Leaf-Level Hyperspectral Reflectance As a Tool for Measuring Photosynthetic Capacity in C4 Grasses

Modern genomic and genetic tools are not being fully leveraged to improve the photosynthetic capacity of food and fuel crops due to a phenotyping bottleneck. Currently available techniques for measuring photosynthetic performance are not capable of both accurately estimating photosynthetic capacity and achieving the required high throughput for phenotyping a population. Recently, optical, near-surface reflectance spectroscopy has been proposed and tested as a method for high-throughput screening of photosynthetic capacity in C3 trees and crops. This approach was tested during the summer growing seasons of 2013 and 2014 in C4 grasses using a family of 180 phenotypically diverse recombinant inbred lines (RILs) resulting from a cross of Setaria viridis and S. Italica. Photosynthetic capacity measured by leaf gas exchange and leaf reflectance spectra (350-2500nm) varied significantly across a subset of the population. Partial Least Squared Regression is being used to model the relationship between photosynthetic capacity and leaf optical properties.  This model will then be applied to spectra measured on all 180 RILs in 4 different environments (high planting density, low planting density, well-watered, and water-limited) in order to test genotype by environment interactions in quantitative trait loci mapping of photosynthetic capacity in this model C4 grass.