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2026 eBarns Report Ohio State Digital Ag Program

COLLEGE OF VETERINARY MEDICINE

2026 eBarns Report

eBarns

“connecting science to farmers” eBarns is a program at The Ohio State University dedicated to advancing production agriculture through the use of field-scale and applied research. The 2026 eBarns Report is a combination of the research conducted throughout Ohio on partner farms and Ohio State University agricultural research stations. These efforts are led and supported by staff, faculty, students, Ohio State University Extension, and additional partners. Current research is focused on enhancing animal production, growing high-quality forages, precision nutrient management, and to develop analytical tools for digital agriculture. In the 5th edition of eBarns we have included research studies not only from the past year, but studies from previous years that have yet to be published for producers. It is our goal to continue to share results from applied livestock, forage, and manure nutrient management in this publication for years to come.

2026 Research Recap 24 Total Studies • • • • •

17 Forages 3 Cattle 2 Small Ruminant 1 Swine 1 Poultry

47 Counties 150 Research Sites

Over 1,663 Acres Represented and 108,954 Animals Represented

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. 2 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

Editorial Welcome to the 2026 Ohio State eBarns Report. Five years! This is the 5th edition of the eBarns Report, where we share and highlight applied research on forages, pasture management, livestock, and equine topics. These studies are a collection of both individual studies and statewide efforts across Ohio, on farms and at university research facilities. We appreciate the faculty, staff, and students that contributed to this year’s report. Their combined contributions make this work possible. The past year has been a much better year for livestock producers. This is in large part due to strong markets for much of the livestock industry, even as uncertainty looms particularly in the beef industry given the smallest cow herd in roughly 75 years. With profitability comes management decisions on improving production and financial standing of the farm. It is also a time to investigate new management practices by conducting on-farm research! 2025 brought some challenges in making high-quality hay across most of the state. Those results are all part of the hay testing program summarized in this year’s report. Poorer quality hay led to an opportunity to look at alternative ways to feed beef cows during the winter months. Ticks and tick-borne disease is still at the front of many producers minds as we progress towards diseases such as Theileria orientalis becoming endemic in our beef cow herd. Production and variety trials of annual forages continue in this year’s publication, with specific focus on winter annuals and their management. Feed management and volatility of livestock prices are things to keep an eye on moving forward. For the 2026 report, the eBarns team conducted 24 studies in 47 counties, spanning 150 research sites. We hope the data presented in this year’s report will provide value to Ohio’s livestock farmers. If you have an interest in conducting on-farm research for next year’s report learn more on page 8 or contact your local Extension office to get started! Sincerely, The eBarns Team

The eBarns Report is published on an annual basis. To view past reports, visit our website: go.osu.edu/ebarnsreports 2026 eBarns Report | 3

Table of Contents Ohio State Digital Ag Program............................................................................................................................................6 Get Involved in On-Farm Research.....................................................................................................................................8 Report Guide.....................................................................................................................................................................10 Calculations and Statistics................................................................................................................................................12 eBarns Contributors..........................................................................................................................................................14 2025/2026 Growing Season Weather...............................................................................................................................20 Ohio Farm Custom Rates..................................................................................................................................................22 Ohio Crop Enterprise Budgets..........................................................................................................................................24 Ohio Livestock Enterprise Budgets...................................................................................................................................26 Corn Silage Price Decision Tool........................................................................................................................................28 New World Screwworm Resources...................................................................................................................................30 Designing Livestock Systems............................................................................................................................................32 Extension Resources for the Farm....................................................................................................................................34 Ohio State Forages Research...........................................................................................................................................38 Alfalfa Weevil Monitoring Summary......................................................................................................................40 Corn DON Resistance Screening.........................................................................................................................42 DON Resistance - Silage Corn.............................................................................................................................46 Drone Spray Deposition.......................................................................................................................................48 Forage Production Under Solar............................................................................................................................50 Fungicide Application for DON.............................................................................................................................54 Hay Quality Analysis Summary............................................................................................................................62 Hay Quality Analysis.............................................................................................................................................64 Nitrogen Rate - Cereal Rye..................................................................................................................................72 Sulfur on Forages.................................................................................................................................................74 Winter Annual Forage Planting Date....................................................................................................................76 Winter Annual Forage Varieties............................................................................................................................78 Woody Invasives Management............................................................................................................................80 Ohio State Cattle Research 2026 Tick Update..................................................................................................................................................82 Winter Feeding Beef Cows...................................................................................................................................84 Tracking Dairy Antibiotic Use................................................................................................................................86 Ohio State Small Ruminant Research Sheep LAI Summary............................................................................................................................................88 Maternal Effects on Birthweights..........................................................................................................................90 Ohio State Swine Research Understanding PRRS Outbreaks..........................................................................................................................92 Ohio State Poultry Research Highly Pathogenic Avian Influenza ......................................................................................................................94 Acknowledgments ............................................................................................................................................................96

4 | Ohio State Digital Ag Program

DEPARTMENT OF

ANIMAL SCIENCES

WHO WE ARE

OUR IMPACT

In the Department of Animal Sciences, we work alongside animal and livestock producers to enhance animal health, nutrition, sustainability and proﬁtability. We also prepare the next generation of scientists and industry leaders through innovative teaching, experiential learning and research opportunities.

RESEARCH

Our faculty's cutting-edge research has widespread implications in traditional and emerging agriculture and for animal operations.

SUSTAINABLE ANIMAL PRODUCTION

YOUR RESOURCE PARTNERS

Innovating for the well-being of animals, people and our planet.

Research is translated into application through beef, dairy, equine, small ruminants, pork and forage teams that communicate discoveries and provide solutions for Ohio producers. BEEF TEAM Lyda Garcia

Garcia.625@osu.edu

DAIRY WORKING GROUP Maurice Eastridge Eastridge.1@osu.edu

OHIO EQUINE PROGRAM Kimberly Cole Cole.436@osu.edu

INTEGRATED FORAGE MANAGEMENT TEAM Marilia Chiavegato Chiavegato.1@osu.edu

MEAT TEAM Lyda Garcia

Garcia.625@osu.edu

Lynn Knipe

Knipe.1@osu.edu

PORK TEAM Sheila Jacobi

Jacobi.1@osu.edu

SMALL RUMINANT TEAM Braden Campbell Cambell.1279@osu.edu

Advancing knowledge to improve animal health, nutrition and production.

NUTRITION & MICROBIOME Advancing nutrition and microbiome research to improve animal health and performance.

WELFARE & HUMAN-ANIMAL INTERACTION Promoting strong bonds, responsible care and ethical stewardship.

ANIMAL HEALTH & DISEASE Protecting animal health through science, surveillance and care.

ANSCI @osuansci • ansci.osu.edu

Ohio State Digital Ag Program

DigitalAg OHIO STATE

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 sitespecific 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 researchbased information in the age of digital agriculture.

WHAT IS DIGITAL AGRICULTURE? The premise of digital agriculture includes the advancement of farm operations through implementation of precision agriculture strategies, prescriptive agriculture and databased 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.

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. digitalag.osu.edu

6 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

Digital Ag Initiatives and Livestock Resources FORAGES More than 1 million acres are dedicated to permanent pastureland in Ohio. For more information visit the Integrated Forage Management Team online: forages.osu.edu

MANURE NUTRIENTS Right source, right rate, right time, right place, right technology. For more information visit the Ohio Composting and Manure Management: ocamm.osu.edu

BEEF CATTLE Since 2018, Ohio ranks in the top five for Beef Quality Assurance certifications. For more information on beef production visit the Extension Beef Team online: beef.osu.edu

DAIRY CATTLE Ohio is the number one Swiss cheese producing state. For more information on dairy production visit the Ohio Dairy Resource Center: dairy.osu.edu

SMALL RUMINANTS Ohio is the largest sheep producing state East of the Mississippi River. For more information on small ruminant production visit the Extension Sheep Team online: sheep.osu.edu

EQUINE Ohio typically has a population of around 300,000 horses. For more information visit the ATI Equine Center: ati.osu.edu/aboutus/facilities/equine-center

SWINE Ohio is consistently in the top 10 states for hog production. For more information visit Ohio State's Ohio Pork Information Center: porkinfo.osu.edu

POULTRY Ohio is ranked number two in the nation for egg production. For more information visit the OSU Poultry Team online: u.osu.edu/poultry

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

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

DATA ANALYSIS AND MANAGEMENT Developing a digital strategy and making actionable decisions using data, from operational insights to farm level execution.

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

2026 eBarns Report | 7

Get Involved in On-Farm Research

eBarns

“connecting science to farmers” COLLEGE OF FOOD, AGRICULTURAL, AND ENVIRONMENTAL SCIENCES

What is On-Farm Research?

On-farm research, also known as applied research, is when agricultural experiments are done on a working farm. This allows farmers to participate directly and learn more about what works well on their farm. Testing and evaluating practices and products in real-world conditions is beneficial for farmers wanting to be efficient and innovative. OSU Extension On-farm research, known as applied research, is whendata agricultural experiments are done on a then receive partners with farmers to also choose the research trials and complete collection. On-farm research partners working farm.and This allowsthat farmers toinsights participate directlytheir andfarm. learn more about what works well on their recommendations results provide to improve farm. Testing and evaluating practices and products in real-world conditions is beneficial for farmers to be efficient and innovative. Extension partners with farmersIndustry to choose the research trials Howwanting is the research shared? GetOSU Involved! Representatives and complete data collection. On-farm research partners then receive recommendations and results that We are always looking for new There are three on-farm research their farm. There are a variety of experiments provide insights to improve partners to conduct on-farm trials! If programs that provide a wide range of prepared and pre-written for easy you are interested in determining how applied research and relevant health use across the state. New studies Get Involved! How is the research shared? you can support Ohio State University and safety updates. These include can also be designed around On-Farm Research, reach out to There are a variety of experiments prepared and pre-written There are three on-farm research programs that provide eFields, eBarns, and ePLUS. Reports specific production and management for easy use across theyour state.county New studies can also be Natural a wide range of applied research and relevant health and Agriculture and for each program are published questions. Start by contacting your designed around specific production(ANR) and management safety updates. These include eFields, eBarns, and ePLUS. Resources Extension Educator, annually. These reports are collections county Agriculture and Natural questions. Start by contacting your county Agriculture and Reports for each program are published annually. or contact a team member for the of the latest applied agricultural Resources Extension Natural Educator (ANR).Extension Educator. Resources program you are most interested in research from The Ohio State These reports are collections of the latest applied agricultural supporting. Not sure who to contact? University Digital Ag Team, CFAES Not sure who to contact? Start with locating your county research from The Ohio State University Digital Ag Team, Start with locating youroffice: county office: Researchers, ExtensionExtension professionals, extension.osu.edu/LAO CFAES Researchers, professionals, and extension.osu.edu/LAO and Ohio producers. Ohio producers.

Explore the Programs eFields

eBarns

go.osu.edu/efields For studies related to: • Corn • Soybeans • Small Grains • Agronomic Technology

8 | Ohio State Digital Ag Program

DIGITAL AGRICULTURE PROGRAM

go.osu.edu/ebarnsreports For studies related to: • Forages & Pastures • Livestock Management • Meat Sciences • Animal Health

ePLUS

go.osu.edu/eplusreports For studies related to: • Produce • Landscape • Urban Agriculture • Specialty Crops

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

eBarns Report Database Explore past reports to access applied on-farm research from Ohio producers and Extension through the searchable database at: go.osu.edu/eBarnsDatabase. Filter your search by year, county, report author, research area, and topics. To Print a Report or Save as a PDF: Select the reports, check the download box in the far-right column, and click Download at the top of the column.

2026 eBarns Report | 9

Report Guide OBJECTIVE

Location Box

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

Look to see the county where the study was conducted.

STUDY INFORMATION

Planting Date 05/03/2025

Start Date 10/15/2026

Harvest Date 10/20/2025

End Date 08/01/2026 Species Swine

Variety Becks 6076V2P

Start Point 50 lb. (DOF, DOA, DIM)

Population 34,000 sds/ac

End Point 300 lb (DOF, DOA, DIM)

Acres 70 Treatments 5

Treatments 5

Reps 7

Reps 4 Experimental Pen Unit

Treatment Width 40 ft. Tillage Conventional Management Fertilizer, Herbicide, Insecticide

Breed, Genetics Commercial Line, Yorkshire Cross x Duroc

Previous Crop Soybeans Row Spacing 30 in. Soil Type Crosby silt loam, 52% Celina silt loam, 48%

Sex Equal Barrow:Gilt Health Protection As needed

Feeding System Self Feeder, Ad libitum IACUC #

RESULTS 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.

Response Variable

Treatment

Within a row, means without a common superscript letter differ, P-value <0.05.

10 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

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

The summary section proves results and findings from the study. Thank you for taking the time to explore our eBarns Report!

RESULTS Treatments (XXX)

Avg. Emergence (plants/ac)

Moisture (%)

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

LSD: CV:

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 individual study.

2026 eBarns Report | 11

Calculations and Statistics To effectively collect, analyze, and interpret data, statistical calculations were made for each 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. Harvest Data: 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.

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

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.

Treatments

Yield (bu/ac)

230 a

229 a

226 ab

225 b

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.

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.

LSD 3 CV 1.6%

CV 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.

Explanation: •

•

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).

12 | Ohio State Digital Ag Program

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.

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

RESOURCES TO LEARN MORE

Many current resources related to experimental design and statistics are located online with the eFields program, which is the flagship publication for the applied, on-farm research reports at Ohio State. Explore these online at go.osu.edu/OnFarmStudyResources to access additional resources on: • • • •

Experimental Design Agricultural Statistics, Least Significant Difference (LSD) Agricultural Statistics, Why Replication and Randomization are Important Printable Study Layouts If you are interested in seeing these resources expanded more for use by livestock producers, please contact the eBarns Team with your areas of interest: Garth Ruff at ruff.72@osu.edu. Field Specialist, Beef Cattle and Livestock Marketing or Dara Barclay at barclay.67@osu.edu. Program Manager, eFields and eBarns

2026 eBarns Report | 13

eBarns Contributors

Laura Akgerman

Andréia G. Arruda

Natasha Atlas

Dara Barclay

Department of Food, Agricultural and Biological Engineering

College of Veterinary Medicine, Department of Veterinary Preventive Medicine

Extension

John Barker

Tim Barnes

Amanda Barnum

Frank Becker

Extension

Disability Services Coordinator

Assistant Professor Knox County

Associate Professor

Extension Educator Marion County

Program Assistant Madison County

Extension Educator Ashtabula County

Program Manager eFields & eBarns

Extension Educator Wayne County

Lee Beers

Amanda Bennett

Pressley Buurma

Braden Campbell

Extension

Department of Animal Sciences

Assistant Professor Trumbull County

Assistant Professor Miami County

14 | Ohio State Digital Ag Program

Extension Educator Seneca County

Assistant Professor

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

Trevor Corboy

Rafael Domingues

Amanda Douridas

Mike Estadt

Extension

Department of Animal Sciences

Extension

Kara Flaherty

John Fulton

Allen Gahler

Alvaro Garcia Guerra

College of Veterinary Medicine, Department of Veterinary Preventive Medicine

Department of Food, Agricultural and Biological Engineering

Extension

Department of Animal Sciences

Christine Gelley

Greg Habing

Jason Hartschuh

Extension

College of Veterinary Medicine, Department of Veterinary Preventive Medicine

Extension

Extension Educator Brown County

Research Sr. Technician

Mike Gastier

Extension Educator Huron County Extension

Assistant Professor

Professor

Extension Educator Noble County

Extension Educator Madison County

Extension Educator Sandusky County

Assistant Professor Pickaway County

Associate Professor

Professor and Vice Chair

Assistant Professor, Field Specialist

2026 eBarns Report | 15

eBarns Contributors

Elizabeth Hawkins

Janessa Hill

Emily Hornberger

Dee Jepsen

Extension

Department of Food, Agricultural and Biological Engineering

Associate Professor, Field Specialist

Extension Educator Holmes County

Extension Educator Guernsey County

Professor

Minseong Kang

Seth Kannberg

Stephanie Karhoff

Brett Kinzel

Department of Food, Agricultural and Biological Engineering

Extension

Graduate Research Associate

Extension Educator Portage County

Assistant Professor, Field Specialist

Extension Educator Coshocton County

Andrew Klopfenstein Dean Kreager

Ashley Kulhanek

Seungki Lee

Department of Food, Agricultural and Biological Engineering

Extension

Department of Agricultural, Environmental, and Development Economics

Sr. Research Associate Engineer

Extension Educator Licking County Extension

16 | Ohio State Digital Ag Program

Assistant Professor Medina County

Assistant Professor

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

Clifton Martin

Rob Leeds

Dan Lima

Extension

Extension Educator Harrison & Jefferson Counties

Emma Matcham

Tim McDermott

Ryan McMichael

Rich Minyo

Department of Horticulture and Crop Science

Extension

Department of Horticulture and Crop Science

Assistant Professor Delaware County

Assistant Professor

Extension Educator Belmont County

Assistant Professor Franklin County

Erika Lyon

Assistant Professor Muskingum County Extension

Extension

Extension Educator Mercer County

Research Specialist

Nina Nebesh

Heather Neikirk

Sarah Noggle

Pierce Paul

Department of Food, Agricultural and Biological Engineering

Extension

Department of Plant Pathology

Graduate Research Associate

Extension Educator Stark County

Extension Educator Paulding County

Professor and Chair

2026 eBarns Report | 17

eBarns Contributors

Jordan Penrose Extension Educator Morgan County

Rafael PortilloGonzalez

Post Doctoral Scholar

Eric Richer

Associate Professor, Field Specialist - Farm Management

Eric Romich

Professor, Field Specialist

Extension

College of Veterinary Medicine, Department of Veterinary Preventive Medicine

Kendra Rose

Garth Ruff

Muhammad Saad

Beth Scheckelhoff

Extension

Department of Animal Sciences

Extension

Extension Educator Crawford County Extension

Field Specialist

Extension

Graduate Fellow

Extension

Assistant Professor Putnam County

Scott Shearer

Ryan Slaughter

Jacci Smith

Stan Smith

Department of Food, Agricultural and Biological Engineering

Extension

Professor and Chair

Extension Educator Ross County

18 | Ohio State Digital Ag Program

Extension Educator Delaware County

Program Assistant Fairfield County

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Jenny Stoneking

Sandra Trushel

Catelyn Turner

Kyle Verhoff

Extension

Extension Educator Adams County

Extension Educator Carroll County

Extension Educator Monroe County

Poultry

Extension Educator Defiance County

Barry Ward

TJ Wells

Aaron Wilson

Tracy Winters

Extension

Leader, Production Business Management

Extension Educator Washington County

Associate Professor, Field Specialist

Ted Wiseman

Kayla Wyse

John Yost

Extension

Assistant Professor Perry County

Extension Educator Williams County

Extension Educator Wayne County

Extension Educator Gallia County

Chris Zoller

Assistant Director, Agriculture and Natural Resources Extension

2026 eBarns Report | 19

2025/2026 Growing Season Weather

Figure 1. County rankings for average temperature (left) and total precipitation (right) for the 12-month period June 2025 - May 2026 compared to the full period of record (1895-2026). Figure courtesy of ncei.noaa.gov/access/monitoring/climate-at-a-glance/

June 2025 – May 2026 Weather Following the historic drought conditions of 2024, much of Ohio recovered by the end of spring 2025. However, weather challenges remained throughout 2025 including extreme summer warmth and humidity, record dry conditions in late summer/fall, and a colder than normal winter. Overall, 2025 (calendar year) was Ohio’s 28th warmest year on record (1895 – 2025), and ranks as the 59th wettest over the 131-year span according to the National Centers for Environmental Information (ncei. noaa.gov/access/monitoring/monthly-report/national). Figure 1 shows temperature and precipitation rankings for individual counties in Ohio for June 2025 – May 2026 (the period detailed below). For more in-season climate analysis, please visit the State Climate Office of Ohio (climate.osu.edu).

Figure 2. Percent of normal precipitation for June 1 – July 31, 2025. Figure courtesy of (mesonet.agron.iastate.edu/).

20 | Ohio State Digital Ag Program

Summer 2025 (June – August) Summer 2025 brought heat, humidity, and highly variable precipitation. June-July 2025 ranks as the 4th warmest June-July on record, driven by the warmest overnight low temperatures on record (1895 – present). Humidity was also a significant factor. For instance, dewpoint temperatures were at or above 65°F on 66% of all hourly observations between June 1 – July 31 across southern Ohio. The summer started out quite wet as well, with June 2026 ranking as the 28th wettest June on record. Figure 2 shows that precipitation for June – July was mostly above normal across Ohio, especially for south central counties. Some areas remained on the drier side however, including west central and east central Ohio. This trend continued into August, which ranks as the state’s driest August on record. Most locations in Ohio saw rainfall deficits of 2 – 4 inches for the month, kicking off yet another strong drought year in Ohio. Summer 2025 ranks as the 11th warmest and 50th driest summer on record. Fall 2025 (September – November) Dry conditions persisted throughout the fall of 2025 for the northern half of Ohio. According to the U.S. Drought Monitor (droughtmonitor.unl.edu), drought peaked in intensity in October with approximately 11% of Ohio covered by D3-extreme drought conditions (Figure 3). Drought evolved from one impacting agriculture including pasture conditions to a hydrologic concern for northwest Ohio. The Maumee River Basin had historically low depths and flows, with area farm ponds and reservoirs severely impacted. Precipitation deficits of 4 – 6 inches for the fall period (9 –12 inch deficits over the previous 6 months) were common across parts of Van Wert, Paulding, Henry, and Wood counties. Parts of southern Ohio however, received 2 – 6 inches above normal, helping restore soil moisture and stream flows in that region. Fall 2025 ranks as the 29th warmest and 54th driest fall on record.

Forages

Manure

Cattle

Small Ruminant

Figure 3. Drought conditions in Ohio on October 28, 2025. Figure courtesy of the U.S. Drought Monitor.

Winter 2025 – 2026 (December – February) Winter got a quick start during the first week of December, with plenty of snowfall across the state and overnight lows dropping below zero across northwest Ohio. However, conditions turned mild by Christmas, with highs well into the 60s across southern Ohio. January was cold and snowy, particularly for the last half of the month. This included a historic winter storm on January 24 – 25, 2026 (Figure 4). Dayton broke its 24-hour snowfall record with 12.4 inches falling the 25th (previous record from the 1978 blizzard event). Extremely cold conditions followed for the end of January, with a week of overnight low temperatures below zero nearly statewide. The coldest temperature occurred in Warren, Ohio on January 31 at -25°F. February was cold and very dry as well, with a moderation in temperatures occurring by the end of the month. Winter 2025 – 2026 ranks as the 36th coldest and 6th driest winter on record.

Equine

Swine

Poultry

Spring 2026 (March – May) Spring 2026 was yet another volatile season with highly variable conditions month-to-month and across the state. The season started out very warm, with Ohio’s 6th warmest March and warmest April on record. This allowed for a rapid increase in accumulated growing degree days (GDDs), reaching record levels by the beginning of May (Figure 5). However, May 2026 was the 46th coldest May on record which slowed the accumumulation of GDDs toward late spring. Cool May conditions were accompanied by significant rainfall for most of Ohio, with totals ranging 6 – 10 inches across most of southern Ohio. These conditions, along with residual impacts on deeper soil moisture levels from the 2024 and 2025 drought seasons likely hampered hay production across many counties in southern and central Ohio. Spring 2026 ranks as the 3rd warmest and 5th wettest spring on record, the first time Ohio has had a top 10 or top 5 ranking for both variables during the same spring season.

Figure 5. Growing degree day (base 50°F, ceiling 86°F) for March – May 2026 (red solid line) and normal (black solid line). Blue shading indicates the range of GDD accumulation for all March – Mays in the record (1896-present). Differences compared to normal are provided in the bottom panel (red dotted line). Figure courtesy of Iowa Environmental Mesonet (mesonet.agron.iastate.edu/).

PROJECT CONTACT

Figure 4. Total snowfall over the 48-hour period leading up to January 26, 2026, at 8pm. Figure courtesy of the National Operational Hydrologic Remote Sensing Center (nohrsc.noaa.gov/).

For inquiries about this project, contact Dr. Aaron B. Wilson, Extension Field Specialist Ag Weather & Climate (wilson.1010@osu.edu).

2026 eBarns Report | 21

Ohio Farm Custom Rates Farming is a complex business and many Ohio farmers utilize outside assistance for specific farm-related work. This option is appealing for tasks requiring specialized equipment or technical expertise. Often, having someone else with specialized tools perform tasks is more cost effective and saves time. Farm work completed by others is often referred to as “custom farm work” or more simply, “custom work”. A “custom rate” is the amount agreed upon by both parties to be paid by the custom work customer to the custom work provider. 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 2026. The summary information included in this publication is based on the responses of 343 farmers, custom operators, farm managers, and landowners conducted in 2026. 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. The price of Midwest No 2 On-Road Diesel during the survey period (January – April 2026) ranged from $3.36 - $5.81 per gallon. Some custom rates published in this study vary widely, possibly influenced by the type or size of equipment used (e.g. 20-shank chisel plow versus a 9-shank), size and shape of fields, condition of the crop (for harvesting operations), 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, or the 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, or rental agreement. Some providers charge differently because they are simply attempting to spread their fixed costs over more acreage to decrease fixed costs per

To review the full publication, visit the Custom Rates and Machinery Costs page on the OSU Extension, Farm Office website using the link below or the QR code to the right: go.osu.edu/OhioFarmCustomRates

22 | Ohio State Digital Ag Program

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 data from this survey are intended to show a representative farming industry cost for specified machines and operations in Ohio. As a custom provider, the average rates reported in this publication may not cover your total costs for performing the custom service. As a customer, you may not be able to hire a custom service for the average rate published in this factsheet. It is recommended that you calculate your own costs carefully before determining the rate to charge or pay. It may be helpful to compare the custom rates reported in this fact sheet with machinery costs calculated by economic models. The following resources are available to help you calculate and consider the total costs of a given machinery operation. You may consider using the data contained in multiple publications as a base for future custom rates. Note for Tables: The measures shown in the following tables are the summary of the survey respondents. The measures are the Average (Mean), Responses, Maximum, Minimum, Median, Standard Deviation, and Statistical Range. The Average reported in this publication is a simple average of all the survey responses for each operation. Responses indicate the number of survey responses for each given operation. The Maximum and Minimum reported in the table are the maximum and minimum amounts reported from the survey data for a given custom operation. The median represents the middle value of the survey responses. Standard Deviation is a measure of variability. Statistical Range identified in the tables consists of two numbers. The first is the average plus the standard deviation. The second number of the range is the average minus the standard deviation. In cases where there were too few responses to statistically analyze, statistics are not presented due to the low response rate.

PROJECT CONTACT

Barry Ward Assistant Professor and Leader, Production Business Management (ward.8@osu.edu)

John Barker Assistant Professor, Agriculture and Natural Resources (barker.41@osu.edu)

Eric Richer Associate Professor and Field Specialist, Farm Management (richer.5@osu.edu)

Amanda Bennett Assistant Professor, Agriculture and Natural Resources (bennett.709@osu.edu)

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

Forage, Pasture, Cover Crop Seeding

Average Responses

Max

Min

Standard Statistical Range² Low Median Deviation¹ High

Cultipacker Seeding /acre

$14.50

$25.00

$8.00

$15.00

$6.28

$20.78

$8.22

Broadcast /acre

$10.74

$16.00

$3.82

$12.00

$3.20

$13.94

$7.53

Broadcast, incorporate (one pass) /acre

$16.29

$25.00

$8.00

$18.00

$6.07

$22.36

$10.21

Drill Cover Crops /acre

$21.57

$42.00

$10.00

$20.00

$7.36

$28.93

$14.22

Pump & Spread (Tanker-Surface) /gal

$0.014

$0.030

$0.010

$0.00

$0.020

$0.010

Pump & Spread (Dragline-Surface) /gal

$0.011

$0.020

$0.010

$0.00

$0.010

Pump, Inject/Sidedress (Dragline) /gal

$0.013

$0.020

$0.010

$0.00

$0.020

$0.010

Hauling Liquid Manure /hr, with Semi /hr

$136.53

$165.00 $100.00

$135.00

$15.56

$152.08 $120.97

Spreading Manure at Field /hr

$183.33

$230.00 $120.00

$200.00

$40.33

$223.67 $143.00

Spreading Manure at Field /ton

$11.85

$34.00

$1.50

$10.50

$8.63

$20.48

$3.22

Mowing/Conditioning /acre

$17.17

$35.00

$5.00

$17.75

$7.15

$24.33

$10.02

Raking /acre

$11.64

$25.00

$3.00

$10.25

$5.16

$16.80

$6.49

Merging Windrows /acre

$10.71

$19.00

$2.00

$10.00

$4.99

$15.70

$5.72

Tedding /acre

$10.95

$30.00

$2.00

$10.00

$6.00

$16.95

$4.95

Trucking (flatbed or van) /loaded mile

$3.36

$6.00

$1.00

$3.00

$1.46

$4.82

$1.90

Small Square Bales: Baled and Dropped on Ground /bale

$1.12

$2.00

$0.52

$1.00

$0.37

$1.49

$0.76

Large Round Bales: Baled and Left in Field /bale

$11.98

$15.00

$8.00

$12.00

$2.40

$14.38

$9.57

Large Square Bales: Baled and Left in Field /bale

$12.91

$16.00

$10.00

$13.00

$2.26

$15.17

$10.65

General Farm Labor /hr

$18.35

$35.00

$10.00

$18.00

$4.35

$22.70

$14.00

Machinery Operation /hr

$22.22

$40.00

$15.00

$20.00

$5.76

$27.98

$16.46

Semitruck Driving (Seasonal) /hr

$23.10

$50.00

$15.00

$20.75

$6.45

$29.55

$16.65

Average hours worked /week (Hours)

33.5

14.7

48.2

18.8

$3.19

$5.00

$2.25

$2.75

$1.25

$4.44

$1.94

Manure Application - Liquid Manure

Manure Application - Solid Manure

Hay Harvest - Field Operations

Hay Harvest - Baling

Operation - Farm Labor

Operation, Miscellaneous Hauling Livestock /mile

2026 eBarns Report | 23

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 2026 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. ALFALFA HAY PRODUCTION BUDGET - 2026 Late Summer Seeding - 4 Year Stand1

ITEM RECEIPTS3 Alfalfa Hay - High Quality Alfalfa Hay - Fair Quality Total Alfalfa Receipts VARIABLE COSTS Seed4 Fertilizer5 P2O5(lbs)

EXPLANATION

YOUR PROD. Numbers

60% of yield (by ton) 40% of yield (by ton)

19 pounds

PRICE PER UNIT

Updated: YIELD (ton/A)2 Years 1-4 3.0

5.0

8/5/2026 YOUR BUDGET

7.0

$225 /ton $155 /ton

405.00 675.00 945.00 186.00 310.00 434.00 591.00 985.00 1,379.00

945.00 434.00 1,379.00

$6.50 /lb

30.88

31.0

51.6

72.2

$0.947 /lb

29.32

48.87

68.42

K2O(lbs) Lime(ton) Chemicals6 Hauling7 Fuel, Oil, Grease8 Repairs9 Miscellaneous10 Hired Custom Work11 Hired Labor12 Int. on Oper. Cap.13

126.4 0.5

210.7

295.0

$0.404 /lb $44.50 /ton

51.09 22.25 12.10 46.15 17.95 24.60 6.05 22.97 26.62 8.66

85.16 22.25 12.10 76.92 17.95 24.60 6.05 22.97 26.62 10.56

119.22 22.25 12.10 107.69 17.95 24.60 6.05 22.97 26.62 12.46

TOTAL VARIABLE COSTS

-Per Acre -Per Ton

298.65 384.93 99.55 76.99

471.21 67.32

105.51 105.51 29.55 49.25 57.02 57.02 20.23 20.23 187.00 235.00 28.37 28.37

105.51 68.95 57.02 20.23 293.00 28.37

105.51 68.95 57.02 20.23 235.00 28.37

427.68 495.38

573.08

515.08

726.33 880.31 1,044.30 242.11 176.06 149.19

986.30 140.90

-0.27 51.67 292.35 -135.33

567.16 627.70 907.79 392.70

$0.50 /bale

6 mo

FIXED COSTS Labor Charge14 4.4 hours Management Charge15 5% of gross revenue Mach. And Equip. Charge16 Seedbed Preparation/Seeding Costs - Custom Hire17 Land Charge18 Miscellaneous19

65 lb. bales

7.1%

$23.98 /hour

TOTAL FIXED COSTS TOTAL COSTS

-Per Acre -Per Ton

RETURN TO LABOR AND MANAGEMENT20 RETURN TO LAND RETURNS ABOVE VARIABLE COSTS RETURNS ABOVE TOTAL COSTS

259.45 339.69 600.07 104.69

509.16 627.70 907.79 334.70

Values highlighted in gold may be changed to assist in computing the "Your Budget" column using macros embedded within the spreadsheet. Values highlighted in light blue are cells embedded with macros and will be calculated for the user based on data entered. These cells may be input manually, but macros will be overwritten! Values highlighted in gray are stand alone cells that require direct input from the user.

24 | Ohio State Digital Ag Program

Costs Incurred At Planting

123.50

89.00 25.96

80.91

319.37

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

Key points to remember when utilizing 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.

Crop Enterprise Budgets for Livestock Producers: • • • •

Alfalfa Hay Production Budget (2026) Alfalfa Hay, Late Summer Seeding, Production Budget (New in 2026!) Organic Alfalfa Production Budget (2026) Corn Silage Budget 2025

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 achieved 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 For inquiries about this project, contact Barry Ward (ward.8@osu.edu), Eric Richer (richer.5@osu.edu, or Emma Matcham (matcham.3@osu.edu).

Access these budgets by using the QR code to the right, or visit go.osu.edu/enterprise_budgets.

2026 eBarns Report | 25

Ohio Livestock 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 livestock and freezer budgets were developed by a team of OSU Extension faculty and staff. The budgets are tools that growers can use to examine different scenarios on their operation to help in decision making. The full offering of 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. The budgets have color coded cells that will allow users to plug in their own numbers and calculate bottom lines for different scenarios 2025 Ewe Budget Sell Weaned Lambs 50 lbs

ITEM

EXPLANATION

Your Lambing Avg. Weight/ Lambs Sold RECEIPTS Rate Feeder Lamb 150% 50 lbs Wool Sales/Indemnity 4.00 lbs Cull Value % Cull Rate Cull Ewes $100.00 20% Cull Rams $150.00 25%

Your Prod Numbers

11/15/2025

PRICE PER UNIT

Amount

Lambing Rate 150%

Lambing Rate 175%

Lambing Rate 200%

YOUR BUDGET

% Lamb Crop Weaned

98%

$3.25 / lb $238.88 $238.88 $278.69 $318.50 $238.88 $0.40 / lb USDA Rate $1.60 $1.60 $1.60 $1.60 $1.60 % Death Loss Ewes per Ram 1% $19.00 19.00 19.00 19.00 19.00 0.5% 25 $ 1.47 $ 1.47 $ 1.47 $ 1.47 $ 1.47

TOTAL RECEIPTS

$260.95

260.95

300.76

340.57

260.95

15.00

25.00 0 28.13 2.19 $0.00 70.32

25.00 0 28.13 2.19

$0.00 70.32

25.00 0 28.13 2.19 $0.00 70.32

6.00 3.68 24.00 5.00 3.25

8.00 4.29 24.00 8.00 3.25

10.00 4.90 24.00 11.00 3.25

6.00 3.68 24.00 5.00 3.25

112.24

112.25

117.86

123.47

112.24

19.00 7.00 40.00

4.00 2.60 34.73 17.31 5.22

4.00 2.60 34.73 17.31 6.02

4.00 2.60 34.73 17.31 6.81

4.00 2.60 34.73 17.31 5.22

TOTAL FIXED COSTS

129.86

130.66

131.45

129.86

TOTAL COSTS

242.10

242.11

248.51

254.92

242.10

RETURN ABOVE VARIABLE COSTS RETURN ABOVE TOTAL COSTS RETURN TO LABOR AND MANAGEMENT 13

148.70 18.84 43.06

182.90 52.24 77.26

217.10 85.65 111.46

148.70 18.84 43.06

VARIABLE COSTS 2 Feed Days on Pasture Ewes on Pasture (including opens) Acres per Ewe

240 50 ewes 0.3 acres Pasture Acres

Pasture (Hay Equivalent) Days on Feed Concentrate Hay Corn Silage Feed Concentrate 2a Mineral Creep Feed (pelleted) Other TOTAL FEED COSTS

Pasture Value (per acre)

15 acres 125 days 2 lbs 0 lb 1 lbs 0.05 lbs

$50.00 /acre

Feed Value

$200.00 /ton $40.00 /ton $450.00 /ton $0.12 /lb

0 lbs

$0.00 /ton

Health Program Marketing 4 Bedding Supplies and Misc 5 Int. on Operating Cap. 6

2.50 per hd 0.2 tons

$120.000 /ton

12 mo

8.0%

$41

TOTAL VARIABLE COSTS FIXED COSTS Labor Charge 7 Shearing Ewe Replacement 8

1 hrs 1 time/year 20%

Rep. Rate

Breeding (Cost/Ewe) 9 Int. on Breed Stock 10 Equipment Charge 11a Buildings Charge 11b Management Charge 12

20% $ 40.00

$19.00 /hr $7.00 /head $200.00 /ewe

Ram Cost Ewes/Ram

500

25 6.5%

2% of gross revenue

Values highlighted in gold may be changed to assist in computing "Your Budget" Column using macros embeded within the spreadsheet. Values highlighted in light blue are cells embedded with macros and will be calculated for the user based on data entered. These cells may be input manually, but macros will be overwritten! Values highlighted in gray are stand alone cells that require direct input from the user.

26 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Key points to remember: • The budgets represent common, workable, combinations of inputs that can achieve a given output. • Amounts of feed, bedding, supplies, machinery, labor, 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 feed, supplies, and labor 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 genetics, pasture quality, and local cost of supplies in a given season all can cause the actual results to vary greatly from what is presented.

Some Livestock and Freezer Enterprise Budgets: • • • • •

Cow-Calf Budget (2026) Freezer Beef Budget (2025) Sheep Enterprise Budget (2025) (Ewe, Fed Lamb, Freezer Lamb) Market Beef Budget (Self-Fed) (2021) Market Beef Budget (Bunk-Fed) (2021)

Access these budgets by using the QR code to the right, or visit go.osu.edu/enterprise_budgets.

Equine

Swine

Poultry

Context for the Ewe example budget on the left: ¹Average weaning weight = 50 @ % Calf Crop Weaned Lamb Crop Weaned can be calculated by: Number of lambs weaned/Number of ewes Cull ewes: Cull value X (20% cull rate - 1% death loss) Cull rams: Cull value X (25% culling rate - 0.5% death loss)/number of ewes/ram ²Assumes total feeding of 365 days - pasture feeding of 225 days and Hay or Silage feeding of 140 days Days on Hay or Silage automatically adjusts to days on pasture (i.e. 260 days on pasture changes hay formula to 105 days) 2a Feed Concentrate assumes a corn/balancer pellet mix ³ Costs of health program estimated by authors based on industry experience and expertise ⁴ Costs of marketing estimated by authors based on industry experience and expertise ⁵ Costs of supplies and other expenses estimated by authors based on industry experience and expertise ⁶ Includes 1/2 cost of feed, health program, supplies, and misc for 12 months at current interest rate ⁷ Assumes the cost of wages and benefits ⁸ Assumes that the cost to replace is 20% of of the ewe. ⁹ Breeding Cost taking the Replacement rate of 25%, the cost of a ram, and the ewes per ram 10 Average of ram cost and cull ram value divided by ewes per ram plus the average of price per ewes and cull ewes 11a Machinery cost calculations are located on the next worksheet - Click Building and Machinery below 11b Building cost calculations are located on the next worksheet - Click Building and Machinery below 12 Management charge is 2% of gross revenue 13 Return to labor and management is the revenue less total expenses except operator labor and management. It is a measure of the returns to the operator's labor and management.

PROJECT CONTACT For inquiries about this project, contact Barry Ward (ward.8@osu.edu), Garth Ruff (ruff.72@osu.edu), or Brady Campbell (campbell.1279@osu.edu).

2026 eBarns Report | 27

Corn Silage Price Decision Tool UPDATED IN 2026 The process of pricing corn silage can be challenging due to the lack of a public market providing official prices, unlike corn grain. Typically, corn silage prices are established through individual contracts and negotiations, which can lead to concerns about the asymmetry of information. Given that region-specific information, such as local cash corn prices, hay bids, and harvest costs must be entered into the formula for determining corn silage value, it is critical to have a pricing tool that is grounded in timely and localized information from Ohio. While other corn silage pricing tools exist we have a downloadable decision tool to assist buyers and sellers in determining corn silage pricing in Ohio.

The back-end data, including Ohio farm operation costs and county-level cash corn prices, are updated annually. Specifically, in this update for the 2026 version, there are four major remarks:

This tool provides a transparent process for determining the value of silage, requiring only the estimate of either corn grain yield or silage yield wet basis from users. Users can also adjust other parameters, such as operational costs and who pays silage harvest costs, to reflect their unique circumstances. The tool allows for the inclusion of often forgotten costs such as silage shrink and field loss of corn grain during field dry down. The tool provides a reasonable ”range” of corn silage values for negotiation in three units: dollars per acre, dollars per ton wet basis, and dollars per ton dry matter.

4. Other input costs reference the 2026 OSU Custom Rates, which are updated every other year.

1. The default corn grain yield is set at 192.5 bushels per acre – same as the OSU Enterprise Budget, based on the trend-based prediction. Accordingly the default silage yield basis is updated to reflect this assumption. 2. County-specific cash prices are updated based on Barchart data (accessed on August 21, 2026). 3. Hay price is updated based on the Ohio listings from AllHay.com (August 21, 2026).

Finally, we would like to emphasize that this tool should be used for reference only, and users are encouraged to adjust the value of silage based on their individual circumstances when negotiating silage sales.

HOW TO USE THE SPREADSHEET

The downloadable spreadsheet includes 4 stages. To begin, enter the necessary values on the User tab to get the per-acre gross value of silage: 1. Quantity Estimates: Grain and silage yield Enter “Estimated grain yield” and “Dry matter of silage” (%). Then, the tool provides guided “Estimated silage yield wet basis” and “Silage yield dry basis.” If users have a better estimate of silage yield wet basis instead of grain yield, they can enter their own silage yield wet basis (D7) instead of entering the “guided value” (E7). 2. Prices To estimate the price of silage, we refer to cash price* and 2nd hay auction prices**. For corn grain discount and average grain loss for harvest before black layer, the guiding values draw on a conventional estimate and Nielsen et al. (1996), respectively. Users can change these values. The two stages report the per-acre gross value of silage. Sellers mainly rely on the value of grain, while the buyers take hay values—opportunity costs—into account as well as the grain value. The tool shows the potential negotiation range.

28 | Ohio State Digital Ag Program

3. Price adjustment for harvest cost Reflect on additional opportunity costs (harvest costs of grain and nutrient removal) and who will be in charge of silage harvest expenses. 4. Range to negotiate Find the complete range to negotiate based on the inputs entered. The range is displayed in three different units: dollars per acre, dollars per ton wet basis, and dollars per ton dry matter. *Prices from barchart.com **Prices from farmanddairy.com

Access spreadsheet, here: go.osu.edu/CornSilagePrice

Forages

Manure

Small Ruminant

Cattle

Determining Corn Silage Value 1. Grain and silage yield Estimated grain yield Dry matter (DM) of silage Estimated silage yield wet basis Silage yield dry basis

Poultry

Sources Select your base yield Grain yield Silage yield wet basis

Fulton

Mean value between Seller and Buyer 1343.8 ($/acre) 153.9 ($/ton as DM) 56.9 ($/ton as fed)

3. Price adjustment for harvest cost 3-a. Grain Harvest Cost Combine only - corn Grain drying Storage charge $/Bushel/month Storage Length (months) % Harvest and Storage Loss Grain Harvest Costs

$ per acre cost

3-b. Silage Harvest Cost Farm to market - hauling Chopping Fill silage bag Silage Harvest Costs

$ per acre

$37.35 $36.00 $45.00

Seller

←Enter you County name $4.77 $/bushel $0.25 $/bushel $211 $/ton 1.62 bushels/acre

$812.79 $93.08 $34.44

User enter value

$0.25

Buyer

$34.20 $159.30 $157.33 $350.83

User enter value

1 Farmer estimate Farmer estimate Table 1

Barchart: 08-21-2023 Assumed

2nd hay prices Nielsen et al. (1996)

$1,874.86 $/acre $214.71 $/ton, dry matter basis $79.44 $/ton as fed

Guided value $37.4 $/acre $0.04 $/per point of moisture removed/bushel

UWisc Extension

OSU Custom rates OSU Custom rates

$/bushel

$0.05 $/bushel/month 5 months 2.50% percent

$20.32 $138.67

OSU Custom rates Assumed Assumed

Who pays Buyer Buyer Buyer Buyer Seller

Guided value $0.19 $/bushel $6.75 $/ton $6.67 $/ton

$64.37

3-c. Difference in Nutrient Removal (added to minimum value) 3-d. Silage Storage Loss Storage structure (choose one in the C37's dropdown list) Percentage of storage loss Estimated storage loss

Swine

User enter value Guided value Unit 180.0 192.5 bushels/acre 37% 35% percent 23.6 23.6 tons/acre, wet-basis 8.7 tons/acre, dry matter basis

2. Prices Cash price of #2 yellow corn 15.5% Corn Grain Discount $/bushel Tons of low quality hay per acre Average grain loss for harvest before black layer

Gross value per acre

Equine

OSU Custom rates OSU Custom rates OSU Custom rates

Table 2

Select an option: Conventional tower $143.49

Table 3 and 4

14% percent

Assumed the buyer pays for chopping/hauling/storage as default (See Who pays column)

4. Range to negotiate Minimum vs Maximum Value/Acre Corn Silage Value of Standing Corn Silage ($/ton wet basis) Value of Corn Silage ($/ton DM)

Seller

$738.49 $31.29 $84.57

Buyer

$1,731.37 $/acre $73.36 $/ton wet basis $198.28 $/ton DM

PROJECT CONTACT For inquiries about this project, contact Jason Hartschuh (hartschuh.11@osu.edu) Seungki Lee (lee.10168@osu.edu), or Minseong Kang (Ph.D. Candidate).

2026 eBarns Report | 29

New World Screwworm Resources New OhioLine Fact Sheet: The New World screwworm (NWS, Cochliomyia hominivorax) is a parasitic fly that lays eggs in wounds or other openings found in the living tissue of a variety of warm-blooded animals, including wildlife, companion animals, horses, and livestock. Immature flies (larvae or maggots) feed on their host’s flesh, which can worsen wounds and may result in death if untreated. Within livestock production systems, NWS can cause significant economic loss due to mortality and reduction in animal productivity. While NWS infestations can impact livestock health, they are not a food safety issue. NWS was eradicated from the United States in the 1960s. Since then, NWS has been reported in Central America, Mexico, and most recently in limited parts of the United States, making prevention and early detection essential for control. Infestation is typically seasonal, with most cases occurring in spring and summer, as NWS is not tolerant of cold weather. Regularly observing your animals, practicing good biosecurity, and managing flies are the best practices to protect your herd. Identification: NWS adults are usually ~1/3 inch, metallic Signs of NWS Infestation: Producers should be alert for blue blow flies with three dark stripes on their thorax (the possible signs of infestation in their livestock: body segment just behind the head). They have distinct Wounds with discharge or odor as found in rotting flesh orange eyes that take up most of their head. However, Wounds or body cavities with maggots present NWS closely resemble other flies, such as secondary screwworm (Cochliomyia macellaria) which do not pose the Wounds with white egg-like same threat to animal health. structures around the edges Livestock biting or licking at wounds or skin Frequent head shaking or rubbing against objects Decreased appetite, restlessness, or lethargy New World screwworm (USDA, 2026). Photo by the U.S. Department of Agriculture.

New World screwworm (CDC, 2025). Photo by U.S. Centers for Disease Control and Prevention.

Isolation from the herd or flock (reduced interaction)

NWS fly laying eggs around a wound.. Illustration by USDA

If these signs or symptoms are observed, producers should immediately contact their veterinarian and/or local animal health officials.

NWS maggots are the most problematic for animal health, as they infest living tissue. Identifying maggots can be more difficult than identifying NWS adults, and any suspected Treatment, Control, and Prevention: Treatment for NWS cases of NWS should be confirmed by professionals. Not all involves killing and removing maggots from livestock and maggots in wounds are NWS larvae. domestic animal wounds using one of several prescription and/or over-the-counter products the FDA has approved The NWS name comes from the larval behavior of through an Emerging Use Authorization. Discuss with burrowing (“screwing”) into the host’s flesh. While most your veterinarian how to address NWS infestation and species of blow flies feed on dead flesh, NWS adults lay access these emergency treatments. Notably, culling is eggs on living flesh. Maggots feed on and infest the host’s not a recommended approach to NWS management. living flesh (a condition known as myiasis) through three Animals that recover from NWS are permitted to enter stages of their development, after which they drop off into the food supply chain providing they meet all regulatory the environment to pupate (the process required to mature to an adult fly). Adult flies emerge, mate, and lay new eggs, requirements.The following methods can prevent NWS: repeating the cycle.

See the complete Fact Sheet: go.osu.edu/NWSFactSheet For inquiries about the fact sheet, contact: Tim McDermott, DVM (mcdermott.15@osu.edu), Dr. Risa Pesapane (pesapane.1@osu.edu), or Syd Wilson (ENR Graduate Student).

30 | Ohio State Digital Ag Program

Monitor your animals closely for signs of infestation.

Promptly treat all wounds, including umbilical cords of newborn animals, using an approved insecticide or wound dressing to prevent egg-laying by adult flies. If you or your animals travel to an area that has NWS, make sure to follow USDA APHIS recommendations (go.osu.edu/ nwstravel).

Forages

Manure

Small Ruminant

Cattle

Equine

Swine

Poultry

If you suspect NWS: Immediately contact your veterinarian Your veterinarian must report suspect cases to a State Animal Health Official (SAHO) or Area Veterinarian in Charge (AVIC) who will decide if the report is credible and assign a Foreign Animal Disease Diagnostician (FADD) to further investigate the possibility of NWS detection or infestation. Use the QR code to the right (or screwworm.gov) for updated NWS information, status reports, and pesticide recommendations.

The Facts About New World Screwworm (USDA):

How to Reduce Risk of New World Screwworm on Livestock Operations (USDA:)

go.osu.edu/NWSFactsUSDA\

go.osu.edu/NWSReduceRiskNRCS

Detections as of August 19, 2026: 46 confirmed cases in the U.S. •

43 in Texas

•

1 in New Mexico

Species affected: •

22 Cattle

•

16 Sheep

•

5 Goats

•

3 Dogs

Stay up to date using the USDA New World Screwworm Detection site: go.osu.edu/NWSdetectionsUSDA

New World Screwworm Conﬁrmed Detections

Download Data

As of Wednesday, August 19, 2026, last reported animal detection Sunday, August 16, 2026 Data updated daily at 6PM (ET) Select Time Period All Time

Map of Counties with Detections All Time

Outbreak Situation All Time Total Animal Cases

Fly Trap Detections

Domestic Cases

Wildlife & Feral Cases

2 Active 44 Inactive

0 Active 0 Inactive

Note: Only wild ﬂies are reported. A detection means at least one wild ﬂy was found in trap.

Active Cases Inactive Cases Fly Trap Detections

Cases by Month Bars reﬂect most recent 4 months

Domestic

Active County (≥ 1 active animal case) Inactive County (No current active animal cases) Fly Trap Detection County (Wild NWS ﬂies trapped only)

Selected Type (All)

Wildlife & Feral

Fly Traps

30 14 2

Jun 2026 Jul 2026 Aug 2026 Jun 2026 Jul 2026 Aug 2026 Jun 2026 Jul 2026 Aug 2026

©Click 2026for Mapbox © OpenStreetMap International Exports

The ﬁrst reported case of New World Screwworm in the United States occurred June 3, 2026. Since that date, the United States has recorded 46 case(s) in 2 states, 46 in domestic animals and 0 in wildlife.

Table of Cases View of the Confirmed Detections of New World Screwworm dashboard.

State Texas

Conﬁrmed Date

State

County

Case Type

Animal Type

Species

Status

16-Aug-2026

Texas

Val Verde

Domestic

Sheep

Active

05-Aug-2026

Texas

Terrell

Domestic

Sheep

Active

Animal Type All

31-Jul-2026

Animal Species All

30-Jul-2026

Texas

Brewster

Domestic

Cattle

Inactive

29-Jul-2026

Texas

Brewster

Domestic

Cattle

Inactive

Status All

21-Jul-2026

Texas

Pecos

Sheep

Inactive

Domestic

Goats

Inactive

18-Jul-2026

Texas

Schleicher

Domestic

Sheep

Inactive

Texas

Starr

Domestic

Cattle

Inactive

For the NWS Fact Sheet featured: "We would like to thank USDA-APHIS partners Jamie Davis, DVM, and Karen Grube, DVM, Ohio State Veterinarian Dennis M. Summers, DVM, DACVPM, and Assistant State Veterinarian Kristy Shaw, DVM for their review and contributions."

Domestic

17-Jul-2026

ACKNOWLEDGMENTS

Sutton

Domestic

VETERINARY CONTACTS

Gustavo M. Schuenemann, Extension Veterinarian and Professor (schuenemann.5@osu.edu), Jeanette O'Quin, Clinical Professor (oquin.4@osu.edu), and Risa R. Pesapane, Associate Professor (pesapane.1@osu.edu)

2026 eBarns Report | 31

Designing Livestock Systems INTRODUCTION

Designing livestock systems for safety and accessibility: Automation and assistive technology have many benefits for livestock farmers to improve their safety and efficiency while managing their herds and flocks. Technology within livestock systems reduce physically demanding tasks, limits direct contact with animals, and allows routine chores to be completed more accurately and consistently. These technologies are especially valuable for women farmers, youth workers, and persons with physical disabilities, chronic health conditions, or age-related limitations.

Safety Benefits

Efficiency Benefits

Reduced Animal Handling Risks: Automated equipment such as hydraulic chutes, automatic gates, and livestock sorting systems allow farmers to manage animals with less direct physical contact. This reduces the risk of kicks, crushing injuries, and other animal-related accidents.

Consistent Animal Care: Automation delivers feed and water more consistently than manual systems, helping ensure livestock receive the proper amount at the appropriate time. Consistency can improve animal performance and overall operation management.

Less Lifting and Repetitive Motion: Automatic feeders, waterers, conveyors, and feed-delivery systems reduce the need to carry feed bags, haul water, push wheelbarrows, and perform repetitive chores. This lowers the risk of strains, sprains, and overexertion injuries.

Labor Savings: Many automated systems allow one person to perform tasks that previously required multiple workers, such as restraining, sorting, or feeding livestock. This can reduce labor costs and help farms address labor shortages.

Improved Monitoring: Cameras, sensors, and remote monitoring systems allow farmers to observe livestock from a safe location rather than repeatedly entering pens or barns. This is particularly useful during calving, lambing, or when monitoring sick animals.

Increased Productivity: Automated feeders and waterers perform routine chores on a schedule. When less time is spent on repetitive chores, farmers can focus on other production activities.

Table 1. Examples of Automation in Livestock Operations

Automated Technology

Safety Benefit

Efficiency Benefit

Automatic feeders

Less lifting and carrying

Scheduled, consistent feeding

Automated waterers

Less hauling water and ice removal

Continuous water supply

Feed conveyors

Reduced strain injuries

Quicker feed delivery

Automatic manure scraping equipment

Reduced strain injuries and improved barn sanitation

Regular and faster clean-out times

Automatic gates

Fewer manual movements

Improved livestock flow

Hydraulic chutes and turn tables

Reduced animal contact

Faster animal processing

Cameras and sensors

Remote observation

Faster detection of problems

Table 2. Example Modifications for Specific Disabilities

Accessibility Benefits Automation can help farmers with mobility impairments continue operating their farms independently by reducing the physical demands of feeding, watering, and handling livestock. Modifications can be made for farmers who use wheelchairs or have limited mobility to participate in livestock management activities. Some facilities can be modified with accessible work platforms, wider working areas, remote hydraulic controls, and automated gate systems.

32 | Ohio State Digital Ag Program

Limitation

Helpful Equipment

Wheelchair user

Accessible livestock chutes, remote gate controls on pen and pasture gates

Arthritis or reduced grip strength

Hydraulic chutes, powered gates, automatic feeders

Back injury

Hoists, exoskeletons, lift-assist devices

Hearing loss

Visual alarm systems, camera monitoring

Vision impairment

Audible alerts, GPS guidance, accessible controls

Forages

Manure

Cattle

Small Ruminant

Equine

Feeding and Chore Automation Automation can greatly reduce repetitive physical demands. • Automatic feeders in livestock barns • Automatic pan and chain feeders in poultry barns • Automated watering systems, with water conservation features, heaters or insulated bowls • Automated feed mixers and feed pushers • Remote-controlled feed delivery systems • Sensors to create feed usage reports or alert low feed levels • Conveyor systems for feed and bedding • Automatic manure scraping equipment • Manure pit and lagoon monitoring sensors

Swine

Poultry

Automated poultry feeder system.

Livestock Handling Equipment These systems use hydraulic, pneumatic, or electric controls to restrain, position, and release animals with minimal manual effort. Technology tools reduce the need for lifting, pushing, or wrestling animals. Besides safety, these systems reduce the need for additional labor and can help a single operator remain independent. Automated handling systems reduce repetitive physical effort, which helps farmers with chronic pain, fatigue-related conditions, or reduced endurance complete their tasks more comfortably. • Hydraulic squeeze chutes that secure cattle with minimal physical effort • Self-catching head gates that automatically restrain animals • Automatic sorting gates to move livestock without manual handling • Sheep and goat tilt tables to position animals (hoof trimming or treatment) • Remote gate controls Goat in a tilt table chute. • Robotic milking systems Environment Monitors Cameras and Remote Monitoring • Internet-connected surveillance systems for barns and birthing areas allow observation from a phone or computer • Reduce the need for frequent trips to barns Ventilation Systems • Building fans, inlets and curtains adjust automatically based on temperature and humidity • Cage and pen sensors activate heaters, cooling pads, or heat lamps • Help maintain animal comfort and reduce heat stress • Automated heat lamps and environmental controls • Monitor confined space gases, i.e. Oxygen, carbon dioxide, ammonia • Improves air quality for workers and livestock Automated Lighting • Timers and sensors control lighting schedules • Computerized systems regulate light intensity and duration • Improve energy efficiency and animal management

PROJECT CONTACT For inquiries about this project, contact Agricultural Safety and Health, Ohio AgrAbility Program Dee Jepsen (jepsen.4@osu.edu) or Laura Akgerman (akgerman.4@osu.edu).

SUMMARY •

Automation helps livestock producers work safer and more independently by reducing physical demands, minimizing injury risks, reducing labor demands, improving efficiency, and creating a more accessible work environment for all workers. For farmers with disabilities, these technologies can be the key to remaining active, productive, and independent in livestock agriculture.

•

To learn more, visit the Ohio AgrAbility website (agrability.osu.edu/) for additional information about accessible technologies for tractors and machinery, or call to schedule a personal farm assessment to discuss how accessibility can be improved at your livestock operation.

2026 eBarns Report | 33

Extension Resources for the Farm Locate an OSU Extension Office

Farm Office

Extension Publishing

Events: • Ag Lender Seminars • Agri-Law Summit • Workshops (Grain Marketing, Planning for the Future of Your Farm, and more) • Webinars

Small Farm Team

Resources: • Ag Law Library (online) • Farm Management (Cash Rents and Farmland Values, Custom Rates and Costs, Data and Statistics, Decision Aids, Enterprise Budgets, online courses, and more!) • Income Tax Schools • Farm Transition Planning • Ohio Farm Resolution Services (OFRS) - Ohio's Certified Agricultural Mediation Program • Farm Financial Management and Policy Institute (FFMPI)

Find your local office and contact your Agriculture and Natural Resources Extension Educator with questions and more information on available programs: extension.osu.edu/lao

Find 4-H books, Extension bulletins, Field Guides, and more! extension.osu.edu/extension-publishing

Information for new and small farms Mission: To provide a greater understanding of production practices, economics of land use choices, assessment of personal and natural resources, marketing alternatives, and the identification of sources of assistance for new and small farms in Ohio. Events: • New and Small Farm Conference (annually) • New and Small Farm Management Colleges Connect with the Team: u.osu.edu/gofarmohio (QR code to the left)

A team of experts that represents OSU Extension programs in Agricultural and Resource Law, Farm Management, Production Business Management, and Tax. We've collaborated to form the Farm Office, a one-stop shop for navigating the legal and economic challenges of agricultural production.

Connect with the Team: farmoffice.osu.edu YouTube: @osufarmoffice Ag Law Facebook: @OhioAgLaw

YouTube: @osusmallfarmteam5974 Facebook: @GoFarmOhio

Women in Agriculture

Learn. Grow. Connect. Inspire. Empower. Mission: To help women improve their quality of life by providing them with resources to make better business decisions while maintaining a balance with family and personal obligations. Events: • Regional Women in Ag Conferences • Wednesday Women in Ag Webinar Series (monthly) • Networking opportunities, resources, and more! Connect with the Team: u.osu.edu/ohwomeninag

34 | Ohio State Digital Ag Program

College of Veterinary Medicine: Veterinary Extension

Fulfilling the land-grant mission by interpreting knowledge and research so Ohioans can apply scientifically based information to better their lives, businesses, and communities. The Ohio State University Extension, part of Ohio's land-grant institution, serves all 88 counties with a focus on Agriculture and Natural Resources, 4-H Youth Development, Family and Consumer Sciences, and Community Development. Established in 1974, the Veterinary Extension Unit continues to adapt and confront challenges, ensuring that valuable information reaches those who need it. Find Resources and Veterinary Extension Specilalists, here: vet.osu.edu/departments-offices/extension

Forages

Manure

Cattle

Small Ruminant

Forages

Forage Management, Pastures and Grazing, Research and Resources: Forages.osu.edu Fact Sheets: • • • • •

Forage Testing for Beef Cattle ohioline.osu.edu/factsheet/anr-0149 Giving New Life to Tired Pastures ohioline.osu.edu/factsheet/anr-0159 Determining the Grazing Intensity and Move Frequency for Your Rotational Grazing System ohioline.osu.edu/factsheet/anr-0160 Maximizing Forage Quality Through Targeted Grazing of Native Warm-Season Grasses ohioline.osu.edu/factsheet/anr-0166 Forage as Vegetative Cover for Utility-Scale Solar in Ohio ohioline.osu.edu/factsheet/cdfs-4106

Manure

Ohio Composting and Manure Management Program (ocamm.osu.edu). Goal: To facilitate research, development, and communication of sustainable strategies for the management of animal manures and other organics for Ohio farms and communities. Ohio State Extension Environmental and Manure Management Facebook: @OSUnutrients Fact Sheets: • • •

Manure and Compost: Nitrogen Availability in Organic Production ohioline.osu.edu/factsheet/anr-34 Horse Manure Management ohioline.osu.edu/factsheet/AGF-212 Manure to Energy Through Anaerobic Digestion ohioline.osu.edu/factsheet/AEX-653.1

Dairy Resources

Buckeye Dairy News: dairy.osu.edu/newsletter/buckeye-dairy-news Ohio Dairy Industry Resources Center dairy.osu.edu Fact Sheets: •

Calving Management in Dairy Herds: Timing of Intervention and Stillbirth ohioline.osu.edu/factsheet/VME-29

Poultry Team

Equine

Swine

Poultry

Beef Team

Providing information and educational materials on topics like beef cattle management, feeds and feeding, pasture management, marketing, and more. Events and Programs: • Ohio Beef School • Grazing and Pasture Walks, Forage Nights • Clinics • Beef Quality Assurance Certifications • Bovine Emergency Response Planning (BERP) Resources: • BEEF Cattle Newsletter: u.osu.edu/beef • Evaluating Beef Cattle Stockmanship Fact Sheet: ohioline.osu.edu/factsheet/anr-0164 Connect with the Beef Team: u.osu.edu/beefteam

Ohio State Equine Center

Tours, Reproduction Services, Training Services: ati.osu.edu/aboutus/facilities/equine-center Connect on Facebook: @ATIEquineCenter Equine Fact Sheets: • • • • •

Plants Poisonous or Injurious to Horses ohioline.osu.edu/factsheet/1028 Management Considerations for the Cribbing Horse ohioline.osu.edu/factsheet/anr-89 Feeding Horses ohioline.osu.edu/factsheet/anr-6 The Role of Macronutrients in Equine Nutrition ohioline.osu.edu/factsheet/1023 The Role of Micronutrients in Equine Nutrition ohioline.osu.edu/factsheet/1021

Small Ruminant Team

Supporting Ohio small ruminant producers by providing educational information, research conducted at The Ohio State University, resources, and contact information for leaders in Ohio's small ruminant industry. Connect with the Team: u.osu.edu/sheep Resources: •

Small Ruminant Production: Are CIDRs Reusable? Fact Sheet: ohioline.osu.edu/factsheet/as-1025

Resource: Backyard Poultry Certification Course (online) Connect and find resources: u.osu.edu/poultry/ Fact Sheets: • Bird Flu: Health, Nutrition, and Food Safety in the Face of an Outbreak ohioline.osu.edu/factsheet/fcs-1010

Ohio Pork Information Center porkinfo.osu.edu/

2026 eBarns Report | 35

We are tackling today’s grand challenges in every corner of Ohio. Sustainability Simultaneously ensuring viable agricultural production, food security and safety, and environmental and ecosystem sustainability simultaneously. One Health We study the nexus where human, animal, plant, and environmental health intersect or interact.

Rural-Urban Interface We explore the tensions and opportunities created in the communities, industries, policies, economies, and communications between rural and urban residents. Leadership We are preparing the next generation of scientists and leaders.

The Ohio State University College of Food, Agricultural, and Environmental Sciences is Ohio State’s cornerstone college. Through our teaching, research, and Extension, we sustain life.

wooster

columbus

statewide

COLLEGE of FOOD, AGRICULTURAL, and ENVIRONMENTAL SCIENCES

AGRICULTURE AND NATURAL RESOURCES EXTENSION

Ohio State University Extension Agriculture and Natural Resources empowers Ohio’s agriculture and natural resources communities, provides outreach and education based on unbiased research, and cultivates relationships to strengthen the economic viability and quality of life for Ohioans. Follow us @OhioStateANR

agnr.osu.edu

Ohio State Forages Research For 2026, eBarns forage research was focused on growing high quality forages, and improving forage yields by testing management practices and incorporating technology. Below are highlights of the 2026 eBarns forage research: 1,663 acres of forages

17 forage studies

For more forage research and feeding management from Ohio State University Extension, explore the following resources:

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.

Forage Team forages.osu.edu

Dairy Team dairy.osu.edu

Small Ruminant Team go.osu.edu/SmallRuminantTeam

38 | Ohio State Digital Ag Program

Agronomic Crops Team Forage Research

Beef Team u.osu.edu/beefteam/

Equine Team go.osu.edu/EquineTeam

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

Species for Planting by Mid-July Corn Plant 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.

Forage Sorghum Sorghum Sudangrass Sudangrass

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.

Soybean Silage

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

Can be mowed and wilted to correct harvest moisture. Harvesting as hay can be challenging. Earlier planting dates provide more autumn yield.

Oat or Spring Triticale Plus Winter Cereals

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.

Oat or Spring Triticale Plus Field Peas

Field peas can improve forage quality (especially crude protein content) but will increase seed cost.

Italian Ryegrass

Earlier planting dates provide more autumn yield. Excellent forage quality in the fall. Potential for three harvests next year starting in late April.

2026 eBarns Report | 39

Alfalfa Weevil Monitoring Summary OBJECTIVE

eBarns Collaborating Farms

Monitoring the impact and population of alfalfa weevil throughout the state to inform control timing.

STUDY INFORMATION

OSU Extension Statewide

WEATHER INFORMATION

Start Date 4/13/2026

Cumulative Monthly Growing Degree Days (GDDS)

End Date 5/10/2026

LOCATION

Crop Monitored Alfalfa Total # of Fields 16 Ohio Counties Defiance, Perry, Wayne, Represented Ross, Putnam, Mercer, Holmes, Paulding, Huron/Erie, Miami, Madison

JAN FEB

MAR

APR

MAY

JUN

Total

NWARS (Wood County)

156

300

346

642

1464

Jackson ARS (Jackson County)

248

383

402

638

1722

STUDY DESIGN Ohio State University Extension personnel from around the state scouted alfalfa fields within their county and reported their results to compile a summary of alfalfa weevil populations and impact. Alfalfa stems were collected randomly from scouted fields to provide a representative sample of the field. Alfalfa weevil larvae were dislodged from the sample and counted. The larvae per stem count was then compared to the height stage of the alfalfa field to provide a recommendation if a pest control method was necessary. The larvae count was also compared to the regional growing degree day (GDD) accumulation in each region, based on Northwest (NWARS) and Jackson (Jackson ARS) Agricultural Research Stations, to inform growers when scouting may be beneficial for them.

40 | Ohio State Digital Ag Program

Map of participating counties.

Damage from alfalfa weevil.

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

RESULTS

OBSERVATIONS Southern counties experienced greater larvae counts at the initiation of the scouting program due to the greater amount of GDDs accumulated in that region. There was an overall increase in alfalfa weevil larvae over the first week or two but there was a notable decrease in the average number of larvae as insecticide passes were made in the scouted fields. If insecticide was not applied in field numbers did not drop until mid-May when larvae pupated and developed into adults

Alfalfa weevil larvae population development, by county.

Alfalfa weevil on alfalfa.

SUMMARY •

Tracking GDDs is a useful tool for producers to use to know when to begin scouting for alfalfa weevil. In particularly, above average GDDs in March pushed alfalfa weevil development earlier than in past years.

•

There is a great amount of variability from field to field so individual scouting is necessary to make an informed control application.

•

Action threshold recommendations are >1 larvae per stem for a 9-inch alfalfa stand, >2 larvae per stem for a 12-inch alfalfa stand, and >4 larvae per stem for a 16-inch alfalfa stand. Many fields scouted never crossed the economic threshold when assessing the larvae per stem count and the height of the alfalfa.

Alfalfa stand height progression, by county.

FACT SHEET

PROJECT CONTACT

Learn more about alfalfa weevils from the fact sheet "Alfalfa Weevil on Alfalfa" by using the link below, or the QR code to the right.

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

go.osu.edu/AlfalfaWeevilFactSheet

2026 eBarns Report | 41

Corn DON Resistance Screening OBJECTIVE Assess the difference between hybrids in resistance to deoxynivalenol (DON) mycotoxin production from Gibberella Ear Rot (GER).

eBarns Collaborating Farms OSU Extension Statewide

With support from Ohio Corn and Wheat through the Corn Check Off, OSU conducted its third year of corn deoxynivalenol (DON) hybrid susceptibility screening. The objective of this project was to identify hybrids with partial genetic resistance to DON. Use these results with caution as we were not able to evaluate the effects of all possible weather environments on DON production. This trial was conducted at three locations across the state that represent different production regions: Apple Creek, Bucyrus, and South Charleston. Hybrids varied in maturity, so weather conditions may not have been conducive for ear infection and DON production by the fungus Fusarium graminearum during each individual hybrid pollination window, despite using three different environments. Two locations had low levels of natural infection across all maturity groups. To increase Gibberella ear rot (GER) development, and consequently, DON contamination of grain, plots were also inoculated at all three locations. DON levels were not significantly different between inoculated and naturally infected plots across locations. Results were summarized, and hybrids were compared by location.

For 2025, 90 hybrids were submitted from 11 seed companies. Weather conditions varied significantly, with all locations experiencing late-season drought. Two locations had very low DON levels, yet statistically significant differences remained between hybrids at these locations. The third location has a broad range of DON levels from 0.08-21.75 ppm. Across all three locations, 33 hybrids were statistically indistinguishable from the lowest hybrid at all locations in terms of DON contamination.

In 2023, a total of 80 hybrids from 8 seed companies were screened; the results are available at go.osu.edu/ DON2023. While this was only a small subset of the hybrids that are planted in Ohio, the results demonstrated the importance of hybrid selection for achieving low DON levels. Twenty-one of the hybrids entered in 2023 were also in the 2024 screening. All companies that participated had hybrids that showed potential DON resistance and others that were susceptible.

The weather conditions at all locations in 2025 deviated from normal. During the growing season, Bucyrus received 1 inch below normal rainfall; the other two locations received above-normal rainfall. All 3 locations had below-average rainfall and temperatures in August and September. The average relative humidity at Bucyrus was higher in August and September than at the other locations. The full weather data for the growing season are summarized in Table 1. The complete set of monthly weather data is available at: ohiocroptest.cfaes.osu.edu/ corntrials/.

In 2024, 89 hybrids were submitted from 11 seed companies. Although weather conditions were less favorable for GER development in 2024 than in 2023, statistically significant differences were still observed among hybrids in terms of average levels of DON contamination. These Corn silking during inoculation results can be found at with Fusarium graminearum. go.osu.edu/DON2024.

42 | Ohio State Digital Ag Program

These results can be used to select hybrids with natural partial resistance to DON, or at the very minimum, avoid highly susceptible hybrids. Fifty-six hybrids have been included in the trial for more than one year. Comparing hybrid performance across years is encouraged as it represents a wider range of weather conditions. Keep in mind, hybrids with low DON in 2025 are not guaranteed to have low DON in future years or environments. However, hybrids with consistently high DON across environments are likely susceptible and should not be planted when possible if DON contamination is a concern for your operation.

Weather conditions during pollination are critical: infection typically occurs while silks are still wet, with relative humidity >80% and temperatures between 59°F and 86°F. Table 1 summarizes DON contamination (in ppm) by hybrid. The inoculated and natural infection treatments were analyzed together instead of as separate, as inoculation was not significant. DON values with an asterisk (*) are not statistically different from the lowest average DON level at that location. The lowest DON average was at the South Charleston location, where 38 hybrids had all plots below the DON detection limits. Ten hybrids have statistically higher DON levels than the lowest hybrid, and two hybrids are statistically the same as the highest

Forages

Manure

Small Ruminant

Cattle

hybrid. Wooster also had low levels with 50 hybrids, being statistically the same as the lowest hybrids, which have non-detectable levels of DON. However, only one hybrid had all plots with a non-detectable DON level. The highest hybrid plot average DON was only 0.88 ppm; however, no other hybrids were statistically equivalent to this hybrid. While a different hybrid had the highest average DON in

Equine

Swine

Poultry

Bucyrus, the highest average DON hybrid in Wooster and South Charleston was the same, ranking 2nd highest in Bucyrus. DON levels in Bucyrus were much higher, with no hybrids having non-detectable DON levels across all plots. The LSD was much higher in Bucyrus at 1.28 ppm, leading to 59 hybrids being the same as the lowest hybrids. 19 hybrids had an average DON level of 2 ppm or higher.

Table 1. DON contamination of 90 hybrids averaged across locations and across plots at each location. Locations were South Charleston (SC), Bucyrus (B), and Wooster (W). Brand

Hybrid

DON (ppm) SC

Brand

Hybrid

DON (ppm) SC

B&A Genetics

BA20-12 VT2P

112

0.17*

21.75

0.22

Channel

204-54TRERIB

104

0.79

1.59

0.31

B&A Genetics

BA22-05 VT2P

105

0.02*

0.76*

0.16*

Channel

208-62VT2PRIB

108

0.02*

0.18*

0.04*

B&A Genetics

BA23-09 VT2P

109

3.60

0.11*

Channel

210-46VT2PRIB

110

0.01*

0.44*

0.21

B&A Genetics

BA23-12 AA

112

1.41

1.38

0.14*

Channel

215-42TRERIB

115

0.60

4.30

0.43

B&A Genetics

Dekalb

DKC092-14RIB

0.35

5.33

0.27

BA25-07 PCE

107

0.02*

2.27

B&A Genetics

Dekalb

DKC099-11RIB

0.02*

1.70

0.20

BA25-11 VT2P

111

0.27*

0.15*

0.28

Dekalb

DKC101-35RIB

101

0.06*

1.68

0.11*

B&A Genetics

BA25-12 VT2P

112

0.03*

0.28*

0.18*

Dekalb

DKC102-13RIB

102

0.26*

1.68

B&A Genetics

BA26-06 PCE

106

0.29*

0.13*

Dekalb

DKC108-64RIB

108

1.25

0.49*

0.25

Dekalb

DKC110-10RIB

110

0.08*

0.17*

0.09*

B&A Genetics

BA26-10 PCE

110

0.05*

0.74*

0.24

Dekalb

DKC110-41RIB

110

0.41*

0.04*

Dekalb

DKC45-74RIB

0.08*

0.29*

0.11*

B&A Genetics

BA26-12 PCE

112

0.9*

0.43

Dekalb

DKC56-26RIB

106

0.03*

2.60

0.06*

B&A Genetics

Dekalb

DKC63-91RIB

113

1.57

0.44

BA26-14 PCE

114

0.26*

0.38

Dekalb

DKC64-22RIB

114

0.64*

0.14*

B&A Genetics

EXP55-10

110

0.04*

2.10

0.16*

Ebberts 7209TR Field Seeds

109

0.03*

0.29*

0.31

Beck's Hybrids

5530PCE

105

0.53*

0.23

Ebberts 7335PC Field Seeds

105

0.4*

Beck's Hybrids

5794V2P

107

1.37

0.14*

Ebberts 7442PC Field Seeds

112

0.11*

1.07*

0.44

Beck's Hybrids

5824AM

108

1.50

0.04*

Ebberts 7557PC Field Seeds

107

0.83*

0.06*

Beck's Hybrids

6184V2P

111

0.37*

0.05*

Ebberts 7660PC Field Seeds

110

0.02*

1.45

0.21

Beck's Hybrids

6216PCE

112

0.09*

0.26

Ebberts 7770PC Field Seeds

110

0.87*

0.22

Beck's Hybrids

6235D1

112

0.16*

0.40

Ebberts 7993PC Field Seeds

113

0.46*

0.41

Beck's Hybrids

6280TCV2P

112

0.55*

0.12*

Ebberts 7993PC Field Seeds

113

0.46*

0.41

Numbers with an asterisk (*) are not significantly different from the lowest DON level at the location - Table continues on following pages -

2026 eBarns Report | 43

Corn DON Resistance Screening Brand

Hybrid

DON (ppm) SC

Brand

Hybrid

DON (ppm) SC

0.16*

NK Seeds

NK1523-V

115

1.56

7.00

0.88

PC Seedco

PC 3305

105

0.7*

0.13*

0.43*

0.11*

PC Seedco

PC 5510

110

0.15*

2.09

0.16*

0.26*

0.27

PC Seedco

PC 5511

111

0.15*

1.48

0.38

PC Seedco

PC 6313

113

0.44*

0.33

105

0.6*

0.08*

PC Seedco

PC 8407

107

0.45*

0.09*

Pioneer

P01851PCE

101

0.7*

0.05*

G06M39-DV

106

0.18*

Pioneer

P07147PCE

107

0.03*

2.29

0.14*

Golden Harvest

G08U00-V

108

0.6*

0.04*

Pioneer

P09312V

109

0.2*

0.04*

Pioneer

P10300PCE

110

0.84

5.80

0.1*

Golden Harvest

G10L16-DV

110

0.2*

0.20

Pioneer

P10796PCE

110

6.73

0.1*

Golden Harvest

Pioneer

P1136AM

111

0.01*

0.49*

0.38

G10M87-AA

110

0.02*

0.08*

Pioneer

P12517V

112

1.51

0.30

Golden Harvest

G10U97-V

110

1.41

0.14*

Pioneer

P13777PCE

113

0.02*

1.52

0.41

Pioneer

P14364PCUE

114

8.00

1.43

0.13*

Golden Harvest

G12S75-D

112

0.05*

0.29*

0.37

Golden Harvest

AGI-C-4111 PWE

111

0.08*

0.75*

0.22

G12U11-AA

112

0.04*

0.17*

Seed Genetics Direct

Golden Harvest

G13M31-AA

113

0.91*

Seed Genetics Direct

AGI-C-5105 PWE

105

0.06*

0.53*

0.13*

Golden Harvest

G14B32-DV

114

0.03*

0.11*

0.37

Direct 2111-AA

111

0.08*

0.37*

0.13*

Golden Harvest

G15U34-V

115

0.05*

0.2*

0.18*

Seed Genetics Direct

LG Seeds

LG52C90VT2RIB

102

0.01*

0.69*

0.26

Direct 3111-3110

111

1.11*

0.12*

LG Seeds

LG53C44VT2RIB

103

0.01*

0.32*

0.07*

Seed Genetics Direct

LG Seeds

LG58C16VT2PRO

108

2.34

0.55

Direct 5107-V

107

0.02*

0.25*

0.15*

LG Seeds

LG60C05-3110

110

0.04*

0.2*

0.04*

Seed Genetics Direct

LG Seeds

LG62C73VT2RIB

112

0.02*

2.63

0.05*

Hybrid A

113

0.27*

6.23

0.39

NK Seeds

NK0415-AA

104

0.01*

0.78*

0.46

Minyo Check

NK Seeds

NK0880-V

108

0.43*

0.06*

Hybrid A - 1

113

0.43

4.20

0.31

NK Seeds

NK1056-V

110

0.45*

0.14*

Minyo Check

NK Seeds

NK1188-AA

111

0.35

0.27*

0.14*

Minyo Check

Hybrid B

109

0.61

0.77*

0.42

NK Seeds

NK1228-AA

112

1.24*

0.25

NK Seeds

NK1386-VZ

113

0.04*

0.72*

0.26

Minyo Check

Hybrid C

101

4.20

0.41

Golden Harvest

G01U74-AA

101

0.08*

0.09*

Golden Harvest

G03U08-D

103

0.07*

Golden Harvest

G04M93-AA

104

Golden Harvest

G05U86-DV

Golden Harvest

DON (ppm) Results Across Locations HIGH

South Charleston (SC)

Bucyrus (B)

Wooster (W)

1.56

21.75

0.88

AVERAGE

0.12

1.50

0.20

LOW

0.00

0.08

0.00

LSD .10

0.29

1.28

0.19

CV (%)

343

Numbers with an asterisk (*) are not significantly different from the lowest DON level at the location.

44 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

WEATHER INFORMATION

PROJECT CONTACT For inquiries about this project, contact: Jason Hartschuh Figure 1. Bucyrus Growing Season Weather Conditions. (hartschuh.11@osu.edu), Planting date: May 19. Harvest date: Nov. 11. Stephanie Karhoff (karhoff.41@osu.edu), Pierce Paul (paul.661@osu.edu), or Rich Minyo (minyo.1@osu.edu)

Figure 2. Wooster (Apple Creek) Growing Season Weather Conditions. Planting date: June 5..Harvest date: Nov. 24.

Our Corn Checkoff Research Sponsored by Ohio Corn Farmers: “The presentation of results in this report does not imply endorsement of any hybrid by The Ohio State University.”

Figure 3. South Charleston Growing Season Weather Conditions. Planting date: May 3. Harvest date: Oct. 27.

2026 eBarns Report | 45

DON Resistance - Silage Corn OBJECTIVE

Wooster Campus Farms

Evaluate corn silage hybrid resistance to the production of the mycotoxins deoxynivalenol (DON) and zearalenone.

STUDY INFORMATION

OARDC Wayne County

WEATHER INFORMATION

Planting Date 6/5/2025 Harvest Date 9/19/2025 Variety See Treatments Population 34,800 sds/ac Acres 2 Treatments 16 Reps 4 Treatment Width 10 ft. Tillage Minimum Management Fertilizer Previous Crop Corn Row Spacing 30 in. Soil Type Canfield Silt Loam, 100%

Growing Season Weather Summary APR

MAY

JUN

JUL

AUG

SEP

Total

Precip (in.)

3.92

4.25

5.22

4.91

0.73

2.27

21.30

Cumulative GDDs

200

493

1140

1892

2455

2935

STUDY DESIGN This experiment was designed as a randomized complete block split-split plot design to evaluate the resistance of corn silage hybrids to the production of the mycotoxins deoxynivalenol (DON) and zearalenone. Plots were managed for high yields. At wet silk, plants were inoculated with Fusarium graminearum spores using a backpack inoculation sprayer method. Plots were harvested using a 2-row plot corn silage harvester. Silage samples were fermented for 30 days in vacuum-sealed bags. Mycotoxins were tested using high-performance liquid chromatography, and tandem mass spectrometry.

46 | Ohio State Digital Ag Program

Fermented corn silage for analysis.

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

Foliar disease pressure was low in plots, but hybrids responded very differently to drought conditions. Some ears were still at 0% milk line; however, whole plant plot moisture was 46-60%. Yield averages by hybrid ranged from 16.3-26.3 ton/ac at 65% moisture.

•

DON and zearalenone levels between hybrids were significantly different.

•

The within hybrid plot to plot variations in DON and earalenone levels were large this year.

•

Recent research shows that conditions that slow silage fermentation such as lower moisture can promote mycotoxin growth during storage.

PROJECT CONTACT For inquiries about this project, contact Jason Hartschuh (hartschuh.11@osu.edu) or Rich Minyo (minyo.1@osu.edu).

RESULTS Treatments

DON (ppm)

Zearalenone (ppm)

Channel 208-18

0.70 ab

15.06 b

Channel 210-46

1.66 ab

19.54 b

Channel 214-78

1.35 ab

36.45 b

Channel 215-09

1.25 ab

2.88 b

Channel 215-60

4.81 a

63.07 b

Channel 216-36

1.40 ab

24.08 b

NK 0604-DV

3.26 ab

23.86 b

NK1480-DV

2.85 ab

19.03 b

NK 1228-AA

0.83 ab

18.37 b

Enogen E102K7-D

1.10 ab

0.00 b

Enogen E107C1-D

2.84 ab

173.83 a

Enogen E108K4-DV

1.94 ab

21.22 b

Enogen E111V7-D

1.35 ab

2.73 b

Enogen E112S5-D

0.99 ab

0.00 b

Enogen E114C4-DV

0.49 b

18.45 b

Enogen E117Z7-D

0.28 b

0.00 b

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: 4.16 CV: 172.5%

LSD: 105.7 CV: 326.4%

2026 eBarns Report | 47

Drone Spray Deposition OBJECTIVE Evaluate differences in fungicide deposition in corn resulting from applications made with drone sprayers and ground sprayers.

Northwest Ag Research Station OARDC Wood County

Figure 1. Water-sensitive card images collected from the inside ear leaf for the different application methods: (a) Hylio at 5 gal/ac, (b) Hylio at 2.5 gal/ac, (c) DJI at 5 gal/ac, (d) DJI at 2.5 gal/ac, (e) 360 Undercover drops at 20 gal/ac, (f) 360 Undercover drops at 15 gal/ac, (g) boom sprayer at 20 gal/ac, and (h) boom sprayer at 15 gal/ac.

STUDY DESIGN This study was conducted in a corn field at the Northwest Agricultural Research Station, with 96 plots divided into 32 untreated controls and 64 treated plots. Eight application methods were tested, each replicated four times. Ground sprayer treatments included a boom sprayer with XR 11003 VS tips at 40 psi and a 360 Undercover sprayer with Turbo TeeJet 015 nozzles (three per drop) at 40 psi, both applied at 15 and 20 (gal/ac). Drone treatments included the DJI AGRAS T20P and Hylio AG130, each flown 8 feet above the canopy at 2.5 and 5 gal/ac. Ground sprayer plots measured 100 feet by 10 feet, while drone plots measured 100 feet by 20 feet. Deposition was assessed using water-sensitive cards placed at 30 and 60 feet within each plot, with cards positioned above, below, and on the ear leaf, as well as outside the ear leaf. Cards were scanned with a DropeScope analyzer to quantify coverage, droplet size (VMD), droplet count, and applied volume (gal/ac).

48 | Ohio State Digital Ag Program

Figure 2. Example of card placement on curled, erect leaves due to drought and heat stress at the time of fungicide application.

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

Application equipment and spray rate significantly influenced fungicide deposition in corn. Deposition patterns differed by equipment type: drones tended to deposit more above the ear leaf, while ground sprayers deposited more below the ear leaf. This difference is likely explained by operating height, as ground sprayers applied just above the canopy whereas drones sprayed from approximately 8 feet above the canopy. Wind speed during the study was considered negligible. Additional factors such as spray height and time of day may have affected results along with hot, droughty conditions at the time of application.

•

Statistically, the boom ground sprayer applied more on the top side of the ear lead whereas the ground sprayer using Y-Drops uniformly applied to the top and bottom of the ear leaf.

•

The ground machine had statistically larger diameter droplets and higher quantity of droplets compared to the drones. This result is most likely due to the different in spray volume between the two types of applicators.

•

Increasing application rate generally increased deposition on the ear leaf.

RESULTS Treatments (gal/ac) Ground w/ Boom 15 Ground w/ Boom 20 Ground w/ Drops 15 Ground w/ Drops 20 DJI 2.5 DJI 5 Hylio 2.5 Hylio 5

Ear Leaf Location

Coverage (%)

Quantity of Drops

VMD (μm)

Top Bottom Top Bottom Top Bottom Top Bottom Top Bottom Top Bottom Top Bottom Top Bottom

0.6 3.0 6.1 0.2 5.0 8.9 4.2 4.8 0.4 0.2 1.7 0.1 0.7 0.3 0.4 0.9

1100 bcd 274 ef 1603 ab 65 f 1541 ab 1735 a 1310 abc 1315 ab 120 f 112 f 721 de 81 f 413 ef 208 ef 328 ef 758 cde

230 ab 171 f 254 abc 105 cde 251 ab 281 a 268 a 231 ab 165 de 85 f 234 ab 132 ef 196 bcd 118 ef 125 ef 126 ef

LSD: 557 CV: 131%

LSD: 59 CV: 54%

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 AG130 is a high-capacity agricultural spray drone with dual 4‑gallon tanks, GPS navigation, and radar systems for precision application. It can autonomously cover up to 50 acres per hour, delivering efficient fungicide, herbicide, or fertilizer deposition.

PROJECT CONTACT For inquiries about this project, contact Nina Nebesh (nebesh.3@osu.edu), Jason Hartschuh (hartschuh.11@osu.edu), John Fulton (fulton.20@osu.edu), Stephanie Karhoff (karhoff.41@osu.edu), Pierce Paul (paul.661@osu.edu).

2026 eBarns Report | 49

Forage Production Under Solar OBJECTIVE Evaluate the impact of the solar site on forage crop yield and quality and demonstrate the feasibility of producing a forage crop on a utility-scale solar site.

eBarns Collaborating Farm OSU Extension Madison County

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.

Figure 1. Forage harvest field activities with the solar panels mowing (left), raking (middle), and baling (right).

Figure 2. Alfalfa stand in June (left), cool-season hay stand in June (middle), and bale coring for quality analysis (right).

50 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

FORAGE YIELD AND QUALITY RESULTS To evaluate the impacts of the solar site on the production of forage, two forage types (alfalfa and cool-season hay mix) 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. Plots were mowed and baled as small square bales in June, July, and August of 2025. Yields were calculated from total bale weights for all three cuttings. Forage quality was tested using near-infrared spectroscopy from cores taken from each bale. Wet areas with poor drainage were observed, preventing forage establishment in some plots. Improving drainage could lead to higher yield in this environment.

Figure 3. Total yields for alfalfa (left) and quality (right). Letters indicate significant differences between treatments.

Figure 4. Total yields for cool-season hay (left) and quality (right). Letters indicate significant differences between treatments.

SUMMARY •

First year forage yields were comparable between the control and within the panels for the alfalfa and higher within the panels for the cool-season hay.

•

Forage establishment and performance was impacted by abnormally dry conditions in both 2024 and 2025.

•

Forage quality improved with later cuttings both within the panels and in the control area, with the highest quality cutting being the third cutting in August. Crude protein was similar when comparing treatments within each cutting.

•

This demonstrates the agronomic feasibility of forage production on a utility-scale solar site. Continued research will lead to improved recommendations for forage production on solar sites.

2026 eBarns Report | 51

Forage Production Under Solar OBJECTIVE

eBarns Collaborating Farm

Evaluate compaction remediation strategies and identify key considerations for agrivoltaics implementation.

OSU Extension Madison County

STUDY OVERVIEW This project seeks to better understand how building utility-scale solar farms affects farmland and topsoil, and to identify effective ways to restore and improve soil health. In addition, we aim to identify the main obstacles preventing the widespread use of agrivoltaics, so we can better connect the solar industry with agriculture. This will help guide important decisions about system design and ensure that farming remains possible on these sites after construction. 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.

COMPACTION REMEDIATION Cone penetrometer sampling was conducted to evaluate soil compaction caused by site construction. Figure 1 shows compaction for both pre-construction (2023 – red), post construction (2024 – black), and following the first year cropping season (2025 – gray). Compaction through the root zone increased post-construction, on average. A distinct compaction layer was present near an 8-inch depth on average across the site. Various cover crops were compared for their ability to remediate this compaction. Compaction levels, on average, was reduced to near the pre-construction baseline after one year of cropping. No differences were observed between the cover crop or forage species. Compaction data will be continued to be monitored on an annual basis during the spring of each year.

2023 - Pre-Construction 2024 - Post-Construction 2025 - 1 year Cropping

0 -2

Depth (in)

-4 -6 -8 -10 -12 -14 -16 -18

100

150

200

250

300

350

Soil Cone Index (psi) Figure 1. Cone penetrometer readings (psi) taken to 18-inches pre-construction, post-construction, and following the first cropping year. Dashed lines indicate +/- one standard deviation.

52 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

KEY CONSIDERATIONS FOR UTILITY-SCALE AGRIVOLTAICS

SUMMARY •

One year of crop production on the site reduced soil compaction across all treatments including the cover crops and forage species. Drought conditions in 2024 limited cover crop establishment success.

•

The team noted four key considerations that may impact successful production: challenges with post-construction drainage (surface and subsurface), adequate turning radii for equipment between panels, alley obstructions, and row spacing. These potential limitations should be considered pre-construction and may impact equipment selection and logistical feasibilty.

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.

Learn more about agrivoltaics here.

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 and Kubota Tractor Corporation for their collaboration on this research effort.

2026 eBarns Report | 53

Fungicide Application for DON OBJECTIVE

Western Ag Research Station

Evaluate the effects of application method and fungicide on corn yield and deoxynivalenol (DON) levels.

STUDY INFORMATION

OSU Extension Clark County

WEATHER INFORMATION

Planting Date 5/26/2025 Harvest Date 10/29/2025 Hybrid Disease susceptible control Population 34,000 sds/ac Acres 1 Treatments 8 Reps 4 Treatment Width 10 ft.(ground sprayer) 20 ft (drone) Tillage Vertical Tillage Management Fungicide Previous Crop Corn Row Spacing 30 in Soil Type Strawn-Crosby complex 100%

STUDY DESIGN Field layout This study had 96 plots divided, into 32 untreated controls and 64 treated plots. Eight application methods were tested, each replicated four times with two fungicides (Miravis Neo and Proline) in a randomized complete block split plot design with application method as the main plot and fungicide product or control as the split plot factor. Ground sprayer plots measured 100 by 10 ft, while drone plots measured 100 by 20 ft. The center 2 rows of each plot were harvested for yield, moisture, and DON analysis. Results are reported by application method in one table and fungicide product in another table on the following report pages, as there was no statistically significant interaction between fungicide and application method.

54 | Ohio State Digital Ag Program

Growing Season Weather Summary APR

MAY

JUN

JUL

AUG

SEP

Total

Precip (in.)

5.00

4.45

6.62

4.54

2.27

0.64

23.52

Cumulative GDDs

246

587

1292

2089

2705

3222

Equipment used Ground sprayer treatments included a boom sprayer (XR 11003 VS tips at 40 psi) and a ground sprayer (TeeJet 24-inch straight drop with Teejet XR8002 VS nozzles at 40 psi), both applied at 15 and 20 gal/ac. Drone treatments included the DJI Agras T20P and Hylio AG130, each flown 8 feet above the canopy at 2.5 and 5 gal/ ac. Deposition was assessed using water-sensitive cards placed at 30 and 60 ft within each plot, with cards positioned above, below, and on the ear leaf, as well as outside the ear leaf – see more on pages 48 - 49. After fungicide application, plots were inoculated with Fusarium graminearum spores using a backpack sprayer inoculation at R1 to ensure disease was present. Plots were harvested with a Kincaid combine with HarvestMaster yield and moisture monitoring. Two pound grain samples were collected at harvest from the combine, dried to 15% moisture, then ground for DON analysis. DON analysis was done using Gas Chromatographic-Mass Spectrometric Analysis (GC-MS).

Forages

Manure

Small Ruminant

Cattle

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

Application methods Visual observations and results indicated that application method influenced fungicide deposition.The Hylio drone also had average lower DON presence. The drops seemed to result in the lowest yield. It was noted that drone application and ground application occurred on separate days with light rain in between, which may have affected results.

Application methods • Application equipment and spray rate influenced fungicide deposition in corn.

RESULTS

•

The Hylio drone resulted in highest yield and lower DON presence.

•

Applications on separate days and light rain may have affected results. However, this represents the real world where drones can fly when it is too wet for a ground application.

•

The boom at a higher rate had a comparable yield to the Hylio. The boom at the lower rate had a comparable yield to the DJI.

Treatments (gal/ac)

Moisture (%)

Yield (dry bu/ac)

DON (ppm)

Control

16.2

214

0.1

Boom 15

216

0.1

Boom 20

17.8

231

Drops 15

205

0.2

Drops 20

15.2

198

DJI 2.5

16.1

209

DJI 5

16.1

215

0.1

Hylio 2.5

16.6

235

Hylio 5

17.3

225

Treatment Means with the same letter are not significantly different test at alpha = 0.1.

CV: 9.4

CV: 12.1

CV: 119.2

Not Significant (NS)

TOOLS OF THE TRADE The DJI Agras T20P Spray Drone is a highcapacity agricultural spray drone with dual atomized spray discs, a 5 gallon tank, GPS navigation, and radar systems for precision application. It can autonomously cover up to 27 ac/hr, delivering efficient fungicide, herbicide, or fertilizer deposition.

- Report continues on the following pages, with the fungicide product results -

2026 eBarns Report | 55

Fungicide Application for DON OBJECTIVE Evaluate the effects of application method and fungicide on corn yield and deoxynivalenol (DON) levels.

Western Ag Research Station OSU Extension Clark County

- Report continued from the previous pages -

STUDY DESIGN This portion of the study is analyzed by fungicide use. It was done across eight application methods with each replicated four times in a randomized complete block split plot design. Application method was the main plot and the fungicide product or control was the split plot factor. The products used were Miravis Neo and Proline. Ground sprayer plots measured 100 by 10 ft, while drone plots measured 100 by 20 ft. The center 2 rows of each plot were harvested for yield, moisture, and DON analysis. The application method results are located in the pages directly before the product results pages seen here.

LEARN MORE A Gibberella Ear Rot/DON Risk Tool was developed by The Ohio State University Cereal Pathology Lab (with support from the Ohio Corn Marketing Program) to help growers estimate the risk of DON contamination in a field. It is a web-based Crop Risk Tool hosted by the Crop Protection Network. Use the QR code to the right or visit go.osu.edu/DONRiskTool.

Hylio AG130 flying during application.

56 | Ohio State Digital Ag Program

Forages

Manure

Small Ruminant

Cattle

Equine

Swine

Poultry

OBSERVATIONS Fungicide products In general, this location had very low presence of DON with the highest amount present being 0.53 ppm and 53 out of 96 plots having no DON present. Wind speed during the study was minimal and considered negligible. There was very low variation in moisture present. It was noted that drone application and ground application occurred on separate days with light rain in between, which may have affected results.

SUMMARY Fungicide products • Fungicide application slightly decreased presence of DON. However, presence in general was low at the location. •

Proline had the best yield and a comparable DON presence.

•

On average, both fungicides showed decreased presence of DON and an increase in yield – with Proline performing the best.

RESULTS Treatments

Moisture (%)

Yield (dry bu/ac)

DON (ppm)

Control

16.2

214

0.1

Miravis Neo

16.5

215

0.1

Proline

16.6

218

0.1

Treatment Means with the same letter are not significantly different test at alpha = 0.1.

CV: 9.9

CV: 12.6

CV: 123.2

PROJECT CONTACT

Our Corn Checkoff Thank you to the Ohio Corn Check-off for funding the 3-year DON Management Research studies.

For inquiries about this project, contact John Fulton (fulton.20@osu.edu), Nina Nebesh (nebesh.3@osu.edu), Jason Hartschuch (hartschuh.11@osu.edu) or Stephanie Karhoff (karhoff.41@osu.edu).

2026 eBarns Report | 57

Fungicide Application for DON OBJECTIVE

NW Ag Research Station

Evaluate the effects of application method and fungicide on corn yield and deoxynivalenol (DON) levels.

STUDY INFORMATION

OSU Extension Wood County

WEATHER INFORMATION

Planting Date 5/29/2025 Harvest Date 10/22/2025 Hybrid Disease susceptible control Population 34,000 sds/ac Acres 1 Treatments 8 Reps 4 Treatment Width 10 ft.(ground sprayer) 20 ft (drone) Tillage Minimum Till Management Fungicide, Insecticide, Fertilizer Previous Crop Corn Row Spacing 30 in. Soil Type Hoytville clay loam, 100%

STUDY DESIGN Field layout This study had 96 plots divided into 32 untreated controls and 64 treated plots. Eight application methods were tested, each replicated four times with two fungicides (Miravis Neo and Proline) in a randomized complete block split plot design with application method as the main plot and fungicide product or control as the split plot factor. Ground sprayer plots measured 100 by 10 ft, while drone plots measured 100 by 20 ft. The center 2 rows of each plot were harvested for yield, moisture, and DON analysis. Results are reported by application method in one table and fungicide product in another table on the following report pages, as there was no statistically significant interaction between fungicide and application method.

58 | Ohio State Digital Ag Program

Growing Season Weather Summary APR

MAY

JUN

JUL

AUG

SEP

Total

Precip (in.)

4.87

4.74

5.17

2.61

0.26

1.19

18.84

Cumulative GDDs

208

522

1199

1994

2613

3119

Equipment used Ground sprayer treatments included a boom sprayer (XR 11003 VS tips at 40 psi) and a 360 Undercover sprayer (Turbo TeeJet 015 nozzles, three per drop, at 40 psi), both applied at 15 and 20 gal/ac. Drone treatments included the DJI Agras T20P and Hylio AG130, each flown 8 feet above the canopy at 2.5 and 5 gal/ac. Deposition was assessed using water-sensitive cards placed at 30 and 60 ft within each plot, with cards positioned above, below, and on the ear leaf, as well as outside the ear leaf – see more on pages 48 - 49. After fungicide application, plots were inoculated with Fusarium graminearum spores using a backpack sprayer inoculation at R1 to ensure disease was present. Plots were harvested with a Kincaid combine with HarvestMaster yield and moisture monitoring. Two pound grain samples were collected at harvest from the combine, dried to 15% moisture, then ground for DON analysis. DON analysis was done using Gas Chromatographic-Mass Spectrometric Analysis (GC-MS).

Forages

Manure

Small Ruminant

Cattle

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

Application methods Yields were well below average due to drought conditions. Ground applications were made in the morning while drone applications were made in the afternoon when leaves were curled, decreasing surface area for fungicide deposition.

Application methods • Results indicate application method influenced fungicide deposition. Typically, increased rate resulted in both increased yield and moisture.

RESULTS

•

Undercover ground application resulted in the highest average yield, likely because the droplets are deposited closer to the ear leaf. The drops also had lower average moisture values, but resulted in much higher average DON values (comparable to control plots).

•

The DJI application resulted in the lowest average yield, whereas the Hylio drone had a yield comparable to both ground sprayers. Despite the lower yield, DJI had much lower DON values than the control.

Treatments (gal/ac)

Moisture (%)

Yield (dry bu/ac)

DON (ppm)

Control

19.4

0.2

Boom 15

18.6

0.1

Boom 20

19.7

0.1

Undercover 15

18.8

102

0.2

Undercover 20

18.8

0.2

DJI 2.5

20.9

0.1

DJI 5

19.5

0.1

Hylio 2.5

19.6

0.1

Hylio 5

20.3

102

0.1

Treatment Means with the same letter are not significantly different test at alpha = 0.1.

CV: 10.8

CV: 15.6

CV: 106.1

Not Significant (NS)

TOOLS OF THE TRADE The Hylio AG130 Spray Drone is a highcapacity agricultural spray drone with dual 4‑gallon tanks, GPS navigation, and radar systems for precision application. It can autonomously cover up to 50 ac/hr, delivering efficient fungicide, herbicide, or fertilizer deposition.

- Report continues on the following pages, with the fungicide product results -

2026 eBarns Report | 59

Fungicide Application for DON OBJECTIVE

NW Ag Research Station

Evaluate the effects of application method and fungicide on corn yield and deoxynivalenol (DON) levels.

OSU Extension Wood County

- Report continued from the previous pages -

LEARN MORE Vomitoxin (or deoxynivalenol / DON) is a mycotoxin produced by the fungus Gibberella zeae (Fusarium graminearum), which causes Gibberella ear and stalk rot in corn. Contamination of this in corn grain can be harmful to humans and animals at certain levels. It provokes vomiting and may also cause feed refusal and suppression of the immune system. Use the QR code below or visit go.osu.edu/WhatisVomitoxin to learn more.

The DJI AGRAS T20P flying during application.

60 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS Fungicide products On average, both fungicides showed decreased presence of DON and an increase in yield – with Proline performing the best. Wind speed during the study was minimal and considered negligible. In general, this location had very low presence of DON with the highest amount present being 1.2 ppm and 43 out of 96 plots having no DON present. Additional factors such as spray height and time of day may have affected results along with the hot, droughty conditions at the time of application.

SUMMARY Fungicide products • Fungicide application decreased DON presence by half. Proline had the best yield and lowest DON presence. •

On average, both fungicides showed decreased presence of DON and an increase in yield.

•

Both ground spraying and drones can be used for effective fungicide application.

RESULTS Treatments

Moisture (%)

Yield (dry bu/ac)

DON (ppm)

Control

19.4 a

92 a

0.23 a

Miravis Neo

19.4 a

96 b

0.21 a

Proline

19.6 a

95 b

0.07 b

Treatment Means with the same letter are not significantly different test at alpha = 0.1.

LSD: 0.3

LSD: 2.2

LSD: 0.02

CV: 11.2

CV: 15.8

CV: 102.2

PROJECT CONTACT

Our Corn Checkoff Thank you to the Ohio Corn Check-off for funding the 3-year DON Management Research studies.

For inquiries about this project, contact John Fulton (fulton.20@osu.edu), Nina Nebesh (nebesh.3@osu.edu), Jason Hartschuch (hartschuh.11@osu.edu) or Stephanie Karhoff (karhoff.41@osu.edu).

2026 eBarns Report | 61

Hay Quality Analysis Summary OBJECTIVE

eBarns Collaborating Farms

Determining hay quality across Ohio during a summer of delayed first cutting harvest.

OSU Extension Statewide

BACKGROUND Ohio forage producers continue to face highly variable growing conditions, and 2025 was no exception. After one of the most severe droughts in recent history in 2024, 2025 shifted abruptly to persistent spring rainfall and scattered summer storms. While the precipitation supported pasture growth and allowed for multiple hay cuttings, it also created difficult drying conditions that increased the risk of mold, spoilage, and reduced forage quality. Ohio State University Extension has been collecting forage samples for several years to monitor these trends. Across multiple seasons a consistent pattern has emerged: first cutting hay frequently falls below the critical energy (Total Digestible Nutrients, TDN) and crude protein (CP) values required for spring calving beef cows. Weather related harvest delays remain a major driver of these nutrient deficits. To better understand the extent of the issue in 2025, the Extension Beef Team and 29 county educators collected and analyzed 180 forage samples from 80 locations across Ohio. This statewide effort provided an updated snapshot of forage quality under another year of weather extremes and highlighted the continued need for forage testing to guide winter feeding decisions. Table 1. Producer hay analysis report from 2025 Ohio Hay Analysis. 2025 Grass 2025 Grass 2025 Grass 2025 Grass Hay Hay Hay Hay County Fairfield Fairfield Fairfield Fairfield Farm-# SmithEvans1 SmithEvans2 SmithDal1 SmithBB1 % Dry Matter (DM) 88.60% 85.70% 88.60% 88.30% % Crude Protien (CP) 10.45 12.12 8.42 12.72 Type

Figure 1. Map of Ohio Counties that submitted forage samples in red.

STUDY DESIGN Forage samples (hay, haylage, mixed hay, and legume hay) were collected by Extension educators and livestock producers from 29 Ohio counties (Figure 1), using a hay sampling probe. Samples were weighed to determine dry matter, dried at 60° C for 72 hours and then submitted to a certified forage analysis laboratory at The Ohio State University for grinding and nutrient evaluation using Near Infra-Red (NIR) Spectroscopy analysis. Analysis reports included standard forage quality factors (Dry Matter, Crude Protein, Digestibility, and minerals). The forage analysis as presented in Table 1. also included energy reported as TDN using methods described by (Weiss et. al., 1992 doi.org/10.1016/03778401(92)90034-4) and Relative Feed Value (RFV) (Weiss, 1998 doi.org/10.3168/jds.s00220302(98)75641-3).

62 | Ohio State Digital Ag Program

% Acid Detergent Fiber (ADF)

40.93

37.11

44.27

39.62

% Neutral Detergent Fiber (NDF)

63.69

56.05

68.95

62.43

% Calcium % Phosphorus % Potassium % Magnesium

0.38 0.25 1.97 0.14

0.51 0.32 2.65 0.19

0.38 0.21 1.56 0.12

0.50 0.26 1.99 0.17

NDFD30 (Digestibility at 30 hours)

42.94

48.92

33.85

40.94

NDFD48 (Digestibility at 48 hours)

56.98

64.45

47.57

56.28

% ASH % FAT % Lignin % Starch % TDN Relative Feed Value

11.56 1.95 5.79 1.07 48.20 83.27

12.53 2.51 4.58 0.88 54.20 99.56

7.89 1.76 7.49 2.43 44.60 73.41

10.04 1.98 6.26 1.66 50.60 86.48

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

RESULTS

SUMMARY

Results were compared to the nutritional requirements of a 1,200 lb spring calving beef cow, including late gestation (fall) and peak lactation needs (National Research Council (NRC), 2016).

•

Extreme weather in 2025 once again disrupted forage quality across Ohio. Late hay cutting due to a wet spring contributed to the decline in hay quality during the production season

Crude Protein: CP levels were highly variable. Forage species, plant maturity, and nitrogen applications, especially in predominantly grass crops, will impact CP content in a hay sample. CP ranged from 5.4% - 21.0% across all samples. Low quality forage with less than 7% CP can impair rumen function and livestock performance.

•

Most hay and haylage samples did not meet the nutritional needs of spring calving beef cows

•

Forage testing is essential to identify deficiencies and guide ration adjustments.

•

Supplementation, especially energy sources like corn or byproducts, is often necessary to maintain body condition and herd performance.

•

Forage species, age of the forage stand, soil fertility are all agronomic factors to consider when high quality forage is a goal. Adaptive management strategies, including flexible harvest timing and alternative forage crops, can help reduce weather related risk in future seasons.

Energy (TDN) Findings: Spring calving cows require 49% TDN three months before calving in the fall and 60% TDN at peak lactation (NRC, 2016). Many samples fell short of these thresholds: Grass hay tested 25.3% - 59.0% TDN and Haylage tested 42.8% - 58.0% TDN. Higher quality forages, mixed hay tested up to 69.6% TDN and Legume hay tested up to 61.9% TDN.

TDN

Protein (%)

Very few of the 180 samples met the TDN requirement (NRC, 2016) for a lactating beef cow at peak lactation. These values are only for maintenance levels (Figure 2).

(Values expressed on a Dry Matter basis)

Figure 2. 2025 Hay Analysis. TDN and Crude Protein forage levels compared to needs (NRC, 2016) of a spring calving beef cow. Table 2. Mean forage quality metrics for 2025 Ohio Hay Testing Program. Sample Type

# of Samples

DM (%)

CP (%)

ADF (%)

NDF (%)

Ash (%)

TDN

RFV

Grass Hay

90.1

9.8

40.5

63.4

8.3

49.3

84.9

Legume Hay

94.6

14.6

40.8

51.8

7.7

54.8

106.5

Mixed Hay

105

91.1

10.1

41.8

63.3

5.9

51.7

84.5

Haylage

9.4

40.5

6.9

52.9

88.1

PROJECT CONTACT For inquiries about this project, contact Garth Ruff (ruff.72@osu.edu).

2026 eBarns Report | 63

Hay Quality Analysis OBJECTIVE

eBarns Collaborating Farm

Evaluate the summarized quality results of forages harvested in Brown County, Ohio in the 2025 growing season.

STUDY INFORMATION

OSU Extension Brown County

WEATHER INFORMATION

Sampling Date 7/15/2025 Number of 21 Samples Number of 9 Farms Sampled

Estimated 1,225 Total Acres Represented

Growing Season Weather Summary MAR

APR

MAY

JUN

JUL

Total

Precip (in.)

4.25

7.51

7.61

7.35

3.93

30.65

Cumulative GDDs

204

488

870

1580

2432

STUDY DESIGN Hay samples were collected from partner farms using a forage probe. Mostly round baled dry and wet-wrapped hay were sampled. Samples were sent to a lab as part of a statewide testing program. Samples were collected from Wet-wrapped haylage, grass and mixed grass/legume dry hay.

Hay bales in a Brown County field.

64 | Ohio State Digital Ag Program

Forages

Manure

Small Ruminant

Cattle

Equine

Swine

Poultry

SUMMARY

PROJECT CONTACT

•

Brown County forage quality in 2025 was highly variable, with crude protein ranging from 6 - 17.5% and RFV ranging from 61 - 114. Most forage samples tested in the moderate-quality range, suitable for maintaining mature beef cows with other supplementation.

•

Wet-wrapped haylage generally showed better feed value than dry hay, highlighting the importance of timely harvest.

•

Above-average spring precipitation likely delayed harvest in some fields, contributing to wide variation in forage quality observed across the county.

For inquiries about this project, contact Trevor Corboy (corboy.3@osu.edu).

RESULTS Acid Neutral Detergent Detergent Fiber Fiber (%) (%)

Total Relative Digestible Feed Nutrients Value (%) (RFV)

Hay Type

Dry Matter (%)

Crude Protein (%)

Mixed

Dry hay

91.1

7.9

45.2

71.5

5.8

47.1

69.8

Mixed

Dry hay

92.9

8.5

44.6

66.9

46.6

75.4

Mixed

Dry hay

94.7

7.1

49.4

76.5

6.6

39.4

61.3

Mixed

Dry hay

44.5

68.7

5.4

73.5

Mixed

Dry hay

89.7

6.8

44.8

70.3

3.9

47.7

71.5

Mixed

Dry hay

91.2

9.1

45.1

5.6

46.8

71.5

Mixed

Dry hay

91.5

8.9

44.5

4.9

48.9

72.1

Grass

Dry hay

91.3

9.9

39.9

66.8

8.6

46.1

80.5

Mixed

Dry hay

85.2

10.1

43.4

64.5

49.5

79.4

Grass

Dry hay

92.6

39.6

60.9

9.4

50.3

88.6

Mixed

Dry hay

85.3

14.4

40.7

56.8

7.2

56.8

93.7

Grass

Dry hay

92.9

7.3

43.2

66.8

8.5

45.2

76.9

Grass

Dry hay

91.4

7.9

42.6

65.3

9.5

45.7

79.4

Grass

Dry hay

91.3

8.9

38.7

63.5

52.3

86.1

Mixed

Dry hay

92.6

43.2

66.5

4.9

47.4

77.3

Grass

Dry hay

17.5

33.4

51.4

9.3

58.7

113.7

Haylage

Wet-wrapped

51.6

7.8

38.7

63.7

4.1

56.3

85.8

Haylage

Wet-wrapped

7.2

37.7

5.4

53.5

95.5

Haylage

Wet-wrapped

52.8

7.5

39.6

62.9

4.3

54.6

85.8

Haylage

Wet-wrapped

50.6

12.2

57.8

6.9

56.5

94.2

Haylage

Wet-wrapped

41.8

8.9

44.9

67.7

5.4

74.1

Forage Type

Ash (%)

2026 eBarns Report | 65

Hay Quality Analysis OBJECTIVE

eBarns Collaborating Farm

Evaluate the summarized quality results of forages harvested in Delaware County, Ohio in the 2025 growing season.

STUDY INFORMATION

OSU Extension Delaware County

WEATHER INFORMATION

Sampling Date 6/27/2025 - 7/14/2025 Number of 11 Samples Number of 7 Farms Sampled

Estimated 225 Total Acres Represented

Growing Season Weather Summary MAR

APR

MAY

JUN

JUL

Total

Precip (in.)

2.63

4.53

5.07

4.03

5.23

21.49

Cumulative GDDs

145

379

710

1398

2195

STUDY DESIGN Hay samples were collected from partner farms using a forage probe. All samples collected were from dry hay. Samples were sent to lab as part of a statewide testing program and results were shared back with counties. Forage types included mixes and grass.

Baling hay in Delaware County.

66 | Ohio State Digital Ag Program

Forages

Manure

Small Ruminant

Cattle

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

From mid-May through July 2025, hay producers in Delaware County faced a challenging season marked by wet spring and early summer conditions. Frequent rain and humidity made first cutting especially difficult, noting that getting two or more consecutive days without rain was rare during spring/summer 2025.

•

Overall, producers encountered a season where yield was around average, but quality and drying conditions were major concerns.

•

These conditions likely delayed harvest, reduced forage quality, and increased the risk of rainedon hay.

RESULTS Acid Neutral Detergent Detergent Fiber Fiber (%) (%)

Total Relative Digestible Feed Nutrients Value (%) (RFV)

Hay Type

Dry Matter (%)

Crude Protein (%)

Mixed

Dry hay

15.8

53.6

53.5

96.4

Mixed

Dry hay

8.7

61.1

5.4

53.4

Mixed

Dry hay

16.0

52.5

7.6

62.3

109.2

Mixed

Dry hay

12.4

56.1

5.9

97.7

Grass

Dry hay

8.4

58.7

6.5

54.1

Mixed

Dry hay

13.5

57.2

7.6

53.6

92.1

Mixed

Dry hay

11.0

60.3

7.4

51.8

86.8

Grass

Dry hay

8.1

64.5

8.7

48.5

81.6

Grass

Dry hay

8.4

75.3

Mixed

Dry hay

9.8

60.3

6.7

87.5

Mixed

Dry hay

6.7

61.8

123.8

Forage Type

Ash (%)

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

2026 eBarns Report | 67

Hay Quality Analysis OBJECTIVE

eBarns Collaborating Farm

Evaluate the summarized quality results of forages harvested in Licking County, Ohio in the 2025 growing season.

STUDY INFORMATION

OSU Extension Licking County

WEATHER INFORMATION

Sampling Date 6/15/2025 - 7/30/2025 Number of 17 Samples Number of 6 Farms Sampled

Estimated 170 Total Acres Represented

Growing Season Weather Summary MAR

APR

MAY

JUN

JUL

Total

Precip (in.)

2.15

4.12

7.25

6.72

4.55

24.79

Cumulative GDDs

188

451

816

1528

2328

STUDY DESIGN

OBSERVATIONS

Hay samples were collected from partner farms using a forage probe. All but one sample was collected from dry hay. Samples were sent to lab as part of a statewide testing program and results were shared back with counties. Forage types included mixes, grass, legumes.

Hay harvest for first cutting in Licking County was delayed for many producers due to frequent rain events in May and June.

68 | Ohio State Digital Ag Program

Forages

Manure

Small Ruminant

Cattle

Equine

Swine

Poultry

RESULTS Acid Neutral Detergent Detergent Fiber Fiber (%) (%)

Total Relative Digestible Feed Nutrients Value (%) (RFV)

Hay Type

Dry Matter (%)

Crude Protein (%)

Mixed

Wet-wrapped

9.4

61.8

5.0

54.9

88.5

Mixed

Dry hay

9.4

66.7

8.8

46.9

77.0

Mixed

Dry hay

8.5

66.2

6.0

49.8

78.5

Mixed

Dry hay

8.4

60.5

5.5

54.2

90.9

Mixed

Dry hay

8.9

63.0

6.4

51.3

84.7

Mixed

Dry hay

11.5

61.8

6.2

49.3

87.8

Mixed

Dry hay

8.2

69.5

6.4

45.9

73.4

Mixed

Dry hay

9.6

65.1

6.4

80.5

Mixed

Dry hay

63.2

10.9

49.5

85.0

Mixed

Dry hay

10.2

66.6

12.6

44.7

77.2

Mixed

Dry hay

55.8

4.9

56.1

99.3

Mixed

Dry hay

11.2

65.4

5.9

52.2

80.7

Mixed

Dry hay

58.8

5.5

59.5

94.0

Mixed

Dry hay

6.9

66.4

6.1

48.4

79.6

Legume

Dry hay

63.6

5.8

56.4

85.7

Grass

Dry hay

14.9

53.3

7.3

56.1

103.2

Mixed

Dry hay

8.6

69.6

8.6

48.3

73.2

Forage Type

Ash (%)

PROJECT CONTACT For inquiries about this project, contact Dean Kreager (kreager.5@osu.edu).

2026 eBarns Report | 69

Hay Quality Analysis OBJECTIVE

eBarns Collaborating Farm

Evaluate the summarized quality results of forages harvested in Morgan County, Ohio in the 2025 growing season.

STUDY INFORMATION

OSU Extension Morgan County

WEATHER INFORMATION

Sampling Date 7/25/2025 Number of 13 Samples Number of 3 Farms Sampled

Growing Season Weather Summary MAR

APR

MAY

JUN

JUL

Total

Precip (in.)

1.95

4.52

6.93

4.78

5.18

23.36

Cumulative GDDs

208

490

843

1531

2332

Hay in storage.

70 | Ohio State Digital Ag Program

Forages

Manure

Small Ruminant

Cattle

Equine

Swine

Poultry

OBSERVATIONS During May and June there were limited windows to make hay for producers due to frequent rain events. As a result, first cutting was delayed across Morgan County causing some quality concerns for producers.

RESULTS Acid Neutral Detergent Detergent Fiber Fiber (%) (%)

Total Relative Digestible Feed Nutrients Value (%) (RFV)

Hay Type

Dry Matter (%)

Crude Protein (%)

Mixed

Dry hay

87.7

11.2

46.3

67.9

6.5

48.5

72.3

Mixed

Dry hay

89.1

7.2

46.1

71.5

5.2

68.9

Mixed

Dry hay

88.1

8.6

44.6

70.8

4.7

50.6

71.2

Mixed

Dry hay

87.5

7.7

42.9

67.8

3.2

51.5

Mixed

Dry hay

80.5

8.1

50.7

76.8

3.9

42.4

59.8

Mixed

Dry hay

86.1

11.9

38.1

59.1

7.2

59.2

93.2

Mixed

Dry hay

87.4

7.9

43.9

66.8

4.2

50.1

76.1

Mixed

Dry hay

86.

8.8

44.2

3.9

50.1

73.5

Mixed

Dry hay

88.9

8.5

47.8

73.1

5.1

45.8

65.8

Mixed

Dry hay

88.1

13.3

40.3

63.7

6.2

57.2

Mixed

Dry hay

88.9

44.8

69.2

4.6

48.8

72.6

Mixed

Dry hay

89.6

12.2

36.9

60.5

57.1

92.5

Mixed

Dry hay

89.5

9.3

46.6

71.6

3.88

49.1

68.4

Forage Type

Ash (%)

PROJECT CONTACT For inquiries about this project, contact Jordan Penrose (penrose.30@osu.edu).

2026 eBarns Report | 71

Nitrogen Rate - Cereal Rye OBJECTIVE

NC Ag Research Station

Determine if fall applied nitrogen (N) increases density, yield and crude protein in cereal rye, and the effect of spring N on those factors across multiple fall N rates.

STUDY INFORMATION

OARDC Sandusky County

WEATHER INFORMATION

Planting Date 10/14/2025 Harvest Date 5/4/2026 Variety Cereal Rye VNS Population 2 bu/ac Acres 1 Treatments 16 Reps 4 Treatment Width 10 ft. Tillage Minimum Till Management Fertilizer Previous Crop Soybean Row Spacing 7.5 in. Soil Type Rimer loamy fine sand, 100%

Growing Season Weather Summary OCT NOV-FEB

MAR

APR

MAY

JUN

Total

Precip (in.)

2.07

3.95

4.36

4.37

2.83

3.4

20.98

Cumulative GDDs

278

372

526

798

1129

1730

STUDY DESIGN This trial was a randomized complete block split-plot design with four N rates in the fall (0, 25, 50, and 75 lbs N/ac) and four N rates in the spring (0, 30, 60, and 90 lbs N/ac) over the fall rates in cereal rye. Rye was planted in October and harvested in May. Normalized Difference Vegetative Index (NDVI) and Canopeo readings were taken in December before fall dormancy and in the spring after greenup to determine the greenness and density of green matter. Visual differences between fall (left) and spring (right) tillering.

72 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

The growth of the forage was stunted due to the warm March and the cool, wet May. There was no visual difference between treatment growth in the fall, but there was in the spring and at harvest.

•

Forage tillering increased with fall N application.

•

Crude protein was statistically the highest 60 or 90 lbs/ac of spring N depending on fall N application rate.

•

To maximize yield, at least 25 lbs/ac of fall N was needed, with a 30 lbs/ac spring N application.

PROJECT CONTACT For inquiries about this project, contact Jason Hartschuh (hartschuh.11@osu.edu), Kendra Rose (rose.1919@osu.edu), or Al Gahler (gahler.2@osu.edu).

RESULTS Treatments (lbs/ac)

TDN (%)

NDF (%)

Crude Protein (%)

Yield (tons/ac)

Fall 0, Spring 0

67.3 ab

49.8 bcde

10.4 f

1.2 e

Fall 0, Spring 30

67.7 ab

49.9 abcd

10.3 f

1.8 d

Fall 0, Spring 60

66.8 abc

49.5 abcde

13.2 de

2.0 bcd

Fall 0, Spring 90

66.6 abc

48.2 ab

15.5 abc

1.9 bcd

Fall 25, Spring 0

67.8 a

50.7 abcd

8.9 f

1.8 cd

Fall 25, Spring 30

67.2 ab

50.8 bcde

10.1 f

2.2 ab

Fall 25, Spring 60

64.9 d

52.6 abc

13.8 cde

2.1 abc

Fall 25, Spring 90

65.3 cd

50.2 bcde

15.8 ab

2.1 bcd

Fall 50, Spring 0

67.3 ab

52.0 a

10.0 f

2.0 bcd

Fall 50, Spring 30

67.4 ab

49.6 de

12.6 e

2.0 bcd

Fall 50, Spring 60

66.0 bcd

50.3 abcd

14.7 abcd

2.2 ab

Fall 50, Spring 90

66.6 abc

49.2 abcde

13.5 de

2.4 a

Fall 75, Spring 0

66.6 abc

51.2 abcde

13.1 de

2.0 bcd

Fall 75, Spring 30

66.3 abcd

50.7 e

14.1 bcde

2.0 bcd

Fall 75, Spring 60

66.8 abc

47.8 cde

15.8 ab

1.8 d

Fall 75, Spring 90

66.3 abcd

48.3 de

16.3 a

2.0 bcd

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: 1.7 CV: 2.1

LSD: 2.8 CV: 4.7

LSD: 1.8 CV: 11.4

LSD: 0.3 CV: 13.0

2026 eBarns Report | 73

Sulfur on Forages OBJECTIVE

eFields Collaborating Farms

Evaluate if the addition of sulfur (S) to deficient eastern Ohio soil can improve quantity or quality of forage.

STUDY INFORMATION

OSU Extension Statewide

WEATHER INFORMATION

Start Date 2023 End Date Mid-September 2025 Variety Established mixed, cool-season grass Acres 1 Treatments 3 Reps 3 Treatment Width 10 ft. Management Fertilizer Soil Type by Licking: Brownsville County Sites channery silt loam, 100% Muskingum: Watertown Sandy Loam, 100% Jefferson: Morriston silty clay loam, 90.5% Gilpin-Lowell silt loams, 9.5%

2023 Precipitation (in.) Summary Location

APR

MAY

JUN

JUL

AUG

SEP

Total

Licking

3.43

2.68

3.61

3.38

3.94

0.39

17.43

Muskingum

3.39

3.3

4.87

4.01

5.97

0.69

22.23

Jefferson

2.21

1.87

4.56

3.54

4.82

0.97

17.97

2025 Precipitation (in.) Summary Location

APR

MAY

JUN

JUL

AUG

SEP

Total

Licking

4.12

7.26

6.71

4.59

0.28

1.84

24.8

Muskingum

4.52

6.93

4.78

5.17

0.54

2.89

24.83

Jefferson

4.14

5.89

5.1

3.37

0.58

2.86

21.94

STUDY DESIGN Three counties in Eastern Ohio were chosen for the study: Jefferson, Licking, and Muskingum. Plot sizes were 10 by 45 feet. All treatments were replicated three times in a randomized complete block design at each site for the years 2023 and 2025. The Control treatment received no nutrient input. The Urea treatment received 50 lbs/ac of nitrogen (N). The Ammonium Sulfate (AMS) treatment received 50 lbs N/ac and approximatley 50 lbs S/ac. Treatments were completed in late July at a grass height of approximately 3 inches. Plots were harvested mid-September and analyzed for dry matter (DM) and forage quality. DM results were averaged by location.

74 | Ohio State Digital Ag Program

View of site location in Licking County.

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

Sulfur Soil Test Values: Acid rain from coal production historically led to high soil sulfur content in eastern Ohio. Mitigation of the acid rain had led to soil sulfur levels falling below the sufficient level (less than 15 ppm). Jefferson County was at a 7.7 ppm in 2023 and 7.4 ppm in 2025. Licking County was at 8.6 ppm in 2023 and 9.8 ppm in 2025. Muskingum County was at 18 ppm in 2023 and 12.5 ppm in 2025. Jefferson and Licking were grouped together because they were considered low sulfur (averaging below 15 ppm). Muskingum was initially analyzed separately and categorized as high sulfur (averaging above 15 ppm).

•

Large areas of eastern Ohio are used for grazing and forage production, so this experiment was conducted to evaluate whether the addition of sulfur to deficient eastern Ohio soil would lead to improved quantity or quality of forage.

•

There were no significant yield response to sulfur application by AMS in either the high or low sulfur soil conditions. The addition of sulfur did not improve DM, CP, ADF, or TDN over the addition of N alone.

•

There was a N fertilizer response both years at all locations. Addition of N in the form of either urea or AMS led to similar increases in DM production over control plots, regardless of soil sulfur test designation as low or high in both years.

•

This study supports the use of N to increase production, as well as more studies to further investigate forage quality improvement in eastern Ohio.

Effects on Forage Quality: Crude protein (CP) (P<0.0004) and acid detergent fiber (ADF) (P<0.05) both had significant changes independent of fertilizer types. Total digestible nutrients (TDN) stayed constant through the treatments and a trend of increased sulfur uptake in the AMS compared to the urea was noted.

RESULTS Treatments

TDN (%)

CP (%)

ADF (%)

Sulfur (%)

Control

62.52 a

14.97 b

31.68 a

0.28 a

Urea

61.92 a

19.53 a

29.72 b

0.32 a

AMS

62.72 a

19.82 a

29.55 b

0.37 a

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: 4.4

LSD: 2 CV: 8.7

LSD: 1 CV: 4.6

LSD: 0 CV: 14.6

TOOLS OF THE TRADE

PROJECT CONTACT

Comprehensive analysis by a lab that can assess forages through Near Infrared Spectroscopy (NIRS) is essential for understanding forage quality results. One resource is the Sustainable Agroecosystems Lab. Visit go.osu.edu/ForageAnalysis or use the QR code to the right for more information.

For inquiries about this project, contact Dan Lima (lima.19@osu.edu), Clifton Martin (martin.2422@osu.edu), Dean Kreager (kreager.5@osu.edu), or Jason Hartschuh (hartschuh.11@osu.edu).

2026 eBarns Report | 75

Winter Annual Forage Planting Date OBJECTIVE

Wooster Campus Farms

Evaluating the effect of planting date and small grain species on forage yield.

STUDY INFORMATION

OARDC Wayne County

WEATHER INFORMATION

Planting Date Varied Harvest Date Varied Variety Barley, Rye, Triticale (VNS) Population 1.2 million sds/ac Acres 1 Treatments 5 Reps 4 Treatment Width 10 ft. Tillage No-Till Management Fertilizer Previous Crop Corn Row Spacing 7.5 in. Soil Type Canfield silt loam, 100%

STUDY DESIGN This study was a completely randomized design comparing three winter annual small grains (Rye, Triticale, Barley) at five planting dates (Sept. 15, Sept. 29, Oct. 13, Oct. 27, and Nov. 6). Fall and pregreen-up canapeo measurements were taken to estimate percent ground cover, and Normalized Difference Vegetative Index (NDVI) measurements were taken in the fall, pregreen-up, and at harvest. All plots were harvested based on maturity, with a target of early boot stage, resulting in three harvests (April 21, April 27, and May 11). Fall nitrogen (N) application was 25 lbs N/ac, and then 90 lbs N/ac in the spring. Plots were harvested with a small plot forage harvester and subsamples were pulled for dry matter (DM) estimation.

76 | Ohio State Digital Ag Program

Growing Season Weather Summary OCT NOV-FEB

MAR

APR

MAY

JUN

Total

Precip (in.)

3.53

3.95

4.02

5.25

3.87

2.65

23.27

Cumulative GDDs

230

324

491

785

1097

1658

Harvesting part of the trial in Spring 2026.

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

Although percent ground cover estimation decreased with each successive planting date, for both Fall and Spring measurements, there was no relationship between Canapeo estimation and final DM yields. Spring and Fall NDVI values also did not correlate to final yield, but NDVI measured at harvest was moderately correlated to total DM yield. There was a significant species by planting date interaction. Barley and Triticale performed better at the three earliest planting dates. Rye showed the highest yield on the fourth planting date. There was no difference in yields between the three species at the latest planting date.

•

Percent ground cover in the Fall, and prior to Spring green-up, did not provide an accurate prediction of final yield.

•

NDVI at harvest was related to final dry matter yield. Across all planting dates, triticale yield consistently higher dry matter per acre than barley or rye.

•

Barley benefited from earlier planting dates were rye tended to yield the most at the later dates.

RESULTS DM yield (lbs/ac)

Canapeo (% ground cover)

Treatments (planting date)

Barley

Rye

Triticale

Barley

Rye

Triticale

September 15, 2025

2,359 a

1,888 b

2,826 a

73.8 a

57.3 a

72.3 a

September 29, 2025

2,287 a

1,999 b

2,707 a

74.1 a

39 a

56.1 a

October 13, 2025

3,602 a

2,136 b

3,642 a

53.9 a

55.5 a

40 a

October 27, 2025

2,180 b

2,948 a

2,817 a

26.2 a

36.7 a

32.9 a

November 6, 2025

2,168 a

2,720 a

2,437 a

28.1 a

24.7 a

28.4 a

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: 645

LSD: 37

TOOLS OF THE TRADE

PROJECT CONTACT

A small plot harvester can harvest and weigh forages, then a sample can be pulled to test moisture and perform quality analysis.

For inquiries about this project, contact John Yost (yost.77@osu.edu).

2026 eBarns Report | 77

Winter Annual Forage Varieties OBJECTIVE

NC Ag Research Station

Analyzing the growth and quality of different varieties of rye and triticale at different planting dates.

STUDY INFORMATION

OARDC Sandusky County

WEATHER INFORMATION

Planting Date 10/14/2025, 10/29/2025 11/17/2025 Harvest Date 5/4/2026, 5/8/2026, 5/14/2026, 5/19/2026 Variety Hazlet Rye, Albon Rye, Trical Flex 719 Trit, Outlaw Tricivale, VNS Rye, Graze King 90 Population 2 bu/ac Acres 1 Treatments 12 Reps 6 Treatment Width 10 ft. Tillage No-Till Management Fertilizer Previous Crop Soybean

Growing Season Weather Summary

Row Spacing 7.5 in.

OCT NOV-FEB

MAR

APR

MAY

JUN

Total

Precip (in.)

2.07

3.95

4.36

4.37

2.83

3.4

20.98

Cumulative GDDs

278

372

526

798

1129

1730

Soil Type Hoytville clay loam, 100%

STUDY DESIGN This trial was a randomized complete block split bock deisgn six varieties (Hazlet Rye, Albon Rye, Trical Flex 719 Trit, Outlaw Tricivale, VNS, and Graze King 90) planted at three different planting dates (PD) and harvested in the spring at boot stage. Fall nitrogen (N) application was 110 lbs N/ac, and then 113 lbs N/ac in the spring. The rye and triticale was planted on three differerent dates: 10/14/25 (PD 1), 10/29/25 (PD 2), 11/17/25 (PD 3) and harvested on multiple dates in May (5/4/26, 5/8/26, 5/14/26, 5/19/26). View of forage growth at harvest.

78 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

The growth of the forage was stunted due to the warm March and the cool, wet May. There was a visual difference between treatment growth in the fall, in the spring and at harvest. Forage matured at different times, requiring four different harvest dates.

•

Planting date had a significant effect on crude protein (CP), with the latest planting date in November having the highest CP.

•

Planting date had no effect on the yield.

•

CP was the lowest in the variety Hazlet Rye, and highest in Albon Rye and Tricale Flex 719 Trit.

•

Variety Albon Rye & Graze King 90 had the lowest yield, while Hazlet Rye and Outlaw Tricivale had the highest.

PROJECT CONTACT For inquiries about this project, contact Jason Hartschuh (hartschuh.11@osu.edu), Kendra Rose (rose.1919@osu.edu), or Al Gahler (gahler.2@osu.edu).

RESULTS Treatments (planting date, variety)

TDN (%)

NDF (%)

Crude Protein (%)

Yield (tons/ac)

PD 1, Hazlet Rye

68.8 a

43.2 j

15.1 cde

1.1 efgh

PD 1, Albon Rye

66.3 cde

49.0 cd

14.5 cdef

1.3 abcd

PD 1, Trical Glex 719 Trit

67.7 abc

43.5 ij

16.1 bc

1.1 cdefgh

PD 1, Outlaw Tricivale

67.3 abc

45.5 fghij

13.5 ef

1.5 ab

PD 1, VNS Rye

68.2 ab

45.1 ghij

15.8 bc

1 fgh

PD 1, Graze King 90

66.9 bcde

48.2 cdef

14.6 cdef

1.2 bcdef

PD 2, Hazlet Rye

67.3 abc

48.9 cd

12.9 f

1.5 a

PD 2, Albon Rye

67.3 abc

45.7 efghij

16.2 bc

0.9 h

PD 2, Tricale Flex 719 Trit

62.1 f

55 a

15.6 cd

1.3 abc

PD 2, Outlaw Tricivale

65.6 de

49.9 bc

14.1 def

1.4 ab

PD 2, VNS Rye

68.2 ab

46.5 defghi

13.2 f

1.3 abc

PD 2, Graze King 90

66.3 cde

48.1 cdefg

15.1 cde

2 gh

PD 3, Hazlet Rye

67.7 abc

46.9 cdefgh

15.6 cd

1.5 ab

PD 3, Albon Rye

67.7 abc

44.2 hij

18 a

1.1 defgh

PD 3, Trical Flex 719 Trit

62.6 f

52.1 ab

18.1 a

1.4 ab

PD 3, Outlaw Tricivale

65.4 e

48.8 cde

18 a

1.3 abcde

PD 3, VNS Rye

67.9 abc

44.7 hij

15.6 cd

1.2 cdefg

PD 3, Graze King 90

67.1 bcd

46 defghij

17.4 ab

1.1 cdefgh

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: 1.6 CV: 2.0

LSD: 3.1 CV: 5.5

LSD: 1.7 CV: 9.2

LSD: 0.2 CV: 16.1

2026 eBarns Report | 79

Woody Invasives Management OBJECTIVE

Eastern Ag Research Station

Evaluate the use of different herbicides applied by drone to control woody species in pastures.

STUDY INFORMATION

OSU Extension Noble County

WEATHER INFORMATION

Variety Mixed Species Permanent Pasture Application Date 9/12/2025 Fall Evaluation 10/10/2025 Date Spring 5/19/2026 Evaluation Date Acres 9 Treatments 5 Reps 10 Treatment Width 120 ft. Management Herbicide Soil Type Lowell silty clay loam, 100%

Growing Season Weather Summary

STUDY DESIGN

SEP

OCT

NOVFEB

MAR

APR

MAY

Total

Precip (in.)

4.28

2.57

6.87

4.71

2.35

4.91

25.69

Cumulative GDDs

560

843

1051

1320

1724

2132

This was a completely randomized design trial with 4 treatments and a control. One month after application, 10 woody plants were evaluated for the amount of each plant still alive in each treatment area on a scale of 0-10. Plots were then reevaluated in the spring, after spring green-up, to determine the number of plants that were still alive. Woody species evaluated were Hedge Apple, Autumn Olive, and Multiflora Rose. Treatments were applied using a Hylio T20 drone at 5 gal/ac, with a speed of 15 feet per second and a droplet size of 350 microns. Comparison of two plants that were sprayed.

80 | Ohio State Digital Ag Program

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

Hedge apple was the easiest to defoliate in the fall and still be dead in the spring. Autumn Olive often looked dead in the fall, but had shoots inside that grew green leaves the following spring. With the Multiflora Rose having a dense canopy it would be the most likely to have small center canes that still had green leaves during the fall evaluation.

•

The Garlon 4 Ultra treatment using diesel fuel as the oil emulsion performed worse than the other 4 treatments. The areas where this treatment was applied had more Autumn Olive and shorter flight paths, which may have led to more application misses.

•

All treatments would require a multi-year application plan for complete control of woody invasives.

•

Remedy Ultra based treaments were the most effective.

RESULTS Treatments

Fall - Quantity Alive (between 0-10)

Spring - Quantity Alive (between 0-10)

Control

10 c

10.0 c

Remedy Ultra, Chapparral, Preference Adjuvant, Diesel, and Emulsifier

0.7 a

Remedy Ultra, Chapparral, Preference Adjuvant, Valcheck (Crop Oil Concentrate)

1.3 a

Garlon 4 Ultra, Chapparral, Preference Adjuvant, Diesel, and Emulsifier

5.6 b

Garlon 4 Ultra, Chapparral, Preference Adjuvant, Valcheck (Crop Oil Concentrate)

1.7 a

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: 1 CV: 92.5

LSD: 1 CV: 80.4

TOOLS OF THE TRADE The DJI T20 Agricultural Spray Drone was used in this stury to apply herbicides in pasture settings that had steep slopes.

PROJECT CONTACT For inquiries about this project, contact Jason Hartschuch (hartschuh.11@osu.edu), John Fulton (fulton.20@osu.edu), or Nina Nebesh (nebesh.3@osu.edu).

2026 eBarns Report | 81

2026 Tick Update OBJECTIVE Evaluate the most prevalent tick species in Ohio and the health impacts on both producers and livestock.

eBarns Collaborating Farms OSU Extension Statewide

UPDATE ON TICKS AND IMPACTS ON LIVESTOCK AND PRODUCERS

Ticks and the diseases that they vector to livestock, companion animals, and humans have become an increasingly larger public health risk and a significant impact on our cattle industry.

Biggest Ticks of Medical Concern for Producers The American Dog Tick prefers a more open habitat including pasture, meadows, and lawns. This tick is a prominent vector of Rocky Mountain Spotted Fever and Tularemia.

The Black Legged or Deer Tick prefers to live in wooded habitat. This tick is the primary vector for Lyme disease plus can also vector Babesia and Anaplasmosis.

The Lone Star Tick is an aggressive feeder and prefers wooded habitat. This tick is the reported causative agent for the Alpha-gal or Mammalian Muscle Allergy reaction where person who is bitten can become allergic to meat including beef, pork, lamb, and venison.

Adult Male (left) and Adult Female (right) Black Legged or Deer Ticks*.

Adult Male (left) and Adult Female (right) Lone Star Ticks*.

Adult Male (left) and Adult Female (right) American Dog Ticks*.

*Images sourced from TickEncounter, tickencounter.org

Biggest Tick of Major Medical Concern in Livestock: Longhorned Tick The Longhorned Tick (LHT) is an invasive tick that was identified in Ohio in 2020. This tick is unique in that it reproduces via parthenogenesis, meaning the female does not need a male to breed, allowing it to produce extreme numbers of ticks on its host or in a pasture.

Adult Female LHT, sourced from the CDC Public Health Image Library, phil.cdc.gov

82 | Ohio State Digital Ag Program

Tips for managing Longhorned Ticks on pasture and cattle: •

Frequently scout your animals as this tick can reproduce in large numbers.

•

While we have had detection as early as March, this tick is known to prefer heat and tall grass. The largest numbers of ticks on cattle in Ohio were detected in July.

•

This tick can vector Theileria orientalis IKEDA, a protozoal blood parasite, to cattle that can cause fever, weight loss, inappetence, open cows, stillbirth, jaundice, and mortality.

•

Work with your veterinarian if you think you have ALHT on your animals. Guidance for extra-label application of tick control needs to come from a vet.

•

Management intensive grazing with rotation through paddocks to let your animals maintain the pasture may be an important integrated pest management strategy.

•

Products labelled for pasture application to control ticks that have shown to be effective, but they need to contact the tick to work. Application on top of tall grass or residue may not contact the tick directly and therefore may not be effective for control.

Forages

Manure

Cattle

Small Ruminant

Equine

Swine

Poultry

Map of Longhorned tick and Theileria detections in Ohio as of July 15, 2026 •

Longhorned ticks have been identified in 28 counties with 28 counties having positive cases of Theileria orientalis. LHT are found in 26 states and DC, with the newest being Alabama in June 2026.

•

The Farm Services Agency has a Livestock Indemnity Program that may assist producers who have been negatively impacted by cattle mortality from Theileria.

Longhorned tick scouting locations on cattle include the perineum and tailhead (left) and the ears (Right). Longhorned Ticks

Counties with:

Theileria (theileria orientalis)

The Ohio State University now has a tick testing laboratory where you can submit ticks from humans and animals for identification and pathogen testing. For more information, visit: buckeyeticktest.osu.edu

PROJECT CONTACT Note: This work is supported by the Crop Protection and Pest Management Program grant 20247000643574, from the U.S. Department of Agriculture’s National Institute of Food and Agriculture. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author(s) and should not be construed to represent any official USDA or U.S. Government determination or policy.

For inquiries about this project, contact: Timothy S. McDermott, DVM (mcdermott.15@osu.edu).

2026 eBarns Report | 83

Winter Feeding Beef Cows OBJECTIVE

Eastern Ag Research Station

Evaluate the impact of differing hay feeding strategies on beef cows body weight and condition scores.

STUDY INFORMATION

OSU Extension Noble County

WEATHER INFORMATION

Start Date 1/6/2026 End Date 3/19/2026 Species Beef Start Point 75 days pre-calving End Point Date of First Calf Birth Treatments 3 Reps 1 Experimental Pasture/Paddock Unit Total Units 3 Breed, Genetics Angus Cross Beef Cows Sex Cows Diets Fed 3

Weather Summary

STUDY DESIGN Beef cows were blocked by weight and age on December 22, 2025 and randomly assigned to each treatment. There were 20 cows in each of the three treatments (control, bale grazing, and limit-fed). Limit-fed cows were transitioned to a warm up diet for 14 days prior to the start of the study. Cows were weighed two consectutive days to begin the trial. Weights and Body Condition Scores (BCS) were recorded every 25 days. The control group was carried hay as needed, while the bale grazing group was allotted a weeks worth of hay based on cow and bale weights. Bale grazing bales were placed in the pasture the prior November and net wrap was removed. The same hay was fed to all treatments, with the limit-fed cows being delevered a total mixed ration.

84 | Ohio State Digital Ag Program

JAN

FEB

MAR

Total

Precip (in.)

0.92

0.81

7.24

8.97

Avg Max Temp (°F)

45 (avg)

Avg Min Temp (°F)

24 (avg)

June forage regrowth where bale grazing happened - new grass growing through bale residue.

Forages

Manure

Cattle Beef

Small Ruminant

Equine

Swine

Poultry

OBSERVATIONS

SUMMARY

Bunk fed cows initially lost weight even after a warm up period to a limit-fed diet. However, with the diet formulated to a cow in late gestation, they did gain weight back and ended up being the heaviest weight and conditioned (fattest) group of cows. Hay waste on the bale grazing cows depended on weather and was estimated at approximately 10 percent.

•

Bale grazing and limit feeding cows offer potential management strategies to either maintain or improve cow performance during winter months. Forage regrowth and soil sampling will also continue to determine feeding method impact on nutrient managment.

•

Limit fed cows exhibited numerically greater BCS improvement over time.

•

This study will be replicated to better gauge costs and impact to forage in bale grazing fields.

RESULTS Treatments (Diet)

Weight Change (lbs)

BCS Change

Final Weight

Final BCS

Control (TRT 1)

0.66

1373

6.32

Bunk Fed (TRT 2)

0.82

1386

6.75

Bale Grazing (TRT 3)

0.62

1362

6.4

Figure 1. Body condition score (BCS) change over time, by treatment group.

Figure 2. Weight change over time, by treatment group.

PROJECT CONTACT For inquiries about this project, contact Garth Ruff (ruff.72@osu.edu).

2026 eBarns Report | 85

Tracking Dairy Antibiotic Use OBJECTIVE

eBarns Collaborating Farms

Evaluate the level of agreement and consistency of antimicrobial use data on large conventional dairy farms.

College of Veterinary Medicine Statewide

BACKGROUND

STUDY INFORMATION

Accurately quantifying antimicrobial use (AMU) on farms is essential for understanding its relationship with antimicrobial resistance. In addition, dairy producers want to ensure that antimicrobials are used responsibly, both to maintain animal health and to meet industry and consumer expectations. However, it is still unclear which measurement methods would provide more reliable estimates of AMU on farms. Therefore, this study aimed to determine whether farm treatment records can provide similar estimates of antimicrobial use compared to counting empty drug containers, supporting the development of effective surveillance and benchmarking programs.

Start Date September 2022 End Date February 2024 Species Dairy Cattle Total # of Farms 13 Ohio Counties Wayne, Madison, Represented Van Wert, Crawford, Paulding Experimental Farm Unit

STUDY DESIGN This field study included 13 large conventional dairy farms with adequate antimicrobial treatment records located in Ohio and California. AMU was collected over a six-month period using two different treatment collection methods. For the empty drug container method, used and discarded antimicrobial containers were manually counted by researchers during routine farm visits. For the farm treatment records method, treatment data were extracted from dairy management Timeline of events: software. The incidence of antimicrobial treatment was later estimated to compare the two antimicrobial collection methods.

Treatment records maintained by a farm being collected and reviewed.

Visit 0

Visit 1

Visit 2

Visit 3

Visit 4

Visit 5

Visit 6

Goals discussed

First antimicrobial bins collection

Antimicrobial bins collection

Treatment records retrieved

Last antimicrobial bins collection

Treatment records retrieved

120

150

Consent form signed Bins placed

AMU quantification

End of the study

180

AMU quantification

Figure 1. Timeline of data collection.

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OBSERVATIONS

SUMMARY

Antimicrobial use varied considerably among the enrolled dairy farms, highlighting substantial differences in treatment practices across operations. The estimated mean treatment incidence from farm treatment records was 0.78 times lower than empty drug container, suggesting that treatment records alone underestimated antimicrobial use. Together, these findings underscore the need to improve and expand on-farm antimicrobial use data collection systems. More complete and accurate treatment data are essential for the reliable quantification of antimicrobial use at the farm level and for the development of effective benchmarking and monitoring programs that support antimicrobial stewardship efforts.

•

Farm owners allowed access to herd management software and placement of bins to collect discarded antimicrobial containers used for adult dairy cattle.

•

Regardless of the collection method used, AMU varied significantly among the enrolled dairy farms.

•

The variation in antimicrobial use was substantially larger between farms than within farms.

•

Overall, farm treatment records provided similar estimates of antimicrobial use compared to counting empty drug containers, suggesting they can be a practical and reliable tool for monitoring AMU on dairy farms. Both methods yielded fairly similar results when estimating antimicrobial use on farms.

RESULTS Within each farm, antimicrobial use patterns were generally consistent over time, meaning both methods tracked similar trends in use. For dry-cow therapy, both methods showed nearly the same level of use, with no meaningful difference between them. For other common treatments (such as injectable, intramammary during lactation, and uterine treatments), the two methods also produced similar estimates, although the empty container method tended to give slightly higher values.

Figure 1. Mean treatment incidence (doses of antibiotics per 1000 cows per day) by farm obtained using farm treatment record (FTR) and empty drug container (EDC).

TOOLS OF THE TRADE On-Farm Collection Bins and Farm Treatment Records Bins were placed in convenient locations around each farm. Workers were asked to place all empty antimicrobial containers used to treat adult dairy cattle into the bins. Treatment records, maintained by the farm, were collected and reviewed. These records documented antimicrobial treatments administered to animals (including treatment date, animal identification, and medication used).

Figure 2. Overall total mean treatment incidence (animal daily dose ADD/1,000 cow-days) by collection method. X = mean treatment incidence (FTR = 10.4 and EDC = 13.3). Solid horizontal line = median (FTR = 8.8 and EDC = 7.0). Solid dot = Influential observations within the Ohio data set.

PROJECT CONTACT For inquiries about this project, contact Dr. Greg Habing (habing.4@osu.edu) or Dr. Rafael Portillo-González (portillo-gonzalez.1@osu.edu).

2026 eBarns Report | 87

Sheep LAI Summary OBJECTIVE Measure lambing rates of seedstock farms using frozen semen for their Laparoscopic Artificial Insemination (LAI) genetic improvement program.

eBarns Collaborating Farms OSU Extension Marion County

STUDY INFORMATION Start Date 2017 End Date 7/1/2026 Species Sheep Total # of Farms 3 Start Point Various End Point Various Estrous Yes Synchronizatrion Experimental Head Unit Bred, Genetics

Hampshire, Shropshire, Tunis

An up-close view of LAI by a technician.

Sex Female

STUDY DESIGN Laparoscopic Artificial Insemination (LAI) is the intrauterine method of insemination, to bypass the unique anatomically tortuous cervix of the sheep. Currently, producers follow the synchronization schedule, breed ewes with an above average body condition score, and follow the prescribed timelines. The data for this comparison has been collected the past ten years from seedstock producers with experience and knowledge of the LAI procedures. Three farms participated with 67 ewes total.

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Technician using a cradle device during a LAI.

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OBSERVATIONS The dataset contains 24 producer-year records across 10 lambing years. This includes 643 total ewes bred and 317 ewes lambed for an overall lambing percentage of 49.3%. By season the lambing percentages were 50.7% fall, 53.1% early spring, 50.0% for spring lambs, and 38.7% late spring lambs. For this 10 year period BWB Farms has the highest percentage 50.4% while Barnes Farm Genetics narrowly trails at 49.5%. With 4 years of data Erwin Farms lambing percentage is 43.0%.

SUMMARY •

•

During the past ten years, participating flocks have reviewed, accepted new observations, and adopted new standard procedures for LAI. Efforts have been made to reduce stress, teaser rams are used to confirm the exact timing postcidr removal, high plane of nutrition plus above average body condition scores, consistent hormone protocol, and high semen quality post thaw. The decline in LAI pregnancy rates over the past four years remains a primary concern among participating breeders. Common factors associated with low pregnancy rates following LAI include improper insemination timing, poor semen quality, inadequate ewe body condition, compromised ewe health status, and errors in synchronization protocols. However, these factors have been maintained at industrystandard levels, suggesting that other causes may be contributing to the observed decline in pregnancy rates.

TOOLS OF THE TRADE Laparoscopic Insemination is an industry standard procedure has been used for all years in the participing flocks' laparoscopic insemination program. A laparoscope (image to the right) is used with veterinarian accepted procedures and timings.

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RESULTS Table 1. Summary results from all years of the study.

Year

Bred

Lambed

Birth Rate (%)

2017

69.7

2018

64.3

2019

64.3

2020

47.8

2021

153

59.5

2022

65.5

2023

154

48.7

2024

45.9

2025

35.6

2026

23.9

Totals

761

387

Average: 52.5

Figure 1. Birth rate visual based on Table 1.

PROJECT CONTACT For inquiries about this project, contact: Tim Barnes (barnes.821@osu.edu).

2026 eBarns Report | 89

Maternal Effects on Birthweights OBJECTIVE Identifying maternal factors during pregnancy that are associated with neonatal lamb birthweight in singleton pregnancies.

Eastern Ag Research Station Department of Animal Sciences Noble County

STUDY INFORMATION Start Date 5/29/2023 (first breeding date) End Date 10/08/2023 (last birth) Species Sheep Experimental Head Unit Breed, Genetics Hampshire x Suffolk x Dorset Sex Ewes Feed Access Pasture: free access during breeding and early pregnancy, Off pasture: offered free choice first-cutting hay with 1-1.5lbs grain per/ ewe daily, ~60 days before lambing)

Sheep out on pasture in Noble County.

IACUC # 2019A00000096

STUDY DESIGN Maternal age, body weight, progesterone concentrations, and gestation length were evaluated to identify maternal factors (during pregnancy) that are associated with neonatal lamb birthweight in singleton pregnancies. Records and biological samples from 27 singleton pregnancies were utilized. Ewes were housed together and managed similarly throughout breeding, gestation, and lactation. Maternal body weight was recorded at breeding, while blood samples were collected throughout early pregnancy to quantify concentrations of progesterone (main maternal hormone to maintain pregnancy). Lamb birthweights were measured within one hour of birth and grouped into lower, middle, and higher-birthweight categories (tercile).

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OBSERVATIONS Maternal age and body weight were similar among birthweight groups, suggesting limited influence on neonatal size under the conditions of this study. Blood concentrations of progesterone followed expected physiological patterns throughout pregnancy, but did not differ among lamb birthweight categories. Gestation length was the only maternal factor associated with birthweight. Ewes delivering the lightest lambs had pregnancies that were approximately 3.5 days shorter than those delivering the heaviest lambs.

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SUMMARY •

Low birthweight is a major contributor to lamb mortality, making identification of maternal factors affecting fetal growth an important research priority.

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However, length of pregnancy significantly influenced neonatal size, with shorter pregnancies resulting in lighter lambs.

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In this study, maternal age, body weight, and earlypregnancy progesterone concentrations were not associated with lamb birthweight.

•

These findings suggest that factors regulating gestation length may have a greater impact on lamb birthweight which is ultimately associated with neonatal mortality.

RESULTS

Figure 1. Lamb birthweight grouped by category (tercile) lower, middle, and higher birthweight. Comparisons noted with different superscript letters represent significant difference among groups.

Figure 2. Length of pregnancy according to lamb birthweight category (tercile). Comparisons noted with different superscript letters represent significant difference among groups.

TOOLS OF THE TRADE

PROJECT CONTACT

Livestock Scale and Production Records

For inquiries about this project, contact Rafael Domingues (domingues.2@osu.edu), Alvaro Garcia-Guerra (garciaguerra.1@osu.edu), Muhammad Saad (saad.75@osu.edu), or Parker Swanton (undergraduate student).

Accurate birthweight measurements and detailed lambing records were essential to this project. Weighing lambs provided an objective measure of neonatal size. In addition, breeding and lambing dates were carefully recorded to determine gestation length for each ewe.

2026 eBarns Report | 91

Understanding PRRS Outbreaks OBJECTIVE Determine risk factors associated with the occurrence of a specific Porcine Respiratory and Reproductive Syndrome (PRRS) variant (L1C.5.35).

STUDY INFORMATION Start Date January 2025 End Date April 2026 Species Swine Total # of Farms 21 Ohio Counties Allen, Defiance, Henry, Represented Knox, Madison, Mercer, Paulding, Putnam, Van Wert, Wayne, Wyandot

eBarns Collaborating Farms College of Veterinary Medicine Statewide

BACKGROUND Porcine Respiratory and Reproductive Syndrome (PRRS) is a condition that causes severe reproductive and respiratory consequences in pigs. It is caused by virus, and there are several different ‘types’ circulating in the US, referred to as ‘variants’. Although farmers aim to reduce the introduction and spread of the virus through implementation of biosecurity practices, outbreaks continue to persist across Ohio, leading to negative consequences on health and economic production status of herds.

Experimental Farms Unit

STUDY DESIGN A cross-sectional study was conducted across swine farms from six distinct swine production systems/vet clinics across Ohio. Participants were recruited voluntarily through collaboration with Ohio Pork Council and swine companies. All farms were experiencing an outbreak of PRRS virus, with onset of clinical symptoms ranging from January of 2025 to March of 2026. Sequencing data from the production companies was obtained from diagnostic laboratories to categorize the PRRS virus variant type. In-person interviews using a structured questionnaire were conducted in conjunction with on-site assessments, typically lasting 1 - 4 hours. The survey instrument was designed to collect information on current farm demographics and biosecurity practices. A total of 21 sites were visited from March 2025 to April 2026. Approximately half of all sites housed breeding animals and half housed growing pigs. The average breeding herd size was 3,000 sows (range: 1,600 - 5,000) and the average growing herd size was 4,464 (range: 2,000 - 15,000).

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Figure 1. Breakdown of PRRS Outbreak Strains reported

PROJECT CONTACT For inquiries about this project, contact Kara Flaherty (flaherty.177@osu.edu) or Dr. Andréia Gonçalves Arruda (arruda.13@osu.edu).

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OBSERVATIONS

SUMMARY

When looking at biosecurity protocols on farm, the majority of all sites reported farm-specific boots and coveralls for both staff and visitors. Showering-in was reported to a lesser degree and predominantly for sow sites, while less than half of surveyed sites reported the use of a Danish entry. Most sites reported having a visitor log, but the study team observed inconsistent use or availability of the log itself, pointing to variable implementation.

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Results from this study indicated a high prevalence of L1C.5.35 among surveyed Ohio swine farms experiencing PRRS outbreaks through 2025 and early 2026, a variant which is growing in state and national concern due to detrimental health and production consequences.

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A greater number of feed deliveries and visitor access to sites with an L1C.5.35 outbreak was observed and suggest cross-contamination involving feed trucks could be an important process to investigate and improve from outbreaks associated with this variant.

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The potential for cross-contamination supports the use of environmental sampling during high PRRS incidence periods, which could be useful in raising awareness of virus presence around outside areas.

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Day-to-day implementation of biosecurity practices was found to not always match those stated, so careful monitoring of visitors and practices could help understand introduction of PRRS virus.

Fifteen sites (71.4%) were experiencing an outbreak associated with the L1C.5.35 PRRS virus variant. These sites had a higher proportion of visitors accessing pig and office areas in the two weeks preceding the detection of the outbreak compared to non-L1C.5.35 sites (21% vs 0%). They also had a higher proportion of neighboring sites from a different company within 5 miles (92.3% vs 75%) and a higher number of feed deliveries (2.7 ± 1.6 vs 1.6 ± 0.9) compared to sites affected with other PRRS virus variants. Sites experiencing outbreaks associated with other variants had a higher proportion of maintenance occurring compared to L1C.5.35-affected sites (50% vs 26.7%).

RESULTS

Figure 2. Biosecurity practices across all sites.

Figure 3. L1C.5.35 vs Non-L1C.5.35 Practices and Events.

2026 eBarns Report | 93

Highly Pathogenic Avian Influenza OBJECTIVE

eBarns Collaborating Farms

Evaluate the status of Highly pathogenic avian influenza (HPAI) in Ohio up to 2026.

OSU Extension Statewide

2022 - 2026 HPAI DETECTIONS IN OHIO Avian influenza is a viral disease that can affect multiple species of birds and mammals. It can exist in avian species in a low pathogenic form that causes mild symptoms and can also mutate into a highly pathogenic form that can cause high mortality in multiple avian species including domestic poultry such as chickens and turkeys, shore birds, and raptors including hawks, owls, and eagles. Highly pathogenic avian influenza (HPAI) is devastating in that it is highly contagious with no treatment available. It can be spread widely in migratory waterfowl such as where it can exist in an asymptomatic carrier state. Two of the four major migratory pathways pass through Ohio which increases our risk of outbreaks.

2026 was a quiet year for Highly Pathogenic Avian Influenza in Ohio with only 4 detections in backyard flocks, no detections in commercial flocks, and a total of 150 birds affected. The Highly Pathogenic Avian Influenza (HPAI) outbreak of 2022-2026 has nationally affected over 174,800,000 birds, been detected in 1,708 flocks, all 50 states and one territory. In Ohio during this time period, we have had 25,362,926 birds affected from 78 commercial and 18 backyard flocks.

Source: USDA APHIS go.osu.edu/APHIS-HPAI-Data

Figure 1. Confirmed Detections in the United States for HPAI 2022-2026.

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Small Ruminant

SIGNS OF HPAI IN POULTRY

Equine

Swine

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Sudden death without any prior symptoms of illness

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Gasping for air (difficulty breathing)

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Lack of energy and appetite

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Nasal discharge, coughing, sneezing

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Drop in egg production or soft-shelled, misshapen eggs

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Twisting of the head and neck (torticollis)

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Swelling of the eyelids, comb, wattles, and shanks

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Stumbling or falling down

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Purple discoloration of the wattles, comb, and legs

•

Diarrhea

Poultry

Image example of purple discoloration and Image example of purple discoloration swelling of the legs. of the comb and wattles. Images sourced from USDA APHIS Defend the Flock website: aphis.usda.gov/livestock-poultry-disease/avian/defend-the-flock/signs-illness

OUTBREAK PREVENTION

The key to preventing outbreaks in both backyard poultry and the poultry industry is biosecurity. Biosecurity is the strategy of prioritizing animal health by preventing disease from entering the flock or herd. It comes in two main forms, direct biosecurity, and indirect biosecurity.

Direct poultry biosecurity: Preventing disease transmission from bird to bird. Make sure that your flock cannot encounter wild birds by fencing, bird netting or other exclusion. Indirect poultry biosecurity: Make sure to limit or exclude visitors to your flock whenever possible. Make sure to use proper sanitation procedures as well as include the use of personal protective equipment.

For more information on HPAI and biosecurity to keep your flocks safe head to go.osu.edu/HPAI

Ohio Department of Agriculture: Reporting Sick Birds in Ohio If you have sick or dead poultry, call and ask for the sick bird coordinator: Backyard/hobby flocks, call Ohio Department of Agriculture (ODA) 614-728-6220 or after hours at 888-456-3405 Commercial poultry flocks, contact Ohio Poultry Association (OPA) 614-882-6111

PROJECT CONTACT For inquiries about this project: Timothy S. McDermott, DVM (mcdermott.15@osu.edu).

To report sick or dead wild birds, call 800-WILDLIFE (945-3543). Includes: any raptor (such as a bald eagle), multiple waterfowl (such as geese or ducks), and any other large congregation of sick or dead birds.

2026 eBarns Report | 95

Acknowledgments Research Farm Partners

Research Collaborators, Supporters, and Partners

B R and J farms

Cisco Seed Company

Tim Barnes, Barnes Farms Genetics

Ebberts Field Seeds Inc

Cedar Hill Farm

Granville Milling

Matt Claypool

Jefferson Landmark

Corboy Family Farms

Kubota Tractor Corporation

Cropper Family Farm

Ohio Corn and Wheat

Jerry Dixon

OSU Eastern Agriculture Research Station Staff

Andy Dodderer

OSU North Central Agriculture Research Station Staff

Dan Erwin, Jet Tunis

OSU Northwest Agriculture Research Station Staff

Erwin Farms

OSU Western Agriculture Research Station Staff

Gray Farms

OSU Wooster - Schaffter Farm Staff

Kathy Harrison

Savion Energy

Howser Farms

U.S. Department of Energy Funding Award

Kayla Hughes

USDA NIFA Crop Protection and Pest Management Program grant 20247000643574

Landess Farms Earl Lehner Mike McCormick McIntire Farm Pitzer Farm Tom Pletcher Cody Pletcher John Price Rau Farms Smith Farms We Feed U farms Rick Whiting, BWB Farm Yockey Family Thank you to the farms that participated and all personnel who helped with the PRRS investigations!

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FARM SCIENCE REVIEW Future Dates September 21-23, 2027 September 19-21, 2028 September 18-20, 2029 September 17-19, 2030

FSR.osu.edu 2026 eBarns Report | 97

Glossary 95% CI – Confidence Interval, range of values that can be certain to contain the true mean of a population Ad Libitium – A feeding management in which animals are fed without any restrictions ADF – Acid Detergent Fiber, least digestible plant compounds, including lignin and cellulose ADFI – Average Daily Feed Intake, amount of feed (As Fed) consumed per animal, per day ADG – Average Daily Gain, weight gained per animal, per day Bob Veal – Veal calves marketed up to three weeks of age BW – Body Weight CP – Crude Protein, measures nitrogen content of feedstuffs, including true protein and non-protein nitrogen DCAD – Dietary Cation Anion Difference, measurement used when formulating diets for dry or lactating cows using positively and negatively charged minerals on animal performance DDG – Dried Distillers’ Grain with Soluble, co-product of ethanol production and often used as a protein source in a diet DIM – Days In Milk, number of days a dairy cow has been lactating DM – Dry Matter, fiber and nutrients remaining once water is removed from a feedstuff or diet DMI – Dry Matter Intake, amount of dry matter consumed per animal, per day DON – Deoxynivalenol, Vomitoxin, a common mycotoxin found in grain Dressing Percent – (Hot Carcass Weight divided by Live Weight) x 100 GDD – Growing Degree Days, heat units used to estimate growth and development of crop and pests during the growing season FAMACHA © – Selective treatment method for controlling the level of parasitic barber’s pole worm in small ruminants FEC – Fecal Egg Count, quantitative assessment of how many parasite eggs an animal is shedding at a particular time Feekes Growth Stages 10.0 – Grass Forage at Boot Stage, 10.5 – Grass Forage at Heading FTPI – Failed Transfer of Passive Immunity, when a calf fails to absorb adequate immunoglobulins via colostrum Gain : Feed – Measure of Feed Efficiency, ratio of total pounds gained to total pounds of feed fed 98 | Ohio State Digital Ag Program

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Hot Carcass Weight – Carcass Weight prior to chilling Hypoglycemic – Low Blood Sugar K2O – Potassium fertilizer form, Potash Laparoscopic Artificial Insemination – Intrauterine method of insemination used in small ruminants Mycotoxin – Toxin produced by certain molds or fungi in grains N – Nitrogen NDF – Neutral Detergent Fiber, structural components of plants, specifically the cell wall and predicts voluntary intake because it provides bulk or fill NDF48 – Measure of how much of the forage's NDF can be digested in 48-hours NEL – Net Energy Lactation, amount of energy in a feedstuff that is available for milk production and body maintenance NIR – Near Infrared Reflectance, measures light energy reflected by the feed sample to determine the chemical composition of forages OC% – Percent of Organic Carbon OM% – Percent Organic Matter P205 – Phosphorus fertilizer form Passive Immunity – Immunity acquired via colostrum intake shortly after birth PCV – Packed Cell Volume, a measurement of the proportion of blood that is made up of cells RFID – Radio Frequency Identification, method used to track livestock SEM – Standard Error of the Mean, indicates how different the population mean is likely to be from a sample mean TDN – Total Digestible Nutrients, sum of digestible proteins, fiber, lipids, and carbohydrates in feedstuff TKN – Total Kjeldahl Nitrogen, total concentration of nitrogen and ammonia TMR – Total Mixed Ration, method of feeding that combines feeds to a specific nutrient content Vomitoxin – a mycotoxin that commonly infects corn and other grains Wet Chemistry – Chemistry based analytical methods used to measure chemical compounds in plant material 2026 eBarns Report | 99

eBarns

“connecting science to farmers” eBarns is an Ohio State program dedicated to advancing production agriculture through the use of field-scale research. eBarns 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.