Open AccessPredicting biomass of rice with intermediate traits: Modeling method combining crop growth models and genomic prediction models
Author(s) -
Yusuke Toda,
Hitomi Wakatsuki,
Toru Aoike,
Hiromi KajiyaKanegae,
Mariko Yamasaki,
Takuma Yoshioka,
Kaworu Ebana,
Takeshi Hayashi,
Hiroshi Nakagawa,
Toshihiro Hasegawa,
Hiroyoshi Iwata
Publication year - 2020
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0233951
Subject(s) - biomass (ecology) , crop , predictive modelling , microbiology and biotechnology , biology , biomass partitioning , gene–environment interaction , quantitative trait locus , agronomy , japonica , statistics , mathematics , genotype , botany , genetics , gene
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 testing data by cross-validation performed over two years. Therefore, the proposed model achieved an equivalent or a higher correlation between the observed and predicted values (0.53 and 0.65 for each year, respectively) than the model in which biomass was directly predicted by GP (0.40 and 0.65 for each year, respectively). This result indicated that including growth-related traits enhanced accuracy of biomass prediction. Our findings are expected to contribute to the spread of the use of GP in crop breeding by enabling more precise prediction of environmental effects on crop traits.