title: Crop improvement using life cycle datasets acquired under field conditions.
abstract: Crops are exposed to various environmental stresses in the field throughout their life cycle. Modern plant science has provided remarkable insights into the molecular networks of plant stress responses in laboratory conditions, but the responses of different crops to environmental stresses in the field need to be elucidated. Recent advances in omics analytical techniques and information technology have enabled us to integrate data from a spectrum of physiological metrics of field crops. The interdisciplinary efforts of plant science and data science enable us to explore factors that affect crop productivity and identify stress tolerance-related genes and alleles. Here, we describe recent advances in technologies that are key components for data driven crop design, such as population genomics, chronological omics analyses, and computer-aided molecular network prediction. Integration of the outcomes from these technologies will accelerate our understanding of crop phenology under practical field situations and identify key characteristics to represent crop stress status. These elements would help us to genetically engineer "designed crops" to prevent yield shortfalls because of environmental fluctuations due to future climate change.
authors.affiliation: Cellulose Production Research Team, Biomass Engineering Research Division, RIKEN Center for Sustainable Resource Science , Yokohama, Japan ; Gene Discovery Research Group, RIKEN Center for Sustainable Resource Science , Yokohama, Japan ; Kihara Institute for Biological Research, Yokohama City University , Yokohama, Japan.
authors.forename: Keiichi
authors.initials: K
authors.lastname: Mochida
authors.affiliation: Group of Genome Diversity, Institute of Plant Science and Resources, Okayama University , Kurashiki, Japan.
authors.forename: Daisuke
authors.initials: D
authors.lastname: Saisho
authors.affiliation: Group of Environmental Response Systems, Institute of Plant Science and Resources, Okayama University , Kurashiki, Japan.
authors.forename: Takashi
authors.initials: T
authors.lastname: Hirayama
keywords: crop phenology; epigenome; machine learning; population genomics; transcriptome