Data from: Integration of physiological and remote sensing traits for improved genomic prediction of wheat yield

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.