Global food security is under growing threat as climate change intensifies pressure on agricultural systems. Understanding and predicting how crops respond to climate variability, environmental stressors, and management interventions is critical for improving resilience and informing adaptation. This session highlights advances in crop modeling to better explain and predict agricultural responses to climate variability, extreme events, resource limitations, and biotic stressors. We invite contributions spanning process-based crop models, data assimilation, hybrid modeling frameworks, and AI/ML approaches that improve prediction, scalability, interpretability, and decision relevance across agricultural systems. Topics include model development, calibration, evaluation, uncertainty quantification, remote sensing and sensor integration, and applications from field-level management to regional and global assessments. We welcome research on crop growth, yield, quality, adaptation, mitigation, sustainability, biodiversity, ecosystem services, and decision support for producers, researchers, and policymakers.

• EP - Earth and Planetary Surface Processes
• IN - Informatics