B0459
Title: Predicting environmental response of crop plants via the integration of models from different disciplines
Authors: Hiroyoshi Iwata - The University of Tokyo (Japan) [presenting]
Abstract: Prediction of crop responses to environments is essential to efficiently develop new varieties that can adapt to the target environment. Genetic ability of crop plants can be predicted based on genome-wide DNA polymorphisms (i.e., genomic prediction). The environmental response of crop plants, however, is difficult to predict because it is influenced not only by genetic but also by environmental factors. To predict the environmental responses, it is necessary to extend genomic prediction to take into account the variations caused by environmental factors in the prediction. The integration of models from different disciplines will be necessary for the extension of genomic prediction. Crop simulation models, which enable the prediction of crop growth under given environments, and machine learning models associating the crop response to environments with environmental factors are good candidates for the integration. We will introduce methods for integrating genomic prediction models with crop models and machine learning models to predict the environmental responses of crop plants. We will also introduce methods for integrating multi-omics data to predict the crop responses to environments.
