Genomes To Fields (G2F) Funded Projects

High productivity in crops has been achieved through decades of rigorous selection and breeding. Through this process, genetic variability present in these superior varieties is expected to have diminished compared to their less improved counterparts. The ability of plants to thrive under diverse environmental conditions is largely determined by the extent of the genetic variability present in those individuals. The hypothesis here is that selection for superior performance in crop species has therefore reduced the plasticity that allows plants to change their phenotypic expression depending on the effect of environmental influences. To test this hypothesis, this project will first aim to dissect the genetic architecture of this phenotypic plasticity capacity, also known as genotype by environment (G X E), by evaluating the phenotypic and genotypic variability of a diverse collection of maize hybrids grown as part of the nation-wide collaborative Maize G X E project that is part of the Genomes to Fields initiative. As part of this project, we also plan to exploit the genotypic and phenotypic variability present in a collection of maize inbred lines derived from the Iowa Stiff Stalk (BSSS) maize population. This set of diverse materials include lines derived from the BSSS population before any selection was ever applied on it, also inbreds derived from earlier cycles of selection and then finally elite lines recently derived from materials originated from this population that have undergone intense selection. Comparisons of genotypic and phenotypic variation observed in this collection of materials will provide an initial answer to our primary research questions. In addition to that, increased planting density has been identified as one of the most important agronomic factors enhancing productivity in modern maize. Another objective of this project is to determine if insensitivity to planting density is a major contributor to the ability of plants to tolerate environmental influences. For that, a subset of the BSSS derived lines will be evaluated at different planting densities. Results generated by the overall project are expected to enhance our understanding of how rigorous selection and breeding could affect the ability of plants to interact with their surroundings. Deepening our understanding of how the interaction between plants and environments is modulated will directly impact the decision making process of practical plant breeding programs.

Better matching of test crop growth environments to target crop production environments is key for efficient crop breeding. We propose to optimize promising new envirotyping analysis and modeling methods and develop publicly accessible known-truth genotype-environment simulations to allow improved breeding schemes for better global crop yield. In conjunction with the development of simulations, we will improve our promising PreMiuM profile regression algorithms run speed and develop breeder-relevant output plots and tables. We will combine PreMiuM profile regression covariate variable selection with standard linear model selection and fit methods to create a combined analysis workflow that will allow breeders to fit SNP and environment variates to their data. To illustrate these new analysis methods and inform our breeding program modeling, we will analyze real crop datasets with our improved PreMiuM and PreMiuM+model selection workflow and make spatial results maps to visualize the results in an easily interpretable field context. To leverage better envirotyping within breeding programs, we need modeling tools that allow exploration of program design constraints. We will develop breeding simulation models that incorporate realistic environment covariate features of test and target environments and flexible, extensible specifications of genetic gain within an open-source, widely used web-accessible modeling system that supports both student training and advanced breeder modeling. Modeling tools and better envirotyping tools will support crop breeders. Breeders will be able to design optimal germplasm exchange programs for maximum genetic gain by using PreMiuM results to inform setup of test and target environments.

High throughput field phenotyping (HTFP) using unmanned aerial vehicle (UAV) or ground vehicle systems, equipped with sensors, is a promising new approach for plant breeders and plant scientists to measure different varieties and select the best ones. HTFP approaches are expected to help screen more varieties, for more measurable characteristics, faster and perhaps more accurately than current human-based measurements. These approaches will be useful to improve crops for yield, yield stability, stress-resistance, quality, safety or environmental impact, benefitting farmers, industry, society and the environment. Despite exciting possibilities from these approaches, there are many gaps in knowledge and tools for plant breeders to actually use HTFP approaches in decision making. In this project, new tools for HTFP data will be developed that will make the technology easier to use, from detection through action. The HTFP approaches will be developed and tested using largest and most diverse US corn experiment to date, the Genomes to Fields GxE experiment (G2F-GxE). Because the G2F-GxE will be grown under nine heat and drought stress environments, HTFP will be used to identify varieties and genetics with different tolerances to stress. The major outputs of this project will be big-data methods for HTFP, developed and deployed within the statistical computing environments, and improved characterization of the maize G2F-GxE experiment. Student training, software, presentations, publications, and knowledge useful to public and private breeders will also result. Although HTFP is yet unproven, if successful, it is expected that the private and public sectors will adopt some or all of the methods to improve their own breeding processes; this will result in new industry jobs and more efficient agricultural production benefitting both farmers and society.?

Our civilization depends on continuously increasing levels of agricultural productivity, which itself depends on (among other things) the interplay of crop varieties and the environments in which these varieties are grown. Hence, to increase agricultural productivity and yield stability, it is necessary to develop improved crop varieties that deliver ever more yield, even under the variable weather conditions induced by global climate change, all the while minimizing the use of inputs such as fertilizers that are limiting, expensive or have undesirable ecological impacts. By coupling a network of innovative, low-cost nitrate sensors across multiple environments within the heart of the corn belt and advanced cropping systems modeling (APSIM, the most widely used modeling platform), the proposed research will enhance our understanding of and ability to predict yield and Genotype x Environment interactions. The integration of nitrate (N) dynamics into this model is expected to greatly increase the accuracy of its predictions. Because we will also integrate genotypes into this model, the proposed research outlines a new and innovative approach for breeding crops that exhibit increased yields and yield stability. It will be possible to readily translate this approach to other crops. By generating data on nitrate concentrations in soil and in planta at unprecedented spatial and temporal resolution at multiple sites with different soil characteristics and weather, the proposed research will also improve our understanding of N cycles in both the soil and plant. Although essential to plant growth and high yields, when over-applied N can result in a variety of serious negative externalities, some of which are currently the subject of high-impact litigation in Iowa. Project outcomes have the potential to provide guidance to farmers about how to apply sufficient but not excessive amounts of N fertilizer, resulting in both economic benefits to farmers and positive environmental externalities. Our focus on creating a new approach to breeding for yield stability meets the USDA sustainability goals to "satisfy human food and fiber needs" and "sustain the economic viability of farm operations". Our focus on nitrogen meets the USDA sustainability goals to "enhance environmental quality" and to "make the most efficient use of nonrenewable resources...and integrate, where appropriate, natural biological cycles and controls". More specifically, this proposal addresses the NIFA-Commodity Board co-funded priority for "development and application of tools to predict phenotype from genotype" and the "the development of high-throughput phenotyping equipment and methods".

Iowa State University will develop new sensors that measure the amount of nitrogen in soils and plants multiple times per day throughout the growing season. Nitrogen fertilizer is the largest energy input to U.S. corn production. However, its use is inefficient due to a lack of low-cost, effective nitrogen sensors. Year-to-year variation in nitrogen mineralization, due to differences in soil water and temperature, creates tremendous uncertainty about the proper fertilizer input and can cause farmers to over-apply. As a result, nitrogen fertilizer is lost from croplands to the surrounding environment where it pollutes air and water resources. To address this problem, the team will develop a novel silicon microneedle in-plant nitrogen sensor and a microfluidic soil nitrogen sensor. The microscale needles can be inserted into multiple sites of the plant to provide frequent and accurate monitoring of nitrate uptake, and for the first time provide a view of plant nitrogen use as the plant and roots develop. The team will also develop an automated microfluidic sensor which will measure the amount of nitrate in soil by extracting very small amounts of solution from the soil. The microfludic technology on which soil sensors are based can be produced at low cost. The combination of these two sensors will allow for a deeper understanding of plant nitrogen use and how it correlates with nitrate levels in the soil. These new sensors will accelerate the effort to identify, select, and breed new crops with improved nitrogen use efficiency. And the project will help increase the energy efficiency of our agriculture systems while reducing input costs, greenhouse gas emissions, and nitrate pollution of aquatic ecosystems.

Colorado State University (CSU) will develop a high-throughput ground-based robotic platform that will characterize a plant's root system and the surrounding soil chemistry to better understand how plants cycle carbon and nitrogen in soil. CSU's robotic platform will use a suite of sensor technologies to investigate crop genetic-environment interaction and generate data to improve models of chemical cycling of soil carbon and nitrogen in agricultural environments. The platform will collect information on root structure and depth, and deploy a novel spectroscopic technology to quantify levels of carbon and other key elements in the soil. The technology proposed by the Colorado State team aims to speed the application of genetic and genomic tools for the discovery and deployment of root traits that control plant growth and soil carbon cycling. Crops will be studied at two field sites in Colorado and Arizona with diverse advantages and challenges to crop productivity, and the data collected will be used to develop a sophisticated carbon flux model. The sensing platform will allow characterization of the root systems in the ground and lead to improved quantification of soil health. The collected data will be managed and analyzed through the CyVerse "big data" computational analytics platform, enabling public access to data connecting aboveground plant traits with belowground soil carbon accumulation.

G2F is an umbrella initiative to support translation of maize genomic information for the benefit of growers, consumers and society. This initiative will promote projects that advance integrated research and technologies, combining fields such as genetics, genomics, plant physiology, agronomy, climatology and crop modeling, with computation and informatics, statistics and engineering. The G2F conference will focus on exposing the field-based G2F collaborators, many of whom are plant breeders, to novel tools (such as robots, UAVs and sensors) and approaches for analyzing data. Talks will be geared to the needs of the data generators and the group will discuss strategies to integrate these new technologies into G2F. Participants will discuss, together with International Agroinformatics Alliance (IAA), how to clean up, store and share multi-year geocoded field trial data. The Plant Sciences Institute at Iowa State University has been successful at creating an environment that fosters the development of collaborations among plant scientist, engineers and computational scientists. One of the goals of the proposed conference is to encourage participants to replicate this approach on their own campuses, which if successful will expand the pool of engineers and computational scientists who can contribute the G2F-related research in all crops and bring new ideas and approaches to bear on the challenges facing US agriculture. In addition, by encouraging students who are being mentored by the G2F collaborators (many of whom are public sector plant breeders) to attend the conference we will enrich the population of attendees for students with an interest in plant breeding.

A current challenge for the global community is to secure food provision for the decades to come. The goal of this proposal is to develop a conceptual model to predict hybrid performance in response to hydroclimatic changes. Historically, genetic progresses in maize production have responded to breeding activities. To pursue future successful and sustainable crop production we propose to develop a Genomics-by-Environment model. To implement such hybrid statistical modeling approach, possible sources of predictability of corn hybrid performance thorough changing climate forcings will be investigated and implemented in a conceptual framework as follows: (1) Develop a data management test bed to collect, standardize and integrate data; (2) Characterize spatiotemporal hydroclimatic controls and the associated uncertainties across scales; (3) Develop a conceptual Genetic-, Multi-trait-, and Hydroclimatic-sensitive Model; (4) Perform hydroclimatic-driven hybrid performance forecasts based on (a) the spatial regionalization of phenotypic and environmental data and (b) the temporal influence of EHCEs on phenotypic expressions under standardized indices and absolute values of environmental variables; and (5) Develop a conceptual framework for operational rapid-response hybrid performance forecasts. Ultimately, "simulated" successful hybrids in response to droughts may be obtained by integrating the geospatial expansion of genes at field-scale and the syntheses of global-scale hydroclimatic processes.

Agricultural production is not advancing fast enough to meet the world's needs for food, fiber, and fuel in the coming years. One way to address this issue is the emerging possibility of harnessing the billions of microorganisms growing on, around, and inside every crop plant. Although we know these microbes can affect plants, we know very little about how this interaction happens and how the plants, microbes, and environment influence each other. Through Genomes to Fields, we are sampling a core set of 20-25 maize hybrids at 20 growing locations across the US. Once shipped back to our lab, we will extract DNA from the core of the stalk and use DNA sequencing to identify the bacteria present inside the plant. Each plant's profile of these "endophytes" will be compared against plant genetics, seed sources, and in-field environmental data (temperature, rainfall, humidity, etc.) to identify which of these is/are major drivers of the community. This research will answer fundamental questions about crop-endophyte interactions and lay the foundation for sustained research to determine how to leverage them to improve global food security. It will also train a graduate student in these methods to develop the scientific workforce and train the next generation of scientists.

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.

The overall goal of this project is to establish two complementary high intensity phenotyping sites at University of Nebraska-Lincoln (UNL) and Texas A&M University at College Station, TX (TAMU), focusing on maize and wheat. Our four specific objectives are: (1) Support two ongoing community-based plant phenotyping efforts at both UNL and TAMU; namely, the maize Genomes to Fields Initiative (G2F) and the winter wheat breeding programs; (2) Advance and apply Unmanned Aerial Vehicles (UAVs) as our main platform for sensor deployment and data collection, focusing on the development of new sensors and tools, as well as precise and consistent systems and protocols, to enable high throughput analysis of plant physiological and biochemical traits at leaf and canopy levels; (3) Disseminate phenotyping data broadly and promptly via public data repositories, and create image processing and analytical tools that provide end-to-end solutions to extract target traits from UAV images; (4) Train a next generation workforce in high throughput plant phenotyping through graduate and undergraduate research and by developing in-class curriculum, online educational material, and summer short-term training workshops. Over the life of the project, we anticipate making leaps in the science and engineering of plant phenotyping, including new sensor/tool development, data modeling and analytics, and workforce development. In the long term, we seek to replicate our gains by establishing a network of high throughput phenotyping sites nationwide with what we and others have learned.