research

The Iowa Nutrient Research Center at Iowa State University announces funding of over $1.4 million to support eleven new water quality and nutrient management projects for 2021-2022. Researchers from Agronomy are involved in four of the eleven projects.

Can adjustments to nitrogen rates reduce corn yield drag and disease implications following a cereal rye cover crop? (Licht, McDaniel, et al)

Continued assessment of corncobs as an alternative carbon source to enhance bioreactor performance for improved water quality (Archontoulis, et al)

Held every Thursday at 2:10 pm in 2050 Agronomy Hall* or virtually join us via Zoom. 

Date Time Speaker Topic Location

Sept. 2*

2:10pm  Mario Perez Bidegain, Universidad de la Republica, Uruguay "Soil conservation policy driven by science in Uruguay"

Virtual

Sept. 9

Scientists have invested great time and effort into making connections between a plant’s genotype, or its genetic makeup, and its phenotype, or the plant’s observable traits. Understanding a plant’s genome helps plant biologists predict how that plant will perform in the real world, which can be useful for breeding crop varieties that will produce high yields or resist stress.

Iowa State agronomy researchers teamed up with electrical engineering specialists on campus to create a new sensor that measures soil nitrate at a far faster and more frequent rate than traditional measurements. A new paper on the research will soon be available in the Soil Science Society of American Journal and can be found here.

Prashant Jha, agronomy, Iowa State, and Joseph Shaw, electrical and computer engineering, Montana State, are collaborating on a project that was funded by the Iowa Soybean Research Center in late 2019. The goal of their project, “Hyperspectral Imaging for Early Detection of Herbicide-Resistant Weeds in Soybean,” is to accurately map (drone-based) the location of herbicide-resistant weed biotypes in production fields using advanced optics and computer algorithms.

Greenhouse and laboratory experiments were carried out in 2020 to identify the spectral reflectance of different biotypes of waterhemp plants resistant to ALS inhibitors, atrazine, and/or glyphosate. Hyperspectral imaging and other measurements were taken using artificial light. More plants are being grown from two different species of pigweed (waterhemp vs. Palmer amaranth).

This summer (2021), Jha plans to mount a camera on a drone to collect data in soybean fields with confirmed herbicide-resistant waterhemp populations. This includes imaging herbicide-susceptible and herbicide-resistant weed biotypes at different growth stages to characterize classification accuracies as plants grow. Images will be analyzed to differentiate waterhemp from other weed species in a soybean field and to identify susceptible vs. resistant waterhemp biotypes. A neural network machine learning algorithm will be used to develop classification images for field-scale maps. Using neural networks instead of previously used support vector machine algorithms will improve classification accuracies from 80% to 99%.

Dr. Fernando Andrade, an agronomy alumus in crop physiology, has been awarded a Houssay Prize for 2020, while also being awarded the Top Researcher in Argentina for the past year.

Congratulations to our Ram Yadav, winner of the North Central Weed Science Society Outstanding Graduate Student Award. The award recognizes one outstanding graduate student who is a NCWSS student member actively involved in the Society, as well as contributor to the field of weed science through extension, research, and teaching.

Our. Dr. Fernando Miguez has been awarded a USDA National Institute of Food and Agriculture grant. These grants focus on big data analytics, machine learning, artificial intelligence, and predictive technologies needed to keep U.S. agriculture on the leading edge of food and agricultural production. These grants are awarded through the USDA-NIFA Agriculture and Food Research Initiative (AFRI) Food and Agriculture Cyberinformatics and Tools (FACT).
 
“Big data and artificial intelligence will increasingly play a vital role in the future of agricultural technologies,” said Parag Chitnis, acting director of USDA-NIFA. “As we work to realize precision nutrition for consumers and enhance farmer profitability and agricultural sustainability, these predictive technologies will keep research and development moving quickly to provide the tools needed for success.”
 

A new study from researchers at the University of Minnesota and our Dr. Matt Liebman finds that diversifying crop rotations can greatly reduce negative environmental and health impacts, while maintaining profitability for farmers.

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