Novel Tools for Engineering Apomixis in Soybean 

Although hybrid seeds outperform their parents in yield, this advantage does not transfer to the hybrids’ offspring. Breeders must continually cross-pollinate to develop the next generation of hybrids. Because soybeans reproduce through self-pollination, the structure of the soybean flower makes cross-pollination difficult and expensive. The ApoSoy project seeks to develop a cost-effective hybrid soybean system through a process called apomixis, which creates seeds that are genetic clones of the parent.

Evaluating Biopesticides for the Treatment of Hemp 

Greener Cattle Initiative Awards 

Understanding Plant Parasitic Nematode Virulence Mechanisms: A Valuable Approach to Expedite Breeding Durable & Broad-spectrum Crop Resistance 

Root-knot nematodes are parasitic worms that infect economically important crops by attacking plant roots, reducing yields or even destroying entire fields. Traditional plant breeding has struggled to produce crop varieties with broad, lasting resistance. Rocha’s research studies the molecular genetics of plant nematode interactions to develop plants that use RNA to target proteins produced by nematodes, preventing the parasite from developing in the root.

Novel Biobased Antiviral Treatments for Bee Pollinator Health 

Honey bees are essential agricultural pollinators that are threatened by a constant flux of interacting stressors. Viral pathogens are strongly linked to managed bee colony losses worldwide. Antiviral treatments are needed to support beekeeping industries and uphold crucial pollination services. Ricigliano’s lab is developing bee antiviral treatments that are cost-effective, scalable and do not rely on synthetic chemicals. The project incorporates biotechnological approaches and natural product-based treatments to improve bee immunity and virus resistance.

Integrating Genomic Prediction with Crop Modeling to Develop Climate-Resilient Cultivars 

Plant breeding and improved agriculture practices have increased crops’ yields and nutrition, but severe weather events threaten crop losses and food insecurity. The current genomic prediction models that plant breeders use to develop crops that can withstand weather stresses struggle to make accurate predictions about future weather conditions. To address this, Fernandes is developing machine-learning genomic prediction models that directly integrate crop growth models, which include plant physiology and environmental interactions. The genomic model and crop growth model will inform each other to generate enhanced yield predictions across current and potential future weather scenarios.