title: Predicting grain yield using canopy hyperspectral reflectance in wheat breeding data.
abstract: BACKGROUND
  Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only use some bands and are cultivar-specific; therefore they lose considerable information and are not robust for all cultivars.
  
  
  RESULTS
  This study proposes models that use all available bands as predictors to increase prediction accuracy; we compared these approaches with eight conventional vegetation indexes (VIs) constructed using only some bands. The data set we used comes from CIMMYT's global wheat program and comprises 1170 genotypes evaluated for grain yield (ton/ha) in five environments (Drought, Irrigated, EarlyHeat, Melgas and Reduced Irrigated); the reflectance data were measured in 250 discrete narrow bands ranging between 392 and 851 nm. The proposed models for the simultaneous analysis of all the bands were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square. The results of these models were compared with the OLS performed using as predictors each of the eight VIs individually and combined.
  
  
  CONCLUSIONS
  We found that using all bands simultaneously increased prediction accuracy more than using VI alone. The Splines and Fourier models had the best prediction accuracy for each of the nine time-points under study. Combining image data collected at different time-points led to a small increase in prediction accuracy relative to models that use data from a single time-point. Also, using bands with heritabilities larger than 0.5 only in Drought as predictor variables showed improvements in prediction accuracy.
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico City, Mexico ; Facultad de Telemática, Universidad de Colima, 28040 Colima, Colima Mexico.
authors.forename: Osval A
authors.initials: OA
authors.lastname: Montesinos-López
authors.affiliation: Departamento de Estadística, Centro de Investigación en Matemáticas (CIMAT), 36240 Guanajuato, Guanajuato Mexico.
authors.forename: Abelardo
authors.initials: A
authors.lastname: Montesinos-López
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico City, Mexico.
authors.forename: José
authors.initials: J
authors.lastname: Crossa
authors.affiliation: Epidemiology and Biostatistics Department, Michigan State University, 909 Fee Road, East Lansing, MI 48824 USA.
authors.forename: Gustavo
authors.initials: G
authors.lastname: de Los Campos
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico City, Mexico.
authors.forename: Gregorio
authors.initials: G
authors.lastname: Alvarado
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico City, Mexico.
authors.forename: Mondal
authors.initials: M
authors.lastname: Suchismita
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico City, Mexico ; International Rice Research Institute, Los Baños Research Center, Los Baños, Laguna Philippines.
authors.forename: Jessica
authors.initials: J
authors.lastname: Rutkoski
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico City, Mexico.
authors.forename: Lorena
authors.initials: L
authors.lastname: González-Pérez
authors.affiliation: International Maize and Wheat Improvement Center (CIMMYT), Apdo. Postal 6-641, 06600 Mexico City, Mexico.
authors.forename: Juan
authors.identifier: 0000-0002-1468-4867
authors.initials: J
authors.lastname: Burgueño
keywords: Bayes B; Fourier regression; Genome selection; Prediction accuracy; Spectral data; Spline regression; Vegetation indexes; Wheat