Genomes To Fields (G2F) Funded Projects

Sorted by Project's Start Date

Development of a PhenoNet - an Integrated Robotic Network forĀ Field-basedĀ Studies of Genotype x Environment Interactions

Award #: 1625364
Lie Tang
Patrick Schnable
Srikant Srinivasan
September 15, 2016
August 31, 2019

An award is made to Iowa State University to develop and deploy PhenoNet - an integrated robotic network for field-based studies of genotype crossed with environment (GxE) interactions. The core component of PhenoNet is a set of PhenoBots; lightweight robots that are able to autonomously navigate between crop rows using GPS and local range sensors while employing advanced sensing technologies to phenotype crop plants. The PhenoBots can measure indicators such as stalk size, plant height, leaf angle and tassel/inflorescence properties over time. The robots will be optimized for maize research and can be easily adapted for other row crops. The network (PhenoNet) is a universal platform which enables comprehensive field-based research on genotype and environment interactions. The broader impacts of this project are threefold. First, PhenoNet will have an important impact on society as understanding genome X environment interactions will help address the need for sufficient food, feed, and fiber for the planet's growing population, which is vital in an ever-changing environment. PhenoNet will bring "big data" more deeply into agriculture by cementing connections between plant scientists and engineers in their efforts to reach this goal. Second, this project is synergistic with the NSF-NRT project, "Predictive Phenomics of Plants", recently awarded to Iowa State University. The research and engineering outlined in this Major Research Instrumentation project will provide an outstanding opportunity for students from engineering disciplines, computer science, statistics, and agronomy to collaborate and engage in state-of-the-art interdisciplinary research. This project will also advance the training of current engineers and plant scientists who are experienced with networking, robotics and agronomy. Third, this project will reach out to underrepresented groups by targeting minority-serving institutions for student recruitment and will work with the Society of Women Engineers and other similar groups in seeking women participants to help meet the NSF-NRT award's efforts to broaden participation.

The PhenoBots are an important and essential advancement in the fields of agriculture and technology because they more efficiently characterize tall plants over time to their maturity. Previous technology and platforms are either incapable of, or are greatly hindered by various constraints. The design improvements of the Phenobots enable the robots to be more robust, stable, lightweight, integrated and economical. This creates a pathway for transformative research as it enables in situ, non-invasive monitoring of the traits of tall crops, like maize, over time. PhenoNet will consist of a network of four PhenoBots, which will be deployed by plant scientists in Iowa, Kansas, Minnesota, Nebraska, and Wisconsin. The data generated from high throughput phenotyping will address whether it is possible to predict the phenotype of a given genotype in a specified environment.

Understanding the Effect of Long-Term Selection on the Genetic Control and Modulation of Genotype-by-Environment Interaction

Award #: 2016-67013-24419
Natalia De Leon
November 1, 2015
October 31, 2019

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.

Genetically-Informed Envirotyping Tools to Better Match Test and Target Environments

Award #: 2017-67013-26188
Ann Stapleton
February 15, 2017
February 14, 2020

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.

Aerial and Ground Phenotyping Analytical Tool Development for Plant Breeders Using the Maize G2F Project

Award #: 2017-67013-26185
Seth Murray
Dale Cope, Sorin Popescu
March 15, 2017
March 14, 2020

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.?

Low-cost nitrate sensors to populate genotype-informed yield prediction models for next generation breeders

Award #: 2017-67013-26463
Patrick Schnable
Sotirios Archontoulis, Mike Castellano, Liang Dong
April 1, 2017
March 31, 2019

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".

High-throughput, High-resolution Phenotyping of Nitrogen Use Efficiency Using Coupled In-plant and In-soil Sensors

Award #: DEAR0000824
Liang Dong
Michael Castellano, James Schnable
June 12, 2017
June 11, 2019

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.

Root Genetics in the Field to Understand Drought Adaptation and Carbon Sequestration

Colorado State University [Flow-through funds from Advanced Research Projects Agency-Energy (ARPA-E): DEAR0000826]
Award #: DEAR0000826
Patrick Schnable
Jianming Yu
July 3, 2017
July 2, 2020

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.

From Gene to Global Hydroclimatic Controls on Hybrid Performance Predictability

Award #: 2018-67013-27594
Francisco Munoz-Arriola
February 15, 2018
February 14, 2021

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.

Linking Digital Readouts to Traits in Hybrids of the G2F Project and in Heirloom Corn

Arkansas Research Alliance (ARA) and the University of Arkansas Division of Agriculture
Argelia Lorence
Elizabeth Hood
March 15, 2019
March 14, 2020