Estimating carbon isotope discrimination and grain yield of bread wheat grown under water-limited and full irrigation conditions by hyperspectral canopy reflectance and multilinear regression analysis

Abstract

ABSTRACT Water deficit is the most limiting factor for wheat production, so wheat-breeding programmes are currently focused on developing high-performance genotypes under such conditions. Carbon isotope discrimination (∆13C) in grains is a trait closely related to yield and stress tolerance. However, conventional measurement of ∆13C is expensive, limiting its widespread use for genotype selection in breeding programmes. Predicting ∆13C through remote sensing could be useful for large-scale phenotyping. A set of 384 cultivars and advanced lines of spring bread wheat (Triticum aestivum L.) was grown under contrasting water conditions during two seasons. Grain yield (GY) and the ∆13C of grains were obtained at the end of both seasons, and canopy reflectance measurements were taken at anthesis and grain filling. Hyperspectral canopy reflectance was used to estimate GY and ∆13C through Multilinear Regression Analysis (MRL) considering wavelength selection using a Genetic Algorithm (GA), spectral reflectance indices (SRIs), Partial Least Square Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF) and Artificial Neural Networks (ANN). The best models of both GY and ∆13C explained 78% and 60% of data variability, respectively. Additionally, the MRL models showed higher prediction rates than SRIs and similar or slightly lower rates, in most cases, than multivariate regression models, but required only 4–9 wavelengths instead of the full hyperspectral data used to develop the regression models. The use of canopy spectral reflectance data and MRL models to predict GY and Δ13C via GA for selection of the reflectance wavelengths could be a practical tool for genotype selection in wheat breeding systems.