Potential of high-spectral resolution for field phenotyping in plant breeding: Application to maize under water stress

Abstract

Spectroscopy is today and for two decades strongly used in many fields (pharmacy, agriculture, process, medicine…). This use in a very large number of applications is linked to the great spectral richness of the measurement and therefore to the large amount of accessible chemical information. For plant breeding, spectral reflectance in the visible and near-infrared range (VIS–NIR) embeds a lot of information about vegetation (pigments, structure, water, etc.). Discriminatory power between genotypes can be greatly improved by using high spectral resolution. NIR spectroscopy is still limited in the field for phenotyping compared to existing imaging solutions that are easier to implement.In this study, we will address the potential of high spectral resolution data by using NIR spectroscopy to describe phenotypic responses of maize genotypes to water stress. To that end, data acquired following an experimental design with water-deficient environment are processed using an analysis of variance method adapted to multivariate data called REP-ASCA. For each factor, this method gives its significance, the loadings describing the impacted spectral regions and the scores to classify observations. For a date with proven water stress, the treatment and genotype factors and the interaction term are significant with a p-value threshold at 0.05. Treatment term loadings highlight the spectral regions impacted by the change in irrigation while those of the genotype factor allows to group genotypes according to the yield potential regardless the irrigation. The interaction term loadings are used as a phenotyping trait related to water stress response. Based on this signature, tolerant genotypes are differentiated from sensitive genotypes according to a ranking based on final yield (R = 0.81). This spectral signature was then applied to another environment with a moderate water deficit. For most genotypes, we were able to recover the ranking previously established by the stressed environment (R = 0.60).