Outcome‑based regenerative agriculture emphasizes measurable, farm‑scale improvements in soil, water, and air while promoting resource efficiency, ecosystem vitality, and producer benefits. Because field monitoring provides limited spatial and temporal coverage, integrated assessment—including process‑based models, observations, data analytics, and AI/ML tools—plays an essential role in evaluating conservation practices and projecting long‑term outcomes across soils, crops, livestock, hydrology, and atmospheric processes. This session invites innovative studies that advance quantitative understanding of dynamic agroecosystem interactions, including: (1) Monitoring and modeling of water, carbon, and nutrient cycling across terrestrial, aquatic, and atmospheric domains; (2) Farm‑scale assessments incorporating livestock and mixed production systems; (3) Landscape‑scale analyses using remote sensing and AI/ML to detect management practices and ecosystem responses; and (4) Hybrid frameworks that integrate process‑based models with observations and emerging data‑driven methods. Together, these efforts support rigorous conservation assessment and advance the science of results‑driven regenerative agriculture.

• A - Atmospheric Sciences
• GC - Global Environmental Change
• H - Hydrology