Research Article | Open Access

Volume 2022 |Article ID 9768502 | https://doi.org/10.34133/2022/9768502

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

Xiaoyu Zhi iD ,¹ Sean Reynolds Massey-ReediD ,¹ Alex WuiD ,² Andries PotgieteriD ,³ Andrew Borrell,¹ Colleen Hunt,^1,4 David JordaniD ,¹ Yan ZhaoiD ,^2,3 Scott ChapmaniD ,^2,5 Graeme Hammer,² Barbara George-Jaeggli iD ^1,4

¹The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Hermitage Research Facility, Warwick, QLD, Australia
²The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), St Lucia, QLD, Australia
³The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Gatton, QLD, Australia
⁴Agri-Science Queensland, Department of Agriculture and Fisheries (DAF), Hermitage Research Facility, Warwick, QLD, Australia
⁵School of Agriculture and Food Sciences, The University of Queensland, Gatton, QLD, Australia

Abstract

Sorghum, a genetically diverse C4 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 (), phosphoenolpyruvate (PEP) carboxylation (), and electron transport (), quantified using a C4 photosynthesis model, were evaluated in two field-grown training sets ( plots including 124 genotypes) in 2019 and 2020. Partial least square regression (PLSR) was used to predict  (),  (),  (), SLN (), and LMA () from tractor-based hyperspectral sensing. Further assessments of the capability of the PLSR models for , , , SLN, and LMA were conducted by extrapolating these models to two trials of genome-wide association studies adjacent to the training sets in 2019 ( plots including 650 genotypes) and 2020 ( 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  and two QTL for . Candidate genes within 200 kb of the  QTL were involved in nitrogen storage, which is closely associated with Rubisco, while not directly associated with Rubisco activity per se.  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.