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

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.

Acknowledgements

This work was supported and financed by funds from the National Commission for Scientific and Technological Research CONICYT of Chile (FONDEF IDeA 14I10106, FONDECYT N^o 1150353 and 1180252, FONDECYT postdoctoral N^o 3170253 and FONDEQUIP IQM 130073).

Disclosure statement

The authors declare no conflicts of interest.

Additional information

Funding

This work was supported by the Fondo Nacional de Desarrollo Científico y Tecnológico [1150353,1180252,3170253]; Fondo de Fomento al Desarrollo Científico y Tecnológico [IDeA 14I10106,IQM 130073].