Agroecosystem Modeling Lab

We develop agroecosystem models that represent crop and grassland growth, management practices, soil carbon and nitrogen cycling, water balance, and greenhouse gas emissions. Our work includes process-based models, AI-driven approaches, and hybrid frameworks that integrate mechanistic understanding with machine learning to capture interactions among management, climate, and biophysical processes across field to regional scales.

We examine how agricultural management practices (e.g., tillage, cover cropping, irrigation, and nutrient management) influence productivity, soil health, water quality, and environmental outcomes. This research evaluates management strategies across cropping systems to identify practices that support long-term sustainability and productivity under variable environmental conditions.

We study integrated crop–livestock systems to understand how linking cropping and grazing affects productivity, resource use efficiency, soil carbon storage, and greenhouse gas emissions. Our work focuses on cropland–livestock interactions, grazing management, and nutrient recycling, with an emphasis on system-level tradeoffs and synergies.

We evaluate climate-smart agricultural practices, such as reduced tillage, cover cropping, and crop rotation, and their roles in advancing climate mitigation, productivity, and adaptation outcomes. This research emphasizes quantifying greenhouse gas emissions and soil carbon dynamics to assess mitigation potential, while also examining effects on crop productivity and system resilience under climate change.

We investigate how agroecosystems respond to interacting environmental changes such as atmospheric CO2 fertilization, climate change, and extreme events such as droughts and heatwaves. Using models, remote sensing, and observational data, this research assesses dynamics and resilience of agricultural systems under current and future environmental conditions.