Funded Projects (Sorted by project start date descending order)

NIFA AG2PI Collaborative: Improving Causal Gene Detection across Crop and Livestock Species

Award #: 2023-70412-41087
Patrick Schnable
Jack C. M. Dekkers, Somak Dutta, Vivekananda Roy, Maria G. Salas-Fernandez, James Schnable, Asheesh K. Singh, Jianming Yu
September 15, 2023
September 14, 2026

In the face of 21st-century agricultural challenges, our mission is clear: we must produce more food, feed, and fiber for a growing population with evolving dietary preferences, while dealing with limited rural labor and agricultural land, and the need for bio-energy sources. Moreover, climate change introduces more frequent biotic and abiotic stresses. While global crop productivity has matched these challenges, we must intensify our efforts to sustain this progress. This is especially vital as we navigate the rest of the century. To address these pressing needs, our team of experts, spanning crop and livestock breeding, genetics, biochemistry, and data science, is forging ahead. We're developing innovative tools to decode the genetic basis of traits in crops like maize, soybean, sorghum, and in pigs. Our advanced statistical models, enhancing methods like GWAS, TWAS, and eQTL mapping, empower biologists to explore data in groundbreaking ways, uncovering new insights. We are bridging the gap between genetics and traits, from crop yields to Vitamin B levels in maize. Our research probes the interplay of genetics, weather, and environment using diverse data. This newfound knowledge will steer enhancements in crucial U.S. crops and livestock. Our ambitious endeavor extends beyond discovery. It entails crafting novel statistical tools to comprehend essential genes in both livestock and crops, applicable across species. Aligned with the USDA's strategic goals, our work contributes to an equitable, resilient, and prosperous U.S. agricultural system, ensuring accessible, wholesome food for all. Through education and outreach, we'll empower crop and livestock breeders and cultivate the human capital needed to fulfill these aspirations.

CPS: MEDIUM: COCOLLABORATIVE RESEARCH: Field-scale, Single Plant-Resolution Agricultural Management Using Coupled Molecular and Macro Sensing and Multi-Scale Data Fusion and Modeling

Award #: 2020-67021-31528
Liang Dong
Sotirios V. Archontoulis, Michael J. Castellano, Baskar Ganapathysubramanian, Patrick S. Schnable
June 1, 2020
May 31, 2023 Extended thru May 31, 2024

Water and nitrogen represent two of the most expensive inputs to agricultural systems, and two of the critical constraints on overall agricultural productivity. Today, farmers generally over apply nitrogen fertilizer, because the potential cost of over application is less than the potential cost of achieving suboptimal yields. Similarly, in farm settings water is often over applied, particularly when studies are conducted at high resolution within individual center-pivot fields. We willdesignand validatean integrated cyber-physical system to collect and integrate data from remote sensing and low-cost field deployed wearable sensors and use machine learningand mathematical modeling to guide precision water and nutrient interventions in farmer's fields. This would mean that agricultural productivity can be sustained or increased while reducing overall nitrogen fertilizer and irrigation applications. Among the many beneficial effects to society as a whole would be 1) a decrease the environmental impact of agriculture; 2) decreased competition for scarce water supplies between agriculture and growing urban centers; and 3) increased farmer profitability, improving the economic viability of rural economies.The CPS will enable fusion of a large volume of spatio-temporally distributed multi-modal information to create a data-driven decision support platform that provides actionable information on optimal agricultural managementstrategies.The team will continue to leverage and develop extensive outreach and educational activities to train the next generation of scientists, through many existing STEM programs in Iowa State University and University of Nebraska-Lincoln.

NIFA AG2PI Collaborative: Continuing to Seed the Future of Agricultural Genome to Phenome Research

Award #: 2022-70412-38454
Patrick Schnable
Jennifer Clarke, Jack Dekkers, Brenda Murdoch, Chris Tuggle
September 15, 2022
September 14, 2024

To address the challenges and opportunities of achieving sustainable genetic improvement of agricultural species, thereby enhancing the sustainability and profitability of US agriculture, the current agricultural genome to phenome initiative (AG2PI) project has built a large transdisciplinary community of researchers spanning crops and livestock, as well as integrative disciplines (e.g., engineers, data scientists, and social scientists). The overall objective of the AG2PI is to provide competitive funds to conduct AG2P research that will benefit the broader community. The project will: implement a vision for AG2P research that has been developed with community input over the past two years by providing seed grants that will help close research gaps and support new and emerging opportunities. It features a robust project management plan, involving leaders with the requisite experience in managing large complex projects (including seed grant programs), implementation plans based on best practices, and the science of team science, coupled with a robust assessment plan to refine best practices. A comprehensive and inclusive scientific advisory board, stakeholders, and partner organizations will assist the team with meeting its objectives and ensuring that its activities coordinate and complement existing AG2P programs in crops and livestock. The project will continue developing a cross-kingdom community prepared to tackle AG2P research and offer opportunities to develop novel and creative solutions to enhance our understanding of both kingdoms, for the benefit of US agriculture and society. The current AG2PI team is well positioned to accomplish these tasks to produce quick results with proven procedures and processes.

SCC-IRG Track 1: Connecting Farming Communities for Sustainable Crop Production and Environment Using Smart Agricultural Drainage Systems

Award #: 2125484
Liang Dong
Michael Castellano, Hongli Feng, Matthew Lechtenberg, Xiaobo Tan
Sotirios Archontoulis, Baskar Ganapathysubramanian, David Hennessy, Patrick S. Schnable, Jaqueline Comito
October 1, 2021
September 30, 2025

In the US, agricultural drainage infrastructure benefits >22.6 Mha of cropland and is valued at ~$100B. As a proportion of total croplands, drained croplands produce a disproportionately large amount of grain but also release a disproportionately large amount of eutrophying nutrients to aquatic ecosystems. Drainage systems include individually-owned field drains that depend on the function of community-owned main drains. Climate change and agricultural intensification are causing farmers to increase the extent and intensity of drainage leading to a pressing need to balance productivity, profitability, and environmental quality when making drainage decisions. Further, because drainage systems include individually-owned and community-owned drains, decision-making involves complex techno-economic social issues together with understanding biophysical processes and requires balancing the needs of individual farmers, drainage communities, and surrounding regions. This project will develop an integrated decision-making platform to facilitate community decision making for precise prediction and management of drainage effects on water flow, crop production, farm net returns, and nutrient loss. The platform data will be made possible by new agricultural sensors and robots, innovations in behavioral economics and analytics tools. Development of the drainage decision-making platform will be guided by farmer stakeholders—including, the Iowa and Illinois Drainage Districts Associations, a national-level agricultural drainage management coalition, and directly with farmers—forming a continuous learning environment across scientists and farmers that fosters adoption of new technologies and transfer of the research process to the next generation of scientists, engineers, and agricultural professionals.

The project will build upon a suite of biophysical and social science advances in multiple areas, including bioinspired robotic snake sensors, in-situ soil nutrient sensors, computational modeling, and socioeconomics. The snake sensors will navigate through agricultural drainage networks to generate a high spatial resolution data stream about flow rates and nitrate concentrations throughout the belowground network. The soil sensors will enable continuous monitoring of nitrate dynamics. Process-based ecohydrological models, subsurface water transport models, and multiple spatiotemporal sensor outputs will be integrated to obtain high-resolution information about distributions of water and nitrate. Biophysical scenario analyses will assist decision-making for different agricultural management scenarios to balance resource use efficiency, profitability, and environmental performance. Socioeconomic science innovations will be integrated by learning how current systems are managed in the context of various heterogeneities across individuals and drainage districts, such as demographics, farm size, and presence of wetlands, and how new information provided by the proposed infrastructure interacts with human incentives and choices and consequent policy making.

NIFA AG2PI Collaborative: seeding the future of agricultural genome to phenome research for crops and livestock

Award #: 2021-70412-35233
Patrick Schnable
Jack Dekkers, Chris Tuggle, Eric Lyons, Brenda Murdoch, Jennifer Clarke, Carolyn Lawrence-Dill
September 1, 2021
August 31, 2023 - Extended thru August 31, 2024

To achieve sustainable genetic improvement of agricultural species and thereby mitigate environmental impacts and enhance the sustainability and profitability of US agriculture, the expertise of a broad community of agricultural genome to phenome (AG2P) researchers must be engaged, drawing from both crop and livestock communities, as well as integrative disciplines (e.g., engineers, data scientists, economists, and social scientists). Towards this end an existing NIFA-funded project (2020-70412-32615) is assembling and preparing a transdisciplinary community to conduct AG2P research. This project is: Developing a vision for AG2P research by identifying research gaps and opportunities; fostering first steps towards the development of community solutions to these challenges and gaps; and rapidly disseminating findings to the broader community. The current project will leverage these activities by using a competitive process in coordination with NIFA to provide additional seed grants to the AG2PI community. These seed grants will identify bottlenecks and explore novel solutions to community challenges to AG2P research. The project features a robust project management plan, involving leaders with the requisite experience managing large complex projects, implementation plans based on best practices and the science of team science, coupled with a robust assessment plan to refine best practices. A comprehensive and inclusive group of scientific partner organizations (including those serving the global community), a renowned scientific advisory board, and an external stakeholder group will assist the executive team in meeting its objectives and ensuring that its activities coordinate and complement existing programs.

AI Institute: AIIRA: AI Institute for Resilient Agriculture

Award #: 2021-67021-35329
Baskar Ganapathysubramanian
Sotirios V. Archontoulis, Liang Dong, Cassandra J. Dorius, Shawn F. Dorius, Priyanka Jayashankar, Adarsh Krishnamurthy, Carolyn J. Lawrence-Dill, Ajay Nair, Sreevatsal Nilakanta, Yumou Qiu, Soumik Sarkar, Patrick S. Schnable, Arti Singh, Asheesh K. Singh, Lizhi Wang
September 1, 2021
August 31, 2023 - Extended thru August 31, 2024

Our planet faces a daunting challenge: By the end of the century, world population will increase by 45%, cropland will decrease by 20% and our climate will become increasingly variable, threatening crops and putting communities at risk. We need to increase agricultural productivity by 70% to meet our growing food security needs - a challenge we are not able to meet under our current rate of progress. Now imagine a truly game-changing technology that can greatly accelerate this progress. It already exists in the form of artificial intelligence (AI). Using advanced sensor technology, scientists can create digital twins - virtual simulations that mimic real-world plants, crops and farms. For every year of biological data, digital twin-based AI systems can create hundreds of reality-based simulations that can: Streamline and revolutionize plant breeding to help scientists develop improved crop varieties that can better withstand environmental, pest and disease challenges while delivering higher yields and quality. Help farmers and their advisors adopt improved farming techniques and technologies that can boost their profits and help improve the long-term care of their critical land and soil resources. Provide governments with the insight they need to encourage and incentivize adoption of policies and practices that deliver the most benefit with the least environmental cost. Give agricultural companies the data and knowledge needed to develop more effective precision management systems and improved plant varieties that thrive with less water, fertilizer and pesticides. Drive economic development across the rural landscape through AI-inspired ventures. The leaders of the AI Institute for Resilient Agriculture (AIIRA) believe these breakthroughs - and more - can be a reality in the very near future. The Institute is bringing together AI experts with plant breeders, agronomists, geneticists and social scientists to accelerate the adaptation and use of AI-based technologies to transform agriculture to meet the needs of our world's growing population and increasingly climate-challenged food systems.

ARANET: Wireless Living Lab for Smart and Connected Rural Communities

US Ignite (Funded in part by the NSF award CNS-1827940 and PAWR Industry Consortium)
Award #: CNS-1827940
Hongwei Zhang
Yong Guan, Ahmed E. S. Kamal, Daji Qiao, Mai Zheng
Thomas Daniels, A-Ram Kim, Sang W. Kim, James E. Koltes, Joshua M. Peschel, Patrick S. Schnable, Anuj Sharma, Lie Tang
June 1, 2021
May 31, 2025

Agriculture and Rural Communities (ARA) is funded in part by the NSF award CNS-1827940 and PAWR Industry Consortium. See project website (https://arawireless.org/) for details.

NIFA AG2PI Collaborative: Creating a shared vision across crop and livestock communities

Award #: 2020-70412-32615
Patrick Schnable
Jack Dekkers, Chris Tuggle, Eric Lyons, Brenda Murdoch, Jennifer Clarke, Carolyn Lawrence-Dill
September 1, 2020
August 31, 2023 - Extended thru August 31, 2024

To address the challenges and opportunities of achieving sustainable genetic improvement of agricultural species, thereby enhancing the sustainability and profitability of US agriculture, the expertise of a broad community of agricultural genome to phenome (AG2P) researchers must be engaged, drawing from both crop and livestock communities, as well as integrative disciplines (e.g., engineers, data scientists, economists, and social scientists). The overall objective of this AG2PI is to assemble and prepare a transdisciplinary community to conduct AG2P research. The project will: Develop a vision for AG2P research by identifying research gaps and opportunities; foster first steps towards the development of community solutions to these challenges and gaps; and rapidly disseminate findings to the broader community. Towards these ends, AG2PI will sponsor and coordinate field days, conferences, training workshops, and seed grants. AG2PI features a robust project management plan, involving leaders with the requisite experience managing large complex projects, implementation plans based on best practices and the science of team science, coupled with a robust assessment plan to refine best practices. A comprehensive and inclusive group of scientific partner organizations (including those serving the global community), a renowned scientific advisory board, and an external stakeholder group will assist the AG2PI in meeting its objectives and ensuring that its activities coordinate and complement existing programs in plant and livestock G2P. Development of a cross-kingdom community prepared to tackle AG2P research offers opportunities to develop novel and creative solutions to enhance our understanding of both kingdoms, for the benefit of US agriculture and society.

High intensity phenotyping sites: a multi-scale, multi-modal sensing and sense-making cyber-ecosystem for genomes to fields

Award #: 2020-68013-30934
Patrick Schnable
Michael J. Castellano, Liang Dong, Baskar Ganapathysubramanian, Carolyn J. Lawrence-Dill, Lie Tang
June 1, 2020
May 31, 2023 - Extended thru May 31, 2024

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.

BTT EAGER: A Wearable Plant Sensor for Real-Time Monitoring of Sap Flow and Stem Diameter to Accelerate Breeding for Water Use Efficiency

Award #: 1844563
Liang Dong
Michael J. Castellano, Patrick S. Schnable
May 15, 2019
April 30, 2021 - Extended thru April 30, 2024

Breeding plants for increased drought resistance without sacrificing yield is a major goal of breeding efforts around the world. However, drought resistance and yield tend to be inversely correlated. The rate that water flows through the stalk of plants on its way to the leaves is a critical variable in explaining differences in drought tolerance between different varieties of plants. However, current technologies for measuring the rate of this flow are bulky and can damage the plant when they remain applied for long time periods; thus they are not able to monitor plants throughout a growing season. In addition, the data collected from current sensors requires measurements of stem size in order to accurately measure flow rates. If stems grow over the course of the experiment, these measurements can introduce error is. This project develops a wearable plant sensor that enables accurate long-term quantification of flow rates across many environments and genotypes. Large numbers of low-cost sensors can be deployed in breeding programs enabling direct evaluation of lines. From these lines specific genetic loci controlling variation in sap flow rates under different environmental conditions can be identified. Likewise data from these sensors can be used in genomic prediction models that prioritize new breeding lines prior to the investment of resources field trials. This research will enhance workforce development by providing research opportunities to next-generation researchers at the intersection of engineering and plant science.

This collaborative project will integrate advances in sensors, microsystems, nanomaterials, and plant sciences to realize a novel sap flow measurement method that ultimately advances functional genomics research and the breeding of drought tolerant crops. The objective is to develop a wearable plant sensor for long-term, accurate, and affordable monitoring of sap flow over an entire growing season. The sensor design allows efficient thermal insulation of the microscale sap flow sensing unit from external environments, thus eliminating the traditional need of additional bulky thermal insulation setup and increasing the response to sap flow. Spatial averaging of multiple sap flow measurements around the stem enhances measurement accuracy. By using stretchability of the sensor materials and structures, physical constraints of the sensor on plant growth is minimized for long-term monitoring. The proposed wearable sensors can be manufactured at large scale and low cost, allowing it to be incorporated into breeding programs tolerating drought tolerance. Lastly, the sensors are characterized, calibrated and validated over time using gravimetric measures of plant water use in the greenhouse. Initial pilot field measurements are performed, where the sensors are applied to several maize hybrids grown under irrigated and non-irrigated conditions as part of the Nebraska contribution to Genomes to Fields (an existing public-private partnership).