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 

Building Agroecosystem Soil Health to Adapt to Climate and Regulatory Realities while Protecting Rural Community Health 

California’s agricultural watersheds face the dual challenge of water scarcity and water quality issues. This research is tackling complex and seemingly unsolvable problems affecting California’s agricultural communities by conducting on-farm work to gather information on how various soil health practices influence carbon, nitrogen and water in soil across diverse regions. The project will combine this data into a causal model that can help determine how different soil health practices will impact carbon sequestration, water scarcity and water quality under different climates and regulations, to increase farmer adoption of the practices.

Leveraging multi-scale datasets and modeling tools for improved soil health and carbon management 

Farmers and other agricultural decision makers often have trouble accessing reliable soil health information to guide their choices. This project is using existing data along with building new, integrated databases to develop a novel modeling system that combines process-based methods, advanced sensor technologies and stakeholder input to track soil health outcomes better. By delivering timely and interactive information connected to management scenarios, the project aims to help farmers and ranchers adopt practices that improve soil health, reduce nutrient loss and strengthen ecosystem resilience.

Next-generation Sustainable Weed Management Enabled by Micro-targeted Legged Robots 

Weeds are developing herbicide resistance faster than new herbicides can be created. As a result, farmers face lower profits and yields, while greater chemical use threatens soil and water health. Xiang’s research seeks to replace the costly machinery and large quantities of chemicals used for weed control with AI-powered lightweight robots for targeted weed management. These robots can apply micro levels of herbicide at the site of weed growth, reducing chemical use by 99%, lowering costs and limiting farm workers’ exposure to chemicals. This treatment also minimizes field disturbance by protecting crop roots.