2024 eFields Report

2024 eFields Report

Ohio State Digital Ag Program

2024 Research Recap

eFields

“connecting science to fields”

eFields is a program at The Ohio State University dedicated to advancing production agriculture through the use of fieldscale research. The 2024 eFields Report is a culmination of the research conducted over the past year on partner farms throughout Ohio. Current research is focused on precision nutrient management strategies and technologies to improve efficiency of fertilizer placement, enable on-farm evaluation, automate machine functionality, enhance placement of pesticides and seed, and to develop analytical tools for digital agriculture.

eFields has expanded from 39 on-farm research sites in 13 counties in 2017, to 95 on-farm research sites covering 25 counties in 2018, 88 on-farm research sites in 30 counties in 2019, 218 on-farm research sites in 39 counties in 2020, 249 on-farm research sites in 45 counties in 2021, 292 trials in 49 counties in 2022, and 184 on-farm research sites in 47 counties in 2023.

2024 Research Recap

New for 2024

• Highlighted Certified Organic Trials

• UAS Cover Crop and Herbicide Applications

3,075 Total Acres

• 1,390 Corn

42 Ohio Counties

260 On-Farm Research Sites

Disclaimer Notice: The information provided in this document is intended for educational purposes only. Mention or use of specific products or services, along with illustrations, does not constitute endorsement by The Ohio State University. The Ohio State University assumes no responsibility for any damages that may occur through adoption of the programs/techniques described in this document.

Soybean Forages Tech

Small Grains Water Quality

Welcome to the 2024 edition of the Ohio State eFields Report.

The 2024 Ohio State eFields report showcases the dedication of our entire Ohio State team and collaborating partners. We extend our heartfelt gratitude to everyone who continues to support the eFields program, making it a resounding success. Special thanks go to our farm partners for their invaluable time, resources, and suggestions for each study. We also want to recognize the OSU Extension Educators, field specialists, faculty, staff, students, industry partners, and many others who contribute their time and resources to these studies. eFields exemplifies the collaborative efforts across all sectors of agricultural production, helping farmers and consultants use the results to enhance crop production in Ohio and the surrounding regions.

The 2024 eFields Report marks its 8th year. Like every growing season, 2024 brought unique challenges and valuable learning opportunities. The weather was the standout story of the year. While weather patterns varied across Ohio, crops generally got off to a good start with rains through July. However, summer turned hot and dry, with most regions experiencing drought conditions in August and September, and limited precipitation until December. Harvest began in early September and wrapped up quickly, with much of the fall work completed well before Thanksgiving. Overall, crop yields varied significantly across Ohio but were near USDA averages for Ohio. Another major concern in 2024 was the decline in commodity prices, which, when adjusted for inflation, hit their lowest levels in years. This issue remains a top priority for the U.S. as we head into the 2025 growing season. The ongoing conflict between Russia and Ukraine, along with the war between Israel and Hamas, dominated headlines throughout the year. Additionally, the agricultural sector faced challenges such as a growing shortage of ag mechanics and an outbreak of avian flu affecting dairy farms. The top concerns going in 2025 are the low commodity prices, high input costs, weather, labor shortages and global market conditions. Despite a grim outlook for crop production in 2025, Ohio farmers remain optimistic, seeking ways to improve efficiency and manage costs effectively.

In the 2024 eFields Report, our team conducted 260 studies across 42 counties. We’re thrilled by the growing number and diversity of studies each year. The eFields team eagerly anticipates even more exciting projects in the future. You can access the eFields Reports from 2017-2024 online at go.osu.edu/efieldsreports or delve deeper into the data at kx.osu. edu/efields

We hope you find the 2024 eFields Report both informative and valuable. If you’re interested in collaborating with us in 2025 or have any feedback, please reach out to us at digitalag@osu.edu

Sincerely,

The 2024 eFields Team

Get Involved

Are you interested in contributing to the 2025 eFields Report? If so, visit go.osu.edu/efields to review study implementation plus tips and tricks. See below for details on how to get involved and who to contact. We look forward to working with you!

Growers

Growers interested in hosting on-farm research trials for publication in the annual eFields report should reach out to their county Agriculture and Natural Resources Extension Educator (agcrops.osu.edu/people). To view a list of those educators who are already involved, see page 14. Standard protocols for seeding rates, nitrogen rates, and other management practices have been developed for statewide implementation. Contact us today to find out how to get involved. Additional protocols and topics are being developed and can be customized to fit your questions and needs!

Industry Representatives

We are always looking for new partners to conduct on-farm trials! If you are interested in determining how you can support Ohio State University On-Farm Research, reach out to your county Agriculture and Natural Resources (ANR) Extension Educator, email Dr. Elizabeth Hawkins (hawkins.301@osu.edu), or email the eFields Program Manager, Dara Barclay (barclay.67@osu.edu). We would love to discuss your involvement with the eFields program!

Extension Educators and Field Specialists

If you are a current ANR Educator and are interested in getting involved with eFields, contact the Program Manager, Dara Barclay (barclay.67@osu.edu) to get started.

“ “

On-farm research is incredibly important because it allows me to test and validate practices and products in my own growing environment. This ensures that the results are directly applicable to my operation, rather than relying on generalized recommendations that may not fit my conditions. Pairing with programs like eFields and working with my local extension educator, I can ensure the research is sound and reliable. On-farm research keeps me on the cutting edge while ensuring I’m investing wisely, not just spending for the sake of it.

-Mike Kryling

I appreciate the opportunity to do my part in on-farm research with Ohio State University. Data collaboration is very important to improving any industry, and eFields is a great program to help row crop production farming advance to the next level.

-Doug Yoder

“

We believe that on-farm research is essential to improving agriculture across Ohio and we appreciate the continued efforts of OSU Extension to help us confidently determine what management practices will work best for our farm and determine areas of needed improvement. We would strongly encourage other farmers to get involved with the eFields program, and we look forward to continuing research on our farm in the future.

- Curtis Jones and Travis Newcomb, Sandburr Ridge Farms

Ohio State Digital Ag Program

OHIO STATE

DigitalAg

ABOUT US

The Digital Agriculture Program at The Ohio State University embodies the best of the land grant mission – creation, validation, and dissemination of cutting-edge agricultural production technologies. The central focus of this program is the interaction of automation, sensing, and data analytics to optimize crop production in order to address environmental quality, sustainability, and profitability. Research is focused on execution of site-specific nutrient management practices, development of hand-held devices for in-field data capture, autonomous functionality of machinery, remote sensing solutions, and data analytics to enhance timing, placement and efficacy of inputs within cropping systems.

VISION

The Digital Agriculture Program at The Ohio State University strives to be the premier source of research-based information in the age of digital agriculture.

MISSION

• Uniting the private and public sectors to drive innovation for the benefit of farmers.

• Partnering with farmers to translate innovation into long-term profitability for production agriculture.

• Delivering timely and relevant information for the advancement of digital agriculture technologies.

WHAT IS DIGITAL AGRICULTURE?

The premise of digital agriculture includes the advancement of farm operations through implementation of precision agriculture strategies, prescriptive agriculture and data-based decision making. Digital agriculture is a holistic picture of the data space in agriculture, trends related to services directing input management and the value of data usage for improving productivity and profitability of farm operations.

“Digital Agriculture” combines multiple data sources with advanced crop and environmental analyses to provide support for on-farm decision making.

Quality

Digital Ag Initiatives

“Helping growers make the most of Precision and Digital Ag technologies”

PRECISION SEEDING

Utilizing the latest digital ag technologies to place every seed in an environment optimized for its growth and development.

ON-FARM RESEARCH

Deploying field-scale studies to advance production agriculture through efficiency and profitability using data-driven decisions.

PRECISION CROP MANAGEMENT

Management of crop inputs in a manner that maximizes efficiency and profitability

HARVEST TECHNOLOGIES

Taking advantage of available technologies to improve harvest efficiencies and improve data quality.

SOIL COMPACTION MANAGEMENT

Mitigation of soil compaction to enhance crop health and soil structure.

REMOTE SENSING

Providing the ability to remotely assess field conditions, crop health, nutrient needs, and productivity levels on a sub-field scale.

APPS FOR AGRICULTURE

Embracing the power of smart phones and tablets to utilize mobile applications and farming smarter.

PRECISION NUTRIENT MANAGEMENT

Ensuring that all applied nutrients are in a position to maximize crop uptake. Right source, right rate, right time, right place, right technology.

PRECISION LIVESTOCK

Making use of data and digital tools to manage or automate animal well-being, food safety, pasture sustainability, waste products and more.

DATA ANALYSIS AND MANAGEMENT

Developing a digital strategy and making actionable decisions using data, from operational insights to field execution.

Report Guide

OBJECTIVE

Find study information, objectives, study design, weather graph, and summary on the left page. Find results, summaries, project contact, and statistical summary on the right page.

STUDY INFORMATION

Planting Date 4/30/2024

Harvest Date 10/16/2024

Variety Becks 6076V2P

Population 34,000 sds/ac

Acres 70

Treatments 5

Reps 7

Treatment Width40 ft.

TillageConventional

Management Fertilizer, Herbicide, Insecticide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Crosby silt loam, 52% Celina silt loam, 48%

STUDY DESIGN

The study design provides a background on the study. This could include a brief history of research, observations that led to the implementation of this study, explanation of the study design, etc.

Location

Box

Look to see the county where the study was conducted.

WEATHER INFORMATION

Here you will find visuals of the study with short descriptions.

OBSERVATIONS

The observations section of the report allows us to provide any relevant information that the researchers noticed throughout the growing season. Observations allow for a deeper understanding of the study results.

RESULTS

Treatments (XXX)

SUMMARY

• The summary section proves results and findings from the study.

• Thank you for taking the time to explore our 2024 eFields Report!

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

This section allows us to display the tools and technology used to make each study possible.

PROJECT CONTACT

The Project Contact section provides the name of the researcher along with their email address. We encourage you to contact them if you have questions about an indvidual study.

Calculations and Statistics

To effectively collect, analyze, and interpret data, statistical calculations were made for each eFields study when possible. All statistical calculations were conducted using the OSU PLOTS Research App or calculated using the ANOVA spreadsheet, using Fisher’s Protected Least Significant Differences (LSD, alpha = 0.1) method to determine if treatment differences are statistically significant.

Stand Counts and Harvest Data:

All stand counts were conducted for individual plots by counting the number of plants in 30 linear feet along two adjacent rows. All yield data was collected using calibrated yield monitors or weigh wagons. Data was processed and cleaned to ensure accuracy with yields adjusted to a standard moisture prior to analysis.

Take a look at this example from a

study:

Replication

• Allows one to estimate the error associated with carrying out the experiment itself.

• Without replication, it would be impossible to determine what factor contributed to any treatment differences.

• A minimum of 3 replications is required for a proper evaluation, with 4 or more recommended for field-scale research.

Randomization

• Randomization is as important as replication to help account for any variations in production practices and field conditions.

• Even if treatments are replicated, the conclusions you reach may not be correct if a treatment was always applied to the same part of the field.

• Randomization prevents data from being biased due to its field location.

Explanation:

Treatments

Results show the average of the response variable (i.e. yield) for each treatment.

a

a

ab

b

LSD

Least Significant Difference (LSD) is used to compare means of different treatments that have an equal number of replications. For this report, a significance level of 0.1 (or 10%) was used, which means when a treatment is statistically significant, a 90% confidence is attributed to that treatment actually being different from the comparisons.

• For treatment A to be statistically significant from treatment B, they must differ by at least 3 bu/ac. (They do not, so they are not statistically different and are marked using the same letter).

• For treatment D to be statistically different from treatment A, they must differ by at least 3 bu/ac (here they differ by 5 bu/ac, so they are statistically significant and are marked using different letters).

Defined as the coefficient of variation (CV) is a measure of the variability between treatments (i.e. yields) reported as a percentage (%). CV is an indicator of data uniformity. Higher CV’s indicate more treatment or environmental variability.

In this example, since treatment A is different from treatment D by 5 bu/ac, there is 90% certainity that the results of the treatments were indeed different. Treatment differences are represented by using a letter beside the reported value. Since the averages for treatment A and treatment B differ by less than 3, it cannot be concluded that the treatments are different from each other, so the same letter (e.g. “a”) is used to indicate they are the same.

For more information and examples on statistics and experimental setup, visit go.osu.edu/efieldsinvolved.

Return above analysis allows farmers to consider not only yield increase, but also economic return which ultimately impacts the farm’s bottom line. For studies where economics were calculated, return above is labeled in the right-most column of the results table. To standardize return above calculations state-wide, the OSU Extension budgets were used for a partial profit calculation, farmoffice.osu.edu.

Seed Costs:

For the seeding rate studies, a uniform corn seed cost of $3.75/1,000 seeds was used. Soybean seed cost was $0.452/1,000 seeds. These are based on the Ohio Crop Enterprise Budgets developed by Barry Ward, OSU Extension. Learn more about the budgets on page 26.

Commodity Prices:

Price received was determined by the Chicago price at planting and adjusted with a historical basis to represent an Ohio price. The corn price used in the 2024 report is $4.20/bu and the soybean price is $10.00/bu. We then calculated a 10% price increase and decrease to reflect price variability.

Nitrogen Costs:

A nitrogen cost of $0.43/lb used in this report is from the 2025 Corn Production Budget. For the nitrogen timing studies, application costs were also considered. The average costs of application the report uses are from the 2024 Ohio Custom Farm Rates. Learn more about the 2024 custom rates on page 28.

Example economic calculator for corn seeding rate studies:

The “Return above” line includes only the input expense of what was being studied (i.e. seed cost) to provide a clear indication of economic return. To calculate your own economic return, you can access the eFields Economic Calculators at: go.osu.edu/econcalculator.

eFields Contributors

Glen Arnold Professor Field Specialist

OSU Extension

Tim Barnes Extension Educator

Marion County

OSU Extension

Mark Badertscher

Extension Educator

Hardin County

OSU Extension

Nic Baumer

Extension Educator

Hardin County

OSU Extension

Dara Barclay Program Manager, eFields & eBarns

OSU Extension

Frank Becker Extension Educator Wayne County

OSU Extension

JT Benitez

Extension Educator

Butler County

OSU Extension

Amanda Bennett

Extension Educator

Miami County

OSU Extension

Jocelyn Birt

Water Quality Extension Associate

OSU Extension

John Barker Extension Educator Knox County OSU Extension

Lee Beers Extension Educator Trumbull County

OSU Extension

Maddie Brillhart

Student Assistant Department of Entomology

Soybean Forages Tech Other Small Grains

Quality

Bridget Britton Field Specialist OSU Extension

Brady Campbell Assistant Professor Department of Animal Sciences

Grant Davis Extension Educator Champaign County OSU Extension

Cassandra Brown Program Manager School of Environment and Natural Resources

Caden Buschur Extension Educator Darke County OSU Extension

Marilia Chiavegato Assistant Professor Department of Horticulture and Crop Science & Department of Animal Sciences

Rachel Cochran Water Quality Extension Associate OSU Extension

Wayne Dellinger Extension Educator Union County OSU Extension

Amanda Douridas Extension Educator Madison County OSU Extension

Pressley Buurma Extension Educator Seneca County OSU Extension

Trevor Corboy Extension Educator Brown County OSU Extension

Nate Douridas Farm Manager Molly Caren Agricultural Center

eFields Contributors

Nick Eckel Extension Educator Wood County OSU Extension

Amber Emmons

Water Quality Extension Associate OSU Extension

Louceline Fleuridor

Post Doctoral Scholar School of Environment and Natural Resources

Ken Ford Extension Educator Fayette County OSU Extension

Alyssa Essman Assistant Professor Department of Horticulture and Crop Science

John Fulton Professor Department of Food, Agricultural and Biological Engineering

Paige Garrabrant

Water Quality Extension Associate

OSU Extension

Christine Gelley Extension Educator Noble County OSU Extension

Yang Geng Post Doctoral Scholar Department of Food, Agricultural and Biological Engineering

Mike Estadt Extension Educator Pickaway County OSU Extension

Allen Gahler Extension Educator Sandusky County OSU Extension

Ryan Haden Associate Professor Ohio State ATI

Soybean Forages Tech Other Small Grains

Quality

Jason Hartschuh Assistant Professor, Field Specialist OSU Extension

Dee Jepsen Professor Department of Food, Agricultural and Biological Engineering

Andrew Klopfenstein Sr. Research Associate Engineer

Department of Food, Agricultural and Biological Engineering

Elizabeth Hawkins Associate Professor, Field Specialist OSU Extension

Douglas JacksonSmith Professor and Kellogg Endowed Chair School of Environment and Natural Resources

Seth Kannberg Extension Educator Portage County OSU Extension

Grace Koppelman Student Assistant Department of Food, Agricultural and Biological Engineering

Stephanie Karhoff Assistant Professor, Field Specialist OSU Extension

Carri Jagger Extension Educator Morrow County OSU Extension

Sami Khanal Assistant Professor Department of Food, Agricultural and Biological Engineering

Greg LaBarge Professor, Field Specialist OSU Extension

Abby Leatherman Summer Student Assistant

Department of Food, Agricultural and Biological Engineering

eFields Contributors

Rob Leeds

Extension Educator

Delaware County

OSU Extension

Alan Leininger Extension Educator

Henry County

OSU Extension

Clif Little

Extension Educator

Guernsey County

OSU Extension

Horacio LopezNicora Assistant Professor Department of Plant Pathology

Clifton Martin

Extension Educator

Muskingum County

OSU Extension

Ryan McMichael

Extension Educator

Mercer County

OSU Extension

Ed Lentz

Extension Educator

Hancock County

OSU Extension

Kendall Lovejoy Extension Educator

Fulton County

OSU Extension

Laura Lindsey Associate Professor Department of Horticulture and Crop Science

Steve Lyon Professor, Assistant Director Wooster Operations School of Environment and Natural Resources

Logan Minter Associate Professor, Field Specialist

OSU Extension

Richard Minyo Research Specialist Department of Horticulture and Crop Science

Soybean Forages Tech Other Small Grains

Quality

Marina Miquilini Extension Educator Greene County OSU Extension

Asmita Murumkar Assistant Professor, Field Specialist OSU Extension

Osler Ortez Assistant Professor Department of Horticulture and Crop Science

Ricardo Ribeiro Visiting Scholar Department of Horticulture and Crop Science

Erdal Ozkan Professor Department of Food, Agricultural and Biological Engineering

Eric Richer Associate Professor, Field Specialist Farm Management

Gigi Neal Extension Educator Clermont County OSU Extension

Stephanie Pflaum Student Assistant Department of Entomology

Sarah Noggle Extension Educator Paulding County OSU Extension

Amy Raudenbush Researcher Department of Entomology

Eric Romich Professor, Field Specialist Energy Development

Beth Scheckelhoff Extension Educator Putnam County OSU Extension

eFields Contributors

Clint Schroeder Program Manager Ohio Farm Business Analysis

Ajay Shah Professor Department of Food, Agricultural and Biological Engineering

Scott Shearer Professor and Chair Department of Food, Agricultural and Biological Engineering

Vinayak Shedekar Assistant Professor Department of Food, Agricultural and Biological Engineering

Haley Shoemaker Extension Educator Columbiana and Mahoning County OSU Extension

Kendra Stahl Extension Educator Crawford County

OSU Extension

Ryan Slaughter Extension Educator Ross County OSU Extension

Ambria Small Student Associate Department of Plant Pathology

Jenny Stoneking Extension Educator Adams County OSU Extension

Alan Sundermeier Professor Emeritus, OSU Extension (Conservation Action Project Coordinator)

Jacci Smith Extension Educator Delaware County OSU Extension

Alex Thomas Graduate Research Associate, Department of Food, Agricultural and Biological Engineering

Soybean Forages Tech Other Small Grains

Quality

Kelley Tilmon Professor Department of Entomology

Brooks Warner Extension Educator Clinton County OSU Extension

Amit Prasad Timilsina Post Doctoral Scholar Department of Food, Agricultural and Biological Engineering

Aaron Wilson Assistant Professor, Field Specialist OSU Extension

Tiffany Woods Graduate Research Associate School of Environment and Natural Resources

Kayla Wyse Extension Educator Williams County OSU Extension

Kyle Verhoff Extension Educator Defiance County OSU Extension

Dena Wilson Student Assistant Department of Food, Agricultural and Biological Engineering

Barry Ward Leader, Production Business Management OSU Extension

John Yost Extension Educator Wayne County OSU Extension

Jacob Winters Extension Educator Auglaize County OSU Extension

Chris Zoller Extension Educator Tuscarawas County OSU Extension

2024 Growing Season Weather

The weather of 2024 brought yet another challenging year for producers across Ohio in the name of drought. Ohio experienced one of its historically most intense droughts on record, especially across central and southern counties, while temperatures also averaged well above normal. After ample spring moisture and decent planting conditions in May, extreme heat ensued in June followed by very dry conditions for the bulk of the growing season. Compared to our longterm average (1991-2020), growing season temperatures (March – November) were 2-5°F above normal (Fig. 1). Most counties received less than 30 inches over this nine-month period, with southeastern counties particularly hard hit (5075% of normal; much less if March is not included). Through November, 2024 ranks as the warmest and 40th driest on record (1895-present) for Ohio according the National Centers for Environmental Information. For more inseason climate analysis, please visit the State Climate Office of Ohio. The following is a summary of the growing season and seasonal breakdown of 2024.

Figure 1: (Left) Temperature departures (°F), (Middle) Precipitation departures (inches), and (Right) Precipitation departures (percent of normal) from the long-term (1991-2020) normal for March-November 2024. Figure courtesy of the Midwestern Regional Climate Center.

Spring (March – May)

Following the second warmest winter on record for Ohio, warmer than normal conditions persisted throughout spring. All three months ranked near or in the top ten warmest all time. The last spring freeze (32°F) occurred between April 21-30, with a few spots seeing their last spring freeze before April 10. Overall, precipitation was above average for spring (Figure 2), especially across northwestern and eastern Ohio. This was primarily due to a wet April, while March and May precipitation was close to average. Severe weather was a big story before the summer’s drought, as historic tornado numbers accumulated for the season. Six days with five or more confirmed tornadoes occurred in 2024, including March 14, April 2, and April 17, among others. Ohio’s largest tornado outbreak for the year occurred on May 7-8, with twenty tornadoes confirmed across the state. This led to planting delays and interruptions in spring activities in affected areas. Spring 2024 ranks as the 2nd warmest and 28th wettest for Ohio.

Figure 2. County precipitation ranks for Ohio for spring 2024 (March – May). White shading indicates near normal conditions, while the top third (lighter colors) and top ten (darker colors) of the record are depicted for both dry (brown) and wet (green) conditions. Figure courtesy NOAA

Summer (June – August)

June got off to a hot start as temperatures across the state frequently soared well into the 90s. Stations throughout the state recorded seven to nine consecutive days of at least 90°F during the middle of the month, as June 2024 ranks as the 15th warmest June on record. Both July and August were also warmer than normal. Drought conditions initiated in June then intensified throughout the three-month period. Total summer precipitation for central and southeastern Ohio was less than 7.5 inches, 6-8 inches below normal (50% of normal; Figure 3). This caused significant stress on livestock producers in southeast Ohio, with very little hay production and widespread intensive water hauling. Soil moisture, rivers, and streams were well below average all summer, and crops in the driest areas of the state suffered yield loss. Summer 2024 ranks as the 25th warmest and 7th driest on record.

Figure 3. Accumulated precipitation (inches) for June-August 2024. Figure courtesy of the Midwestern Regional Climate Center.

Fall (September – November)

Drought conditions persisted throughout much of fall as well, peaking in both intensity and areal covereage on September 24, 2024 (Figure 4). This year was the first time in the U.S. Drought Monitor’s inception that D4 (Exceptional Drought) conditions have been designated in Ohio. This year also set records for the greatest coverage of D2-D4 drought conditions. The only relief in September and October came in the form of the remnants from Hurricane Helene in late September, which mainly struck the southern counties of Ohio. Some areas that had expericenced significant drought conditions received 6-10” of rain from this system (e.g., Fayette, Ross, Pike, and Scioto Counties), along with strong winds that caused additional lodging and sprouting issues in row crops. Prior to this system, producers in southern Ohio reported 6% moisture in some soybeans, as harvest activies ran two to three weeks early across the state. The large amount of pod shatter to emergence of fall soybeans when moisture returned a few weeks later. Depite Helen’s moisture, October reverted to very dry and warm conditions, ranking as the 7th driest for the state. All three fall months rank near or in the top 10 warmest on record. As November ended, early season cold swept across Ohio with a major lake snow event across Lake and Ashtabula Counties, where more than five feet of snow fell in some locations into the first week of December. Overall, Fall 2024 ranks as the 3rd warmest and 38th driest on record.

TOOLS OF THE TRADE

FARM (Field Application Resource Monitor)

This tool (farm.bpcrc.osu.edu) allows users to define their locations of interest and receive 12- and 24-hour precipitation forecasts (current and historical) to aid in the application of fertilizer, manure, and/or pesticides.

Figure 4: Drought conditions in Ohio on September 24, 2024. Figure courtesy of the U.S. Drought Monitor.

PROJECT CONTACT

For inquiries about this project, contact Dr. Aaron B. Wilson (wilson.1010@osu.edu).

Crop Progress and Suitable Days

OBJECTIVE

Summarize Ohio planting progress and days suitable for fieldwork reported each year the National Agricultural Statistics Service (USDA-NASS).

Corn Planting Progress

eFields Collaborating Farm

OSU Extension Statewide

Corn planting pace was split across the state with the southern counties having great stretches of dry weather and the northwestern counties having a wet and delayed start. In 2024, Ohio farmers reached the 50% planted mark for corn on May 19th and completed planting by June 16th. Figure 1 illustrates Ohio’s corn planting progress for all years between 1979 and 2024. This is the seventh latest finish recorded. This continues the recent trend of continuing to push corn planting dates later towards the end of spring.

Soybean Planting Progress

Figure 1. Ohio corn planting progress reported by USDA NASS from 1979 –2024. 2024 progress is shown by the scarlet dashed line. Data source: USDA NASS

Soybean planting progress in 2024 was typical for Ohio. Figure 2 shows Ohio’s soybean planting progress for all years between 1979 and 2024. Ohio reached 50% planted by May 26th and soybean planting was reported completed on June 30th. The 2024 soybean crop was ties with 1985 for the seventh latest finish recorded by USDA NASS.

Figure 2. Ohio soybean planting progress reported by USDA NASS from 1979 –2024. 2024 progress is shown by the scarlet dashed line. Data source: USDA NASS

Soybean Forages Tech

Days Suitable for Fieldwork

The 2024 season was a great season for getting work done in the field. The weather provided more favorable conditions for fieldwork with all months after May having more than average days suitable for fieldwork except July (Figure 3). Unfortunately, this increase in days suitable was a reflection of the drought conditions being experienced across much of the state. The dry summer led to an early start to harvest and the dry fall enabled consistent progress throughout the fall.

Harvest Progress

Figure 3. Monthly days suitable for fieldwork. The average number of days per month from 1995 to 2023 (scarlet squares) compared to the number of days available for fieldwork per month in 2024 (gray circles). Monthly totals are calculated based on weekly reports. Data source: USDA NASS

Persistent drought conditions throughout much of Ohio led to quicker than normal crop maturity and an early start to harvest. Figure 4 shows the reported harvest progress dating back to 1981. While both corn and soybean harvests started earlier than normal, the start to soybean harvest was notably early. 2024 was just the fifth earliest soybean harvest start on record, beat by memorable seasons like 1987, 1998, 1999, and 2012. Average reported soybean moistures were the lowest every recorded, nearly 2% lower than the all-time average of 13%.

4. Reported crop hrvest completion dates for corn (left) and soybean (right) in Ohio reported by USDA NASS from 1979 – 2024. 2024 progress is shown by the scarlet dashed line. Data source: USDA NASS

SUMMARY

The 2024 spring planting season was slower than normal for the corn crop and near normal for the soybean crop. Later planting, especially for corn, is becoming a common occurence as weather conditions pose challenges in Ohio and across the country. Dry conditions most of the summer led to higher than average days suitable for fieldwork. Harvest started earlier than typically, especially soybeans due to drought conditions and early maturation.

PROJECT CONTACT

For inquiries about this project, contact Elizabeth Hawkins (hawkins.301@osu.edu), or Aaron Wilson (wilson.1010@osu.edu).

Figure

Ohio Crop Enterprise Budgets

What are Enterprise Budgets?

Enterprise Budgets have been developed by faculty of the College of Food, Agricultural, and Environmental Sciences (CFAES) for several decades. The 2025 Ohio Crop Enterprise Budgets were developed by Barry Ward, Leader, Production Business Management at Ohio State. The budgets are tools that growers can use to examine different scenarios on their operation to help in decision making. The Enterprise Budgets can be found on Excel spreadsheets that users can download. Growers can then input their own production and price levels to calculate their own outputs. As seen below, the budgets have color coded cells that will allow users to plug in their own numbers and calculate bottom lines for different scenarios.

PRODUCTION BUDGET -

No-Tillage Practices*

Cell Color Key:

Gold: Values may be changed to assist in computing the “Your Budget” Column using macros embedded within the spreadsheet.

Light Blue: Values will be calculated for the user based on data entered. These cells may be input manually, but macros will be overwritten!

Gray: Values are stand-alone cells that require direct input from the user.

Soybean Forages Tech

Key points to remember when utilizing the budget sheets:

• The budgets represent common, workable, combinations of inputs that can achieve a given output.

• Amounts of seed, types and quantities of fertilizer, chemicals, and other items reflect University recommendations and the experience of many Ohio farmers.

• The combinations of inputs and prices presented will not likely precisely reflect any given farm.

• In practice, actual costs will be higher or lower than shown. Thus the most important column is “Your Budget”.

Characteristics of an Enterprise Budget:

• Estimates the costs and returns expected for a single enterprise.

• Represents one combination (from among hundreds available) of inputs such as seed, chemicals, and fertilizer to produce some level of output.

• A written plan for a future course of action including estimated costs and returns for that particular enterprise.

• Provides a format and a basis for developing enterprise budgets appropriate for a given farm situation.

Things not implied by an Enterprise Budget:

• It is not the only combination of inputs that can be used to produce this crop.

• It does not imply that anyone whose costs are different from this must have incorrect data or poor records.

• It does not imply that all producers can achieve these costs and yields. Different soil types, different ways in which the soil has been utilized and cared for in the past, and different weather in a given season all can cause the actual results to vary greatly from what is presented.

Yield Levels

Three yields are provided in each budget sheet. The middle yield is the long term trend yield for Ohio. The other two yields are 20% lower and higher than the middle yield. These yields levels reflect differing yield potential.

Variable Costs

Seed, fertilizer, and chemical requirements are based on agronomists’ recommendations. Fertilizer amounts vary by yield level to reflect crop removal, based on typical soil test values for P2O5 and K2O. These quantities and prices can changed to reflect your soil tests and local prices to provide a more accurate estimate of your costs of production.

Fixed Costs

Five items are included as fixed costs, some of which may or may not be fixed for a particular operation. These items include labor, management, machinery and equipment, land, and miscellaneous charges.

Costing Methods

The budgets report all costs including cash, depreciation, and opportunity costs. Cash costs likely include categories such as seed, fertilizer, and chemical costs. Depreciation on machinery is included in the “Machinery and Equipment Charge.” Some items may contain opportunity costs, which reflect returns to a producer’s labor, capital, and managerial resources. Opportunity costs should be included in budgeting because they account for the use of a producer’s resources.

Interpretation of Returns

All budgets report “return above variable costs” and “return above total costs”. Return above variable costs is useful in examining decisions that must be made within a year. Return above total costs would be used to examine “long-run” decisions.

Pricing Methods

Prices for crops and inputs reflect estimates for the given year. Crop prices are estimates of harvest prices. No costs are included for grain storage. If an improved price is acheived by your farm due to storage or marketing strategies, then any increased costs to achieve that price should either be netted out of returns or added to costs.

PROJECT CONTACT TOOLS OF THE TRADE

Enterprise Budgets

Access the Ohio Crop Enterprise Budgets by visiting go.osu.edu/enterprise_budgets or by using the QR code to the right.

For inquiries about this information, contact Barry Ward (ward.8@osu.edu).

Ohio Farm Custom Rates

This publication reports custom rates based on a statewide survey. Surveys were mailed/emailed to past respondents and distributed at various Extension programs through the winter of 2024. The summary information included in this publication is based on the responses of 333 farmers, custom operators, farm managers, and landowners conducted in 2024. These rates, except where noted, include the implement and tractor if required, all variable machinery costs such as fuel, oil, lube, twine, etc., and labor for the operation.

Some custom rates published in this study vary widely, possibly influenced by:

• Type or size of equipment used

• Size and shape of fields

• Condition of the crop

• Skill level of labor

• Amount of labor needed in relation to the equipment capabilities

• Cost margin differences for full-time custom operators compared to farmers supplementing current income

• Region of Ohio with different custom services supply and demand characteristics

Some custom rates reflect discounted rates as the parties involved have family or community relationships. Discounted rates may also occur when the custom work provider is attempting to strengthen a relationship to help secure the custom farmed land in a future purchase, cash rental or other rental agreement. Some providers charge differently because they are simply attempting to spread their fixed costs over more acreage to decrease fixed costs per acre and are willing to forgo complete cost recovery. Charges may be added if the custom provider considers a job abnormal such as distance from the operator’s base location, difficulty of terrain, amount of product or labor involved with the operation, or other special requirements of the custom work customer.

The eFields nitrogen studies utilize the Ohio Farm Custom Rates to calculate return above total N. As you read through our nitrogen studies, you can reference these rates to better understand our calculations. Below is a sample of how we utilize these rates for our return above N calculations. The treatment data below is from an eFields Late Season Nitrogen study. The total nitrogen rate and yield were inputted in the Nitrogen Timing Calculator that is found in a downloadable Excel file at go.osu.edu/econcalculator.

Treatment: Rate 1

In this example, the “Late Season N ApplicationCoulters/Acre” rate of $14.60 was used to calculate the return above N. After inputting the application rate, yield, and total N rate into the calculator, the Return Above N for this treatment is $997.00 per acre.

TOOLS OF THE TRADE

PROJECT CONTACT

Quarterly Fertilizer Price Summary

To better understand local fertilizer prices across Ohio, OSU Extension personnel initiated a fertilizer pricing survey of retailers across Ohio beginning in December 2023. Respondents were asked to quote spot prices as of the first day of the quarter. Sale types included pickup (ie. freight on board or FOB) at the plant (any quantity), direct-to-farm delivery (truckloads), or delivered and applied (poultry litter only). No blending or application charges were to be included in the spot price. Results from the survey data can be used to gauge pricing trends and compare Ohio prices to the national average. We recognize that many factors influence a company’s spot price for fertilizer including but not limited to availability, geography, volume, cost of freight, competition, regulation, etc.

The summary report each quarter contains a chart (Table 1) which includes the number of responses for each fertilizer product. The minimum and maximum values are also reported, as well as a simple average of all responses for each product. The spring quarter (April 1st) summary had the greatest participation with 32 retailers from 19 counties completing the survey. In general, when comparing the results from this survey with national average prices, Ohio’s prices were slightly lower.

With a year of completed results we can begin to see pricing trends over the 2024 calendar year.

Results from the quarterly survey are shared through the Ohio Ag Manager blog : u.osu.edu/ohioagmanager

A summary of those reports can also be found under the Farm Management tab of the Farm Office site: farmoffice.osu.edu

Figure 1: Nitrogen Products 2024 Price Trend

Figure 2: Phosphate Products 2024 Price Trend

PROJECT CONTACT

For inquiries about this project, contact: Clint Schroeder (schroeder.307@osu.edu), Amanda Bennett (bennett.709@osu.edu), or Eric Richer (richer.5@osu.edu)

Table 1: Quarterly Fertilizer Prices go.osu.edu/fertilizersummary

Access the Ohio Quarterly Fertilizer Price Summaries:

Figure 3: Potash 2024 Price Trend

Figure 4. Ammonium Sulfate 2024 Price Trend

Reducing Dust Exposure

In the fields or around the grain storage bins, dust exposure was a significant factor for the 2024 harvest season. In a study funded by Ohio Bureau of Workers Compensation, respirable dust measurements were taken at 14 on-farm grain storage facilities in Ohio during the time that workers were cleaning their bins. Workers wore an N-95 mask during the cleaning process, and calculations were made on the mask’s protective factors. In each case, the N-95 performed well, meeting the OSHA assigned protection factor for a half-mask respirator. However, fit tests and seal checks were not conducted by the farmer prior to N-95 mask use, so it is possible that implementing these two practices could have increased respirator effectiveness.

Humans can suffer significant and irreversible health effects from grain dust exposure. Working in an enclosed tractor or combine cab while in the fields can reduce worker exposure to the generated dust, but additional protection from a properly fitted respirator ensures a safer working environment while unloading grain and working around the grain bin facilities.

On large farms with over 11 non-family employees or commercial grain facilities, workers undergo a fit test procedure to comply with OSHA’s Respiratory Protection Standard, 1910.134(e). On private farms, few regulations are in place for fit testing. However, wearing a proper fitted respirator is a good practice to follow. Medical centers can perform fit tests for a small fee. The Ohio Bureau of Workers’ Compensation also offers fit test training. Just as tractors have specifications for the most effective air filter for their specific make/model, respirators should also fit the specific size and shape of the worker’s face.

STEPS FOR RESPIRATOR FIT TESTING

1. CONDUCT A RESPIRATOR FIT TEST EVERY YEAR

Medical Evaluation

Before wearing a respirator, it is good to know if the worker has the physical ability to wear a respirator. This can be determined through a Respirator Medical Evaluation Questionnaire completed by the worker The answers are reviewed by a physician or licensed health care professional. A positive response to any of the questions 1-8 on the Medical Evaluation questionnaire requires a follow-up medical examination. An OSHA Info Sheet can be accessed at: osha.gov/sites/default/files/publications/OSHA3790.pdf

Qualitative Fit Test

Qualitative fit testing is a pass/fail test that uses the wearer’s sense of taste or smell, or their reaction to an irritant to detect leakage into the respirator facepiece.

In this test, the worker wears his or her respirator inside a tent or hood into which the substance is piped (Figure 1), and the worker indicates the ability to sense the irritant. Air leaks around the respirator allows the contaminant to be detected. There are four qualitative fit test substances accepted by OSHA: Isoamyl acetate, which smells like bananas; Saccharin, which leaves a sweet taste in the mouth; Bitrex, which leaves a bitter taste in the mouth; and Irritant smoke — which is only seldom used because of the specific training needed to administer the OSHA Irritant Smoke Protocol.

Once a worker finds a respirator that is effective against smelling the substances used in their fit test, this respirator becomes “the assigned respirator” for the worker. The worker should only wear this make/model and size of respirator for the entire year. An annual fit test and training is done each year to ensure the worker is in the correct size of respirator.

Figure 1. A qualitative fit test, showing the hood where substances are piped into for detection.

For additional information or to find a fit testing facility, contact the Ohio Bureau of Workers Compensation office (focusing on their virtual or in-person fit testing training event) or the Ohio Pesticide Program: pested.osu.edu/resources/wps/respiratorrequirements

2. CONDUCT A USER SEAL CHECK EACH TIME A RESPIRATOR IS WORN

Each time a respirator is used, the worker should conduct a positive- and negative-pressure seal test. This quick check determines if the respirator has a good seal.

How to complete a positive user seal check:

1. Put on the respirator:

Make sure the respirator is properly positioned on your face, covering both your nose and mouth. Ensure the straps are in place, with one strap above the ears and one below.

2. Cover the respirator surface: For respirators with an exhalation valve, cover the valve with your hand. For respirators without an exhalation valve, gently cover the respirator’s surface with both hands to avoid shifting the mask position.

3. Exhale gently:

Breathe out gently and feel for air escaping from around the edges of the respirator.

4. Check for leaks:

If you feel air leaking around the edges, adjust the straps or re-position the respirator and repeat the check.

SUMMARY

Figure 2. A user seal check for disposable respirators.

How to complete a negative user seal check:

1. Put on the respirator:

Make sure the respirator is properly positioned on your face, covering both your nose and mouth. Ensure the straps are in place, with one strap above the ears and one below.

2. Cover the respirator inlets: For respirators with cartridges or filters, cover the air inlets (filters or cartridges) with your hands. For other respirator types, use your hands to cover the surface of the mask, being careful not to disturb the fit.

3. Inhale gently:

Breathe in gently and the respirator should collapse slightly against your face due to the lack of air entering.

4. Check for leaks: If the respirator collapses and you don’t feel air entering through the sides, the seal is adequate. If you feel air coming in at the edges, adjust the straps or re-position the respirator and try again.

Always wear an N95 or P100 respirator when working in high dust, agricultural environments. A disposable face mask respirator is best when replaced each day, and when a user seal check is performed every time it is worn. Facial hair limits the effectiveness of the seal, as does certain eyeglasses and other personal protective equipment (PPE). However, wearing a properly-sized and fitted respirator protects against organic dusts that can deposit deep in the lungs, and promotes better worker health.

Figure 3. Research participant cleaning a grain bin while wearing a backpack of respiratory monitoring equipment.

PROJECT CONTACT

For inquiries about this project, contact the Agricultural Safety & Health Program: Dee Jepsen, Professor (jepsen.4@osu.edu) or Yang Geng (geng.83@osu.edu)

Digital Ag Download (DAD)

In an effort to bring you the latest news and research related to Digital Agriculture, The Ohio State Digital Ag Team has a newsletter. Special updates are sent out periodically for breaking news, important events, and research updates. The newsletter features articles, podcasts, videos, studies, and publications from our team to provide you with relevant information from the industry.

From tips for fine-tuning your planter, to protecting your harvest data, the Digital Ag Download covers it all and keeps you informed throughout each season. By joining our email list, you never have to worry about missing an update from our team.

GET UPDATES

Help grow the popularity of “The Digital Ag Download” by joining our email list and sharing with growers, industry professionals, and anyone interested in your neck of the woods! To sign-up, scan the QR Code or visit go.osu.edu/DigitalAgDownload.

VIDEOS

If you are looking for more information on eFields studies, Precision University, or other demonstrations, the Digital Ag Download regularly features videos from across the state. Find information on the Knowledge Exchange, spray drone application demonstrations, and interviews with eFields partner farms there.

PODCASTS

From the Agronomy and Farm Management podcast, to Precision Ag Reviews, and Precision Farming Dealer, our team has been featured on more than a podcast or two. Whether you are in the office or on the go, find their most recent podcasts with indsutry updates from the Digital Ag Download.

PUBLICATIONS

The Digital Ag website features seven different research focuses from precision seeding to remote sensing. Get timely updates right your inbox to help keep your operation running smoothly with the most recent work from our team with their research publications, quick start guides, and presentations.

Farm Stress Resources

Mental health plays a vital role in overall health and farm success. Agriculture can become stressful with numerous demands and challenges out of our control. There are many resources available to help you cope through difficult times. If you have additional questions or want help, please reach out.

For information on programs and factsheets on Rural and Mental Health Resources, contact our Farm Stress Team at:

go.osu.edu/farmstress (using the “About” tab)

Ohio Resources and Handouts

• The Ohio Crisis Text Line and Call Line provides confidential, 24/7 support for anyone experiencing a mental health crisis, substance use concerns, or suicidal thoughts. It connects individuals to trained crisis counselors who offer emotional support, guidance, and resources. Both services below are free and available for anyone in need of immediate support or guidance.

• Call or Text 988 to connect with a trained counselor for support with a mental health challenge, substance use issue, or thoughts of suicide—whether for yourself or a loved one.

• Text “4HOPE” to 741741: This service is specific to Ohio and links individuals to crisis counselors who can assist with stress, anxiety, depression, and other emotional challenges.

• Resources relating to Rural and Farm economics, Workforce Development, and Personal Stress Management: extension.osu.edu/about/resources/extension-task-forces/rural-and-farm-stress

• Ohio Mental Health Resource Guides by County: u.osu.edu/cphp/ohio-mental-health-resource-guides/

• Ohio Department of Agriculture Farm Stress: gotyourbackohio.org

Now available - Farm Stress Certified Providers!

A new certification for mental health professionals was introduced in 2024. This three-module program is designed to give mental health professionals the understanding and resources needed to assist the agricultural community.

Find a Certified Farm Stress Provider: go.osu.edu/FarmStressProfessionals

Listen to the “Now at Ohio State” Podcast

Hear from Bridget Britton (Behavioral Health Field Specialist) and Aaron Wilson (Extension Field Specialist - Ag Weather and Climate) on the Ohio State podcast where they discuss the growing mental health crisis in farming communities: go.osu.edu/OSUAgMentalHealth

A Farm Stressor in 2024: Drought

See more on the response to the 2024 drought, here kx.osu.edu/page/early-drought-response

Additional Resources:

• Center for Rural Affairs - 10 Helpful Resources for Farmers: cfra.org/news/180130/10-helpful-resources-farmers

• Iowa State University - Iowa Concern 24-hour hotline: 1-800-447-1985

• National Suicide Prevention Lifeline: 1-800- 273-TALK (8255)

• National Suicide Prevention Lifeline Crisis Chat: suicidepreventionlifeline.org/talk-to-someone-now/ Call or Text 988 to connect with a trained licensed professional counselor within five minutes. Free, confidential, anonymous, and secure 24/7. Features active rescue where trained counselors connect with emergency services to save texters from immediate self-harm.

• Veterans Crisis Line: 1-800-273-8255, Press 1 (website also has a chat option) veteranscrisisline.net/

• My Coping Strategies Plan, Kansas State: bookstore.ksre.ksu.edu/pubs/MF3418.pdf

• Responding to Distressed People, NDSU: ag.ndsu.edu/publications/kids-family/responding-to-distressed-people/fs1805.pdf

PROJECT CONTACT

For inquiries about this project contact Sarah Noggle (noggle.17@osu.edu) or Chris Zoller (zoller.1@osu.edu).

2024 Preharvest Weed Escape Survey

PREHARVEST SURVEY

Just prior to harvest each year, the weed science program at Ohio State evaluates the frequency and distribution of problematic weed species by conducting a survey late-season in soybean fields throughout Ohio. Transects are driven through the top soybean producing counties, and information is collected on weed escapes in each field encountered. A rating scale of 0 to 3 is used to classify population level of each weed, with 0 being no weed present to 3 being dense, widespread infestations. Each field is given these ratings for ten weed species, namely: marestail, giant ragweed, common ragweed, waterhemp, Palmer amaranth, redroot pigweed, volunteer corn, common lambsquarters, giant foxtail/grass, and velvetleaf. Performing the survey at this time provides results that reflect the effect of control measures during the season. Conducting this survey on an annual basis allows for the monitoring of weed species shifts, evaluation of weed management program efficacy, and forecasting of potential issues and threats for the following growing season.

In 2024, 62% of fields encountered on the survey were clean, or at least free of the 10 weeds being evaluated.

The most commonly encountered weeds during the 2024 preharvest survey – percent of total fields with weeds present summed across rating levels:

1. Waterhemp - 11%

2. Giant ragweed - 10%

3. Grass/foxtail spp. - 8%

4. Volunteer corn - 8%

5. Marestail - 7%

The most concerning outcome was that waterhemp was encountered at a higher frequency than in previous years, based on percent of fields surveyed with waterhemp present (Figure 1).

• In 2024, waterhemp was the #1 most encountered weed, found in 91% of the counties surveyed and 11% of the 4,100 fields evaluated.

• In 2023, waterhemp was the #3 most encountered weed in the 4,000 fields evaluated, where waterhemp was found in 89% of surveyed counties and 13% of fields.

• In 2022, waterhemp was the #2 most encountered weed, found in 94% of the counties surveyed, and 11% of the 4,200 fields evaluated.

Figure 1. Results of the waterhemp distribution from the 2022, 2023, and 2024 preharvest surveys evaluating weed escapes in soybean. A county labeled with 0% does not mean no waterhemp exists in the county, but that it was not encountered on the survey.

WATERHEMP DISTRIBUTION

The migration of waterhemp and Palmer amaranth into and around the state of Ohio has been closely monitored. These weeds are especially difficult to control and pose a serious threat to crop yields. Palmer amaranth has been reported in various regions around Ohio. Upon discovery, it has generally been confined to the areas of introduction or eradicated. In the years conducting this survey, it has been uncommon to spontaneously encounter Palmer amaranth. Waterhemp has become increasingly common and is now found in crop fields across Ohio, largely across the western portion of the state.

WATERHEMP BIOLOGY

The following biological characteristics give waterhemp a competitive edge:

• Immense seed production – from 100,000 seeds in competitive situations to over 1 million in noncompetitive situations

• Fast growth rate – approximately 1 to 1 ¼ inches per day

• Prolonged emergence window – can emerge later in the growing season than other weed species

• High genetic diversity – male and female flowers are on separate plants and must outcross to reproduce

• Herbicide resistance – due in part to high genetic diversity, waterhemp populations in the Midwest have developed resistance to several herbicides and even multiple resistance to two or more sites of action

WATERHEMP CONTROL

Waterhemp produces a tremendous number of seeds, but the seeds are relatively short lived in the soil. Excellent control over 3-5 years can help eradicate this weed. Successful waterhemp control programs include:

• Prevention – evaluate potential sources of seed. Waterhemp can be introduced to and spread within an operation via contaminated equipment, livestock feed or manure, or seed for CREP or cover crops.

• Control – start clean with a burndown application or tillage. Use a full rate of an effective preemergence herbicide with residual control. A timely POST application, before waterhemp reaches three inches in height, and an overlapping residual can facilitate season long control. See the Weed Control Guide for Ohio, Indiana, Illinois, and Missouri for product and application recommendations [ANR-789].

• Monitoring – late-season escapes are always a risk. Being able to identify waterhemp, scouting late-season, and removing plants before they set seed will greatly reduce contributions to the soil seedbank.

• Mechanical – deep tillage in fields with large seedbanks can bury seed to prevent emergence.

• Cultural – control can be improved with the use of narrow row spacing or fall-seeded cover crops.

TOOLS OF THE TRADE

Waterhemp Identification and Control

You can find more information on the identification and control of problematic pigweed species like waterhemp at: u.osu.edu/osuweeds/super-weeds/pigweeds/ (or use the QR code to the right)

PROJECT CONTACT

For inquiries about this project, contact Alyssa Essman (essman.42@osu.edu).

Figure 2. Waterhemp in a field in Mercer County, Ohio.

Follow us on Social Media

Follow us on Facebook, X, and Instagram to get instant updates and special relseases from our team! Posts feature students, eFields contributors, helpful resources, and research completed by faculty in the department. Connect with us @OhioStatePA on each platform to ensure you are informed about trending topics and events in agriculutre.

Subscribe to our YouTube page @ohiostateprecisionag9203 or @OSUAgronomicCrops to view past presentations, learn more about current research, and gain valuable insight for innovative farming strategies!

Your Checkoff Dollars at Work

Many eFields projects are made possible by the generous support of our commodity partners, Ohio Corn and Wheat and Ohio Soybean Council. Funding provided by their farmer-led boards make it possible for us to focus research on emerging and critical needs for Ohio farmers.

The Ohio Corn Checkoff and the Ohio Small Grains Checkoffs work on behalf of growers, promoting the use of Ohio-grown corn and wheat. Our partnership with national and state organizations is helping to expand trade markets, grow domestic demand, and work with state and national partners to provide education and research that lead to solutions to issues like vomitoxin in corn and increasing the profitability of wheat. Learn more about what we do at ohiocornandwheat.org.

The Ohio Soybean Council invests in research that provides technologies and information that will increase profit opportunities for Ohio farmers. Through basic and applied soybean research the long-term goals are to protect and increase yield, to provide understandable and usable data to farmers, and to inform them of best management practices to ensure the economic well-being of their operations. The Council’s investment helps develop the next generation of soybean researchers to help Ohio farmers realize their future potential. Learn more at soyohio.org

The 2024 eFields Report is proud to share the results from six projects funded by Ohio Corn and Wheat and Ohio Soybean Council. Thank you for helping drive research and innovation to support Ohio farmers.

Preharvest Weed Survey, page 38

Vomitoxin, DON, Management, page 110

Fungicide for White Mold, page 142

Soybean Cyst Nemotode in Ohio, page 160

Cover Crop Population, Planting Date, page 204

Soil Health Survey across Ohio Farms, page 210

Ohio State Corn Research

For 2024, eFields corn research was focused on improving the production and profitability of corn in the greater Ohio area. Some exciting and innovating projects were conducted this year, with 34 unique studies implemented across the state. 2024 eFields corn research investigated many of the topics listed in the eFields focus areas. Highlights include biologicals, fungicide, nitrogen rate, and many other innovative practices. Here is the 2024 eFields corn research by the numbers:

1390 acres

34 corn studies

For more corn research from Ohio State University Extension, explore the following resources:

2024 Ohio Corn Performance Tests

The purpose of the Ohio Corn Performance Trials is to evaluate corn varieties for yield and other agronomic characteristics. This evaluation gives corn producers comparative information for selecting the best varieties for their unique production systems. For more information visit: go.osu.edu/corntrials

Agronomic Crops Team - Corn Research

The Agronomic Crops Team performs interesting research studies on a yearly basis. Resources, fact sheets, and articles on corn research can be found here on the Agronomic Crops Team website: go.osu.edu/ CropsTeamCorn.

The Ohio State Digital Ag Program

The Ohio State Digital Ag Program conducts studies related to all aspects of corn production. Research related to planting, inputs, and harvesting technology can be found on the Digital Ag website: digitalag.osu.edu.

Growth Stages - Corn

For all corn studies in this eFields report, we define corn growth stages as the following:

VE - Emergence - coleoptile is fully visible, yet no leaves are fully developed.

V1 - Full development of the first (flag) leaf, achieved when the collar of the leaf is fully visible.

VN - N fully developed leaves with collars visible.

VT - Tassels fully visible and silks will emerge in 2-3 days.

R1 - Silking - silks are visible and pollination begins.

R2 - Blister - silks darken and dry out, kernels are white and form a blister containing clear fluid.

R3 - Milk - kernels are yellow and clear fluid turns milky white as starch accumulates, kernels contain 80% moisture.

R4 - Dough - starchy liquid inside kernels has dough-like consistency, kernels contain 70% moisture and begin to dent at the top.

R5 - Dent - nearly all kernels are dented and contain about 55% moisture.

R6 - Black layer - physiological maturity is reached and kernels have attained maximum dry weight at 30-35% moisture.

Image Source: University of Illinois Agronomy Guide, 1999.

Biologicals - Beauveria Bassiana

OBJECTIVE

Determine if seed treated with Beauveria bassiana (Bb) reduces ear mold in organic corn.

STUDY INFORMATION

Planting Date 5/21/2024

Harvest Date 10/23/2024

HybridPrairie 5883

Population 32,500 sds/ac

Acres20

Treatments2

Reps4

Treatment Width 60 ft.

TillageConventional

Management Fertilizer

Previous CropClover

Row Spacing30 in.

Soil Type Crosby-lewisburg silt loams, 76% Kokomo silty clay loam, 24%

STUDY DESIGN

This project used a split planter layout. Half of the planter was loaded with treated seed and half was untreated. The treatments were replicated four times but were not randomized. The fourth block was unable to be analyzed so three replications were used to determine the statistical analysis.

eFields Collaborating Farm

OSU Extension

Madison County

WEATHER INFORMATION

Corn was scouted for leaf disease.

OBSERVATIONS

There was minimal insect and weed pressure in this field. The corn was scouted for leaf disease at R5 and no difference between treated or untreated was seen. This was organic corn with a decent yield given drought conditions.

RESULTS

SUMMARY

• No statistical yield difference was observed.

• Only one sample was positive for DON (vomitoxin). All other samples were clean of the 13 toxins that were tested. Considering drought conditions, which are unfavorable for toxin development, this result was not surprising.

• Due to low toxin presence, it is impossible to determine if Bb can reduce levels. Treating again in a high toxin year would be more ideal but impossible to predict.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Beauveria bassiana (Bb) is a soil-borne fungus that has been used widely to control various insect species. Recent research has documented growth enhancement and control of ear mold diseases in corn. We utilized the seed treatment product SPE 120 from JABB of the Carolinas.

PROJECT CONTACT

For inquiries about this project, contact Amanda Douridas (douridas.9@osu.edu).

Biologicals - Beauveria Bassiana

OBJECTIVE

Assess the effect of biological seed treatment (Beauveria bassiana, Bb) in corn growth and yield.

STUDY INFORMATION

Planting Date 5/7/2024

Harvest Date 10/8/2024

HybridPioneer P0035AM

Population 34,800 sds/ac

Acres 6

Treatments2

Reps4

Treatment Width30 ft.

Tillage Strip-Till

Management Fertilizer, Herbicide, Insecticide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Kibbie fine sandly loam, 78%

Gilford fine sandy loam, 17%

Hoytville clay loam, 5%

STUDY DESIGN

This project was laid out in Randomized Complete Block Design (RCBD) with four replications. Each replication consists of one large strip of Bb treated and one large strip of untreated. Bb treatment was applied as seed treatment before corn planting, following product label rate. Beauveria bassiana, is a natural, beneficial fungus that forms a symbiotic relationship with plants. JABB strains are isolated.

eFields Collaborating Farm

OSU Extension

Sandusky County

WEATHER INFORMATION

OBSERVATIONS

There was better growth of corn in treated strips relative to non-treated (control) strips for multiple variables, including more biomass production in the treated strips. Statistical analyses used p-value of 0.1.

RESULTS

SUMMARY

• There was no significant difference between the treated and control strips in stand count and brix concentration across different stages of data collection.

• The chlorophyll content was higher in treated than control strips at the V6 stage (6 collared leaves).

• The biomass production was higher in treated than control strips across different growth stages.

• Follow-up experiments are being conducted in the field and greenhouse.

Bassiana Treated

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Beauveria bassiana (Bb) is a soil-borne fungus that has been used as a biopesticide. Recent research has documented biostimulant benefits of Bb and helping plant growth enhancement in several crop species. We utilized the powdered seed treatment product with Bb as active ingredient from JABB of the Carolinas.

PROJECT CONTACT

For inquiries about this project, contact Allen Gahler (gahler.2@osu.edu) or Osler Ortez (ortez.5@osu.edu).

Biologicals - Bioreactor Extract

OBJECTIVE

Determine the effect of Johnson-Su compost bioreactor extract on corn production.

STUDY INFORMATION

Planting Date 5/25/2024

Harvest Date 10/29/2024

HybridPioneer P04511 AM

Population 32,000 sds/ac

Acres 15

Treatments2

Reps4

Treatment Width300 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous CropWheat

Row Spacing30 in.

Soil Type Blount silt loam, 72%

Pandora silt loam, 25% Haskins loam, 4%

STUDY DESIGN

Randomized complete block design with two treatments. Liquid extract was applied at planting at 6 gal/ac as pop-up on the corn seed. Extract treatments were reduced 5 gal/ ac sidedress UAN nitrogen compared to no extract.

eFields Collaborating Farm

OSU Extension

Seneca County

WEATHER INFORMATION

Corn growing through thick mixed cover crop residue.

OBSERVATIONS

Corn was planted green into a thick stand of mix cover crop. Wet spring conditions were followed by drought mid-summer to fall. No visual difference between treatments.

RESULTS

SUMMARY

• No extract treatments received a total of 67 gal/ ac 28% N compared to extract treaments with 62 gal/ac 28% N.

• Fall application of 2 ton/ac poultry litter on entire plot.

• Extract treatments were not significantly different in yield compared to no extract.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Johnson-Su compost extract is a liquid made by diluting Johnson-Su compost in water to create a soil amendment that is rich in beneficial microbes, especially fungi.

PROJECT CONTACT

For inquiries about this project, contact Pressley Buurma (buurma.20@osu.edu) or Alan Sundermeier (sundermeier.5@osu.edu).

Biologicals - HopperThrottle

OBJECTIVE

Evaluate the use of Meristem HopperThrottle on-seed biological product in the place of conventional graphite seed lubricant.

STUDY INFORMATION

Planting Date 5/24/2024

Harvest Date 11/1/2024

HybridAxis 59A25VT2PRIB

Population 33,000 sds/ac

Acres20

Treatments2

Reps 12

Treatment Width 15 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide, Insecticide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Crosby silt loam, 27% Miamian silt loam, 26% Celina silt loam, 22%

STUDY DESIGN

This study was conducted with a 12-row planter, using the HopperThrottle product in the left 6 rows and conventional graphite in the right 6 rows. Application was made at planting. All other product applications were done uniformly over the field. The field was then harvested with a 6-row combine head. Final analysis was done using yield data from the calibrated combine yield monitor.

eFields Collaborating Farm OSU

Extension

Champaign County

WEATHER INFORMATION

Growing Season Weather Summary

Corn was planted at 30-inch row spacing.

OBSERVATIONS

It did seem that the treated rows had a slight growth advantage to the untreated rows, but it was slight and difficult to measure or document. The treated rows also did seem to flower slightly earlier than the untreated rows. Though this did not seem to last through the season, or result in a difference in yield.

RESULTS

SUMMARY

• This study evaluated the use of Meristem RevLine HopperThrottle graphite replacement product. This product is a 80/20 talc/graphite and micronutrient blend, that also features a biological product that is mixed at the time of use.

• This product is used in the place of conventional seed lubricants, while supplying the micronutrients and biological product right on the seed.

• While some differences in growth rate and plant size were observed, it did not make a difference in yield. Although a greater yield advantage could be possible in a lower yield environment, it was captured in the data.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

REVLINE® HOPPER THROTTLE™ CORN is a best-in-class 80/20 talc/graphite and micronutrient blend that ensures optimum planting performance. It can replace any seed fluency agent.

PROJECT CONTACT

For inquiries about this project, contact Grant Davis (davis.1902@osu.edu).

Biologicals - HopperThrottle

OBJECTIVE

Evaluate the use of Meristem HopperThrottle on-seed biological product in the place of conventional graphite seed lubricant.

STUDY INFORMATION

Planting Date 5/21/2024

Harvest Date 10/24/2024

HybridAxis 61A66PCE

Population 34,000 sds/ac

Acres 26

Treatments2

Reps 11

Treatment Width 15 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide, Insecticide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Crosby silt loam, 46% Brookston silty clay loam, 39% Celina silt loam, 15%

STUDY DESIGN

This study was conducted with a 12-row planter, using the HopperThrottle product in the left 6 rows and conventional graphite in the right 6 rows. Application was made at planting. All other product applications were done uniformly over the field. The field was then harvested with a 6-row combine head. Final analysis was done using yield data from the calibrated combine yield monitor.

eFields Collaborating Farm

OSU Extension

Champaign County

WEATHER INFORMATION

Corn was harvested in October.

OBSERVATIONS

It did seem that the treated rows had a slight growth advantage to the untreated rows, but it was slight and difficult to measure or document. The treated rows also did seem to flower slightly earlier than the untreated rows. Though this did not seem to last through the season, or result in a difference in yield.

RESULTS

SUMMARY

• This study evaluated the use of Meristem RevLine HopperThrottle graphite replacement product. This product is a 80/20 talc/graphite and micronutrient blend, that also features a biological product that is mixed at the time of use.

• This product is used in the place of conventional seed lubricants, while supplying the micronutrients and biological product right on the seed.

• While some differences in growth rate and plant size were observed, it did not make a difference in yield. Although a greater yield advantage could be possible in a lower yield environment, it was captured in the data.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Grant Davis (davis.1902@osu.edu).

Biologicals - Multiple

OBJECTIVE

Determine if biological seed lubricant increases yield and is economical to use on corn compared to a micronutrient seed lubricant and a traditional talc seed lubricant.

STUDY INFORMATION

Planting Date 5/20/2024

Harvest Date 10/21/2024

HybridSeed Genetics Direct4111PWE

Population 33,800 sds/ac

Acres 107

Treatments3

Reps3

Treatment Width20 ft.

Tillage No-Till

Management Fertillizer, Fungicide, Herbicide

Previous Crop Soybean Row Spacing30 in.

Soil Type Scioto sIlt loam, 33% Stone silty clay loam, 33%

Glynwood silt loam, 14%

STUDY DESIGN

This study was designed in a randomized complete block design. Each plot was 20 feet wide. There were three treatments a traditional talc, a biological product named HopperThrottle® and a micronutrient product named Awaken Flowboost®. Products were applied to the corn seed in the planter box.

eFields Collaborating Farm

WEATHER INFORMATION

OBSERVATIONS

Weed and insect pressure was low but some late tar spot set in. Drought conditions were a concern in August and September which accelerated the ripening and harvest.

SUMMARY

• The yield of both the biological and micronutrient product were statistically different than the traditional talc application.

• Even with a higher cost of product, the biological product had the highest return above cost netting almost $50 more per acre compared to the traditional product.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Jacci Smith (smith.11005@osu.edu) or Rob Leeds (leeds.2@osu.edu).

Biologicals - Multiple

OBJECTIVE

Compare two biological products, PROVEN 40 and Envita SC, to determine their impact on plant health and yield.

STUDY INFORMATION

Planting Date 5/14/2024

Harvest Date 10/24/2024

HybridRob-See-Co D01-90VT2P

Population VRT

Acres20

Treatments3

Reps4

Treatment Width40 ft.

Tillage Vertical Till

Management Fertilizer, Herbicide

Previous CropCereal Rye

Row Spacing30 in.

Soil Type Gilford fine sandy loam, 45%

Colonie fine sand, 18%

Tedrow loamy fine sand, 17%

STUDY DESIGN

Three treatments were evaluated across four replications in a randomized complete block design (RCBD). The same starter fertilizer was used for all three treatments, one as a check; the other two treatments in this study were starter plus PivotBio PROVEN 40 and starter plus Envita SC, all at a rate of 180 lbs of N/ac. The treatments were applied in-furrow at planting. Sixteen rows were planted with the center eight being harvested. Yield data was calculated using a weigh wagon and a DICKEY- John GAC 2700Agri moisture tester.

eFields Collaborating Farm

WEATHER INFORMATION

Growing Season Weather Summary

Crop experienced stress during tassel and grain fill.

OBSERVATIONS

This farm is situated in a sandier region of the county. Due to missed rains in July and August, the crop experienced stress during tassel and grain-fill. No visible differences could be seen between the treatments through the growing season. These biological products are not guaranteed to raise yield, but are aimed to increase nitrogen efficiency.

SUMMARY

• There was no significant difference realized between any of the treatments and the starter fertilizer check.

• Future trials should be conducted with varying nitrogen rates to determine the biological’s impact on nitrogen efficiency.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

This DICKEY-john® GAC 2700-Agri Grain Analysis Computer provided precise grain moisture and test weight results for the plot treatments. This table top unit also has temperature sensing capabilites allowing the moisture of hot, dried corn to be tested as well.

PROJECT CONTACT

For inquiries about this project, contact Kendall Lovejoy (lovejoy.59@osu.edu).

Biologicals - Multiple

OBJECTIVE

Determine yield impact of biological additives when applied in-furrow at planting in organic corn.

STUDY INFORMATION

Planting Date 5/24/2024

Harvest Date 10/16/2024

HybridMerit 5454

Population 34,500 sds/ac

Acres33

Treatments 5

Reps4

Treatment Width40 ft.

TillageConventional Management Fertilizer

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Del rey-fulton silt loams, 39%

Pewamo silty clay Loam, 34%

Toledo silty clay loam, 26%

STUDY DESIGN

This study consisted of five treatments in a randomized complete block design. All treatments receved 2 tons/ac of poultry litter preplant and incorporated. At planting, there were four different biological treatments applied and one untreated check. The treatments were applied in-furrow with a Fendt Momentum planter. Yield and moisture data were collected with a collaborated yield monitor and analyzed with a simple analysis of variation (ANOVA).

eFields Collaborating Farm

OSU Extension

Putnam County

WEATHER INFORMATION

Biological products were applied in furrow at planting with Fendt row units.

OBSERVATIONS

This field received lower than average rainfall for the growing season. Weed control was exceptional across all treatments in this trial. One block of treaments was removed from analysis due to crop damage noted at harvest.

SUMMARY

• There was no significant difference in yield among all treatments.

• With no significant difference in yield this year, none of the products provided a return on investment.

• Additional replication of this study, especially in variable weather conditions, are needed to confirm the results of this study.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Fed ‘n Happy Compost Extract is one of many biological additives on the market used to enhance soil biology in today’s modern row crop production. Photo courtesy of www. fednhappy.com.

For inquiries about this project, contact Beth Scheckelhoff (scheckelhoff.11@osu.edu) or Eric Richer (richer.5@osu.edu).

Biologicals - Pivot Proven 40

OBJECTIVE

Confirm Pivot Bio was producing the yield equivalent to synthetic Nitrogen.

STUDY INFORMATION

Planting Date 4/28/2024

Harvest Date 9/9/2024

HybridBeck's 6241

Population 33,000 sds/ac

Acres3

Treatments3

Reps3

Treatment Width40 ft.

TillageMinimum

Management Fertilizer, Fungicide, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Miamian-Lewisburg silt loams, 51% Crosby silt loam, 25% Kokomo silty clay loam, 24%

STUDY DESIGN

This study was a randomized block design with three treatments and three replications. Treatment 1 was 190 lbs of synthetic nitrogen fertilizer applied at planting and sidedress; Treatment 2 was 150 lbs of synthetic nitrogen fertilizer applied at planting (with Pivot Bio Proven 40) and sidedress; Treatment 3 was 150 lbs of synthetic nitrogen fertilizer applied at planting and sidedress (without Pivot Bio Proven 40).

eFields Collaborating Farm

OSU Extension

Pickaway County

WEATHER INFORMATION

OBSERVATIONS

The growing season in Pickaway County started off well as corn stands were nearly perfect. Drought set in first part of June. From June 1 - September 1, the area near trials received only 2.88 inches of precipitation (CoCoRaHS station Circleville, Ohio). Earleaf nitrate levels at VT were all less than 20 ppm due to lack of uptake from the soil. Stalk nitrate test results for the three treatments ranged from 739 ppm to 1060 ppm. All indicating that the lack of rainfall and not nitrogen deficiency was the limiting factor for yield.

SUMMARY

• Due to histroical drought there is no conclusive evidence to determine any significant differences in yield between treatments.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Pivot Bio Proven 40. Leaf tissue and stalk analysis is a diagnostic tool used to assess the nutrient content and health of crops, specifically nitrogen leves. This tool helps monitor the performance of Pivot Bio Proven 40.

PROJECT CONTACT

For inquiries about this project, contact Mike Estadt (estadt.3@osu.edu).

Composted vs Raw Manure

OBJECTIVE

Compare the yield response from appling bed pack cattle manure, compost manure, and commercial fertilizer.

STUDY INFORMATION

Planting Date 5/23/2024

Harvest Date 11/18/2024

Hybrid Seed Consultants SC1122

Population 32,000 sds/ac

Acres 10

Treatments3

Reps4

Treatment Width40 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide

Previous CropWheat Row Spacing30 in.

1%Soil TypeHoytville clay loam, 61% Nappanee loam 38% Haney fine sandy loam, 1%

STUDY DESIGN

The study was designed as a randomized complete block design with three treatments replicated four times. Manure and compost was applied in 2023 after wheat harvest and 150 lbs of potash was applied to the control field in the fall of 2023 per soil test data. All nitrogen applications were equally applied to all treatments during planting as starter fertilizer and sidedress.

eFields Collaborating Farm

OSU Extension

Henry County

WEATHER INFORMATION

Compost and manure was turned prior to application.

OBSERVATIONS

In the first year of the project there were no observations that were unique to this trial of corn. Future soybean and wheat crops will be observed to compare yield responses. Soil test levels will also be compared to see if each treatment affect the Phosphorus and Potassium levels in each treatment area.

SUMMARY

• There were no statistical differences between treatments of bed pack cattle manure, compost, and commercial fertilizer in 2024.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

HCL 120 compost turner (6 feet by 12 feet model). Used for mixing, sizing and aerating composted manure. Requires 110 horsepower at the PTO and variable-speed or hydrostatic transmission to travel slowly.

PROJECT CONTACT

For inquiries about this project, contact Alan Leininger (leininger.17@osu.edu).

Flaming for Weed Control

OBJECTIVE

Determine if flaming corn for weed control sets back the development of corn leading to higher moisture at harvest.

STUDY INFORMATION

Planting Date 5/21/2024

Harvest Date 10/23/2024

HybridPrairie 5883

Population 32,500 sds/ac

Acres 10

Treatments2

Reps4

Treatment Width30 ft.

TillageConventional Management Fertilizer

Previous CropClover

Row Spacing30 in.

Soil Type Crosby-lewisburg silt loams, 80%

Kokomo silty clay loam, 20%

STUDY DESIGN

The study was setup up in alternating passes across the field. Every other pass was flamed and there were four total replications. Flaming occured on June 7 while the corn was at V3V4 growth stages.

eFields Collaborating Farm

OSU Extension

Madison County

WEATHER INFORMATION

Flamed corn showed signs of severe leaf burn.

OBSERVATIONS

Within 5 hours of flaming, differences in corn could be seen. Severe leaf burning occured on leaves around the base and tips of larger leaves. The field was relatively weed free at time of flaming due to cultivation but small weeds could be found within the rows of corn where the field cultvator is ineffective. Weed scouting at V8 revealed minimal weeds but a difference in weed severity was observed using a Chi-squared test. On a scale of 1-3 for weed severity, with 1 being a few weeds to 3 being total coverage, the flamed treatment averaged 1 while non-flamed pass averaged 1.4. Giant ragweed was found in all four flamed plots while morning glory and giant foxtail were found in all four untreated plots. See weed breakdown tables for more details.

RESULTS

Treatments

SUMMARY

• We observed no difference in corn moisture at harvest. This may be due to drought conditions and late harvest.

• No yield difference was observed between treatments indicating damage suffered from flaming does not impact end yield.

• A difference was observed in growth stage at the V8V10 with the flamed treatments being a growth stage behind at that point. Weed control was better in the flamed plots when scouted at the V8 growth stage.

1

1

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

PROJECT CONTACT

Propane flame toolbar covers 12 rows at 30-inch spacing for weed control. For inquiries about this project, contact Amanda Douridas (douridas.9@osu.edu).

Fungicide - Trivapro

OBJECTIVE

Determine if an application of either Trivapro or Azoxy Prop fungicide has an impact on corn yield.

STUDY INFORMATION

Planting Date 4/26/2024

Harvest Date 10/15/2024

HybridBeck’s 6241Q

Population 32,000 sds/ac

Acres200

Treatments3

Reps3

Treatment Width 100 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Crosby silt loam, 60%

Celina silt loam, 24% Brookston silty clay loam, 15%

STUDY DESIGN

This is a randomized complete block study with three treatments and three repititions. Three plots will recieve no fungicide, three recieve a fungicide application of Trivapro, and three recieve a fungicide application of Azoxy Prop. Yield and moisture are measured at harvest to determine the difference in response to each treatment.

eFields Collaborating Farm

OSU Extension

Darke County

WEATHER INFORMATION

Fungicide was applied to corn.

OBSERVATIONS

The dry growing conditions throughout the growing season may have led to lower overall disease pressure. However, tar spot was present in all plots. The yield increase in plots receiving fungicide seems to indicate that both fungicide products showed effectiveness in reducing the negative impact of tar spot and other diseases.

RESULTS

SUMMARY

• This study showed a signficant difference between corn plots without fungicide applied, and plots with fungicide applied.

• However, there was no significant difference in yield between the two different fungicide products that were applied.

• This could indicate that the presence of fungicide on corn had more impact in this study than the specific product used.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

A John Deere self-propelled sprayer was used to apply fungicide and insecticide to the corn in this study.

PROJECT CONTACT

For inquiries about this project, contact Caden Buschur (buschur.46@osu.edu).

High Clearance Robotic Irrigator

OBJECTIVE

Demonstrate in-season application of commercial nutrient sources and water application as a unified strategy to reduce nutrient losses while improving profitability with increased grain yields.

STUDY INFORMATION

Planting Date 5/2/2024

Harvest Date 10/21-23/2024

HybridUSA1093GT

Population 35,000 sds/ac

Acres 140

Treatments3

Reps 8

Treatment Width 160 ft. strips

Tillage Strip-Till

Management Fertilizer, Fungicide, Herbicide

Previous Crop Soybean

Row Spacing30 in.

Soil Type Crosby-Lewisburg Silt Loams, 63% Kokomo Silty Clay Loam, 37%

STUDY DESIGN

Field demonstrations was laid-out in a RCBD strip trial design with 160 feet wide blocks and treatments that include: in-season nutrient management of nitrogen to determine the mineralization rate differences based on irrigated versus non irrigated treatments. There were three treatments with two of the treatments receiving full nitrogen application per Tri-State Fertilizer recommendations and the third irrigated treatment getting only twothirds of the nitrogen. The irrigated treatments received the nitrogen through fertigation at 15 gal/ac. The 360 Yield Center Rain Irrigator unit was used to apply water in a 7-inch band at the base of the corn plant during the growing season.

Molly Caren Agricultural Center OARDC

Madison County

WEATHER INFORMATION

The robotic irragator dispereses water towards the base of the plants during the duration of the growing season in a 7-inch band.

OBSERVATIONS

This crop was planted in the early planting window. This year did not receive adequate rain through the summer and area was in drought. The irrigator began watering in mid-June and completed in the early part of September. In-season applied nitrogen was injected into the water stream and put on at a rate of 15 gal/ac. The corn crop exhibited drought and heat stress for all treatments throughout the growing season. This caused yield loss for the crop. The irrigated portion of the field was watered 11 times throughout the growing season. Total nitrogen applied for irrigated and non-irrigated treatments was 180 lbs/ac and irrigated two-thirds nutrients was 136 lbs/ac.

SUMMARY

• Irrigation had a statiscally significant affect on yield over all treatments. 48 bu/ac between irrigated two-thirds nutrients and non-irriated and 44 bu/ac between irrigated and non-irrigated.

• A total of 773 gallons of diesel was used to run the irrigator for this trial for 2024 cropping season across 71 acres.

• A total of 25,739 kWh were used to run the electric pumps, base station, and well for 2024 growing season across 71 acres.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

The 360 RAIN has a 80-foot boom that covers 32 rows and applies water through Y-DROP like hoses. This method of irrigation increases efficiency by reducing the amount of water used. It is also advantageous to use in a field that is irregular shaped and cannot fit a standard center pivot.

PROJECT CONTACT

For inquiries about this project, contact Andrew Klopfenstein (klopfenstein.34@osu.edu), John Fulton (fulton.20@osu.edu), Scott Shearer (shearer.95@osu.edu), or Elizabeth Hawkins (hawkins.301@osu.edu).

Project Funded by NRCS, ODA, and 360 Yield Center.

Manure Sidedress

OBJECTIVE

Evaluate two different manure systems for nutrient sources in an organic corn system.

STUDY INFORMATION

Planting Date 5/22/2024

Harvest Date 10/16/2024

HybridGreat Harvest 5077

Population 36,000 sds/ac

Acres40

Treatments2

Reps4

Treatment Width40 ft.

TillageConventional Management Fertilizer

Previous CropRed Clover Row Spacing30 in.

Soil Type Crosby silt loam, 48%

Celina silt loam, 22% Miamian silt loam, 17%

STUDY DESIGN

This study was replicated four times in a non-randomized block design. The two treatments were fall-applied poultry litter at a rate of 2.5 tons/ac check and fall-applied poultry-litter plus 3,800 gallons of liquid swine manure injected as a sidedress application. Poultry litter was surface applied with a Tebbe spreader and incorporated. Sidedress manure was applied with a Balzer tank and Dietrich injection sweeps. All other field operations were consistent across treatments. Treatment centers were harvested and yield data and moisture were collected using a calibrated yield monitor.

eFields Collaborating Farm

OSU Extension

Fulton County

WEATHER INFORMATION

Growing Season Weather Summary

Sidedress manure applied with Balzer tank and Dietrich injection sweeps.

OBSERVATIONS

The corn crop was monitored for yield limiting factors throughout the growing season. The last soaking rain on this field was the last week of June. Rainfall was significantly limited throughout pollination and grain fill, resulting in moistures lower than anticipated. Mechanical weed control provided above average control with the exception of block four which exhibited increased grass pressure.

SUMMARY

• A significant difference between treatments was returned.

• The addition of sidedressed swine manure showed a 23 bu/ac yield advantage over the check.

• Additional site years of data will add to the validity of these results.

RESULTS

Litter + Swine Manure

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Poultry litter is an excellent source of organic nutrients, often supplying sufficient amounts of nitrogen, phosphorous, potassium, and calcium for a corn crop when applied at two to four tons per acre.

22 CV: 8.6%

PROJECT CONTACT

For inquiries about this project, contact Kendall Lovejoy (lovejoy.59@osu.edu), Glen Arnold (arnold.2@osu.edu).

Nitrogen Rate

OBJECTIVE

Evaluate the impact of three different nitrogen rates applied at sidedress on corn yield and economic return.

STUDY INFORMATION

Planting Date 5/13/2024

Harvest Date 10/17/2024

HybridSchlessman 646VT2

Population 34,500 sds/ac

Acres30

Treatments3

Reps4

Treatment Width40 ft.

TillageConventional

Management Fertilizer, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Hoytville clay loam, 72% Blount loam, 28%

STUDY DESIGN

This study was designed to evaluate three different sidedress application rates. The grower typically applies 180 lbs N/ac at preplant; however, wet field conditions prompted a switch to a sidedress application. To assess the impact of varying nitrogen levels, the study included the grower’s standard rate of 180 lbs N/ac, as well as a reduced rate of 160 lbs N/ac and an increased rate of 200 lbs N/ac. A randomized complete block design (RCBD) with four replications was utilized in this trial. The treatments were applied to 16 rows and the center 8 rows were harvested. A calibrated yield monitor was used to report moisture and yield results.

eFields Collaborating Farm

OSU Extension Fulton County

WEATHER INFORMATION

Corn experienced early stress due to drought in July and August.

OBSERVATIONS

This farm is situated in a sandier region of the county. Due to missed rains in July and August, the crop experienced early stress, causing it to shut down sooner than expected. As a result, the corn began to exhibit leaf firing approximately two weeks earlier than anticipated. There were no visible differences observed between treatments throughout the growing season.

RESULTS

SUMMARY

• There was no statistical yield difference between the 160 lbs N treatment and the 200 lbs N treatment.

• Similarly, there was no statistical difference between the 160 lb N treatment and the 180 lbs N treatment.

• Additional years of study and data will add to the validity of this data.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

The John Deere S780 used to harvest this study was equipped with ActiveYield. This system uses the three load cells pictured here to estimate the change in weight of grain which continually calibrates the mass flow sensor returning accurate dry yield to the operator.

PROJECT CONTACT

For inquiries about this project, contact Kendall Lovejoy (lovejoy.59@osu.edu).

Nitrogen Rate

OBJECTIVE

Determine the optimal nitrogen rate for a cornfield by considering yield variability, economic profitability, and nitrogen requirements.

STUDY INFORMATION

Planting Date 5/22/2024

Harvest Date 10/19/2024

HybridSeed Consultants, SC 1054AM

Population 33,000 sds/ac

Acres 68

Treatments 5

Reps4

Treatment Width40 ft.

TillageConventional

Management Fertilizer, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Tiro silt loam, 64%

Pandora silt loam, 25%

Sebring silt loam, 3%

STUDY DESIGN

Planted corn, looking at the varible rates in nitrogen, applied five treatments of various levels of anhydrous: 100, 120, 140, 150, and 160 units of N.

eFields Collaborating Farm

OSU Extension

Seneca County

WEATHER INFORMATION

Nitrogen was applied to corn at varying rates.

OBSERVATIONS

Before tassel, no change in green, average moving speed 3.5 mph, averaging 4 inches deep with anhydrous. Was in a drought most of the season, field was dryer than normal.

SUMMARY

• The sufficient nitrogen rate from the trial to reach maxium yields was 140 units of N.

• With the common practice generally being 150 units of N, it can be determined the grower can drop their nitrogen rate to 140 units to still reach maxium yields.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 4 CV: 1.3%

TOOLS OF THE TRADE

Blu-Jet Anhydrous Bar

The bar uses a pressure-based system to push the liquid NH3 from the tanks into the ground. It uses a knife and coulter combo to cut the ground before the NH3 is placed under the soils surface and then the rear coulters cover up the trench.

PROJECT CONTACT

For inquiries about this project, contact Pressley Buurma (buurma.20@osu.edu).

Nitrogen Rate

OBJECTIVE

Measure the effect of nitrogen rate on corn yield.

STUDY INFORMATION

Planting Date 5/20/2024

Harvest Date 10/27/2024

HybridBeck's 5413

Population 34,000 sds/ac

Acres33

Treatments2

Reps 5

Treatment Width40 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Blount silt loam, 72% Pandora silt loam, 25% Haskins loam, 4%

STUDY DESIGN

Nitrogen is a costly input for corn production. Two nitrogen rates were compared for corn yield. At planting 125 lbs/ac UAN fertilizer (28% N) was surface applied with Herbicide. On June 22, sidedress N was coulter injected at full rate of 70 lbs/ac N and reduced rate 51 lbs/ac N. A normal nitrogen rate of 195 lbs/ac was compared to 176 lbs/ac.

eFields Collaborating Farm

WEATHER INFORMATION

Corn at V6 stage with killed rye.

OBSERVATIONS

Cereal rye cover crop was seeded October 2023 after soybean harvest. Corn was planted into standing rye which was terminated at the same time. No visual difference between treatments. Drought conditions occurred late summer.

SUMMARY

• Yields were significantly improved with additional nitrogen.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

LSD: 6 CV: 2.4%

PROJECT CONTACT

For inquiries about this project, contact Amber Emmons (emmons.118@osu.edu) or Alan Sundermeier (sundermeier.5@osu.edu).

Nitrogen Rate and Poultry Litter

OBJECTIVE

Evaluate the impact of a 30% reduction in total nitrogen applied at sidedress to a corn crop in a system with regular poultry litter applied.

STUDY INFORMATION

Planting Date 5/22/2024

Harvest Date 11/7/2024

HybridPioneer PO5725AM

Population 34,000 sds/ac

Acres 159

Treatments2

Reps 8

Treatment Width30 ft.

TillageConventional

Management Fertilizer, Fungicide, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Blount loam, 52%

Pewamo silty clay loam, 25%

Glynwood loam, 11%

STUDY DESIGN

The study was designed with eight replications of each of the two treatments. The two treatments were an application of 65 gallons of 28% at sidedress and 45 gallons of 28% at sidedress. The field was planted at a population of 34,000 sds/ac. In addition to both treatments, 5 gallons of Thio-Sul was applied at sidedress.

eFields Collaborating Farm

WEATHER INFORMATION

OBSERVATIONS

The corn exhibited the expected decrease in yield with the reduction in total nitrogen applied. Taking a look at the economics of each treatment, the cost of the additional nitrogen in the higher nitrogen treatment was not made up by the sale price of the grain from the yield increase. Using the cost of $1.75/gallon of 28% and $3.75/bu as the grain sale price the higher nitrogen treatment cost an additional $35/acre and returned only $23/ac more than the reduced nitrogen treatment. The reduced nitrogen treatment has a $12/ac better return on investment than the higher nitrogen treatment.

SUMMARY

• There was a significant decrease in yield with the 30% reduction in N applied.

• However, the savings of $35/ac on fertilizer cost, made up for the $23/ac decrease, on the reduced N treatment plot.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

3

PROJECT CONTACT

For inquiries about this project, contact Kyle Verhoff (verhoff.115@osu.edu) or Alan Sundermeier (sundermeier.5@osu.edu).

Corn at V3 stage.

N Rate Following Cover Crops

OBJECTIVE

Evaluate the effect on manure and compost on corn production.

STUDY INFORMATION

Planting Date 5/22/2024

Harvest Date 11/8/2024

HybridSeed Consultants 1122

Population 32,000 sds/ac

Acres30

Treatments3

Reps4

Treatment Width40 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Hoytville clay loam, 46%

Mermill loam, 19%

Millgrove loam, 12%

STUDY DESIGN

Randomized complete block design. Manure and compost applied September 25, 2023 surface applied. Corn planted green into living cover crop.

eFields Collaborating Farm

OSU Extension

Henry County

WEATHER INFORMATION

Corn was planted into living cover crop mix.

Pictured: Barley Clover Mix Biomass 85% canopy.

OBSERVATIONS

There was a thick cover crop stand when the corn was planted no-till. There was no visual difference between treatments.

SUMMARY

• No significant difference between treatments.

• Compost has aeration tubes in cattle manure which condensed the material.

• Compost applied at 10 ton/ac, manure applied at 20 ton/ac.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Amber Emmons (emmons.118@osu.edu) or Alan Sundermeier (sundermeier.5@osu.edu).

Corn a month after planting in crimped - roller planting.

N Rate Following Cover Crops

OBJECTIVE

Measure the effect of nitrogen rate on corn yield.

STUDY INFORMATION

Planting Date 5/25/2024

Harvest Date 10/19/2024

HybridPioneer P0843 AM

Population 34,000 sds/ac

Acres 27

Treatments2

Reps 5

Treatment Width40 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous CropWheat

Row Spacing30 in.

Soil Type Paulding Clay, 100%

STUDY DESIGN

This study is a randomized complete block design. The two sidedressed N rates selected were 50 gal/ac UAN (full rate, 28%) and 38 gal/ac UAN (reduced rate). A mixture of cover crops seeded on July 3 with Y-drop sidedress N.

eFields Collaborating Farm

OSU Extension

Putnam County

WEATHER INFORMATION

Interseeded cover crops were limited in growth.

OBSERVATIONS

Interseeded cover crops were limited in growth due to drought conditions in late summer. No visual differences between treatments.

SUMMARY

• Corn yields were not significantly affected by nitrogen rate.

• Interseeding of cover crops did not have an effect on corn production.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

LSD: 5 CV: 2.0%

PROJECT CONTACT

For inquiries about this project, contact Paige Garrabrant (garrabrant.42@osu.edu) or Alan Sundermeier (sundermeier.5@osu.edu).

Nitrogen Rate MRTN + 50

OBJECTIVE

Determine if the MRTN (Maximum Return to Nitrogen) application rate is adequate to supply necessary N to a corn crop, compared to 30 pounds more or less than the MRTN rate.

STUDY INFORMATION

Planting Date 5/21/2024

Harvest Date 10/16/2024

HybridDekalb DK56-64

Population 32,000 sds/ac

Acres 80

Treatments3

Reps4

Treatment Width30 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Paulding clay, 63% Haskins loam, 30% Roselms loam 6%

STUDY DESIGN

The study is a randomized complete block design, which allows for better control of the in-field variability. The three N rates selected were 130 lbs N/ac, 160 lbs N/ac, and 190 lbs N/ac. In this case, the MRTN rate was approximately 160 lbs of N, and the farmer wanted to apply a rate 30 lbs N/ac less and 30 lbs more to determine if any measureable difference in yield could be found. 45 lbs of N via 28% UAN was applied at plant, with the remainder applied at sidedress as anhydrous ammonia.

eFields Collaborating Farm

OSU Extension

Van Wert County

WEATHER INFORMATION

Corn appeared healthy, showing no signs of stress.

OBSERVATIONS

During the growing season, there was no visual difference between treatments. Any corn color and height differences were indistinguishable between treatments. Corn appeared to be healthy and did not show any signs of drought stress.

RESULTS

SUMMARY

• The yield data for this trial demonstrated that our MRTN rate had significantly higher yield than our lowest N rate, but was not significantly different than our highest N rate.

• Our economics also showed the MRTN rate coming out on top, with an $19 higher return over N when compared to the highest N rate, and a $40 higher return over N when compared to the lowest N rate.

• Our N use efficiency shows that our lowest N rate was the most efficient in terms of lbs N per bushel, with the MRTN rate having the second highest efficiency.

• Overall, the MRTN rate is the rate that we would recommend moving forward to give the producers the best bang for their buck.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 5

TOOLS OF THE TRADE

Maximum Return to Nitrogen (MRTN) is a calculated N rate based on the price of corn and N fertilizer. It is not an agronomically-determined rate, rather a rate that will provide the highest economic return.

PROJECT CONTACT

For inquiries about this project, contact Rachel Cochran (cochran.474@osu.edu).

Nitrogen Rate - Green Lightning

OBJECTIVE

Determine if Green Lightning was an adequate substitution for commercial nitrogen fertilizer while looking at nitrogen rates.

STUDY INFORMATION

Planting Date 5/20/2024

Harvest Date 10/16/2024

HybridBeck’s 6038VR

Population 36,000 sds/ac

Acres 15

Treatments 36

Reps4

Treatment Width20 ft.

Tillage Minimum Till

Management Fertilizer, Herbicide

Previous CropWheat

Row Spacing30 in.

Soil Type Brookston silty clay loam, 86% Crosby silt loam, 14%

STUDY DESIGN

This study was laid out to compare standard commercial nitrogen source, liquid 28%, with Green Lightning in RBCD 20 feet strips while looking at different nitrogen rates. Green Lightning is a new product that looks at taking nitrogen from the atmosphere and creating psuedo lightning through its production process to create a nitrogen rich product. This product is meant to be produced on farm with grower providing electricity, water, and storage for product once produced.

eFields Collaborating Farm OSU

Extension Fayette County

WEATHER INFORMATION

Drone applying the Green Lightning during the growing season.

OBSERVATIONS

Corn crop was planted later than normal for this area. It then turned extremely dry and went into drought for the duration of the growing season. Nitrogen program was 200 units with 30 units coming at planting via 2x2 and then different rates sidedressed for commercial N. The rates for sidedress were 30, 48 and 65 gal/ac of 32% for commercial N treatments and equivalent application rates of Green Lightning product as well. Additionally, Green Lightning treatments had the balance of the nitrogen program applied via drone: 2 applications 25 gal/ac for treatment D, 2 applications at 25 and 12 gal/ ac for treatment E, and 1 application of 23 gal/ac for treatment F.

SUMMARY

• Commercial N was not statiscally significant on yield versus Green Lightning, meaning this product could be a solution for commerical.

• There was no statiscal difference in nitrogen rate for any of the treatments.

• Rain was the limiting factor in this location due to extreme drought.

• More research is needed to confirm as with the growing season for this location that limited rainfall and high temps determined the yield not nitrogen.

• For upcoming cropping season different rates of Green Lightning will be used to account for differences in nitrogen content per gallon.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant

Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Andrew Klopfenstein (klopfenstein.34@osu.edu) or Ken Ford (ford.70@osu.edu).

Nitrogen Rate - Green Lightning

OBJECTIVE

Determine if Green Lightning was an adequate substitution for commercial nitrogen fertilizer.

STUDY INFORMATION

Planting Date 5/22/2024

Harvest Date 10/21/2024

HybridBeck’s 5794V2P

Population 34,000 sds/ac

Acres30

Treatments2

Reps2

Treatment Width30 ft.

Tillage Vertical Till

Management Fertilizer, Fungicide, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Hoytville silty clay, 100%

STUDY DESIGN

This study was laid out to compare standard commercial nitrogen source, liquid 28%, with Green Lightning in alternating 30 feet strips. Green Lightning is a new product that looks at taking nitrogen from the atmosphere and creating psuedo lightning through its production process to create a nitrogen rich product. This product is meant to be produced on farm with grower providing electricity, water, and storage for product once produced.

eFields Collaborating Farm

OSU Extension Paulding County

WEATHER INFORMATION

Harvesting the corn crop.

OBSERVATIONS

Corn crop was planted later than normal for this area. Corn crop was well established and had excellent yields but limiting factor was still rain due to drought conditions in July and August. Nitrogen program was 180 units with 44 gal/ac of 28% sidedressed and the balance coming at planting. Green Lightning treatments had 22 gal/ac of 28% and 2 applications of 25 gal/ac of Green Lightning.

RESULTS

SUMMARY

• Commercial N was statiscally significant on yield versus Green Lightning.

• While there was a statiscally signaficant difference this product needs to continue to be tested as a higher rate of Green Lightning could have been used to meet the needs of the corn crop.

• For upcoming cropping season different rates of Green Lightning will be used to account for differences in nitrogen content per gallon.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Green Lightning™ nitrogen strives to create long term farm profitability, higher yields, and a lasting legacy for their families and communities to prosper by creating a commercial nitrogen substitute through a different nitrogen fixation process.

PROJECT CONTACT

For inquiries about this project, contact Andrew Klopfenstein (klopfenstein.34@osu.edu).

Phosphorus Placement

OBJECTIVE

Measure the yield impact of strip-till performed in marginal (wet) conditions with subsurface P vs. no-till with surface band P.

STUDY INFORMATION

Planting Date 5/25/2024

Harvest Date 10/24/2024

HybridPioneer P0487

Population 30,000 sds/ac

Acres3

Treatments2

Reps3

Treatment Width 15 ft.

Tillage Strip-Till

Management Fertilizer, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Millsdale silty clay loam, 60% Dunbridge sandy loam, 40%

STUDY DESIGN

Split block, not randomized due to limited field size. Strip till has shown significant improvements on corn yields across many soil types in this area, but conditions are not often ideal for performing the strip till, espcially in heavier clay soils. The researchers have opted to still perform strip till even late in the fall or when the ground is too wet for a side by side comparison with the more common no-till practice.

eFields Collaborating Farm

OSU Extension Sandusky County

Unverferth 130 Zone Builder strip-till bar used during fall tillage.

OBSERVATIONS

The yield benefit was most pronounced in the hoytville soil and less in the courser textured soils. Our smartfirmer reported the seed trench in the ‘notill’ treatment was not as clean as in the strip-tilled treatment. The no-till treatment may have preformed better if we had fine-tuned the planter to those conditions.

SUMMARY

• The strip-tilled plots, even though the strips were not ideal, significantly out-yielded the plots where the fertilizer was only dribbled on the surface.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Unverferth 130 zone builder was the strip-till tool bar used, equipped with shanks set at 8-10 inches deep and colters which created a uniform mound. Ag Leader liquid controller was used ot turn liquid fertilizer on and off, as well as adjust the application rate. RTK (Real Time Kiinematic), was used as an extra correction for the GPS signal so accuracy remains at +/- 2. This accuracy was critical for successful strip tilling.

PROJECT CONTACT

For inquiries about this project, contact Allen Gahler (gahler.2@osu.edu).

Phosphorus Starter

OBJECTIVE

Evaluate phosphorus fertilizer’s impact on corn yield by comparing no phosphorus applied to starter phosphorus fertilizer.

STUDY INFORMATION

Planting Date 5/8/2024

Harvest Date 11/2/2024

HybridPioneer P0720

Population 34,000 sds/ac

Acres 7

Treatments2

Reps4

Treatment Width30 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous CropAlfalfa

Row Spacing30 in.

Soil Type Dixboro fine sandy loam, 58% Colwood loam, 42%

eFields Collaborating Farm

OSU Extension Fulton County

WEATHER INFORMATION

STUDY DESIGN

This study is a randomized complete block design with two treatments: phosphorus starter fertilizer (7-24-4 at 6 gal/ac) compared to no starter phosphorus fertilizer.

Corn planted into terminated alfalfa.

OBSERVATIONS

No visual difference in treatments throughout the growing season. Drought conditions were observed late summer which was followed by wet spring conditions.

SUMMARY

• A total rate of 112 lbs/ac nitrogen was applied to entire plot.

• Alfalfa cover crop contributed nitrogen.

• No significant difference among treatments.

• This study supports H2Ohio goal of reducing phosphorus fertilizer application.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Alan Sundermeier (sundermeier.5@osu.edu).

Phosphorus Starter

OBJECTIVE

Evaluate phosphorus fertilizer impact on corn yield.

STUDY INFORMATION

Planting Date 5/19/2024

Harvest Date 10/21/2024

HybridAxis 54T64

Population 33,880 sds/ac

Acres33

Treatments 5

Reps3

Treatment Width 60 ft.

Tillage Strip-Till

Management Fertilizer, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Hoytville silty clay loam, 51%

Haskins sandy loam, 21%

Mermill loam, 21%

STUDY DESIGN

This study is a randomized complete block design with 5 treatments. MAP fertilizer was applied at 115 lbs/ac (full rate) and then at a half rate. Struvite fertilizer was applied at 275 lbs/ac (full rate) and then at a half rate. The control was a zero phosphorus treatment.

eFields Collaborating Farm

OSU Extension Sandusky County

WEATHER INFORMATION

MAP and Struvite applied during fall strip-till.

OBSERVATIONS

Drought conditions were observed during the months of August and September but sufficient rain during June enabled corn to maintain good yield potential.

RESULTS

SUMMARY

• No significant difference in corn yield among treatments.

• No phosphorus deficiency symptoms were observed.

• Struvite has the potential to maintain corn yield sustainability as compared to soluble MAP fertilizer. Treatments

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Struvite (SmartPhosDG analysis 4-22-0 with 8% Mg) is a slow release phosphorus byproduct from wastewater treatment plants.

PROJECT CONTACT

For inquiries about this project, contact Alan Sundermeier (sundermeier.5@osu.edu) or Vinayak Shedekar (shedekar.1@osu.edu).

Planter Technology

OBJECTIVE

Determine whether planter equipment impacts final yield of organic corn.

STUDY INFORMATION

Planting Date 5/24/2024

Harvest Date 10/16/2024

HybridMerit 6026

Population 34,500 sds/ac

Acres 14

Treatments2

Reps4

Treatment Width40 ft.

TillageConventional

Management Fertilizer

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Del rey-fulton silt loams, 36%

Toledo silty clay loams, 34%

Pewamo silty clay loam, 31%

STUDY DESIGN

The study was a randomized complete block design with four replications. The treatments consisted of planting with either a low-tech John Deere 7200 MaxEmerge 2 (factory vacumeters, spring down force, rubber closing wheels, no row cleaners) or a high-tech Fendt Momentum (Precision Planting electric drives, FurrowForce, hydraulic downforce, pnuematic row cleaners). Both treatments were planted at 1 1/2 inches deep, 34,500 sds/ac and at 5.0 mph.

eFields Collaborating Farm

OSU Extension

Putnam County

WEATHER INFORMATION

High-Tech Fendt Momentum planter used for planting.

OBSERVATIONS

This field received lower than average rainfall for the growing season. Mechanical weed control was variable across all treatments in this trial and could have impacted results of this trial.

SUMMARY

• There was no significant difference in yield among the two planters.

• Additonal replication of this study, especially in variable weather conditions, is needed to confirm the results of the study.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 21 CV: 7.6%

TOOLS OF THE TRADE

Modular row unit. This Fendt row unit can be equipped with a variety of components to maximize planter performance in changing field conditions. This unit included in-furrow fertilizer placement which allowed the biological additives to be placed near the seed.

PROJECT CONTACT

For inquiries about this project, contact Beth Scheckelhoff (scheckelhoff.11@osu.edu), or Eric Richer (richer.5@osu.edu).

Seeding Rate

OBJECTIVE

Determine the economic optimum corn seeding rate for a field.

STUDY INFORMATION

Planting Date 4/26/2024

Harvest Date 9/26/2024

HybridEbberts 6883

Population See Treatments

Acres 26

Treatments 5

Reps3

Treatment Width30 ft.

TillageConventional

Management Fertilizer, Fungicide, Herbicide, Insecticide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Eldean loam, 62% Warsaw silt loam, 14% Eldean-Casco gravelly loams, 13%

STUDY DESIGN

A randomized complete block design was used for this corn study. The five seeding rate treatments included 28,000, 32,000, 36,000, 40,000 and 44,000 sds/ac planted in strips and replicated 3 times. A pivot exists in the this field with the study site located under the irrigation. The as-planted map was used for verifying the actual seeding rate with stand counts collected across 3 transects for emergance estimates within each treatment. All other management practices were kept the same across the field. A John Deere S680 combine with a calibrated yield monitor was used at harvest. Statisitcal analysis was conducted using a confidence interval of 0.01.

eFields Collaborating Farm

OSU Extension

Miami County

WEATHER INFORMATION

Corn planted in late April.

OBSERVATIONS

The 2024 growing season was unique for this field with excellent field conditions at planting and timely rain fall events early in the season. Stand counts plus early season scouting indicated excellent emergence with a percent stand at or above 97% across the study site regardless of the treatment. Emergence was uniform across the study site. Of note, the emergence for the 28,000 plants per acre was slightly above the target at 28,542 plants per acre. Late July through harvest were dry, hot months. During this time, ear growth, pollination, and seed fill occurred. Irrigation provided supplementary water during this period. Grain moisture at harvest did have some variation with the 28,000 and 32,000 treatments being 21.0% and 20.1% respectively. The grain moisture for all other treatments was between 18.4% and 19.5%. Though the growing season turned dry, yields were good for the higher seeding rates. Stalk diameters between treatments were similar and there was no lodging at harvest. There were no harvest issues encountered.

RESULTS

• There were significant yield differences between treatments except between the 40,000 and 44,000 sds/ac treatments.

• Practically, yields increased as the seeding rate increased for this study. This result was attributed to the growing season, in particular, the hot and dry conditions mid-season through harvest.

• As expected, the 28,000 and 32,000 sds/ac treatments had the lowest yield at 246 and 263 bu/ac, respectively.

• Grain moisture at harvest tended to decrease as the seeding rate increased.

SUMMARY PROJECT CONTACT

For inquiries about this project, contact John Fulton (fulton.20@osu.edu).

Seeding Rate

OBJECTIVE

Determine the optimum corn seeding rate to maximize returns.

STUDY INFORMATION

Planting Date 5/25/2024

Harvest Date 10/20/2024

HybridChannel 202-24STXRIB

Population See Treatments

Acres 5

Treatments3

Reps3

Treatment Width30 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Fitchville silt loam, 56%

Muskingum silt loam, 23%

Sebring silt loam, 21%

STUDY DESIGN

Variable rate seeding prescriptions have the potential to better match seeding rate to productivity zones in an effort to maximize profit.

eFields Collaborating Farm OSU Extension Tuscarawas County

WEATHER INFORMATION

Corn grew very well during season, with good weed control and no insect or disease concerns.

OBSERVATIONS

This field was planted timely and performed very well. There was no evidence of pest problems, including insect and disease. Weed control was excellent.

RESULTS

SUMMARY

• There was a statistical difference amongst the three seeding rates evaluated.

• The highest seeding rate (36,000 sds/ac) achieved the greatest return above seed cost.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 5

PROJECT CONTACT

For inquiries about this project, contact Chris Zoller (zoller.1@osu.edu).

Seeding Rate RX

OBJECTIVE

Understand the yield impact of varying corn seeding rates within Ohio considering in-field variability and cultural practices implemented.

STUDY INFORMATION

Planting Date 4/26/2024

Harvest Date 9/26/2024

HybridEbberts 6883

Population See Treatments

Acres 16

Treatments2

Reps3

Treatment Width30 ft.

TillageConventional

Management Fertilizer, Fungicide, Herbicide, Insecticide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Eldean loam, 62% Warsaw silt loam, 14% Eldean-Casco gravelly loams, 13%

STUDY DESIGN

eFields Collaborating Farm

OSU Extension

Miami County

WEATHER INFORMATION

A randomized complete block design was used for this corn study. The two treatments included a fix population of 36,000 sds/ac (farmer typical population for this field) and an RX treatment. All treatments were planted in strips and replicated 3 times. A pivot exists in this field and the study site is located under the irrigation. The RX included seeding rates ranging from 31,000 sds/ac up to 38,000 sds/ac with an average estimated seeding rate of 34,300 sds/ac for the field. The as-planted map was used for verifying the actual seeding rate with stand counts collected across 3 transects for emergance estimates within each treatment. All other management practices were kept the same across the field. A John Deere S680 combine with a calibrated yield monitor was used at harvest. Statistical analysis was conducted using a confidence interval of 1%.

Harvest results were good despite dry growing season.

OBSERVATIONS

The 2024 growing season for this field was notably distinct, beginning with excellent field conditions at planting time and receiving timely rainfall events early in the season. Stand counts and early season scouting revealed excellent emergence across the study site, regardless of the treatment applied. Emergence was uniformly consistent throughout the site. The weather patterns for this field in 2024 were unique, with favorable rain and ambient temperatures early on. However, as the season progressed into late July, the conditions turned hot and dry, and this persisted through harvest. Despite the dry spell, corn yields were impressive, averaging just over 270 bushels per acre. Stalk diameters were consistent across different treatments, and there was no lodging observed at harvest. Despite the dry conditions, the overall quality of the corn harvest was relatively good. Additionally, the harvest proceeded smoothly without any issues.

RESULTS

SUMMARY

• There was no significant difference in corn yield between the two treatments.

• There was no significant difference in stand counts between the treatments.

• There was no significant difference in grain moisture at harvest; both treatments were the same at 19%.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

LSD: 6 CV: 1.0%

TOOLS OF THE TRADE

As-planted Maps serve to verify what was completed wihtin a field by a planter. These maps provide value by verifying what occurred in terms of planted population and other planter performance across fields. They can also help direct scouting once a crop emerges to evaluate plant stands and note possible issues.

PROJECT CONTACT

For inquiries about this project, contact John Fulton (fulton.20@osu.edu).

Starter Fertilizer

OBJECTIVE

Understand the impact of additional bio-nutrients when applied with starter fertilzer on harvest moisture and yield in corn.

STUDY INFORMATION

Planting Date 5/24/2024

Harvest Date 10/13/2024

HybridAgrigold 64205

Population 32,900 sds/ac

Acres 17

Treatments2

Reps4

Treatment Width40 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide, Insecticide

Previous Crop Soybeans

Row Spacing30 in.

Soil Type Blount silt loam, 82% Glynwood silt loam, 16% Pewamo silty clay loam, 2%

STUDY DESIGN

This study was a randomzied complete block trial consisting of two starter fertilizer treatments. One being the Control, containing: 5 gal/ac 10-34-0, 1 quart per acre zinc, 9 gal/ ac 27-0-0-2, and 3 gal/ac 12-0-0-26. The second treatment additionally contained RootRx (potassium in the form of 0-0-5 and other soluble carbon compounds) at a rate of 1 quart per acre. Harvest moisture and yield data were collected in the fall.

eFields Collaborating Farm

OSU Extension

Union County

WEATHER INFORMATION

Corn was harvested in mid October.

OBSERVATIONS

The rainfall from July through September was well below average. There were no visual differences between the corn with the added bio-nutrients and the corn without. Disease pressure was relatively mild, likely due to the dry conditions.

RESULTS

SUMMARY

• There was no statistical yield difference between the starter fertilizer program used at this location and the starter fertilizer program with additional bio-nutrients.

• There was not a harvest moisture difference between the starter fertilizer program used at this location and the starter fertilizer program with additional bio-nutrients.

• Additional years of data, particularly due this extreme weather season, are needed to confirm results of this study.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

360 BANDIT Planter Liquid Placement System

This system places the liquid fertilizer on both sides of the seed, three inches away, at a depth of three quarters of an inch.

PROJECT CONTACT

For inquiries about this project, contact Wayne Dellinger (dellinger.6@osu.edu).

Sulfur Post-Applied

OBJECTIVE

Evaluate the effect of sulfur applied to post-emergent corn.

STUDY INFORMATION

Planting Date 4/29/2024

Harvest Date 10/18/2024

HybridBeck's 5824 AM

Population 34,000 sds/ac

Acres 110

Treatments2

Reps 8

Treatment Width 120 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide

Previous CropCorn

Row Spacing30 in.

Soil Type Bogart silt loam, 66%

Wheeling silt loam, 15% Glenford silt loam, 10%

STUDY DESIGN

This study was designed to evaluate yield differences with the additon of sulfur applied to post-emergent corn. The Sulfur was applied at a rate of 0.1 quarts per acre. The two treatments consisted of eight replications each.

eFields Collaborating Farm OSU Extension Tuscarawas County

WEATHER INFORMATION

Corn was harvested in mid-October.

OBSERVATIONS

There was a uniform stand across the field with little weed, insect, or disease issues. The addition of postapplied sulfur did not result in a yield increase.

SUMMARY

• The addition of sulfur applied to post-emergent corn did not result in a yield increase at this location.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 9

TOOLS OF THE TRADE

There may be instances when the application of sulfur benefits crop growth. On-farm research can help evaluate crop needs and responses when evaluating the allocation of nutrient dollars.

PROJECT CONTACT

For inquiries about this project, contact Chris Zoller (zoller.1@osu.edu).

Vomitoxin (DON) Management

Ohio has a long history, unfortunately, of Vomitoxin issues affecting our cereal grain crops. Vomitoxin is a common name for the toxin Deoxynivalenol, which is abbreviated to DON most of the time and can affect many crops with high toxin concentrations found in both the plant and grain of corn, wheat, barley, oats, rye, oats, and triticale. The name Vomitoxin came from the original detection of this toxin in small grains. At the time, barley was a major livestock feed for pigs. When this toxin is present, pigs will refuse to eat their feed, but when they do eat the contaminated feed, they will vomit multiple times. Vomitoxin (DON) has a long history of not only domestic feeding challenges but also creating challenges in the export of grains dating back to the export of feed-grade barley in 1928. DON is the poisonous toxin produced by the fungus Fusarium Graminearum which cause stalk, ear, and grain rot in corn.

Management of DON in corn requires an integrated pest management approach. We have been working on the complete IPM system.

Hybrid selection

Weather conditions

Fungicide selection and application

Post-harvest grain management and cleaning

Hybrid Selection

Significant reductions in DON levels in wheat have been achieved through genetic selection, the same will be true for reducing DON levels in corn. In 2023 we screened 80 corn grain hybrids in 3 locations the full results can be found here: go.osu.edu/DON2023 The variability among some of the hybrids with high DON levels led to a high coefficient of variation and LSD, especially at Bucyrus. A total of 13 hybrids had an average DON below 1 ppm (range 0 to 2.4) and 28 hybrids had an average DON below 2 ppm (range 0 to 4.1) at all locations. A total of 43 hybrids had average DON below 3 ppm at all 3 locations, but the range of DON for these hybrids was 0-6.5 ppm across all plots at these locations. All companies have hybrids that across all locations remained low. In 2024 we screened 90 hybrids from 11 seed companies. All seed companies that sell corn in Ohio are invited to submit hybrids to be screened for DON resistance. While DON levels across the state were much lower in 2024 visual ear rot symptoms were different between hybrids.

Weather Conditions

Weather conditions during the corn pollination window play a critical role in the ability of the fungi to survive and infect the corn ear. There has to be an infection by the fungus Fusarium graminearum during pollination for the production of the DON toxin. This fungus thrives when the temperatures are between 59-86°F and the relative humidity is above 80%. By calculating the number of hours during the 21 days before green silk that weather conditions are in this range, a predictive model can be used to assess the risk of the corn grain having DON levels above 1 ppm and the potential need for additional management through a fungicide application. These differences in weather conditions during corn pollination explain much of the variability seen every year

Figure 1. 2024 Corn ear rot on the tip of one hybrid in the 2024 hybrid resistance screening trial.

Fungicide Selection and Application

Fungicides can be an effective tool to assist in the management of high DON corn. Currently, only the fungicides Proline and Miravis Neo are labeled for the control of Gibberela ear rot. However, these fungicides must be applied during the VT/R1 growth stage while corn silks are wet. The fungicide must also penetrate the corn canopy and land on the silks. In corn silage, we have successfully used both ground and drone fungicide applications to significantly reduce DON levels. Ground-based applications provide a much higher percentage of coverage however the drone application penetrates the canopy with a higher concentration fungicide solution.

Post-harvest grain management thorough cleaning

While the best way to have low DON corn is through variety selection and in-season management, there may still be times when levels are higher than desired. The highest levels of DON are in the cobs and grain fines. The most recent grain clean research we conducted utilized a rotary-style cleaner. The grain cleaner reduced DON levels in the clean grain by 10 percent. The screening that was removed from the grain sample tested over 20 ppm DON at all flow levels.

Ohio and the Great Lakes region continue today to be an epicenter for DON in corn and small grains. Through an IPM approach corn DON levels can be reduced just as wheat levels have been reduced.

All Corn DON research done in Ohio can be found at: go.osu.edu/vomitoxin

The DON management research done in Ohio is generously sponsored by your check-off dollars through the Ohio Corn Marketing Board.

PROJECT CONTACT

For inquiries about this project, contact

Jason Hartschuh (hartschuh.11@osu.edu)

Figure 2. Water-sensitive paper showing the fungicide coverage at silk level between a ground application on top and a Hylio drone application on the bottom.

Yield Impact of Cover Crops

OBJECTIVE

Observe the long term yield impacts of a cereal rye cover crop in a corn-soybean rotation.

STUDY INFORMATION

Planting Date 5/6/2024

Harvest Date 10/17/2024

HybridSplit Planter: Pioneer

P0035AM/P04511AM

Population 33,500 sds/ac

Acres 16

Treatments2 Reps4

Treatment Width200 ft.

Tillage Strip-Till

Management Fertilizer, Herbicide, Pesticide

Previous Crop Soybean Row Spacing30 in.

Soil Type

Glenford silt loam, (27%)

Kibbie fine sandy loam, (23%)

Lenawee silty clay loam, (20%)

STUDY DESIGN

Randomized Block design with four replications and two treatments of no cover crop and cover crop (cereal rye 50 lbs/ac). Blocks not seeded with rye are 400 feet by 200 feet. There is much debate over the use of cover crops when rotating to corn as to whether it affects yield and health of the corn plant. This may take several years to monitor long term performance of both corn and soybeans with the use of cover crop rye on an annual basis.

eFields Collaborating Farm

OSU Extension

Sandusky County

WEATHER INFORMATION

Cover crop in corn at emergence.

OBSERVATIONS

No visual difference in plant health was observed between the two treatments during the season.

RESULTS

SUMMARY

• After one year, there was not a consistent yield response to the presence of cover crops amongst the replications.

• This is the first year for the study and we intend to replicate it in subsequent years.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Allen Gahler (gahler.2@osu.edu).

Department of Horticulture and Crop Science

grow your career with us.

Are you interested in Controlled Environment Agriculture, Sustainable Agriculture, Agronomy, Agroecology, Horticulture, Plant Biosciences, Turfgrass Science, or Professional Golf Management?

So are we! Check out our undergraduate and graduate programs, cutting edge research and extension teams perfect for helping you grow.

Learn more by scanning the code, following us on social media or visiting hcs.osu.edu

RESEARCH IN ENTOMOLOGY INCLUDES

Management of bees to ensure pollination of Ohio’s crops

Crop protection from insect pests

Integrated pest management in controlled environments

Specialty crop entomology

Vector biology, mosquitoes, & ticks

Insect population genetics & adaptation

Turfgrass pest management

Urban ecology & pest management

Landscaped ecosystem & forest entomology

Ohio State Soybean Research

For 2024, eFields soybean research was focused on improving the production and profitability of soybeans in the Ohio. Some exciting and innovating projects were executed this year, with 31 studies being conducted across the state. 2024 soybean research presented in eFields covers biologicals, fungicide, sulfur applications, and more. Below are highlights of the 2024 eFields soybean research.

828 acres

31 soybean

studies

For more soybean research from Ohio State University Extension, explore the following resources:

2024 Ohio Soybean Performance Tests

The purpose of the Ohio Soybean Performance Trials is to evaluate soybean varieties for yield and other agronomic characteristics. This evaluation gives soybean producers comparative information for selecting the best varieties for their unique production systems. For more information visit: go.osu.edu/OhioSoyTest.

Agronomic Crops Team - Soybean Research

The Agronomic Crops Team performs interesting research studies on a yearly basis. Resources, fact sheets, and articles on soybean research can be found here on the Agronomic Crops Team website: go.osu.edu/ CropsTeamSoybean.

The Ohio State Digital Ag Program

The Ohio State Digital Ag Program conducts studies related to all aspects of the soybean production cycle. Research related to soybean planting, inputs, and harvesting technology can be found on the Digital Ag website: digitalag.osu.edu.

Growth Stages - Soybeans

For all soybean studies in this eFields report, we define soybean growth stages as the following:

VE - Emergence - Cotyledons appear above the soil surface and provide nutrients for 7 to 10 days.

VC - Cotyledons have fully expanded and unifoliate leaves have unfolded.

V1 - First Trifoliate: Second true node, first node at which a trifoliate leaf is produced. Nodules visible.

V2 - Two fully developed trifoliates unfolded. The plant is roughly 8 in. tall. Nodules are actively fixing nitrogen. Cotyledons have fallen off plant.

V3 - V4 - A dramatic increase in the number of nodules visible on roots takes place by these stages.

V5 - VN - Lateral roots extend 15 in. away from main stem and grow to the center of 30 in. rows. Branches begin developing on the lowest nodes. Total number of nodes the plant may produce is set at V5.

R1 - Beginning Bloom - one flower is open at any node on the main stem.

R2 - Full Bloom - An open flower at one of the two uppermost nodes of the main stem with a fully developed leaf.

R3 - Beginning Pod - Pods are 3/16 in. long at one of the four uppermost nodes on the main stem.

R4 - Full Pod - Pod is 3/4 in. long at one of the four uppermost nodes on the main stem. This the most critical period for seed yield.

R5 - Beginning Seed - Seed in one of the four uppermost nodes with fully developed leaves is 1/8 in. long.

R6 - Full Seed - Pod containing a green seed filling the pod cavity is present at one of the top four nodes.

R7 - Beginning Maturity - One normal pod on the main stem has reached its mature pod color

R8 - Full Maturity - Ninety-five percent of the pods on the plant have reached their mature color Approximately 5 to 10 days of good drying weather is needed to bring crop to less than 15% moisture. Image Source: University of Illinois Agronomy Guide, 1999.

Adjuvant - Fulltec

OBJECTIVE

To determine how the addition of the adjuvant Fulltec impacts the effectiveness of fungicide and insecticide.

STUDY INFORMATION

Planting Date 5/2/2024

Harvest Date 10/8/2024

Variety Beck’s 3650E3

Population 140,000 sds/ac

Acres44

Treatments2

Reps3

Treatment Width 100 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide, Insecticide

Previous CropCorn

Row Spacing 15 in.

Soil Type Crosby Silt Loam, 71%

Brookston Silty Clay Loam, 13%

Celina Silt Loam, 10%

STUDY DESIGN

This is a randomized complete block study with two treatments and three replications. Three plots recieved fungicide (Trivapro) and insecticide (Province II) with added Fulltec, and three recieved fungicide and insecticide without the addition of Fulltec. Yield and moisture were measured at harvest to determine the difference in response to each treatment.

eFields Collaborating Farm OSU Extension Darke County

WEATHER INFORMATION

John Deere self propelled sprayer applying fungicide and insecticide with Fulltec adjuvant added.

OBSERVATIONS

Given the dry conditions, the effectiveness of fungicide is likely to be reduced compared to a wetter year. Due to the limited disease pressure, the fungicide applied likely had less impact on yield, and the adjuvant does not appear to have made the fungicide any more effective.

SUMMARY

• The yield results of this study showed no significant difference between treatments.

• Based off of these results, the adjuvant did not appear to improve the effectiveness of the fungicide product that was applied.

RESULTS

6

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Caden Buscher (buschur.46@osu.edu).

Biologicals - Compost Tea

OBJECTIVE

Determine if a foliar application of a bioreactor extract, or compost tea, to soybeans had any measurable impact on yield.

STUDY INFORMATION

Planting Date 5/24/2024

Harvest Date 10/3/2024

Variety USA 251 E

Population 160,000 sds/ac

Acres 6

Treatments2

Reps4

Treatment Width 45 ft.

Tillage No-Till

Management Herbicide

Previous CropCorn

Row Spacing 7.5 in.

Soil Type Paulding Clay, 55% Haskins Loam, 38% Roselms Loam, 7%

STUDY DESIGN

The study was a randomized complete block design in a small, irregularly shaped field. The two treatments were no application, and application of a biological compost tea as a foliar feed during early vegetative growth. The grower wanted to determine if a foliar application would have the same yield increase he had anecdotally seen in an in-furrow application of the biological product in another location.

eFields Collaborating Farm

OSU Extension

Defiance County

WEATHER INFORMATION

Harvesting the soybean trial.

OBSERVATIONS

No visual difference between plots through the growing season. Inadequate moisture in the region may have had some impacts to yield and crop development.

RESULTS

SUMMARY

• This study did not provide any statistically significant differences in yield after foliar application of a biological ‘compost tea’ product.

• The growing season in Northwest Ohio was dry, and did experience drought toward late summer/ early fall.

• This could have impacted crop yield, but the addition of the foliar extract was not enough to raise the yield by a significant margin.

Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

A Johnson-Su Bioreactor is a method of composting that produces a mature compost, as well as a liquid known as ‘compost tea’ that can both be applied to cropping systems to improve soil health and microbial presence.

PROJECT CONTACT

For inquiries about this project, contact Rachel Cochran (cochran.474@osu.edu).

Biologicals - Biostim

OBJECTIVE

Evaluate the effect of EnviroKure EK-L Biostim biofertlizer on soybean yield.

STUDY INFORMATION

Planting Date 6/12/2024

Harvest Date 10/21/2024

Variety Pioneer P31T64E

Population 180,000 sds/ac

Acres 5

Treatments2

Reps4

Treatment Width 60 ft.

Tillage No-Till

Management Herbicide

Previous CropCorn

Row Spacing 15 in.

Soil Type Del Rey Variant Silt Loam, 58% Mermill Loam, 42%

eFields Collaborating Farm

OSU Extension Defiance County

WEATHER INFORMATION

STUDY DESIGN

The study was designed as a randomized complete block design with four replications of two treatments. The treatments were a foliar application of the EnviroKure Biostim biofertilizer product and an untreated control. The Biostim product was applied via drone at 5 gal/ac at R1. Plots were 60 feet wide and the center 30 feet were harvested and recorded with a calibrated yield monitor.

Soybeans grew well despite dry growing season and adverse conditions.

OBSERVATIONS

The soybeans emerged well and maintained a good stand despite the adverse environmental conditions this season. There was no observable difference between the treatments, and no significant disease, insect, or weed pressure was present.

SUMMARY

• There was no significant difference in yield between the foliar application of the EnviroKure Biostim product and the untreated control, for the 2024 growing season.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

DJI Agras T40. The AGRAS T40 is equipped with the revolutionary Coaxial Twin Rotor design, enabling it to carry a spray load of 40 kg and a spread load of 50 kg (70 L). The aircraft is built in with a Dual Atomized Spraying System, DJI Terra, Active Phased array Radar and Binocular Vision. It supports multiple missions from surveying, mapping, to spraying and spreading.

PROJECT CONTACT

For inquiries about this project, contact Kyle Verhoff (verhoff.115@osu.edu)

Biologicals - Beauveria Bassiana

OBJECTIVE

Assess the effect of biological seed treatment Beauveria bassiana (Bb) in soybean growth and yield.

STUDY INFORMATION

Planting Date 5/19/2024

Harvest Date 10/6/2024

Variety Pioneer P33A62E

Population 132,000 sds/ac

Acres 6

Treatments2

Reps4

Treatment Width30 ft.

Tillage No-Till

Management Herbicide

Previous CropCorn

Row Spacing30 in.

Soil Type Kibbie fine sandy loam, 76% Gilford fine sandy loam, 19%

STUDY DESIGN

This project was laid out in randomized complete block design with four replications. Each replication consists of one large strip of Bb treated and one large strip of untreated. Bb treatment was applied as seed treatment before corn planting, following product label rate. Bb is a natural,beneficial fungus that forms a symbiotic relationship with plants. JABB strains are isolated from plants, for plants, and applied as inoculants in the spring.

eFields Collaborating Farm

were harvested in early October.

Soybeans

OBSERVATIONS

There were no noticeable differences in growth of soybean plants between the treated and control strips. This observation was also supported by the data collected on biomass production. Yield results were adjusted to 13% moisture. Statistical analyses used p-value of 0.1.

RESULTS

SUMMARY

• There was no significant difference between the treated and control strips in measured variables, including stand count, GreenSeeker, chlorophyll content, brix concentration, and biomass production at vegetative and reproductive stages.

• Grain yield and stand counts were slighly higher numerically for the treated strips. However, the differences were not statistically different.

• Follow-up experiments are being conducted in the field and greenhouse.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 3

PROJECT CONTACT

For inquiries about this project, contact Allen Gahler (gahler.2@osu.edu) or Osler Ortez (ortez.5@osu.edu).

Cover Crop Termination Timing

OBJECTIVE

Evaluate the effect of cover crop mixes that winterkill and cover crop mixes that do not winterkill and the impact on the following year’s soybean yields.

STUDY INFORMATION

Planting Date 6/12/2024

Harvest Date 10/21/2024

Variety Pioneer P31T64E

Population 180,000 sds/ac

Acres 10

Treatments3

Reps 5

Treatment Width40 ft.

Tillage Minimum Till

Management Fertilizer, Herbicide

Previous CropWheat

Row Spacing 10 in.

Soil Type Haskins Loam, 37%

Rawson Sandy Loam, 30%

Mermill Loam, 26%

STUDY DESIGN

The study was designed as a randomized complete block design with five replications of three treatments. The treatments were a winterkill cover crop mix (oats, radish, and berseem clove)r, a non-winterkill cover crop mix (rye, rapeseed, and crimson clover), and a control of no cover crops. Soybeans were planted into the standing cover crops and then the cover crops were terminated 3 days later.

eFields Collaborating Farm

OSU Extension Defiance County

WEATHER INFORMATION

Growing Season Weather Summary

No cover crops (left) is fallow, winterkill mix (middle) is yellow, and the non winterkill mix (right) is green.

OBSERVATIONS

The soybeans in the non-winterkill cover crop treatment took a longer time to fully emerge and at the time that stand counts were taken the non-winterkill cover crop treatment was noticeably behind developmentally. Other than the differences in the development of the soybeans the were no other noticeable differences between the treatments.

SUMMARY

• There was no statistcally significant difference in soybean yield between the no cover crop control and the winterkill cover crop treatment.

• There was a significant decrease in yield following the non-winterkill cover crop treatment when compared to the control.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Kyle Verhoff (verhoff.115@osu.edu)

Foliar Nutrient - Terramar

OBJECTIVE

Determine if a micronutrient application is economical and increases soybean yield.

STUDY INFORMATION

Planting Date 5/24/2024

Harvest Date 10/8/2024

Variety Beck’s 2630E3

Population 145,000 sds/ac

Acres40

Treatments2

Reps3

Treatment Width 120 ft.

Tillage Vertical Tillage

Management Fertilizer, Fungicide, Herbicide

Previous Crop Soybean

Row Spacing 15 in.

Soil Type Pewamo Silty Clay Loam, 64%

Blount Silt Loam, 36%

STUDY DESIGN

This study was designed in a randomized complete block. Each plot was 120 feet wide. We had two treatments a control, and a Terramar treatment. Terramar was applied at R1 growth stage and at 32 ounces per acre rate. The product was applied with the post herbicide pass.

eFields Collaborating Farm OSU Extension

Delaware County

WEATHER INFORMATION

Field showed signs of stress due to dry conditions.

OBSERVATIONS

This field was stressed heavily due to drought in August and September. Compared to fields that were even 2 miles east, this field displayed visual stress and suffered yield reduction. Minor marestail weed pressure weed pressure was seen.

RESULTS

SUMMARY

• There was a statistically significant difference in yield, with plots treated with Terramar yielding lower than the untreated control.

• Despite the product’s claims to enhance plant resilience under stress conditions, the results did not support its effectiveness, particularly in light of the drought conditions experienced this year.

• The return above cost was best for the control treatment by $35.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 2 CV: 1.4%

MORE INFORMATION

Here is the link and the QR code to the right with more information about this project (https://go.osu.edu/24efieldsvideo).

PROJECT CONTACT

For inquiries about this project, contact Jacci Smith (smith.11005@osu.edu) or Rob Leeds (leeds.2@osu.edu).

Foliar Nutrient - Terramar

OBJECTIVE

Determine if a micronutrient application is economical and increases soybean yield.

STUDY INFORMATION

Planting Date 4/26/2024

Harvest Date 10/6/2024

Variety Genesis G3171ES

Population 148,000 sds/ac

Acres 16

Treatments3

Reps3

Treatment Width 120 ft.

Tillage No-Till

Management Fertilizer, Herbicide, Insecticide

Previous Crop Soybean

Row Spacing20 in.

Soil Type Bennington Silt Loam, (86%)

Cardington Silt Loam, (8%) Pewamo Silty Clay Loam, 4%

STUDY DESIGN

This study was design in a randomized complete block. Each plot was 120 feet wide. There were 3 treatments, a control with no micronutrient applied, a B Sure micronutrient product applied at 16 ounces per acre, and a Terramar micronutrient applied at 32 ounces per acre.

eFields Collaborating Farm

OSU Extension

Delaware County

WEATHER INFORMATION

Plots were sprayed with treatments on June 18, 2024.

OBSERVATIONS

No visual differences were observed during the growing seasons between plots. Dry conditions in June and from the end of July through October stressed plants and rapidly sped up ripening of the crops. Disease, weed, and insect pressure was low.

RESULTS

SUMMARY

• There is no statistical yield difference between any of the treatments.

• The control yielded better than either of the products that claim to promote plant health during stresses such as the drought conditions seen this year.

• The control treatment was best in terms of return per acre above the product.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 5 CV: 4.0%

MORE INFORMATION

Here is the link and the QR code to the right with more information about this project (https://go.osu.edu/24efieldsvideo).

PROJECT CONTACT

For inquiries about this project, contact Jacci Smith (smith.11005@osu.edu) or Rob Leeds (leeds.2@osu.edu).

Fungicide

OBJECTIVE

Determine how a treatment of fungicide and insecticide impacts yield in soybeans.

STUDY INFORMATION

Planting Date 4/29/2024

Harvest Date 10/8/2024

Variety Beck’s 3140E3

Population 140,000 sds/ac

Acres 70

Treatments3

Reps3

Treatment Width 100 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide, Insecticide

Previous CropCorn

Row Spacing 15 in.

Soil Type Crosby Silt Loam, 52%

Celina Silt Loam, 31%

Brookston Silty Clay Loam, 16%

STUDY DESIGN

This is a randomized complete block study with three treatments and three repititions. Three plots recieved no fungicide or insecticide, three recieved only a fungicide application, and three recieved a tank mix application containing both fungicide (Trivapro) and insecticide (Province II). Yield and moisture were measured at harvest to determine the difference in response to each treatment.

eFields Collaborating Farm

OSU Extension Darke County

WEATHER INFORMATION

being planted in April.

Soybeans

OBSERVATIONS

The growing season was very dry with drought conditions throughout the beans entire lifecycle. The lack of moisture could likely be the reason for low disease pressure, which in turn could be the reason for the lack of difference between treatments.

SUMMARY

• The results of this study show no significant difference in yield between the soybeans receiving each treatment and the control.

• Neither the beans that had only fungicide, or the combination of fungicide and insecticide, showed a significantly higher yield than the beans that received no pesticide.

• In all plots, disease and insect pressure seemed to be low, which could be the reason for the lack of increased yield in plots receiving pesticide applications.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Caden Buscher (buscher.46@osu.edu).

Fungicide

OBJECTIVE

Evaluate the effect of a program (fungicide/foliar fertilizer mix) and a single biostimulant program (TerraMar) applied at soybean growth stage R3.

STUDY INFORMATION

Planting Date 5/16/2024

Harvest Date 10/9/2024

Variety Epley ESB2302ENRL

Population 130,660 sds/ac

Acres 87

Treatments3

Reps4

Treatment Width 60 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide

Previous CropCorn

Row Spacing 15 in.

Soil Type Houcktown-Glynwood-Jenera complex, 62%

Pewamo Silty Clay Loam, 30%

Jenera-Shinrock Till, 8%

STUDY DESIGN

Study consisted of three treatments: 1) a fungicidefoliar fertilizer mix: Revytek (fungicide -- 0.06 gal/ ac), ReaX Complete (N, S, B, Cu Fe, Mn, Mo, Zn -0.25 gal/ac), Re-NForce K (5-0-20 20%S -- 1.01 gal/ ac) and Radiate (growth regulator -- 0.02 gal/ac), 2) TerraMar (seaweed extract and leonardite, analysis 0-0-4, 0.25 gal/ac) and 3) a control (no product applied). Treatments 1 was recommended by a local dealer as a complete soybean health program. Treatments 1 and 2 were applied with 20 gal/ac of water at R3. Treatment areas were 60 feet wide -- the center 35 feet were harvested for grain yield. Experimental design was a completely randomized block replicated four times. Analysis was by SAS.

eFields Collaborating Farm

Hancock County

WEATHER INFORMATION

Three treatments were tested in this trial.

OBSERVATIONS

Yields were reduced for this field because of droughtlike conditions that occurred during grainfill. The fungicide-foliar fertilizer mix treatment had significantly larger yields than the Terramar or control treatment. There was no yield difference between the Terramar and the control. Since the fungicide-foliar was a mix of products, it is uncertain whether the yield increase was from the fungicide, fertilizer, or a combination. There was no evidence of disease, so if it was the fungicide, it would be from a plant health benefit rather than control of a specific disease. Available water may have affected the outcome -- 20 gallons of water were used as a carrier for foliar products, which may have affected yields in a drought year (the control did not receive additional water). The Terramar yield being slightly larger (not statistically) than the control would suggest that the extra water may have been beneficial.

RESULTS

SUMMARY

• Terramar had similar yields to the control treatment.

• The intensive fungicide-foliar fertilizer program yielded 5 bushels more than the control and 3 bushels more than the Terramar product.

• The design of this study does not provide adequate information to know whether the yield increase was from fungicide, foliar feed, of combination of both. However, available water may have enhanced the yields in the fungicidefoliar feed program rather than the products themselves since the field was drought-stressed during grainfill. Regardless, the cost of the fungicide-foliar feed program may limit its use on most farms.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Ed Lentz (lentz.38@osu.edu).

Fungicide

OBJECTIVE

Measure soybean yield response to a foliar fungicide application.

STUDY INFORMATION

Planting Date 6/4/2024

Harvest Date 10/20/2024

Variety Stewarts 3144XF

Population 165,000 sds/ac

Acres20

Treatments2

Reps3

Treatment Width 120 ft.

Tillage Vertical Till

Management Fungicide, Herbicide

Previous Crop Soybean

Row Spacing 7.5 in.

Soil Type Hoytville clay loam, 98%)

Mermill-aurand complex, 2%

STUDY DESIGN

Experiment was a complete randomized block design with two treatments and three replications. Treatments were 120 feet wide and 800 feet in length. Quilt XL fungicide was applied at 12.5 ounces per acre and was applied at the R3 growth stage. The middle 80 feet was harvested of each treatment. Yields were recored by using a calibrated yield monitor.

eFields Collaborating Farm

OSU Extension

Wood County

WEATHER INFORMATION

No visible disease pressure at application.

OBSERVATIONS

At the time of application there was no disease pressure in the field.

SUMMARY

• There was no disease pressure.

• There was not a statistically significant difference in yield between the 2 treatments.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 2

2.0%

TOOLS OF THE TRADE

Turbo Twin jet nozzels are ideal for applying fungicides, insecticides, or herbicides. Its twin style flat spray tip is best suited for broadcast spraying where superior leaf coverage and canopy penetration are important.

PROJECT CONTACT

For inquiries about this project, contact Nick Eckel (eckel.21@osu).

Fungicide

OBJECTIVE

Measure soybean yield response to a foliar fungicide application.

STUDY INFORMATION

Planting Date 6/11/2024

Harvest Date 10/7/2024

Variety Stewart 2964XF

Population 165,000 sds/ac

Acres20

Treatments2

Reps3

Treatment Width 120 ft.

Tillage Vertical Till

ManagementFungicide

Previous CropCorn

Row Spacing 7.5 in.

Soil Type Hoytville clay loam, 100%

eFields Collaborating Farm

OSU Extension Wood County

WEATHER INFORMATION

STUDY DESIGN

Experiment was a complete randomized block design with two treatments and three replications. Treatments were 120 feet wide and 800 feet in length. Approach Prima fungicide was applied at 6.8 ounces per acre and was applied at the R3 growth stage. The middle 80 feet was harvested of each treatment. Yields were recored by using a calibrated yield monitor.

Soybeans being sprayed by John Deere sprayer during growing season.

OBSERVATIONS

At the time of application there was no disease pressure in the field.

SUMMARY

• There was no disease pressure,

• There was not a statistically significant difference in yield between the two treatments.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

3

2.0%

PROJECT CONTACT

For inquiries about this project, contact Nick Eckel (eckel.21@osu).

Fungicide

OBJECTIVE

Measure soybean yield response to a foliar fungicide application.

STUDY INFORMATION

Planting Date 6/11/2024

Harvest Date 10/8/2024

Variety Stewarts 2964XF

Population 165,000 sds/ac

Acres20

Treatments2 Reps3

Treatment Width 120 ft.

Tillage Vertical Till

ManagementFungicide

Previous CropCorn

Row Spacing 7.5 in.

Soil Type Hoytville clay loam, 100%

STUDY DESIGN

Experiment was a complete randomized block design with two treatments and three replications. Treatments were 120 feet wide and 800 feet in length. Delaro fungicide was applied at 6 ounces per acre and was applied at the R3 growth stage. The middle 80 feet was harvested of each treatment. Yields were recored by using a calibrated yield monitor.

eFields Collaborating Farm OSU

Extension Wood County

WEATHER INFORMATION

Soybeans were planted at 7.5 inch row spacing.

OBSERVATIONS

At the time of application there was no disease pressure in the field.

SUMMARY

• There was no disease pressure,

• There was not a statistically significant difference in yield between the 2 treatments.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 5

PROJECT CONTACT

For inquiries about this project, contact Nick Eckel (eckel.21@osu).

Fungicide for White Mold

OBJECTIVE

Evaluate the the use of Lactofen herbicide to suppress the development of white mold in soybeans.

STUDY INFORMATION

Planting Date 5/2/2024

Harvest Date 10/16/2024

Variety Asgrow AG30XF4

Population 110,000 sds/ac

Acres 15

Treatments2

Reps3

Treatment Width 100 ft.

Tillage Minimum Till

ManagementFungicide

Previous CropCorn

Row Spacing 15 in.

Soil Type Soil Type, 52% Soil Type, 23%

eFields Collaborating Farm

OSU Extension Trumbull County

WEATHER INFORMATION

STUDY DESIGN

This study used a randomized strip design to compare Lactofen application to non-treated controls. Six treated and six non-treated controls were used for the comparison. Lactofen was applied to flowering soybeans at R1-R2 with incidence and severity ratings being collected approximately five weeks after application.

White mold present in soybeans.

OBSERVATIONS

Northeast Ohio experienced prolonged dry periods during peak white mold infection periods (R1-R3). This dry period was less favorable conditions for white mold infection development resulting in very low pressure. Despite this, white mold was found thorughout the area, including this research plot.

SUMMARY

• Application of lactofen at a rate of 6 oz/ac did not result in significant differences in yield.

• Lactofen application also did not suppress the incidence or severity of white mold.

• There was no significant difference between white mold presence or yield with the application of lactofen.

• Incidence and severity ratings were collected (data not shown) but there was no significant difference between the treatment and control.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Lee Beers (beers.66@osu).

High Clearance Robotic Irrigator

OBJECTIVE

Demonstrate in-season application of commercial nutrient sources and water application as a unified strategy to reduce nutrient losses while improving profitability with increased grain yields.

STUDY INFORMATION

Planting Date 5/1/2024

Harvest Date 10/8/2024

Variety Croplan CP3550XF

Population 135,000 sds/ac

Acres 25

Treatments2

Reps 7

Treatment Width 80 ft.

TillageVertical

Management Fertilizer, Fungicide, Herbicide

Previous CropCorn

Row Spacing30 in.

Soil Type Crosby-Lewisburg Silt Loams, 64%

Kokomo Silty Clay Loam, 36%

STUDY DESIGN

Field demonstrations was laid-out in a RCBD strip trial design with treatments that include: in-season irrigated versus non irrigated treatments for soybeans. There were no nutrients applied to the soybean crop during the 2024 growing season. The 360 Yield Center Rain Irrigator unit was used to apply water in a 7-inch band at the base of the soybean plant during the growing season. These beans were planted on 30-inch spacing.

Molly Caren Agricultural Center OARDC

Madison County

360 Rain Unit irrigating soybean crop during August of growing season..

OBSERVATIONS

This crop was planted in the early planting window. This year did not receive adequate rain through the summer and area was in drought. The irrigator began watering in late August at pod fill R4/5 and completed in the early part of September. The soybean crop exhibited drought and heat stress for the non-irrigated treatment. This caused yield loss for the crop. The irrigated portion of the field was watered 6 times for a total of 4.5 inches of applied water. Additionally, the hurricane that was recieved in late fall caused significant losses in nonirrigated treatments with shatter losses from previous drought conditions. There were no losses from the hurricane in the irrigated treatment.

RESULTS

SUMMARY

• Irrigation had a statistically significant affect on yield over non-irrgated.

• A total of 211 gallons of diesel was used to run the irrigator for this trial for 2024 cropping season across 11 acres.

• A total of 3,475 kWh were used to run the electric pumps, base station, and well for 2024 growing season across 11 acres.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Andrew Klopfenstein (klopfenstein.34@osu.edu), John Fulton (fulton.20@osu.edu), Scott Shearer (shearer.95@osu.edu), or Elizabeth Hawkins (hawkins.301@osu.edu).

Project Funded by NRCS, ODA, and 360 Yield Center.

Phosphorus Starter

OBJECTIVE

Evaluate phosphorus fertilizer impact on soybean yield by comparing no phosphorus applied to starter phosphorus.

STUDY INFORMATION

Planting Date 5/24/2024

Harvest Date 10/5/2024

Variety Pioneer P28A83PR

Population 170,000 sds/ac

Acres20

Treatments2

Reps3

Treatment Width 120 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous CropCorn

Row Spacing 7.5 in.

Soil Type Lenawee silty clay loam, 76% Hoytville clay loam, 24%

STUDY DESIGN

This study supports H2Ohio goal of reducing phosphorus fertilizer application. Randomized complete block design with two treatments. Treatment locations were maintained from 2023 corn starter phosphorus compared to no phosphorus applied. No fertilizer applied in 2024.

eFields Collaborating Farm

OSU Extension

Henry County

WEATHER INFORMATION

matured and were harvested in early October.

Soybeans

OBSERVATIONS

Drought conditions were observed late summer which followed wet spring conditions.

RESULTS

SUMMARY

• No significant difference in soybean yield among treatments.

• No phosphorus deficiency symptoms were observed.

• This follows the same treatment locations from 2023 when corn yields were also not significantly different among the same treatments.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Alan Sundermeier (sundermeier.5@osu.edu).

Potassium In-Season

OBJECTIVE

Evaluate the yield impact of in-season applied potassium.

STUDY INFORMATION

Planting Date 4/27/2024

Harvest Date 10/6/2024

Variety Enlist 3 Soybean

Population 140,000 sds/ac

Acres44

Treatments2

Reps 6

Treatment Width 60 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide, Insecticide

Previous Crop Soybean

Row Spacing30 in.

Soil Type Blount Silt Loam, 81%

Pewamo Silty Clay Loam, 14%

Glynwood Silt Loam, 5%

STUDY DESIGN

The study was to see in-season applied potassium would increase soybean yields. RE-NFORCE K was applied to the at a rate of five gallons per acre on May 21, 2024. Tissue testing done mid season to see if there was a difference in plant uptake.

eFields Collaborating Farm

OSU Extension

Morrow County

WEATHER INFORMATION

Applying RE-NFORCE K to the soybean field.

OBSERVATIONS

Tissue sampling showed that the beans treated with RE-NFORCE K did not have anymore K than the beans that were not treated with the product. The treated beans did have a visual difference in that they were taller but that did not convert to a higher yield.

SUMMARY

• Adding RE-ENFORCE K this year did not show a yield increase.

• Had we received more rain the outcome might have been different. Drought conditions likely limited K uptake across treatments.

RESULTS

gal/ac RE-NFORCE K

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 2 CV: 1.6%

PROJECT CONTACT

For inquiries about this project, contact Carri Jagger (jagger.6@osu.edu).

Relative Maturity

OBJECTIVE

Evaluate how different relative maturity bean varieties planted at the same time compared in yield harvested.

STUDY INFORMATION

Planting Date 5/2/2024

Harvest Date 10/9/2024

Variety Alloy A28E34 15340300, Alloy A30E33 13330102, Alloy A32E33 16420501

PopulationSee Results

Acres 5

Treatments3 Reps3

Treatment Width 65 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous CropCorn

Row Spacing 7.5 in.

Soil Type Blount Silt Loam, 73% Glynwood Silt Loam, 27%

STUDY DESIGN

This study was organized as a randomized complete block of 9 plots looking at three different relative maturity bean varieties planted at the recommended population to see how the yields compare at harvest time.

eFields Collaborating Farm OSU

WEATHER INFORMATION

Extension

Morrow County

Producers learning about the relative maturity trials.

OBSERVATIONS

Seeds were slow to germinate and slug damage was a problem because of consistantly moist soils and plant debris the average county rainfall for May was 5 inches. Once the weather warmed up and the rains slowed down the beans took off and grew well until it got hot and dry. The county average rain fall from June - October was 2.79 inches. The beans had very good growth and pod development but the pod fill and pod size was small.

SUMMARY

• In summary we did not see a statistically significant difference in yield between the three different relative maturities.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Carri Jagger (jagger.6@osu.edu).

Relative Maturity

OBJECTIVE

Observe how different relative maturity bean varieties planted at the same time compared in yield harvested.

STUDY INFORMATION

Planting Date 5/2/2024

Harvest Date 10/9/2024

Variety Alloy A28E34 15340300, Alloy A30E33 13330102, Alloy A32E33 16420501

Population RM2.8 - 215,000

RM3.0 - 195,00

RM3.2 - 200,000 sds/ac

Acres4

Treatments3

Reps3

Treatment Width 65 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous CropCorn

Row Spacing 7.5 in.

Soil Type Blount Silt Loam, 73% Glynwood Silt Loam, 27%

STUDY DESIGN

This study was organized as a randomized block of 9 plots looking at three different relative maturity bean varieties planted at the recommended population to see how they compared between the varieties at harvest. They were also treated with a microbiological. Manuever was applied at 6 grams of biological with 12 gallons of water per acre.

eFields Collaborating Farm

OSU Extension

Morrow County

WEATHER INFORMATION

Soybeans harvest in October.

OBSERVATIONS

Seeds were slow to germinate and slug damage was a problem because of consistantly moist soils and plant debris the average county rainfall for May was 5 in. Once the weather warmed up and the rains slowed down the beans took off and grew well until it got hot and dry. The county average rain fall from JuneOctober was 2.79 inches. The beans had very good growth and pod development but the pod fill and pod size was small.

RESULTS

SUMMARY

• We did not see a yield difference between the three different relative maturities evaluated.

• This plot had Manuever which is an integrated system for microbial acquired nutrition and it utilizes complementary modes of action, which enhance the crops’ access to critical nutrients that would otherwise be plant unavailable.

• The plots treated with Manuever yielded 6 bushels less than plots in another part of the field that were not treated with Manuever.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Carri Jagger (jagger.6@osu.edu).

Relative Maturity

OBJECTIVE

Evaluate soybean varieties with varying maturity dates for yield at early harvest to aid planting and establishment of cover crops.

STUDY INFORMATION

Planting Date 5/21/2024

Harvest Date 10/4/2024

Variety Beck's: 2550E3, 2950E3, 3650E3, 4060E3

Population 140,000 sds/ac

Acres3

Treatments4

Reps 5

Treatment Width30 ft.

TillageConventional Management Herbicide

Previous CropWheat

Row Spacing 15 in.

Soil Type Cut and Fill Land, 100%

STUDY DESIGN

The study was a completely randomized block design with four treatments (Beck’s Hybrids soybean varieties) and three replications. Plots were 30 feet wide and 220 feet in length.

eFields Collaborating Farm

OSU Extension

Putnam County

WEATHER INFORMATION

Field image of soybean maturity groups.

OBSERVATIONS

Target planting date for this trial was May 1, though planting occurred three weeks later. Two replications of each treatment were not harvested and included in the study due to weed pressure from grassy weeds that infiltrated plots during drought conditions.

RESULTS

SUMMARY

• Soybean varieties with maturities of 2.5 to 3.6 had no significant difference in yield but were significantly lower yielding than the 4.0 maturity variety.

• Under ideal planting and growing conditions, these varieties would have been established three to four weeks earlier in the growing season, providing an opportunity for higher yields at the time of harvest in early October.

• All plots were harvested with ample time to plant and establish a cover crop mix.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Soybean Maturity Groups

These allow producers to plant soybean varieties that best fit with their production goals, balancing maximized yield and anticipated harvest date.

PROJECT CONTACT

For inquiries about this project, contact Beth Scheckelhoff (scheckhelhoff.11@osu.edu).

Row Cleaners

OBJECTIVE

Determine if the presence of a row cleaner unit on the planter row provides better emergence and/ or yield when planting green into an unterminated cover crop.

STUDY INFORMATION

Planting Date 5/25/2024

Harvest Date 10/10/2024

Variety Asgrow 33XF3

Population 148,000 sds/ac

Acres40

Treatments3

Reps3

Treatment Width30 ft.

Tillage No-Till

Management Herbicide

Previous CropCorn

Row Spacing 15 in.

Soil Type Latty Silty Clay, 89% Nappanee Silty Clay, 11%

eFields Collaborating Farm

OSU Extension Paulding County

WEATHER INFORMATION

STUDY DESIGN

This study was replicated six times across a field, for a total of twelve plots. Row cleaner plots utilized row cleaners to move cover crop residue out of the way, where plots without the row cleaners planted straight into the residue on the soil surface.

Martin-Till ACCR1360 Row Cleaners planter setup.

OBSERVATIONS

Rye cover crop residue was light in this year’s trial due to poor emergence of the cover crop in the fall of 2023. Approximately 75% of the trial, if not more, did not have significant rye residue on the soil surface when the field was planted to soybean. This may have impacted the efficacy of the row cleaners, stand counts, and yield data of the trial.

RESULTS

SUMMARY

• The results of this study indicate there was no significant yield increase from the use of the row cleaners after a cereal rye cover crop.

• Stand counts were collected four times through the growing season, and averages across time indicate there was no significant difference in stands between treatments.

• Poor cover crop germination in the majority of the trial area may have contributed to these results.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 3 CV: 4.2%

TOOLS OF THE TRADE

Martin-Till ACCR1360 Row Cleaners

Used to move cereal rye cover crop residue out of the planter row to improve seed-to-soil contact.

PROJECT CONTACT

For inquiries about this project, contact Rachel Cochran (cochran.474@osu.edu) or Sarah Noggle (noggle.17@osu.edu).

Seeding Rate

OBJECTIVE

Understand the yield impact of varying soybean seeding rates considering infield variability.

STUDY INFORMATION

Planting Date 4/28/2024

Harvest Date 10/11/2024

Variety Pioneer 37A18

PopulationSee results

Acres 50

Treatments4

Reps4

Treatment Width40 ft.

Tillage No-Till

Management Fertilizer, Fungicide, Herbicide, Insecticide

Previous CropCorn

Row Spacing30 in.

Soil Type Xenia Silt Loam, 50%

Miamian Silt Loam, 31% Fincastle Silt Loam, 19%

STUDY DESIGN

A randomized complete block design was used to compare three fixed seeding rates and one variable rate seeding treatment. The variable rate treatment was determined by Precision Planting SmartFirmers responding to estimated soil organic matter on the go. Seeding rate was reduced in higher organic matter zones and raised in lower organic matter zones.

eFields Collaborating Farm

OSU Extension

Clinton County

WEATHER INFORMATION

Field map showing fixed seeding rates, variable rate seeding treatment, and organic matter zones.

OBSERVATIONS

Favorable spring conditions allowed this trial to be planted in late April. Warm temperatures led to rapid GDD accumulation and growth and development was ahead compared to typical years. Because of the 30 inch spacing, the herbicide program was adjusted to include more residuals both pre and post-planting. Observed stand counts in mid-June showed lower than expected populations across all of the treatments. This was not unexpected due to being low productive soils.

SUMMARY

• The lowest seeding rate was the highest yielding. This may be due to reduced competition at the lower population during the drought conditions through late summer.

• A strong herbicide program helped manage weed pressure due to later canopy in the 30-inch rows.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Precision Planting SmartFirmers

Enables on-the-go estimates of soil properties at planting. The estimates of soil organic matter were used to guide the seeding prescription.

PROJECT CONTACT

For inquiries about this project, contact Elizabeth Hawkins (hawkins.301@osu.edu).

Soybean Cyst Nematode in Ohio

Mapping and Understanding

Soybean Cyst Nematode (SCN) Challenges in Ohio

Soybean cyst nematode (SCN) stands as the most damaging and yield-limiting pathogen for soybean in Ohio and throughout North America. This stealthy nematode can reduce yields by 30% or more, often without any visible signs of its presence until the damage is severe. By proactively testing your fields for SCN, you can detect its presence early and implement management strategies that prevent irreversible harm.

With funding from the Ohio Soybean Council (soyohio.org) we will continue offering free testing for up to two soil samples per grower in 2025. The samples will be tested for SCN. This initiative aims at assisting farmers in identifying their nematode populations accurately. To submit your samples, get started by using the QR code or link to the right. There you can find more information on how to collect samples and to download the Soil Sample Submission Form that will accompany the samples. Samples can then be mailed to:

OSU Soybean Pathology and Nematology Lab

Attention: Horacio Lopez-Nicora, Ph.D.

110 Kottman Hall

2021 Coffey Rd. Columbus, Ohio 43210

Email: lopez-nicora.1@osu.edu

SCN is steadily gaining ground in Ohio, as nematode numbers increase (Fig. 1). Their ability to thrive on soybean cultivars labeled as ‘SCN-resistant’ poses a critical risk to the state’s soybean industry. Knowing your SCN levels is essential –managing SCN effectively starts with taking a proper soil sample. At the OSU Soybean Pathology and Nematology Lab, between 2018 and 2024, we have processed over 2,000 soil samples from Ohio growers (Fig. 2). In the majority of samples, SCN was either undetected (40% of samples) or found in very low quantities (21% of samples with fewer than 200 eggs per 100 cc of soil). However, 39% of samples contained SCN levels exceeding 200 eggs/100 cc soil. Notably, some fields (8%) had levels surpassing 5,000 eggs/100 cc soil, which is known to significantly reduce soybean yield (Fig. 3). If SCN count for a field exceeded 500, its population was preserved for detailed virulence profiling.

go.osu.edu/SCNsamples

Figure 1. Distribution and average SCN counts (eggs/100cc soil) in Ohio.

Figure 3. Levels of SCN (eggs/100cc soil) in Ohio.

Figure 2. Soil samples received and analyzed for SCN in Ohio. Years, left to right: 2018, 2019, 2020, 2021, 2022, 2023, 2024

We have characterized the virulence profiles of several Ohio’s SCN populations (Fig. 4). Alarmingly, over 85% of these SCN populations can reproduce on PI 88788 (SCN Type 2), at rates of 30% to 60% compared to susceptible soybeans. This poses a substantial challenge because more than 90% of SCN-resistant soybean cultivars rely on PI 88788 for resistance. Meanwhile, only a minority of SCN populations in Ohio can reproduce at low levels on Peking (SCN Type 1), and none significantly on PI 437654. Just 10% of samples fell into SCN Type 0, which can be effectively managed with any resistance source. Understanding your SCN population type (the nematode’s virulence profile) is crucial for selecting soybean varieties with effective resistance. In 2025, we are strongly encouraging growers to submit soil samples for SCN testing. If a high level of nematode presence is detected, we strongly advise determining your SCN virulence type –Know your numbers! Know your type!

Introducing the SCN Profit Checker Calculator

Figure 4. SCN population type in Ohio. From left to right: 1-, 0-, 2-

To underscore the importance of soil testing in 2025, TheSCNCoalition.com has launched the SCN Profit Checker Calculator. By entering specific field data – such as SCN egg count, soil sand percentage, pH, and the SCN female index on PI 88788 (with an average value provided for Ohio if unavailable from the grower ’s field) – farmers can estimate potential yield losses due to nematode infestations. Explore this valuable tool: TheSCNCoalition.com/ProfitChecker

Curious about our SCN sample processing methods?

Use the QR code (or this link: go.osu.edu/SCNTestingVideo) to learn more about how we collect and quantify SCN in soil samples. Remember, effective SCN management begins with a precise soil sample to detect its presence, know your SCN numbers, and implement an integrated management strategy.

PROJECT CONTACT

For inquiries about this project, contact Horacio Lopez-Nicora (lopez-nicora.1@osu.edu)

Soybean Oil and Protein Mapping

OBJECTIVE

Evaluate the variability in soybean oil and protein concentration.

STUDY DESIGN

Seven fields across Ohio were sampled in 2024. Soybean grain samples and soil samples were collected across each field. Sampling sites were established using a spatial analysis of terrain and soil types. The spatial classification routine identified nine locations for collecting soybean grain samples and three zones for soil samples within each field. Data on production practices, timing of field operations (including planting, fertilization, and spraying dates), and types of field operations were gathered for each field.

All samples were analyzed and summarized by the Ciampitti Lab at Kansas State University. Soybean grain samples were analyzed using a benchtop NIR analyzer. Soil samples were analyzed using traditional soil lab methods. Results were plotted to illustrate differences between fields while soybean oil and protein maps were created for each field.

Weather data reflects Troy, OH (location is in the middle of the testing area).

eFields Collaborating Farm

WEATHER INFORMATION

Soybean concentration map for a field ranging from 21.7% to 23.4%.

RESULTS

Median and variation of oil concentration by field.

Median and variation of protein concentration by field.

TOOLS OF THE TRADE

John Deere HarvestLab™ 3000

Sensor is based on near-infrared (NIR), which is capable of measuring the concentration of various grain components like protein, oil, moisture, and even fiber content in real-time during harvest. When mounted on a combine it monitors soybean crop quality as it is being harvested, and allows for more informed decisions based on specific nutrient levels within a field.

SUMMARY

• Oil and protein concentrations were above the averages in export vessels based on the USSEC survey.

• Oil concentration was similar between fields 1 through 4 and then fields 5 through 7. However, oil concentration was different between the two groups of fields.

• Protein varied among the fields samples with Field 5 above 40%.

• Oil and protein spatially varied across fields with about a 2% range in the concentrations for individual fields.

PROJECT CONTACT

For inquiries about this project, contact John Fulton (fulton.20@osu.edu).

Sulfur Rate

OBJECTIVE

Determine if sulfur applied in the form of ammonium sulfate (AMS) increases yield in soybeans.

STUDY INFORMATION

Planting Date 5/20/2024

Harvest Date 10/22/2024

Variety NK Seeds NK37-C1E3

Population 160,000 sds/ac

Acres 83

Treatments2

Reps4

Treatment Width 24.5 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous CropCorn

Row Spacing 15 in.

Soil Type Ockley Silt Loam, 38%

Millsdale Silty Clay Loam, 23% Milton Silt Loam, 14%

STUDY DESIGN

The study followed a completely randomized design with four replicates. Each plot was field length (varying from 1,351 to 2,554 feet) and 24.5 feet wide. Treatments consisted of control (no AMS application) and AMS (AMS application at approximately 100 lbs/ac on April 19, 2024).

eFields Collaborating Farm

OSU Extension

Greene County

WEATHER INFORMATION

No statistical signifcant difference was found when looking at the yield of the control and AMS treatments.

OBSERVATIONS

Soil and yield variations within this field have been consistently observed by growers, and applying AMS was an experimental strategy aimed at improving overall yield under variable conditions. The high coefficient of variation (CV) indicates considerable variability in the experiment, suggesting that the results are less consistent and reliable, which affects the overall precision and validity of the findings.

SUMMARY

• There was no statistically significant difference in yield between the treatments.

• Therefore, this study shows that applying ammonium sulfate (AMS) did not lead to an increase in soybean yield.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

6 CV: 8.3%

PROJECT CONTACT

For inquiries about this project, contact Marina Miquilini (miquilini.1@osu.edu).

Sulfur Rate

OBJECTIVE

Evaluate the effectiveness of a preemergence treatment of ammonium sulfate (21-0-0-24) to soybeans.

STUDY INFORMATION

Planting Date 4/23/2024

Harvest Date 9/14/2024

Variety B&A Genetics BA25EN23

Population 151,000 sds/ac

Acres20

Treatments 1

Reps 8

Treatment Width 60 ft.

TillageVertical

Management Fertilizer, Herbicide

Previous CropCorn

Row Spacing 15 in.

Soil Type Ockley Silt Loam, 33%

Fitchville Silt Loam, 14%

Fox Gravelly Loam, 14%

STUDY DESIGN

This study was designed as a randomized complete block study. Each plot was 60 feet wide and encompassed the full length of the field. Treatments consisted of 100 lbs/ac of ammonium sulfate (21-0-0-24) and no AMS (Check). A calibrated yield monitor was used to collect yield data.

eFields Collaborating Farm

OSU Extension

Knox County

WEATHER INFORMATION

Soybeans being planted in April.

OBSERVATIONS

There were no visual differences in this plot throughout the growing season. This field had good weed control and insect and disease pressure was very low throughout the season. Crop condition was very good through the end of July. It was abnormally dry durring August and Septermber. Even with the dry conditions 2024 yields were above average.

RESULTS

SUMMARY

• AMS was applied preemergence at a rate of 100 lbs/ac.

• This application did not result in a yield increase. The yield difference was not statistically significant.

• This treatment resulted in a decrease in net returns due to a lower yield and an additional cost of $33 (AMS and application charge).

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

LSD: 2 CV: 2.4%

PROJECT CONTACT

For inquiries about this project, contact John Barker (barker.41@osu.edu).

Sulfur Rate

OBJECTIVE

Determine if ammonium sulfate (AMS) increases yield across different organic matter levels.

STUDY INFORMATION

Planting Date 4/29/2024

Harvest Date 10/8/2024

Variety Xitavo XO3752E

Population 140,000 sds/ac

Acres 54

Treatments2

Reps9

Treatment Width 60 ft.

TillageConventional

Management Fertilizer, Herbicide

Previous Crop Soybean

Row Spacing 15 in.

Soil Type Kokomo Silty Clay Loam 45%

Crosby-Lewisburg Silt Loam, 55%

STUDY DESIGN

This trial was laid out in a randomized complete block design. One rate of AMS was applied at 80 lbs/ac (19.2 lbs actual sulfur) compared to untreated strips. Yield results were compared by percent organic matter.

eFields Collaborating Farm

OSU Extension

Madison County

WEATHER INFORMATION

Field layout for ammonium sulfate fertilizing prescription.

OBSERVATIONS

No visual differences were seen throughout the growing season. Yield results were analyzed over the entire trial and broken down by organic matter levels. Neither returned statistically significant results.Organic matter categories used were low (under 2.5%), medium (2.51-3.49%), and high (over 3.5%).

SUMMARY

• No statistical difference was seen when using AMS in any organic matter categories.

• Previous research has shown a greater benefit in areas with low organic matter.

• Given severe drought conditions reaching D2, yield results were likely impacted more due to lack of water.

RESULTS

Zone (Low)

OM Zone (Medium)

OM Zone (High)

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Dry Ammonium Sulfate (AMS)

AMS is often used as a water conditioner for spray applications but is also a readily available source of sulfur. AMS was broadcast with a fertilizer spreader

PROJECT CONTACT

For inquiries about this project, contact Amanda Douridas (douridas.9@osu.edu).

Sulfur Rate

OBJECTIVE

Determine if ammonium sulfate (AMS) increases yield across different organic matter levels.

STUDY INFORMATION

Planting Date 4/29/2024

Harvest Date 10/7/2024

Variety Xitavo XO3752E

Population 140,000 sds/ac

Acres33

Treatments2

Reps 10

Treatment Width 60 ft.

TillageConventional

Management Fertilizer, Herbicide

Previous Crop Soybean

Row Spacing 15 in.

Soil Type Kokomo Silty Clay Loam, 64%

Crosby-Lewisburg Silt Loam, 36%

STUDY DESIGN

This trial was laid out in a randomized complete block design. One rate of AMS was applied at 80 lbs/ac (19.2 lbs actual sulfur) compared to untreated strips. Yield results were compared by percent organic matter.

eFields Collaborating Farm

OSU Extension

Madison County

WEATHER INFORMATION

Field layout for ammonium sulfate fertilizing prescription.

OBSERVATIONS

No visual differences were seen throughout the growing season. Yield results were analyzed over the entire trial and broken down by organic matter levels. Neither returned statistically significant results. Organic matter categories used were low (under 2.5%), medium (2.51-3.49%), and high (over 3.5%).

SUMMARY

• No statistical difference was seen when using AMS in any organic matter categories.

• Previous research has shown a greater benefit in areas with low organic matter.

• Given severe drought conditions reaching D2, yield results were likely impacted more due to lack of water.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Amanda Douridas (douridas.9@osu.edu).

Sulfur Rate

OBJECTIVE

Determine if liquid sulfur applied in a 2x2 band (30-inch) would increase soybean yields on sandy fields.

STUDY INFORMATION

Planting Date 5/16/2024

Harvest Date 10/3/2024

Variety Pioneer 28A93E

Population 120,000 sds/ac

Acres4

Treatments2

Reps4

Treatment Width30 ft.

Tillage No-Till

Management Fertilizer, Herbicide, Insecticide

Previous CropCorn

Row Spacing 15 in.

Soil Type Tedrow Loamy Fine Sand, 59%

Kibbie Fine Sandy Loam, 41%

STUDY DESIGN

Randomized block design. With the sulfur flowing through the corn starter system on the planter, sulfur was dropped in 30-inch rows, while the soybeans were planted in 15-inch rows.

eFields Collaborating Farm

OSU Extension Sandusky County

WEATHER INFORMATION

Soybeans being planted with liquid sulfur applied in a 2x2 band.

OBSERVATIONS

The plots with sulfur looked better from the combine, however, this did not translate to a significant yield difference. The test plot area was very sandy and low in organic matter. The productivity potential of this field is about 60 bu/ac beans and 220 bu/ac corn.

SUMMARY

• There was no statistically significant yield difference between the two treatments.

• Future research into sulfur response in heavier soils is planned for the coming year.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Kinze 3600 Planter with Splitters

Used the 3 point hitch tank for the sulfur by plumbing it into the liquid 2x2 single disk opener system with check valves and an electric pump. Using the implement switch, sulfur was applied while soybeans were planted.

PROJECT CONTACT

For inquiries about this project, contact Allen Gahler (gahler.2@osu.edu).

Sulfur Rate

OBJECTIVE

Determine if liquid sulfur applied in a 2x2 band (30-inch) would increase soybean yields on sandy fields.

STUDY INFORMATION

Planting Date 5/16/2024

Harvest Date 10/4/2024

Variety Pioneer 28A93E

Population 120,000 sds/ac

Acres9

Treatments2

Reps3

Treatment Width30 ft.

Tillage No-Till

Management Fertilizer, Herbicide, Insecticide

Previous CropCorn

Row Spacing 15 in.

Soil Type Tedrow-Dixboro Complex, 67%

Colwood Fine SandyLoam, 33%

STUDY DESIGN

Randomized block design. With the sulfur flowing through the corn starter system on the planter, sulfur was dropped in 30-inch rows, while the soybeans were planted in 15-inch rows.

eFields Collaborating Farm OSU

WEATHER INFORMATION

Extension

Sandusky County

Soybeans near harvest in early October.

OBSERVATIONS

The difference between the treated and untreated plots were not very noticeable from the cab. This location also has a little more organic matter than other adjacent fields, and a productivity potential of about 60 bu/ac beans and 225 bu/ac corn

SUMMARY

• No difference between the sulfur and control plots were observed.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Allen Gahler (gahler.2@osu.edu).

Vegetative Stage Rolling of Soybeans

OBJECTIVE

Evaluate if rolling soybeans at planting and V1 increases yield by stressing soybean plants and increasing pod set.

STUDY INFORMATION

Planting Date 5/2/2024

Harvest Date 10/7/2024

Variety Pioneer P26A20

Population 160,000 sds/ac

Acres 14

Treatments3

Reps3

Treatment Width40 ft.

Tillage Minimum Till

Management Fertilizer, Herbicide

Previous CropCorn

Row Spacing 15 in.

Soil Type Haskins Loam, 46%

Sebring Silt Loam, 33%

Fitchville Silt Loam, 21%

eFields Collaborating Farm

OSU Extension Trumbull County

WEATHER INFORMATION

STUDY DESIGN

Randomized complete block design used three treatments and was replicated three times. Treaments included no rolling (control), rolling after planting but before emergence (VE), and rolling at V1.

OBSERVATIONS

No significant differences in yield were observed among treatments. Rolling soybean fields after planting pushes rocks, residue, and other materials into the soil. While harvesting the non-rolled control plots, the combine had to stop four times to remove rocks from the header. Each stoppage to remove a rock resulted in approximately two minutes of downtime.

SUMMARY

• No yield significant yield reponse was observed in any of the treatments.

• No yield response was observed in three years of on-farm research trials.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

40 Foot Land Roller

Designed to compact soil by rolling over a field, primarily used to flatten uneven ground, crush large soil clods, and improve seed-to-soil contact after planting.

PROJECT CONTACT

For inquiries about this project, contact Lee Beers (beers.66@osu.edu).

Vertical Till vs No-Till

OBJECTIVE

Determine how tillage and no-till prior to planting impact soybean yield.

STUDY INFORMATION

Planting Date 4/25/2024

Harvest Date 9/21/2024

Variety Pioneer P37A18E

Population 155,000 sds/ac

Acres 31

Treatments2

Reps4

Treatment Width 80 ft.

TillageVertical

Management Fertilizer, Herbicide

Previous CropCorn

Row Spacing 15 in.

Soil Type Brookston Silty Clay Loam, 29%

Fox Silt Loam, 22% Westland Silty Clay Loam, 18%

STUDY DESIGN

The trial consisted to two treatments, no-till and vertical till. Treatments were laid out in a randomized complete block design with four replications. Vertical tillage was completed in the spring ahead of planting. Harvest passes were taken down the middle of each treatment.

eFields Collaborating Farm

OSU Extension Fayette County

WEATHER INFORMATION

No statistical difference was found between the no-till and vertical till treatments.

OBSERVATIONS

This trial was initiated after farmers expressed concern over negative impacts of vertical tillage ahead of soybeans in 2023. Spring planting conditions were very different from 2023 to 2024 with wetter conditions during planting in 2023 compared with 2024 in this area.

SUMMARY

• Vertical tillage ahead of planting soybeans was compared to no-till to determine soil management impacts on planting.

• No statistical difference was found between the two treatments indicating one does not increase yield more than the other.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Great Plains Turbo Max

A vertical tillage machine that vertically cuts residue and fractures the soil to create a smooth seedbed for uniform seed emergence. Set at 2 to 3 degrees, running 2 to 3 inches deep.

PROJECT CONTACT

For inquiries about this project, contact Amanda Douridas (douridas.9@osu.edu).

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The Ohio State University College of Food, Agricultural, and Environmental Sciences is Ohio State’s cornerstone college. Through our research, teaching, and engagement with Ohioans and the world, we sustain life

Ohio State Small Grain Research

For 2024, eFields small grains research was focused on improving the production and profitability of wheat in Ohio. Some exciting and innovating projects were executed this year, with 3 studies being conducted across the state. 2024 small grains research presented in eFields covers nutrient management and seeding rate initiatives. Below are highlights of the 2024 eFields small grains research:

54 acres of small grains 3 small grains studies

For more small grains research from Ohio State University Extension, explore the following resources:

2024 Ohio Wheat Performance Tests

The purpose of the Ohio Wheat Performance Test is to evaluate wheat varieties for yield and other agronomic characteristics. This evaluation gives wheat producers comparative information for selecting the best varieties for their unique production systems. For more information visit: go.osu.edu/OhioWheat

Agronomic Crops Team - Wheat Research

The Agronomic Crops Team performs interesting research studies on a yearly basis. Resources, fact sheets, and articles on wheat and barley research can be found here on the Agronomic Crops Team website: go.osu.edu/CropsTeamWheat and go.osu.edu/CropsTeamBarley.

The Soybean and Small Grain Crop Agronomy Program

The Soybean and Small Grain Crop Agronomy Program in the Department of Horticulture and Crop Science at The Ohio State University is directed by Dr. Laura Lindsey. The goal of the research program is to meet the needs of Ohio farmers through research-based agronomic recommendations. Research related to small grains planting, cropping inputs, and harvesting technology can be found on the program’s website: stepupsoy.osu.edu/home.

Growth Stages - Small Grains

For all wheat and barley trials in this eFields report, we define growth stages as the following:

Feeke’s 1.0 - Germination period to the first emerged leaf.

Feeke’s 2.0 – Tillers become visible.

Feeke’s 3.0-4.0 – Tiller formation.

Feeke’s 5.0 – Strongly erect leaf sheaths. Growing point is still below the soil surface.

Feeke’s 6.0 – First node visible. The growing point is above this node. Tiller production is complete.

Feeke’s 7.0 – Second node visible. Rapid stem elongation is occurring.

Feeke’s 8.0 – Flag leaf visible.

Feeke’s 9.0 – Flag leaf completely emerged and leaf ligule is visible.

Feeke’s 10.0 – Boot stage. Head is fully developed and can be seen in the swollen section of the lead sheath below the flag leaf.

Feeke’s 10.5 – Heading and flowering. Head is fully emerged.

Feeke’s 10.5.1 – Early flowering, anthers are extruded in the center of the head.

Feeke’s 10.5.2 – Mid flowering, anthers are extruded in the center and top of the head.

Feeke’s 10.5.3 – Late flowering, anthers are extruded in the center, top, and base of the head.

Feeke’s 11.0 – Ripening.

Feeke’s 11.1 – Milk stage.

Feeke’s 11.2 – Mealy stage.

Feeke’s 11.3 – Hard kernel.

Feeke’s 11.4 – Harvest ready.

Image adapted from: Ohio Agronomy Guide, 15th Edition.

Biologicals - Biostim

OBJECTIVE

Evaluate the effect of EnviroKure EK-L

Biostim biofertilizer on wheat yield.

STUDY INFORMATION

Planting Date 10/4/2023

Harvest Date 7/3/2024

Variety FS 606

Population 2,400,000 sds/ac

Acres3

Treatments2

Reps3

Treatment Width 60 ft.

Tillage No-Till

Management Fertilizer

Previous Crop Soybean

Row Spacing 7.5 in.

Soil Type Ottokee Loamy FIne Sand, 57% Haskins Loam, 16% Mermill Loam, 14%

STUDY DESIGN

The study was designed as a randomized complete block design with three replications of two treatments. The treatments were a foliar application of the EnviroKure Biostim biofertilizer product on wheat and an untreated control. The Biostim product was applied via drone at 5 gal/ac, just before flowering. Plots were 60 feet wide and the center 30 feet was harvested and weighed using a weigh wagon.

eFields Collaborating Farm OSU Extension Defiance County

WEATHER INFORMATION

Foliar application of EnviroKure Biostim product on wheat.

OBSERVATIONS

There were no observable differences throughout the research plot. The stand was uniform and had no major disease, insect, or weed pressures.

SUMMARY

• There was no significant difference in yield resulting from a foliar application of the EnviroKure Biostim product, for the 2024 growing season.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

PROJECT CONTACT

For inquiries about this project, contact Kyle Verhoff (verhoff.115@osu.edu).

Organic Nitrogen Sources

OBJECTIVE

Evaluate four different nitrogen sources in an organic cropping system to determine their effects on crop yield and assess the overall economic benefits associated with each treatment.

STUDY INFORMATION

Planting Date 10/9/2023

Harvest Date 7/2/2024

Variety Great Harvest 4722

Population 1,800,000 sds/ac

Acres 45

Treatments4

Reps4

Treatment Width30 ft.

TillageConventional

Management Fertilizer

Previous Crop Soybean

Row Spacing 7.5 in.

Soil Type Mermill Loam, 52%

Hoytville Clay Loam, 19%

Glynwood Loam, 13%

STUDY DESIGN

This study evaluated the efficacy of fallapplied poultry litter alone versus poultry litter combined with three spring top-dressed nitrogen sources, applied separately. Treatments were arranged in a randomized complete block design (RCBD) with four replications. Poultry litter was applied at 2.35 tons/ac in the fall as the check treatment. In March, three top-dress nitrogen sources were applied: bone meal (220 lbs/ac), chilean nitrate (135 lbs/ac), and feather meal (150 lbs/ac), each adding the equivalent of 20 lbs N/ac. Yield and moisture data were collected using a calibrated yield monitor.

eFields Collaborating Farm

OSU Extension

Fulton County

WEATHER INFORMATION

Growing Season Weather Summary

Different poultry litters applied as a nitrogen source.

OBSERVATIONS

No visible differences were seen in the treatments throughout the growing season. Each treatment had an excellent stand with no lodging. Other farms in the area experienced some lodging due to excess nitrogen and a wind storm in late spring. The return above treatment costs were calculated using industry provided material costs and the 2024 Ohio Farm Custom Rates for each respective application. Fall applied poultry litter could serve as an easy, economical option for supplemental wheat nitrogen in both conventional and organic systems.

SUMMARY

• A significant difference in wheat yield was realized between the check and the poultry litter plus Chilean nitrate treatment.

• There was no significant difference between the three spring applied treatments.

• Similarly, there was no significant difference in yield between the check, poultry litter plus bone meal, and the poultry litter plus feather meal.

• Poultry litter applied in the fall as the sole source of N returned the greatest profit per acre.

• Additional site years of data will add to the validity of these results.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

5 CV: 3.6%

TOOLS OF THE TRADE

Has a high capacity box to help streamline spreading efficiency while also applying at precise rates across the field.

PROJECT CONTACT

For inquiries about this project, contact Kendall Lovejoy (lovejoy.59@osu.edu).

Tebbe Spreader

Seeding Rate

OBJECTIVE

Compare yield response of multiple wheat seeding rates and row spacings.

STUDY INFORMATION

Planting Date 10/13/2023

Harvest Date 7/2/2024

Variety Williams WSC3252

PopulationSee Results

Acres 6

Treatments4

Reps4

Treatment Width40 ft.

Tillage No-Till

Management Fertilizer, Fungicide

Previous Crop Soybean

Row SpacingSee results

Soil Type Hoytville Silty Clay Loam, 84%

Nappanee Silty Clay Loam, 16%

STUDY DESIGN

The study was designed as a randomized complete block design with four treatments replicated four times. The treatments included 1.8 million sds/ac at 7.5-inch rows, 1.8 million sds/ac at 3.25-inch rows, 900,000 million sds/ ac at 7.5-inch rows, and 900,000 sds/ac at 3.25-inch rows. All treatments were planted in the fall of 2023 after soybean harvest was complete.

eFields Collaborating Farm

OSU Extension

Henry County

WEATHER INFORMATION

Treatments of 900,000 and 1.8 millon seeds per acre planted in 3.25 -inch and 7.5-inch rows.

OBSERVATIONS

Row spacing did not affect yield response. Seeding rate of 1.8 million sds/ac yielded significantly higher than 900,000 sds/ac.

SUMMARY

• Both 1.8 million sds/ac treatment yields were statistically higher than those in the 900,000 sds/ ac seeding rate.

• Row spacing had no statistical difference on yield.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1. LSD: 3 CV: 2.4%

TOOLS OF THE TRADE

Tye 2020 Drill

Has the ability to plant seeds with 8-inch row spacing, often used for planting crops directly into undisturbed soil without prior tillage.

PROJECT CONTACT

For inquiries about this project, contact Alan Leininger (leininger.17@osu.edu).

COLLEGE OF FOOD, AGRICULTURAL, AND ENVIRONMENTAL SCIENCES

Agronomy and Farm Managemend Podcast

Stay on top of what is happening in the field and the farm office.

This podcast takes a bi-monthly dive into specific issues that impact agriculture, such as: weather, land value, policies, commodity outlooks, and more.

Available on Apple Podcasts and Youtube.

podcast.osu.edu/agronomy/

Read the latest eBarns Report to discover advances in technology, farm management practices, and feed production. This report collects the latest applied research from The Ohio State University Digital Ag Team, the Department of Animal Sciences, Ohio Producers, and Extension Educators.

View the report online and contact the eBarns Team to get more information: go.osu.edu/eBarnsReports

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Are you a livestock producer interested in learning more about your operation through data collection and on-farm research?

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Ohio State Forages Research

For 2024, eFields forage research was focused on increasing forage production in Ohio. Some exciting and innovating projects were executed this year, with 3 unique studies being conducted across the state. 2024 Forage research presented in eFields covers both precision nutrient management and species selection. Below are highlights of the 2024 eFields Forage research:

7 acres of forages 3 forage studies

For more forage research and feeding management from Ohio State University Extension, explore the following resources:

Ohio Forage Performance Tests

The purpose of the Ohio Forage Performance Test is to evaluate forage varieties of alfalfa, annual ryegrass, and cover crops for yield and other agronomic characteristics. This evaluation gives rorage producers comparative information for selecting the best varieties for their unique production systems. For more information visit: go.osu.edu/OhioForages.

Agronomic Crops Team - Forages Research

The Agronomic Crops Team performs interesting research studies on a yearly basis. Resources, fact sheets, and articles on alfalfa, winter annuals, and summer annuals can be found here on the Agronomic Crops Team website: go.osu.edu/CropTeamForages.

Ohio Forage Performance Tests

Agronomic Crops Team Forage Research

Forage Team

Dairy Team

Beef Team

Soybean Forages Tech Other Small Grains Water Quality

Species for Planting by Mid-July

Corn Plant Silage

Forage Sorghum Sorghum Sudangrass Sudangrass

Soybean Silage

Highest single cut forage yield potential of all choices. Silage quality will be lower than with normal planting dates. Risk will be getting it harvested at right moisture for good fermentation.

Best harvested as silage. Brown midrib (BMR) varieties are best for lactating cows. Conventional varieties are okay if BMR seed is not available. Can produce 3-4 tons of dry matter/acre. Risk of prussic acid (hydrogen cyanide gas) if frosted.

Reasonable alternative to replace alfalfa forage. Check seed treatment and herbicide labels, many restrict forage use.

Teff Grass

Best suited to beef and sheep; lower yield than sorghum grasses. Can harvest as hay or silage. Millets

Best suited to beef and sheep; many produce a single harvest. Best harvested as silage. Pearl millet does not produce prussic acid after frost damage.

Mixtures of annual grasses with soybean

Best harvested as silage. Mixtures of sorghum grasses or millets or even oats and spring triticale with soybean are feasible and can improve forage quality characteristics.

Species for Planting Late-July to Mid-September

Oat or Spring Triticale

Oat or Spring Triticale Plus Winter Cereals

Oat or Spring Triticale Plus Field Peas

Italian Ryegrass

Can be mowed and wilted to correct harvest moisture. Harvesting as hay can be challenging. Earlier planting dates provide more autumn yield.

Winter cereals (Winter rye, Winter wheat, Winter triticale) can be added to oat or spring triticale to add a forage harvest early next spring. Winter rye can also contribute a little extra autumn yield to the mixture.

Field peas can improve forage quality (especially crude protein content) but will increase seed cost.

Earlier planting dates provide more autumn yield. Excellent forage quality in the fall. Potential for three harvests next year starting in late April.

Cereal Leaf Beetle

OBJECTIVE

Determine cereal leaf beetle larval abundance and feeding damage across varieties of organic small grain forages.

STUDY INFORMATION

Planting Date 4/22/2024

Harvest Date (Boot Stage) 6/11/2024 - 6/18/2024

Harvest Date (Late Heading) 6/28/2024 - 7/3/2024

VarietySee Results

Population 100 lbs/ac

Acres 1

Treatments 13

Reps4

Treatment Width 10 ft.

Management Fertilizer

Previous Crop Soybean

Row Spacing 7.5 in.

Soil Type Wooster-Riddles Silt Loams, 51% Canfield Silt Loam, 49%

STUDY DESIGN

This study was a randomized complete block design with 4 replicates and 13 treatments. Each plot was 10 feet wide x 40 feet long with 7.5 inch rows drilled. On June 6, twenty plants from each plot were randomly selected, and the tallest leaf was identified and cut from the base of each plant. Leaf height (cm), number of cereal leaf beetle larvae, and damage ratings were collected for each of the twenty leaves.

Damage was a visual rating on a scale of 1-5 (1 = less than 25%, 2 = 25%, 3 = 50%, 4 = 75%, 5 = more than 75%). The center 4 ft were harvested from each plot and dry matter yield was calculated in tons/ac.

Wooster Campus Farms OARDC

Wayne County

WEATHER INFORMATION

OBSERVATIONS

There was lower overall insect pressure than in the 2023 field season. The barley varieties were susceptible to powdery mildew which likely adversely affected plant quality and palatability.

Cereal leaf beetle larvae under a microscope.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

SUMMARY

• Cereal leaf beetle larvae were obersvered in higher numbers on all oat varieties compared to triticale and barley.

• There was significantly less visible feeding damage in the triticale varieties.

• At the boot yield, triticale had the highest yield; however, at forage yield the oat varieties out-performed triticale and barley.

PROJECT CONTACT

For inquiries about this project, contact Amy Raudenbush (raudenbush.3@osu.edu), Ryan Haden (haden.9@osu.edu), Stephanie Pflaum, Maddie Brillhart, or Kelley Tilmon.

No-Till Organic Summer Annuals

OBJECTIVE

Assess yield differences between summer annual forage species and nitrogen rate in a no-till organic system, with frost seeded Red Clover.

STUDY INFORMATION

Planting Date 08/10/2023

Harvest Date 10/9/2023

Variety See Treatments

PopulationSee Study Design

Acres 5

Treatments 16 Reps4

Treatment Width 15 ft.

Tillage No-Till

Previous CropWheat

Row Spacing 7.5 in.

Soil Type Hoytville Clay Loam, 90%

Rimer Loamy Fine Sand, 10%

NC Ag Research Station

OARDC

Sandusky County

WEATHER INFORMATION

STUDY DESIGN

This study was conducted on an organic farm after wheat harvest. Red Clover was frost interseeded at 15 lbs/ac into the wheat in March. After wheat harvest Red Clover was mowed to slow the growth. A randomized complete block split-plot design was used with nitrogen rate as organic poultry litter pellets as the main effect and forage species as the secondary effect. Nitrogen was applied using a calibrated horn spreader, and then forages were planted with a John Deere no-till drill. Seeding rates of 100 lbs/ac of oats, 35 lbs/ac BMR Sorghum, and 30 lbs/ac Sorghum Sudan were planted in August.

OBSERVATIONS

Cooler temperatures after planting favored clover and oats growth over the summer annuals BMR Sorghum and Sorghum Sudan. The cooler conditions also slowed summer annual plant growth with plant height being only half of normal at 60 days. The weed plant count was lower in the plots with at least 50 pounds of nitrogen and a grass species than in the clover-only plot.

PROJECT CONTACT

For inquiries about this project, contact Jason Hartschuh (hartschuh.11@osu.edu) or Allen Gahler (gahler.2@osu.edu).

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

SUMMARY

• In this trial, the Red Clover dominated the plots reducing the growth of the grass species. With the clover alone plots having the highest yields.

• However, the higher rates of nitrogen did assist the oats in competing with the clover. As a blend, the higher nitrogen rates increased the growth of the grass species, decreasing the total forage protein.

• As the amount of grass species present increased so did the Neutral Detergent Fiber (NDF) and the Total Digestible Nutrients (TDN) of the forage.

• While the grasses competed with the clover when higher nitrogen rates were applied, a better method of stunting the clover than mowing is needed to maximize forage yields from the grass species.

Winter Annual Rye Nitrogen Rates

OBJECTIVE

Use spring NDVI readings to assist making spring nitrogen application management decisions and determine if fall applied nitrogen increases density and protein in cereal rye.

STUDY INFORMATION

Planting Date 10/6/2023

Harvest Date 05/2/2024

VarietyCereal Rye VNS

Population 2 bushels per acre

Acres 1

Treatments 16

Reps4

Treatment Width 10 ft.

Tillage Minimum Till ManagementNone

Previous Crop Soybean Row Spacing 7.5 in.

STUDY DESIGN

This trial was a randomized complete block split-plot design with four nitrogen rates in the fall (0, 30, 60, and 90 lbs/ac) and four nitrogen rates in the spring (25, 50, 75, and 100 lbs/ac) over the fall rates in cereal rye. Rye was planted in October and harvested in May. NDVI and Canopeo readings were taken in late November before fall dormancy and in the spring after greenup to determine the greenness and density of green matter.

OBSERVATIONS

NC Ag Research Station OARDC

Sandusky County

WEATHER INFORMATION

The 0 nitrogen fall treatment was thin and yellow compared to the other treatments in the spring. Ninety pounds of spring nitrogen improved the 0-fall nitrogen color, but plots were still thin. The highest nitrogen plots had wider leaves than the low nitrogen plots.

View of plot layout.

Soybean Forages Tech

SUMMARY

• At least 30 pounds of fall nitrogen was needed to maximize the yield.

• Spring nitrogen is the primary driver of Crude Protein (CP). CP was significantly affected by each 25 pound increase in spring nitrogen regardless of fall nitrogen rate.

• Total Digestible Nutrients (TDN) of cereal rye was negatively affected by increased nitrogen rates and was not affected at all by fall nitrogen treatments.

• NDVI had a correlation with all the spring applied nitrogen treatments - Canopeo did not. NDVI is a better tool for measuring ground cover and potential nitrogen available in spring for the crop, compared to Canopeo.

TOOLS OF THE TRADE

Treatment Means with the same letter are not significantly different according

Normalized Difference Vegetation Index (NDVI)

A handheld sensor that is walked across each plot to estimate the percent of live green vegetation in an area. The NDVI scale is between 0 and 1, with values closer to 1 indicating a higher percent of green vegetation.

PROJECT CONTACT

For inquiries about this project, contact Jason Hartschuh (hartschuh.11@osu.edu) or Kendra Stahl (stahl.221@osu.edu).

Figure 2. Plot harvester in action.

eFields Through the Years...

Ohio State Water Quality Research

Agriculture plays a key role in meeting water quality challenges in Ohio. In 2024, eFields research was expanded to better understand how management practices can help improve environmental stewardship, sustainability, and profitabilty. This research aims to help Ohio farmers improve the resiliency of their farm operations. Below are highlights of some of the 2024 eFields soil health and water quality research:

167 soil health trials 5 other studies

For more soil health and water quality research from The Ohio State University’s College of Food, Agricultural, and Environmental Sciences and the Department of Extension, explore the following resources:

CFAES Water Quality Inititative

Faculty and staff in the College of Food, Agricultural, and Environmental Sciences have a long and productive record of conducting research, teaching, and extension/outreach activities to address Ohio’s pressing water quality challenges. For more information, please visit: waterquality.osu.edu/

OSU Extension Water Quality

Local solutions will be critical to solving water quality challenges. OSU Extension has assembled a team of water quality associates to help meet local needs of farmers in Northwest Ohio. Learn more at the OSU Extension Water Quality website here: go.osu.edu/waterqualityext

OSU Soil Health

Soil health is a critical impact for many areas of agronomy, horticulture, and natural resources, with ties to entomology, plant pathology, engineering, chemistry, and many other disciplines. Information related to soil health assessment, management, and research can be found on the Soil Health website: soilhealth.osu.edu

OSU Ag Best Management Practices

Selecting the most effective best management practices (BMPs) for the specific field situation is critical for success. Learn about critical concerns and the BMPs that can help address them at the Ag BMP website here: agbmps.osu.edu

H2Ohio

H2Ohio is Governor Mike DeWine’s initiative to ensure safe and clean water for all Ohioans. It is a comprehensive, data-driven approach to improving water quality over the long term. H2Ohio focuses specifically on reducing phosphorus, creating wetlands, addressing failing septic systems, and preventing lead contamination. Learn more at: h2.ohio.gov

Cover Crop Population, Planting Date

OBJECTIVE

Evaluate effect of cereal rye seeding rate and planting date on cereal rye biomass, ground cover, and soybean final stand and yield.

STUDY INFORMATION

Planting Dates

Early: 10/19/2023

Mid: 11/3/2023

Late: 11/15/2023

Cover Crop Termination Cereal rye chemically terminated 5/7/2024

VarietyVNS Cereal Rye

Population 25 lbs/ac, 50 lbs/ac, and 75 lbs/ac

Acres 1

Treatments9

Reps4

Treatment Width 10 ft.

TillageConventional

Management Herbicide

Previous CropCorn

Row Spacing 7.5 in.

Soil Type Hoytville Clay Loam, 50.3%, Hoytville Silty Clay Loam, 49.7%

STUDY DESIGN

The study design was a randomized complete block replicated four times. There were a total of nine treatments consisting of three different seeding rates (25, 50, and 75 lbs/ac) and three different planting dates (October 19, November 3, and November 15). Cereal rye was established with drill and chemically terminated on May 7, two weeks before soybeans were planted. Biomass and ground cover were measured on April 21.

NW Ag Research Station OARDC Wood County

WEATHER INFORMATION

OBSERVATIONS

Intense rainfall events this spring and above normal precipitation from May 1 - June 30, 2024 reduced cereal rye growth. Total precipitation from May 1 - June 30 was 9.34 inches, compared to normal precipitation of 7 inches.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Canopeo

A mobile application was used to calculate fractional green canopy cover in a 0.25 meters2 area and estimate ground cover provided by cereal rye cover crop.

SUMMARY

• Planting date and seeding rate significantly increased ground cover and biomass, though planting date had a greater impact.

• Cereal rye planting date also had a significant effect on soybean yield, with a 6 bu/ac difference between plots with early and late cereal rye establishment.

• This trial was also conducted at Western ARS in Clark County where we observed a significant interaction between seeding rate and planting date. Equivalent biomass and ground cover levels were achieved at WARS despite seeding rate. At the earliest planting date, no statistical difference between 50 or 75 lb/ac seeding rates were observed at the mid planting date. At WARS, planting date also had a significant effect on soybean yield, but with the late treatment yielding higher.

PROJECT CONTACT

For inquiries about this project, contact Stephanie Karhoff (karhoff.41@osu.edu) or Elizabeth Hawkins (hawkins.301@osu.edu)

Nutrient Management Tools

OBJECTIVE

Provide agricultural communities with a brief overview of available decision support tools for nutrient management and conservation planning to improve soil health and water quality along with crop productivity.

Decision support tools for planning and managing nutrient and conservation practices

Field-to-watershed scale decision support tools help farmers and conservation staff assess the impacts of farm management and conservation practices on soil health, water quality, and nutrient runoff, enabling data-driven decisions that improve environmental outcomes and farm productivity. Selecting the right tool for the user’s needs is key to achieving sustainable agricultural productivity and environmental quality. Here is information about the water quality focused decision support tools.

AGRICULTURAL CONSERVATION PLANNING FRAMEWORK (ACPF)

Purpose: Locate suitable practices based on site-characteristics at field-watershed level (HUC-12 level).

Key Features:

1. Developed by the U.S. Department of Agriculture’s Agricultural Research Service.

2. Identifies locations for different types of conservation practices that can be placed within and below fields to reduce, trap, and treat water flows and thereby improve water quality in agricultural watersheds.

3. Help local farming communities better address their soil and water conservation needs.

4. ManureMap Toolbox: ACPF-compatible tool that provides information for application of manure from the source to proximal cropland.

5. FiNRT: ACPF-compatible tool that provides estimated costs and nitrate reduction outcomes for ACPF-generated conservation practices.

OHIO APPLICATOR FORECAST

[National Weather Service]

acpf4watersheds.org

Purpose: Minimize the risk of nutrient runoff by providing accurate timing for fertilizer application based on environmental conditions.

Key Features:

1. Also known as the Runoff Risk Advisory Tool in the Great Lakes Region and was developed by National Weather Service.

2. Uses daily weather forecasts and soil conditions to predict potential risk of runoff for any field or location in a 3- and 7-day window.

3. Nutrient applicators can use the runoff risk forecast to make decisions on whether or not to conduct nutrient applications in the field on a future date. The goal is to apply nutrients when the risk of runoff is low.

4. Provides the spatial extent of frozen soils to help make decisions on nutrient application during cold conditions.

NUTRIENT TRACKING TOOL (NTT)

[Texas Institute for Applied Environmental Research]

Purpose: Evaluate the environmental impact of agricultural practices on nutrient losses.

Key Features:

1. Developed by the modeling team of Texas Institute for Applied Environmental Research at Tarleton State University in cooperation with USDA’s Office of Office of the Chief Economist, Natural Resource Conservation Service (NRCS), and Agricultural Research Service (ARS).

2. Online, User-friendly interface with minimal input requirements.

3. Immediate feedback on nutrient runoff and water quality.

4. Suggests cost-effective management practices to reduce loss of nitrogen, phosphorus, sediments, and soil carbon.

CORN SPLIT N

[Purdue Extension]

go.osu.edu/NutrientTracking

Purpose: Improve nitrogen application practices, reducing runoff, and enhancing nutrient use efficiency.

Key Features:

1. Recommends optimal timing for nitrogen applications to reduce runoff.

2. Improves nitrogen use efficiency, preventing excess fertilizer from polluting waterways.

3. Uses weather and soil data to support environmentally friendly fertilization practices.

IRRIGATORPRO [Flint River Soil and Water Conservation District]

Purpose: Enhance water use efficiency in agriculture while reducing water-related environmental impacts.

Key Features:

1. Offers data-driven irrigation scheduling tailored to crop growth stages.

2. Promotes water use efficiency to avoid over-irrigation and runoff.

3. Reduces nutrient leaching and protects water quality in agricultural settings.

SMARTIRRIGATION APPS

[Southeast Climate Consortium]

go.osu.edu/CornSplitN_purdue

irrigatorpro.org

Purpose: Provide customized irrigation schedules that maximize water efficiency and protect water resources.

Key Features:

1. Offers data-driven irrigation scheduling tailored to crop growth stages.

2. Promotes water use efficiency to avoid over-irrigation and runoff.

3. Reduces nutrient leaching and protects water quality in agricultural settings.

IRRIGATION INVESTMENT CALCULATOR

[Useful to Usable Project]

Purpose: Evaluate the economic feasibility and sustainability of irrigation practices.

Key Features:

1. Calculates cost-benefit analysis of irrigation systems for economic sustainability

2. Assesses water use efficiency to minimize waste and environmental impact.

3. Guides decisions to ensure sustainable water management practices.

PROJECT CONTACT

For inquiries about this project contact Asmita Murumkar (murumkar.1@osu.edu)

Mahesh R. Tapas (tapas.4@osu.edu)

smartirrigationapps.org

hprcc.unl.edu/agroclimate/iic.php

Soil Health and Integrated Livestock

OBJECTIVE

Document long-term soil health impacts of using manure and perennial crops in rotation, by leveraging farm management history against on-farm soil samples.

eFields Collaborating Farm

Douglas Jackson-Smith, Louceline Fleuridor, Steve Lyon, Tiffany Woods, and Cassandra Brown - SENR; Ryan Haden - OSU ATI; Hemendra Kumar - Univ. of Maryland; Marilia Chiavegato - HCS/Animal Sciences; Ryan McMichael (Mercer County), Frank Becker (Wayne County) - OSU Extension; Ajay Shah and Amit Prasad Timilsina - FABE

BACKGROUND

Specialization of livestock and crop production has led to rapid gains in farm productivity and efficiency. However, the long-term sustainability of specialized farming systems has come under increased scrutiny due to concerns about soil health, nutrient losses, resilience to extreme weather, and growing scrutiny of agriculture’s greenhouse gas footprint. Reintegrating livestock and cash grain production offers the potential to address some of these concerns by recycling nutrients, replacing chemical fertilizers with manure, and diversifying crop rotations.

METHODS

Rather than applying treatments, this study compared farm management histories with soil health test results from working farm fields. In 2022 and 2023, the team gathered composite soil samples in 86 fields of 31 Ohio farms located in Wayne, Stark, and Holmes counties (eastern Ohio) and Mercer, Shelby, Auglaize and Darke counties (western Ohio). Fields were selected and grouped into four field class (FC) categories based on their 5-year cropping and management history: (1) cash grain fields that used no manure, (2) cash grain fields that used cattle manure, (3) fields that rotated cash grains with perennial hay, and (4) long-term perennial pasture and hay fields.

Composite soil samples were analyzed for each field to look at various soil health qualities. Annual interviews were conducted with farmers to document field management from 2017-2023. Analysis of variance was used to identify differences between the field classes. Multivariate analysis was then used to see how the intensity of various management practices influenced soil health outcomes.

RESULTS

Comparing cash grain fields without (FC1) and with manure (FC2), evidence was found to associate the use of cattle manure with increased soil total organic matter, soil respiration rates, and active carbon (POXC). Fields that used manure (FC2) also had higher arylsulfatase enzyme activity, which helps in cycling sulfur. In fields where perennials were part of the cropping system

Manure being stored in western Ohio, prior to being used on neighboring fields.

(FC3 and FC4), even more pronounced increases were found in most measures of soil health, including soil organic matter, ACE-protein and C, as well as the enzymes Arylsulfatase and Betaglucosidase, which aids in the breakdown of organic matter. The multivariate assessment confirmed that higher usage of perennial crops and manure applications was associated with increases in most soil health metrics. Indicators of cover crop use, fertilizer nutrient applications, and soil disturbance from tillage were not strongly associated with these soil health metrics after manure and perenniality effects were accounted for. Soil texture (especially clay content) did have a significant association with all soil health metrics.

Key to Field Classes (FC):

FC1: Row Crops, No Manure

FC2: Row Crops + Manure

FC3: Annuals + Perennials + Manure

FC4: Pasture/Hay + Manure

Study included beef and dairy operations (spreading own manure) and cash grain farms (importing manure from beef or dairy operations). To target ruminant manure, results excluded fields that received poultry or swine manure.

SUMMARY

• Our results suggest that reintegrating manure and perennial crops into row crop systems would lead to significant improvements in soil health. Several practical and logistical challenges exist, such as the current separation of crop and livestock operations, which is compounded by policies that often lack incentives for integration.

• With rising fertilizer costs there is a growing desire for strategies that reduce dependence on purchased inputs.

• Reintegration of crops and livestock appear to be a potentially valuable strategy to build resiliency and sustain productivity in Ohio agriculture.

ACKNOWLEDGEMENTS

The authors express appreciation to our 31 participating farms, and to the USDA NIFA IDEAS program for funding.

PROJECT CONTACT

For inquiries about this project, contact: Doug Jackson-Smith (jackson-smith.1@osu.edu).

Soil Health Survey Across Ohio Farms

OBJECTIVE

Survey Ohio farm fields to understand how past management practices of notill, cover crops, and manure applications influence laboratory-analyzed soil health values and crop yield.

STUDY DESIGN

eFields Collaborating Farm

Soil samples were collected from 167 fields in 28 counties in 2024, 108 fields in 29 counties in 2023, and 167 fields in 37 counties in 2022. Soil cores (15-20 cores per field) were taken at a depth of 8 inches. Entire samples were aggregated in 2023 and 2024 for laboratory analysis at Brookside Inc. of routine soil nutrients and soil health (Permanganate oxidizable carbon (POxC), respiration, and wet aggregate stability).

Farmer management information was collected from farms including years in no-till, years of cover crops, manure application history, and crop yield averages.

SUMMARY

Three years of soil health survey data have provided valuable information about the effects of soil health on crop yields and the historical soil properties that affect expected baseline soil health values.

This work shows that soil health analysis can be run with your traditional 8-inch routine soil nutrient test. Based on the correlation between soil health values and routine soil tests like Organic Matter and CEC the best way to evaluate soil health is to test the same location in your fields over time. Comparing your field to book benchmark values does not tell you how your management has changed your soils. Instead choosing select points in your field to test along with your routine soil test will allow you to evaluate if your management is improving your farm’s soil health.

POxC is sensitive to recent management changes that improve soil health (cover crops, tillage, rotations, etc.). Figure 1 demonstrates the correlation of POxC to the routine soil test OM and CEC as these two increase so does POxC. Figure 2 illustrates that corn yields increase as POxC increases while soybean yields are not affected, corn would benefit from the additional nitrogen that is held in the increased organic matter more than soybeans.

CO2 respiration is a measure of how fast the soil food web can ‘wake back up’ and become active again. Figure 3 shows the relationship between CO2 respiration and corn and soybean yields. Soybean yields benefit greatly from this increase in CO2 respiration while corn yields have a slight negative relationship. Conservation practices of no-till and cover cropping increase CO2 respiration but show a trend toward lower corn yields over time. Soybean yields increased with more years of cover cropping and were not affected by no-till. When analyzing your farm’s CO2 respiration Figure 4 demonstrates that we would expect higher values in historically higher OM and CEC soils.

Figure 1. Routine soil testing for CEC and organic matter both have an underlying effect on POxC. In soils where OM and CEC are historically higher a greater POxC will be expected.

RESULTS

Figure 2. Relationship of corn and soybean yield to POxC. Corn yields show a positive yield response to increasing POxC while soybean yields are unaffected by this increase.

Figure 3. Relationship of corn and coybean yield to soil CO2 respiration. Corn yields show a slightly negative yield response to increasing respiration while soybean yields were strongly improved by increasing soil respiration.

Figure 4. Routine soil testing for CEC and organic matter both have an underlying effect on soil respiration. In soils where OM and CEC are historically higher a greater CO2 respiration is expected.

ACKNOWLEDGEMENTS

Thank you to Ohio Soybean Council for providing funding for this effort.

PROJECT CONTACT

For inquiries about this project contact Jason Hartschuh (hartschuh.11@osu.edu), Elizabeth Hawkins (hawkins.301@osu.edu), Dara Barclay, Ambria Small, Horacio LopezNicora, and John Fulton.

VRT and BMP Adoption (Sandusky)

OBJECTIVE

Determine what practices are being used by farmers within HUC8 Sandusky watershed to manage water and nutrients.

STUDY DESIGN

eFields Collaborating Farm

In the field survey process, all the cropped fields within the watershed were considered in the randomized selection process regardless of farm and field size. However, only fields that were greater than 20 acres were selected for the survey; the average size of the fields surveyed was 72.7 acres. A trained Soil and Water Conservation District employee interviewed the landowner or farm manager for each field surveyed. The Ohio State University and the Center for Survey Statistics and Methodology at Iowa State University helped in designing the sampling strategy and data analysis.

KEY FINDINGS

• Approximately 57% of the fields surveyed were currently enrolled in a cost share conservation program, including both state and federal level programs.

• The assessment found that most farmers were testing their soil, with 92% of the surveyed fields being sampled at least once every four years. The vast majority of soil samples (85%) were completed using precision agriculture, via grid or zone methods.

• Approximately 62% of fields surveyed had phosphorus applied using variable-rate technology (VRT); 21% of fields had nitrogen applied using VRT.

• Nearly 55% of the fields were either no tilled or minimally tilled.

• The assessment found that 59% of the farmland assessed was owned by the farmer and 41% was in a lease.

• Farm familiarity is very high as 92% of the fields were managed by the farmer for three years or longer with only 8% being farmed less than three years.

• Farmers utilized fertilizer retailers and crop consultants for 87% of fields surveyed.

• Commercial fertilizer is the majority nutrient source (80%) used in this region, followed by manure (14%).

Voluntary/Comprehensive Nutrient Management Plans

Percentage of fields covered by a nutrient management plan. Approximately 46% of fields surveyed were covered by an approved voluntary or comprehensive nutrient management plan (VNMP/CNMP) with 48% not covered with a VNMP These plans were completed either by their local Soil and Water Conservation District (SWCD) or Natural Resources Conservation Service (NRCS). Assessment respondents indicated using the Tri-State Fertilizer Recommendations on 98% of the fields surveyed for determining the amount of commercial phosphorus (P) fertilizer to apply

OTHER NUTRIENT MANAGEMENT STRATEGIES

Variable Rate Technology (VRT) Application:

• 62% of fields surveyed used variable-rate P application versus 38% using fixed-rate application

• 21% of fields surveyed used variable-rate N application versus 79% using fixed-rate application

• 37% have VRT capabilities that exist on farm versus 63% that are through a supplier Manure Application:

• 98% of fields surveyed were fertilized using appropriate setback distances to critical areas for manure application, according to USDA-NRCS 590 standards

• 31% of fields surveyed were using subsurface manure application

• 80% of fields surveyed incorporated the manure

• 44% of fields surveyed had subsurface manure applied into vegetative cover or an actively growing crop, which helps keep nutrients in the field

Phosphorus Application

Distribution of amount of phosphorus applied on surveyed fields Approximately 86% of phosphorus applied is for the 1-2 year crop need

Nitrogen Placement Method

Distribution of nitrogen (N) placement methods on fields. The majority of farmers surveyed (58%) applied nitrogen before or during planting. The remaining (42%) side-dressed most of their nitrogen in-season.

CONCLUSION

This survey was completed throughout 2023 referencing crop year 2022, prior to the implementation of H2Ohio practices. The assessment results establish a baseline of adoption for various farming practices in the Sandusky watershed. This information allows for a more targeted approach to increase best management practice adoption. Demonstrated by data, certain practices are elevated to yield optimal results. We will continue to assess more watersheds around the state in the coming years, revisiting previously assessed watersheds in a few years to determine levels of change. We encourage Ohio’s farmers to get involved in the OACI’s Farmer Certification program, H2Ohio and any other conservation focused program to learn about new practices, share information and become better stewards of the land.

PROJECT CONTACT

For inquiries about this project, contact Dr. John Fulton (fulton.20@osu.edu) or Ty Higgins (thiggins@ofbf.org).

VRT and BMP Adoption (Upper Scioto)

OBJECTIVE

Determine what practices are being used by farmers within HUC8 Upper Scioto watershed to manage water and nutrients.

STUDY DESIGN

eFields Collaborating Farm

OSU Extension Statewide

In the field survey process, all the cropped fields within the watershed were considered in the randomized selection process regardless of farm and field size. However, only fields that were greater than 20 acres were selected for the survey; the average size of the fields surveyed was 38.9 acres. A trained Soil and Water Conservation District employee interviewed the landowner or farm manager for each field surveyed. The Ohio State University and the Center for Survey Statistics and Methodology at Iowa State University helped in designing the sampling strategy and data analysis.

KEY FINDINGS

• Approximately 62% of the fields surveyed were currently enrolled in a cost share conservation program, including both state and federal level programs.

• The assessment found that most farmers were testing their soil, with 76% of the surveyed fields being sampled at least once every four years. The vast majority of soil samples (91%) were completed using precision agriculture, via grid or zone methods.

• Approximately 64% of fields surveyed had phosphorus applied using variable-rate technology (VRT); 24% of fields had nitrogen (N) applied using VRT.

• Nearly 77% of the fields were either no-tilled or minimally-tilled.

• The assessment found that 60% of the farmland assessed was owned by the farmer and 40% was in a lease.

• Farm familiarity is very high as 92% of the fields were managed by the farmer for three years or longer with only 8% being farmed less than three years.

• Farmers utilized fertilizer retailers and crop consultants for 85% of fields surveyed.

Nutrient Management Plan

Percentage of fields covered by a nutrient management plan. Approximately 22% of fields surveyed were covered by an approved voluntary or comprehensive nutrient management plan (VNMP/CNMP) with 69% not covered with a VNMP These plans were completed either by their local Soil and Water Conservation District (SWCD) or Natural Resources Conservation Service (NRCS).

OTHER NUTRIENT MANAGEMENT STRATEGIES

Variable Rate Technology (VRT) Application:

• 64% of fields surveyed had been using variable-rate P application versus 36% using fixed-rate application

• 24% of fields surveyed had been using variable-rate N application versus 76% using fixed-rate application

• 51% have VRT capabilities that exist on farm versus 49% that are through a supplier

Manure Application:

• 79% used appropriate setback distances according to USDA-NRCS 590 standards

• 21% used subsurface application

• 85% incorporated nutrients into the soil

• 16% applied into vegetative cover or an actively growing crop, keeping nutrients in the field

CONCLUSION

Application

Distribution of amount of phosphorus applied on surveyed fields. Approximately 86% of phosphorus (P) applied is for the 1-2 year crop need.

Nitrogen Placement Method

Distribution of nitrogen (N) placement methods on fields. The majority of farmers surveyed (58%) applied nitrogen (N) before or during planting. The remaining (42%) side-dressed most of their nitrogen in-season.

This survey was completed in 2023 referencing crop year 2022, prior to the implementation and availability of H2Ohio practices. The assessment results establish a baseline of adoption for various farming practices in the Upper Scioto Watershed.* This information allows for a more targeted approach to increase best management practice adoption. Demonstrated by data, certain practices are elevated to yield optimal results. We will continue to assess more watersheds around the state in the coming years, revisiting previously assessed watersheds in a few years to determine levels of change. We encourage Ohio’s farmers to get involved in the OACI’s Farmer Certification program, H2Ohio and any other conservation focused program to learn about new practices, share information and become better stewards of the land.

PROJECT CONTACT

For inquiries about this project, contact Dr. John Fulton (fulton.20@osu.edu) or Ty Higgins (thiggins@ofbf.org).

Phosphorus

ePLUS represents an Ohio State University program dedicated to advancing production agriculture and wise use of natural resources through on-location research. This program utilizes modern technologies and information to conduct applied, cooperative research with an educational and demonstration component used to help growers, managers, and advisors understand how new practices and techniques can improve farm, forest, and garden sustainability. The program is dedicated to delivering timely and relevant, data-driven, actionable information. Projects to enhance decision making in a wide range of systems involving pests, management, conservation, technology, mechanization, and community engagement are being developed.

CONTACT US!

Interested in contributing to the 2025 ePLUS Report? If so, visit go.osu.edu/eplus to review study implementation plus tips and tricks. Please reach out to Dr. Logan Minter, Field Specialist for Specialty Crops (minter.21@osu.edu) to learn more. We look forward to working with you!

Ohio State Technology Research

Helping growers make the most of precision and digital ag technologies. The Digital Ag program at The Ohio State University embodies the best of the land grant mission - creation, validation and dissemination of cutting-edge agricultural production technologies. The central focus of this program is the interactions of automation, sensing and data analytics to optimize crop production in order to address environmental quality, sustainability and profitability. The team works on the development of hand-held devices for in-field data collection, apps that aid in calibration of applicators, remote sensing and monitoring, and enhanced data analysis for shorter turnaround time.

For more technology research and information from The Ohio State University’s Department of Food Agricultural and Biological Engineering and industry partners, explore the following resources:

The Ohio State Digital Ag Program

The Ohio State Digital Ag Program conducts studies related to all aspects of the corn production cycle. Research related to corn planting, cropping inputs, and harvesting technology can be found on the Precision Ag website: digitalag.osu.edu

Ohio No-Till Council

Experience and learn about cover crops, nutrient management, soil health, no-till equipment, digital ag, and other topics essentials for success.

2025 Events: March 11-12

Conservation Tillage and Technology Conference

ctc.osu.edu

December 2

Ohio No-Till Winter Conference Plain City

Several field days (full or half day), including one on July 23 at Archbold, will be scheduled this summer. Dates and details will be announced on our website and in Ohio’s Country Journal (see below).

We anticipate partnering with the the All-Ohio Chapter of SWCS, The Nature Conservancy, Ohio Farm Bureau, Conservation Action Project (CAP), OSU Extension, and Cargill RegenConnect to present top speakers and hands-on learning opportunities. The Ohio Soybean Council and Ohio Corn Marketing program are major sponsors.

Visit ohionotillcouncil.com to view event details and register.

Look for an updated “Ohio No-Till News” page in each mid-month issue of Ohio’s Country Journal.

Corn Height and Yield by Planting Date

OBJECTIVE

Evaluate the relationship between corn plant height and yield based on planting dates.

STUDY INFORMATION

Planting Date 4/13/2023 - 6/8/2023

Harvest Date 10/23/2023 - 11/6/2023

CropCorn Acres3

Treatments20

Reps4

Row Spacing30 in.

Sensor Data LiDAR on Freefly’s Alta X

Western Ag Research Station

OARDC

Clark County

STUDY DESIGN

The study site included 80 plots based on a split-plot randomized complete block design with four replications of each treatment. Main plot factor included five planting dates spaced approximately every two weeks from mid-April to midJune. The subplot-factor was four different hybrids (H1, H2, H3, H4) of varying relative maturities (100, 107, 111, and 115 days). Each replicate included a border plot on both ends of the block to reduce any edge-of-field effects on the measured plots. Yield measurements were based on the center two rows (out of four). Height data of corn plants were collected on August 15, 2023 using a LiDAR sensor mounted on a FreeFly Alta X drone.

Battle for the Belt research plots.

Soybean

OBSERVATIONS

Corn plants planted in late May/early June (PD4) and mid/late June (PD5) were found to be taller and high yielding than those planted earlier in the growing season. This suggests that potential stress factors earlier in the season may have slowed down the growth of the earlier-planted corn.

A positive correlation (r = 0.55) was observed between crop height and corn yield, suggesting crop height as a potential indicator of crop yield.

SUMMARY

• Early planting of corn does not necessarily lead to higher yields. Corn planted in May and June achieved the highest yields, unlike those planted in March and April.

• Corn plant height and yield data were positively correlated.

• Spatially explicit crop height data can serve as an early indicator of corn yield.

TOOLS OF THE TRADE

FreeFly Alta X is a quadcopter that can be combined with a variety of optional modules, payloads, and accessories. The Alta X possess extra payload capacity of up to 35 lb. The battery life without extra payload is 50 minutes and with extra payload is 10.75 minutes.

PROJECT CONTACT

For inquiries about this project, contact Sami Khanal, (khanal.3@osu.edu).

Crop Emergence after Cover Crops

OBJECTIVE

Assess the impact of cover cropping history on the emergence of corn and soybeans.

STUDY INFORMATION

Planting Date 5/6/2024 to 6/10/2024

Number of fields16 (8 Corn, 8 Soybean)

Crop Corn and Soybean

Treatments (T) 4; by cover crop history

T1Corn-Soybean rotation without cereal rye

T2 Corn-Soybean-Wheat rotation without cereal rye

T3 Corn-Soybean rotation with < 3 years of cereal rye

T4 Corn-Soybean rotation with > 5 years of cereal rye

Data collection V4-V8 (corn)

V1-V4 (soybean)

STUDY DESIGN

The study was conducted on 16 farmers’ fields planted in corn or soybean during the 2024 growing season. These fields were grouped under four treatment groups based on their crop rotation and cover cropping history. Before field data collection, each field was divided into four 1-acre zones, as shown in the figure below. During the summer, data collection included cash crop stand counts at the V4-V8 growth stage for corn and V1-V4 growth stage for soybean. Initially, a 1/1000th of an acre was measured out for each zone within a field. Then, the number of live plants in that area was measured and the process was repeated for three times per zone. These counts were averaged and multiplied by 1000 to get approximate crop stand counts within a zone.

eFields Collaborating Farm

Study locations.

Sample field showing four zones within it that were used for data collection.

OBSERVATIONS

Differences in corn and soybean emergence were visually observed in box plots for the different treatments.

A one-way ANOVA test also provided evidence of the effects of the different treatment groups on both corn and soybean emergence (p < 0.05).

For corn, T1 and T4 (corn-soybean rotation with less than three years and more than five years of cover cropping, respectively) showed higher emergence compared to T1 and T2 (no cover cropping history).

In contrast, soybean showed higher emergence for T1 and T2, and lower emergence for T3 and T4.

SUMMARY

• Overall, different corn-soybean rotations and varying frequencies of cover cropping affected corn and soybean emergence.

• Persistent adoption of cereal rye had a positive impact on corn emergence but a negative impact on soybean emergence.

• Additional data from another growing season could help clarify some of these patterns in crop emergence.

Drone footage of corn and soybean fields under T2 and T3 treatments. T2 refers to fields in a corn-soybean rotation without cereal rye, while T3 refers to fields in a corn-soybean rotation with cereal rye for less than three years.

ACKNOWLEDGEMENTS

We express our appreciation to our participating farms and USDA AFRI program for funding.

PROJECT CONTACT

For inquiries about this project, contact Sami Khanal, (khanal.3@osu.edu).

Deposition of Cover Crops in Canopy

OBJECTIVE

Evaluate the effectiveness of broadcasting cereal rye using a UAS in various canopy conditions for corn and soybeans.

STUDY INFORMATION

Planting DateVaries

Harvest DateVaries

VarietyVaries

PopulationVaries

Acres 27

Treatments3

Reps4

Treatment Width 60 ft.

TillageVaries

ManagementVaries

Previous Crop Corn-Soybean Rotation

Row SpacingVaries

Soil Type Varies

STUDY DESIGN

For this study, three 30-inch corn fields, three 15-inch soybean fields, and two 30-inch soybean fields, had 60 lbs/acre of cereal rye broadcasted with a drone. Broadcasting took place when the canopy was green, 50% dry, and harvest ready, with 4 replications for each planting date in each field. Each plot had three passes made at 20 feet route spacing. A row of pans was placed across the center pass. The seed was weighed to calculate application rate and variance across the swath. Two different pans were used based on 30-inch or 15-inch row spacing. The data analysis was the Kruskal-Wallis test because the data did not meet the requirements of the ANOVA test. If the p-value is less than 0.10, then there is a statistical difference between the median material collected for the listed canopy conditions.

Molly Caren Agricultural Center OARDC

Madison County

WEATHER INFORMATION

Pans placed in 15-inch row spacing soybeans at complete dry down to collect cereal rye being interseeded.

OBSERVATIONS

When interseeding cover crops aerially, the crop canopy stage could effect where the material falls on the ground. In all corn stages, seeds were seen being captured by the corn leaves and collecting near multiple leaf collars of each plant. The number of seeds seen decreased as the corn dried down. In the soybeans, green and 50% dry down applications saw a funneling effect on the seeds. This caused the aerially broadcast seeds to gather between the rows, possibly causing the increased deposition to the pans.

RESULTS

SUMMARY

• Overall, there were statistical differences between the material by the collection pans on the ground for some of the different planting dates.

• Visual observations indicated that waiting until dry down reduces seed displacement by the canopy.

• This study highlighted that spreading cover crops into standing corn and soybeans prior to harvest can have varying deposition depending upon plant stage (green to fully desiccated).

Soybeans (30 in. rows)

Soybeans (15 in. rows)

- 50% Dry

Dry - Harvest Ready

- 50% Dry

- Harvest Ready

Dry -Harvest Ready

- 50% Dry

Dry - Harvest Ready

The Dunn test is used after a Kruskal-Wallis test to find out which specific groups are different from each other. If the p-value from the Dunn test is less than 0.10, it means there is a significant difference between those groups.

PROJECT CONTACT

For inquiries about this project, contact Alex Thomas (thomas.5083@osu.edu) or John Fulton (fulton.20@osu.edu).

Drones for Spraying Pesticides

Although most aerial pesticide spraying in the United States is done using traditional fixed-wing aircraft, use of smaller, remotely piloted aircraft has been gaining significant acceptance by pesticide applicators. Although UAV (Unmanned Aerial Vehicle) and UAS (Unmanned Aerial System) are the most common names given to this kind of technology, the name used most by the public is “drone.”

Drones successfully and effectively monitor plant growth by collecting and delivering real-time data from the moment of plant emergence to harvest. With the help of fast and accurate GPS (Global Positioning System) or GNSS (Global Navigation Satellite System) technology, a high-resolution camera, and variable flying speeds and altitudes, drones can provide a wealth of information on the condition of every half square inch of crop or soil.

Why are drones attractive for spraying pesticides?:

• Can avoid topography or soil conditions that do not allow the use of traditional ground sprayers or conventional agricultural aircraft.

• An alternate option if airplanes and helicopters are not available (or too expensive to use).

• Can efficiently spray small, irregular-shaped fields.

• Can significantly reduce the risk of applicators being contaminated by the pesticides.

• Emerging problems (i.e. tar spot on corn) may need more targeted pesticide application, like what drones provide.

BEST SPRAYING PRACTICES WITH DRONES

Success in pesticide application is heavily dependent on knowing and following best spraying practices. Unfortunately, not much research data is available on best spraying practices for spray drones. Some recommendations to start with:

Determining Effective Spray Swath

Avoid skips, misses, and excessive overlap of adjacent spray swaths. The best way to determine the optimum spray swath (whether the spray is uniform or not) is to perform spray deposition pattern testing.

Find the Right Altitude

Flight height should be as close to the target as possible to reduce spray drift. Unintended issues may result from flying too low, such as, the collision avoidance radar being frequently triggered and skips in spray deposition. Wind velocity is usually the most critical factor affecting drift. Even in light breezy wind conditions, leave a buffer zone between the area being sprayed and any field with sensitive crops downwind.

LIMITATIONS AND OBSTACLES

Acceptance of spray drones by individual farmers has been slow for several reasons:

• Not enough research data comparing drone to ground sprayers and conventional aircraft is available.

• Fewer acres are covered per hour of operation compared to airplane and ground sprayers.

• Batteries require recharging between tank refills (5–15 minutes with a full tank).

• Pesticide labels do not provide specific instructions for drone use.

Most commercial spray drones today are the multi-rotor type either with a boom or without a boom.

Spray drone with no boom. Photo by Erdal Ozkan.

a

The newest design uses rotary disc atomizers positioned under large propellers, as seen below.

Spray pattern produced by the dual spray atomizer. Photo by DJI.com.

CERTIFICATION AND TRAINING

• To fly a drone: FAA “Part 107 Certificate”

• To apply pesticides using drones (or to do so while under the direct supervision of a person who holds this certificate): “Part 137 Certificate”

• Ohio Commercial Pesticide Category 1 You must complete this category when receiving your pesticide applicator license. Contact your county’s OSU Extension office or the Ohio State University Extension Pesticide Safety Education Program (pested.osu.edu) for details.

Find all requirements to be a drone pilot for pesticide application, here: faa.gov/uas/commercial_operators.

ADDITIONAL RESOURCES

Ohio Department of Agriculture agri.ohio.gov/divisions/plant-health/pesticides/uas/

Close up of Controlled Droplet Atomizer

Photo by Dr. Steve Li, Auburn University.

SUMMARY

Drones are now a viable option when choosing equipment to spray pesticides, and the number of companies offering drone spraying services is rapidly increasing in Ohio and throughout the United States. But is drone spraying a good option for everyone? If you are well informed about this technology, aware of all the rules and regulations, and have viable usages identified, then consider buying one.

Otherwise, wait until you are adequately informed about all aspects of drone spraying. Regulations may change rapidly, and more research will likely emerge regarding effectiveness in comparison to other forms of application. Stay up to date by regularly checking in with the appropriate resources, such as, the FAA website and your county Extension office for updated information on certification and licensing required to apply pesticides using any kind of sprayer, including drones.

PROJECT CONTACT

For inquiries about this project contact Dr. Erdal Ozkan (ozkan.2@osu.edu).

Ohio State University Extension Publication, FABE-540 “Drones for Spraying Pesticides—Opportunities and Challenges” ohioline.osu.edu/factsheet/fabe-540

A spray drone with

boom. Photo by Erdal Ozkan.

Forage Production Under Solar

OBJECTIVE

Evaluate the impact of the solar site on forage crop growth and identify soil health impacts and remediation strategies.

STUDY OVERVIEW

Agrivoltaics is the practice of farming within solar sites including under the panels, between the rows, and around the border of the site. The primary goal of this project is to build upon the understanding of forage production in solar farms to reduce land use impacts of utility-scale solar installations by developing best practices for establishing forages, integrating complimentary grazing strategies, maximizing soil health, and utilizing precision agriculture technologies and equipment to minimize error and risk. Overall, results from this research will be used to identify strategies to implement hay, grazing, and solar production with key developer considerations such as system design, equipment, contracts, liabilities, and economic gain or loss.

eFields Collaborating Farm

OSU Extension Madison County

SITE SOIL HEALTH IMPACTS AND REMEDIATION

Cone penetrometer sampling was conducted to evaluate soil compaction caused by site construction. Figure 1 shows compaction for both preconstruction (2023 – red) and post construction (2024 – black). Compaction through the root zone increased postconstruction, on average. A distinct compaction layer is now present near an 8-inch depth on average across the site. Various cover crops are being compared for their ability to remediate this compaction. Compaction data will be collected on a yearly basis during the spring of each year.

This project is being funded in part by

Logo was developed by the U.S. Department of Energy to indicate receipt of DOE funding. Not an endorsement by DOE.

Cone penetrometer readings taken to 18-inches pre- and postconstruction. Dashed lines indicate +/- 1 standard deviation.

PROJECT CONTACT

For inquiries about this project, contact Christine Gelley (gelley.2@osu.edu), Amanda Douridas (douridas.9@osu.edu), Brady Campbell (campbell.1279@osu.edu), Elizabeth Hawkins, Eric Romich, Trevor Corboy, Scott Shearer, and Andrew Klopfenstein.

Special thanks to Savion Energy, Sarah Moser, and Kubota Tractor Corporation for their collaboration on this research effort.

Cool-season hay mix established between the panels in the solar arrays (left). Aerial view of small-scale tractor and no-till drill planting between the the solar panels (right).

Soybean Forages

PRELIMINARY FORAGE YIELD RESULTS

To evaluate the impacts of the solar site on the production of forage, three forage types (alfalfa, cool-season hay mix, and teff grass) were compared inside the solar arrays to control plots outside of the arrays. The control plots were planted outside of the solar arrays at 100% of the current recommended seeding rate. Within the solar arrays between the rows of panels each forage type was planted at three seeding rates: 75%, 100%, and 125% of the current recommended seeding rate. The trial was a randomized complete block design replicated four times.

Due to drought conditions and weed pressure during the establishment year, the forage plots were not harvested and baled but yield was estimated using a rising plate meter. Plate meter readings were collected in a zigzag pattern across the sampling area, gathering 30 readings per plot. The average estimated yield was calculated for each treatment using the plate meter readings and a calibration curve developed from clipped samples that were dried and weighed. The results for each forage type are shown in Figure 2. These preliminary yield estimates depict 45 days of growth from August to September and indicate similar establishment success between the control plots and the treatments located in the solar arrays. These plots will be maintained and harvested in upcoming seasons to better understand forage yield and quality.

Collecting forage samples for analysis.

SUMMARY

Figure 2. Estimated yields for alfalfa (top), cool -season hay mix (center), and teff grass (bottom) collected using a rising plate meter. Letters indicate statistically significant differences between treatments.

• Weather and weeds were the most significant factors that impacted forage crop establishment in 2024.

• In early spring, the site received heavy precipitation that caused standing water on the site and prohibited planting until late-May/early-June.

• Drought conditions throughout summer hindered forage crop growth.

• Research efforts will continue through 2027 to better understand the feasibility of agrivoltaics.

Soil Moisture with Cover Crops

OBJECTIVE

Quantify the differences in soil moisture and temperature between fields under conventional tillage and cover crop management.

STUDY INFORMATION

Cover Cropped Field A

Cover Cropped Field B Tilled Field

Planting Date 5/1/2024 4/28/2024 4/28/2024

Harvest Date 10/20/202410/4/2024 10/20/2024

eFields Collaborating Farm

OSU Extension

Greene County

VarietySeed Consultants SC112AM Stewarts 09DP409Seed Consultants SC112AM

Population 29,700-30,40030,400-31,90031,000

Acres24 10 13

Tillage Fall - Vertical Till Spring - No-Till

Fall - Vertical Till Spring - No-Till

Fall - No-till Spring - Vertical Till

Cover CropCereal RyeCereal RyeNone

Management Fertilizer, Herbicide, Fungicide

Fertilizer, Herbicide, Fungicide

Fertilizer, Herbicide, Fungicide

Previous Crop Soybeans Soybeans Corn

Row Spacing30 in.

Soil Type Miamian Silt Loam, 58%

Russell-Miamian

Silt Loams, 38%

Xenia Silt Loam, 4%

STUDY DESIGN

30 in. 30 in.

Russell-Miamian

Silt Loams, 38%

Miamian Silt Loam, 35%

Russell Silt Loam, 14%

Miamian Silt Loam, 78% RussellMiamian Silt Loams, 22%

Sensors were placed in two no-till fields with cover crops (Cover Cropped Field A and Field B) and one conventionally tilled field (Conventional Tilled Field). Two sensors were placed in each field at depths of two inches and four inches below the soil surface. The sensors were placed in spring and remained until harvest, except during planting and spraying, when they were temporarily removed and reinstalled afterward.

Data logger and sensors installed in cover cropped field.

Cereal rye terminated in preparation for planting.

RESULTS

Both cover cropped fields exhibited similar soil moisture patterns, consistently differing from the tilled field. At a depth of 2 inches, the tilled field showed slightly higher soil moisture levels until July, when a notable shift occurred, with both cover cropped fields exhibiting considerably higher soil moisture than the tilled field. At a depth of 4 inches, the soil moisture levels in the cover cropped fields and the tilled field were initially similar, with the tilled field maintaining slightly higher levels until mid-June. After this point, both cover cropped fields showed significantly higher soil moisture compared to the tilled field. Overall, during the drought period, the cover cropped fields retained more soil moisture on average than the tilled field. The use of cover crops appears to enhance the fields’ resilience to extreme weather events, such as drought. Additionally, the cover-cropped fields produced higher corn yields with the cover cropped fields averaging 164 and 170 bu/ac and the tilled field averaging 160 bu/ac.

Average daily soil moisture at 2 inches (top) and 4 inches (bottom) under cover cropped and tilled fields.

TOOLS OF THE TRADE

METER TEROS 11 sensors with ZL6 data loggers

These soil sensors allow for monitoring soil moisture and temperature throughout the season. Sensors were placed at depths of 2 and 4 inches for the duration of this project and measurements were taken hourly.

PROJECT CONTACT

For inquiries about this project, contact Marina Miquilini (miquilini.1@osu.edu) or Elizabeth Hawkins (hawkins.301@osu.edu).

Sugarcane Mosaic Virus UAS Detection

OBJECTIVE

Determine the role of UAS imagery in monitoring Sugarcane Mosaic Virus (SCMV) infection in corn.

STUDY INFORMATION

Inoculation TimeJune-July, 2021-2022

CropCorn

Acres2

Number of Fields 3

Treatments2

Reps4

Row Spacing30 in.

Sensor Data Micasense RedEdge-MX multispectral sensor; Zenmuse XT2 sensor

UAS DJI Matrice 200

Disease Monitoring

2021 - 6/30, 7/8, & 7/12

2022 - 7/6, 7/15, 7/18, & 7/19

STUDY DESIGN

Wooster Ag Research Station OARDC Wayne County

Experimental layout in in 2021, overlaid on an aerial image captured on July 14, 2021. Red and blue boxes represent the randomized block design containing four single-row replicates of inoculated (red) and noninfected/ mock-inoculated (blue) for each of the 51 hybrid varieties grown.

Forty-eight commercial field corn hybrid varieties were evaluated using a randomized block design in the 2021 field experiment. These varieties represent some of the highest-yielding varieties currently available from five major commercial seed companies and were found to be susceptible to SCMV from previous trials.

Based on the 2021 field experiment, four susceptible varieties were selected for more in-depth characterization in the 2022 experiment, considering their disease incidence scores and yield penalty due to infection. In each year, there were four replications per hybrid for each treatment. For both years, the best agronomic practices achieved optimal yields without irrigation, as well as without the application of insecticides or fungicides.

UAS flights were conducted on 3 dates each year for each field—28 June, 14 July, and 28 July in 2021 and 30 June, 28 June, and 1 September in 2022—to capture trends in the spectral signatures of inoculated corn plants. During these flights, multispectral imagery with five spectral bands was collected using a multispectral sensor at an altitude of 30m and a thermal sensor at 20m. Yield values were recorded at the end of the season during harvest on 9 November 2021 and 21 November 2022. The percentage of plants that displayed mosaic symptoms for each hybrid plot was recorded using human scouting.

(Left) A corn crop that does not have SCMV infection or visual SCMV symptoms. (Right) A corn crop that displays visual symptoms of SCMV infection.

OBSERVATIONS

The effect of SCMV on corn vigor were assessed using 36 vegetation indices based on various combinations of spectral bands were calculated for each plot, alongside the individual spectral bands from multispectral (5) and thermal (1) sensors.

On average, SCMV-inoculated plants had higher reflectance values for blue, green, red, and rededge bands and lower reflectance for near-infrared as compared to mock-inoculated samples.

Significant relationships between disease incidence and various vegetation indices (or spectral bands) were identified on 14 July 2021.

On 28 July 2022, no significant correlations were detected for disease incidence at Snyder. However, at Schafter, disease incidence was significantly correlated with the NIR spectral band, MCARI1, and MTVI1. On 1 September 2022, significant correlations were discovered at both locations.

When evaluating the binary disease classification models independently for each year (using a 70/30 random split), the models in 2021 demonstrated higher accuracy compared to those in 2022. When data from both years were combined, the models performed better than those from 2022 but still did not reach the accuracy levels of the 2021 models.

SUMMARY

• Multispectral imagery has the potential for detecting sugarcane mosaic virus (SCMV) in corn fields.

• Machine learning models, trained on remotely sensed imagery, can serve as a tool for farmers to precisely identify and map SCMV infection in corn.

TOOLS OF THE TRADE

The MicaSense RedEdge multispectral camera captures high-resolution images across five spectral bands, enabling precise agricultural analysis and monitoring.

Table 1. Top vegetative indices and spectral bands with statistically significant correlation with disease incidence at each trial location.

Details relating to the vegetation indices (VI) such as SCCI, TCAR/OSAVI and others that are reported above can be found in this publication. Sugarcane Mosaic Virus Detection in Maize Using UAS Multispectral Imagery. Remote Sensing, 16(17), 3296. https://www.mdpi.com/2072-4292/16/17/3296.

Table 2. Accuracy of three classification models for predicting SCMV inoculated vs. mock-inoculated plots.

Models included 41 spectral features. Accuracy reported is the average of three model runs with three randomly selected training and testing datasets.

PROJECT CONTACT

For inquiries about this project, contact Sami Khanal, (khanal.3@osu.edu).

Target Spray- Pre Applications

OBJECTIVE

Evaluating potential cost savings with a target spray application system compared to broadcast applications on soybeans.

STUDY DESIGN

Molly Caren Agricultural Center

OARDC

Madison County

This project is evaluating potential cost savings with a target spray application system compared to broadcast applications on soybeans. We evaluated six pre-planting spray applications across multiple fields. These fields had a combination of no-till and tillage. Weed control historically across this farm has been excellent. Field details are included in Table 1.

The technology used in this study was John Deere See and SprayTM. In 2024 it had a technology fee of $1 per acre for pre-planting applications. Costs of broadcast applications were calculated to determine cost savings with a target spray application. No broadcast application was actually made.

Table 1. Fields receiving pre-planting target spray herbicide applications.

Table 2. Economic analysis of pre-planting target spray.

1 Cost includes

1 FT- Full Tillage, NT- No Till, VT- Vertical Tillage

Soybean Forages Tech

OBSERVATIONS

The broadcast spray application cost for pre-planting ranged from $5.47 to $12.29/ac with the target spray ranging $6.47 to $13.29/ac. Since only target spray was used in 2024, visual observations were used to evaluate weed control effectiveness.

2024 Application: Soybean Burndown Target Spray Field:4A

|

Start:Apr 22, 2024 12:30 PM

End:Apr 22, 2024 12:49 PM

RESULTS

*This is the area the machine covered but did not spray. The "Target Rate" and "Target Total" represent the product saved by using See & Spray technology.

Map:Target Rate

For all target spray applications, it was determined that weed control was as good as broadcast expectations. Cost savings were seen in all but one application. Field 6 received target spray to 97% of its acres which cost the producer $22 more for that field than a regular broadcast application. The largest savings was seen in field 8A where only 11% of the acres were sprayed. This led to an 88% savings over broadcast. Across all fields, this farm saved $1,250 by using target spray technology on the pre-planting application.

TOOLS OF THE TRADE

3D Target Spray Nozzle

Inclined fan design that can maximize coverage. Improved pattern quality that reduces drift potential. PWM compatible for ExactApply up to 30Hz pulsing. Installed backwards on the boom for target spray applications.

PROJECT CONTACT

For inquiries about this project, contact Amanda Douridas (douridas.9@osu.edu) or Nathan Douridas (douridas.2@osu.edu).

Weed pressure map depicting areas of increased weed presence (left). Target spray application map (right), application summary (below).

Operator Name:Michael Schmid Operator License:150426

Target Spray - Post Applications

OBJECTIVE

Evaluating potential cost savings with a target spray application system compared to broadcast applications on soybeans.

STUDY DESIGN

This project is evaluating potential cost savings with a target spray application system compared to broadcast applications on soybeans. We evaluated eight post-planting spray applications across multiple fields.

These fields had a combination of no-till and tillage. Weed control historically across this farm has been excellent. Field details are included in Table 1. The industry reporting method for target spray coverage is in acres NOT applied as a percent so that is why we reported coverage as such in Table 1.

The technology used in this study was John Deere See and SprayTM. In 2024 it had a technology fee of $4/ac for postplanting applications. Costs of broadcast applications were calculated to determine cost savings with a target spray application. No broadcast application was actually made.

Molly Caren Agricultural Center

OARDC

Madison County

Table 1. Fields receiving post-planting target spray herbicide applications.

Target spray display.

OBSERVATIONS

The broadcast spray cost for all post-planting applications was $15.05 per acre. Adding $4 per acre for the tech fee brings the target spray application for post-planting to $19.05 per acre. In years past, broadcast applications with the same chemistry have resulted in zero weed escapes. Since only target spray was used in 2024, visual observations were used to evaluate weed control effectiveness. It should be noted that residual herbicides were used in all fields. This was done simultaneously with the target spray application since the sprayer used was equipped with a second tank system for this purpose.

Table 2. Economic analysis of post-planting target spray.

1 Cost includes $4/ac tech fee. Total cost per field.

2 Broadcast spray cost minus target spray cost. Total savings per field.

RESULTS

For all target spray applications, it was determined that weed control was as good as broadcast expectations. Cost savings were seen in all but one field application. Field 3MISD received target spray to 79% of its acres which resulted in close to breakeven. The greatest cost savings were seen in Field 5 where only 10% of the acres were sprayed resulting in an 87% savings or $944. Overall, total saved by using target spray for post-planting applications was $2,636 across this study.

TOOLS OF THE TRADE

See and SprayTM Camera

With up to 36 cameras mounted on the boom it covers 2,100 square feet, scanning for weeds. It works with a boom mounted processor to see, selectively target, and kill weeds while operating. Weed detection can be done on fallow and incrop including corn and soybeans.

PROJECT CONTACT

For inquiries about this project, contact Amanda Douridas (douridas.9@osu.edu) or Nathan Douridas (douridas.2@osu.edu).

UAS Application Deposition

OBJECTIVE

Evaluate the influence of different nozzle styles on spray deposition from a boom-style drone sprayer.

STUDY DESIGN

Northwest Ag Research Station OARDC Wood County

The experimental design for this study was a Complete Randomized Design (CRD) with 4 replications. Three styles of Teejet 015 nozzles were selected; XR 110015, TT 110015 and TTAI 11015 (see below). The drone sprayer used for this project was a Hylio AG110 with a boom-style nozzle configuration. The boom had 4 nozzles spaced equally across a 7 feet boom, 4 rotors, and a 2.5-gallon tank. The Hylio drone sprayers have a flow meter installed between the filter and the spray boom. The water-sensitive cards were placed 15 inches apart at in order to capture the full spray width on each pass. After each pass, cards were collected and placed in labeled bags according to lateral position on the frame. Cards were then analyzed using a DropScope analyzer to measure median droplet size, droplet size distribution, and coverage (%) for individual cards. SAS (Ver. 9.4) was used to analyze the data. Only the main effects are presented to determine the influence of flight height and speed on deposition.

Graph shows each nozzle type change in droplet size across the swath pattern.

ACKNOWLEDGMENTS

We would like to aknowledge the following individuals for their support of this project. Kendall Lovejoy, Grant Davis, Stephanie Karhoff, Kyle Verhoff, Kayla Wise, Amber Emmons, Alex Thomas, John Fulton, Ryanna Tietje, Nick Eckel, Matt Davis, and the NWOARDC Staff.

Soybean

OBSERVATIONS

Wind did not impact the results of this study as it remained negligible throughout all tests. This minimized any drift concerns and allowed the drone to operate under stable conditions while speed and altitude were varied during testing. The expected results were that the TTAIG nozzle would produce the largest droplet size, followed by the TTG, and then the XRG. Calibration of the drone was conducted before testing to ensure the application rate met the target rate for all tests. Feedback from the flow meter was used to verify that the target rate was achieved for each test.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

The Hylio AG110 has a boom nozzle configuration and can be flown in either autonomous or manual flight control modes. You can command up to three UAS from a single ground station as they complete fully automated operations. Also, each AgroDrone comes with an RC controller for optional manual mode.

SUMMARY

• As expected, there were statistical differences between the different nozzle styles.

• There was significant difference in percent coverage between theTTAIG nozzle style and the other nozzles.

• The TTAIG nozzle was significantly higher in overall coverage but had more variation in coverage than the TTG and XRG nozzles.

• There was a signifcant difference in median droplet diameter between all three nozzle styles.

• Nozzle type did not affect the resulting swath width.

Nozzle Style

LSD: 0.096

38

PROJECT CONTACT

For inquiries about this project, contact Alan Leininger (leininger.17@osu.edu). or John Fulton (fulton.20@osu.edu).

UAS Application Deposition

OBJECTIVE

Evaluate how altitude and flight speed impacts deposition from a spray drone.

STUDY DESIGN

Northwest Ag Research Station

The experimental design for this study was a Complete Randomized Design (CRD) as a 2-way factorial (3 heights x 2 speeds) with 4 replications. The drone sprayer used for this project was a Hylio AG110 with a boom-style nozzle configuration. The boom had 4 nozzles spaced equally across a 7 feet boom, 4 rotors, and a 2.5-gallon tank. The Hylio drone sprayers have a flow meter installed between the filter and the spray boom. A structure was fabricated to mount spray cards at three different heights. The structure provided the ability to operate the spray drone one time while collecting data at 5 feet, 10 feet, and 15 feet. The water-sensitive cards were placed 15 inches apart at each of the 3 levels in order to capture the full spray width on each pass. After each pass, cards were collected and placed in labeled bags according to height and lateral position on the frame. Cards were then analyzed using a DropScope analyzer to measure median droplet size, droplet size distribution, and coverage (%) for individual cards. SAS (Ver. 9.4) was used to analyze the data. Only the main effects are presented to determine the influence of flight height and speed on deposition.

OBSERVATIONS

When conducting this trial, wind speed was not a factor as it was a relatively still day. The swath width was calculated by the software based on operating factors such as flight height and speed set by the pilot. For this testing, the spray swath was approximately 12 feet. Calibration of the spray drone was conducted prior to testing. It should be noted that as drone speed increases, the pitch of the drone also increases, causing it to tilt forward more. This results in the nozzles being at different angles between the speed treatments.

ACKNOWLEDGMENTS

We would like to aknowledge the following individual for their support of this project. Kendall Lovejoy, Grant Davis, Stephanie Karhoff, Kyle Verhoff, Kayla Wise, Amber Emmons, Alex Thomas, John Fulton, Ryanna Tietje, Nick Eckel, Matt Davis, and the NWOARDC Staff.

Hylio AG-110 flying over stand with water sensitive card placed across the entire swath.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

SUMMARY

Altitude

• The 5 feet altitude had the maximum coverage statistically at 0.65% but it was not statistically different from the 10 feet altitude.

• The 15 feet altitude had the least mean coverage at 0.37%.

• Practically, as altitude increased, coverage decreased.

• Median droplet diameter was not significantly different between the 3 altitude treatments, though practically the 10 feet and 15 feet were larger than the 5 feet altitude.

• Of note, the 15 feet altitude had the widest swath width. Speed

Speed

• There was a significant difference in coverage between the speed treatments.

• The 15 mph treatment had about 1.7 times more coverage than the 20 mph.

• In general, as ground speed increased, coverage decreased, which is a consideration for those operating spray drones.

• Speed had no statistical difference on droplet size between the two ground speeds.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha =

TOOLS OF THE TRADE

The Hylio AG110 spray drone has a boom nozzle configuration. This drone is a fully autonomous, four-rotor UAS platform and comes equipped with a high-precision spraying system consisting of one 2.5 gallon tank, TeeJet nozzles, and electronic flowmeters.

PROJECT CONTACT

For inquiries about this project, contact Alan Leininger (leininger.17@osu.edu). or John Fulton (fulton.20@osu.edu).

UAS Applied Herbicide

OBJECTIVE

The objective of this project is to compare different post herbicide applications from a spray drone.

STUDY INFORMATION

Planting Date 6/22/2024

Harvest Date 10/17/2024

Variety SC7293E

Population 160,000 sds/ac

Acres 8

Treatments4

Reps4

Treatment Width40 ft.

Tillage No-Till

Management Fertilizer, Herbicide

Previous CropCorn

Row Spacing 15 in.

Soil Type Haskins loam, 61% Hoytville clay loam, 22% Nappanee loam, 17%

STUDY DESIGN

The study was designed as a randomized complete block design with three treatments replicated four times. The treatments included Glyphosate label rate with 3 gallons of water per acre, Cobra label rate with 5 gallons of water per acre, Glyphosate / Cobra mixed at the label rate with 5 gallons of water per acre, and a control with no application of herbicide. Yield data was compared to evaulate treatements.

eFields Collaborating Farm OSU Extension

Henry County

WEATHER INFORMATION

Hylio drone with nozzles under the rotors.

OBSERVATIONS

All treatments were applied on the same day with the glyphosate and Cobra herbicide treatments applied based on label. Based on post-application scouting, weed pressure was reduced by each herbicide application but not for the control treatment. The growing season can be characterized by extended hot periods without rain which resulted in reduced soybean yields for this field. Further testing is needed to decide if this practice of using drones to apply herbicides is effective. The results also show that weed pressure observed in this study was not the limiting yield factor. Other products need to be compared in future studies that have labels that permit aerial application.

RESULTS

SUMMARY

• There was no statistical yield difference between the control and the glyphosate treatments.

• A significant yield difference existed between the Control Treatment and the Cobra and Cobra / Glyphosate mix treatments with the Cobra and Cobra / Glyphosate having reduced yield of 3 to 4 bu/ac.

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

Hylio AG210 with hydraulic forked nozzles mounted under the rotors. This drone is a fully autonomous, four-rotor UAS platform and comes equipped with a high-precision spraying system consisting of one 2.5 gallon tank, TeeJet nozzles, and electronic flowmeters.

PROJECT CONTACT

For inquiries about this project, contact Alan Leininger (leininger.17@osu.edu) or John Fulton (fulton.20@osu.edu).

UAS Cover Crop Pattern Tests

OBJECTIVE

Identify the ideal route spacing for broadcasting various cover crops, including mixes, using an unmanned aerial system (UAS).

STUDY DESIGN

Standardized collection pans were placed at least 2 feet apart with no space between each pan. A DJI Agras T20P drone equipped with a single disk spreading system was used in this study. The spreader utilizes a single-disk for spreading products. The drone spreader was flown transversely and over the center of the row of pans. The four cover treatments were 1) cereal rye, 2) crimson clover, 3) radish, 4) cereal rye/crimson clover, and 5) cereal rye/radish. The application rates for the single seed type tested were according to the USDA NRCS Appendix -A (Cover Crops). When broadcasting cover crops, due to the possibility of lower emergence, the rates included a 20% increase to the recommended rates. These rates were 60 lbs/ac of cereal rye, 22 lbs/ac of crimson clover, and 4 lbs/ac of radish. The application rate for the seed mixes was the single seed type rates added together and mixed appropriately for comparing when the seed is spread when mixed and not mixed. All tests were replicated 4 times. For the mixed seeds, the two different seed types were separated and weighed individually. This data was then used to calculate the average single pass pattern for each cover crop treatment. These patterns were then used to calculate the uniformity of spread (CV) and route spacing by product. A CV of 20% or less was considered acceptable spread uniformity for a drone spreader.

Molly Caren Agricultural Center

Cover crop seeds being collected from the collection pans after a flight pass was made.

This graph shows the averages of the data collected for each cover crop seed.

OBSERVATIONS

The change in the variation from when the seed is spread by itself versus when it is mixed with other materials is likely from the change in the flow of each seed type. The short optimal route spacing of the radish is likely due to the testing being done with the large hopper gate, but trying to apply a low rate. Further testing will be done using the small hopper gate, which will better meter the seed at the lower rates. The shorter optimal route spacing of radish in Mix B is likely because the difference between the cereal rye seed mixed with the radish seed. Further testing will adjust the rates to follow the USDA Appendix - A recommendations.

RESULTS

SUMMARY

• Cereal rye had an acceptable CV up to a 26 feet route spacing when individually applied but when mixed with crimson clover the route spacing decreased to 20 feet, and when mixed with radish, route spacing increased to 39 feet.

• Crimson clover had an acceptable CV up to a 30 feet route spacing when spread individually and when mixed with cereal rye, the route spacing increased to 39 feet.

• The low route spacing for radish contributed to its higher spread pattern variability plus low target rate in combination of using the large hopper gate for metering the product.

TOOLS OF THE TRADE

The DJI Agras T20P drone can be equipped with a single disk spreading system provided through DJI. The spreader box is capable of carrying approximately 55 pounds of dry product. The spreader comes with different hopper gates (small and large) in order to meter product onto the spinner disk depending on the target rate.

PROJECT CONTACT

For inquiries about this project, contact Alex Thomas (thomas.5083@osu.edu) or John Fulton (fulton.20@osu.edu).

UAS Spreader Collection Pans

OBJECTIVE

Assess various collection devices for analyzing spread patterns in dronebased granular applications.

DRONE SETTINGS

Application Rate 60 lbs/ac

Flight Speed 32.8 ft/s

Spinner Disk Speed 1300 RPM

Route Spacing20 ft

AltitudeSee Study Design

STUDY DESIGN

This study utilized 21 pan collection devices placed in a row every 2 feet. Pans A, B, and C were laid out with the drone flight altitude set to 15 feet. Pan D (cardboard box) was flown separately to adjust the altitude by the height of the box to ensure the collection height would be 15 feet above the top of the boxes. Pan D was used as the control treatment and was assumed to have a 100% collection efficiency. A DJI T20P application drone equipped with the available DJI spreader box was used for all tests. The spreader had one spinner disc. The drone was flown over the center of the row of collection pans. Each test was replicated 4 times with individual patterns averaged for each pan. The average patterns for each pan were plotted and compared. Cereal rye and ammonium sulfate (AMS) were the two products used in this study. Collection efficiency for Pans A, B, and C was calculated based on the total amount of material captured for Pan D.

Molly Caren Agricultural Center OARDC Madison County

Pan A is the SCS Spreader Trays (top left), Pan B is the Rauch Practical Test Set (top right), Pan C is the New Leader Manufacturing Test Kit (bottom left), and the 20 in. x 20 in. x 30 in. Carboard box (bottom right).

OBSERVATIONS

When broadcasting cover crops and fertilizer sources with a UAS under field applications, a conflict was found between the target rate being applied and the rate being captured by the pans. Thereby, this study was conducted to evaluate different collection pans and determine the amount of material that may be bouncing out of the pans. The amount of material bouncing out allows one to calculate the collection pan efficiency for an individual pan design. Two different materials were selected; one a common cover crop seed (cereal rye) and the other a common granular fertilizer source (AMS). When spreading granular products such as seeds and fertilizer with a drone, rotor wash or turbulence can impact particle velocity plus influence material behaviors in the pans. All tests were conducted during a time when wind speed was low thereby not impacting particle behavior.

SUMMARY

• Collection pan efficiency differed between the two products; cereal rye and AMS. This result was attributed to the physical differences like particle size and hardness along with bulk density.

• For cereal rye, the collection efficiency for pan B did not statistically differ from pan D.

• Pan C consistently had the lowest collection efficiency for both products.

• For AMS, the differences between pans were likely from particle size differences and hardness.

• Pan B resulted in the best option for both cereal rye and AMS collection efficiency.

RESULTS

PROJECT CONTACT

For inquiries about this project, contact Alex Thomas (thomas.5083@osu.edu) or John Fulton (fulton.20@osu.edu).

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

DJI T20P flying over the row of Pan D (cardboard boxes).

UAS Spreader Spinner Disk Speed

OBJECTIVE

Evaluate the effect of spinner disk speed (RPM) on the distribution patterns of granular products when using a drone spreader.

STUDY INFORMATION

Application Rate 60 lbs/ac

Flight Speed 32.8 ft/sec

Spinner Disk Speed See Results

Altitude 15 ft

STUDY DESIGN

Molly Caren Agricultural Center

OARDC Madison County

Standard pattern testing protocol for fertilizer application equipment was used in this study. Standardized collection pans are available through Rauch (Practical Test Set kit; Rheinmünster, Germany) with pan dimensions of 0.5 meter by 0.5 meter pans were used. Baffles that minimize product bouncing out of the pans were used. For this study, 29 collected placed in one and spaced 2 feet apart were used. A DJI T20P drone with its spreader box was used in this study. The spreader uses a single spinner disk to spread products. Four spinner disk speeds were selected as treatments: 550, 800, 1050 and 1300 RPM. It is common for this particular drone and spreader box to use 1300 RPM for the application of cover crops and fertilizers. The drone was flown over the center of the row pans. After each test, material was collected and weighed to characterize the spread pattern. Cereal rye seed was used for this trial. Each spinner disk speed was replicated four times, and the materials were collected after each pass. The results were then averaged for analysis to characterize the single pass pattern for each spinner disk speed. The patterns were then used to determine the maximum spread width at each spread setting. Results highlight the need for additional testing using different products to fully understand the relationship between spinner disk speed and route spacing for drone applications.

Graph showing the results of the averages of each spinner disk speed. The drone flew over the pans at the 0 ft transverse distance.

DJI T20P flying over the row of collection pans.

OBSERVATIONS

When this testing was conducted, wind speeds remained at or below 5 mph and the flight route was flown towards the wind so there was no effect to pattern due to cross-wind. When doing a single pass, each of the spinner disk speeds showed to have a skewed pattern to the left of the drones flight path. Previous studies have shown that that spreaders with a single spinner disk will have a skewed pattern, applying more material to the left or the right of the spreader. The general understanding is as spinner disk speed increases, route spacing increases. It is expected that the light seed weight for cereal rye along with the impact rotor wash caused these results. Further testing should be done to determine how altitude will effect the ideal spinner disk speed.

RESULTS

TOOLS OF THE TRADE

The Rauch Practical Test Set comes with standard half meter by half meter collection pans, baffled dividers, measuring cylinders, and a tape measure. The baffled dividers are needed to keep materials from bouncing out of the collection pans are required for standardized pan testing.

SUMMARY

• The spinner disk speed of 800 RPM provided the widest route spacing of 26 feet.

• As expected, the 550 RPM had the smallest route spacing at 8 feet.

• Unexpectedly, the 1050 and 1300 RPM treatments generated route spacings of 14 and 18 feet, respectively for the cereal rye.

PROJECT CONTACT

For inquiries about this project, contact Alex Thomas (thomas.5083@osu.edu) or John Fulton (fulton.20@osu.edu).

Strip Intercropping

OBJECTIVE

Demonstrating strip intercropping, utilizing alternating strips of corn and soybeans to identify the relationship between them and maximize yield potential.

STUDY INFORMATION

Planting Date 5/7/2024

Harvest Date 10/18/2024

HybridBeck’s 6038VR

Variety Beck’s 3650E3XF

Corn Population 37,000 sds/ac

Soybean Population 150,000 sds/ac

Acres 72

Treatments4

Reps 11

Treatment Width 10 ft. and 60 ft.

Tillage Minimum Till

Management Fertilizer, Herbicide, Insecticide

Previous Crop Corn/Soybean Rotation

Row Spacing30 in. Twin Row

Soil Type Brookston silty clay loam, 61% Crosby silt loam, 32% Celina silt loam, 5%

STUDY DESIGN

A full production 72 acres has been planted in these strips. The rows are in perfectly polar North/South orientation studying the effects of the sun and the row direction. This year, all strips were harvested as full passes. For this year’s configuration, we did 60 feet of 10 feet alternating strips followed by 60 feet of corn then 60 feet of beans. This allowed for full evaluation of monoculutre crops.

eFields Collaborating Farm

OSU Extension Fayette County

WEATHER INFORMATION

Strip intercropping plots being planted.

OBSERVATIONS

This test location got off to an excellent start with the earliest plant date we have had at this location for the duration of trials. It then turned extremely dry and went into drought for the duration of the growing season. We also had issues with weed pressures for this location that also caused issues in some locations of the field. During harvest crops were short in stature and difficult to harvest along with being damage from hurricane that was recived in the fall.

SUMMARY

• When comparing monoculture corn results versus strips, the strips were not statistically better than monoculture corn.

• When comparing monoculture bean results versus strips, the monoculture beans were statistically significantly better than strip beans.

• This year, the worst yields were produced for all crops to date for this location.

RESULTS

Treatment Means with the same letter are not significantly different according to Fisher’s Protected Least Significant Differences (LSD) test at alpha = 0.1.

TOOLS OF THE TRADE

vDrive from Precision Planting is a maintenance-free electric drive system, simplifying your planter. A vDrive motor mounts to each vSet meter and makes that row a single row planter, because that row is controlled individually.

PROJECT CONTACT

For inquiries about this project, contact Andrew Klopfenstein (klopfenstein.34@osu.edu).

Ohio Crop Performance Trials

The purpose of the Ohio Crop Performance Trials is to evaluate corn hybrids, soybean varieties, and wheat varieties for grain yield and other important agronomic characteristics. Results of the trials can assist farmers in selecting hybrids and varieties best suited to their farming operations and production environments and can complement recommendations made by seed companies and breeding programs.

VIEW FULL RESULTS ONLINE

Yields and other agronomic performance characteristics are reported annually for each crop at each respective trial location. A combined regional summary is included. The brand, seed source, hybrid or variety number, and summary table for all genetics tested are summarized. Additionally, the technology traits (e.g., herbicide and insect resistant events) and seed treatments (e.g., insecticide and fungicide) associated with each hybrid or variety entry are also reported. Weather results are also summarized in each annual report. Results can be sorted online by yield, brand, and other variables.

Annual crop performance and archive results are available online: u.osu.edu/perf/archive/

USING RESULTS TO IMPROVE PROFITABILITY:

Selecting a hybrid or variety is one of the most important decisions for your farming operation. Many factors need to be considered: crop maturity, herbicide resistance, disease resistance, and yield across multiple locations. Hybrid and variety selection should be based on proven performance from multiple test locations and years. Confidence in test results increases with the number of years and the number of locations in which the hybrids and varieties are tested. Annual reports provide multiple-year performance data as well when available. Look for consistency in a hybrid’s or variety’s performance across a range of environmental conditions. The presentation of results in these reports do not imply endorsement of any hybrid or variety by The Ohio State University. We suggest using information from the Ohio Crop Performance Trials along with industry information to select a hybrid or variety for your farming operation to maximize your profitability.

IS YOUR FAVORITE SEED BRAND MISSING FROM THE TRIAL?

If your favorite seed brand is missing from the trial, please contact your seed representative and encourage them to enter hybrids or varieties in the 2025 and future crop performance trials at Ohio State.

SUMMARY OF THE OHIO WHEAT PERFORMANCE TEST - 2024

The wheat performance test was conducted in five counties (Wood, Wayne, Darke, Union, and Pickaway) during the 2023 - 2024 growing season with 84 varieties evaluated. In fall 2023, wheat was planted within 14 days of the fly-free date. Fall growth was good, and wheat entered dormancy in great condition. Higher than normal growing degree days in March through June accelerated crop development, with wheat harvest about 10 days earlier than normal. Overall, wheat yield was lower than previous years likely due to higher levels of disease and shorter grain fill period. Grain yield averaged between 65.8 and 96.1 bu/acre among the five locations.

SUMMARY OF THE OHIO CORN PERFORMANCE TEST - 2024

Ten sites were available for hybrid evaluation covering three regions. Corn planting conditions were variable. Corn yields varied across the state depending on planting dates, rainfall distribution, and disease pressure. The Northeast and Northwest areas had better precipitation conditions during grain fill. The Western and Southern regions had less rainfall available, including moderate to severe drought.

Despite fluctuating temperatures and variable precipitation, averaged across hybrid entries in the early and full season tests, yields were 260 bu/acre in the Southwestern/West and Central region, 216 bu/acre in the Northwestern region, and 253 bu/acre in the North Central/Northeastern region. Average yields at individual test sites ranged from 208 bu/acre at Hoytville to 285 bu/acre at Hebron.

SUMMARY OF THE OHIO SOYBEAN PERFORMANCE TEST - 2024

In 2024, soybean varieties were tested in six counties (Henry, Sandusky, Mercer, Union, Preble, and Clinton). Soybean yield varied greatly among the six trial locations. Soybean yield was lowest in Henry and Sandusky, averaging as low as 37.8 bu/acre for the early maturity trial (2.0-3.1 relative maturities) in Sandusky County. Soybean yield was greatest in Licking, Preble, and Mercer Counties, averaging as high as 87.3 bu/acre for the late maturity trial (3.7-4.4 relative maturities) in Preble County. Season-long rainfall in Licking, Preble, and Clinton totaled 22.0, 17.3, and 15.6 inches, respectively, whereas rainfall totaled 11.7, 12.4, and 14.3 inches in Henry, Mercer, and Sandusky, respectively. Locations with greater season-long rainfall were also the highest yielding locations.

HOW TO SUBMIT ENTRIES:

The Ohio Crop Performance Trials solicit entries from all seed companies. Trial entry forms are available online: u.osu.edu/perfentry/

ACKNOWLEDGEMENTS

The crop performance test programs at the Ohio State University have been in place for over 50 years. We appreciate the university-industry-farmer partnerships that have made these programs possible. We thank many farmer cooperators for contributing to these trials over the past several decades/ years. We are grateful for the assistance provided by OSU research farms at Wooster, Western Agricultural Research Station, and Northwest Agricultural Research Stations for hosting performance test sites.

PROJECT CONTACT

For inquiries about this project, contact: Osler Ortez (ortez.5@osu.edu), Laura Lindsey (lindsey.233@osu.edu), or R.J. Minyo (minyo.1@osu.edu)

M.A. Lowe, D.G. Lohnes, A. Geyer, M.W. Hankinson, J. McCormick

Ohio Crop Performance Trials

2024 Ohio Corn Performance Test

EVALUATION PROCEDURES

R.J. Minyo and O. Ortez, The Ohio State University College of Food, Agricultural, and Environmental Sciences (CFAES) Department of Horticulture and Crop Science, M.A. Lowe, CFAES Wooster Farm Operations, and D.G. Lohnes, CFAES Information Technology

The purpose of the Ohio Corn Performance Test (OCPT) is to evaluate corn hybrids for grain yield and other important agronomic characteristics. Results of the test can assist farmers in selecting hybrids best suited to their farming operations and production environments as well as complement recommendations made by seed companies and breeding programs. Corn hybrids differ considerably in yield potential, standability, maturity, and other agronomic characteristics that affect profitable crop production. Hybrid selection should be based on proven performance from multiple test locations and years. The presentation of results in this report does not imply endorsement of any hybrid by The Ohio State University.

Seed companies marketing corn hybrids in Ohio are invited to enter hybrids in the test. An entry fee is charged to cover expenses. In 2024, companies were permitted to enter an unlimited number of hybrids. Ten sites were available for hybrid evaluation covering three regions of Ohio (Southwestern/West Central/Central, Northwestern, North Central/Northeastern). Seed companies were required to enter a hybrid at all the sites within a testing region. Each hybrid entry was evaluated using three replications per site in a randomized complete block design. Hybrids were planted either in an early or full season maturity trial based on relative maturity information provided by the companies. In the Southwestern/West Central/Central region, the relative maturity of hybrid entries in the early maturity trial was 111 days or earlier; the relative maturity of hybrid entries in the full season trial was 112 days or later. In the Northwestern and North Central/Northeastern regions, the relative maturity of hybrid entries in the early maturity trial was 108 days or earlier; the relative maturity of hybrid entries in the full season trial was 109 days or later. Hybrids were planted with an Almaco Seed Pro 360 plot planter with SkyTrip GPS. Each plot consisted of four 30-inch rows approximately 25 feet long. Force 6.5 soil insecticide was applied in a T-band to all test plots. Seed companies selected a final stand and percent overplant for each hybrid entered. Fertilizer, herbicides, insecticides, and foliar fungicides were applied according to recommended cultural practices for obtaining optimum grain yields. Details concerning the establishment and management of each 2024 test site are listed in footnotes below the tables for each location.

MEASUREMENTS AND RECORDS

YIELD. The center two rows of each plot were harvested with a self propelled two row picker sheller combine. Yields are reported as bushels of grain per acre (Bu/A) at 15.5% moisture.

MOISTURE (Harv Mst). A grain moisture determination was made from each plot with an electrical conductance moisture meter. Grain moisture is reported as percent grain moisture.

LODGING (Stk Ldg). The number of broken stalks in each plot was determined prior to harvest. Only those plants with a stalk broken below the ear were considered stalk lodged. Stalk lodging is reported as a percentage of final plant stand.

FINAL STAND (Final Std). Seed companies selected a desired planting rate for each hybrid entered. Differences between the planting rate and the final stand may be attributed to seed quality and/or environmental conditions present. Plant populations are reported in hundreds per acre (100/A).

EMERGENCE (Emg). A plant count was made on each plot after plant emergence. The emergence percentage was computed based on the number of plants and the number of seeds planted, and reported as the percentage of the seeds planted.

TEST WEIGHT (TW). Test weights were recorded in pounds (lbs.) per bushel on grain samples at field moisture. The results are a summary (average) of all sites in each region.

LSD 0.10. Least Significant Differences at probability level 0.10 (LSD 0.10) are reported for yield and other agronomic characteristics. Differences between hybrids are significant only if they are equal to or greater than the LSD value. If a given hybrid out-yields another hybrid by as much or more than the LSD value, then we are 90% confident (i.e., the odds are 10:1) that the yield difference is real, with only a 10% probability that the difference is due to chance variation (such as soil variation, etc.). For example, if Hybrid X is 19 Bu/A higher in yield than Hybrid Y, then this difference is statistically significant if the LSD is 19 Bu/A or less. If the LSD is 20 Bu/A or greater, then we are less confident that Hybrid X is higher yielding than Hybrid Y under conditions of the test. If ‘NS’ is indicated for a characteristic, then the differences among hybrid entries are not significant at the 10% probability level.

Soybean Forages Tech

2024 GROWING CONDITIONS

The 2024 Ohio growing season will be remembered for its extreme variability. One of the main challenges was a delayed start to the planting season. Despite above average temperatures in April and May, persistent rain events limited suitable days for field work, especially in the Northwestern region of the state. By May 5, 26% of corn was planted in Ohio according to USDA reports. By May 19, 46% of Ohio’s corn acres were planted and reported planted acres reached 90% by June 2. Temperatures in June through August were generally below average and precipitation was well below average. Rainfall during the growing season was variable across sites ranging from 18.5 inches (7.8 inches below average) at Upper Sandusky to 25.7 inches (2.2 inches below average) at Hoytville in the Northwest region. Averaged across the 10 Ohio Corn Performance Test locations, precipitation was 5.9 inches below the 10-year average. Heat-unit accumulation was generally greater at OCPT sites in the Southwestern/West Central/Central and Northwestern regions (with heat-unit accumulation ranging from 3,491 to 3,619 Growing Degree Days - GDDs) than sites in the North Central/ Northeastern region (3,112 and 3,375 GDDs). Overall, the heat-unit accumulation was 470 GDDs higher in 2024 when compared to 2023.

Foliar diseases and insect pests were generally not a major yield limiting factor in 2024. Gray leaf spot (GLS) and northern corn leaf blight (NCLB) were present at nearly all sites in mid-July. Nine of the 10 test sites had fungicide applied between VT/R1 and brown silk (R2). Additionally, tar spot was observed late in the season (R4 and R5 stages) at all locations, except Hebron. When tar spot appears late in the season, less yield impact is expected. Above normal temperatures returned in September. However, with limited soil moisture, the corn crop was unable to add the late season starch, increase test weight, and achieve the top end yields we have seen in past years. The extended dry periods also delayed crop maturation and dry down in the full season hybrids until mid-October. Field conditions were suitable for harvest in October and early November.

RESULTS

Results Results of the 2024 testing program are presented in Tables 1 to 10. Yields and other agronomic performance characteristics are averaged across the individual test sites and shown under the “Summary” heading for each region in Tables 1 through 9. A combined regional summary of hybrid performance is presented in Table 10. The brand, seed source, hybrid number, and table location for hybrids tested in 2024 are summarized in Table 11. Hybrids are listed in alphabetical order by brand. Additionally, the technology traits (e.g., herbicide and insect resistant events) and seed treatments (e.g., insecticide and fungicide) associated with each hybrid entry are indicated in Table 11 (information provided by seed companies).

Yields varied across the state depending on planting dates, rainfall distribution, timing, total precipitation received, and disease pressure. Despite fluctuating temperatures and variable precipitation during grain fill, OCPT yields exceeded expectations. Averaged across hybrid entries in the early and full season tests, yields were 260 Bu/A in the Southwestern/West Central/ Central region, 216 Bu/A in the Northwestern region, and 253 Bu/A in the North Central/Northeastern region. Yields at individual test sites, averaged across hybrid entries in the early and full season tests, ranged from 208 Bu/A at Hoytville to 285 Bu/A at Hebron.

Saturated soil conditions delayed planting in the Northwestern and Northeastern regions until mid-to-late May. Precipitation was below normal for the rest of the growing season, which limited yield potential compared to other regions in the state. The precipitation timing and totals were extremely variable across the state throughout the growing season. Foliar diseases and insect pests were generally not a major factor. GLS and NCLB could be found at a few locations in mid-July. Tar spot, however, was statewide with the highest incidence of disease pressure in central and southern Ohio.

CFAES provides research and related educational programs to clientele on a nondiscriminatory basis. For more information, visit cfaesdiversity.osu.edu. For an accessible format of this publication, visit cfaes.osu.edu/accessibility.

Ohio Corn Performance 2024 Test Sites

Southwest/West Central Region

Northwest Region

Wood - Corn Grain Silage

North Central/Northeast Region

Wayne - Organic and Conventional

Tar spot appeared late (R5) in the Northwestern region with limited yield impact. Gibberella ear rot (GER) and other ear molds were observed in some hybrids at most locations. Stalk lodging was largely absent across locations except at South Charleston where strong winds from hurricane Helen caused moderate-to-severe root lodging in several hybrids.

Confidence in test results increases with the number of years and the number of locations in which the hybrid is tested. Table 10 presents combined performance data for hybrids tested at six and eight locations in 2024. Tables 2, 3, 5, 6, 8, and 9 provide multiple year performance data as well. Look for consistency in a hybrid’s performance across a range of environmental conditions. Yield, standability, grain moisture, and other comparisons should be considered between hybrids of similar maturity to determine those best adapted to each location or region. Results of the corn performance trials for 2024 and previous years (archive tab, 2000 to 2023) are available online at: ohiocroptest.cfaes.osu.edu/corntrials. Results and hybrids can be sorted by yield, brand, and other variables online.

ACKNOWLEDGEMENTS

We thank our farmer cooperators for their contributions to the 2024 Ohio Corn Performance Trial testing program. We are grateful for the assistance provided by Matt Lowe, CFAES Wooster Farm Operations with establishing the test plots; Joe Davlin, Western Agricultural Research Station; Matt Davis, Northwest Agricultural Research Station; and Ken Scaife and Mike Sword, CFAES Wooster. We thank Stacy Cochran and Juliette Portisch, CFAES Marketing and Communications, for their assistance in preparing the 2024 test results for publication.

ohiocroptest.cfaes.osu.edu

Ohio Crop Performance Trials

TABLE 1E. Performance of hybrids in the early maturity trial. SOUTHWESTERN/WEST CENTRAL/CENTRAL Ohio, 2024.

AUGUSTA SEED

AUGUSTA SEED

AXIS SEED

AXIS SEED

AXIS SEED

RHTCe

60C61

BA 25-07 PCE

BA 25-11 VT2P

BA 26-06 PCE

BA 26-08 PCE

BA 26-10 PCE 204-54TRERIB 205-85VT2PRIB 207-87VT2PRIB 208-62VT2PRIB

210-08VT2PRIB 210-46VT2PRIB 211-11VT2PRIB DKC106-98RIB DKC110-10RIB

DKC110-41RIB DKC56-26RIB DKC59-82RIB D49PN05RA D51VC95RIB 1335C 1660C 7188PC 7209TR RIB 7557PC

9779SSX

FS 5947T RIB

FS 5949PC RA FS 6017V RIB FS 6133VDG RIB

G10L16-DV

G11V76-AA NK0922-V NK1040-AA NK1056-V

PC 3305 PC 5510 PC 6610 PC 8407 PC 8408

SEED CONSULTANTS

SEED CONSULTANTS

SEED CONSULTANTS

SEED CONSULTANTS

SEED CONSULTANTS

SC 1084AM SC 1093AM

SC 1094PCE SC 1105PCE SC 1112AM

AGI 4106PWE AGI 4111PWE

AGI 5105PWE

Direct 0110-3110

Direct 1111

Direct 2111-AA

Direct 3109

Direct 3111-3110

Direct 3111-D

SG-6310PCE

SG-6550PCE

SG-6707V

SG-6884PCE

SG-7054AA

SG-7124PCE

SG-7153V

AGRIGOLD

Soybean Forages Tech

Small Grains

1L. Performance of hybrids in the full-season trial. SOUTHWESTERN/WEST CENTRAL/CENTRAL Ohio, 2024.

FC 8345 TRE

FC 8420 VT2P RIB

FC 8455 VT2P RIB

A642-32VT2PRO A644-64VT2RIB

A646-30VT2RIB A6362

62C60 63J77 63M73

64H70

65W75

BA 23-14 VT2P

BA 25-12 VT2P

BA 25-16 VT2P

BA 26-12 PCE

BA 26-14 PCE 214-78DGVT2PRIB 215-09VT2PRIB 215-42TRERIB 215-70TRERIB DKC114-99RIB DKC64-22RIB DKC66-06RIB DKC68-35RIB

D53SP85RIB D53VC54RIB D56TC44RIB 6883DGVT2P RIB 7442PC

7993PC

FS 6245V RIB

FS 6349PC RA FS 6447T RIB G12U11-AA

G14B32-DV G15U34-V NK1228-AA NK1480-DV

PC 2212

PC 6313

SC 1135PCE AGI 3113PWE AGI 3114PWE AGI 4115PWE AGI 5113PWE

Direct 2113-3110

Direct 4113-DV Direct 5112-AA

Direct 5114-D

Direct 8115-3110

Direct 8116-3110

SG-7233DGVT2

SG-7244PCE SG-7275V

SG-7347PCE SG-EX112B

Ohio Crop Performance Trials

TABLE 2. Two year hybrid performance in SOUTHWESTERN/WEST CENTRAL/CENTRAL Ohio, 2023-2024.

AGRIGOLD HYBRIDS

AXIS SEED

AXIS SEED

A644-64VT2RIB 59D20 63M73 65W75

BA 23-14 VT2P

BA 25-12 VT2P

BA 25-16 VT2P 207-87VT2PRIB 211-11VT2PRIB 214-78DGVT2PRIB

DKC56-26RIB

DKC59-82RIB

DKC64-22RIB DKC66-06RIB DKC68-35RIB

D53VC54RIB D56TC44RIB 1660C 6883DGVT2P RIB 7188PC

7209TR RIB 7993PC 9779SSX FS 6017V RIB FS 6133VDG RIB

G10L16-DV G11V76-AA G14B32-DV

SEED CONSULTANTS SEED CONSULTANTS

SEED CONSULTANTS

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED

SEED

1112AM

Direct 0110-3110

Direct 2111-AA Direct 2113-3110 Direct 3109 Direct 3111-D

Direct 8115-3110

Direct 8116-3110

SG-7054AA SG-7124PCE SG-7153V

SG-7233DGVT2 SG-7244PCE

SEED

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SEED

SEED

Soybean Forages Tech

Small Grains

TABLE 4E. Performance of hybrids in the early maturity trial. NORTHWESTERN Ohio, 2024.

53M77

55D61

57K72

BA 25-07 PCE

BA 26-06 PCE

BA 26-08 PCE 204-54TRERIB 205-85VT2PRIB 207-87VT2PRIB 208-62VT2PRIB

DKC099-11RIB DKC101-35RIB

DKC102-13RIB DKC103-07RIB DKC106-98RIB

DKC56-26RIB D44PN25RA 1335C 7188PC 7557PC

FS 5335P RIB

FS 5525VDG RIB G03U08-D G08U00-V

LG 53C44VT2RIB

LG 58C48VT2RIB NK0252-D NK0880-V

PC 3305

PC 8407

PC 8408

SC 1042Q

SC 1055PCE

SC 1084AM

AGI 4106PWE

AGI 5105PWE

Direct 0101-DV

Direct 2107-AA

Direct 4102-AA

Direct 5101-D

Direct 5107-V

Direct 9107-3010

SG-5653GT

SG-5885PCE

SG-5940DV

SG-6122V

SG-6310PCE

SG-6550PCE

SG-6707V

SG-6884PCE

SG-EX105A 75-07 84-04

Ohio Crop Performance Trials

4L. Performance of hybrids in the full-season trial. NORTHWESTERN Ohio, 2024.

210-08VT2PRIB 210-46VT2PRIB 211-11VT2PRIB 214-78DGVT2PRIB 215-09VT2PRIB 215-42TRERIB 215-70TRERIB DKC110-10RIB DKC110-41RIB DKC114-99RIB

DKC59-82RIB DKC64-22RIB DKC66-06RIB DKC68-35RIB D49PN05RA

D51VC95RIB 1660C 6883DGVT2P RIB 7209TR RIB 7442PC

7993PC 9779SSX FS 5947T RIB FS 5949PC RA FS 6017V RIB

FS 6133VDG RIB FS 6245V RIB FS 6349PC RA FS 6447T RIB G10B61-AA

G10L16-DV G11V76-AA G12U11-AA GM 61PC85 LG 62C73VT2RIB NK0922-V NK1056-V NK1188-AA PC 2212 PC 5510 PC 6313 PC 6610 SC 1093AM SC 1094PCE SC 1105PCE SC 1112AM AGI 3113PWE AGI 3114PWE AGI 4111PWE AGI 4115PWE AGI 5113PWE Direct 0110-3110 Direct 2111-AA Direct 2113-3110 Direct 3109 Direct 3111-3110 Direct 3111-D

Small Grains

Soybean Forages Tech

TABLE 5. Two year hybrid performance in NORTHWESTERN Ohio, 2023-2024. AXIS

VT2P 207-87VT2PRIB 211-11VT2PRIB 214-78DGVT2PRIB

DKC101-35RIB

DKC56-26RIB

DKC59-82RIB

DKC64-22RIB

DKC66-06RIB

DKC68-35RIB 1335C 1660C

6883DGVT2P RIB 7188PC 7209TR RIB 7993PC 9779SSX FS 5525VDG RIB FS 6017V RIB FS 6133VDG RIB G10B61-AA

TABLE 6. Three year hybrid performance in NORTHWESTERN Ohio, 2022-2024.

Ohio Crop Performance Trials

TABLE 7E. Performance of hybrids in the early maturity trial. NORTH CENTRAL and NORTHEASTERN Ohio, 2024.

SEED

BA 25-07 PCE

BA 26-06 PCE

BA 26-08 PCE 204-54TRERIB 205-85VT2PRIB 207-87VT2PRIB 208-62VT2PRIB

DKC099-11RIB

DKC101-35RIB

DKC102-13RIB DKC103-07RIB DKC106-98RIB

DKC56-26RIB D44PN25RA 1335C 7000TR 7188PC

7557PC FS 5335P RIB FS 5525VDG RIB G03U08-D G08U00-V

LG 51C62VT2RIB LG 53C44VT2RIB LG 58C48VT2RIB NK0252-D NK0880-V

SC 1042Q

SC 1055PCE

SC 1084AM AGI 4106PWE AGI 5105PWE

Direct 0101-DV

Direct 2107-AA

Direct 4102-AA

Direct 5101-D

Direct 5107-V

Direct 9107-3010 SW 0123PE SW 9504VT SW 9600SS SW 9822VT SW 9876SS

SG-5653GT

SG-5885PCE

SG-5940DV

SG-6122V

SG-6310PCE

SG-6550PCE

SG-6707V

SG-6884PCE

SG-EX105A

75-07 84-04

Soybean Forages Tech Other Small Grains

TABLE 7L. Performance of hybrids in the full-season trial. NORTH CENTRAL and NORTHEASTERN Ohio, 2024.

FC 8047 C

FC 8235 VT2P RIB

FC 8257 PC A6362 59A25

AXIS SEED

AXIS SEED

AXIS

FS

DIRECT

SEED GENETICS DIRECT

SEED GENETICS DIRECT

SHUR GROW

59D20 59D20 RHTCe 60C61 62C60 63J77

63M73

BA 23-14 VT2P

BA 25-11 VT2P

BA 25-12 VT2P

BA 25-16 VT2P

BA 26-10 PCE

BA 26-12 PCE

BA 26-14 PCE

210-08VT2PRIB 210-46VT2PRIB

211-11VT2PRIB 214-78DGVT2PRIB 215-09VT2PRIB 215-42TRERIB 215-70TRERIB

DKC110-10RIB DKC110-41RIB

DKC114-99RIB DKC59-82RIB DKC64-22RIB

DKC66-06RIB DKC68-35RIB D49PN05RA D51VC95RIB 1660C

7209TR RIB 7442PC 7993PC 9779SSX

FS 5947T RIB

FS 5949PC RA

FS 6017V RIB

FS 6133VDG RIB

FS 6245V RIB

FS 6349PC RA

FS 6447T RIB

G11V76-AA

NK1040-AA

NK1056-V NK1228-AA

SC 1093AM

SC 1094PCE

SC 1105PCE SC 1112AM AGI 4111PWE

Direct 0110-3110

Direct 2111-AA

Direct 3109

Direct 3111-3110

Direct 3111-D

SG-7054AA

SG-7124PCE

SG-7153V

SG-EX109A

SG-EX110A

76-11 78-13 85-09

Ohio Crop Performance Trials

TABLE 8. Two year hybrid performance in NORTH CENTRAL and NORTHEASTERN Ohio, 2023-2024.

SEED CONSULTANTS SEED CONSULTANTS SEED CONSULTANTS

SEED GENETICS DIRECT SEED GENETICS DIRECT

SEED GENETICS DIRECT SEED GENETICS DIRECT SEED GENETICS DIRECT SEED GENETICS DIRECT SEED GENETICS DIRECT

SEED

63M73 BA 23-14 VT2P BA 25-12 VT2P BA 25-16 VT2P 207-87VT2PRIB

211-11VT2PRIB 214-78DGVT2PRIB

DKC101-35RIB

DKC56-26RIB

DKC59-82RIB

DKC64-22RIB

DKC66-06RIB

DKC68-35RIB 1335C 1660C

7000TR 7188PC 7209TR RIB 9779SSX FS 5525VDG RIB

FS 6017V RIB FS 6133VDG RIB G11V76-AA NK1040-AA SC 1042Q

SC 1084AM SC 1093AM SC 1112AM AGI 4106PWE AGI 4111PWE

Direct 0101-DV

Direct 0110-3110

Direct 2111-AA Direct 3109 Direct 3111-D

Direct 9107-3010 SW 9600SS SW 9876SS SG-7054AA SG-7124PCE SG-7153V 84-04 85-09

TABLE

Soybean Forages Tech Other Small Grains

TABLE 10. Combined regional summary of hybrid performance, 2024.

AUGUSTA

AUGUSTA

AUGUSTA

AXIS

AXIS

CHANNEL DEKALB

BA 23-14 VT2P

BA 25-07 PCE

BA 25-11 VT2P

BA 25-12 VT2P

BA 25-16 VT2P

BA 26-06 PCE

BA 26-08 PCE

BA 26-10 PCE

BA 26-12 PCE

BA 26-14 PCE 204-54TRERIB 205-85VT2PRIB 207-87VT2PRIB 208-62VT2PRIB 210-08VT2PRIB 210-46VT2PRIB 211-11VT2PRIB 214-78DGVT2PRIB 215-09VT2PRIB 215-42TRERIB 215-70TRERIB DKC106-98RIB DKC110-10RIB DKC110-41RIB DKC114-99RIB

DKC56-26RIB DKC59-82RIB DKC64-22RIB DKC66-06RIB DKC68-35RIB

D49PN05RA D51VC95RIB 1335C 1660C 6883DGVT2P RIB

7188PC 7209TR RIB 7442PC 7557PC 7993PC

9779SSX

FS 5947T RIB FS 5949PC RA FS 6017V RIB FS 6133VDG RIB

FS 6245V RIB FS 6349PC RA FS 6447T RIB

G10L16-DV G11V76-AA

G12U11-AA NK0922-V NK1056-V PC 2212 PC 3305

PC 5510 PC 6313 PC 6610 PC 8407 PC 8408

SEED CONSULTANTS

SC 1084AM SC 1093AM SC 1094PCE SC 1105PCE SC 1112AM

AGI 3113PWE AGI 3114PWE AGI 4106PWE AGI 4111PWE AGI 4115PWE

AGI 5105PWE AGI 5113PWE Direct 0110-3110 Direct

Ohio Crop Performance Trials

TABLE 11. Seed source, table location, technology traits, and seed treatments for hybrids tested in 2024.

1st CHOICE

AGRIGOLD HYBRIDS

AUGUSTA SEED

AXIS SEED

B&A GENETICS

1st CHOICE SEEDS 310 EAST 3rd ST. RUSHVILLE, IN 46173 765-938-3000 1stchoiceseeds.com

AGRIGOLD 1122 EAST 169th ST. WESTFIELD, IN 46074 317-896-5551 agrigold.com

AUGUSTA SEED PO BOX 899 STAUNTON, VA 24482 540-886-6055 augustaseed.com

AXIS SEED 2974 MARION GREEN CAMP RD. MARION, OH 43302 614-348-6314 axisohio.com

FC 8047 C

FC 8235 VT2P RIB

FC 8257 PC

FC 8345 TRE

FC 8420 VT2P RIB

FC 8455 VT2P RIB

A642-32VT2PRO A644-64VT2RIB A646-30VT2RIB

A2360 A4961 A6362

53M77 55D61 57K72 59A25 59D20 59D20 RHTCe 60C61 62C60 63J77 63M73 64H70 65W75

BA 23-14 VT2P

B&A GENETICS 2180 PINE RD. CELINA, OH 4822 419-305-5481 bagenetics.us

CHANNEL

DEKALB

DYNA-GRO

EBBERTS

BAYER CROP SCIENCE 1819 N. STAR RD. COLUMBUS, OH 43212 308-529-1371 channel.com

BA 25-07 PCE

BA 25-11 VT2P

BA 25-12 VT2P

BA 25-16 VT2P

BA 26-06 PCE

BA 26-08 PCE

BA 26-10 PCE

BA 26-12 PCE

BA 26-14 PCE

204-54TRERIB 205-85VT2PRIB 207-87VT2PRIB 208-62VT2PRIB 210-08VT2PRIB 210-46VT2PRIB 211-11VT2PRIB 214-78DGVT2PRIB 215-09VT2PRIB 215-42TRERIB 215-70TRERIB

BAYER CROP SCIENCE 800 N. LINDBERGH BLVD. ST. LOUIS, MO 63167 800-768-6387 cropscience.bayer.us NUTRIEN 5296 HARVEST LAKE DR. LOVELAND, CO 80538 740-207-0182 dynagroseed.com

EBBERTS FIELD SEEDS, INC. 6840 NORTH ST. RT. 48 COVINGTON, OH 45318 937-473-2521 ebbertsseeds.com

FS InVISION

GROWMARK, INC. 1705 TOWANDA AVE. BLOOMINGTON, IL 61701 309-557-6399 fsseeds.com

DKC099-11RIB DKC101-35RIB DKC102-13RIB DKC103-07RIB DKC106-98RIB DKC110-10RIB DKC110-41RIB DKC114-99RIB DKC56-26RIB DKC59-82RIB DKC64-22RIB DKC66-06RIB DKC68-35RIB

D44PN25RA D49PN05RA D51VC95RIB D53SP85RIB D53VC54RIB D56TC44RIB 1335C 1660C 6883DGVT2P RIB 7000TR 7188PC 7209TR RIB 7442PC 7557PC 7993PC 9779SSX

FS 5335P RIB

5525VDG RIB

4L, 4L, 4L, 1L 1L 1L 1L 1L 1L 1E, 1E, 1L,

4E, 4E, 1E, 4L, 1E, 1E, 1E, 1L, 1L, 1L, 1L 1L

7L, 7E, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7E, 7L, 7L, 7L, 7E, 7E, 7L, 7L, 7L, 7E, 7E, 7E, 7E, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7E, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7E, 7L, 10 7E, 7L, 7L, 7E, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, Hybrid No.

NON-GMO

CruiserMaxx / Vibrance

Acceleron

RR,CB RR,CB,LL, Enlist RR,CB,TRE,WBC RR,CB RR,CB

RR,CB RR,CB RR,CB

4L, 4L, 4L,

7E 7E 4E, 7L 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4E, 4L, 4L, 4L, 4E, 4E, 4L, 4L, 4L, 4E, 4E, 4E, 4E, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 7E 7E 7E 7E 4E, 4L, 4L, 4L, 4E, 4L, 4L, 4L, 4L, 7E 4L, 4L, 4E, 4L, 4L, 4E, 4L, 4L, 4E, 4L, 4L, 7E 7E 4L, 4L, 4L, 4L, 4L, 4L, 4L,

1L, 1E, 1E, 1L, 1L, 1E, 1E, 1E, 1L, 1L, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1L, 1L, 1L, 1L, 4E, 4E, 4E, 4E, 1E, 1E, 1E, 1L, 1E, 1E, 1L, 1L, 1L, 4E, 1E, 1E, 1L 1L 1L 1E, 1E, 1L, 7E 1E, 1E, 1L, 1E, 1L, 1E, 4E, 4E, 1E, 1E, 1E, 1E, 1L, 1L, 1L, 7L 7L 7L

RR,CB,LL, Enlist GT,CB,LL GT,CB,RW,LL,BCW

RR,CB,TRE,WBC RR,CB,LL, Enlist RR,CB,TRE,WBC RR,CB RR,CB RR,CB RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,TRE,WBC RR,CB,TRE,WBC RR,CB,TRE,WBC RR,CB,TRE,WBC

RR,CB RR,CB,LL, Enlist RR,CB RR,CB RR,CB RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,LL, Enlist

RR,CB,TRE,WBC RR,CB RR,CB RR,CB RR,CB RR,CB RR,CB RR,CB,DT RR,CB RR,CB,TRE,WBC RR,CB,TRE,WBC

RR,CB RR,CB RR,CB,BCW,WBC RR,CB,TRE,WBC RR,CB,BCW,WBC RR,CB,RW,LL,CEW RR,CB,TRE,WBC RR,CB,BCW,WBC RR,CB,TRE,WBC RR,CB RR,CB RR,CB,TRE,WBC RR,CB

RR,CB,LL, Enlist

RR,CB,LL, Enlist RR,CB RR,CB,RW,LL,CEW RR,CB RR,CB,TRE,WBC NON-GMO NON-GMO RR,CB,DT RR,CB,TRE,WBC RR,CB,LL, Enlist

RR,CB,TRE,WBC RR,CB,LL, Enlist

RR,CB,LL, Enlist

RR,CB,LL, Enlist

RR,CB,RW,LL,CEW

RR,CB,RW,LL,CEW

RR,CB,DT

RR,CB,TRE,WBC

RR,CB,LL, Enlist

RR,CB

RR,CB,DT

RR,CB

RR,CB,LL, Enlist

RR,CB,TRE,WBC

CruiserMaxx / Vibrance

Acceleron

Acceleron

Acceleron

Acceleron Acceleron

Acceleron

Acceleron

Acceleron

Acceleron

Acceleron

Acceleron

Acceleron

Acceleron

Acceleron

Acceleron

Corn Complete

Corn Complete

Corn Complete

Corn Complete

Corn Complete

Soybean Forages Tech Other Small Grains

Brand Seed Source

GOLDEN HARVEST 2001 BUTTERFIELD RD. DOWNERS GROVE, IL 60515 800-944-7333 goldenharvetseeds.com

LUCKEY FARMERS 1200 W. MAIN ST. WOODVILLE, OH 43469 419-575-4905 luckeyfarmers.com

LG SEEDS 1122 EAST 169th ST. WESTFIELD, IN 46074 317-896-5552 lgseeds.com

NK SEEDS 2001 BUTTERFIELD RD. DOWNERS GROVE, IL 60515 937-414-3559 syngenta-us.com/corn/nk

PC SEEDCO 3250 GLADY RD. LYNCHBURG, OH 45142 937-218-8836

SEED CONSULTANTS, INC. 648 MIAMI TRACE RD. SW WASHINGTON C. H., OH 43160 800-780-2676 seedconsultants.com

SEED GENETICS DIRECT 9983 JEFFERSON WEST LANCASTER JEFFERSON, OH 43128 740-505-0073, 740-505-1544 seedgeneticsdirect.com

G03U08-D

G08U00-V

G10B61-AA

G10L16-DV

G11V76-AA

G12U11-AA

G14B32-DV G15U34-V GM 61PC85

LG 51C62VT2RIB

LG 53C44VT2RIB

LG 58C48VT2RIB LG 62C73VT2RIB

NK0252-D NK0880-V NK0922-V NK1040-AA NK1056-V NK1188-AA NK1228-AA NK1480-DV

2212

3305

5510

6313

6610

8407

8408

SC 1042Q SC 1055PCE

1084AM

1093AM SC 1094PCE SC 1105PCE SC 1112AM SC 1135PCE

AGI 3113PWE AGI 3114PWE AGI 4106PWE AGI 4111PWE AGI 4115PWE AGI 5105PWE AGI 5113PWE Direct 0101-DV Direct 0110-3110 Direct 1111 Direct 2107-AA Direct 2111-AA Direct 2113-3110 Direct 3109 Direct 3111-3110 Direct 3111-D Direct 4102-AA Direct 4113-DV Direct 5101-D Direct 5107-V Direct 5112-AA Direct 5114-D Direct 8115-3110 Direct 8116-3110 Direct 9107-3010

SEEDWAY, LLC 1734 RAILROAD PL. HALL, NY 14463 800-836-3710 seedway.com

SHUR GROW 6239 ST. RT. 187 MECHANICSBURG, OH 43044 800-231-SEED heritagecooperative.com

SW 0123PE SW 9504VT SW 9600SS SW 9822VT SW 9876SS

7E, 7L, 7L, 7L, 7L, 10 10 7E, 7L, 10 7E, 10 7L, 7L, 10 7L, 7L, 7L, 10 10 10 7E, 7E, 7E, 7E, 7L, 7L, 7L, 7E, 7L, 7L, Hybrid No.

4E, 4E, 4L 1E, 1E, 1L, 1L 1L 4L 7E 4E, 4E, 4L

4E, 4E, 1E, 1E, 1E, 4L 1L, 1L

1L, 1E, 1E, 1L, 1E, 1E, 1E, 4E, 4E, 1E, 1E, 1E, 1E, 1E, 1L

7E 7E 4L, 4L, 4L, 7E 7E 7E 7E 4L, 7L 4L, 7L

GT,CB,RW,LL,WBC,DT GT,CB,LL,VIP GT,CB,LL GT,CB,RW,LL,BCW,VIP GT,CB,LL GT,CB,LL GT,CB,RW,LL,BCW,VIP GT,CB,LL,VIP RR,CB,LL, Enlist

RR,CB RR,CB RR,CB RR,CB

ALBERT LEA SEED HOUSE 1414 W. MAIN ST. / PO BOX 127 ALBERT LEA, MN 56007 800-352-5247 alseed.com

SG-5653GT SG-5885PCE SG-5940DV SG-6122V SG-6310PCE SG-6550PCE SG-6707V SG-6884PCE SG-7054AA SG-7124PCE SG-7153V SG-7233DGVT2 SG-7244PCE SG-7275V SG-7347PCE SG-EX105A SG-EX109A SG-EX110A SG-EX111A SG-EX112B 75-07 76-11 78-13 84-04 85-09

1L, 1L, 1E, 1E, 1L, 1E, 1L, 4E, 1E, 1E, 4E, 1E, 1L, 1E, 1E, 1E, 4E, 1L, 4E, 4E, 1L, 1L, 1L 1L 4E, 7E 7E 7E 7E 7E 4E, 4E, 4E, 4E, 1E, 1E, 1E, 1E, 1E, 1E, 1E, 1L 1L 1L 1L 1E, 1E, 1E, 1E 1L 4E, 4L, 4L, 4E, 4L,

4L, 4E, 4L, 4L, 4L, 4E, 4E, 7E 7E 4E, 4L, 4L, 4L, 4L, 4L, 4L, 4E, 4L, 4L, 4E, 4L, 7E 4L, 7E 4L, 4L, 4L, 4L, 4L, 7E 4L, 7E 7E 4L, 4L, 7E 7E 7E 7E 7E 4E, 4E, 4E, 4E, 4L, 4L, 4L, 4E, 4L, 4L, 7E 7E 7L 7L 7L

Cruiser Maxx Corn 500 / Vayantis

Cruiser Maxx Corn 500 / Vayantis

Cruiser Maxx Corn 500 / Vayantis

Cruiser Maxx Corn 1250 / Vayantis

Cruiser Maxx Corn 500 / Vayantis

Cruiser Maxx Corn 500 / Vayantis

Cruiser Maxx Corn 1250 / Vayantis

Cruiser Maxx Corn 500 / Vayantis

Cruiser Maxx 250

Acceleron

Acceleron

Acceleron

Acceleron

GT,CB,RW,LL,BCW GT,CB,LL,VIP GT,CB,LL,VIP GT,CB,LL GT,CB,LL,VIP GT,CB,LL GT,CB,LL GT,CB,RW,LL,BCW,VIP NON-GMO NON-GMO NON-GMO NON-GMO NON-GMO NON-GMO NON-GMO

RR,CB,RW,LL RR,CB,LL, Enlist RR,CB,LL RR,CB,LL RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,LL RR,CB,LL, Enlist

RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,LL, Enlist RR,CB,LL, Enlist GT,CB,RW,LL,BCW,VIP GT,CB,LL,VIP NON-GMO GT,CB,LL GT,CB,LL GT,CB,LL,VIP NON-GMO GT,CB,LL,VIP GT,CB,RW,LL,BCW GT,CB,LL GT,CB,RW,LL,BCW,VIP GT,CB,RW,LL,BCW GT,CB,LL,VIP GT,CB,LL GT,CB,RW,LL,BCW GT,CB,LL,VIP GT,CB,LL,VIP GT,CB,LL,VIP

RR,CB, LL, Enlist RR,CB RR,CB,RW,LL,CEW RR,CB RR,CB,RW,LL,CEW

GT

RR,CB, LL, Enlist GT,CB,RW,LL,BCW,VIP GT,CB,LL,VIP RR,CB, LL, Enlist RR,CB, LL, Enlist GT,CB,LL,VIP RR,CB, LL, Enlist GT,CB,LL RR,CB, LL, Enlist GT,CB,LL,VIP RR,CB,DT RR,CB, LL, Enlist GT,CB,LL,VIP

GT,CB,LL,VIP

Apron Max / Vibrance / Vayantis Apron Max / Vibrance / Vayantis Apron Max / Vibrance / Vayantis Apron Max / Vibrance / Vayantis

Apron Max / Vibrance / Vayantis Apron Max / Vibrance / Vayantis

Apron Max / Vibrance / Vayantis

Apron Max / Vibrance / Vayantis

Cruiser Maxx / Vibrance / Vayantis

Cruiser Maxx / Vibrance / Vayantis

Cruiser Maxx / Vibrance / Vayantis

Cruiser Maxx / Vibrance / Vayantis

Cruiser Maxx / Vibrance / Vayantis

Cruiser Maxx / Vibrance / Vayantis

Cruiser Maxx / Vibrance / Vayantis

LumiGen LumiGen

LumiGen

LumiGen

LumiGen

LumiGen

Pro TEC C4+

Acknowledgements

Research Collaborators and Supporters

Brice Acton

Andy Albers

Allgyre Farms

Greg Arnett

Matt Arnos

Ayers Farms

Glenn Bard

David Barrett

Collin Bauerbach

Mike Beam

Beam Precision Ag

Beck’s Hybrids

Matt Bell

Jason Berchtold

Dean Bixel

Bock Family Farms

Craig Bowsher

Braddock Farms - Jim & Susan Braddock

John Branstrator

David Brautigam

Brett and Brandon Kenworthy

Geoff Brooks

Mike Burke

Matt Burkholder

Butch Foster Farms

Buurma Farms

Daniel Call

Jillian Carbone

Jim Carter

Carter Farms

Jared Chester

Scott and Tyler Clark

Eric and Cordelia Clawson

Richard Clifton/Anna Snyder

Clifton Farms

Conner Collins

Conkle Family Farm

Mike Cook

Rachel Cornell

Terri Cory

Casey Coughlin

Chandler Cox

John Coyne

Darke County Commissioners

Matt Davis

Grant Davis

Joe Davlin

Doug Dawson

Chris Dean

Allen Dean

Defiance County Board of Commissioners and Defiance County SWCD

Jonathan Dorsten

Nate Douridas

Duling Family Farms

Jeff Dulling

Stephanie Dunkel

Eckel Grain Farms

Egger Farms

Matt Eggers

Matt and Scott Ellis

Randy Engel

Fayette County Agronomy Club

Fayette County Commissioners

Jonathan Francis

Terry Frey

Fry Farms

Generations Family Farm

Maren Gilbreth

Grant Farms - Jason Grant

Don Hammersmith

Hardscrable Farms

HARTC

Hawkins Farms - Lucas and Keith Hawkins

Nick Heitz

Von Herron

Todd Hesterman

Hestermen Farm

David Hogle

Richard “Dick” Hoshock

Justin Hunter

Jacobs Family Farm - Kevin and Josh Jacobs

Jeff Truster Farms

Scott Jenks

Doug Jennings

Jeremy Fruth Farms

Don Johnson

Matt Karhoff

Todd Kelly

Brett Kenworthy

Brianna, Ryan, Ethan, Leah, and Jenna Klopfenstein

Evan Klopfenstein

Gary Klopfenstein

Roy Klopfenstein

Leon Klopfenstein

Matthew Klopfenstein

Jan Layman

Lee Farms

Adam Lesch

Jacob Lesch

Jim Love

Will Luli

Bret Margraf

Curt Mauer

Geoff and James Mavis

Jacob McCarty

Andrew McCord

Alan Mehl

Dave Mielke

Mike Heyob Farms

Deanna Miller

Molly Caren Agricultural Center

Morrow County Commisioners and Morrow Soil and Water Conservation District

Bennett Musselman

David Myerholtz

Myerholtz

Jason Myers

Erin Neal

Neal Farms

NextGen Organic Farms

Northwest ARS

Garrett Nowak

OARDC North Central Ag Research Center

OARDC Northwest Ag Research Center

OARDC Western Ag Research Center

Soybean Forages Tech Other Small Grains

OARDC Wooster

Steve Okuley

Bob Ott

Overholser Farms

Alex Parsio

Steve Payn

David Peart

Tom Pitstick

Putnam County Commissioners and Putnam Soil and Water Conservation District

Radcliff Farms

Carl and Bruce Renner

John Rethmel

Rod Rethmel

Eric Rife

Dan Robinette

Tom Rufenacht

Tyson Rufenacht

Matt Ruff

Sandburr Ridge Farms

Evan and Tim Schafer

Andrew Schaub

Michael Schmid

Paul Schmitmeyer

Bryce Schott

Brittany Schroeder

Ryan Schubert

Seiler Farms

John Settlemyre

Dale Sheridan

Dusty Smith

Cory Smith

RJ Snider

Southwest Ohio Corn Growers

Spillman Farms

Tom Stannard

Stephan Janos Farms

Sean Stuckey

Lynn Stuckey

Stuckey Family Farms

Matt Sullivan

Kris Swartz

Kirk Swensen

Mike Sword

Ron Synder

Chris Tkach

Mike Trausch

Triple H Farms

Triple K Farms

Brett Unverferth

Utter Farms

Luke VanTilburg

Ramarao Venkatesh

Clayton Waddle

We Feed U Farms

Nathan Weaver

Mike Weible

Lynn Whittlesey

Eddie Willing

John Wilson

Winkle Farms

Martha Winters

Kristen Wisecarver

Woodruff Farms

Jonathan Wyse

Doug Yoder

Nich Zachrich

Aaron Zimmerman

Acknowledgements Industry Partners

Acknowledgements Industry Partners

eFields is an Ohio State program dedicated to advancing production agriculture through the use of field-scale research. eFields utilizes modern technologies and information to conduct on-farm studies with an educational and demonstration component used to help farmers and their advisors understand how new practices and techniques can improve farm efficiency and profitability. The program is dedicated to delivering timely and relevant, data-driven, actionable information to farmers throughout Ohio.