Predicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models.

ABSTRACT: Genomic prediction (GP) is expected to become a powerful technology for accelerating the genetic improvement of complex crop traits. Several GP models have been proposed to enhance their applications in plant breeding, including environmental effects and genotype-by-environment interactions (G×E). In this study, we proposed a two-step model for plant biomass prediction wherein environmental information and growth-related traits were considered. First, the growth-related traits were predicted by GP. Second, the biomass was predicted from the GP-predicted values and environmental data using machine learning or crop growth modeling. We applied the model to a 2-year-old field trial dataset of recombinant inbred lines of japonica rice and evaluated the prediction accuracy with training and test

SUBMITTER: Toda Y 

PROVIDER: S-EPMC7304626 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature