HIGH INTENSITY PHENOTYPING SITES:  A MULTI-SCALE, MULTI-MODAL SENSING AND SENSE-MAKING CYBER-ECOSYSTEM FOR GENOMES TO FIELDS

Increasingly variable weather patterns have already begun to negatively impact agriculture. We currently lack the knowledge and tools necessary to efficiently develop resilient crop varieties that will provide stable and economically viable yields across increasingly variable environments. To address the challenge of breeding next generation resilient crop varieties we require accurate and mechanistically based models that can predict phenotypic outcomes based on genetic, environmental, and crop management data. Developing accurate predictive crop models requires an enhanced understanding of GxE (Genotype X Environment). This in turn requires large collections of phenotypic and environmental data gathered from common sets of crop varieties grown in diverse environments. With support from state and national Corn Growers, the Genomes to Fields (G2F) initiative has been conducting community-based experiments to do just that. Since 2014, G2F participants have been generating and analyzing genotypic, environmental, and crop management data from commercially relevant maize germplasm to learn how GxE interactions influence phenotypeThe proposed project, G2F-HIPS, will support and intensify G2F by deploying, evaluating and validating a combination of established, image-based sensing technologies and promising new field-based agricultural sensors, generating and sharing reference data to foster community innovation, developing and democratizing analysis pipelines for phenotypic data, conducting proof-of-principle research projects to identify genes responsible for crop responses to environmental variation, and contributing in a substantial manner to the training of current and future agricultural researchers to make use of these innovations.The objectives of the proposed project are to:Objective 1. Deploy and evaluate for use by G2F, a combination of established, image-based sensing technologies and highly promising new field-based agricultural sensors (for nitrate and water) and generating and sharing reference phenomic data to foster community innovation.Objective 2. Develop and democratize analysis pipelines for phenotypic data.Objective 3. Conduct proof-of-principle research projects to identify agronomically relevant genes from phenomic data.Objective 4. Contribute in a substantial manner to the training of current and future agricultural researchers to make use of these innovations.