February 2025 Arkansas Ag Research Report
A new cotton grading score is in the works to help breeders select the best possible cotton varieties for producers. Arkansas cotton breeder Fred Bourland is partnering with Cotton Incorporated to complete the new tool.
IN THIS ISSUE:
- Find out how machine learning is used to improve poultry processing and ecology.
- Soybean research offers discoveries on impacts of flooding in early reproductive stages.
- Agricultural education is evolving to include artificial intelligence and microcontrollers.
- Who is the new director of our Veterinary Diagnostics Lab?
- See who the Experiment Station’s top-cited scientists are.
Top Notch
Seventeen current Arkansas Agricultural Experiment Station researchers are among those ranked as the world’s most-cited scientists, an indication of their impact across multiple fields of inquiry.
The rankings are based on a composite score that includes metrics such as citation counts and what’s known as “h-index,” which is an indicator of the impact of an author’s publications.
“We are proud to see so many of our scientists on this list,” said Jean-François Meullenet, director of the Arkansas Agricultural Experiment Station. “Faculty with experiment station appointments have an average h-index of 15.9 and were cited more than 25,000 times last year. This is a true testament to the impact they are having on agriculture, food and life sciences in Arkansas and beyond.”
Arkansas Agricultural Experiment Station researchers bring real-world benefits by conducting experiments and publishing their findings with the broader scientific community.
AI in Ag
Chicken ‘woody breast’ detection improved with advanced machine learning model
Arkansas Agricultural Experiment Station researchers developed a new machine learning model that significantly improves detection of a defect in chicken breast meat called “woody breast” — a $200 million problem in the United States.
Publishing in the journal Artificial Intelligence in Agriculture, Dongyi Wang, assistant professor of biological and agricultural engineering and food science, and his team showed the new machine learning model can classify three woody breast defect levels with an overall accuracy of 95 percent, outperforming the traditional models which offered no more than 80 percent accuracy.
Dongyi Wang, assistant professor of biological and agricultural engineering and food science, joined Chaitanya Pallerla, a food science graduate student, and Casey Owens, professor of poultry processing and products, in a study to improve “woody breast” detection.
Machine learning maps animal feeding operations to improve sustainability
Understanding where farm animals are raised is crucial for managing their environmental impacts and developing technological solutions, but gaps in data often make it challenging to get the full picture.
Becca Muenich, associate professor of biological and agricultural engineering, and her team built a machine learning model that can predict the location of feeding operation locations without using aerial images by considering key predictors of feeding operation presence, such as surface temperature, phosphorus levels and surrounding vegetation.
Their work was published in Science of the Total Environment.
Becca Muenich, associate professor of biological and agricultural engineering and a researcher with the Arkansas Agricultural Experiment Station, used machine learning tools to model the locations of animal feeding operations in the U.S.
Listen — Food, Farms & Forests Podcast
Don Johnson, a University Professor in the Department of Agricultural Education, Communications and Technology, discusses the importance of agricultural education, the challenges and successes in the field, and how ag education is evolving to include concepts like artificial intelligence and microcontrollers.
New episodes every other week! Subscribe on your favorite platform!
New Faces
Second-gen land-grant poultry veteran Barton takes helm of Veterinary Diagnostic Lab
James Barton recently took the helm of the University of Arkansas System Division of Agriculture’s Veterinary Diagnostic Lab following a 30-plus-year career in the poultry industry.
“Dr. Barton’s career experiences and knowledge in poultry production, sales, technical services and laboratory management make him a great fit and asset to the Veterinary Diagnostic Lab,” said Nathan Slaton, associate vice president for agriculture and assistant director of the Arkansas Agricultural Experiment Station. “His experience and leadership are welcomed to help grow laboratory services to support the poultry industry during the continued Highly Pathogenic Avian Influenza outbreak.”
Barton is no stranger to the Division of Agriculture. His father, Lionel Barton, was an extension poultry specialist for the Division of Agriculture and professor emeritus.
James Barton, DVM, was hired as the director of the UADA Veterinary Diagnostic Lab in January.
Research Spotlights
How flooding soybeans in early reproductive stages impacts yield, seed composition
Caio Vieira, assistant professor of soybean breeding, published research in the journal Crop Science that offers more insight into how soybean plants respond to flooding in the critical early reproductive stage.
With an increasing frequency and intensity of flooding events and an eye to capitalize on a common rice production technique, soybean breeders are on a quest to develop varieties with flood tolerance at any stage in the plant’s development.
A two-year study on 31 soybean varieties showed that some genotypes visually classified as “moderately tolerant” to flooding had higher yields than those classified as “tolerant.” Seed composition was also not significantly different after a four-day flood during the early reproductive stages.
Cold plasma-treated seeds show potential to protect plants, reduce pesticide use
Food science and entomology researchers from the Arkansas Agricultural Experiment Station teamed up to harness the fourth state of matter, plasma, and measure its effects on rice seed to ward off armyworms and improve plant growth.
Mahfuzur Rahman, assistant professor of food science, and Rupesh Kariyat, associate professor of crop entomology, have worked with Kariyat’s graduate student Deepak Dilip and postdoctoral fellow Nikitha Modupalli from Rahman’s lab to publish a study on the project in Nature’s Scientific Reports.
The results found that rice seeds treated with cold plasma could negatively impact fall armyworms’ growth and development. Researchers also observed signs of improved plant growth such as more leaf growth.
A machine acquired by Mahfuzur Rahman, assistant professor of food science, is used to treat rice seeds with cold plasma for a study examining its effects on plant growth and protection from the fall armyworm.
They also saw a faster germination rate in cold plasma-treated plants, though this was not statistically significant.
New cotton grading score app aims to improve yield stability
A new cotton grading score is under development to help breeders and producers determine if high-yielding cotton plants are obtaining yield from more seed or more lint per seed.
Fred Bourland, professor of plant breeding and genetics and the Altheimer Chair for Cotton Research and Development, is on the verge of finalizing the Yield Component Score, or YC-Score, in partnership with Cotton Incorporated. The score will be available as an app in the coming year with an associated publication.
Fred Bourland, professor of plant breeding and genetics, is working on his third cotton grading score. The new Yield Component Score was developed to help cotton producers improve yield stability.
Watch
Derico Setyabrata – Preserving Meat, Reducing Waste
When it comes to the color of beef in the store or refrigerator, there’s more going on than meets the eye. Animal science researcher Derico Setyabrata is looking for ways to extend the shelf life of beef products to reduce food waste and boost the bottom line. Setyabrata hopes to support the long-term sustainability of the beef industry. “I still want to eat beef in the next 30, 40, 50 years,” he said. “I want to make sure that can happen.”
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