When people think about climate solutions in agriculture, they often picture new equipment, alternative fuels or regenerative farming practices. Fewer people think about math. Yet mathematical modeling and artificial intelligence are quietly reshaping how agricultural waste moves through supply chains, cutting costs, reducing emissions and helping farmers adapt to climate uncertainty.

For Laura Mora, an Operations Research Ph.D. student working at the intersection of operations research, AI and agricultural systems, climate action starts with logistics.

“Operations research uniquely positions me to address climate-related challenges in agriculture because it connects data to decisions under real-world constraints,” Mora said. “In my work, optimization lets me evaluate how changes in participation, technology deployment, and logistics affect costs and environmental outcomes across a region.”

One of the most tangible examples of this work lies in an unlikely place: manure. Livestock waste is a persistent challenge for farmers, contributing to nutrient runoff, greenhouse gas emissions and rising management costs. Mora’s research shows that with smarter planning, manure can become part of a climate solution rather than a liability.

Her mentor, Daniela Jones (assistant professor), has seen this transformation firsthand. “These experiences showed her how optimization can redesign manure logistics to reduce costs and improve consistency,” Jones said, referring to Mora’s work in the IDEALS Lab and her internship at Idaho National Laboratory. By rethinking how waste is collected, transported and reused, optimization tools can make agricultural systems more efficient and environmentally responsible.

Rather than focusing on a single farm or technology, Mora’s work considers entire regions and supply chains. “OR also overlaps strongly with statistics, economics and with machine learning (for surrogate models that speed up complex planning problems),” Mora explained. “This combination supports practical, scalable interventions rather than isolated, one-off solutions.”

That scalability is increasingly important as climate change introduces more variability into agriculture, from weather extremes to shifting resource availability. According to Jones, AI and optimization help producers navigate that uncertainty. “Operations research, AI, and optimization offer powerful tools for building supply chains that are reliable and efficient,” she said. Through projects like REFRAME, these tools enable “rapid scenario evaluation and integrated planning tools that help producers operate with greater confidence.”

Beyond technical modeling, Mora is focused on ensuring that research insights reach the people who need them. As a member of the 2026 KIETS Climate Leaders Program, she is gaining experience translating complex analyses into actionable guidance. “By connecting technical modeling with practical decision-making, this interdisciplinary exchange can spark new ideas and generate ripple effects across projects, institutions, and communities,” she said.

Mentorship plays a key role in that translation. “Mentorship is essential for helping students connect rigorous modeling with meaningful real-world impact,” Jones said. With Mora, that includes learning how optimization results inform “nutrient management regulations, cost-share programs, or planning for adding value to waste.”

Looking ahead, Mora hopes her work will extend beyond individual studies. “During the KIETS fellowship, I want to build frameworks that future students and researchers can expand to address evolving challenges,” she said. “My goal is to leave behind tools and insights that strengthen long-term efforts in climate mitigation and adaptation.”

In a field where climate solutions are often framed as sweeping transformations, Mora’s work highlights the impact of quieter changes, better planning, smarter logistics and tools that help farmers turn waste into value rather than harm.