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.

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.

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.

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 p henotypes 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.

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).

Predicting crop yield is central to addressing emerging challenges in food security, particularly in an era of global climate change. Currently, machine learning and crop modeling are among the most commonly used approaches for yield prediction. This award supports fundamental research to combine the strengths of machine learning and crop models. Machine learning algorithms will be used to predict intermediate plant traits, which will then be fed into a crop model to predict grain yields across different environment and field management practices. Both conception and execution of this EAGER project depend on collaborations across multiple disciplines, including high-throughput phenotyping, object recognition, machine learning, optimization, computer simulation, and crop modeling. If successful, this research is expected to improve not only accuracy but also interpretability of yield prediction models, which will o pen numerous opportunities for downstream research and discoveries. The interdisciplinary effort will enhance the impact of science and engineering education across disciplines, while providing a collaborative and inclusive environment for all students to engage in cutting edge research activities.

Underlying yield prediction is one of the grand challenges of biology: understanding how phenotype is determined by genotype, environment, and their interactions. Machine learning algorithms are able to predict crop phenotype to reasonable accuracy based on genotype information, but most models have a black box structure and their results are hard to interpret. On the other hand, crop models offer biological insights into causes of phenotypic variation by providing explicit explanations of the interactions between traits and environmental conditions in different phases of the crop growth cycle, but the collection of trait measurement data and calibration of model coefficients are labor intensive, time consuming, and costly. The proposed approach is a nested model. Deep learning algorithms will be trained to predict leaf appearance rate from genotype and empirically measured trait data. Training data will be extracted from images of plant leaves obtained via field experiments that employ novel phenotyping technique. Next, the resulting predicted traits and environment data will be fed into the crop model to predict yield. If proven effective, this approach can be applied to study other plant traits to improve crop yield prediction.

This proposal will develop and deploy "wearable" (i.e., non-destructive, leaf-mountable) sensors for the measurement of water transport dynamics in maize. The sensors will be used to enable a high-throughput phenotyping platform that demonstrates the sensors' ability to discriminate among maize genotypes for plant water transport dynamics. The new sensors will advance plant sciences and agricultural research in a manner similar to how wearable human body sensors have advanced human health and biomedical sciences.Two types of sensors to be developed and deployed in field research plots: a relative humidity (RH) sensor and leaf water content sensor. The RH sensor will measure humidity and temperature at the leaf surface, and can self-adjust its size and shape to adapt to the growth of leaves. The leaf water content sensor will be developed using advanced Micro-Electro-Mechanical Systems technology, and will measure leaf thickness and water content. In years one and two, the sensors will be calibrated and validated. In years two and three, 400 of each type of sensor will be deployed across 50 maize hybrids in replicated plots. Each hybrid, selected from the Genomes to Fields Initiative (G2F), includes 24+ site-years of yield, weather and phenotype data from locations spanning Arizona to NY. Using sensor and grain yield data generated during the project in combination with yield and weather data from the G2F site-years, we will test the association of variation in water transport dynamics with variation of yield and yield stability among hybrids and in relation to environmental parameters.