Integrating research on plant responses to light limitation across scales

Climate change and agricultural intensification have made light limitation a key constraint on crop light use efficiency and yield potential. Research has progressed from description to quantification, mechanistic understanding, and multiscale modeling. However, indicator systems remain fragmented, and cross-scale integration is weak. These gaps limit translation to breeding and on-farm practice.

This review integrates the multidimensional networks and key strategies of crop adaptation to light limitation. It identifies core methodological bottlenecks that hinder prediction and application. It proposes a forward-looking paradigm to support mechanistic insight, breeding for shade efficiency, and precision management under complex light environments.

Crop adaptation to light limitation arises from coordinated networks of light signaling, metabolic regulation, and architectural optimization. Two strategic modes dominate: shade avoidance and shade tolerance. C₃ and C₄ species show distinct response logic due to differences in energetics, anatomy, and regulatory control. Progress is constrained by the absence of a unified, cross-scale indicator framework and by bottlenecks in model prediction and scale coupling. This review proposes an integrated systems-biology approach enabled by high-throughput phenotyping and artificial intelligence for science. The framework standardizes indicators and fuses multimodal data across scales. It further links data assimilation with physics-informed learning to build multiscale digital twins. This approach reduces indicator fragmentation, strengthens cross-scale coupling, and supports uncertainty-aware decisions and breeding targets for shade-efficient cultivars. It opens a path to quantitative prediction and actionable management of crop performance under complex light regimes.