Project Database

Tropospheric ozone (O₃) frequently exceeds phytotoxic thresholds in Taiwan’s farmlands, posing serious threats to crop productivity, soil health, and agroecosystem resilience. Yet, existing air-quality models lack the resolution and biological coupling needed to inform field-level agricultural decisions. This project proposes Taiwan’s first crop-sensitive early-warning platform for O₃ stress, integrating high-resolution modeling, plant ecophysiology, soil microbiology, and AI-enabled diagnostics across four coordinated subprojects (SP1-SP4). SP1 will deploy a 1-km modeling system over Taiwan‵s rice rice-soybean belt, incorporating crop-specific ozone–yield functions derived from SP2 and SP3‵s open-top chamber experiments. SP2 will explore O₃-induced changes in photosynthesis, carbon–nitrogen allocation, and rhizosphere microbiota, while SP3 will develop non-invasive, real-time diagnostics using chlorophyll fluorescence and spectral indices with machine learning. SP4 will quantify vegetation–O₃–VOC feedbacks via field monitoring and lab-based oxidation-flow reactor studies to constrain atmospheric chemical responses. Together, this project will deliver a predictive platform capable of issuing multiday O₃ risk forecasts and early stress warnings tailored to Taiwan‵s cropping systems. This research fills a critical knowledge and technological gap, enabling proactive, biologically meaningful ozone risk management. The platform will support national sustainability goals (SDGs 2, 6, 12, 13) and serve as a replicable model for subtropical agriculture under rising oxidative stress.