Progress 02/15/18 to 02/14/19
Target Audience:The team reached theweather, climate, and water community at the American Meteorological Society annual meeting in Phoenix, AZ. The purpose was tocommunicate about breeding programs and oportunities inthe iintersection of environmental-genotype area. Also, we provided persoectives onclimate,data, and machine learning techniques to the "OMICS" community at the Genomes to Fields and PHENOME 2019 annual conference in Tucson, AZ. The purpose was to communicate our activities on environmental data collection and improvement. Recruitment activities were focused on training "Digital Breeders". We hired two PhD students, an statistitian and Biological Systems engineer. Our commitment to supportwomen and minority groups in STEM education was reflected in this process. We also recruited an undergraduate research experience, whom will join later this year University of California Davis to pursue her Masters. We held interdisciplinary group meetings, where statistitians and Biological and Agricultural Engineers created a collaborative community. Two oral presentations and two posters emerged from these collaborative efforts. Changes/Problems:
What opportunities for training and professional development has the project provided?PI- Munoz-Arriola taught one formal courses (Soil and Water Resources Engineering). Undergraduate and graduate students, evidencing the need of non-stationary approaches to address water, food, and energy security through the presentation of sixteen final projects. Also, support for undergraduate research experience is evidenced in the projects developed in Munoz-Arriola research lab with 2 undergraduates working on independent research projects (all women in science and engineering); all undergraduates in the lab received funding from our project and UNL's Institute of Agriculture and Natural Resources in response to competitive grants solicitations. The PI continued his involvement with the National Science Foundation Research Training program on Resilient Complex Agricultural Landscapes. Four PhD students (two part-time and to fully funded in year one) have been involved in the project so far. Alessandro Amaranto was partially funded for one semester to produce a data-driven model to improve environmental databases. Manas Khan, was also partially funded to create the analytics and geospatial aggregations of EHCEs. Parisa Sarzaeim and Denise Bradford have been fully funded and work on the database improvement and statistical tool development in te project. Co-PI-Diego Jarquin developed and taught multiple sections of a one-week workshop on Statistical Methods for Omics Assisted Breeding at the University of Tokyo in Japan in November of 2018. There, he discussed prediction models involving the genotype x environmental interaction, and computational techniques integrating data for these models. Diego Jarquin also delivered two 90 minute lectures at the University of Nebraska - Lincoln on the resources of the Holland Computing Center at University of Nebraska - Lincoln which contribute to address the computational needs of the Objectives of this project. He also had the opportunity to become a visiting scientist for three months in the Laboratory of Biometry and Bioinformatics in the Department of Agricultural and Environmental Biology at the University of Tokyo in Japan, and work with scientist on developing prediction models for multi-omics data. Diego Jarquin trained two PhD students in the Department of Statistics to understand the reaction-norm model used for predictions involving the genotype x environmental interaction, and to be able to utilize a pipeline developed for simple predictions. How have the results been disseminated to communities of interest?PI- Munoz-Arriola: member of the American Meteorological Society Water Resources Committee. Consolidated the international research collaborative programs on Hydroinformatics and Integrated Hydrology with the Delft Institute for Water Education (The Netherlands); and software development with the Universidad Autonoma de Baja California (Mexico). The G2F commutity invited Parisa Sarzaeim (PhD student) and Munoz-Arriola to give a talk on "Environmental Data Generation, Collection, and Storage for Cross-Scale Phenotype Predictability in the G2F Initiative" and at AMS Munoz-Arriola talked about "Weather/climate data collection for large-scale phenotype predictability in the Midwest". Five additional posters were presented in regional and international venues by students involved in the project. Co-PI. Diego Jarquin have lead and coauthored at least 12 manuscripts where similar models have been used successfully (four of them in 2018). Also, some of these results have been presented in at least 6 international invited conferences. Diego Jarquin had numerous meetings and presentations as a visiting scientist in the Laboratory of Biometry and Bioinformatics in the Department of Agricultural and Environmental Biology at the University of Tokyo in Japan where he discussed models similar to what is proposed here. What do you plan to do during the next reporting period to accomplish the goals?PI- Munoz-Arriola expects to consolidate the software developed in the past cycles and publish findings in scientific journals, addressing the complexity of extreme precipitation and drought in agricultural and urban areas. PI-Munoz-Arriola expects to continue strengthen communication and collaboration with the G2F initiative, as well as private companies, exploring new synergies with such sectors. Two PhDs will continue working as part of the work proposed for year 2. UNL's Agriculture Research Department, the Robert Daugherty Water for Food Global Institute, and the National Science Foundation Research Training graduate program on Resilient Complex Landscapes have evidenced interest in the present project. We expect this could be translated into funding opportunities for graduate students. Co-PI. Continue the model development stage for evolving the current model and allow it to perform predictions under stress conditions. A student will be continuously trained to utilize the developed models.
What was accomplished under these goals?
MA= Major Activities; DA=Data Collected; SM=Summary Statistics; KO=Key Outcomes Objective 1. MA: A pipeline was designed to retrieve phenotypic, genomic and environmental data from the Genomes to Fields initiative for years 2014, 2015 and 2016. The team also developed pipelines to collect environmental data from public sources including remote sensing, grided and station-based networks. DC: The team developed the analytics to correct inconsistencies in the G2F database (data gaps) using data driven models. The artificial neural network used integrated G2F and environmental data for precipitation, minimum and maximum temperature, wind speed, and relative humidity. Some of the sources of data are listed below: -High Plains Regional Climate Center (station data) -National Solar Radiation Database -National Weather Service -NASA's Prediction of Worldwide Energy Resources (combination of Model and RS data) -Moderate Resolution Imaging Spectroradiometer -Global Precipitation Measurement -The Tropical Rainfall Measurement Mission SM: This objective has resulted in 0 peer-reviewed publications, 0 extension publications, 6 conference presentations, 0 extension presentation, 1 undergraduate project, 0 MS projects, and 2 PhD projects. The results of the research have led to 0 additional grant applications. KO: To date the greatest impact has been a change in knowledge with the conference presentations. The team has introduced the complexity and opportunities to advance breeding programs to the Weather, Cliamte and Water communities of the American Meteorological Society (AMS, 2019 annual meeting, Phoenix, AZ). The "Omics" community (at the PHENOME 2019 annual meeting, Tucson, AZ) on the other hand, received possitevely our efforts on environmental database development and predictive analytics using machine learning techniques and decision support tools. We anticipate a change of action in the coming years as we interact with more producers and water managers across the US. Objective 2. MA: The team developed analytics to identify extreme hydrometeorological and climate events (EHCEs). Tests have been made to spatiotemporaly characterize extreme precipitation, drought, and heat waves and the associated return periods. DC: Algorithms for the metrics of EHCEs have been developed. These algorithms incorporated the World Meteorological Organization libraries for EHCEs. Currently, analytics and data sources are being connected to create spatiotemporal aggregates of environmental data, which will be coupled with phenotype assessments. SM: In 2018, this objective resulted in 0 peer-reviewed journal articles, 0 extension publications, 1 conference presentations, 0 extension presentations, 0 undergraduate projects, 0 MS project, and 1 PhD project (partially funded). This objective has resulted in funded proposals including 0 internal grants and 0 external grants (two pending for funding from NSF coupled natural-human systems and innovations on the nexus food-energy-water). KO: To date, the greatest impact has been a change in knowledge with the presentations in the American Society of Agriculture and Biological Engineers (ASABE annual meeting, Detroit, MI). Objective 3. MA: A baseline model to perform predictions based on covariance structures have been developed for analyzing current observed environmental data. DC: No new data collected. However, we have identified areas where the present project and the G2F initiative can improve collection, storage, and distribution of data to users. SM: Preliminary results have been analyzed. At the AMS 2019 annual conference, improvements of 16% (2016) to 33% (2016) on environmental data. This represent an addition of 8 to 12 experiments with data gaps, respectively. KO:Automated pipeline for running the analysis and predictions is developed. Objective 4. MA:Work in progress since data obtained in Obective 2 was obtained during the first year of the project. DC: No results are expected in year 1. SM: No results are expected in year 1. KO: No results are expected in year 1. Objective 5. MA: This objective is an integration of Objectives 1-4. Thus, it is in working progress. Some of the prediction models are already developed and thir implemented through computational pipelines and arechitectures of software are in progress. DC: Some of the phenotypic, genotypic and environmental data are already collected, but more data will be collected. SM: KO: No results are expected in year 1.