Year: 2026
Type: preprint
Abstract: Photosynthesis is a key determinant of crop productivity, yet conventional gas-exchange measurements are labor-intensive and unsuitable for high-throughput assessment of dynamic photosynthetic acclimation across large genotype panels. Consequently, genetic variation in photosynthetic acclimation remains poorly characterized and difficult to exploit in breeding programs. Here we present a computational pipeline that integrates rapid optical sensing (chlorophyll meters and hyperspectral reflectance) with a mechanistic model of photosynthetic protein-turnover to estimate dynamic nitrogen allocation among photosynthetic components. The pipeline was demonstrated using 60 winter wheat ( Triticum aestivum L.) cultivars sampled at 10 time points spanning leaf emergence to senescence. Uncertainties associated with each pipeline component were quantified and benchmarked against gas-exchange ground-truth measurements under three controlled-environment light and temperature regimes. Dynamic nitrogen allocation to light harvesting, electron transport, and carboxylation was characterized using three biologically interpretable parameters: maximum synthesis rate, degradation rate, and age-dependent decline in synthesis. Although hyperspectral models showed moderate predictive accuracy for photosynthetic capacity (R² ≈ 0.49), prediction errors were predominantly random, allowing robust parameter estimation when observations across the leaf lifespan were integrated. The pipeline successfully resolved genotype-by-environment interactions in photosynthetic acclimation, with environmental and interaction effects contributing more strongly than genotype main effects to nitrogen dynamics. These results demonstrate a scalable, uncertainty-aware sensor-to-trait architecture for dynamic physiological phenotyping that enables high-throughput germplasm screening across diverse environments and provides novel physiological selection targets for improving crop adaptation under variable climates.
Institution: Humboldt-Universität zu Berlin
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W7213973556