Genomic Bayesian functional regression models with interactions for predicting wheat grain yield using hyper-spectral image data

Abstract

BackgroundModern agriculture uses hyperspectral cameras that provide hundreds of reflectance data at discrete narrow bands in many environments. These bands often cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra. With the bands, vegetation indices are constructed for predicting agronomically important traits such as grain yield and biomass. However, since vegetation indices only use some wavelengths (referred to as bands), we propose using all bands simultaneously as predictor variables for the primary trait grain yield; results of several multi-environment maize (Aguate et al. in Crop Sci 57(5):1–8, 2017) and wheat (Montesinos-López et al. in Plant Methods 13(4):1–23, 2017) breeding trials indicated that using all bands produced better prediction accuracy than vegetation indices. However, until now, these prediction models have not accounted for the effects of genotype × environment (G × E) and band × environment (B × E) interactions incorporating genomic or pedigree information.ResultsIn this study, we propose Bayesian functional regression models that take into account all available bands, genomic or pedigree information, the main effects of lines and environments, as well as G × E and B × E interaction effects. The data set used is comprised of 976 wheat lines evaluated for grain yield in three environments (Drought, Irrigated and Reduced Irrigation). The reflectance data were measured in 250 discrete narrow bands ranging from 392 to 851 nm (nm). The proposed Bayesian functional regression models were implemented using two types of basis: B-splines and Fourier. Results of the proposed Bayesian functional regression models, including all the wavelengths for predicting grain yield, were compared with results from conventional models with and without bands.ConclusionsWe observed that the models with B × E interaction terms were the most accurate models, whereas the functional regression models (with B-splines and Fourier basis) and the conventional models performed similarly in terms of prediction accuracy. However, the functional regression models are more parsimonious and computationally more efficient because the number of beta coefficients to be estimated is 21 (number of basis), rather than estimating the 250 regression coefficients for all bands. In this study adding pedigree or genomic information did not increase prediction accuracy.

Genomic Selection in Plant Breeding: Methods, Models, and Perspectives

“…A recent study developed statistical models to assess hyperspectral wavelength  environment interactions in HTP, incorporating genomic and pedigree G  E interactions [78]. Although little GP accuracy was achieved, important hyperspectral wavelength  environment interactions were observed, demonstrating that GS coupled with HTP can be a powerful tool applied to early-generation testing of a large number of selection candidates.…”

Predicting dark respiration rates of wheat leaves from hyperspectral reflectance

“…Radiation reflected from leaves can provide information about the internal composition of the leaf (Blackburn, 2007;Jacquemoud et al, 1996;Jacquemoud & Baret, 1990). Reflectance over a broad range of narrow and contiguous wavelength bands, termed hyperspectral reflectance, is increasingly used to predict plant or crop traits including water status (Gutierrez, Reynolds, & Klatt, 2010;Sims & Gamon, 2003); photosynthetic metabolism (Ainsworth, Serbin, Skoneczka, & Townsend, 2014;Barnes et al, 2017;Serbin, Dillaway, Kruger, & Townsend, 2012;Silva-Pérez et al, 2018); leaf mass per area (LMA; Asner & Martin, 2008;Ecarnot, Compan, & Roumet, 2013); concentrations or contents of nitrogen (N), lignin, and photosynthetic pigments (Martin & Aber, 1997;Yendrek et al, 2017); and grain yield (Montesinos-López, Montesinos-López, Montesinos-López, Cuevas, et al, 2017;Weber et al, 2012). ; Atkin et al, 2015;Reich, Walters, Ellsworth, et al, 1998;Reich, Walters, Tjoelker, Vanderklein, & Buschena, 1998;Ryan, 1991).…”

Increasing Predictive Ability by Modeling Interactions between Environments, Genotype and Canopy Coverage Image Data for Soybeans

“…For the aerial method, the highest correlations (0.33 and 0.44) were reached on days 45 and 41, respectively. To assess the usefulness of canopy coverage data in the early stages of the vegetative growth, the information from days [14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] (for the four data sets) was included in the prediction models, and the results were compared with those obtained using canopy information from the whole season. …”

“…Dealing with high-throughput phenotypic information, several authors [11][12][13] have shown improvements in predictive ability with the inclusion of these sources of information in the models for wheat and maize. Montesinos-Lopez et al [14] showed that accounting for the band (hyper-spectral image data)-by-environment interaction also improved yield predictability in wheat when compared with those models that did not include this component in the models.…”