Estimating Photosynthetic Attributes from High-Throughput Canopy Hyperspectral Sensing in Sorghum

Sorghum, a genetically diverse C(4) cereal, is an ideal model to study natural variation in photosynthetic capacity. Specific leaf nitrogen (SLN) and leaf mass per leaf area (LMA), as well as, maximal rates of Rubisco carboxylation (V(cmax)), phosphoenolpyruvate (PEP) carboxylation (V(pmax)), and electron transport (J(max)), quantified using a C(4) photosynthesis model, were evaluated in two field-grown training sets (n = 169 plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict V(cmax) (R(2) = 0.83), V(pmax) (R(2) = 0.93), J(max) (R(2) = 0.76), SLN (R(2) = 0.82), and LMA (R(2) = 0.68) from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for V(cmax), V(pmax), J(max), SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 (n = 875 plots including 650 genotypes) and 2020 (n = 912 plots with 634 genotypes). The predicted traits showed medium to high heritability and genome-wide association studies using the predicted values identified four QTL for V(cmax) and two QTL for J(max). Candidate genes within 200 kb of the V(cmax) QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se. J(max) QTL was enriched for candidate genes involved in electron transport. These outcomes suggest the methods here are of great promise to effectively screen large germplasm collections for enhanced photosynthetic capacity.