Rising global temperatures and more frequent heatwaves threaten crop productivity by impairing Photosystem II (PSII) function, thereby reducing photosynthetic efficiency, growth, and yield. PSII heat tolerance (Tcrit) – the temperature at which PSII function is compromised – is a key indicator of heat tolerance. However, the current measurement methods are too slow and destructive for large-scale screening and phenotyping, creating a bottleneck in breeding for heat-tolerant varieties. Research suggests that Tcrit can acclimate to sustained heat; however, the molecular mechanisms driving this acclimation remain unknown, limiting efforts to harness this trait for genetic improvement and accelerate breeding for heat tolerance.
This project integrates high-throughput phenotyping, machine learning algorithms, and molecular insights to develop a non-invasive method to predict Tcrit and uncover the regulatory mechanisms of its acclimation. This project provides an innovative method for selecting heat-tolerant genotypes, improving breeding efficiency and stress adaptation strategies, which is applicable to horticultural and broadacre crops, where heat stress affects yield and quality. Ultimately, this research will provide practical tools for breeders and farmers to enhance breeding efficiency and contribute to long-term food security in a warming climate.
