DOI: 10.5061/dryad.rbnzs7hqq
title: Data from: Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield

description: Genomic selection is an extension of marker-assisted selection by
 leveraging thousands of molecular markers distributed across the genome to
 capture the maximum possible proportion of the genetic variance underlying
 complex traits. In this study, genomic prediction models were developed by
 integrating phenological, physiological, and high-throughput phenotyping
 traits to predict grain yield in bread wheat (Triticum aestivum L.) under
 three environmental conditions: irrigation, drought stress, and terminal
 heat stress. Model performance was evaluated using both five-fold
 cross-validation and leave-one-environment-out (LOEO) schemes. Under
 five-fold cross-validation, the model incorporating vegetation indices
 derived from spectral datasets from the grain-filling phase achieved the
 highest accuracy. In LOEO validation, the model that included days to
 heading performed best under irrigation, whereas under drought stress, the
 model utilizing vegetation indices from the vegetative stage showed the
 highest accuracy. Under terminal heat stress, three models performed best:
 one incorporating genotype by environment interaction, one using
 vegetation indices during the vegetative stage, and one integrating
 spectral reflectance data from both the vegetative and grain-filling
 phases. Although incorporating multiple covariates can improve prediction
 accuracy or reduce the normalized root mean square error, using an
 extended model with all available covariates is not recommended due to the
 marginal predictive accuracy gains, increases in phenotyping, costs, and
 complexity of data collection analysis. Overall, our findings show the
 importance of tailored phenomic inputs to specific environmental contexts
 to optimize genomic prediction of wheat yield.
description: # Data from: Integration of physiological and remote sensing traits for
 improved genomic prediction of wheat yield Dataset DOI:
 [10.5061/dryad.rbnzs7hqq](10.5061/dryad.rbnzs7hqq) ## Description of the
 data and file structure Dataset used in the publication "Integration
 of physiological and remote sensing traits for improved genomic prediction
 of wheat yield".  The data was collected at the Campo Experimental
 Norman E. Borlaug (CENEB) Field station from the International Maize and
 Wheat Improvement Center (CIMMYT), Ciudad Obregon, Sonora, Mexico in the
 growth season 2022-2023. We collected agronomic, physiological and remote
 sensing traits to predict wheat yield using genomic prediction in three
 environments: irrigation, drought stress and terminal heat stress. Raw
 data and BLUEs for grain yield, grain filling percentage, grain filling
 rate, plant height, days to heading, stomatal conductance, transpiration,
 quantum yield of photosystem II and high-throughput traits in the MOLPAN
 wheat population. ### Files and variables #### File:
 Garcia-Barrios_et_al_2025_TPG_raw_BLUEs_datasets.xlsx **Description:** 
 ##### Variables * Environment: irrigation, drought, terminal heat * Entry:
 genotype number * Rep: replicate  * CIMMYT ID: ID used for internal CIMMYT
 identification control * Cross Name * Grain Yield_gm2: Grain yield
 harvested at physiological maturity in grams per square meter * GFP_%:
 Grain filling percentage (%) in days, this means how many days each
 genotype was in the grain-filling period * GFR_gm2: Grain filling rate in
 grams per square meter * Height_cm: Plant height at physiological maturity
 * Heading_days: Days passed from emergence to heading * gs_molm2s1: Leaf
 stomatal conductance * E_mmolm2s1: Leaf transpiration rate * PhiPS2:
 Quantum yield of photosystem II * R_Blue_vg: Reflectance value of the blue
 band at the vegetative stage * R_Green_vg: Reflectance value of the green
 band at the vegetative stage * R_Red_vg: Reflectance value of the red
 band at the vegetative stage * R_RedEdge_vg: Reflectance value of the red
 edge band at the vegetative stage * R_NIR_vg: Reflectance value of the
 near infrared band at the vegetative stage * Cired_edge_vg: Chlorophyll
 index calculated with the red edge band at the vegetative stage * NDVI_vg:
 Normalized Differenced Vegetation Index at the vegetative stage *
 LAI_PROSAIL_vg: Leaf area index derived from the inversion of the
 radiative transfer model PROSAIL at the vegetative stage *
 Fcover_PROSAIL_vg: Fraction of vegetation cover derived from the inversion
 of the radiative transfer model PROSAIL at the vegetative stage *
 ALA_PROSAIL_vg: Average leaf angle derived from the inversion of the
 radiative transfer model PROSAIL at the vegetative stage * SR_vg: Simple
 ratio index at the vegetative stage * FIPAR_PROSAIL_vg: Fraction of
 Intercepted Photosynthetically Active Radiation derived from the inversion
 of the radiative transfer model PROSAIL at the vegetative stage *
 Cigreen_vg: Chlorophyll index calculated with the green band at the
 vegetative stage * Cab_PROSAIL_vg: Chlorophyll a+b content derived from
 the inversion of the radiative transfer model PROSAIL at the vegetative
 stage * LAICab_PROSAIL_vg: Leaf area index based on chlorophyll content
 derived from the inversion of the radiative transfer model PROSAIL at the
 vegetative stage * MCARI_vg: Modified Chlorophyll Absorbed Reflectance
 Index at the vegetative stage * R_Blue_gf: Reflectance value of the blue
 band at the grain-filling stage * R_Green_gf: Reflectance value of the
 green band at the grain-filling stage * R_Red_gf: Reflectance value of the
 red band at the grain-filling stage * R_RedEdge_gf: Reflectance value of
 the red edge band at the grain-filling stage * R_NIR_gf: Reflectance value
 of the near infrared band at the grain-filling stage * Cired_edge_gf:
 Chlorophyll index calculated with the red edge band at the grain-filling
 stage * NDVI_gf: Normalized Differenced Vegetation Index at the
 grain-filling stage * LAI_PROSAIL_gf: Leaf area index derived from the
 inversion of the radiative transfer model PROSAIL at the grain-filling
 stage * Fcover_PROSAIL_gf: Fraction of vegetation cover derived from the
 inversion of the radiative transfer model PROSAIL at the grain-filling
 stage * ALA_PROSAIL_gf: Average leaf angle derived from the inversion of
 the radiative transfer model PROSAIL at the grain-filling stage * SR_gf:
 Simple ratio index at the grain-filling stage * FIPAR_PROSAIL_gf: Fraction
 of Intercepted Photosynthetically Active Radiation derived from the
 inversion of the radiative transfer model PROSAIL at the grain-filling
 stage * Cigreen_gf: Chlorophyll index calculated with the green band at
 the grain-filling stage * Cab_PROSAIL_gf: Chlorophyll a+b content derived
 from the inversion of the radiative transfer model PROSAIL at the
 grain-filling stage * LAICab_PROSAIL_gf: Leaf area index based on
 chlorophyll content derived from the inversion of the radiative transfer
 model PROSAIL at the grain-filling stage * MCARI_gf: Modified Chlorophyll
 Absorbed Reflectance Index at the grain-filling stage ## Code/software To
 analyze this dataset we used Rstudio with the packages lme4 and prosail. 
 The package lme4 was further implemented using the graphic user interface
 META-R (v6.04) ## Access information Other publicly accessible locations
 of the data: * None Data was derived from the following sources: * None