Modelling and Using Spatial Effects in Nationwide Historical Data Improve Genomic Prediction of Rice Heading Date in Japan.

ABSTRACT: Genomic prediction is a promising strategy for enhancing crop breeding efficiency. Historical data of breeding and cultivation tests from geographically wide regions presumably contain rich information for training genomic prediction models. Therefore, it is essential to explore methodologies to effectively handle such data. To improve the prediction accuracy of models using historical data, we incorporated a spatial model to account for spatial structures among field stations, in addition to conventional genomic prediction models. Targeting the rice heading date from historical data across Japan, we first constructed conventional genomic prediction models using genomic and/or meteorological elements as predictors. Next, we obtain the residual terms. Assuming that the residual terms were p

SUBMITTER: Taniguchi S 

PROVIDER: S-EPMC11992326 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature