Source: IOWA STATE UNIVERSITY submitted to
HIGH INTENSITY PHENOTYPING SITES:  A MULTI-SCALE, MULTI-MODAL SENSING AND SENSE-MAKING CYBER-ECOSYSTEM FOR GENOMES TO FIELDS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
Reporting Frequency
Annual
Accession No.
1022368
Grant No.
2020-68013-30934
Project No.
IOW05612
Proposal No.
2019-05478
Multistate No.
(N/A)
Program Code
A1141
Project Start Date
Jun 1, 2020
Project End Date
May 31, 2023
Grant Year
2020
Project Director
Schnable, P. S.
Recipient Organization
IOWA STATE UNIVERSITY
2229 Lincoln Way
AMES,IA 50011
Performing Department
Agronomy
Non Technical Summary
To date much of the focus of agricultural research has been on increasing yield rather than ensuring the stability of yields within and across regions and years. It is of course important to develop higher yielding crop varieties. However, 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. This problem is exacerbated by the fact that breeding new crop varieties takes 7-10 years, and at many locations today's weather may not be an accurate representation of the spectrum of weather new varieties will experience at that same locations 10 years from now. 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. Fortunately, advances in the plant sciences, computational and data sciences, and engineering offer the potential to help us address this challenge and thereby create a more sustainable, resilient and profitable US agricultural system.Developing accurate predictive crop models requires an enhanced understanding of the combined effect of crop variety (G) and environment (9), GxE. This in turn requires large collections of plant traits 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 plant traits.The 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. As such, G2F-HIPS will promote the widespread adoption of new sensing technologies, methods of data analysis and thinking across the many G2F sites. In combination, these activities have the potential to facilitate a more mechanistic understanding of how phenotypes respond to genotypic and environmental variation, thereby facilitating the development of more resilient crop varieties that make more efficient use of agricultural inputs such as nitrogen and water, with corresponding environmental benefits.
Animal Health Component
0%
Research Effort Categories
Basic
70%
Applied
15%
Developmental
15%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2011510108180%
2011510108020%
Knowledge Area
201 - Plant Genome, Genetics, and Genetic Mechanisms;

Subject Of Investigation
1510 - Corn;

Field Of Science
1080 - Genetics; 1081 - Breeding;
Goals / Objectives
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.
Project Methods
During years 1 & 2 replicated yield plots will be grown in 10 unique environments at 6 locations across a 700 mile west to east transect that varies in elevation from 4,100-600' and that experiences between 18-37" of rainfall annually. At one site we will apply zero, partial or full irrigation to create three distinct water environments. At another, we will apply three levels of N fertilizer to create three distinct N environments. In year 1, two replications of ~45 core exPVP hybrids that have been included annually in all G2F field trials since 2014 will be grown in each of the 10 G2F-HIPS environments. In Year 2, in addition to the exPVP hybrids we will grow two replications of an association population (the "SAM panel") that consists of approximately 380 inbreds, which has been previously genotyped with a set of 1.2M high quality segregating SNPs and that has provided single-gene association mapping resolution. To provide baseline data for comparison to new phenotyping methods, in years 1 & 2, 12 traits will be manually collected at maturity from the 10 G2F-HIPS environments using G2F's SOP. Additional data will be collected using robots, UAVs and sensors. All data will be released to the community to accelerate development of improved statistical approaches for use of high throughput phenotyping data gathered from diverse environments to analyze GxE and promote gene discovery.Objective 1G2F-HIPS will provide the first comprehensive multimodal crop-sensing infrastructure. We will deploy imaging-based sensors using both ground-based (e.g., field robots) and aerial (e.g., UAV/drone) platforms, as well as next-generation plant sensors specifically designed to directly measure key traits (e.g. stalk and soil N, leaf transpiration and soil moisture). G2F-HIPS will collect hierarchical phenotypic data of structural, physiological, and performance-related traits ranging from molecular to plant to plot levels, and will conduct validations of sensor measurements to ensure data integrity and biological interpretations, and perform proof-of-principle experiments to demonstrate the value of coupled direct soil and plant measurements of N concentrations and water/transpiration.Objective 2G2F requires scalable software tools that can assimilate data and provide actionable feedback on data collected. This will be particularly true as increasingly complex, diverse, and multi-scale sensor data are collected. We envision a generalizable, Lego-like modular framework that provides end-users the ability to extract physiologically meaningful traits, fuse diverse data, and obtain actionable feedback on amount, and quality of data collected for specific analysis outcomes. Such a framework will only be truly impactful when done at scale and made accessible to a wide community. This will result in a sustainable community enabling consistent and reproducible data analysis. Our vision is supported by the following sub-objectives: Sustainable tool deployment to democratize data analytics pipelines, and tool development for G2F-HIPS data: annotation tool chains, turking on CyVerse.Objective 3G2F-HIPS will deploy conventional, high throughput, and high intensity phenotyping techniques across a diverse set of natural environments, management practices, and maize germplasm. The resulting dataset will be released to the community to enable both a wide range of future biological analyses and the development of new quantitative genetics and image processing analysis methods. G2F-HIPS will also conduct three "showcase" analyses using these data.Objective 4We will use lessons learned from ISU's Predictive Plant Phenomics (P3) paradigm graduate training program to offer traditional courses that are foundational for helping new graduate students to think differently. Courses will be offered in-person at ISU and virtually at UA; course materials will be disseminated broadly. We also will deliver just-in-time workshops, bootcamps, and challenges for students, researchers, and agriculture learners at national and international venues so that they, too, can develop the skills and expertise required to effectively participate in high-intensity phenotyping. Although personnel related to G2F will have priority, these activities will be open to the entire plant phenomics community on a space-available basis.Meeting project goals will require the collaborative efforts of researcher/educators with expertise in diverse fields. Towards this end, a diverse, highly collaborative group of investigators has been assembled and individual co-PIs will have sole or shared responsibilities for specific project objectives as outlined.