2024 eBarns Report

Barns Report

COLLEGE OF FOOD, AGRICULTURAL, AND ENVIRONMENTAL SCIENCES

COLLEGE OF VETERINARY MEDICINE

2024 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 2024 eBarns Report is a combination of the research conducted on partner farms and Ohio State agricultural research stations throughout Ohio. 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 third edition of eBarns we have included research studies not only from the past year, but studies from previous years that have yet to be summarized in a producer friendly manner. It is our goal to continue to share results from applied livestock, forage, and manure nutrient management in this publication for years to come.

2024 Research Recap

29 Total Studies

• 9 Forages

• 1 Manure Nutrients

• 5 Cattle

• 1 Cattle (Combined Beef & Dairy)

• 1 Beef

• 3 Dairy

• 6 Small Ruminant

• 3 Equine

• 4 Swine

• 1 Poultry

15 Counties

31 Research Sites

4 Statewide Projects

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.

Welcome to the 2024 Ohio State eBarns report.

This is the third edition of the eBarns report, where we share and highlight livestock, equine, and foragebased research that is conducted across Ohio on farms and university research facilities. We appreciate the faculty, staff, and students that contributed to this year’s report, without their cooperation none of this would be possible.

The past year has been an interesting one in the livestock industry. We came out of a nearly ideal 2023 growing season for feedstuffs and forage. Now we are well into a 2024 growing season where drought and poor pasture conditions are prominent especially in southern and eastern Ohio.

We entered 2024 with the smallest beef cow herd in the U.S. since 1960. Livestock prices have been all over the board from setting record highs in beef, while hog and dairy prices have remained relatively low. Small ruminants continue to remain a topic of discussion with small farm clientele and have now drawn the attention of those looking to control vegetation in the many solar energy fields across the state.

In the past year we had a further infestation of the Longhorned Tick into the state and have dealt with Highly Pathogenic Avian Influenza (HPAI) in poultry and dairy. Ohio livestock producers are resilient as they come and hopefully rain will fall in the drought impacted areas of the state soon.

For the 2024 report the eBarns team conducted 29 studies in 15 counties, with 4 of the studies being statewide efforts. We hope that some of the data presented in this year’s report will provide value to Ohio’s livestock and equine industries at the local level. If you have an interest in conducting on-farm research for next year ’s report, contact your county OSU Extension office or go to digitalag.osu.edu/ebarns to get started.

Sincerely,

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

The 2024 eBarns Team

Ohio State University Department of Animal Science

Advancing knowledge of animal sciences for the betterment of animals and humans…

Our areas of research and teaching includes:

• Growth, development, and meat science

• Breeding, genetics, and reproduction

• Mammary biology and milk quality

• Animal welfare and behavior

• Nutrition and microbiology

• Waste management and bio-fuels

• Human-animal interactions

Our Purpose

Ohio State Digital Ag Program

OHIO STATE

DigitalAg

ABOUT US

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

VISION

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

MISSION

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

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

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

WHAT IS DIGITAL AGRICULTURE?

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

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

Ohio State Livestock Production Resources

Forages

Pasture comprises 7.5% of Ohio's farm land.

For more information visit the Integrated Forage Management Team at 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 at 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 OSU Extension Beef Team at 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 at 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 OSU Extension Sheep Team at sheep.osu.edu

Equine

Ohio has the 6th largest horse population in the country.

For more information visit the Ohio Equine Resource Center at equine.osu.edu

Swine

See Ohio State's new 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 at u.osu.edu/poultry

Report Guide

OBJECTIVE

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.

STUDY INFORMATION

Planting Date 05/03/2023

Harvest Date 10/20/2023

Variety Becks 6076V2P

Population 34,000 sds/ac

Acres 70

Treatments 5

Reps 7

Treatment Width 40 ft.

Tillage Conventional

Management Fertilizer, Herbicide, Insecticide

Previous Crop Soybeans

Row Spacing 30 in.

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

Location Box

Look to see the county where the study was conducted.

STUDY INFORMATION

Start Date 10/15/2023

End Date 08/01/2024

Species Swine

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

End Point 300 lb (DOF, DOA, DIM)

Treatments 5

Reps 4

Experimental Unit Pen

Breed, Genetics Commercial Line, Yorkshire Cross x Duroc

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.

OBSERVATIONS

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.

RESULTS

Treatments (XXX)

Emergence (plants/ac)

SUMMARY

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

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

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

TOOLS OF THE TRADE

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

PROJECT CONTACT

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

eBarns Contributors

Tim Barnes Extension Educator

Marion County

OSU Extension

Dara Barclay Program Manager, eFields & eBarns

OSU Extension

Ben Bohrer Assistant Professor Department of Animal Sciences

Nick Eckel

Extension Educator

Wood County

OSU Extension

Alora V. Brown Undergraduate Researcher Department of Animal Sciences

Amber Emmons

Water Quality Extension Associate

OSU Extension

Amanda Bennett Extension Educator Miami County

OSU Extension

Karen BennettWimbush Associate Professor

OSU ATI

Brady Campbell Assistant Professor Department of Animal Sciences

Ben Enger Associate Professor Department of Animal Sciences

Chris Clark Eastern Agricultural Research Station Manager

Kellie Enger Research Associate Department of Animal Sciences

Alyssa Essman Assistant Professor Department of Horticulture and Crop Science

Allen Gahler Extension Educator Sandusky County OSU Extension

Kara Flaherty Education Program Specialist Department of Animal Sciences

Gregg Fogle Small Ruminant Research Center Manager

Cori Gammariello Graduate Research Associate Department of Animal Sciences

Ryan Haden Associate Professor OSU ATI

Jason Hartschuh Assistant Professor, Field Specialist OSU Extension

Manure Nutrients Forages Poultry

John Fulton Professor Department of Food, Agricultural and Biological Engineering

Christine Gelley Extension Educator Noble County OSU Extension

Elizabeth Hawkins Associate Professor, Field Specialist OSU Extension

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

Kate Hornyak Program Coordinator Delaware County OSU Extension

eBarns Contributors

Dee Jepsen Professor Department of Food, Agricultural and Biological Engineering

Dean Kreager Extension Educator Licking County OSU Extension

Dr. Mark Loux Emeritus Faculty Department of Horticulture and Crop Science

Alejandro Relling Associate Professor Department of Animal Sciences

Clifton Martin Extension Educator Muskingum County OSU Extension

Talita Resende Assistant Professor, Swine Health

Extension Specialist Department of Animal Sciences

Rob Leeds Extension Educator Delaware County OSU Extension

Sara Mastellar Associate Professor OSU ATI

Eric Richer Associate Professor, Field Specialist OSU Extension

Clif Little Associate Professor, Extension Educator Guernsey County OSU Extension

Tim McDermott Extension Educator Franklin County OSU Extension

Garth Ruff Field Specialist OSU Extension

Clint Schroeder Program Manager, Ohio Farm Business Analysis Program

OSU Extension

Melva Tacuri Vera Undergraduate Researcher Department of Animal Sciences

Barry Ward Leader, Production Business Management

OSU Extension

Manure Nutrients Forages Poultry

Elizabeth Share Program Specialist Department of Animal Sciences

Brayden Thompson Summer Research Intern Department of Animal Sciences

Benjamin Wenner Former Associate Professor Department of Animal Sciences

Jacci Smith Extension Educator

Delaware County OSU Extension

Catelyn Turner Extension Educator Monroe County OSU Extension

Aaron Wilson Assistant Professor, Field Specialist OSU Extension

Kendra Stahl Extension Educator

Crawford County

OSU Extension

Yifei Wang Graduate Researcher Department of Animal Sciences

Ted Wiseman Extension Educator

Perry County OSU Extension

John Yost

Extension Educator

Wayne County

OSU Extension

Chris Zoller

Extension Educator

Tuscarawas County

OSU Extension

Examine the world of agricultural production in more detail than ever before with the eBarns Hub. Access on-farm research from Ohio producers and Extension through the searchable database.

Report Database

Discover a wealth of knowledge available in the yearly eBarns Report now online. Easily find and print specific research reports from every report edition at once.

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.

2023/2024 Growing Season Weather

June 2023 – May 2024 Weather

From a smoky yet comfortably cool summer in 2023 to a very active severe weather season to start 2024, the past year has offered plenty to talk about when it comes to weather. Parts of Ohio ebbed in and out of drought, while numerous extreme precipitation events were felt across the state as well. Overall, 2023 (calendar year) was Ohio’s 4th warmest and 49th driest year on record (1895-2023) and over the 12-month period (June 2023-May 2024), it was Ohio’s 3rd warmest 12-month (June to May) on record according the National Centers for Environmental Information. Figure 1 shows temperature and precipitation rankings for individual counties in Ohio for June 2023 – May 2024. For more inseason climate analysis, please visit the State Climate Office of Ohio at climate.osu.edu

Figure 1. County rankings for average temperature (left) and total precipitation (right) for the 12-month period June 2023May 2024 compared to the long-term mean (1895-2023). Figure courtesy of NOAA Climate at a Glance.

Summer 2023 (June – August): Cooler than average conditions prevailed in June as the state experienced its 25th coldest June on record (1895-present). July and August only featured a few days of extreme heat as well, with near to below average temperatures recorded for the season (Fig. 2. Temperature differences (°Fahrenheit) from the long-term (1991-2020) average conditions for June – August 2023). The cool conditions were the result of frequent episodes of northerly flow that brought cooler and drier air south from Canada. This pattern also brought periods of persistent wildfire smoke to the region, including three major episodes on June 6-7, June 27-29, and July 16-17. Overall, June was drier than normal, though rain finally started falling around the state on June 10, with heavy rainfall observed across the northern Miami Valley to the Cleveland area. An active pattern followed, with both July and August depicted as wetter than normal. Riding along the edge of a heat dome that brought temperatures up close to 100°F in late August in southwest Ohio, a complex area of showers and storms dropped upwards of 10 inches of rain in 6 hours across north central Ohio. Summer 2023 ranks as the 39th coldest and 32nd wettest summer on record.

Fall 2023 (September – November): Conditions turned dry once again in September, as rainfall was 3-4 inches below average across most of Ohio, and September 2023 ranks as the 5th driest on record. The U.S. Drought Monitor showed expanding abnormally dry and moderate drought conditions across the state that reached a peak on October 10, 2023 (Fig. 3). There were multiple reports of combine and field fires as a result of the dry conditions. More frequent rainfall events returned later in October and November, though amounts generally stayed below average throughout the fall.

3. U.S. Drought Monitor for Ohio as of 10/10/2023.

Figure 2. Courtesy of the Midwestern Regional Climate Center

Figure

Fall 2023 (September – November), continued: Temperatures were fairly mild throughout the season, a bit above average for October but close to long-term normals for September and November. The lingering warmth and dry conditions may have helped slightly offset the slow drydown of corn due to the slow growing degree day accumulation throughout the growing season. The first hard freeze conditions (28°F or colder) came to the northwest half of Ohio the last week of October and to southeast Ohio during the first week of November. Fall 2023 ranks as the 24th warmest and 13th driest fall on record.

Figure 4. The Accumulated Winter Season Severity Index for the 2023-2024 winter season. Figure courtesy of the Midwestern Regional Climate Center

Winter 2023-2024 (December - February): Typical of El Niño patterns, dry weather persisted into the first part of winter and returned in February, with wetter than normal conditions recorded in January 2024. Temperatures remained above to much above normal for most of the winter, exceeding 8°F above average in some counties. The warm conditions led to a lack of frozen ground and off and on muddy conditions in fields and pastures throughout the winter, though cold stress on livestock was minimal. The Accumulated Winter Season Severity Index (AWSSI), which uses a mixture of maximum and minimum temperatures along with snow depth and snow duration to quantify the severity of a winter season, shows mild to "record" mild conditions prevailed for much of the region during the 2023-2024 winter season (Fig. 4). While snowfall fell behind normal values, one mid-February event brought upwards of 8” to portions of central Ohio, which was more than half of the season’s total.

By the end of February though, warm, moist conditions from the Gulf of Mexico led to an outbreak of severe weather during the early morning hours of February 28th. Several hail and damaging wind reports were reported along with nine tornadoes. One of these included an EF2 tornado (winds ~135 mph) that moved across eastern Clark County into Madison County, and brought damage to the Molly Caren Agricultural Center, home of the Farm Science Review. In summary, the Winter 2023-2024 ranks as the 2nd warmest and 57th wettest winter on record.

Spring 2024 (March - May): Frequent severe weather outbreaks and warmer than average temperatures headlined the weather stories for spring 2024. Numerous tornado and severe weather events occurred across the state, as Ohio approached the tornado record with 58 by the end of the month (eventually eclipsed its record number of tornadoes for a year and currently at 65 as of August 1, 2024). Destructive and deadly tornadoes hit rural areas hard, with major destruction in places like Mercer, Darke, Auglaize, and Logan Counties. Ohio experienced its third largest tornado outbreak on record on May 7th with 19 tornadoes officially reported. All three months had above average and top ten warmest temperature rankings, and the last hard freeze conditions (28°F or colder) occcured before April 10th for most of Ohio. Precipitation extremes were also prevalent, as northwest Ohio seemed to catch the bulk of the rainfall during the planting season (Fig. 5. Precipitation differences (percent of normal) from the long-term (1991-2020) average conditions for March – May 2024). Mercer to Marion Counties northward to Defiance and Lucas Counties all experienced much above normal precipitation for the spring season which hampered planting efforts. Wet conditions were also noteworthy in parts of eastern and southeastern Ohio where high river levels were recorded on the Ohio River and flooding fatalities were reported near Marietta. Across the south however, wet conditions in March turned a bit drier by April and May and precipitation was timely for planting and pasture growth throughout the region. However, these drier conditions would initiate more drought like conditions that set in during the summer. Overall, Spring 2024 ranks as the 2nd warmest and 28th wettest spring on record.

PROJECT CONTACT

Dr. Aaron B. Wilson, Extension Field SpecialistAg Weather & Climate (wilson.1010@osu.edu).

Figure 5. Figure courtesy of the Midwestern Regional Climate Center

Ohio Farm Custom Rates, 2024

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.

The 2024 publication, released on July 1, 2024, reports custom rates based on a statewide survey. Surveys were mailed/ emailed to past respondents and distributed at various Extension programs through the winter of 2024 (beginning of 2024). The summary information included in the publication is based on the responses of 333 farmers, custom operators, farm managers, and landowners. These rates, except where noted, include the implement and tractor if required, all variable machinery costs such as fuel, oil, lube, twine, etc., and labor for the operation.

Some custom rates published in this study vary widely, possibly influenced by type or size of equipment used (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, and region of Ohio with different custom services supply and demand characteristics.

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

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.

Note for Tables included here:

The measures shown 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 indicates 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. The data from this survey are intended to show a representative farming industry cost for specified machines and operations in Ohio.

To review the full publication, visit the Custom Rates and Machinery Costs on the OSU Extension, Farm Office website:

go.osu.edu/FarmCustomRates2024

PROJECT CONTACT

Barry Ward

Leader, Production Business Management

ward.8@osu.edu

Eric Richer Field Specialist, Farm Management richer.5@osu.edu

Clint Schroeder Program Manager, Farm Business Analysis

schroeder.307@osu.edu

Amanda Bennett Extension Educator, Agriculture and Natural Resources bennett.709@osu.edu

Ohio State Forages Research

For 2024, eBarns forage research was focused on increasing forage production, forage quality, and pasture management in Ohio. The forage research presented in here covers precision nutrient management, species selection, and looking critically at effective management strategies. Below are highlights of the 2024 eBarns Forage research:

22 acres of forage 9 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.

Ohio Forage Performance Tests

The purpose of the Ohio Forage Performance Test is to evaluate forage varieties of alfalfa, annual rye grass, and cover crops for yield and other agronomic characteristics. This evaluation gives forage producers comparative information for selecting the best varieties for their unique production systems.

Agronomic Crops Team Forage Research go.osu.edu/CropTeamForages

Ohio Forage Performance Tests go.osu.edu/OhioForages

Species for Planting by Mid-July

Manure Nutrients Poultry Small Ruminant Equine Cattle Forages

Corn Plant Silage

Forage Sorghum Sorghum Sudangrass Sudangrass

Soybean Silage

Teff Grass

Millets

Mixtures of annual grasses with soybean

Highest single cut forage yield potential of all choices. Silage quality will be lower than with normal planting dates. Risk will be getting it harvested at right moisture for good fermentation.

Best harvested as silage. Brown midrib (BMR) varieties are best for lactating cows. Conventional varieties are okay if BMR seed is not available. Can produce 3-4 tons of dry matter/acre. Risk of prussic acid (hydrogen cyanide gas) if frosted.

Reasonable alternative to replace alfalfa forage. Check seed treatment and herbicide labels, many restrict forage use.

Best suited to beef and sheep; lower yield than sorghum grasses. Can harvest as hay or silage.

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.

Best harvested as silage. Mixtures of sorghum grasses or millets or even oats and spring triticale with soybean are feasible and can improve forage quality characteristics.

Species for Planting Late-July to Mid-September

Oat or Spring Triticale

Oat or Spring Triticale Plus Winter Cereals

Oat or Spring Triticale Plus Field Peas

Italian Ryegrass

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

Winter cereals (Winter rye, Winter wheat, Winter triticale) can be added to oat or spring triticale to add a forage harvest early next spring. Winter rye can also contribute a little extra autumn yield to the mixture.

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

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

Autumn Olive Control

OBJECTIVE

Autumn Olive is a non-native and invasive shrub/tree that can be found in Ohio woodlands, pastures, and abandoned fields. The objective of this study was to evaluate dormant, winterapplied treatments for the management of Autumn Olive in pasture systems.

STUDY INFORMATION

Application Date 02/07/2024

Evaluation Date 05/21/2024

Species Autumn Olive

System Pasture

Location Caldwell, OH

Treatments 6

Reps 5

Application Cut Stump, Basal Bark, or Dormant Stem

Location EARS

STUDY DESIGN

Eastern Ag Research Station

CFAES and OSU Extension Noble County

This study was conducted in 2023 and repeated in 2024 at one location evaluating six Autumn Olive treatments. The experimental design was a completely randomized design (CRD). The study was conducted at the Eastern Agricultural Research Station (EARS) in Caldwell, OH. Applications were made in late-winter (February) and treatments were evaluated approximately three months later (May). View the 2023 data in the 2023 eBarns Report on pages 20-21.

Figure 1. Autumn Olive dormant management trial at the Eastern Agricultural Research Station in Caldwell, OH.

Figures 2, 3, and 4. Treated Autumn Olive plants in May following applications in late-February.

OBSERVATIONS

In the two years conducting this trial, all treatments except treatments 2 and 4 provided adequate control of Autumn Olive with little to no regrowth. Plant size and environmental conditions may influence efficacy.

RESULTS

SUMMARY

Manure Nutrients Poultry Small Ruminant

• Several options exist for control of Autumn Olive in pasture systems.

• With appropriate product, carrier, and rate selection, applications can be made in late-winter to cut stumps, basal bark, or dormant stems.

Figure 5. Results of the 2024 trial evaluating different treatments for the control of Autumn Olive. Treatment means with the same letter are not significantly different at a = 0.05.

Rating structure, based on regrowth following applications:

1: none

2: slight regrowth

3: moderate regrowth

4: extensive regrowth

TOOLS OF THE TRADE

Find more information on the identification and control of problematic weed species by visting: u.osu.edu/osuweeds/ (or use the QR code to the right)

PROJECT CONTACTS

• Ted Wiseman (wiseman.15@osu.edu)

• Dean Kreager (kreager.5@osu.edu)

• Clifton Martin (martin.2422@osu.edu)

• Christine Gelley (gelley.2@osu.edu)

• Dr. Alyssa Essman (essman.42@osu.edu)

• Chris Zoller (zoller.1@osu.edu)

• Clif Little (little.16@osu.edu)

• Dr. Mark Loux

Corn DON Resistance Screening

OBJECTIVE

Assessing the difference between hybrids in resistance to DON mycotoxin production from Gibberella

Ear Rot

STUDY INFORMATION

Population 34000 sds/ac

Total Acres 5

Treatments 80 Reps 4

Treatment Width 10 ft.

Row Spacing 30 in.

STUDY DESIGN

This study was conducted in South Charleston, Apple Creek, and Bucyrus in a randomized complete block design.

The Bucyrus and Apple Creek locations had natural DON infection and was inoculated at green silk by spraying Fusarium Graminearum spores on the silks.

The South Charleston location was not inoculated due to stalk lodging at tassel and only had natural infection.

Grain samples were collected at harvest from each plot and finely ground for DON analysis which was conducted using Liquid chromatography-mass spectrometry.

Bucyrus

eBarns Collaborating Farms

1. Weather condition data in Bucyrus, OH. GDD = Growing Degree Days

2. Weather condition data in Apple Creek, OH.

South Charleston Planting: 05/23/2023 Harvest: 11/20/2023

Figure 3. Weather condition data in South Charleston, OH.

Figure

Figure

OBSERVATIONS

• At all locations, tassel window was compressed with all hybrids in green silk during a 2-week window.

• Weather conditions during tassel were most favorable in Bucyrus, then Apple Creek, with the least favorable conditions in South Charleston.

RESULTS

SUMMARY

• A total of 13 hybrids had an average DON below 1 ppm (range 0 to 2.4) and 28 hybrids had an average DON below 2 ppm (range 0 to 4.1) at all locations.

• A total of 43 hybrids had average DON below 3 ppm at all 3 locations, but the range of DON for these hybrids was 0-6.5 ppm across all plots at these locations.

• With one year of data we can not guarantee a hybrids resistance, but hybrids with high DON levels are susceptible.

Numbers are DON in ppm

Figure 4. Summary of Results. RM = Relative Maturity

Figures 5 and 6. Images of corn ears during this study

PARTICIPATING BRANDS

• Dekalb

• Golden Harvest

• LG Seeds

• NK

• Minyo Check

See the C.O.R.N. Newsletter article for a full list of data (detailed by hybrid), and more weather details (by location):

• PC SEED

• Pioneer

• Revere

• Seed Genetics Direct

go.osu.edu/don2023

Thank you to the Ohio Corn Board for funding this research project.

TOOLS OF THE TRADE

Almaco Small Plot Combine with Air Cyclone Grain Sampler

This tool collects a uniform grain sample from the entire plot.

PROJECT CONTACT

Jason Hartschuh (hartschuh.11@osu.edu)

Mechanical Control of Pasture Weeds

OBJECTIVE

Determining if weed populations in pastured forages could be changed or reduced by varying the timing of mowing throughout the late spring and summer growing period without the use of herbicides.

STUDY INFORMATION

STUDY INFORMATION

Planting Date Established Pasture

Harvest Date Treatment Dependent

Variety Pasture Forages

Acres 1

Treatments 8

Reps 4

Plot Size 15 ft. wide x 20 ft. long, 1 ft. border outside

Management No additional fertilizer

STUDY DESIGN

A randomized complete block design with 8 treatments (including a control) and 4 replications of each treatment in this multiyear experiment (2020 - 2022). Forage samples (61cm x 61cm, hand harvested) taken near the beginning of June, July, August and September each year. Broadleaf weed species were separated out. Fresh weights were recorded and a sub-sample was dried in a forage dryer at 46 degrees Celsius (114.8 degrees Fahrenheit).

After harvesting samples, cow/calf pairs grazed the plots paddock until the farm manager determined cattle should be moved. After each grazing, treatment plots were cut with a rotary mower according to treatment (1 pass over the plot, cutting to height of approximately 4 inches above the soil surface).

Eastern Ag Research Station

OSU Extension

Noble County

WEATHER INFORMATION

Figure 2. Image of pasture used in study with weeds present. Site is a predominately tall fescue and mixed grass pasture field.

Figure 1. The toal rainfall for each year of the study, from the month of May - September.

Manure

OBSERVATIONS

• Data showed that mowing monthly significantly reduced the number of weeds compared to no mowing or mowing in June (Figure 4).

• Mowing in only June had higher weed yields than did the control (no mowing). The increased weeds present in the June treatment may be the result of opening up the canopy too early in the growing season exposing weeds to increased sunlight.

RESULTS

• With cocklebur being a summer annual, early mowing may have contributed to it being so prevalent.

• Cocklebur was also able to produce seed under the mowing height in all treatments.

TOOLS OF THE TRADE

2’ x 2’ Grid

The grid is placed in a different quadrant of the plot, samples were collected by hand. Weeds can be separated from the forages and dried. Samples were collected at the beginning of every month during this study.

SUMMARY

• Mowing in just July maximized forages and minimized weeds at this location.

• Some weeds, such as Cocklebur, will require a herbicide treatment for optimum control.

Control, No Mowing 2 Mowing in June 3 Mowing in July 4 Mowing in August 5 Mowing in September 6 Mowing in June and August 7 Mowing in July and September 8 Mowing in June, July, August, and September

Figures 3 and 4: Letters indicate significant difference of p> 0.10 between treatments for all three years.

PROJECT CONTACT

Ted Wiseman (wiseman.15@osu.edu)

Christine Gelley (gelley.2@osu.edu)

Catelyn Turner (turner.1630@osu.edu)

Figure 3.

Figure 4.

Oats Nitrogen Rate August Planting

OBJECTIVE

Assess the effects of nitrogen rate on mid-August planted oats.

STUDY INFORMATION

Planting Date 08/31/2023

Harvest Date 11/09/2023

Variety FSM Oats

Population 100 lbs/ac

Acres 2

Treatments 5

Reps 4

Treatment Width 10 ft.

Tillage No-Till

Management Fertilizer

Previous Crop Wheat

Row Spacing 7.5 in.

Soil Type Hoytville Clay Loam, 90%

Rimer Loamy Fine Sand, 10%

STUDY DESIGN

This study was designed as a randomized complete block design with nitrogen rates of 0, 23, 46, 69, and 92 lbs/ac of nitrogen as urea applied at planting. Plots were no-till planted with a Great Plains grain drill at 100 pounds of oats per acre. A fungicide was applied 3 weeks after planting for crown rust control. Plots were harvested once they began to head using an RCI plot harvester which harvested the center 3 feet of the plot by 30 feet long. Sub-samples for the forage were collected and dried for lab analysis of forage quality.

NC Ag Research Station OARDC

Sandusky County

WEATHER INFORMATION

Growing Season Weather Summary

Figure 1. Planted oats - planted at 100 pounds per acre.

OBSERVATIONS

Plots were slower growing than normal and never reached head emergence. Harvest was done after the first frost that appeared to stunt plant growth. Plots were moisture stressed with too much rainfall. While the field is well tiled, harvest was further complicated by muddy field conditions.

SUMMARY

• Nitrogen rate had a significant effect on oats yield and quality factors.

• The zero nitrogen treatment contained about 50% wheat stubble in the samples.

• Yields were below our trend line oats yield for late August plantings by about half a ton.

• Nitrogen had a significant effect of increasing Crude Protein and Total Digestible Nutrients (TDN).

• Even though yields and Crude Protein were lower than our historic trend, the 69 lbs/ac rate of nitrogen was the most economical for both yield and quality.

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

PROJECT CONTACT

Jason Hartschuh (hartschuh.11@osu.edu)

Allen Gahler (gahler.2@osu.edu)

Oats Nitrogen Rate July Planting

OBJECTIVE

Evaluate July planted oats after wheat harvest response to nitrogen fertilizer rate.

STUDY INFORMATION

Planting Date 07/30/2023

Harvest Date 09/26/2023

Variety FSM Oats

Population 100 lbs/ac

Acres 1

Treatments 5 Reps 4

Treatment Width 10 ft.

Tillage No-Till

Management Fungicide, Insecticide

Previous Crop Wheat

Row Spacing 7.5 in.

Soil Type Hoytville Clay Loam, 90%

Rimer Loamy Fine Sand, 10%

STUDY DESIGN

This study was designed as a randomized complete block design with nitrogen rates of 0, 23, 46, 69, and 92 lbs/ac of nitrogen as urea applied at planting. Plots were no-till planted with a Great Plains grain drill at 100 pounds of oats per acre. A fungicide was applied 3 weeks after planting for crown rust control. Plots were harvested once they began to head using an RCI plot harvester which harvested the center 3 feet of the plot by 30 feet long. Sub-samples for the forage were collected and dried for lab analysis of forage quality.

NC Ag Research Station OARDC

Sandusky County

WEATHER INFORMATION

Growing Season Weather Summary

1. Planted oats - yield was not significanly impacted by the change in nitrogen rate.

Figure

OBSERVATIONS

Oats growth was very slow this year due to suffering from both too wet and too dry of conditions. Wet conditions delayed planting after wheat harvest for 2 weeks. After planting another large rain fall event may have tied up nitrogen or caused leaching.

RESULTS

SUMMARY

• July planted oat yields were much lower than normal, with our highest yielding treatments being less than half a ton per acre on average. This led to nitrogen not having a significant impact on yield but higher nitrogen rates did trend towards a higher yield.

• With these low yields, about 200 pounds per acre or half of the forage yield in some of the treatments was wheat stubble.

• Nitrogen didn’t have a significant effect on Crude Protein.

• The 46 lbs/ac of nitrogen treatment had the highest digestibility and energy which was significantly different from the untreated control.

• Results followed trends seen in previous years but yields and protein levels were below average.

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

PROJECT CONTACT

Jason Hartschuh (hartschuh.11@osu.edu)

Allen Gahler (gahler.2@osu.edu).

N Rate on Organic Summer Annual Species

OBJECTIVE

Evaluate the effect of nitrogen rate across four summer annual organic forage species planted into Red Clover after wheat.

STUDY INFORMATION

Planting Date 08/10/2023

Harvest Date 10/09/2023

Variety See Treatments

Population See Treatments

Acres 5

Treatments 16

Reps 4

Treatment Width 15 ft.

Tillage Light Tillage

Management None

Previous Crop Wheat

Row Spacing 7.5 in.

Soil Type Colwood Loam, 80%

Kibbie Fine Sandy Loam, 20%

eFields Collaborating Farm

OSU Extension

Ottawa County

WEATHER INFORMATION

Growing Season Weather Summary

STUDY DESIGN

This study was laid out in a randomized complete block split block design. With the treatments of species (oats, BMR Sorghum, Sorghum Sudan, and Red Clover) and nitrogen rates of (0, 50, 100, and 150 pounds per acre) from pelletized chicken litter. All species were planted and harvested on the same day. Plots were planted after wheat harvest and frost seeded into Red Clover. The field was lightly disc to slow down clover growth and allow for grass crop establishment.

Figure 1. Organic Sudan Oats.

OBSERVATIONS

In this trial, we lightly disced the red clover that was already established to slow its growth, so that the grass crops could establish. In all plots, the red clover survived the discing and grew with the grass crop. The visual amount of clover was inversely related to nitrogen rates, ie, more nitrogen less clover, but more grass crops. Plots were harvested before grass crop heading, possibly reducing yield.

PROJECT CONTACT

Jason Hartschuh (hartschuh.11@osu.edu)

Nick Eckel (eckel.21@osu.edu)

Allen Gahler (gahler.2@osu.edu)

RESULTS

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

SUMMARY

• Compared to other species trials, there was less yield increase from nitrogen application showing that the grass crop was able to find soil nitrogen from the clover that was present.

• However, nitrogen applications did significantly increase crop yields in all plots but the Red Clover only plots. Both species and nitrogen had a significant effect on Crude Protein (CP). While in some species CP decreased with increased nitrogen applications, this was caused by the change in species mix with less clover at higher nitrogen application rates and more grass crop.

• Neither species nor nitrogen rate affects energy. Planting other summer annual forages into frost seeded red clover can increase yield and may decrease CP so that the forage is closer to livestock’s nutritional needs.

No-Till Organic Summer Annuals

OBJECTIVE

Asses yield differences between summer annual forage species and nitrogen rate in a no-till organic system, with frost seeded Red Clover.

STUDY INFORMATION

Planting Date 08/10/2023

Harvest Date 10/09/2023

Variety See Treatments

Population See Treatments

Acres 5

Treatments 16

Reps 4

Treatment Width 15 ft.

Tillage No-Till

Previous Crop Wheat

Row Spacing 7.5 in.

Soil Type Hoytville Clay Loam, 90% Rimer Loamy Fine Sand, 10%

STUDY DESIGN

This study was conducted on an organic farm after wheat harvest. Red clover was frost interseeded into the wheat in March. After wheat harvest Red Clover was mowed to slow the clover. A randomized complete block split-plot design was used with nitrogen rate as organic poultry litter pellets as the main effect and forage species as the secondary effect. Nitrogen was applied using a calibrated horn spreader, and then forages were planted with a John Deere no-till drill. Seeding rates were 100 lbs/ac of oats, 35 lbs/ac BMR Sorghum, 30 lbs/ac Sorghum Sudan, and 15 lbs/ac Red Clover with no additional forage.

NC Ag Research Station OARDC

Sandusky County

WEATHER INFORMATION

Growing Season Weather Summary

Manure

OBSERVATIONS

• Cooler temperatures after planting favored clover and oats growth over the summer annuals BMR Sorghum and Sorghum Sudan.

• The cooler conditions also slowed summer annual plant growth with plant height being only half of normal at 60 days.

• The weed plant count was lower in the plots with at least 50 pounds of nitrogen and a grass species than in the clover-only plot.

SUMMARY

In this trial, the Red Clover dominated the plots reducing the growth of the grass species. With the clover alone plots having the highest yields. However, the higher rates of nitrogen did assist the oats in competing with the clover. As a blend, the higher nitrogen rates increased the growth of the grass species, decreasing the total forage protein. As the amount of grass species present increased so did the Neutral Detergent Fiber (NDF) and the Total Digestible Nutrients (TDN) of the forage. While the grasses competed with the clover when higher nitrogen rates were applied, a better method of stunting the clover than mowing is needed to maximize forage yields from the grass species.

PROJECT CONTACT

Jason Hartschuh (hartschuh.11@osu.edu)

Allen Gahler (gahler.2@osu.edu)

RESULTS

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

N Rate on Summer Annual Species

OBJECTIVE

Evaluate the effect of nitrogen rate across four summer annual forage species.

STUDY INFORMATION

Planting Date 08/04/2023

Harvest Date 10/19/2023

Variety See Treatments

Population See Treatments

Acres 2

Treatments 16 Reps 4

Treatment Width 10 ft.

Tillage No-Till

Management None

Previous Crop Wheat

Row Spacing 7.5 in.

Soil Type Hoytville Clay Loam, 89%

Rimer Loamy Fine Sand, 11%

STUDY DESIGN

This study was laid out in a randomized complete block split block design. With the treatments of species (oats, BMR Sorghum, Sorghum Sudan, and red clover) and nitrogen rates of (0, 50, 100, and 150 pounds per acre). All species were planted and harvested on the same day. Harvest was planned for 60 days after planting but was delayed due to slower than normal growing conditions with all plots harvested once the oats reached the heading stage. Plots were harvested with a small plot forage harvester with sub-samples pulled to dry and submit to the lab for forage quality analysis.

NC Ag Research Station OARDC

Sandusky County

WEATHER INFORMATION

Growing Season Weather Summary

Figure 1. Summer annual species were harvested on a plot harvester.

OBSERVATIONS

This trial suffered from wet soil conditions through much of the growing season along with below average growing degree days (GDD). All nitrogen rates on BMR Sorghum and Sorghum Sudan had a light green color throughout the growing season instead of the normal dark green color. Plots were harvested just before a frost in muddy conditions which slightly increased ash content in some plots.

PROJECT CONTACT

Jason Hartschuh (hartschuh.11@osu.edu)

Allen Gahler (gahler.2@osu.edu)

RESULTS

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

SUMMARY

• Both species and nitrogen rate had a significant effect on yield and forage quality.

• On all species but Red Clover, as nitrogen rates increased so did forage yield, but in all the grass species there was no statistical significance between the 100 and 150 lbs/ac nitrogen rates.

• All grass species had significantly greater Crude Protein and Total Digestible Nutrients (TDN) when 150 pounds of nitrogen was applied.

• Under more normal growing conditions we would have expected the BMR Sorghum and the Sorghum Sudan to have out yielded the oats.

• Each year is different and producers must decide which species will best fit there operations needs.

Winter Annual Rye Nitrogen Rates

OBJECTIVE

Use spring NDVI readings to assist making spring nitrogen application management decisions and determine if fall applied nitrogen increases density and protein in cereal rye.

STUDY INFORMATION

Planting Date 10/06/2023

Harvest Date 05/02/2024

Variety Cereal Rye VNS

Population 2 bushels per acre

Acres 1

Treatments 16

Reps 4

Treatment Width 10 ft.

Tillage Minimum Till

Management No herbicides, insecticides, or fungicides applied

Previous Crop Soybean Row Spacing 7.5 in.

STUDY DESIGN

This trial was a randomized complete block split-plot design with four nitrogen rates in the fall (0, 30, 60, and 90 lbs/ac) and four nitrogen rates in the spring (25, 50, 75, and 100 lbs/ac) over the fall rates in cereal rye. Rye was planted in October and harvested in May. NDVI and Canopeo readings were taken in late November before fall dormancy and in the spring after greenup to determine the greenness and density of green matter.

OBSERVATIONS

• The 0 nitrogen fall treatment was thin and yellow compared to the other treatments in the spring.

• 90 pounds of spring nitrogen improved the 0-fall nitrogen color, but plots were still thin.

NC Ag Research Station OARDC

Sandusky County

WEATHER INFORMATION

• The highest nitrogen plots had wider leaves than the low nitrogen plots.

Figure 1. View of plot layout.

RESULTS

View the treatments to the right.

SUMMARY

• At least 30 pounds of fall nitrogen was needed to maximize the yield.

• Spring nitrogen is the primary driver of Crude Protein (CP). CP was significantly affected by each 25 pound increase in spring nitrogen regardless of fall nitrogen rate.

• Total Digestible Nutrients (TDN) of cereal rye was negatively affected by increased nitrogen rates and was not affected at all by fall nitrogen treatments.

• NDVI had a correlation with all the spring applied nitrogen treatments - Canopeo did not. NDVI is a better tool for measuring ground cover and potential nitrogen available in spring for the crop, compared to Canopeo.

TOOLS OF THE TRADE

Treatment Means with the

Normalized Difference Vegetation Index (NDVI)

A handheld sensor that is walked across each plot to estimate the percent of live green vegetation in an area. The NDVI scale is between 0 and 1, with values closer to 1 indicating a higher percent of green vegetation.

PROJECT CONTACT

Jason Hartschuh (hartschuh.11@osu.edu)

Kendra Stahl (stahl.221@osu.edu)

Figure 2. Plot harvester in action.

Manure Sidedress

OBJECTIVE

Evaluate the potential yield difference between swine manure and commercial fertilizer applied at sidedress.

STUDY INFORMATION

Planting Date 05/18/2023

Harvest Date 11/02/2023

Variety Becks 5413Q

Population 31,100 sds/ac

Acres 36

Treatments 2

Reps 3

Treatment Width 30 ft.

Tillage No-Till

Management Fertilizer

Previous Crop Soybeans

Row Spacing 30 in.

Soil Type Del Rey silty clay loam, 13%

Fulton silty clay loam, 37%

Latty silty clay loam, 50%

eFields Collaborating Farm

OSU Extension Williams County

WEATHER INFORMATION

STUDY DESIGN

The study was a randomized complete block design. There were two treatments, swine manure and commercial fertilizer (28%). This study had 3 replications.

Figure 1. Swine manure was applied to the field on 6/9/2023.

OBSERVATIONS

This field had a dry period in May, June and July during the vegetative growth of the corn. Harvest was later than usual as fall dry-down was slower than usual. Disease pressure and insect pressure were minor.

RESULTS

Treatments

SUMMARY

• As with the previous year, corn sidedressed with manure had a significantly greater yield than corn sidedressed with commercial fertilizer.

• There was a significant difference in yield. There was a 48 bu/ac advantage in the manure treatment.

243 a

195 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: 7 CV: 1.9%

Amber Emmons (emmons.118@osu.edu)

2024 Tick Update

Introduction

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

Top 3 Ticks for Medical Concerns in Humans

American Dog Ticks prefer a more open habitat including pasture, meadows, and lawns. This tick is a prominent vector of Rocky Mountain Spotted Fever and Tularemia.

American Dog Tick (Dermacentor variabilis)

Black Legged or Deer Ticks prefer to live in a wooded habitat. This tick is the primary vector for Lyme disease, plus it can also vector Babesia and Anaplasmosis.

Black Legged or Deer Tick (Ixodes scapularis)

Lone Star Ticks are aggressive feeders and prefer a wooded habitat. This tick is the reported causative agent for the Alpha-gal (Mammalian Muscle Allergy reaction) where the person who is bitten can become allergic to meat including beef, pork, and venison.

Lone Star Tick (Amblyomma americanum)

Top Tick for Medical Concerns in Livestock

Longhorned Ticks are 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.

Longhorned Tick (Haemaphysalis longicornis)

Tips for

Managing Longhorned

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

Ticks in Pastures and on Cattle

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

• This tick has been known to vector Theileria (protozoal blood parasite) to cattle that can cause fever, inappetence, open cows, jaundice, and mortality.

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

• Intensive grazing management with rotation through paddocks to let animals maintain the pasture will be an important integrated pest management strategy.

• Products labelled for pasture application to control ticks 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.

*Map reflects confirmed data prior to June 1, 2024. Some suspected LH Ticks and theileria locations are pending confirmation. Future updates will contain an updated map.

Longhorned Ticks in Ohio Fact Sheet https://ohioline.osu.edu/factsheet/vme-1035

PROJECT CONTACT

Timothy S. McDermott, DVM Extension Educator, Franklin County mcdermott.15@osu.edu (614)-292-7916

Note: This tick outreach is supported by a grant from USDA NIFA 20217000635562

Beef Breeding Program Impact Survey

OBJECTIVE

Gaining a greater understanding of how producers manage beef cattle operations through a survey regarding reproductive data management.

STUDY INFORMATION

Time Surveyed Spring / Summer 2024

Species Beef

Topic Surveyed Reproduction

County Distributing Survey Delaware County

Type of Respondents Farmers/Herdsman

States Surveyed

Ohio 57 (83%)

Indiana 3

Kentucky 2

States with 1 Response each Arkansas, Oklahoma, Pennsylvania, Tennessee, West Virginia, Wisconsin

Total 68

Breeding Age Cattle Surveyed

Mature Cows 3,068

Replacement Heifers 617

Bulls 147

Total Animals Represented 3,832

STUDY DESIGN

A survey was distributed that contained questions designed to provide insights into the current practices of beef cattle reproductive data management. This survey aims to understand how producers manage their beef cattle operations, particularly focusing on the types of reproductive records kept and the methods used for record keeping. The goal is to gather valuable information that will aid in maintaining high-quality livestock through effective breeding program management.

50% of the survey participants have a commercial production enterprise

Figure 1. Responses to: Which best describes your cow-calf enterprise?

SUMMARY

Results emphasize the diversity in record-keeping and breeding techniques used to maintain highquality livestock.

This survey aims to aid producers in refining breeding strategies for enhanced operational effectiveness.

See more about Delaware County's eBarns studies at go.osu.edu/24ebarnsvideos

RESULTS

What is the biggest benefit of your preferred breeding method?

17.65% Genetics and Genetic Improvement

14.71% Time and Labor Savings

10.29% Variety and Flexibility in Sire Selection

Figure 2. Responses to: What is your preferred breeding methods?

• 46.15% of those who preferred NS answered that they mated 100% using this method.

• Of those who preferred AI, 36.12% answered that they mated 80-100% using this method. (13.89% said 80%, 8.34% said 90%, 13.89% said 100%).

• 23.08% of those who preferred ET answered that they only mated 20% using this method, A smaller percentage of 15.38% answered that they mated 70% using this method.

#1 = Time (15%)

Perceived barriers for not using AI:

#2 = Facilities (7%)

#3 = Cost (2%)

TOOLS OF THE TRADE

Social Media

Distribution of this survey went through digital newsletters and on social media.

Beef Team: u.osu.edu/beefteam

facebook.com/delawarecountyag

Sire Selection: Ranking the most importanct traits considered when making sire selection decisions:

Ranked as #1 Importance

Birth Weight (25.42%)

Reproductive Performance / Fertility (25.42%)

Conformation/Structural Soundness (23.72%)

Ranked as #2 Importance

Reproductive performance / Fertility (32.20%)

Conformation/Structural Soundness (22.03%)

Growth Rate (11.86%)

Ranked as #3 Importance

Maternal Ability (23.73%)

Conformation/Structural Soundness (15.25%)

Answers for "Other"

Figure 3. Responses to: Who breeds your cows? Answers for "Service"

• 60% - Bull

• 30% - Family

• 20% - Natural Service

• 37.5% said ABS

• 25% listed individuals

• 6.25% listed Specific Companies

PROJECT CONTACT

Kate Hornyak (hornyak.26@osu.edu)

Jacci Smith (smith.11005@osu.edu)

Rob Leeds (leeds.2@osu.edu)

Mastitis’ Effects on Milk Composition

OBJECTIVE

Evaluating biochemical changes in blood and milk to determine how subclinical mastitis affects local mammary tissue function and metabolism.

STUDY INFORMATION

Start Date 05/01/2022

End Date 03/01/2023

Species Dairy Cattle

Start Point Primiparous

End Point Primiparous

Treatments 2

Reps 4

Experimental Unit Head

Breed, Genetics Holstein

Reproduction Stage Heifers

Feed Access Hand Fed

Diet Standard Krauss Dairy Diet (OSU Location)

IACUC # 2022A00000020

STUDY DESIGN

Figure 1. Study timeline. Red lines denote milking time points (every 8 hours). Asterisk denotes blood sampling time point (every 2 h. 40 min., beginning 1 h. 20 min. after milking and intramammary infusions.

Part 1 of Study: Four primiparous cows (in first transition period, both pre and postpartum) were milked 3 times a day. Each cow had both quarters of either the right or left side of her udder infused with oyster glycogen (an inducer of neutrophil recruitment), and the opposite quarters were infused with saline (a control). Milk yields were recorded for each udder half. Milk samples were taken at each milking and evaluated by the Dairy Herd Improvement Association (DHIA) for somatic cell count (SCC), protein, lactose, and fat for 3 days after infusions.

Part 2 of Study: Part 1 did not result in a milk yield response, so in Part 2 the experimental design was changed to try to elicit subclinical mastitis that would also result in a decrease in milk yield. Part 2 utilized 4 mid-lactation primiparous Holstein cows. One udder half of each cow was challenged with killed, non-viable, Staphylococcus aureus, and the opposite quarters were infused with saline. Milk and blood measures were taken for 2 days postinfusions.

Figure 2. Graphic of study design

Figure 3. Collection of mammary tissue for further examination.

OBSERVATIONS (PART 1)

• No significant milk yield response.

• Differences in milk components: large increase in milk somatic cell count, marginal alterations in lactose and protein percentages.

RESULTS

OBSERVATIONS (PART 2)

• No significant milk yield response.

• Significant increase in SCC.

• Pronounced changes in milk composition.

• Milk protein concentrations increased in Staph. aureus challenged udder halves while lactose concentrations reduced.

SUMMARY

Figure 4. Data point visual: Saline (SAL, solid blue line, n = 4), formalin-fixed Staphylococcus aureus (FX-STAPH, dashed red line, n = 4).

Mean SCS (A), Lactose Content (B), Fat Content (C), Protein Content (D), Milk Yields (E),

Energy corrected milk yields (ECMY) (F) of whole milk from udder halves receiving designated intramammary infusions of SAL or FX-STAPH at time point 0.

Error bars denotes SEM, asterik = P ≤ 0.05, † = P = 0.10 for SAL vs. FX-STAPH comparisons within time point.

The fact that a large increase in SCC did not result in a decrease in milk yield was a surprising result. A change in tissue metabolism may be due to utilization of nutrients by the immune cells recruited to the mammary gland during mastitis.

In Part 2 of the study there was a notable increased concentration of milk lactate, which is a byproduct of anaerobic metabolism. This indicates that mammary tissue metabolism and milk synthesis pathways are altered during disease. Future research is needed to further elucidate what is happening during subclinical mastitis.

PROJECT CONTACT

Dr. Ben Enger, Associate Professor (enger.5@osu.edu)

Kellie Enger, Research Associate (enger.6@osu.edu)

Cori Gammariello

Dairy Cattle Behavior During Solar Eclipse

OBJECTIVE

Evaluating behavior and milk production of dairy cattle during the solar eclipse on April 8, 2024.

STUDY INFORMATION

Start Date 04/05/2024

End Date 04/11/2024

Species Dairy Cattle

Experimental Unit Head

Total Units 165

Breed, Genetics Holstein

BACKGROUND

CFAES Wooster

OSU Extension Wayne County

It is understood that animals do not have the ability to think, but they are able to learn. Dairy cattle quickly habituate to a daily routine of feed delivery and milking timing. The processes by which animals learn is still not understood, but is affected by their interactions with humans and the environment. On April 8, 2024 a rare solar eclipse occurred, with Wooster, OH in the path of the darkness totality at approximately 3:14pm.

STUDY DESIGN

All production cattle at The Ohio State University CFAES Krauss Dairy are fitted with a SRS collar. The collar system at Krauss Dairy records in 2-hour increments, on each even hour (2:00am, 4:00am, 6:00am, etc.), 24 hours/day. It also records milk production totals (milking twice daily at 4:00am and 4:00pm).

A report was generated for all cattle at the dairy to include activity and milk yield from April 4, 2024 to April 11, 2024. All cattle with complete records from April 5 - 11 (N=165) were used in the analysis. Data for total daily activity (minutes/day), total activity between 2:00pm and 4:00pm, and second shift milk yields (4:00pm) were used to create time blocks:

• Pre-Eclipse average (April 5 - 7)

• Day of Eclipse (April 8)

• Post-Eclipse average (April 9 - 11)

A paired sample t-test analysis (JMP Pro) was used to determine if there was a difference in activity level and production between these timing blocks.

OBSERVATIONS

• Slight variations in herd averages for: total daily activity, activity between 2 - 4pm, and second shift milk yield.

• No significant activity differences were found when comparing (Pre-Eclipse and Post-Eclipse activity levels to the activity level on April 8th (Figure 3).

• There was a significant difference in the second shift (4:00pm) milk yield for all comparisons as the averages gradually increased over the total days observed (Figure 2).

Figure 1. Dairy cattle feeding at CFAES Wooster.

SUMMARY

• The dairy’s average per cow production increased each day from April 7 - 10. This increase can not be directly attributed to any environmental impact related to the decrease in sunlight associated with the April 8th solar eclipse.

• The short period of decreased sunlight from the April 8th, 2024 had no observable impact on the activity level or milk production of dairy cattle at the OSU CFAES Krauss Dairy.

• The findings of this observation agree with another published study (on grazing dairy cattle) that a solar eclipse does not significantly alter dairy cattle behavior.

RESULTS

Figure 2. Table displaying data collected on Total Activity, Activity between 2:00pm and 4:00pm, and the second shift milk yield.

Figure 3. Probabilities from paired t-test comparisons of total daily activity, 2:00pm - 4:00pm activity, and milk yield by time block.

Asterik (*) signifies a statistical difference between averages. Alpha (∞) = 0.05

TOOLS OF THE TRADE

SRS Collar System

The collars record an individual animal’s activity in 2-hour increments (total minutes active in that time).

PROJECT CONTACT

Circadian Temperature or Activity Detection

OBJECTIVE

Evaluating if a significant circadian temperature or activity rhythm can be detected with indwelling rumen boluses in dairy calves and if that rhythm changes by season.

STUDY INFORMATION

Start Date 10/01/2021

End Date 10/31/2023

Species Dairy

Start Point 3 weeks of age

End Point 13 weeks of age

Treatments 9

Reps 189

Experimental Unit Head

Breed, Genetics Holstein

Sex Females

Feeding System Ad Libitum, Self Feeder

IACUC # 2020A00000111

eBarns Collaborating Farm

OSU Extension

Crawford County

STUDY DESIGN

For this study, each calf was treated as an individual study unit. The rhythm was analyzed for calves that were 3, 6, and 12 weeks of age to determine if rhythms change as the calf ages.

Season was also analyzed as a factor to determine if winter and summer have different rhythm patterns since during these seasons calves experience either heat stress or cold stress.

Figure 1. Holstein calf lying down.

Figure 2. Average temperature (blue line) and activity (orange line) of calves at 3 weeks of age, over a weeks time.

OBSERVATIONS

• Calves are most active at the time of day when they are fed.

• Sick calves were visually slower and less active than healthy calves.

• Calf temperature decreased any time the calf's activity increased.

RESULTS

SUMMARY

Season had a significant affect on both calf temperature and activity rhythms. Spring and fall had similar rhythm, but summer and winter showed increased variation with less calf activity between meals.

Age of the calf was also significant. The 12 week old calves activity had increased variation between calves.

The drop in calf temperature during times of increased activity could lead to low grade fevers being missed by manual rectal temperature monitoring.

Only Significant rhythm powers are shown

3. Results table for temperature rhythm power

TOOLS OF THE TRADE

ST Farmfit Rumen Bolus (Fig 5.) and Management app (Fig. 6)

Used to monitor dairy calf temperature and activity for illness detection.

/ Fall, 12 weeks

Only significant rhythm powers are shown

Figure 4. Results table for activity rhythm power

PROJECT CONTACT

Jason Hartschuh (hartschuh.11@osu.edu)

Figure

Figure 5. Figure 6.

Effect of Dietary Fiber Management

OBJECTIVE

Evaluate the effects of feeding different types and amount of fiber during gestation on both ewes and lambs.

STUDY INFORMATION

Start Date January 2020

End Date March 2020

Species Sheep

Start Point Multiparous

End Point Multiparous

Treatments 3

Reps 3

Experimental Unit Pen

Breed, Genetics Dorset x Hampshire

Crossbred Sheep

Sex 87 Mature Ewes

Feeding System Self Feeder

IACUC # 2019A00000001

STUDY DESIGN

Small Ruminant Research Center CFAES Wooster Wayne County

Two separate groups of non-pregnant ewes (91) were put on a fescue-based pasture with rams for breeding. Ewes were exposed to rams for 2 complete estrus cycles (34 days). When environmenatal conditions no longer allowed for grazing, ewes were moved to indoor housing and fed a diet containing concentrate and soybean hulls (1:3 ratio). Ewes were then blocked by body weight into 3 blocks and randomly allotted to a pen (10 ewes/pen). Diet fiber treatments were as follows: soybean hulls (SH), limit fed hay (LH), and ad libitum hay access (CONT). Diets were offered to 9 pens (3 pens/treatment, 9-10 ewes/pen). On day 20 of the feeding period, ewes were pregnancy checked. Four ewes were not confirmed pregnant and were removed from the experiment. Dietary treatments for hay were chosen to reflect winterfeeding systems commonly used in the midwestern United States. For lambing, ewes moved to lambing pens for 48 hours, then to maternity pens following birth. During lambing, and until ewes were moved back to pasture (day 112), ewes were fed the same diet and amount as when they were in the group pen setting.

Data Collected:

• Daily: Concentrate/roughage offerings and refusals (measuring dry matter intake (DMI)), Total pen feed refusal for SH and LH fed ewes

• Weekly: Hay refusal for CONT fed ewes

• Every 2 weeks: feed samples (for dry matter (DM) content and nutrient composition)

• Ewe body weight (BW): at the beginining of the experiment (d -28), mid-gestation (d 34), late-gestation (d 76), and 2 days after lambing.

• Lamb BW: at lambing and at weaning (60 days of age)

Figure 1. View of ewes using a fence line feeder during this study.

OBSERVATIONS

Cattle Small Ruminant

• Ewes consuming SH had to be limited in their intake of SH as these ewes demonstrated an eating behavior that drove them to eat more of what was really needed, thus pushing them to gain more body weight gain.

• The SH wes over ate on the SH offered which led to additional complications including excessive hoof growth, leading to foundering.

• The average day of lambing was day 103 ± 5 with respect to the initiation of the experiment on d -51 (starting/finishing dates of the experiment were set by the ability to return the sheep back to pasture).

RESULTS

Ewe Data Results: (Figure 2, right)

Effect of feeding unrestricted access to hay (CONT) and a fixed amount of SH or LH during gestation on ewe body weight before and during gestation as well as at lambing.

SUMMARY

Under the conditions of this experiment, SH ewes demonstrated an increase in body weight, and lambs of SH ewes were born heavier, but had a slower growth until weaning. Management of the hay (ad libitum or restricted) did not affect the amount of hay consumed by the ewe.

Lamb Data Results: (Figure 3, right)

Effect of feeding CONT and a fixed amount of SH or LH on lamb body weight at birth and weaning.

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

TOOLS OF THE TRADE

Soybean Hulls

A by-product of soy or vegatable oil production. Once processed, soybeans can produce oil, meal, fiberous hulls. The hulls (outer coating of the seed) mainly consists of fiber that has no current use in human consumption or fuel production. However, this fiber source can be useful in ruminant production systems.

PROJECT CONTACT

Dr. Braden J. Campbell Assistant Professor, State Small Ruminant Extension Specialist (campbell.1279@osu.edu)

Dr. Ale Relling, Associate Professor (relling.1@osu.edu)

Effect of Overprocessing Distiller Grains

OBJECTIVE

Investigate how different heat processing methods of Dried Distillers Grain (DDG) affects the growth of finishing lambs.

STUDY INFORMATION

Start Date 06/16/2023

End Date 07/28/2023

Species Sheep

Start Point ~ 100 days of age

End Point ~ 150 days of age

Treatments 2 Reps 6

Experimental Unit Pen

Total Units 34

Breed, Genetics Hampshire × Dorset × Ile de France × South African Meat Merino (SAMM) crossbred lambs

Sex 18 Wethers, 16 Ewes

Feeding System Self Feeder

Space 66 ft²

IACUC # 2023A00000070

STUDY DESIGN

Thirty-four cross-bred lambs were weighed and blocked by body weight and sex, then assigned to 1 of 12 pens. Then, each pen was randomly assigned 1 of 2 treatments (Diet A or Diet B). Lambs were offered water ad libitum and offered feed daily using slick bunk management. The bunk feeder was considered slick when there was no feed remaining in the bunk 24 hours post feeding. When this occurred for 2 consecutive days, the amount of feed offered increased by 0.75 lbs./pen. If a pen left feed for 2

Small Ruminant Research Center CFAES Wooster

Wayne County

Figure 1. Nutrient composition of commercial (Diet A) and heat processed* (Diet B) dried distillers grain (DDG) fed to finishing lambs, reported as a percent (%) on a dry matter basis.

Figure 2. Visual comparison of DDG sample. Diet A (left) is a commercial DDG source received from the mill (no additional heat processing). Diet B (right) is the same commercial DDG, but was further processed using heat*

consecutive days, the amount of feed offered was decreased by 0.5 lbs./pen. Prior to beginning the experiment, all lambs were fed diet A (CON) for 10 days during an adaptation period. Lamb body weight (BW) was measured at the start of the experiment (day 1), and every 14 days throughout the duration of the experiment (42 days). Dry matter intake (DMI) was recorded daily for each pen prior to feeding by weighing the previous day’s feed refusals.

OBSERVATIONS

Cattle Small Ruminant

• There were no differences observed in lamb body weight or Average Daily Gain (ADG).

• There was no difference in diet nutritional composition (NDF, ADF, CP, EE, or Ash).

SUMMARY

Completion of this experiment revealed that processing DDG at 302°F for 70 minutes does not affect lamb growth, ADG, DMI, or Gain to Feed Ratio (G:F).

Further research should be conducted to focus on processing DDG at greater temperatures for longer periods of time.

RESULTS

Figure 3. Diet composition and ingredient inclusion rates for both Diet A and heat processed* Diet B.

**48.50% limestone, 24.30% salt, 19.40% ammonium chloride, 4.40% Vitavet selenium, 2.40% Vitamin E, 0.49% Vitamin A and D

Figure 4. Data collected and calculated on finishing lambs consuming a diet with 20% DDG that differ through heat processing*. SEM = Standard Error of the Mean.

¹All variables were analyzed as repeated measurements considering the fixed effects of treatment, day, and their interaction. Because of the lack of observed differences in the time by treatment interaction (P ≥ 0.36) only the main effect of treatment are presented.

* Dried Distillers Grain (DDG) heat processing: 40% moisture was added to the DDG and heated at 150°C for 70 minutes. Then, Diet B was placed in a second oven at 56°C for 2 days to allow for humidity evaporation.

TOOLS OF THE TRADE

Drying Oven

A commerical drying oven was used to heat process the DDG further to elicit the Milard reaction. The Milard reaction is noted by the color change in the two examples of DDG here. The darker sample (Diet B) was heat processed. (See Fig. 2 for an example of this color change)

PROJECT CONTACT

Dr. Braden J. Campbell Assistant Professor, State Small Ruminant Extension Specialist (campbell.1279@osu.edu)

Dr. Ale Relling, Associate Professor (relling.1@osu.edu)

Brayden Thompson, Animal Sciences Undergraduate, Summer Research Intern

Feeding Different Distiller Concentrations

OBJECTIVE

Investigating the effect of feeding increasing concentrations of Dried Distillers Grains (DDG) on finishing lambs.

STUDY INFORMATION

Start Date June 2023

End Date July 2023

Species Sheep

Start Point ~ 120 days of age

End Point ~ 180 days of age

Treatments 3

Reps 5

Experimental Unit Pen

Breed, Genetics Dorset

Sex 75 Wethers

Feeding System Self Feeder

Space 118 ft²

IACUC # 2023A00000070

STUDY DESIGN

During the summer of 2023, 75 wethers were identified for this experiment, with each lamb having intial body weights of 95 lbs. and at approximately 120 days of age. They were weighed and blocked by body weight (BW), then were assigned to 1 of 15 pens. Each pen was randomly assigned 1 of 3 treatments (diets containing different concentrations of Dried Distillers Grains (DDG) at 10, 20, or 30%).

Soybean meal and soybean hulls were used to replace the difference of the DDGs to ensure diets were isonitrogenous and isocaloric. Lambs were weighed every 28 days for a feeding period of 56 days. Feed samples were collected every other week and sent for nutrient analysis. The following was calculated at the conclusion of the experiment: Average Daily Gain (ADG), Dry Matter Intake (DMI), and Gain to Feed Ratio (G:F).

Eastern Ag Research Station

OSU Extension

Noble County

Figure 2. Treatment table for the different DDG concentrations.

Figure 1. Feedlot lambs at the Eastern Ag Research Station in Caldwell, OH.

OBSERVATIONS

Cattle Small Ruminant

• No differences in lamb performance were observed when evaluating lamb final BW, ADG, and G:F.

• A decrease in lamb DMI was noted when inclusion rates of DDG increased in the diet.

SUMMARY

Increasing the concentration of DDG in lamb finishing diets from 10% to 30% does not affect lamb growth, but linearly decreases feed intake. These results should be interpreted with caution as different sources of DDG may present differing results. Further research is required to determine the impacts of this feeding strategy.

RESULTS

Figure 4. The effect of increasing the concentration of dried distillers grains (DDG) in the finishing diet of feedlot lambs on lamb body weight gain, average daily gain, dry matter intake, and gain to feed ratio. SEM = Standard Error of the Mean.

TOOLS OF THE TRADE

Dried Distillers Grain (DDG)

Dried Distillers Grain is a popular by-product from ethanol production used in the livestock feed industry as it is a cost-effective feedstuff that is rich in crude protein and minerals.

PROJECT CONTACT

Dr. Braden J. Campbell Assistant Professor, State Small Ruminant Extension Specialist (campbell.1279@osu.edu)

Dr. Ale Relling, Associate Professor (relling.1@osu.edu)

Figure 3. Feedlot lambs eating research diets from the feed bunk.

Pumpkin Seed Impact on Parasites in Sheep

OBJECTIVE

Evaluating the effect of pumpkin seeds as a supplement for sheep and as a natural anthelmintic (de-worming treatment), while reducing chemical anthelmintic use and fall pumpkin waste.

STUDY INFORMATION

Start Date 11/06/2023

End Date 12/11/2023

Species Sheep

Start Point 8 months of age

End Point 9 months of age

Treatments 2

Reps 5

Experimental Unit Pen

Genetics Commercial

Breed Dorset, Suffolk, Hampshire, South African Meat Merino (SAMM), and Ile de France Crossbred lambs

Sex 20 ewe lambs

Space 66 ft²

Feeding System Self Feeder

IACUC # 2023A00000082

STUDY DESIGN

Small Ruminant Research Center CFAES Wooster Wayne County

Figure 1. Lamb eating pumpkin seeds

Figure 2. Data comparing “as Fed Intake“ for the 2 treatment groups

Pumpkin seeds (PS) were sourced from pumpkins grown at the Western Agricultural Research Center and Waterman Student Farm at The Ohio State University. Pumpkin seeds were collected and stored in 1 lb. increments and frozen for future use. Samples of PS were sent to a commerical laboratory for nutrient composition analysis.

Initially, 32 ewe lambs (grazing on pasture) were identified based upon parasite load using fecal egg counts (FEC). The top 20 greatest FEC sheep were used and sorted for this experiment. Lambs were randomized and allocated to 1 of 10 pens (5 pens/treatment; 2 lambs/pen). All lambs were fed a common diet, but differed in supplement or top dressing offered. Lambs in PS group received 1 lb. of PS/head(hd)/day whereas control lambs (CON) recieved 0.5 lbs. of whole shelled corn (WSC)/hd/day to ensure both groups of lambs were offered isoenergetic diets. Lambs were fed for 28 days.

Data collected every 7 days: lamb live body weight, fecal samples for FEC evalution to quantify parastic burden, blood samples for Packed Cell Volume (PCV) observation, and FAMACHA eye scores (FAM)

Data collected daily (prior to feeding): feed refusals (amount of feed not eaten from the previous days feed offering)

OBSERVATIONS

Cattle Small Ruminant

RESULTS

Manure Nutrients Forages Poultry Equine Swine

• There was no difference in lamb FAM or FEC. However, lambs in the PS group had a greater PCV (38%) when compared to the CON lambs (36%) (see Figure 5).

• There was also a decrease in lamb body weight for lambs offered PS when compared with lambs in the CON group (see Figure 4).

• Although not quantified, it was observed that lambs in the PS treatment group excreated tapeworm segments (proglotids) whereas CON lambs did not.

SUMMARY

• Although there was no change observed in lamb FEC or FAM, lamb PCV increased in the PS feeding group.

• Lamb body weight decreased in the PS group, which was associated with the decrease in feed intake or refusal of PS in the PS lambs. The decrease in PS consumption may have negatively impacted the effectiveness of PS as a natural parasite control method.

• The PS fed lambs were the only group that was observed to have shed tapeworm proglotids. Although not measured, this unique observation indicates the potential for some type of anthelmintic effect which warrents additional research.

TOOLS OF THE TRADE

McMaster Slide

The McMaster’s Technique uses a special slide (McMaster Slide) with 2 chambers (6 columns/chamber). This slide is used to quantify the parasitic burden using a fecal sample that is collected directly from an animal.

representing the recorder average PCV from blood samples every week of the study.

PROJECT CONTACT

Dr. Braden J. Campbell Assistant Professor, State Small Ruminant Extension Specialist (campbell.1279@osu.edu)

Dr. Benjamin Wenner

Melva Tacuri Vera Animal Sciences Undergraduate Honors Thesis Project

Figure 3. Lamb with pumpkin on their nose

Figure 4. Data representing the recorded average body weights every week of the study.

Figure 5. Data

Sheep LAI Summary

OBJECTIVE

Measuring the lambing rate in Ohio Seedstock Sheep Flocks with an extended history of using frozen semen for Laparoscopic Artificial Insemination.

STUDY INFORMATION

Start Date 09/01/2016

End Date 12/01/2023

Species Sheep

Start Point Various

End Point Various

Estrous Synchronization Yes

Breeding Method Artificial Insemination (AI)

Experimental Unit Head

Breed, Genetics Shropshire, Tunis, Hampshire

Sex Ewes

Feeding System Self Feeder, Ad Libitum

STUDY DESIGN

Laparoscopic artificial insemination (LAI) is an intrauterine method of insemination to bypass the unique anatomically tortuous cervix in sheep. Success of LAI programs depends on proper implementation of the estrus synchonization program, animal selection, and knowledge of the LAI process.

Data was collected through a questionnaire from seedstock flocks using LAI standarized protocol administered by experienced technicians. Source flock selection was based on multiple years of LAI experience.

eBarns Collaborating Farm

OSU Extension

Marion County

Figure 1.Technician using a cradle device during a LAI.

Figure 2. An up-close view of LAI by a technician.

OBSERVATIONS

Cattle Small Ruminant

RESULTS

• Our study participants felt a better understanding of how sperm traits alter fertility in the ewe after insemination are warrented.

• Rams need to be screened for semen factors (concentration during storage, motility at insemination, number of sperm inseminated, morphology of sperm inseminated, viability of sperm inseminated, and acrosome intergrity) that are known to influence the success of LAI.

SUMMARY

Extensive efforts have been focused to understand the causes of such variability in lambing rates. The degree to which various factors interact and influence LAI remain unknown. The standardized process for LAI with frozen semen has not changed significantly since it was first introduced in 1980.

Today, the following variables remain at the core for lambing rate success:

• Preparation of the ewe (age, body condition, plane of nutrition, body weight, synchronistion of estrus, and use of teaser)

• Quality of semen

• Enviromental condition (before, during, and after the program).

TOOLS OF THE TRADE

Laparoscope

Used with veterinarian accepted procedures and timings for Laparoscopic Artificial Insemination in Ohio seeddtock ewes.

Figure 3. Results Table showing a summary of the calculated birth rate for each year of this multi-year study.

4. Birth Rate Visual, based on Figure 3.

PROJECT CONTACT

Tim Barnes (barnes.821@osu.edu)

Figure

Small Ruminant Record Keeping Survey

OBJECTIVE

Assess common methods of records being kept and types of records being kept by small ruminant producers.

STUDY INFORMATION

Time Surveyed Spring / Summer 2024

Species Small Ruminant

Topic Surveyed Farm Management

County Distributing Survey Delaware County

Locations Surveyed USA, Canada, United Kingdom

Type of Respondents Farmers/Herdsman

Total Number of Survey Responses 192

What type of livestock do you raise?

eBarns Collaborating Farms

OSU Extension Statewide

STUDY DESIGN

A survey was distributed digitally and contained questions designed to give insight on current small ruminant record keeping practices, how producers keep records, and what records they keep.

Figure 2. Chart showing that over 96% of the respondents keep some type of records.

What benefits do you see from keeping livestock management records?

Figure 1. Bar chart showing the breakdown of what types of producers responded. Mainly sheep vs. goat producers.

Animals Represented in Survey

Sheep 14,551

Goats 3,735

Other 846

Figure 3. Word cloud generated from answers provided, with the larger words being the most frequently mentioned.

OBSERVATIONS

Cattle Small Ruminant

RESULTS

• The top 4 types of records that small ruminant managers keep are birthing, breeding, health, and genetics (Fig. 4).

• The top uses for management records are Breeding and selection/ genetic progress, Health and death loss tracking, Performance/ Show results, Financial, Reference guide, Birthing rates, Feed and nutrition, and Compliance and regulation.

• Most respondents (64.81%) use ear tags as their identification method.

• Method of keeping records are split close to even for paper versus electronic.

• Electronic record keepers tend to use spreadsheets.

• The most prevalent pre-made record system represented in our survey was the “HerdBoss” app.

SUMMARY

The 192 respondents of the survey manage 18,286 head of small ruminants, made up of over 14,500 head of sheep and over 3,700 head of goats. Over 96% of the respondents keep some type of livestock management records, however it’s worth noting that over 3% of respondents do not keep any type of records (Fig. 2).

Survey answers reflected that producers see records as a benefit to their livestock management; however, they are not always updating them right after a handling event (Fig. 5).

In conclusion, this survey showed that most sheep and goat producers keep dedicated livestock records and see benefits to their livestock business.

TOOLS OF THE TRADE

Social Media

Distribution of this survey went through digital newsletters and on social media.

Sheep Team: u.osu.edu/sheep facebook.com/delawarecountyag

that these

update their permanent records most often after each handling of an animal (67.86%).

PROJECT CONTACT

Jacci Smith (smith.11005@osu.edu)

Kate Hornyak (hornyak.26@osu.edu)

Rob Leeds (leeds.2@osu.edu)

See more about Delaware County's eBarns studies at go.osu.edu/24ebarnsvideos

Figure 5. Pie chart showing

producers

Figure 4. Chart showing most commonly recorded livestock operation data.

Cool-Season Grass Hay Preferences

OBJECTIVE

Measure yield, macronutrient content, and ergovaline presence within monocultured coolseason grass hays. Then, evaluate relative horse preference focusing on Fescue and Ryegrass families.

STUDY INFORMATION

Start Date 05/01/2019

End Date 12/31/2020

Species Equine

Start Point Mature

End Point Mature

Treatments 8 Reps 2

Unit per Rep 4

Experimental Unit Head

Breed, Genetics Stock type

Sex M:F 2 mares : 2 geldings

IACUC # 2019A00000070

STUDY DESIGN

ATI Equine Facility

CFAES Wooster Wayne County

First cutting chopped hay (7 cultivars) was created in 2019 and analyzed for nutrients and ergovaline concentrations. Taste preferences were evaluated during summer 2019 and winter 2020 using a randomized complete block design with cultivar and bucket location as variables.

Stalls had 8 buckets along a wall and visible to pre-installed foaling cameras for recording. The same 4 mature horses at maintenance were used for the preference testing both years. Horses were fasted 30 minutes before entering the testing stalls and had access to the cultivars for 30 minutes. Weight consumed was recorded. Equine preference data for cultivar and bucket location were analyzed in JMP Pro 15.

ACKNOWLEDGMENTS: We would like to thank Ben Traver, Ben Lowe, and Ben Wasson for their assistance in harvesting, chopping, and weighing the 2019 hay. The study was supported by an Ohio State ATI Research, Creative, and Other Scholarly Activities (RCOSA) grant. This data was originally presented/published as an abstract at the virtual 2021 Equine Science Society Symposium: Bennet-Wimbush, K., B. Lowe, V. R. Haden, and S. L. Mastellar. 2021. 44 Relative preferences for cool season grass hay cultivars in adult horses. Journal of Equine Veterinary Science 100:103507. doi: 10.1016/j.jevs.2021.103507.

Figure 2. Example camera view of preference test set up, using a foaling camera.

Figure 1. 2019 Yield of the seven cool season cultivars grouped by horse preference.

OBSERVATIONS

• In 2019 Meadow Fescue, Festulolium and the three Tall Fescue cultivars all had significantly greater first cutting yields than the Perennial Ryegrass and Orchardgrass cultivars (P < 0.05; Figure 1).

• Consumption varied by cultivar (P < 0.05; Figure 3). Cultivar ranks by amount consumed by the horses were similar across years.

• Ergovaline was below detectable limits in all 2019 samples, so 2020 fescue hay was analyzed for endophyte presence (via tiller staining) confirming very low levels of infection: Bronson (3%), Texoma Max Q (17%), and KY 31 (0%).

RESULTS

SUMMARY

In this study, horses preferred Perennial Ryegrass and Orchardgrass hay which were also the lowest yielding cool season cultivars.

Horses demonstrated a consistent preference for some species of cool season grasses in both 2019 and 2020. However, the preference does not appear to be due to endophyte presence or macronutrient content.

For hay producers looking to strike a balance between moderately high yield and feeding preference by horses, Festulolium is likely to be a promising option.

Figure 3. Preference of mature horses for monocultured cultivars first cutting hay during two years. Total consumed is across all preference tests within year. Means within columns with different superscripts are statistically different (P<0.05).

TOOLS OF THE TRADE

Foaling Cameras

Cameras mounted above the stalls allowed researchers to record and quantify horse behavior.

PROJECT CONTACT

Dr. Sara Mastellar (mastellar.1@osu.edu)

Dr. Karen Bennett-Wimbush (wimbush.4@osu.edu)

Dr. Ryan Haden (haden.9@osu.edu)

Meal Frequency Effects on Heart Rate

OBJECTIVE

Evaluate equine heart rate in response to feeding frequency.

STUDY INFORMATION

Start Date 05/27/2022

End Date 06/30/2022

Species Equine

Start Point Mature

End Point Mature

Treatments 3

Reps 2

Unit per Rep 6

Experimental Unit Head

Breed, Genetics Light horses used for riding

Sex M:F 6 mares : 6 geldings

Feeding System Pellets in corner feeders and hay in slow feeder nets with 5 cm x 5 cm holes.

Diet Fed At a 2% body weight (BW) on a dry matter (DM) basis with 1.55% BW DM basis in grass hay (slow feed hay net). Concentrate provided at 0.45% BW on a DM basis.

IACUC # 2019A00000081-R1

STUDY DESIGN

ATI Equine Facility CFAES Wooster Wayne County

Two groups of 6 horses each received a meal frequency of 1x, 2x, or 3x for one week each in a randomly generated order. Diets were the same for all treatments. On day 6 of each week, using Polar H10 heart rate monitors connected to iPad tablets, data was collected (heart rate each second) for 12 hours while the horses were stalled. Data was imported into Excel. Outliers (>2.5 x mode) were removed from the original data set (1,514,799 data points) relative to the individual horses’ resting heart rate. Mode was used to represent the horses’ resting heart rate. After outliers were removed, the number of values above, at, and below the mode were quantified. Chi Square tests were used to evaluate statistical significance.

Horses received daily allotment of feed at 8 am

Horses received daily allotment of feed split across two feedings (8 am & 6 pm) 3x

Horses received daily allotment of feed split across three feedings (8 am, 1 pm, & 6 pm)

Figure 1. Horse with the Polar heart rate monitor during sampling. Wet sponges (pink and blue) were placed under the electrodes. Sweat ensured subsequent conductivity. Inset image on bottom right: Tablets connected via Bluetooth to each heart rate monitor collecting data every second.

Figure 2. Image of heart rate recording (split across both pages). Heart rates of 12 horses fed 1x, 2x, or 3x/day over 12 hour period.

OBSERVATIONS

• Of the 12 horses, 9 did have a significant difference in the proportion of heart rate readings above or below mode based on meal frequency (P<0.05).

RESULTS

SUMMARY

With this information it does appear that one meal daily was the most stressful for the horses within this study despite the use of slow feeder hay nets. For these reasons it may be beneficial for the horse’s well-being if managers and owners can feed horses more often. However, it is important to note that, ideally, stress is evaluated using multiple measures. For the final analysis of the heart rate data, 1,479,854 of the original data points were used. Outliers (>2.5 x mode) accounted for 2.3% of the data.

This data was part of a larger study evaluating additional effects of meal feeding frequency in horses of varying body condition scores.

Figure 3. Each horse with their mode, and the percentages of their heart rate data that was above, at, or below mode.

Figure 2. continued

ACKNOWLEDGMENTS: We would like to thank Oriana Tillery-Sucre for assistance with horse care and sampling. We would also like to thank Ohio State ATI’s equine facility and staff for providing the horses. The project was funded by an Ohio State ATI’s Research, Creative & Other Scholarly Activities (RCOSA) grant and Buckeye Nutrition. This research was previously presented/ published as a poster: Brown, A. V., E. R. Share, N. Liburt, J. K. Bedore, P. Harris, and S. L. Mastellar. 2024. Effects of Meal Frequency on Heart Rate in Horses American Society of Animal Science Midwest No. PSIV-A-8, Madison, WI.

TOOLS OF THE TRADE

Used to track heart rates. Data can be collected by connecting the monitors to portable devices, like iPad tablets, for data analysis.

PROJECT CONTACT

Dr. Sara Mastellar (mastellar.1@osu.edu)

Elizabeth E. Share (share.8@osu.edu)

Alora V. Brown

Polar H10 Heart Rate Monitors

Meal Frequency Effects on Plasma Amino Acids

OBJECTIVE

Evaluate the effect of feeding frequency on plasma amino acid responses in metabolically healthy horses of various stable body conditions.

STUDY INFORMATION

Start Date 05/27/2022

End Date 06/30/2022

Species Equine

Start Point Mature

End Point Mature

Treatments 3 Reps 2

Unit per Rep 6

Experimental Unit Head

Breed, Genetics Light horses used for riding

Sex M:F 6 mares : 6 geldings

Feeding System Pellets in corner feeders and hay in slow feeder nets with 5 cm x 5 cm holes.

Diet Fed At a 2% body weight (BW) on a dry matter (DM) basis with 1.55% BW DM basis in grass hay (slow feed hay net). Concentrate provided at 0.45% BW on a DM basis.

IACUC # 2019A00000081-R1

STUDY DESIGN

ATI Equine Facility CFAES Wooster Wayne County

Figure 1. Body composition has been known to affect basal metabolism, circulation of metabolites, and response to meals in other species. Visual was adapted from: buckeyenutrition.com/tools/body-condition-score.aspx

Treatment Description

1x

2x

3x

Horses received daily allotment of feed at 8 am

Horses received daily allotment of feed split across two feedings (8 am & 6 pm)

Horses received daily allotment of feed split across three feedings (8 am, 1 pm, & 6 pm)

Horses were fed to maintain condition with a complimentary feed (16% Cruide Protein (CP)) and hay (11% CP). Diet and feeding system is listed above, and treatments are listed in the Table to the right. After a 14 day adaptation, 2 groups (6 horses each) were randomly allocated to meal frequency regimens (lasting 7 days each) in a crossover design.

Jugular vein catheters were placed on day 7 of each regimen. Blood samples were taken relative to the morning feeding at −30, 0, 30, 60, 90, 120, 180 minutes. A mixed model with repeated measures in SAS was used to determine the effects of meal frequency, time point, and body condition score on plasma amino acid concentrations.

ACKNOWLEDGMENTS: We would like to thank Oriana Tillery-Sucre for assistance with horse care and sampling and Dr. Shaun Wellert for assistance with placing catheters. We would also like to thank Ohio State ATI’s equine facility and staff for providing the horses. The project was funded by an Ohio State ATI’s Research, Creative & Other Scholarly Activities (RCOSA) grant and Buckeye Nutrition. This research was previously presented/published at a scientific conference: Mastellar, S. L., E. R. Share, J. K. Suagee-Bedore, K. Bennett-Wimbush, N. R. Liburt, A. Krotky, B. Cassill, K. L. Urschel, and P. A. Harris. 2023. 74 Effects of meal frequency on plasma amino acid concentrations in horses of various body condition scores. Journal of Equine Veterinary Science 124:104376. doi: https://doi. org/10.1016/j.jevs.2023.104376

OBSERVATIONS

• With the exception of glutamate, all plasma amino acid concentrations increased after meal consumption (P < 0.01).

• While meal feeding frequency did affect plasma amino acid concentrations for most amino acids (P < 0.01), there was no interaction between meal frequency and time point for any amino acid (P > 0.7).

• Indispensable postprandial plasma amino acid concentration increases over baseline at 60 min (average± SE) were 56 ± 7%, 56± 8%, and 59 ± 7% for the 1x, 2x, and 3x meal feeding regimens, respectively. Body condition score affected plasma concentrations of alanine, arginine, aspartate, and tyrosine (P < 0.01).

SUMMARY

The findings demonstrate that even one third of the dietary allotment was able to create increases in plasma amino acid concentrations similar to the 1x feeding regimen. Feeding smaller, more frequent meals could be a simple way to improve protein and amino acid bioavailability in equine diets. More research is needed to investigate plasma amino acid profiles related to disease states and body composition.

RESULTS

2. Plasma amino acid responses to the 8 am meal were similar across treatments (1x, 2x, & 3x), despite the difference in meal size.

8 am meal varied with body condition.

TOOLS OF THE TRADE

Henneke body condition scoring system

To learn more and find scoring sheets: ohioline.osu.edu/factsheet/as-1024

PROJECT CONTACT

Dr. Sara Mastellar (mastellar.1@osu.edu)

Dr. Karen Bennett-Wimbush (wimbush.4@osu.edu)

Elizabeth E. Share (share.8@osu.edu)

Figure

Figure 3. Plasma amino acid responses to the

Intestinal Parasite Surveillance

OBJECTIVE

Characterizing the occurrence of intestinal parasites of pigs raised on free-range systems located in Ohio and surrounding states.

STUDY INFORMATION

Start Date Summer 2024

End Date On-going, Expected Spring 2025

Species Swine

Breed Various

Sex Males and Females

STUDY DESIGN

Fecal samples are collected directly from the rectal ampulla of pigs, or, if this method is not successful, freshly dropped feces are carefully collected to avoid contact with soil. Each sample receives a unique identification and is stored under refrigeration until submission to the Veterinary Parasitology Laboratory at the OSU-College of Veterinary Medicine for parasitology tests. Laboratory test results are emailed to the producers along with a summary about the parasites observed in the samples.

A survey is conducted at each farm to gather characteristics related to the farm management and health status of pigs. Information from surveys is compiled and analyzed using descriptive summary statistics.

Learn more about the project and other swine topics:

eBarns Collaborating Farms

CFAES Animal Sciences and Center for Food Animal Health Statewide

STUDY INTRODUCTION

Intestinal Parasite Surveillance in Free-Range Pig Farming: Farms are being recruited and enrolled continuously throughout the project period based on the OSU CFAES Extension network and on direct contact with farmers listed on pastured pig farms and pork products directories available on the internet. The criterium for farm inclusion is: “pigs have access to out-door, non-concreted areas.”

1. Our research/Extension team visiting a farm to collect pig fecal samples and to interact with producers in person.

Figure

OBSERVATIONS

• At least 1 pig of each farm was positive for at least 1 intestinal parasite.

• Only 8 pigs (14.03%) were negative for intestinal parasites.

• Coccidia and Ascasis suum were the most prevalent parasites observed, with 80.7% and 54.3% positive samples, respectively. Strongyloides sp. was present in 31.58% of the samples and Trichuris sp. in 14.04%. Giardia and Cryptosporidium were detected punctually.

PRELIMINARY RESULTS

SUMMARY

• Five farms have been included in the project up to now, with 57 samples collected (as of July 2024).

• All 5 farms included up to this point are raising their pigs in a near-organic condition.

• Current age of sampled pigs range from 8 weeks to 12 years old.

• None of the intestinal parasites detected can be transmitted to humans through consumption of pork.

Figure 2. Chart with data as of Summer 2024.

Acknowledgements for project: Dr. Antoinette Marsh, Dr. Arruda, and Abby Waldrop

Are you curious to know if your pasture pigs have intestinal parasites?

Contact us and enroll in the project! All costs related to sampling and parasitology tests are covered by the project team.

PROJECT CONTACT

Dr. Talita Resende, DVM Assistant Professor and Swine Health Extension Specialist (resende.2@osu.edu)

Kara Flaherty BS Animal Sciences Education Program Specialist (flaherty.177@osu.edu)

Noise on the Swine Farm

How much noise are Ohio farmers exposed to during their daily livestock routines?

The answer is more than expected. Sounds that register 90 decibels for an 8-hour shift are considered hazardous for workers. In fact, the noise-contributing sources at swine facilities are cumulative over one’s lifetime. Common high-noise sources include squealing pigs when receiving health care, feeding times in the breeding and finishing barns, and pressure washing the pens. Additional noise from tractors, machinery and grain augers contribute to a farmer’s daily noise dose. Debilitating hearing loss is a common hazard outcome of farm work.

Figure 1. A sound pressure meter (left) or a smart phone with a sound APP (right) provides quick measurement of the decibel level in the work environment.

How to measure sound on the farm:

Sound is measured in units called decibels (dB). Knowing the decibel level of a workplace makes it easy to determine if and where protection practices should be put in place.

To know if noise levels exceed the 90dB threshold, take a sound pressure reading. Handheld sound pressure meters record on the spot. Local feed elevators may have these units available to monitor a workspace – or at least discuss noise exposure hazards at their facility. If a farm is covered under the Ohio Bureau of Workers’ Compensation (BWC) Program, BWC field specialists can provide a workplace noise reading upon request.

Another tool to use for sound measurements is a smart phone with a sound APP. These APPS have similar capacity to sound meters and provide a convenient and economical way to get instantaneous results. Search the APP store for “sound level meter” to find a variety of free options.

Personal Noise Exposure Level Recorded in an 8-Hr Day

In this noise exposure sample a worker wore a personal noise dosimeter inside a swine animal facility for 470 minutes (from 7:40 to 15:30 on the 24-hour clock). From 7:40 to 12:00 (Period 1) the worker fed pigs and cleaned pens, except for a break at 10:10am where the worker stepped outside the building (shown as Period 2). After lunch (from 12:00 –12:45), the worker resumed pen cleaning (Period 3). Excluding the outlier noise at 15:10, the highest recorded point was 109.6dB.

To read the graph: x axis = time of day, y axis = noise sound level in (dB), red line marks 90dB (the OSHA standard for permissible sound level). Anything over the red line requires hearing protection, however it is recommended to start wearing hearing protection at 85dB. In this example, the worker spent a majority of their workday in a high noise environment that required hearing protection.

Figure 2. A graph of the data collected during the noise exposure sampling

Prevention tips for the workers

1. Limit daily exposure to high noise areas. Prominently post “High Noise Area” signs in work areas where the sound level exceeds 85dB.

2. Wear hearing personal protective equipment (PPE) in workplaces that exceed 90dB. Farm chores measuring 90dB and higher require protection while doing that task, but these activities may not take an entire workday. It’s a good practice to start wearing hearing protection at 85dB as a preventative measure.

3. There are many types of PPE ranging from ear plugs to muffs. All are acceptable if they are worn correctly. Ear plugs need to be inserted into the ear canal, while earmuffs cover the entire outer ear.

4. Choose PPE with a Noise Reduction Rating (NRR) of 20 or higher. The NRR is included on the package of each product. This number indicates the decibels that are reduced by wearing the hearing protection. The higher the rating, the better the product. As an example, if the workplace measures 100dB, wearing hearing protection with an NRR rating of 22, makes the worker’s exposure 78dB.

SUMMARY

Don’t wait to put a noise protection practice in place. Hearing loss is permanent! Unlike wearing corrective eyeglasses, hearing aids cannot restore a person’s hearing; these devices can only amplify the sounds detected by the auditory nerves.

TOOLS OF THE TRADE

Sound Level Meter

A sound level meter takes instantaneous measurements of the environmental noise conditions at their source.

Control the noise at the source

1. Select equipment and ventilations systems with lower sound levels. Often times, newer equipment has housing and insulation that reduces noise output.

2. Perform routine maintenance on equipment and fans. Replace worn, loose or unbalanced parts to reduce the noise generated by vibration and friction. Well-lubricated equipment can reduce vibration and friction. Also ensure engines have properly installed mufflers and are in good condition.

3. Isolate the noise source from the worker. Insulating walls in the barns and farm shop will reduce the transfer of noise to other work or office spaces. Also, cab tractors are good options for muffling engine noises and protect workers while performing other farm tasks.

4. While it is difficult to contain noises created by the livestock and pressure washers, workers can protect themselves by wearing hearing protection and rotate their work so that they limit the amount of time exposed to high noise activities.

5. If the farm hires more than 10 non-family members, employers should follow the recommended practice in the OSHA 1910.95 standard for Occupational Noise Exposure.

To learn more about noises on the farm, watch a video produced by the OSU Ag Safety and Health program in partnership with Ohio Bureau of Workers’ Compensation. This video explains the common ways hearing loss can occur in agricultural environments, how audiograms are used to detect hearing impairment, and the steps to take to prevent damage.

https://youtu.be/YxH10xQVTok

PROJECT CONTACT

Agricultural Safety and Health Program

For inquiries about this article, contact Dee Jepsen (jepsen.4@osu.edu) or Yang Geng (geng.83@osu.edu)

pH and Temperature of Different Pork Muscles

OBJECTIVE

Characterize the relationship between pH and temperature decline with color and water-holding capacity of five different pork muscles.

STUDY INFORMATION

Start Date 02/20/2024

End Date 05/24/2024

Species Swine

Treatments 5

Reps 15

Experimental Unit Head

STUDY DESIGN

OSU Meat Lab

CFAES Animal Sciences

Franklin County

Fifteen pigs (hot carcass weight = 93 ± 3 kg) were slaughtered at the Ohio State University Meat Lab. The pH and temperature of 5 muscles were measured at 1-, 3-, 6-, 9-, 12-, and 24-hours(h) postmortem (during the conventional chilling process):

• longissimus dorsi (LD)

• psoas major (PM)

• semitendinosus (ST)

• triceps brachii (TB)

• gluteus medius (GM)

Data Collection and Evaluation:

Instrumental CIE (L*, a*, b*) Color Values: using a Minolta CM600d on the cut surface of each muscle following carcass fabrication at 24-h postmortem.

Drip Loss of Pork (loss of fluid from fresh, non-cooked pork as muscle transitions to meat): assessed using 25-mm diameter cores with the EZ cup method over a 48-hour period.

Muscles were randomly allocated to 1 of the 3 postmortem aging time points (1, 3, and 10 day(s)), and 1 of the 2 endpoint cooking temperatures (145°F (63°C) or 160°F (71°C)) using sous-vide technique before Warner-Bratzler shear force analysis. The rate and the extent of pH and temperature decline, color, and drip loss were analyzed using a randomized complete block design in PROC GLIMMIX of SAS v9.4, with muscle as the fixed effect, and pork carcass nested within slaughter event as a random effect. Stepwise regression analysis was performed with PROC REG of SAS to develop prediction equations for the parameters of interest while including slaughter event as dichotomous variables in the model.

SUMMARY

This study characterized the relationship between pH and temperature decline with color and waterholding capacity of 5 different pork muscles. The results indicated that the rate and extent of pH and temperature decline, color, and drip loss appear to be muscle-specific. The relationship between pH and temperature decline with color and drip loss also appears to be muscle-specific.

While both pH at 1h and 24h postmortem played critical roles in color and drip loss, pH at 1h postmortem is the best predictor for the quality of the LD muscle, but not for other muscles.

Figure 2. Yifei Wang with pigs prior to project.

Figure 1. Swine carcass graphic.

OBSERVATIONS

• The main effect of muscle was significant for both pH and temperature at each time point, as well as for color and drip loss.

• The LD, ST, and GM muscles exhibited greater pH than TB and PM at 1h postmortem. Then the PM, ST, and TB muscles demonstrated greater pH than LD and GM at 24h postmortem.

• The LD muscle demonstrated the greatest rate of pH decline from 1h to 3h postmortem, while no differences in the rate of pH decline at other time points were observed between muscles. LD also had the greatest drip loss among the five muscles.

RESULTS

• PM and ST had the lowest temperature at 1h postmortem, while LD and PM had the lowest temperature at 24h postmortem. Portable MPI

• Pork LD, ST, and GM demonstrated greater "lightness" in color than PM and TB. PM demonstrated the greatest "redness" among the 5 muscles.

• For ST, pH at 1h postmortem explained greater variation in drip loss (50%) than pH at other time points. However, for the PM, TB, and GM, pH and temperature (when measured at each of the timepoints used in this study) explained less than 33% of the variation in color and drip loss.

• Cooking Temperatures: GM had the greatest WBSF compared to other muscle cuts when cooked to both temperatues used; PM demonstrated the lowest WBSF when cooked to 145°F (63°C); and LD, PM, ST, and TB exhibited similar WBSF when cooked to 160°F (71°C).

Figure 4. Quality results for the Longissimus Dorsi (LD), Psoas Major (PM), Semitendinosus (ST), Triceps Brachii (TB), and Gluteus Medius (GM) muscles. a-c Means with different letters in each row and different muscles are significantly different (P < 0.05)

TOOLS OF THE TRADE

(Meat Probes Inc., Topeka, KS)

PROJECT CONTACT

Dr. Ben Bohrer, Assistant Professor Department of Animal Sciences (bohrer.13@osu.edu)

Yifei Wang, Graduate Research, Ph.D Department of Animal Sciences (wang.10408@osu.edu)

pH Meter

Figure 3. Carcass data collection in the OSU Meat Lab

Quality of the Pork Tenderloin

OBJECTIVE

Determine the associations between meat quality and sensory traits for the pork tenderloin (psoas major).

STUDY INFORMATION

Start Date 07/27/2023

End Date 05/24/2024

Species Swine

Treatments 6

Reps 103

Experimental Unit Meat Pieces

STUDY DESIGN

OSU Meat Lab

CFAES Animal Sciences

Franklin County

A total of 103 pork tenderloins (IMPS# 415; psoas major muscle) were utilized on the same day of production. Each sample was cut into 2 sections: One 10cm section for quality analysis and one 5cm section for sensory analysis.

Quality Analysis consisted of instrumental color, pH, cooking loss when vacuum-packaged samples were cooked in a water bath to an endpoint cooking temperature of 160°F (71°C), and WarnerBratzler shear force. Desmin degradation was analyzed using western blot.

Sensory Analysis consisted of juiciness, tenderness, flavor, and overall acceptability assessments using a trained sensory panel that utilized a 15cm line scale (0 = extremely dry, extremely tough, no flavor, or very unacceptable, and 15 = extremely juicy, extremely tender, very flavorful, or very acceptable).

Data Categorization:

• 3 groups based on pH values of low, average, or high pH (< 5.60, 5.60-5.80, or > 5.80)

• 3 groups based on Warner-Bratzler shear force values of tender, intermediate, or tough (< 2.5kg, 2.5-3.0kg, or > 3.0)

Data were analyzed using PROC CORR of SAS (SAS Inst. Inc., Cary, NC) to generate Pearson correlation coefficients. Data was analyzed with PROC GLIMMIX of SAS with fixed effects of pH value group or Warner-Bratzler shear force group.

SUMMARY

This study characterized the associations between meat quality and sensory traits for the pork tenderloin. Data from this study suggests that changes in important meat quality traits such as pH and shear force are influential to sensory attributes for pork tenderloins. Future research efforts to quantify the underlying biological mechanisms of such quality traits will be beneficial for the pork industry.

Figure 1. Preparing samples for analysis.

Figure 2. Image of a cooked pork tenderloin. Photo credit: Food Network

OBSERVATIONS

Manure Nutrients Forages Poultry Small Ruminant Cattle Equine Swine

Data initially suggested weak and moderate associations between the quality parameters and sensory characteristics. However, when samples were categorized into groups (based on data categories), interesting trends began to emerge.

• Low pH tenderloins: lighter in color; greater levels of cooking loss; tended to be perceived as less juicy and tougher (by the sensory panel); had lower levels of sensory acceptability compared with high pH tenderloins.

• Average pH tenderloins: intermediate tenderness (compared with low and high pH tenderloins) with a linear effect for instrumental lightness, cooking loss, sensory juiciness, sensory tenderness, and sensory acceptability.

RESULTS

Response

• Tender category: perceived as more tender, with greater levels of acceptability (compared to tough tenderloins).

• Intermediate category: tenderloins were an intermediate tenderness (compared to tender and tough tenderloins).with a linear effect for both sensory tenderness and sensory acceptability.

• Low pH tenderloins and tough tenderloins had greater abundance of degraded desmin (last row in Fig. 3), which is an indicator of the extent of protein degradation in meat - believed to contribute to meat tenderization.

Lightness, L*

Cook loss, % WBSF, kg

Sensory juiciness

Sensory tenderness

Sensory flavor

Sensory acceptance

"Desmin intact (55 kDa)"

Degraded desmin (42 kDa)

3. Quality and sensory results for pork tenderloin samples. n = 103 pork tenderloin muscles; superscripts within each row with different letters indicate significant differences (P < 0.05).

TOOLS OF THE TRADE

TA-XT Plus Texture Analyzer (Texture Technologies Corp., MA)

Measures shear force values to determine: tender, intermediate, or tough categorization.

PROJECT CONTACT

Dr. Ben Bohrer, Assistant Professor Department of Animal Sciences (bohrer.13@osu.edu)

Yifei Wang, Graduate Research, Ph.D Department of Animal Sciences (wang.10408@osu.edu)

Figure

Highly Pathogenic Avian Influenza

An Update on 2022-2024 HPAI Detections in Ohio

The Highly Pathogenic Avian Influenza (HPAI) outbreak of 2022-2024 affected 96,809,026 birds, making it even more devastating than the 2014-2015 outbreak. This has included 495 commercial flocks and 653 backyard flocks in 48 states (as of June 5, 2024). The first confirmed case of Highly Pathogenic Avian Influenza detected in Ohio was confirmed on March 30, 2022, in a Franklin County non-commercial backyard flock.

Ohio Numbers

7 affected commercial flocks

10 affected backyard flocks

9,646,496 total birds affected

Most recent detection: February 27, 2024

Avian influenza is a viral disease that can affect multiple species of birds. It can exist in a low pathogenic form that causes mild symptoms of disease 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, in which it can exist in an asymptomatic carrier state. Two of the four major migratory pathways pass through Ohio which increases our risk of outbreaks.

Confirmed Detections in the United States for HPAI 2022-2024. Source: USDA APHIS

go.osu.edu/APHIS-HPAI-Data

Prevention

The key to preventing outbreaks in both backyard poultry and the poultry industry is biosecurity. Biosecurity is the strategy of prioritizing the health by preventing disease from entering the flock or herd. It comes in 2 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.

HPAI in Dairy Cattle

In the United States over 99% of the commercial milk supply produced by U.S. dairy farmers participates in the Grade “A” milk program that follows the Pasteurized Milk Ordinance, which includes controls that help ensure the safety of dairy production. These dairy products are first pasteurized or processed in a fashion that kills pathogens similar to the cooking of meat. When H5N1 (subtype that causes avian influenza) was first detected in dairy cattle in early 2024 it was also detected in raw milk. To be sure that pasteurization killed the H5N1 virus the USDA conducted a study testing 297 retail dairy products in 17 states, including Ohio.

Results for Ohio are summarized below. A total of 8 products were tested in Ohio. None of the products tested in Ohio, or anywhere in the country, contained a live virus that could be grown and cause viral infection. However, in 3 of the 8 products the virus that was inactivated by the pasteurization process could be found. This confirmed that pasteurization is an effective way to kill the H5N1 virus that may be in milk.

Detection of Live Virus in Retail Product(s)

Number of Retail Product Samples Tested

Retail Product Samples Negative for Viral RNA (qRTPCR Screening -)

Retail Product Samples Positive for Viral RNA (qRTPCR Screening +)

Retail Product Sample Results for Live Virus (Viability Testing by Egg Inoculation)

An additional study investigated the different types of pasteurization to be sure all processes were effective, and it is estimated that pasteurization can eliminate about 1 trillion virus particles per milliliter. This study found that all pasteurization techniques approved by the Pasteurized Milk Ordinance were effective.

For updates on HPAI from the FDA, use this QR code or the link below.

go.osu.edu/FDA-HPAI-updates

PROJECT CONTACT

For inquiries about this project: Timothy S. McDermott, DVM Extension Educator, Franklin County mcdermott.15@osu.edu (614)-292-7916

For inquiries about HPAI in Dairy: Jason Hartschuh, Extension Field Specialist, Dairy Management & Precision Livestock hartschuh.11@osu.edu

Acknowledgments

Research Collaborators and Supporters

Eastern Agricultural Research Station and Staff:

Chris Clark

Dalton Huhn

Edwin Pickenpaugh

Kevin Stottsberry

Jim Jasinski, OSU Extension

Ben Lowe

Dewey Mann, Director of Waterman

Ohio Corn Board

Ohio Sheep and Wool Program

OSU ATI’s Equine Facility and Staff

OSU Beef Team

OSU Sheep Team

OSU Small Ruminant Research Center in Wooster:

Gregg Fogle

Roger Shearer

Erin Weisgarber

ST Farmfit

Dr. Mark Sulc

Oriana Tillery-Sucre

Ben Traver

Ben Wasson

Dr. Shaun Wellert

Will C. Hauk Endowment

Future Dates

September 16-18, 2025

September 22-24, 2026

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 nonprotein 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

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

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

Wet Chemistry – Chemistry based analytical methods used to measure chemical compounds in plant material

eBarns

“connecting

science to farmers”

eBarns is a The Ohio State University 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.