Management of Soybean Root Diseases for Multiple Planting Dates and Environments of North Dakota in 2015: Langdon, Carrington and MinotPrincipal Investigators: Dr. Venkat Chapara, Dr. Pravin Gautam and Amanda Arens, NDSU Langdon Research Extension Center; Dr. Michael Wunsch, NDSU Carrington Research Extension Center; Eric Eriksmoen, NDSU North Central Research Extension Center Funded Project

$33,525

Figure 1. Soybeans’ plant stand for three locations and two planting dates in 2015

Minot Early

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Langdon Early

Langdon Late Carrington Late

Carrington Early

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Acquire

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The goal of this project was to study the efficacy of seed-treatment chemistries to control soybean root

disease as well as their impact on yield for multiple planting dates and environments in North Dakota .

We also wanted to improve our understanding of the prevalent root-rot pathogens and effective seed-treatment options to manage them. In 2015, six field experiments were conducted in North Dakota at three NDSU ag extensions located in Langdon, Carrington and Minot with funding from the North Dakota Soybean Council (NDSC) .Generally, farmers receive recommendations to plant earlier because the soybeans’ yield potential increases . However, cool and wet soils are frequently encountered at early planting dates, slowing the seedlings’ growth and making them more vulnerable to seed and root rots that are caused by plant

—continued on page 12

Dr. Venkat Chapara

Figure 2. Root-rot severity for three locations and two planting dates in 2015

Minot Early

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Carrington Early

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—continued from page 11pathogenic fungi . Eventually, there is a reduced stand . The pathogens’ sensitivity to the fungicides used in seed-treatment products are evaluated in this study . Most fungicide seed treatments are only active for 2 to 3 weeks . Thus, when germination and emergence are slowed due to cool soil temperatures, seedlings are still vulnerable to infection by pathogens . This is the main reason for choosing two different planting dates and the data generated will help to explain why a seed treatment benefits the yield at one location and not necessarily at another . Obviously, choosing a fungicide seed treatment will depend on the knowledge about what disease problems are prevalent in a particular field. Research about

pathogens is limited for the prevalence of soil-borne pathogens that can infect soybean seed and seedlings in North Dakota . Our pathogen-isolation results from the lab indicate that Pythium and Rhizoctonia are the major pathogens at all three locations we tested . Products that contain the active ingredient metalaxyl or mefenoxam are effective against Pythium and Phytophthora . Other active ingredients, e .g ., azoxystrobin, trifloxystrobin, ipconazole, captan, carboxin, fludioxonil, thiram, sedaxane and thiophanate methyl, are effective against Fusarium and Rhizoctonia . Fungicides alone and in combination with the

above-mentioned seed-treatment products were selected to manage these seedling rots . The soybean seed (Variety S007-Y4 for Minot and Langdon; Variety S04-D3 for Carrington) was treated alone and in combination of products . In all 15 chemical treatments and a non-treated control in three locations x two planting dates were planted . The two-year field research objectives were rigorously tested . The findings were compared with the root-rot severity to plant stand and yield. No significant differences among the treatments were observed for any location on either plant stand or yield. The benefit of a seed treatment on soybean plantstand establishment was not fully evident. In our 2015 field trials, the weather was very cooperative for the Langdon and Minot locations, whereas in Carrington, very low yields were recorded (Figure 3; graph document attached) due to dry weather during the critical pod-fill period in August. The field where these trials were conducted had well-drained soils .

Figure 3. Yield in bu/ac for three locations and two planting dates in 2015

Minot Early

Minot Late

Langdon Early


Carrington Early

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Rancona Summit

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EverGol Energy

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Results obtained were compared

with the root-rot severity to plant stand and yield . No significant differences between the treatments were observed in any location .


Iron is less available to soybean plants in high pH soils . The symptoms of iron-deficiency chlorosis (IDC) include leaves that are yellow in June and sometimes through July . Soybean growers with fields that have a past IDC history need information to aid them with side-by-side comparisons of varieties from many different companies . There are genetic differences among cultivars for tolerance to IDC . Even a small amount of yellowing in the soybean leaves can reduce the final yield by 20 percent . We measure the IDC tolerance by the amount of yellowing in the leaves. For fields with IDC, visual yellowing is closely correlated with yield . These data provide unbiased information that enables growers to choose the best variety for their IDC-prone fields. The objective was to screen all private-company varieties that have been entered into the Langdon

Research and Extension Center (REC), Carrington REC, Minot REC, Williston REC and Fargo Main Station yield trials for visual ratings of IDC at multiple field locations with a past history of IDC symptoms . A second objective was to provide visual IDC screening for the advanced NDSU breeding lines . Comparing soybean varieties from different companies required that all varieties be evaluated with side-by-side comparisons in the same field. Otherwise, a fair comparison was not possible .In 2015, four locations on farmer-cooperator fields with a past history of IDC symptoms were identified and were later planted with hill-plots . There were 272 Roundup Ready company varieties tested, and 70 Liberty Link and non-GMO company varieties tested . Also, the NDSU soybean breeder evaluated

112 advanced NDSU breeding lines for visual IDC symptoms . Those locations included Leonard, Prosper, Hunter, and Galesburg, North Dakota . A total of 7,264 hill-plots were planted . Because there was too much rain at the Colfax site, the site had to be abandoned . Data was analyzed and reported in the NDSU bulletin titled North Dakota Soybean Performance; the data was also posted online . This data set is the largest one with the most comparisons of many company varieties, including Roundup Ready, Liberty Link and non-GMO, for North Dakota and western Minnesota . Because the 2015 data are averaged across three locations with four replications per location, the data are quite reliable in helping growers select the best varieties for their IDC-prone fields. This data enables growers to increase the yield on their IDC-prone fields because varieties with the least amount of yellow IDC symptoms will yield the best on fields with that problem.

Visual Ratings for Iron-Deficiency ChlorosisPrincipal Investigator: Dr. Ted Helms, Plant Sciences, NDSU

There were 272 Roundup Ready

company varieties tested and 70 Liberty Link and non-GMO varieties tested .

Dr. Ted Helms rated iron-deficiency chlorosis damage among company RR2 varieties.

Funded Project$86,749

14www.ndsoybean.org

Growers would like to purchase glyphosate-resistant soybean varieties and to save their own seed for planting the next year . These varieties need to be high-yielding, lodging-resistant, tolerant to iron-deficiency chlorosis (IDC), and have good disease and pest resistance . Soybean varieties are protected by a patent on the glyphosate-resistant gene (construct) and are often protected by a second patent on the variety . Monsanto has provided a website to explain these issues (http://www .soybeans .com/patent .aspx) . The purpose of this research is to provide superior glyphosate-resistant varieties that have been developed by North Dakota State University (NDSU) .

Development of glyphosate-resistant experimental lines was initiated at NDSU through a new breeding program . Creating a new soybean variety requires seven years . As the

first step in the breeding process, crosses were initiated in the summer of 2010 . Experimental lines that were crossed in 2010 are in the final stages of yield evaluation and seed increase . As part of the continuing process to develop new lines, 5,563 plant-rows were planted in the spring of 2015 . During that growing season, the first year of replicated yield testing was conducted for 1,320 new glyphosate-resistant, experimental lines . In 2015, there were 100 experimental lines tested for the second year, and 11 of them were advanced for the third year of yield evaluation during the 2016 growing season . These 11 advanced experimental lines vary from a 00 .7 to a 0 .7 maturity and, at this point, yield very competitively with Roundup Ready 2 Yield® varieties . A

total of 5,240 plots were devoted to this project in 2015 .

Two glyphosate-resistant experimental lines were increased during the 2015-2016 winter season in Chile, South America . These advanced, experimental, glyphosate-resistant lines have the potential to be released in January 2017 . ND12-24081(GLY) is a 00 .7 maturity experimental line, and ND12-21598(GLY) is a 00 .9 maturity experimental line . Both lines have been approved for pre-release, and seed is being increased in the summer of 2016 . The release of either or both experimental lines is contingent on the 2016 yield tests . Commercial quantities of seed would be available for farmers to plant during the 2018 growing season .

The benefit to the North Dakota soybean industry would be reducing the cost of soybean seed for varieties that are glyphosate resistant . This would reduce the input costs because growers could save their own glyphosate-resistant soybean-variety seed that was developed at NDSU . They could then plant the seed without paying a technology fee . Currently, farmers must purchase expensive new seed each year . According to Monsanto, when the patent is no longer viable for the Roundup Ready One technology, growers are supposed to be able to save their seed and plant it without fines or penalty.

There were 272 Roundup Ready

company varieties tested and 70 Liberty Link and non-GMO varieties tested .

As part of the continuing process to develop new

lines, 5,563 plant-rows were planted .

Breeding of Glyphosate-Resistant Soybean CultivarsPrincipal Investigator: Dr. Ted Helms, Plant Sciences, NDSU; Co-Investigator: Dr. Berlin Nelson Jr., Plant Pathology, NDSU

Dr. Ted Helms discusses his Chile, South America winter nursery in January 2015.



Breeding Improved Non-GMO Cultivars and GermplasmPrincipal Investigator: Dr. Ted Helms, Plant Sciences, NDSU; Co-Investigator: Dr. Berlin Nelson, Plant Pathology, NDSU Funded Project

$224,671

The North Dakota State University (NDSU) breeding program provides growers with the option to grow non-genetically modified organism (GMO) varieties . As glyphosate-resistant weeds, such as Kochia and Water Hemp, become more common, producers may benefit from growing non-GMO varieties . Farmers have the option to purchase non-GMO varieties that were developed by North Dakota State University without paying a “technology fee” and can save their own seed . This research had four broad objectives: i) to provide North Dakota soybean growers with cultivars which are genetically superior to cultivars that are currently grown; ii) to collect soybean samples from the Variety Fee Tests and to report the protein and oil data in the NDSU Soybean Performance bulletin; iii) to increase yield on IDC-prone soils and to increase the profit for those problem soils; and iv) to enable private companies and growers to compare the yield of SCN-resistant cultivars

on sites that are infested with SCN .A new variety, ‘ND Bison’, was released in January of 2016 . ND Bison is a non-GMO variety with high-yield, good IDC tolerance, resistance to races 3 and 4 of phytopthora root rot, and moderate resistance to SCN . Three non-GMO experimental lines were pre-released in March of 2016 . In 2015, the grain samples for protein and oil analyses were collected for all company varieties entered in the LaMoure, Northwood, Grandin, Fairmount, Walcott, and Milnor, North Dakota, testing sites . These samples were analyzed, and the data were reported in the North Dakota Soybean Performance Bulletin (A-843) . In 2015, yield data were collected for 40 private company RR varieties at 3 sites that had IDC symptoms . Averaged across the Hunter, Leonard, and Galesburg, North Dakota, locations for 2015, the yield range varied from a low of 15 .7 bu/A for an IDC-susceptible company variety to a high of

49 .9 bu/A for an IDC-tolerant company variety . Also, four sites that were infested with Soybean Cyst Nematode (SCN) were planted with 40 Roundup Ready company varieties to test for SCN resistance . These data were published in the North Dakota Soybean Performance Bulletin (A-843) and also online .Growers benefit when there are non-GMO varieties that give them a choice between buying new seed each year and being able to save seed from non-GMO varieties to plant the next year . NDSU’s past success to develop non-GMO varieties for the oilseed market includes ‘ND Henson’, ‘Ashtabula’ and ‘ND Bison’ . Also, tofu and natto specialty-type varieties, such as ‘ProSoy’, ‘ND1406HP’, and ND1100S, have been developed . NDSU’s non-GMO varieties provide growers with an alternative for using glyphosate and permit various herbicides to be rotated in soybean fields across different years. Growers benefit when different companies’ varieties are compared at the same field sites because this enables growers to increase the yield for fields that have soil or pest problems .

A field of the new NDSU released soybean variety ND Bison.

A new variety, ND Bison, was

released in January of 2016 .

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16www.ndsoybean.org

Based on USDA statistics, 5 .75 million acres of soybeans were planted in North Dakota in 2015 . Soybean yield in North Dakota was almost 185 .9 million bushels . One factor that reduces soybean yield is the lack of useable iron, which causes Iron-Deficiency Chlorosis (IDC). In 2013, the north-central U .S . land area where soybeans were grown was approximately 4 .7 million acres . IDC caused a 375,000-ton loss in soybean grain production, with a value of $120 million .The most effective step to manage this problem is to select an IDC-tolerant variety that is suitable for a specific area because IDC occurrences vary widely under different

environmental conditions . The tolerance of different varieties to IDC can be determined with an IDC visual rating by experts in variety research trials . Some scientists suggest that visual ratings at an early stage could help farmers identify the severity and to apply iron chelates in order to prevent yield loss . Visual rating is too subjective and requires experts . Leaf color can be used to identify chlorosis in soybean leaves, so a digital-imaging technique can detect IDC’s presence when the crop is growing . In this study, a digital-imaging technique is used to rate IDC in soybeans with a camera, a task which does not require expertise .The digital image has different bands

of red, green and blue (RGB) . Research studies at the University of Arkansas showed that the amount of red and blue in the color scheme indicates how green an image or a plant looks . Therefore, those researchers suggested a vegetation index called the Dark Green Color Index (DGCI) based on the color scheme or values of hue, saturation and

intensity (HSI) . The DGCI has been used to detect a nitrogen deficiency for corn .The greener the soybean plots are, the higher the DGCI value will be . The DGCI’s average value for the entire plot was used as a base value to rate the IDC . Then, by segmenting the image and detecting the low DGCI values for chlorosis, the rating was increased . IDC scores are from 1 to 5, with 0 .5 point increments, where 1 is no chlorosis and 5 is severe chlorosis with dead tissue .The chlorophyll-meter values for individual leaflets were also measured . Digital images were captured from the same leaflets with two different smartphones to determine the technique’s ability to estimate the chlorophyll amount . The DGCI values determined by the digital-image processing technique were significantly correlated (r2 = 0 .89) to the chlorophyll-meter reading for the same leaflets. Moreover, the correlation between the DGCI values with two different smartphones was significantly high (r2 = 0 .98), meaning that DGCI can be accountable for variations with different cameras .In summary, a lot of people carry smartphones . The phones can be used to rate the soybeans’ IDC by using DGCI . We are planning to use DGCI accompanied with an unmanned aerial vehicle (UAV) to delineate IDC and to detect the IDC’s growing pattern in the field. As a result, susceptible field regions can be detected for future management .

Digital Imaging Techniques to Detect and Rate Iron-Deficiency Chlorosis (IDC) in SoybeansPrincipal Investigators: Oveis Hassanijalilian and John Nowatzki, Agricultural and Biosystems Engineering, NDSU;Co-Investigators: Dr. Hans Kandel and Dr. Ted Helms, Plant Sciences, NDSU; Dr. Sreekala G. Bajwa, Dr. Saravanan Sivarajan and Alimohammad Shirzadi, Agricultural and Biosystems Engineering, NDSU Funded Project

$26,094 A new variety, ND Bison, was

released in January of 2016 .

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Polymers Derived from Biodiesel Waste for Road-Dust ControlPrincipal Investigator: James A. Bahr, Research and Creative Activity, NDSU Funded Project

$85,000

Road dust is a common problem in the rural United States, as well as at industrial sites such as mining areas, and can lead to health issues for people living and working in these dusty environments . At the same time, the growing biodiesel industry has an excess of crude glycerin that is expensive to dispose of and/or purify . This research explored the possibility of combining soy biodiesel with inexpensive crude glycerin (from biodiesel waste) to synthesize a non-toxic, biodegradable and non-corrosive dust-suppressant material that is dispersible in water for application onto gravel roads as a “drop-in” replacement for chloride-based dust-control agents .Funding from the ND Soybean Council allowed us to successfully develop a soy-based dust-control agent that performed as well as magnesium chloride in laboratory testing . This effort required the optimization of the chemical-synthesis metrics, the development of lab-scale testing methods and the determination of effective application rates that are required for its use on roadways . Our results indicated that this soy-based material can be dispersed in water and applied to gravel surfaces, using

standard application equipment, at rates comparable to chloride brines . The material is noncorrosive, biodegradable and performs well in dry conditions . Furthermore, it is water stable and resists being washed away in the rain . Finally, lab results indicated that it forms cross-linked networks that bind the dust particles together to minimize fugitive road dust and to reduce the loss of fine particles from the gravel. Fine-particle retention is important for a road bed’s longevity and will reduce the road-maintenance costs associated with the periodic re-graveling and grading operations .Magnesium- and calcium-chloride brines are the most commonly used dust-suppressant materials because they are relatively inexpensive and easy to apply . The main problems with these salts is that they are highly corrosive to vehicles and road infrastructure . They also tend to perform poorly in very dry conditions when they are needed most and can be washed from the road surface during rainfall . Our goal was to overcome these problems while adding more benefits.The U .S . market for dust control is quite large with over 1 .3 million miles of unpaved roads and over $400 million spent annually

to apply dust treatments . The development of a dust-control product derived from soy-biodiesel waste should create a new market for the biodiesel industry . As a result, revenue generated from this new market should decrease the overall cost to produce biodiesel by utilizing the biodiesel’s glycerin waste stream . Because biodiesel and/or soybean oil are also used to make this soy-based road-dust suppressant, a new market for soy biodiesel and/or soybean oil will be created from the manufacture and use of this product .

Treated and untreated class 5 gravel prior to testing.

Soy based dust control agent dispersed in water.

18www.ndsoybean.org

In-field Crop-Sensing Technology Applications for Soybean ProductionPrincipal Investigator: John Nowatzki, Agricultural and Biosystems Engineering, NDSU; Co-investigators: Dr. Sreekala G. Bajwa, Dr. Saravanan Sivarajan and M.M. Maharlooei, Agricultural and Biosystems Engineering, NDSU

This research was conducted to evaluate the use of digital-imaging technology and the Ag leader OptRx sensor for soybean production . The sensor data for this study were collected from North Dakota State University’s (NDSU) NW22 plots in 2014 . Digital images were captured from the NW22 plots and also from the experimental plots at Leonard, North Dakota, to rate iron-deficiency chlorosis (IDC) . The experimental design for the NW22 plots consisted of six nitrogen treatments, and four soybean varieties grown under tiled and non-tiled conditions . The Leonard study plots consisted of different varieties’ trial plots with each plot having a 4-x-11-ft . row . At the NW22 plots, sensor data were collected by mounting two OptRx sensors on a bicycle sprayer, and Vegetation Index (VI) values were collected over the study area on five different dates throughout the

growing season . The current technique utilized to rate different soybean varieties is done visually, but visual rating is too subjective, is labor intensive and requires experts . In this study, we used a digital-imaging technique to rate iron-deficiency chlorosis (IDC) in soybeans with a digital camera which was easy to use . Active optical crop sensors have the potential to identify the need for nitrogen in real time and to make appropriate in-season applications of nitrogen in order to avoid plant stress, thus avoiding yield reduction . Most commercially available, active, optical crop sensors have been evaluated and tested with corn and wheat crops, not soybeans, in North Dakota . This research was conducted to investigate the use of a digital-imaging technique to rate IDC and the potential use of the Ag leader OptRx sensor to monitor

the nitrogen status and needs for soybean production . The digital-imaging technique showed relatively good correlation between the IDC visual rating and color indices, such as the dark green color index (DGCI) and hue, but we didn’t notice a very good correlation between the visual rating and color indices . The reason could be due to variations in the sunlight intensity, which affects the leaf color’s appearance and potentially affects the visual ratings, leading to inconsistency when rating IDC . The OptRx sensor readings showed significant differences for the nitrogen treatments later in the growing season . The sensor readings also showed good correlation between the normalized red-edge vegetative index (NDRE) and the harvested yield for the last date (August 22) for the NuTech (6088) variety compared to other varieties . More investigation needs to be performed regarding the OptRx sensor’s response with different soybean varieties and nitrogen treatments .This study shows that the digital-imaging technique has the potential to rate the IDC and OptRx sensor to detect N treatment differences later in the season . Future work will focus on developing a smartphone app to rate IDC without needing an expert . The trials to rate the new soybean varieties’ IDC resistance will benefit greatly from this new sensing technology . The OptRx sensor also needs to be evaluated for variable-rate, in-season N application during soybean production .

John Nowatzki



From the Ground Up: How Salinity Gradients Damage Soybeans, Contribute to Arthropod Pest Infestations and Impact Soil-Nitrogen ReservesPrincipal Investigators: Dr. Abbey Wick and Dr. Tom DeSutter, Soil Science, NDSU; Dr. Jason Harmon and Dr. Deirdre Prischmann-Voldseth, Entomology, NDSU; Co-Investigators: Kirsten Butcher, Soil Science, NDSU; Jackie Eichele, Entomology, NDSU and Chandra Langseth, Soil Science, NDSU

Approximately 1 .2 million acres in the Red River Valley are classified as slightly saline . Because soybeans are very salt sensitive, even low salt levels can significantly hurt yields . For soybeans, this results in annual crop losses of $57 million . Much of the existing information about soybeans’ response to salinity does not apply directly to North Dakota soils or to the types of soluble salts that we have here . From 2013-2016, a series of greenhouse and field studies were conducted at North Dakota State University to look at the soybeans’ response, and the resulting pest pressures, to low levels of salinity in conditions that are specific to North Dakota .

Soybeans grown in sandy-loam soils were found to be considerably more sensitive to soluble salts than previous studies suggested . We found that soybean yields began to decline at a soluble salt level of 1 .1 mmhos/cm, instead of 1 .9 mmhos/

cm as originally identified in studies that were not conducted in North Dakota . After 1 .1 mmhos/cm there is a 20% yield decline per unit increase in soluble salts . (Figure 1) . No yield reductions

at low salt levels (< 3 mmhos/cm) were observed for a silty, clay-loam soil texture .Increased pest numbers on plants that are grown in higher-salinity soils were consistently observed in our greenhouse studies and were also found in our field study. In general, pests can do better on plants that are grown in more saline soil (Figure 2) . However, there was some variation in these results from field to field, suggesting that there may be other factors involved with determining how salinity affects the pests . In the greenhouse, we performed detailed studies to better understand how pests,



Figure 1. Relative soybean yield response to increased soluble salts for a sandy-loam soil texture.

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Figure 2: Spider mites in field cages have more offspring on plants that are grown in increasingly saline soils.

Commercial Evaluation of a Novel SoyBased ResinPrincipal Investigators: Dr. Dilpreet S. Bajwa, Mechanical Engineering, NDSU and Dr. Dennis P. Wiesenborn, Agricultural and Biosystems Engineering, NSDU

A research project was conducted at North Dakota State University to evaluate the use of soybean oil to develop a safe, environment-friendly resin (glue) that can be used by the wood-product industry . A lot of wood products used by the construction or furniture industry are made from wood particles, veneer or wood flakes that are glued with a resin to produce particleboard, plywood, strandboard, etc . The worldwide demand for wood products has grown for the last 50 years and is expected to increase . At present, most of the resins used for wood products are derived from petroleum . Some petroleum-based glues (formaldehyde) are not healthy . They cause allergies, asthma and skin infections . Now the federal government has put limits on how much formaldehyde can be used in wood products . As a result, there is a great need and market for safe bio-based resins . Soybean oil is a good feedstock to make bio-based resins . In this research, soybean oil, sugar molecules and an epoxide chemical were used to create a new,

strong resin called Epoxidized Sucrose Soyate (ESS) . The resin is 100% bio-based, with over 90 percent soy content, and has been tested with coatings and several other composite products . In our research, the new resin was evaluated to manufacture low-density fiber boards using wheat straw and soybean straw . Several different resin formulations were tested by mixing them with an expensive resin that is currently used in the industry . We wanted to see if we could produce good-quality boards . The boards were manufactured using new glue and various processing conditions . Once the boards were conditioned, they were tested for strength, water absorption, fiber bonding, screw-withdrawal strength and hardness . The test results were compared with other similar products that are available . The results showed that the new soybean-oil based ESS glue was not only strong, but also better than several other resins

used for wood products . It can also be blended with other petroleum-based glues to make them greener . The research’s test results were shared with other wood-product companies to market the ESS resin . Overall, the results showed that the ESS resin has the potential to be a safe, reliable and economical alternative . These results will benefit both North Dakota soybean farmers and the industry. The findings will promote additional value for the soybean crop via product diversification. The huge market potential for bio-based resins will result in an increased demand and price stability for soybeans . This will result in better income for the stakeholders . It will also attract investors and soybean-processing companies to North Dakota .

At present, most of the resins used for wood

products are derivedfrom petroleum .

—continued from page 19such as the soybean aphid, benefit from being on plants in saline soil . In addition to having more babies and creating large populations, we found that aphids on plants that are grown in saline soil live and make babies longer . Plus, the aphids will preferentially move toward plants that are growing in saline soil, meaning that may be able to find and take advantage of high-salinity areas within fields.These results are extremely

important for soybean growers in North Dakota who are experiencing yield reductions as a result of salinity. We are finding that it is not only the yield loss that growers need to consider when selecting a crop for saline areas, but they also need to consider the pest pressures . Managing saline areas differently (for example, tillage management, crop selection, fertilizing, pest scouting and spraying) than the remainder of the field that is not affected by salts becomes important for the grower’s bottom line . Dr. Abbey Wick and Dr. Tom DeSutter


Research and Extension Efforts at the Soil Health and Agriculture Research Extension (SHARE) Farm (Year 3)Principal Investigators: Dr. Abbey Wick, Soil Science, NDSU; Dr. Frank Casey, Natural Resource Sciences, NDSU and Dr. David Ripplinger, Agribusiness and Applied Economics, NDSU;Co-Investigators: Chandra Langseth and Rebecca Schewe, Soil Science, NDSU

The Soil Health and Agriculture Research Extension (SHARE) Farm in Mooreton, North Dakota, is primarily funded with commodity dollars. The goal is to conduct field-scale soil-health research and to develop extension programming that is driven by farmers’ input . Activities for 2015-2016: A perennial-grass buffer strip was seeded in Spring 2015 . An NDAWN Station was installed in Summer 2015 . Conservation-tillage plots were added in Fall 2015 . National Corn Growers Association (NCGA) Soil-Health Partnership plots (cover crops) were added in Spring 2016 (Figure 1) .Quantifying Salt Removal from the Tile Drainage: For this study conducted from 2014-2016, soil cores were removed from the SHARE Farm to leach and collect salts from the cores in the lab . A total of 94 leaching events were required to reduce salt levels from an EC of 4 .5 to 0 .7 mmhos/cm, which means 564” of precipitation with 94 wet-dry cycles . NDSU Soil-Salinity Economics Decision-Making Tool: Since 2013,

North Dakota State University has evaluated the soybean, corn and wheat yield response to salinity . The salinity levels where yields start to decline were set at 1 .1 mmhos/cm (soybean), 2 .0 mmhos/cm (corn) and 4 .5 mmhos/cm (wheat) . Using these salinity thresholds and percentage yield declines, an online tool was developed to help growers visualize salinity’s impacts on yields, revenue, input costs and returns . The tool is available at ndsu .udu/soilhealth .Using baseline market prices and expected yields from the 2016 Crop Budgets for the Southern Red River Valley, prepared by NDSU Extension, the following revenues were calculated . At a soluble salt level of 5 mmhos/cm, the expected per-acre soybean revenues fall from $276 to $50 per acre; corn revenues fall from $472 to $305 per acre; and wheat production falls from $314 to $278 per acre (Figure 2) . The impact of salinity on profitability varied greatly by crop because salinity it impacts the revenue and costs . In fields with moderately or strongly saline soils, salt-sensitive crops

(soybeans) may no longer be viable because profitability is dramatically reduced . Extension Programming: In 2015-2016, extension efforts focused on three areas: (1) soil-health café talks, (2) field days and a two-day soil-health bus tour, and (3) web-based information . Café talks were held in Sargent, Stutsman, Cass, and Grand Forks Counties . Each café talk had between 15 and 35 attendees, which made for great discussion and new connections . In 2015, the Annual Soil Health Field Day was held at the Langseth farm where one set of conservation tillage plots is located (over 100 attendees) . In 2016, a Soil Health Field Tour highlighted the commodity-funded projects (100 attendees) . Thirty videos were produced (8 in 2015-2016) since 2013 and received over 15,800 views . A Twitter account was developed (@NDSUsoilhealth) to reach additional growers . The web page (ndsu .edu/soilhealth) was updated and served as a resource for farmers .


Figure 1. SHARE Farm plot layout.

Figure 2. Salinity revenue response for corn, soybean and wheat based on the yield response to soluble salts.

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Effect of Soil Salinity on Soybeans’ Disease ResistancePrincipal Investigators: Dr. Berlin Nelson Jr., Plant Pathology, NDSU and Dr. Abbey Wick, Soil Science, NDSU

Soil salinity is a serious problem in North Dakota; it can affect the soybeans’ growth and yield . This project tried to determine if low salinity levels would affect the resistance of soybean cultivars to Phytophthora root rot and soybean cyst nematode (SCN) because both those diseases are managed using resistant cultivars . Many commercial cultivars grown in this area have resistance to one or both of these diseases . If salinity reduces the effectiveness of host resistance, then the primary method of disease management is useless in saline fields. The project’s emphasis was on low levels of salinity where the plants still grow, but are under stress from salinity . The project’s initial focus was to examine resistance to Phytophthora sojae race 4, the cause of Phytophthora root rot, under saline soil conditions . The tested saline soil treatments were EC levels of 0, 0 .5, 1 .0, 1 .5, 2 .0 and 2 .5 dS m-1 saturated paste using sodium sulfate and magnesium sulfate . Plants of the Barnes (with resistance gene

Rps6) and LaMoure cultivars were grown with various EC levels . Barnes had resistance to race 4 while LaMoure was susceptible . LaMoure was included in the experiment as a positive check for disease development . The results over four experiments indicated that at, the low salinity levels of EC 0 .5 to 2, the resistant cultivar, Barnes, maintained resistance to P . sojae while the susceptible cultivar, LaMoure, showed susceptibility throughout the salinity treatments . Barnes averaged over 98 percent survival from EC 0 .5 to 2 while the LaMoure plants averaged 15 percent or less survival . Additional experiments at EC 1 .5 and 2 showed that three other resistant cultivars, two with the Rps 1k resistance gene and one with the Rps 3a gene, also maintained their resistance to the pathogen when compared to the treatment with no salinity . Thus, three sources of genetic resistance (Rps 1k, 3a and 6) to P . sojae were not adversely affected by salinity

levels up to EC 2 .One surprising result of this research was the effect of salinity levels above EC 2 on the general plant growth . Soybean plants were smaller and grew poorly, had smaller stems, and had fewer roots . We were unable to inoculate these plants because of their poor condition .Research about the effects of salinity on resistance to soybean cyst nematode is still in progress . We compared the SCN resistance of soybean-plant introduction PI88788 to the susceptible cultivar, Barnes, at EC 0, 1 .5 and 2 . PI88788 was resistant to the common SCN race HG 0 that we have in this area . Resistance was measured by counting the number of female nematodes that were produced on the roots . PI88788 maintained resistance to SCN at all salinity levels, whereas Barnes showed susceptibility (Table 1) . At EC 2, there were 33 times more females on Barnes than on PI88788 after five weeks of plant growth. Thus far, this research indicated that, with low salinity levels of EC 2 or less, resistance to Phytophthora root rot and SCN is still effective .


Reproduction of soybean cyst nematode on two soybean cultivars at three levels of salinity.

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Control of Soybean DiseasesPrincipal Investigator: Dr. Berlin Nelson Jr., Plant Pathology, NDSU;Co-Investigator: Dr. Ted Helms, Plant Sciences, NDSU Funded Project

$54,500

Soybean growers are aware that diseases can impact yields, increase production costs and affect crop-rotation decisions . In North Dakota, we have major soybean diseases that can cause serious problems for soybean production . Our primary diseases are caused by seedling and root-infecting pathogens such as Phytophthora, Fusarium and soybean cyst nematode (SCN) . This project has an emphasis on working with the soybean breeder to test modern cultivars for disease resistance and to develop public cultivars with resistance to major diseases . In addition, we look for and try to determine if there are new pathogen strains that might increase our disease pressure . We try to evaluate new ways of managing diseases .We have a large emphasis on SCN because this major disease threat that has spread rapidly in North Dakota . In the summer of 2015, in cooperation with Dr . Helms, the soybean breeder, four field sites were established in Richland and Cass Counties in order to test commercial cultivars for resistance to SCN under field conditions . Soil samples were

taken from each site in the spring and fall to determine the levels of SCN reproduction on the cultivars’ roots . This is an indication of the cultivars’ resistance levels . Those same cultivars were also tested for resistance under controlled conditions in the greenhouse . In May 2016, another four SCN testing sites were established by Dr . Helms, and the commercial cultivars for those tests are being evaluated for SCN resistance under controlled conditions in the greenhouse . The data from all tests for SCN resistance will be available in extension-type bulletins or on websites .Efforts were also continued to incorporate resistance to SCN and Phytophthora sojae (cause of Phytophthora root rot) into public germplasm and cultivars from Dr . Helms’ soybean-breeding program . We screened breeding lines for resistance to SCN and identified lines with resistance . We made progress toward developing a public cultivar with resistance to SCN . We also screened breeding lines for resistance to P. sojae and identified those lines with resistance . Dr . Helms tries to

incorporate P . sojae resistance into any cultivar or germplasm released from NDSU .We surveyed for new races of P . sojae, one of the most important root pathogens, and in 2015, we obtained soil from 81 soybean fields in eastern North Dakota . The results, thus far, demonstrate that 93 percent of the isolates can defeat Phytophthora-resistance genes Rps 1a, 86 percent can defeat Rps 1c, 36 percent can defeat Rps 1k and 7 percent can defeat Rps 6 . Rps 1k and 6 are the most common genes used for commercial cultivars in North Dakota, thus there is a substantial proportion of the pathogen population that can defeat our most commonly used resistance sources . These results indicate that growers need to carefully monitor this disease in their fields to see if the resistance genes they use for their soybean cultivars are still providing resistance to this disease which is associated with flooded or very wet soil . Another disease that we are investigating is a root disease caused by Fusarium tricinctum which, along with other fungal pathogens, may be involved with the late-season death

of soybeans . The experiments in 2015 to study that pathogen’s role in adult plant death were not successful due to most of the plants dying at the seedling stage . Fusairum root rots are important in our area, and we are continuously studying their role in the fields’ disease damage .

Figure 1. Phytophthora root rot of soybeans

24www.ndsoybean.org

Digital-Imaging Technique to Detect and Count AphidsPrincipal Investigator: Dr. Sreekala G. Bajwa, Agricultural and Biosystems Engineering, NDSU; Co-investigators: Dr. Jason Harmon, Entomology, NDSU; John Nowatzki, Dr. Saravanan Sivarajan and M.M. Maharlooei, Agricultural and Biosystems Engineering, NDSU Funded Project

$29,810

Aphids are a serious soybean pest, with a threshold for economic damage assessed as 250 aphids on a single soybean plant . Currently, aphids on soybean plants are counted manually to make crop-management decisions such as insecticide application . Manual counting is a time-consuming and laborious task . An automated counting method would make the task faster and easier . The project’s objective was to investigate using digital images of soybean leaves captured with regular consumer cameras to accurately detect and count aphids of on soybean leaves . Data were collected from soybean plants grown on test fields near Fargo that were supervised by NDSU entomologist Dr . Jason Harmon . The soybean varieties grown on the test fields were considered as being susceptible to soybean aphids . Images of soybean leaves with

aphids on them were acquired with three different consumer cameras, a Sony W80 (7 .2 MP) digital camera, an AppleTM iPhone 6 (8 MP), and a NokiaTM Lumia 1020 (41 MP) with and without a macro lens . The purpose of using macro lens was to have a better focus on the images taken at close proximity because there were issues with camera focusing for the 2014 greenhouse study when the camera was too close to the target . Images were acquired from soybean fields on July 23, August 4 and August 12 of 2015 . The protocols (camera type, data-collection method, etc .) were similar for all three sets of collected data . The leaves were laid straight on a still board that had a ruler taped on it, and images of the leaves were taken . The acquired images were processed to remove the still-board background from the leaf part by using the leaf color to distinguish the leaf from

the background . Once the leaf was identified, the image was processed again to identify its objects, including aphids, leaf spot, exoskeleton (dead skin of aphid), etc ., using their hue (color tone) . From these objects, aphids were identified using their distinct hue, size, roundness and aspect ratio (length-to-width ratio), and the objects identified as aphids were counted . Randomly selected aphids on the leaf that was processed with the image-processing method were manually checked to verify how accurately the method identified the aphids . The method was tested on images for both the top and bottom sides of the soybean leaves .This research showed that the digital-imaging technique has the potential to detect and count aphids on soybean leaves in field conditions with reasonable accuracy . There was a strong correlation between the aphid count estimated with the digital-imaging technique and the manual count under field conditions . The Sony camera gave the best correlation on the top (0 .93) and the bottom (0 .87) sides of the leaves . The correlations were a little less than what was observed for images collected in greenhouse conditions under bright light . The iPhone camera also performed well . However, the Nokia camera performed poorly for detecting and counting aphids . The misclassifications were caused by an exoskeleton that had a similar size and shape as a small aphid, aphids that were too small and some leaf spots that looked similar to aphids . We continued to refine the algorithm. Overall, the method was successful for detecting and counting aphids .

Figure 1. The original image of soybean leaf (left), apids identified on the leaf (middle), and the correlation between the manual and digital counts (right) for the top and bottom surfaces of a soybean leaf. Aphids are marked, with red circles, on the original image. The correlation curves are for the field images collected with a 7.2 MP SONY W80 camera.

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The Effectiveness of Unmanned Aircraft Systems (UAS) for Soybean Crop ManagementPrincipal Investigator: John Nowatzki, Agricultural and Biosystems Engineering, NDSU; Co-investigators: Dr. Samuel Markell, Plant Pathology, NDSU; Dr. Sreekala G. Bajwa and Dr. Ganesh Bora, Agricultural and Biosystems Engineering Dept., NDSU

The purpose of this project was to demonstrate the effectiveness of UAS imagery to assist soybean producers with crop-management decisions . Investigators used UAS to collect imagery during the growing season; data were obtained for one commercial field and two North Dakota State University (NDSU) Research Extension Center soybean fields. The primary goal was to demonstrate that UAS-collected imagery can be used effectively on field-scale during the growing season in order to identify crop problems, such as iron-deficiency chlorosis, nitrogen deficiencies, relative yield potential and soybean cyst nematode damage, and to compare the relative impacts of saline-affected areas on soybean growth and yield . The project’s objectives were as follows: 1) demonstrate the effectiveness for high-resolution aerial imagery

of soybeans collected at the field level with UAS to identify iron-deficiency chlorosis (IDC), nitrogen deficiencies, soybean cyst nematode damage, relative yield potential, and the relative impacts of saline-affected areas on soybean growth and yield; 2) correlate the UAS imagery with 5-meter RapidEye and 30-meter Landsat imagery; and 3) compare the usefulness of fixed-wing and multi-rotary UAS aircraft platforms for collecting field- level imagery .During the summer of 2014, project personnel used the UAS to collect high-resolution imagery of soybeans at the NDSU Carrington Research Extension Center (June 12 and 23; July 8, 15, and 30; and August 6, 2014) . This imagery allowed us to do stand counts across fields. In 2014, project personnel used a Draganflyer X4 multi-rotary UAS and a Trimble UX5 fixed-wing UAS aircraft to collect field-level imagery (six dates

with the multi-rotary UAS and two dates with the fixed-wing UAS). The researchers collected RGB color and near-infrared imagery . On July 9 and 19, 2015, project personnel used the Trimble UX5 fixed-wing UAS to collect near-infrared imagery at the NDSU Williston Research Extension Center and at the NDSU Nesson Valley Research Center . The researchers also used the Trimble UX5 UAS to collect near-infrared imagery at the NDSU Carrington Research Extension Center on July 14, 2015 . Project personnel also collaborated with Altavian, Inc . to collect color and near-infrared imagery of an irrigated soybean field on the Tyler DeWitz Farm in Kidder County, North Dakota, on July 2 and 20 . Handheld NDVI data were also collected on the same dates .Project personnel were unable to complete the project’s objectives because 1) there were issues with not getting the NDSU UAS pilots qualified to fly our UAVs until August 2016; we were unable to collect imagery when needed; 2) during July 2016 when the Northern Plains UAS Test Site personnel came to the Williston and Carrington Research Extension Centers, the camera malfunctioned . The UAS operators were not aware of the malfunction until after the flights were completed at both locations . The imagery was not usable for research purposes; 3) the imagery collected by Altavian, Inc . at the DeWitz Farm in Kidder County was usable, however, disease or insect problems were recorded in the field. The imagery did not correlate with harvested yield .


John Nowatzki (left) and his UAS research team.

26www.ndsoybean.org

Soil and Water Management for Soybean Production in Fargo ClayPrincipal Investigators: Dr. Amitava Chatterjee and Dr. Aaron Daigh, Soil Science, NDSU Funded Project

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Figure 1. Effect of tile drainage (surface-drained, open- and control-tiled) and tillage practices, chisel (CH), no-till (NT) and strip-till (ST), on soybean yield (Bu/ac) during the 2014-2015 growing seasons.

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Research site in Casselton, N.D.


Nitrogen moves from soybean roots to the tops, mostly in two forms: nitrate from the soil solution and compounds generated in the nodules . The compounds generated in the nodules are called ureides . The ureide molecules consist of two urea molecules that are connected by a small organic molecule . The ureides move upward in the plant, where the nitrogen is converted into protein, chlorophyll and other nitrogen-containing compounds in the leaves, stems, pods and seeds . Thus, the ureide concentration in the plant is a direct indication about how well the nodules are fixing nitrogen . A soybean plant with poor nodulation always has a low concentration of ureides .Recently, we discovered a relatively simple way to analyze soybean tissues for ureides . This method is simple enough to be performed at a soil-testing lab . The research’s objective is to calibrate this test for field-grown soybeans . If the plant axes

(stems plus petioles) contain 300 or 3,000 ppm of nitrogen as ureide-N, what does that mean? We conducted field-inoculation studies at Minot and Carrington, and we compared the crop’s response, with regards to seed yield, protein content and protein yield, to the crop’s ureide-N levels earlier in the year . In 2007, we also conducted similar studies at Carrington .So far, our studies suggest that, if the axes of a soybean plant contain somewhere around 1,200-1,500 ppm of ureide-N at late flowering, the plant is fixing nitrogen well

enough to meet the plant’s growth needs .The ureide test is also helpful to evaluate inoculants and inoculation methods . At our experiment in Minot this year, on a soil with no prior soybean history, there were four treatments .

There was a control, seed inoculation with a liquid inoculant, granular inoculant, and both the liquid and the granular inoculant . The results with regard to seed yield are shown in Figure 1 . The level of ureide-N at flowering was the highest with double inoculation, about 1,500 ppm . This treatment also had the highest yield . The results with regard to seed-protein yield are shown in Figure 2 . Again, with double inoculation, the ureide level was the greatest with double inoculation, and the amount of protein harvested per acre in the seed was also the greatest .Once the ureide test is properly calibrated, it will benefit farmers, agronomists and researchers by allowing a simple test to evaluate the health of the nitrogen fixation system in soybean plants . For example, it could be used to evaluate new inoculant products .

Calibration of a Ureide Tissue Test for SoybeansPrincipal Investigators: Dr. R. Jay Goos and Shiny Mathews, Soil Science, NDSU; Dr. Jasper Teboh, NDSU Carrington Research Extension Center; Shana Forster, North Central Research Extension Center, NDSU

46

44

42

40

38

36

34

32

30 0 200 400 600 800 1000 1200 1400 1600

Ureide – N in Plant Axes, ppm

See

d Y

ield

, Bu

/Ac

Control

Both Liquid and Granular Inoculant

Granular Inoculant

Minot, 2015

Liquid Inoculant

Figure 1

950

900

850

800

750

700

650

600 0 500 1000 1500 2000

Ureide – N in Plant Axes, ppm

So

ybea

n P

rote

in Y

ield

, lb

./A

c

Control

Both Liquid and Granular Inoculant

Granular Inoculant

Minot, 2015

Liquid Inoculant

Figure 2

28www.ndsoybean.org

Soybean Response to Various Management Inputs with Tile-Drained ConditionsPrincipal Investigator: Dr. Hans Kandel, Plant Sciences, NDSU Funded Project

$2,495

From the 1990s through 2015, excess water during part of the growing season has significantly impacted the region’s crop production . This research, near Fargo, evaluated the use of seed treatments, nitrogen, sulfur, and/or foliar-fungicide applications with both tiled and non-tiled conditions . It is important for producers to know the most economic soybean-management practices .The treatments tested were as follows: two soybean varieties with different maturity ratings, AG0434 (0 .4) and AG0934 (0 .9), in combination with 10 input-based treatments under tiled and non-tiled conditions . The seed treatment was Acceleron . The biological was TagTeam, which was seed applied . Sulfur was applied as ammonium sulfate (21-0-0-24S) . The rate was 15 lb S per acre (which also included 13 lb/a N, as ammonium) . Nitrogen

(50 lb/a) was side-dressed as Urea at the full-bloom stage (R2) . The foliar fungicide Priaxor, at a rate of 4 fluid ounces per acre, was applied

at R2 . The trials were planted in 2014 and 2015 with a small-plot seeder that had 14-inch row spacing . The full season (0 .9 RM) yielded 53 .6 bu/a compared to the early variety with 47 .9 bu/a . The two varieties responded differently to tile drainage .

Both varieties had more vigor and were greener, and the earlier variety was taller on the tiled ground . The early variety’s yield when grown on tiled ground was significantly higher (4%) than on the non-tiled ground .At the beginning of the season, a number of treatments resulted in more vigorous and greener plants than the control . The plants in the control treatment were shorter compared with treatments that included sulfur . The control yielded 49 .6 bu/a . Adding a seed treatment significantly increased the yield to 51 .2 bushels per acre, a 3% increase compared with the no-seed treatment . Foliar-fungicide or biological-treatment yields were not different than the “only seed treated” treatment (Table 1) .

Table 1. Mean plant density, vigor, height, and yield for treatments averaged across drainage, varieties, and years (2014-2015), at Fargo, ND.

Seed Treatment

Fertilizer and Other Inputs

Plant Density (plants/

acre)

Vigor Scale 1-9 (9 most

vigorous)

Plant Height

(cm)

Seed Yield

(bu/a)

No None (control) 180,489 5 .5 54 .9 49 .6Yes None 187,917 5 .7 56 .2 51 .2Yes N 50 lb ./ac 181,707 5 .6 55 .8 52 .9Yes Foliar Fungicide 189,992 5 .6 55 .0 50 .4Yes S 15 /b ./ac 194,633 6 .1 59 .0 51 .5Yes Biological 184,676 5 .4 56 .2 52 .0Yes N 50 lb ./ac + S 15 lb ./ac 187,438 6 .1 57 .6 53 .4Yes Fungicide + Biological 194,827 5 .9 57 .1 51 .6Yes N + Fungicide + S + Biological 189,979 6 .5 57 .3 52 .9

Mean 187,962 5 .8 56 .6 51 .7CV% 19 .7 16 .3 8 .6 6 .2

LSD 0 .10 NS 0 .4 2 1 .3

53.5

53

52.5

52

51.5

51

50.5

50No Fertilizer 13 lb./a N + 15

lb./a S50 lb./a N at

R213 lb./a N + 15 lb./a S and 50

lb./a N R2

a

a

b

bSoybean Yield (bu/ac)


Seeding Date, Cultivar and Location Influence on Soybeans’ Performance and Phenology in Eastern North DakotaPrincipal Investigator: Dr. Burton Johnson, Plant Sciences, NDSU Funded Project

$30,075

The soybean-cultivar maturity rating and seeding date were evaluated at the Carrington (northern), Prosper (central), and Lisbon (southern), North Dakota, locations to determine the yield response for cultivars from three maturity ratings because the seeding date was delayed from mid/late May to early/mid July during the 2014 and 2015 growing seasons . The maturity ratings were 00 .9, 0 .7 and 1 .4, and the seeding dates were spaced at about 10-day intervals, for a total of six seeding dates (Table 1) .This research is important for producers from different locations to select seeding dates for optimum yield performance for soybean cultivars with different maturity ratings . Later-maturity varieties can offer higher yield potential; however,

delayed planting and uncertainty about the growing season’s duration often dictates the selection of earlier cultivars to reduce the production risk from early fall frosts that may result in low yield for the later-maturity cultivars because of not reaching physiological maturity .In 2014, the Carrington and Prosper locations’ cultivar-maturity rating and seeding-date yield responses ranged from 30-45 bu/acre to 40-64 bu/acre, respectively, between Date 1 and Date 4 for the 00 .9 and 0 .7 maturity-rated cultivars, respectively (Table 1) . Yield for the late-maturity group 1 .4 cultivar was 17 and 26 bu/acre at seeding Date 4 at Carrington and Prosper, respectively . This indicated that seeding later than June 12 resulted in a substantial

yield reduction at these locations for the late-maturity rating cultivar . At the southern Lisbon location, yield was 36 bu/acre at seeding Date 4 on June 23 and 46 bu/acre at seeding Date 3 on June 12 . In 2015, the Carrington, Prosper and Lisbon locations’ cultivar-maturity rating and seeding date yield responses were 13 to 23 bu/acre, 28 to 42 bu/acre and 24 to 39 bu/acre, respectively, between Date 1 and Date 4 (Table 1) . Consequently, there was no clear maturity-rating seeding-date yield responses at Carrington in 2015 due to the constricted yields . At the Prosper location in 2015, more than 30 bu/acre yields were achieved from Dates 1 through 3 for maturity ratings 00 .9 and 0 .7, and Dates 1 through 4 for maturity rating 1 .4 . At the Lisbon location, the 0 .7 maturity-rating cultivar did not yield more than 30 bu/acre for any of the seeding dates . Yield for the 00 .9 maturity cultivar was greater than 30 bu/acre for


Table 1. Mean soybean seed yield (bu/ac) at six North Dakota environments (three locations and two years) for six seedling dates and three cultivar maturity ratings.

Carrington 2014 Prosper 2014 Lisbon 2014

Seedling Date1 0.92 0.73 1.44 0.9 0.7 1.4 0.9 0.7 1.4

D1 May 23 36 .2 44 .7 41 .6 63 .8 67 .0 70 .3 41 .3 44 .2 51 .4D2 June 4 37 .5 36 .3 35 .6 58 .3 62 .2 54 .2 40 .7 42 .2 47 .8

D3 June 12 35 .8 32 .1 30 .4 50 .9 53 .7 52 .6 42 .4 48 .3 46 .0D4 June 23 30 .0 30 .0 17 .0 39 .6 45 .3 26 .4 39 .7 42 .9 35 .5D5 July 2 10 .8 10 .0 1 .2 21 .1 25 .7 7 .4 31 .3 31 .8 22 .0D6 July 9 9 .4 5 .0 0 .1 12 .3 12 .4 1 .3 25 .5 27 .4 8 .8

Carrington 2015 Prosper 2015 Lisbon 2015

0.9 0.7 1.4 0.9 0.7 1.4 0.9 0.7 1.4D1 May 22 12 .7 15 .8 17 .3 35 .7 40 .0 41 .6 30 .7 28 .4 38 .6D2 June 1 13 .4 15 .5 20 .2 32 .8 38 .4 40 .1 30 .3 28 .0 32 .9

D3 June 10 17 .0 17 .9 22 .8 30 .3 31 .5 37 .1 24 .4 25 .1 31 .3D4 June 20 17 .9 17 .9 20 .3 27 .7 29 .7 33 .6 23 .5 24 .8 30 .0D5 June 30 16 .6 15 .6 17 .2 21 .7 24 .3 27 .7 21 .5 20 .7 25 .3D6 July 9 12 .4 11 .8 3 .8 17 .8 20 .7 13 .4 17 .0 15 .9 20 .1

LSD (0 .05) Environment x Date x Cultivar = 4 .0 1 Average seeding date across three environments in 2014 or 2015; 2 Maturity rating 0 .9, cultivar AG00932; 3 Maturity rating 0 .7, cultivar P90Y70; 4 Maturity rating 1 .4, cultivar P91Y41

Table 2. Mean soybean seed yield (bu/ac) for three cultivar maturity ratings at six seeding dates averaged across six environments (three locations and two years).

Seedling Date1 0.92 0.73 1.44

D1 May 23 36 .7 40 .0 43 .5D2 June 2 35 .5 37 .2 38 .5

D3 June 11 33 .5 34 .8 36 .7D4 June 22 29 .7 31 .8 27 .1D5 July 1 20 .5 21 .3 16 .8D6 July 9 15 .7 15 .5 7 .9

LSD (0 .05) Date x Cultivar = 3 .4 1 Average seeding date across six environments in 2014 or 2015; 2 Maturity rating 0 .9, cultivar AG00932; 3 Maturity rating 0 .7, cultivar P90Y70; 4 Maturity rating 1 .4, cultivar P91Y41

30www.ndsoybean.org

Impact of Drainage-Water Management and Subirrigation on Soil and Water QualityPrincipal Investigators: Dr. Xinhua Jia and Dr. Thomas Scherer, Agricultural and Biosystems Engineering, NDSU Funded Project

$49,829

Soybeans are very sensitive to soil moisture and salinity conditions in the field. For the last 20 years, it has been difficult to plant or harvest many fields in the Red River Valley due to wet conditions . To solve this problem, tile drainage was quickly established in the region to remove excess water, to reduce the soil salinity, and to create critical windows for planting and harvesting . However, tile-drainage water contains high-soluble chemicals, including nitrate and dissolved minerals (salts), that can pollute our streams and lakes . In this study, we used six farm fields, four in Clay County, Minnesota, and two in Richland County, North Dakota, to monitor the nutrients and salts coming from tile-drainage outlets . In our research, we compared four different ways to manage the water and to reduce the nitrate loss for a tile-drained field. These methods included conventional drainage, meaning the drainage water is directly discharged into a surface ditch; controlled drainage, which

has a control board at the drainage outlet to control the time and amount of drainage flow; and subirrigation, which adds water to the tile-drained field at the control drainage structure . We also had two check fields, which had surface drainage and no tile drainage . Our results indicated that the nitrate-nitrogen concentration in the tile drainage water (28 .3 ppm) was 22 times higher than in the surface-drainage ditches (1 .3 ppm) . With controlled drainage, the nitrate was retained in the field. Phosphorus monitoring showed that both surface and subsurface flows contained phosphorus concentrations that exceeded the water-quality standard . A higher amount of salts was found in tile-drainage flows compared to the surface ditch water . With good water-management practices in the field, we can help retain nitrate and salts in the field during the late spring and summer, and can improve the water quality for our surface-water environment .

Soil salinity is a big concern for many soybean growers because it can cause soybean iron chlorosis and reduce the soybean yield . Tile drainage is the only effective way to permanently leach out the salts, to reduce the soil salinity and to keep the land productive . We monitored the soil-salinity changes in the field using soil sampling and salinity maps . Our results clearly indicated that the soil salinity was reduced with tile drainage . The biggest benefit for soybean growers and the soybean industry is to understand that, through drainage-water management, we can achieve better yields in the field and maintain clean water for all citizens in the Red River Valley .

In our research, we compared four

different ways to manage the water and reduce the nitrate loss for a tile-drained field.

—continued from page 29seeding Dates 1 and 2 . At seeding Date 1, the 1 .4 maturity-rated cultivar yielded 39 bu/acre with later seeding Dates 2, 3 and 4 yielding between 30 and 33 bu/acre . Based on this study with six environments, seeding-date recommendations for the 00 .9 and 0 .7 maturity ratings and a yield potential of 30 bu/acre or greater were from seeding on/before June

23 and for maturity-rated cultivars 1 .4 on/before June 11 (Table 2) . Also, the yield was generally the greatest at the earliest and second seeding dates for each maturity rating .This soybean research provides producers with important information about late -or replanting-soybean performance compared to alternative production strategies regarding profit potential. Cropping alternatives for late-seeded

soybean could be short-season crops, such as buckwheat and millet; warm-season forages; and cover crops . Perhaps late-seeded soybean could follow an early maturing winter annual oilseed crop (camelina) that is harvested in late June/early July . The study provides support for a graduate student to develop research skills, leading to a career that will contribute to agricultural sustainability .


Soybean producers make important decisions to maximize the seed yield and economic return by optimizing the planting date and plant population . With the expansion of soybean acres in the northern growing regions of Manitoba and North Dakota, current information about the soybean-yield response to different planting windows is limited . Planting time is critical because growers in these short-growing-season regions face the risk of both a late-spring and early fall frost . If a poor soybean stand occurs, it can be difficult to determine if replanting is necessary due to the soybean plants’ ability to compensate for stand loss by adding branches, pods and seed . Although it is currently recommended to increase seeding rates with late planting to compensate for a potential yield loss, it can still be risky to replant or delay planting until June . The objectives of this study were to 1) identify the impact of a reduced soybean-plant stand on soybean yield; 2) monitor the compensation level for soybean-plant growth based on the planting date and the plant’s population density; and 3) compare the optimum economic soybean plant populations for the early, ideal and late seeding windows .

Six target plant populations, 80,000; 110,000; 140,000; 170,000; 200,000 and 230,000 plants ac-1, were seeded on early (May 4), ideal (May 22) and late (June 10) calendar dates in 2015 at the Ian N . Morrison Research Farm in Carman, Manitoba . The lowest plant population was intended to mimic a 50 percent stand loss, and the “late” planting date was intended to mimic a replanted soybean crop . The study’s findings can be compared

to the results reported from Carman in 2014 and to a partnered study at North Dakota State Univeristy’s Carrington Research Extension Center in Carrington, North Dakota .Yields increased with a higher plant population until reaching maximum yields . These maximums were similar for the early and ideal dates, but different for the late planting date at Carman in 2015 (Figure 1) .


Optimizing the Soybean Plant Population for Early, Ideal and Late Planting DatesPrincipal Investigator: Dr. Yvonne Lawley, Plant Science, University of Manitoba;Co-Investigator: Cassandra Tkachuk, Plant Science, University of Manitoba

Soybeans nearing maturity on September 4, 2015, at the University of Manitoba’s Ian N. Morrison Research Station in Carman, Manitoba. Left to right: block of plots from the first replicate seeded on the early (May 4), late (June 10) and ideal (May 22) planting dates in 2015.


70

60

50

40

30

20

10

0 0 40 80 120 160 200 240 280 320

Plant Population Density (1000 pl ac-1)

Early Planting DateIdeal Planting DateLate Planting Date

Yie

ld (b

u a

c -1

)

Figure 1. Relationship between soybean yield and soybean-plant population density at Carman in 2015 for the early, ideal and late planting dates. The plantings date are described by the quadratic model where yield begins to decline beyond a maximum level at a given plant density.

and M.M. Maharlooei, Agricultural and Biosystems Engineering, NDSU

The Effectiveness of Unmanned Aircraft Systems (UAS) for Soybean Crop Management 25 Principal Investigator: John Nowatzki, Agricultural and Biosystems Engineering, NDSU; Co-investigators: Dr. Samuel Markell, Plant Pathology, NDSU; Dr. Sreekala G. Bajwa and Dr. Ganesh Bora, Agricultural and Biosystems Engineering Dept., NDSU

Soil and Water Management for Soybean Production in Fargo Clay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .26 Principal Investigators: Dr. Amitava Chatterjee and Dr. Aaron Daigh, Soil Science, NDSU

Calibration of a Ureide Tissue Test for Soybeans . . . . . . . . . . . . . . . . . .27 Principal Investigators: Dr. R. Jay Goos and Shiny Mathews, Soil Science, NDSU; Dr. Jasper Teboh, NDSU Carrington Research Extension Center; Shana Forster, North Central Research Extension Center, NDSU

Soybean Response to Various Management Inputs with Tile-Drained Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .28 Principal Investigator: Dr. Hans Kandel, Plant Sciences, NDSU

Seeding Date, Cultivar and Location Influence on Soybeans’ Performance and Phenology in Eastern North Dakota 29 Principal Investigator: Dr. Burton Johnson, Plant Sciences, NDSU

Impact of Drainage-Water Management and Subirrigation on Soil and Water Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .30 Principal Investigators: Dr. Xinhua Jia and Dr. Thomas Scherer, Agricultural and Biosystems Engineering, NDSU

Optimizing the Soybean Plant Population for Early, Ideal and Late Planting Dates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31 Principal Investigator: Dr. Yvonne Lawley, Plant Science, University of Manitoba; Co-Investigator: Cassandra Tkachuk, Plant Science, University of Manitoba

Lease of JD 4520 Tractor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32 Dr. Ted Helms, Plant Sciences, NDSU

Evaluating the Effects of Soybean Plant Stand by Planting Date 33 Principal Investigators: Dr. Mike Ostlie and Greg Endres, NDSU Carrington Research Extension Center and Cassandra Tkachuk, Plant Science, University of Manitoba

Improving the Soil Health and Productivity of Sodic Soils . . . . . . . .34 Principal Investigator: Dr. Tom DeSutter, Dr. Amitava Chatterjee and Dr. Abbey Wick, Soil Science, NDSU; Co-Investigator: Maria Breker, Soil Science, NDSU

Weed Management in Soybeans Contributes to a Field-Based Weed-Control Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .35 Principal Investigators: Dr. Thomas J. Peters, Plant Sciences, NDSU; Andrew Lueck, NDSU and Dr. Rich Zollinger, Plant Sciences, NDSU

Maximizing Soil Warming and Health with Different Tillage Practices in a Corn-Soybean Rotation 36 Principal Investigator: Dr. Aaron Daigh, Soil Science, NDSU; Co-Investigators: Jodi DeJong-Hughes, UMN Extension and Dr. Abbey Wick, Soil Science, NDSU

Impact of Selected Establishment Factors on Soybean Production: Row Spacing and Plant Canopy . . . . . . . . . . . . . . . . . . . . . .37 Principal Investigator: Greg Endres, NDSU Carrington Research Extension Center Co-Investigator: Dr. Mike Ostlie, NDSU Carrington Research Extension Center

Management of Soybean Aphids and Interaction with Soybean Cyst Nematode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38 Principal Investigators: Dr. Janet Knodel and Dr. Samuel Markell, Plant Pathology, NDSU; Co-Investigators: Dr. Ted Helms, Plant Sciences, NDSU; Patrick Beauzay, Plant Pathology, NDSU; Jodi DeJong-Hughes, UMN Extension and Dr. Abbey Wick, Soil Science, NDSU

Determination and Validation of Transcriptomic and Metabolite Biomarkers for Dietary Soybean-Meal Utilization 39 Bridget Owen, Executive Director, Soy Aquaculture Alliance

Utilizing Soybean Maturity Class and Planting-Date Evaluation to Improve Soybean Production . . . . . . . . . . . . . . . . . . . . . .40 Principal Investigator: Dr. Jasper Teboh, NDSU Carrington Research Extension Center; Co-Investigators: John Rickertsen, NDSU Hettinger Research Extension Center and Szilvia Zilahi-Sebess, NDSU Carrington Research Extension Center

Getting to the Root of the Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41 Allie Arp, NCRSP Communications Liaison

32www.ndsoybean.org

—continued from page 31Beyond the maximum level, the yields declined with further increases in the plant population for all planting dates . A marginal cost analysis was used to calculate the economic-optimum seeding rate for soybeans to incorporate yield maximization and economic return . Assumptions for these calculations included a soybean seed cost of $44 per 1,000 seeds, excluding seed treatment; a soybean price of $10 bu-1 and the average mortality from the experiment, 22 percent . The economic-optimum seeding rates calculated for the treatments in the 2015 Carman experiment were 154,000; 144,000 and 198,000 seeds ac-1 for early, ideal and late planting, respectively . Economic-optimum seeding rates were also calculated for the results

previously reported from Carman in 2014 . They were 157,000 and 147,000 seeds ac 1 for the ideal and late planting (no early treatment in 2014) . The econmic-optimum seeding rates calculated for all planting dates in both years were similar but always lower than the plant population that achieved the maximum yield . Comparing trends for the economic- seeding rates between 2014 and 2015 revealed that the likelihood of late-season moisture should be a seeding-rate consideration for late- seeded soybeans . At Carman in 2014, the economic-optimum seeding rate was lower for late compared to ideal planting dates, the opposite of what was expected . This may be explained by the early fall frost and the lack of precipitation during the seed-filling period (R5 to R7 stages)

that occurred in 2014 . In contrast, the 2015 results coincided with current recommendations to increase the seeding rate for late planting when adequate precipitation was received during seed-fill. Thus, the results from this study suggested that soybean growers should consider both growing-season length and the potential for late-season moisture when deciding th erates for late planting .

Lease of JD 4520 TractorDr. Ted Helms, Plant Sciences, NDSU

Yields increased with a higher

plant population until reaching maximum yields .

Pictured is a John Deere 4520 tractor for pre-emergence chemical application. The tractor is really important to help with weed control and end-trimming of plots. We especially appreciate the tractor cab because we are primarily using this tractor to spray herbicides and, possibly, insecticides later in the summer. Because of financial support of the North Dakota Soybean Council, this tractor helps contribute to the success of our research and breeding program at NDSU.


Evaluating the Effects of Soybean Plant Stand by Planting DatePrincipal Investigators: Dr. Mike Ostlie and Greg Endres, NDSU Carrington Research Extension Center and Cassandra Tkachuk, Plant Science, University of Manitoba

Plant date blocks ranging from mid-may (left), early may (center) and mid June (right).

Funded Project$8.400

In 2014 and 2015, a soybean study was conducted in Carrington with 3-4 planting dates and 5 plant populations at each planting date . The plant-population targets ranged from 80,000 to 200,000 established plants per acre . Planting dates ranged from early May to mid-June, each separated by 2 weeks . The study’s goal was to determine the optimum plant population for each planting date, covering the range of the soybean-planting window for central North Dakota . This was designed not only to help soybean farmers manage seed costs, but also to assist with decisions about replanting a field with a poor initial soybean stand . Industry trends, as well as previous North Dakota State University research, pointed to earlier planting dates for soybeans as being a big benefit to yield. For the first time in quite a while, this idea was heavily challenged with cold, near-blizzard conditions occurring in mid-May 2015 . This resulted in the only conditions that reduced emergence in both years . As a result of that cold spell, we saw a nearly 50 percent reduction in plant stand for our first planting date, compared to roughly 20 percent for 2014 and all other 2015

planting dates . As expected, soybeans were sensitive to planting date . In general, June-planted soybeans performed worse than May-planted soybeans, similar to the trend in 2014 (Table 1) . In 2015, the soybeans responded more to early cold temperatures as well as to the moisture status at planting . (The early June date was planted into soil with good moisture .) This skewed the 2015 yield data toward more typical or late planting dates .Even though the yield was heavily influenced by the environment on a particular planting date, calendar dates were a good predictor for the

optimum plant-population trends regardless of the environmental conditions . In other words, soybean populations for maximum yield and maximum economic return increased as the planting date moved later (Table 1) . The recommendation for soybeans is 150,000 established plants/a . Based on this information, that is a pretty good general recommendation . However, you may want to consider altering populations if you are planting earlier or later than typical . Soybeans compensate for stand loss by adding branches and, eventually, more pods/plant . With earlier planting dates, the plants have more time to add branches before flowering and pod fill. The Carrington data, plus data from a mirrored study in Manitoba, substantiate that lower plant populations produce good results with early planting while many more plants may be needed for late planting . Unfortunately, the optimum plant population is different every year for each planting date, depending on soil and weather conditions . The key may be increasing or decreasing the standard population if you are planting outside the typical planting window .

Table 1. The soybean-plant populations needed for maximum yield and economic return in 2014 and 2015 at Carrington, North Dakota.

EnvironmentPlanting

Date

Plant Population for

Maximum Yield (plants/ac)

Maximum Yield

(bu/ac)

Maximum Economic Return

(plants/ac)

Carrington 2014

Mid-May 182,000 70 .3 175,000Early June 315,000 50 .6 282,000

Carrington 2015

Early May 113,000 43 .5 106,000Mid-May 157,000 48 .0 142,000

Early June 146,000 55 .9 139,000Mid-June 164,000 48 .5 152,000

34www.ndsoybean.org

North Dakota has over four million acres of sodic soils, and producers have difficulty managing the soil for soybean production . Sodic soils have excess sodium compared to calcium and magnesium . These soils are difficult to manage because, when they are wet, the trafficability is often reduced; and when these soils are dry, they become very hard, reducing seed germination and root growth . Adding amendments to combat the sodium’s negative influence on soil is a “first-choice” management strategy . The research’s intent was to determine management options that are feasible and cost effective and that promote soil health . This study used flue-gas desulfurization gypsum (a byproduct from coal combustion), sugarbeet-waste “spent” lime (mostly calcium carbonate) and potassium-magnesium sulfate (sold as K-Mag

fertilizer) . Each amendment was applied at three rates at two sites near Delamere, North Dakota; both sites were mapped as having excess sodium . The rates for the gypsum and spent lime were 5, 15 and 30 tons/acre, and the K-Mag was applied at 1, 2 .5 and 5 tons/acre . One site was tile drained, and the other one was only surface drained (no tile) . In 2014, each site was seeded with alfalfa, which was being used as a “nurse crop” to better promote long-term soybean production and soil health . The amendments, coupled with alfalfa’s ability to promote soil health, were hypothesized to improve the soil function and to enhance the long-term soil productivity .Applying the amendments, up to 30 tons/acre for flue-gas gypsum and spent lime, did not alter the alfalfa yield or its quality at either site . The high rate of K-Mag, 5 tons/

acre, reduced the alfalfa yield at the non-tiled site, compared to the zero-amendment treatment, which was likely due to K-Mag’s high solubility and the subsequently increased the soil’s electrical conductivity during seed germination . Over time, however, the soil’s electrical conductivity was not different across treatments and rates of application . The amendment applications’ rates were not high enough, or their solubilities were too low (gypsum and lime) to see treatment differences in the soil %Na (the ratio of Na to Na+Ca+Mg+K) in the 0-6” depth . A laboratory study using soils from both field sites determined that the spent lime greatly increased the soil’s microbial respiration compared to the other field treatments and rates. The other treatments (gypsum and K-Mag) and their application rates did not reduce microbial respiration below that of the zero-amendment treatment . These results were significant because the amendments used for sodic soil management most likely will not decrease the soil’s microbial activity .Improving the soil’s health and function is critical for long-term financial and food security. In precipitation-limited regions, such as North Dakota, increasing the sodic soils’ health is difficult. However, using a nurse crop, such as alfalfa, and amendments may offer improved long-term soil health and soybean productivity .

Dr. Abbey Wick

Improving the Soil Health and Productivity of Sodic SoilsPrincipal Investigator: Dr. Tom DeSutter, Dr. Amitava Chatterjee and Dr. Abbey Wick, Soil Science, NDSU;Co-Investigator: Maria Breker, Soil Science, NDSU Funded Project

$37,405


Weed Management in Soybeans Contributes to a Field-Based Weed-Control StrategyPrincipal Investigators: Dr. Thomas J. Peters, Plant Sciences, NDSU; Andrew Lueck, NDSU and Dr. Rich Zollinger, Plant Sciences, NDSU

Waterhemp control from Dual Magnum + Valor fb Liberty in soybean, Herman, MN, 2015. Borders are running check of waterhemp infestation.


With the advent of resistant weeds, weed management is a renewed production challenge for soybean growers in Minnesota and eastern North Dakota . Weed management must change from a series of tactical sprays to a planned strategy that incorporates regular scouting; positive identification of weeds; and mapping with an emphasis on prioritizing the control of target weeds based on population/density, difficulty to control, and the longevity of seed viability in soil . The physical features of the field should also be a component of the plan, including topography, soil type, and proximity to ditches and field boundaries. The weed-management strategy emphasizes the “field” and targets weed control across crops that comprise the crop sequence in the field. Weed control should be two or three herbicides applied pre-emergence and/or post-emergence alone or in mixtures that are efficacious against target weeds; are safe for rotational crops; and represent herbicide diversity, including herbicide tolerance trait(s) and herbicide site-of-action (SOA) families .In 2014, 2015 and 2016, experiments were conducted with natural populations of waterhemp and kochia in soybeans . (This report focuses on 2015 .) The experimental area also included corn and sugarbeet experiments to simulate waterhemp or kochia control for rotational crops in the field. The experiment was a randomized, complete-block design and four replications . Plots were 6 rows at 22-inch row spacing by 35 feet . Two or three effective soybean herbicides were applied

over LibertyLink soybean . Soybeans, corn or sugarbeets were planted at commercial seeding rates using a John Deere Maxemerge planter . Herbicide treatments were applied with a bicycle-wheel plot sprayer at 17 gallons per acre through 8002 XR flat-fan nozzles pressurized with CO2 at 30 pounds per square inch to the center 4 rows of 6-row plots that were 35 feet in length . Data collection was a visual assessment of soybean safety and weed control at multiple time points during the growing season . Data were analyzed with ARM’s ANOVA procedure, version 2015 .6, software package . In soybeans, herbicide treatments that combined a chloroacetamide herbicide (SOA 15), a PPO inhibitor herbicide (SOA 14), and Liberty (SOA 10) grass and broadleaf weed control were greater than other herbicide treatments . Soybean yield was not measured in these experiments . Seventeen herbicides (19 active ingredients) representing 11 SOA families were applied across the soybean, sugarbeet and corn experiments . Across the crop sequence, 12 herbicides were applied in one of three crops, 2 herbicides were applied in two crops and 3 herbicides were applied to all three crops in the sequence . Multiple herbicides were from the

same SOA family and were used within and across crops . That is, 12 herbicides applied in and across crops represented 4 SOA families . Currently, there is no objective way to quantify herbicide diversity in and across crops in the sequence . Diversity indices that have been successfully used for ecological experiments to document species richness and evenness might be applicable to measure herbicide diversity . Diversity indices, evenness, and models that aid farmers in making herbicide-selection decisions based on SOA families, weed efficacy, rotational crop safety and economic choices are ideas for making informed decisions, for establishing a diversity “baseline” and for quantifying diversity “improvement” across the crop sequence . These ideas represent the project’s next phase .

36www.ndsoybean.org

Maximizing Soil Warming and Health with Different Tillage Practices in a Corn-Soybean RotationPrincipal Investigator: Dr. Aaron Daigh, Soil Science, NDSU; Co-Investigators: Jodi DeJong-Hughes, UMN Extension and Dr. Abbey Wick, Soil Science, NDSU

There are many advantages of reducing soil tillage to build soil health . However, reducing tillage creates concerns about yield reductions because of cool and wet soils in the poorly drained landscape that dominates much of North Dakota and the Red River Valley . The objectives of this study are as follows:1 . To monitor soil warming and

water contents with a chisel plow, vertical tillage, strip till with shank, and strip till with a coulter on various soil series;

2 . To evaluate soil health, crop emergence and yields; and

3 . To transfer information to producers through field days, videos, and others .

This multi-state effort involves North Dakota and Minnesota, and is in year two of a four-year field study . Four on-farm locations have a corn-soybean rotation and rotate every year . At each location, the four tillage practices are demonstrated in a replicated design using full-sized equipment in plots that are 40- or 66-feet wide by 1800-feet long . The evaluated soil series are Fargo silty clay; Lakepark clay loam; Barnes-Buse loam; Delamere fine, sandy loam and Wyndmere fine, sandy loam . These soil series cover over 67 million acres of prime farmland in the Northern Great Plains .Soybean yields for 2015 did not significantly differ among treatments. Soil temperatures and water contents differed among tillage treatments briefly in the spring of 2016 but then converged during the following cool, dry weeks through planting . In the spring of 2016, the crop-

residue cover tended to differ among tillage treatments, as expected, for farms planted with soybeans and farms planted with corn in these corn-soybean rotations . However, stand counts and plant heights did not differ among the tillage treatments at any of the farms . One interesting observation was the fluffy soil in the chisel-plow treatments at some farms . This was also observed in the spring of 2015 (a relatively dry spring, similar to 2016) . Therefore, the planter down pressure and residue managers needed to be adjusted . The residue managers were raised, and the lightest down pressure was used for seeding; otherwise, the planter dug into the soil about six inches .Soil samples were collected to determine soil health and were

analyzed in the laboratory . Crop-residue cover, crop populations, and yields were evaluated during the summer and fall months . One video was produced to deliver the study findings to North Dakota producers. Information obtained during this year will be presented at joint NDSU and UMN Extension events, including the 2016 Soil Health Tour and the 2016 Conservation Tillage Conference, as well as at the joint International America Society of Agronomy, Soil Science Society of America and Crop Science Society of America Conference in Phoenix, Arizona, in November 2016 .


One interesting observation was the

fluffy soil in the chisel-plow treatments .

2016 planting on Dr. Aaron Daigh’s tillage plots at the Soil Health and Agriculture Research Extension (SHARE) Farm in Mooreton, ND.


Impact of Selected Establishment Factors on Soybean Production: Row Spacing and Plant Canopy Principal Investigator: Greg Endres, NDSU Carrington Research Extension Center; Co-Investigator: Dr. Mike Ostlie, NDSU Carrington Research Extension Center

Paired rows (mid foreground) with 14- and 28-inch row plots surrounding a paired-row plot. Greg Endres


In 2015, a field study was initiated at the CREC to examine the soybean response to paired rows compared to wider rows and plant canopy types . The newest interest with soybean row spacing is paired rows, but the research data are very limited in North Dakota . As farmers consider row-spacing options, they also ask about the best plant canopy type when determining what soybean varieties to grow . Data from this study will assist in answering questions regarding the advantages for using paired-row spacing and if the plant canopy type is a contributing factor to increase soybean yield .In 2015, row spacing included a)

7-inch pairs (centered on 28 inches), b) 14 inches and c) 28 inches . The plant canopy types included a) intermediate (Peterson Farms Seed 12R05) and b) bushy (PFS 15R05N) . The varieties had the same maturity group rating; they also had similar yield potential, plant height and several other agronomic traits . The best management practices, as recommended by North Dakota State University, were used for soybean production .Averaged across the two varieties, plant stand was similar among

row spacings (trial average of 118,800 plants/acre) . With 14-inch rows, canopy closure occurred 10 days earlier

than with paired 7-inch rows and 27 days earlier than with 28-inch rows . The pod height with paired and 14-inch rows was slightly over 1 inch higher than with 28-inch rows . Seed yield was similar among row spacings, Averaged across rows, the intermediate canopy type had quicker canopy closure versus the bushy type . Plant response, and seed yield and quality with the row-by-plant type interaction were not statistically significant. No conclusions or recommendations can be generated due to the limited (one site-year) data . The study continues in 2016 .

The newest interest with soybean row

spacing is paired rows, butthe research dataare very limited .

38www.ndsoybean.org

North Dakota Soybean Council Research Committee chairman Troy Uglem scouts for aphids.

Management of Soybean Aphids and Interaction with Soybean Cyst NematodePrincipal Investigators: Dr. Janet Knodel and Dr. Samuel Markell, Plant Pathology, NDSU;Co-Investigators: Dr. Ted Helms, Plant Sciences, NDSU; Patrick Beauzay, Plant Pathology, NDSU; Jodi DeJong-Hughes, UMN Extension and Dr. Abbey Wick, Soil Science, NDSU

The soybean aphid is well-established in the upper Midwest and continues to be a serious pest for soybean production in North Dakota . Soybean producers need to be vigilant and scout their fields regularly, especially from July through mid-August when soybean-aphid populations can increase rapidly . The goal for this portion of our research was to compare alternate management strategies, including the use of an insecticide seed treatment, early (R1) foliar insecticide applications and a Rag1 aphid-resistant variety, to a foliar-insecticide application made at the economic threshold of 250 aphids per plant . We also examined the use of a 10 percent sugar solution and a sugar-based biorational insecticide called sucrose octanoate because

there have been recent questions about whether these products can provide adequate soybean-aphid control . Under heavy soybean-aphid pressure, our results demonstrated that using an insecticide seed treatment (thiamethoxam) and R1 foliar insecticide applications (lambda-cyhalothrin) did not result in a yield advantage compared to foliar insecticide applications made at the economic threshold of 250 aphids per plant, even when the seed treatment and/or R1 applications appeared to keep soybean-aphid numbers below the economic threshold . Utilizing an insecticide seed treatment and

early foliar insecticide applications are no guarantee to keep aphid populations in check through the R6 growth stage when soybeans are no longer susceptible to aphid feeding . These treatments are ineffective for late July and early August aphid influxes, and producers may need to make a second foliar application, with no added yield benefit to offset the additional cost . Furthermore, early foliar insecticide applications can reduce the soybean aphids’ natural

enemies, may flare spider mites and may promote the development of insecticide-resistant aphids . Sugar and sucrose octanoate did not provide acceptable aphid control, and we do not recommend using these products . Soybean cyst nematode (SCN) is a major threat to soybean production in the upper Midwest and has expanded significantly in North Dakota, particularly in the eastern part of the state . Recent research suggests that there is a relationship between soybean-aphid density and SCN reproduction, and soybean-aphid feeding may result in increased SCN, even with SCN-resistant varieties . The goal of this portion of our research was to examine the SCN population growth in SCN-resistant and susceptible varieties with differing soybean-aphid pressure (aphid free, economic threshold and untreated) . We found that, with the SCN-resistant variety, the SCN increased when soybean-aphid numbers increased, but we did not observe the same pattern for the SCN susceptible variety . For SCN levels, the SCN-resistant variety had significantly less SCN than the susceptible variety at the end of the growing season . At this time, there is no compelling evidence to change extension-recommended management practices for either soybean aphid or SCN .


The goal for this portion of our research was to

compare alternate management strategies .


Determination and Validation of Transcriptomic and Metabolite Biomarkers for Dietary Soybean-Meal UtilizationBridget Owen, Executive Director, Soy Aquaculture Alliance

The long-term goal of the research is to develop biomarkers or biomarker signatures that will quantitatively correlate to the growth and efficiency performance indices when soybean meal (SBM) is utilized in the diets of rainbow trout, red drum and cobia . Three primary objectives were addressed: 1) Define the correlations

between growth rates and dietary SBM concentrations (anti-nutrient levels)

2) Validate the molecular markers in the liver and intestine across the three species that correlate with growth performance, intestinal histological evaluation,

3) Define the metabolite changes in the plasma that correlate with the growth performance .

The generated data highlighted seven important points: 1 . With respect to growth and

histological changes, an inflammatory effect was achieved for all three species with the experimental diets .

2 . Growth and/or histological changes were correlated with increased SBM in the diet .

3 . Rainbow trout are generally considered to be more sensitive to SBM than red drum and cobia . This belief was generally borne out by the lower feed efficiency and a larger HSI in rainbow trout compared to what was observed for the other two species which were fed the same diets .

4 . Histological examination revealed putative inflammatory effects for all three species even though the growth was not, apparently, affected in red drum and cobia .

Significant changes were observed for different histological parameters in each species, suggesting that the mechanisms to compensate for the antinutritional factors (ANFs) found in SBM may be different for each species; alternatively, different ANFs have dissimilar effects in these species .

5 . Gene-expression data revealed three genes which may be suitable as biomarkers for SBM-induced gastric enteritis (transforming growth factor β, interleukin 2 and calprotectin) for all three species .

6 . The use of “calprotectin” as a potential biomarker warrants further investigation . In other vertebrates, genes that encode calcium-binding proteins are highly upregulated with inflammation and are translated into protein sequences that form a heterodimer

referred to as “calprotectin .” Sloughing the intestinal cells and neutrophils into the intestinal lumen carries a lot of this protein which can be detected non-invasively by using antibodies . This idea is promising for non-invasive sampling in cultured fish.

7 . Metabolomic analysis revealed a trend toward increasing the dietary SBM inclusion in rainbow trout and illustrated the differences between diets containing 0 to 10 percent SBM and 20 to 40 percent SBM in red drum . Metabolomic analysis of the cobia plasma showed no overall differences between the dietary treatments . This underscores the point that different species have varying metabolic responses to dietary SBM .

Growth and/or histological were correlated with

increased SBM in the diet .

The Soy Aquaculture Alliance (SAA) brings together soy, aquaculture and seafood stakeholders to enhance their own businesses and industries as well as give consumers access to affordable, healthy seafood. To learn more about SAA, visit soyaquaalliance.com.

40www.ndsoybean.org

Utilizing Soybean Maturity Class and Planting-Date Evaluation to Improve Soybean ProductionPrincipal Investigator: Dr. Jasper Teboh, NDSU Carrington Research Extension Center; Co-Investigators: John Rickertsen, NDSU Hettinger Research Extension Center and Szilvia Zilahi-Sebess, NDSU Carrington Research Extension Center

Producers and stakeholders have interest in expanding soybean production and productivity beyond central North Dakota and entering non-traditional production areas that are west of the Missouri River . Dry summers with low available moisture at critical growth stages and a short growing cycle are some limiting factors . The project’s goal is to enhance the farmer’s understanding about how maturity groups (MG) and planting dates may be used to minimize soybean-production risks . This research was conducted to determine the optimum yielding maturity classes for soybeans under dryland conditions

in western North Dakota and under irrigation in central North Dakota to determine if early or late planting has a significant impact on soybeans.Field trials were conducted in Hettinger (HREC), where soybeans from three MGs were established under rain-fed conditions, and in Carrington (CREC), where four MGs were grown under irrigation . Seeds were planted at four planting dates from May 5 (early planting) to June 9 by June 16 (late planting) . The soybean MGs were, 0 .2, 0 .4 and 0 .6 at both sites, including 0 .7 at the CREC . Maturity groups 0 .2 to 0 .4 would be considered

early at HREC while they are considered suitable at the CREC . Each planting date was about one week from the previous date at HREC, and varied between 10 days and 2 weeks from the previous date at Carrington . Maturity groups 0 .2 and 0 .6 were RoundUp Ready varieties while 0 .4 and 0 .7 were conventional varieties . Average yield at the HREC was 31 bu compared to 41 bu/a at CREC . At HREC, yields were significantly greater for MG 0 .6 and 0 .2 than for MG 0 .4 when averaged across planting dates . This suggests that yield differences among MGs may be due to influences from factors other than being an early or late variety . It must be noted that the 0 .2 and 0 .6 varieties were both RoundUp Ready varieties, and the other(s) was(were) conventional . Protein followed a similar trend as yields while oil was lower for any MG with a higher yield . Planting on May 19 produced the highest yields (averaged at 35 bu/a), followed by May 27 (31 bu/ac) . Yield differences for MG 0 .6 had more stability, showing fewer differences between planting dates . Despite the yield dependency of early MG on the planting date, this was not consistent . Overall, later maturing varieties had better yields . At CREC, yield differences were not significant. Average yields varied between 44 bu (MG 0 .7) and 41 bu/a (MG 0 .4) for the fou planting dates at this site . Planting on June 1st produced the highest yield, even though yields and protein were not significantly different. Results in 2015 indicated that early planting can produce similar yields as a later planting date, depending on available moisture (as in CREC), maturity group used .

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Figure 1: Yields of three maturity groups of soybeans planted on four different planting dates at Hettinger, 2015.

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Figure 2: Yields of four maturity groups of soybeans planted on four different planting dates at Carrington, 2015.

*Averaged within dates. Identical upper case letters indicate means are not significantly different (p<0.05). Identical lower case letters within planting date indicate means are not significantly different (p<0.05).

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Getting to the Root of the ProblemAllie Arp, NCRSP Communications Liaison

A multi-year project at North Dakota State University (NDSU) analyzed the genetics of Iron Deficiency Chlorosis (IDC) thanks to funding from the North Central Soybean Research Program (NCSRP) . The uptake of iron is essential to healthy soybean growth, but conditions aren’t always ideal and soybean plants —especially those grown in north central states—can become deficient.Research has shown that many genes have to work together to make proper iron uptake and function possible . The research project, “Iron Deficiency Chlorosis: Getting to the Root of the Problem” was led by Dr . Phillip McClean, Department of Plant Sciences at NDSU with contributions from Dr . Ted Helms, also of NDSU . Dr . Robert Stupar, Department of Agronomy and Plant Genetics, University of Minnesota, Dr . James Orf, Department of Agronomy and Plant Genetic, University of Minnesota have also coinvestigators on the project . The study sought to find and analyze genes related to

ICD . The project’s initiatives included three stages focused on molecular marker discovery and deletion analysis as outlined in their recent annual project report . Stage 1: Marker discovery — The research team initially planned to evaluate 200 molecular markers, but were fortunately able to switch to five times that many for the project . The 1,000 genome-based markers were overlaid onto 284 breeding lines from 30 public and private programs with a focus on Great Plains varieties . The research revealed nine markers were associated with IDC tolerance . Eight of the markers were located near Gm03 — a gene involved in iron metabolism . Moving forward, the team assessed the value of the markers by evaluating them on an additional 711 lines from breeding programs in North Dakota . Results showed three of the original nine markers were associated with IDC performance in these varieties . Further analysis determined which marker was found in the most IDC tolerant lines .

The next step was to determine the marker performance in active breeding programs through the help of NDSU’s breeding program . Gm19 locus was demonstrated as one component of IDC tolerance . Stage 2: Marker discovery — In the second stage of the project the team used genotype-by-sequencing (GBS) technology to collect data from 34,428 single nucleotide polymorphic (SNP) markers . The use of the GBS technology allowed for a large number of markers to be evaluated at once, ultimately determining 10 new molecular markers to target for breeding IDC tolerance . Stage 3: Deletion analysis — The relevance of Gm03 was determined through the final stage of the study’s progress this year . According to the report, advanced genotyping methods allowed the observation of susceptibility to IDC to be mapped to Gm03 in three different forms . Additional phenotyping coupled with this data solidified the importance of Gm03 to iron metabolism .



NCSRP directors Greg Converse of South Dakota and Gene Stoel of Minnesota share a laugh during the tour of the University of Missouri’s worm farm.

NCSRP director and Michigan farmer Ed Cagney looks at SCN eggs through a microscope as part of a tour of the University of Missouri’s worm farm.

42www.ndsoybean.org

—continued from page 41NCSRP funds online tools for farmers2015 marked the first full year of operation for the Soybean Research and Information Initiative (SRII) website . The initiative launched in the spring of 2014 as an effort to build upon the functionality and success of the Plant Health Initiative (PHI) in developing an easy access one-stop shop for soybean research in the Midwest . A project funded by the North Central Soybean Research Program (NCSRP), the site can be found at soybeanresearchinfo .com .In 2015, the SRII site hosted more than 319,000 visits by people who took advantage of the site’s content which features research from thirteen universities across the twelve NCSRP member states . In addition to the latest articles and publications being featured on the homepage, the SRII website offers farmers useful information about soybean diseases and pests as well as diagnostic tools and other agronomic issues .The SRII site contains NCSRP-funded soybean research summaries, detailed reports, Extension bulletins, peer-reviewed journal articles,

videos, photographs, tips and links to many other soybean information resources . Perhaps one of the most beneficial features of the site is the overview of 24 of the most prolific soybean diseases throughout the region . Each summary includes the disease’s life cycle, agronomic impact and how to manage the disease . To help identify the diseases many pictures are included in addition to scouting tips and information about how to distinguish diseases that are

commonly mistaken for each other .Even though NCSRP focuses on the northern part of the Midwest, the SRII website also includes links to the National Soybean Checkoff Research Database; a searchable directory of all the soybean-checkoff funded research programs from across the country . This provides farmers with as much information as possible to help with their operations .The National Soybean Checkoff Research database serves multiple purposes . Most importantly, it is a rich and easily searched source of soybean research projects and results designed to benefit farmers as well as public and private researchers . It is a powerful tool to drive future soybean research priorities and projects that will advance previous efforts, reduce redundancy and drive both efficiency and maximum return on checkoff investments . Increased research collaboration and coordination as well as more public and private partnerships are additional anticipated benefits. NCSRP member states are already using and benefitting from the database.NCSRP director and North Dakota Soybean Council board member Matt Danhuser,

left, works with another attendee to determine the V and R stages of a soybean plant at Farm Journal’s Soybean College.

Daryl Cates, Illinois Soybean Association director and NCSRP directors Ron Ohlde of Kansas, Cecil DeMott of Missouri and Dave Rodibaugh of Indiana inspect a plant for SCN infestation as part of a tour of the University of Missouri’s nematode greenhouse.

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Growing a Healthy Industryndsoybean.org/wp-content/uploads/2018/03/2016-NDSC... · 2018-03-12 · knichols@ndsoybean .org 2016 Research Committee Report North Dakota Soybean Council