Progress 02/15/18 to 06/14/23
Outputs
Target Audience:Raising global temperatures, accelerated melting of the polar caskets, and even the anxiety of the global population about weather extremes and climate change evidence the need to prepare our agricultural sector better when extreme hydrometeorological and climate events occur. As a community, we grapple to respond to whether our agriculture is ready to adapt to unexpected fluctuations of climate and consolidate its role as a Carbon sequestration champion. Thus, the adaptability of agriculture to extreme, compounded, or cascade events (i.e., extreme rainfall, droughts, or floods, respectively) remains unclear for basic research and management practices. Along these lines, we see the need to enhance the conversations between breeders, climatologists, and meteorologists. We aimed to develop a conceptual model to predict hybrid performance in response to hydroclimatic changes. Our premise was that we would address some of the challenges described above by harnessing hydroclimate, genetics, and phenotype by developing and implementing data analytics and software engineering. This project targeted an audience formed by geneticists, agronomists, and statisticians to understand better local to regional maize responses to climate and the relevance of this information to farmers, advisers, and scientists. The formidable agronomic and digital resources provided by the G2F initiative represented an opportunity to accomplish all the Objectives listed and explained above and create products for multiple target audiences. Four project outputs were created and made available to the scientific and agronomic breeding communities. The outputs were: 1.The analytics of deep learning to fulfill environmental data gaps and the consequent enhancement of the G2F multi-dimensional OMICS database. 2.The creation of the CLIM4OMICS database and analytics for data quality control and classification. 3.The development of the coupling GSA-GxE; and 4.The creation of the software GEnomics-by-ENvironment phenotype predictive system (GEEN; www.thegeen.com). The four products described above allowed us to reach three relevant conclusions for the breeding community, and we believe we will inspire more collaborations with the Earth System community. The conclusions are: The connection between climate and OMICS enhanced data with improvements in maize yield predictability, The consistent regional maize yields in the aftermath of the 2019 flooding event in the Northern High Plains and The local-to-sub-continental sensitivity of maize yields to climate variables. This project remains relevant to communities aiming to advance the science and technology of fusing breeding and climate digital resources and information technologies to address some of the challenges climate change imposes on agriculture. Also, the outputs and outcomes open new opportunities to understand better and tackle complexity in breeding success under a changing climate. The open access to these products will allow breeding and Earth System communities to harness the G2F operational digital resources. Changes/Problems:For the first half of the project, the proposed activities toward achieving the project's objectives occurred on time. We hired two minority and underrepresented graduate students. However, one of the PhDs dropped from the graduate program after one academic year. Our approach to mitigate such a problem was to hire a group of undergraduate computer scientists to develop the software's proposed architecture (see below the support provided by the Department of Computer Sciences and Engineering' Senior Design program). We consider this a wise move because we consolidated the design and construction of the software GEnetic by ENvironment (GEEN). On the other hand, the University closures delayed us during the Spring of 2020 due to COVID-19. The spread of COVID-19 and the cancellation of in-person activities impacted our work efficiency. The areas with significant impact have been software development and company outreach.We completed the GEEN prototype during the summer of 2020, but we still plan to release the beta version in the spring of 2021. The testing stage will be implemented during the summer, and the formal release of GEEN will occur during the Fall of 2021. The delays that preclude the conclusion of this project in the last stage are related to (1) once the students graduate, the reviews of manuscripts slowdown, and (2) journals and journals' reviewers have taken an unexpectedly long time. For example, in the manuscript "CLIM4OMICS: a geospatially comprehensive climate and multi- OMICS database for Maize phenotype predictability in the U.S. and Canada," the review process of the latter will take at least two months. The other submissions have taken more than two months since the initial submission. We appreciate the flexibility and the support during the regular and extended time for this project. What opportunities for training and professional development has the project provided?The project provided various opportunities for the PIs and the students involved. The opportunities include leveraging and pursuing local to federal funding. Funded projects or activities by the University of Nebraska included "System Science," "Quantitative Life Sciences Initiative Summer Graduate Student Support Competition," a graduate research assistantship from the School of Natural Resources, and a Senior Design project from the College of Computing. At the federal level, we received funding from the USDA-NIFA CPS Dig, Sip, Breathe: Automated Monitoring of Carbon and Water Cycles in Agriculture and the USGS-CESU OpenET Science Team support and eeMETRIC development, operation, and review. We leveraged the funding provided by the NSF Research Training Program on Resilient Complex Landscapes. Two Ph.D. and one Master's students received their degrees--in addition to nine undergraduate students and four Ph.D. Students were partially funded. Co-PI Jarquin and three Ph.D. students reach postdoctoral, faculty, or Scientist positions at the end of this project. All undergraduates in Computer Science and non-computer Science majors currently work in the private sector, and at least half of them have listed the PIs as their recommendation referees. The remaining partially funded graduate students are pursuing a Ph.D. in Data Science for Agriculture and Natural Resources. PIs and students presented the progress and acknowledged the USDA-NIFA funding for this project. The PIs participated in multiple educational and curricular activities. Seven graduate and undergraduate-level courses (thirteen-semester courses) involving the environment, climate, water, agriculture, and ecosystems were taught at the University of Nebraska-Lincoln. All included at least a lecture about the present project and acknowledging the funding source. How have the results been disseminated to communities of interest?Regional and international conferences allowed us to connect the Breeding and Earth System's communities. This "research bridge," powered by the GxE model, allowed us to publish about weather and climate patterns and their effects on the predictability of phenotypes in Agricultural and Biological journals. We published in Earth System and Environmental journals on how genomics enhances the simulation and geospatial representation of crop responses to climate. We published twelve peer-reviewed scientific articles and six proceedings. Furthermore, we published seven databases and software architectures following the FAIR principles. Also, we disseminated our findings nationally or internationally through four courses, one visit, and eighteen invited talks. All the intellectual contributions listed above reached communities of interest in breeding and climate and acknowledged the funding provided by the USDA-NIFA Plant Breeding for Agricultural Production Program. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
MAMajor Activities;DCData Collected;SMSummary Statistics;KOKey Outcomes Obj 1 MA: We designed and built the data management module as a stand-alone architecture of software or as a module of the software delivered in Objective 5. This module provides a centralized database of all data types used in the GxE model. It has detailed information on different phenotypes and metadata related to all projects. It provides graphic interfaces and APIs for creating, retrieving, updating, and deleting operations. Users can also perform different types of analytics using logic and conditional operators. For development purposes the APIs interface is open access while the user interface is restricted to developers. The visualization module has been integrated and has undergoing continues additions and improvements. Additionally, we developed cosegmentation algorithms and databases for phenotyping and analytics for UAV data integration. DC: The team developed the analytics to correct inconsistencies in the G2F database using data driven models. The artificial neural network used integrated G2F and environmental data for Rainfall, minimum and maximum temperature, wind speed, and relative humidity. SM: The most significant impact has been training six undergraduate, one Master's, and two Ph.D. students to develop environmental analytics for phenotype diagnostics and predictability. We shared our progress and data with the community, leading to successful internal support for and national awards for the students in this project at national and international conferences. We published six peer-reviewed publications, four databases, one architecture of software, ten conference presentations, and one USDA-NIFA and one USGS grants. KO: Students presented at venues relevant to the OMICS and Climate communities. We published in similar venues highlighting the efforts of USDA-NIFA and the G2F initiative, bringing climate analytics and analyses to the OMICS community and analytics and analyses for OMICS in the Earth Science community. The activities above fostered the improvement of the G2F database by enhancing environmental data using deep learning techniques and three publicly available data sources. CLIM4OMICS, contains the improved database and digital tools following the principles of findability, accessibility, interoperability, and reusability. Obj 2 MA: We evidenced how precipitation is one of the key hydroclimate drivers of maize yield across the G2F initiative area of study. To assess the spatial distribution of climate drivers of maize yield predictability, we developed, implemented, and tested the coupling of GxE and global sensitivity analysis (GSA) analytics. The coupled GxE-GSA analytics also represent a novel effort toward quantifying uncertainty in climate data and its propagation into the predictions of yields or other phenotypes. DC: Algorithms for the metrics of EHCEs have been developed. These algorithms incorporated by the WMO for EHCEs were connected to create spatiotemporal aggregates of environmental data, coupled with phenotype assessments, and incorporated into presentations, publications, and developed software. We developed algorithms for planting date metrics and growing season duration. SM: This objective resulted in three peer-reviewed journal articles, three conference presentations, one conference proceeding article, and one PhD graduate. This objective has resulted in two NASA grants funded. KO: Coupled GxE-GSA could lead to the identification of the main patterns of climate variability responsible for the prediction of maize yields. As for now, some of the key findings indicate that ranked Rainfall, followed by Solar Radiation, Relative humidity, and temperature were predictors of maize yields in the G2F stations. SR is the aggregated source of predictability in maize yields in the region. Obj 3 MA: A model to perform predictions based on covariance structures has been implemented for analyzing the updated environmental data. A multi-trait model was used to analyze experiments containing multiple phenotypic covariates randomly selected and organized by environmental attributions. We studied how the selection of superior genotypes is driven by the yield, evidenced in testing single-trait and multi-trait genomic predictions. DC: We increased the number of environments by more than 30% between 2014 and 2018. We are exploring the addition of the years 2019 to 2021. We created data quality control and classification analytics for the enhancement of the G2F initiative. SM: We published four scientific articles and present our research advancements in four venues. KO: One of the key findings evidenced, when some genotypes were fully or partially tested, that phenotype predictions using an alternative single-trait model outperformed the multi-trait model. This experimental design also guided us to identify the contributions of randomly increased environmental data availability and the emerging patterns of rainfall variability. The second key finding illustrated how combining OMICS data and enhanced climate data improved the predictability of maize yield. Obj 4 MA: We estimated the contribution of the climate and OMICS database enhancement to the predictability of maize yields via environmental covariance structures using G×E modeling. We quantified the predictability of yields using trial selection, such as randomization, ranking, and precipitation gradients. Finally, we developed a framework for predicting crop risks before planting. DC: The data collected and data collection algorithms for the climate and OMICS database enhancement are described ABOVE. We collected remote sensing data from multiple platforms and integrated Sentinel-2 NDVI and SMAP soil water content using Google Earth. SM: Three peer-reviewed articles were published, ten conference papers presented, and one Ph.D. student graduated. KO:We proved that the predictability of maize yields was associated with the variability of climate variables such as precipitation. Also, that the main drivers of maize predictability were unevenly distributed across the study area. The designed climate crop-risk framework evidenced the association between climate extremes such as floods and the collective selection of hybrids in the Northern High Plains. This predictive framework also assisted in the identification of the growing season time windows used in objective 3. Obj 5 MA: The progress made on Objectives 1-4 can be synthesized by the contributions above and how we leveraged them to consolidate the GEnomics by ENvironment phenotype predictive system (GeEn; www.thegeen.com). The analytics for climate data improvement, which enabled the enhancement of G2F's OMICS database, were implemented and tested. We used Amazon's Active Web Services (AWS) for the enhanced database (Obj. 1). We are developed a module with quality control and classification features, which has helped to improve G2F data curation and other large-scale experiments in need of climate data. We tested the analytics to quantify climate data uncertainty and their propagation to phenotype simulations (Obj. 2). We tested a model for the simulation of phenotypes using multiple stations across the US. GEEN is operational and new modules are added regularly. DC: Data and software were continuously collected, published, and used. A running prototype was tested, and results on software stability, robustness, and parsimony showed its reliability. A manuscript and the respective tests are under review. SM: This objective resulted in a prototype of software architecture, two conference presentations, and two undergraduate software development experiences. We added two Ph.D. students in Computer Sciences to work on software development and climate analytics, and funded by UNL's School of Natural Resources and a USDA-NIFA project funded last year. KO: Prototype of the architecture of software GeEn in Amazon's AWS.?
Publications
- Type:
Journal Articles
Status:
Awaiting Publication
Year Published:
2024
Citation:
Stuart, L., Hobbins, M., Niebuhr, E., Ruane, A.C., Pulwarty, R. Hoell, A., Thiaw, W., Rosenzweig, C., Mu�oz-Arriola, F. Jahn, M. Farrar, M. Enhancing Global Food Security: Opportunities for the American Meteorological Society. Bulletin of the American Meteorological Society.
- Type:
Conference Papers and Presentations
Status:
Awaiting Publication
Year Published:
2024
Citation:
Rico, D. A., F. Mu�oz-Arriola, J. Bradley, and C. Detweiler (Accepted). Analytics for Real-Time Inertial Localization of the Tethered Aircraft Unmanned System. 18th International Symposium on Experimental Robotics (ISER), 2023, 11 pp.
- Type:
Journal Articles
Status:
Submitted
Year Published:
2024
Citation:
Sarzaeim, P. and F. Munoz-Arriola (Submitted). Climate Drivers of Maize Yield Predictability in the US and Canada. To be submitted to Science of the Total Environment.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Qui�ones R, Samal A, Das Choudhury S and Mu�oz-Arriola F (2023) OSC-CO2: coattention and cosegmentation framework for plant state change with multiple features. Front. Plant Sci. 14:1211409. doi: 10.3389/fpls.2023.1211409.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Sarzaeim, P., Mu�oz-Arriola, F., Jarquin, D., Aslam, H., & De Leon Gatti, N. (2023). CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada. Earth System Science Data, 15(9), 3963-3990.https://doi.org/10.5194/essd-15-3963-2023.
- Type:
Journal Articles
Status:
Published
Year Published:
2023
Citation:
Sarzaeim, P., F. Munoz-Arriola, D. Jarquin, H. Aslam, and N. De Leon Gatti. CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for Maize phenotype predictability in the US and Canada. Earth System Science Data Discussions (2023): 1-35. https://doi.org/10.5194/essd-2023-11.
- Type:
Journal Articles
Status:
Published
Year Published:
2022
Citation:
Sarzaeim1, P., F. Mu�oz-Arriola, and D. Jarquin (2022). Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability. Journal of Experimental Botany. DOI: 10.1093/jxb/erac146.
|
Progress 02/15/22 to 02/14/23
Outputs
Target Audience:Raising global temperatures, accelerated melting of the polar caskets, and even the anxiety of the global population about weather extremes and climate change evidence the need to prepare our agricultural sector better when extreme hydrometeorological and climate events occur. As a community, we grapple to respond to whether our agriculture is ready to adapt to unexpected fluctuations of climate and consolidate its role as a Carbon sequestration champion. Thus, the adaptability of agriculture to extreme, compounded, or cascade events (i.e., extreme rainfall, droughts, or floods, respectively) remains unclear for basic research and management practices. Along these lines, we see the need to enhance the conversations between breeders, climatologists, and meteorologists. We aimed to develop a conceptual model to predict hybrid performance in response to hydroclimatic changes. Our premise was that we would address some of the challenges described above by harnessing hydroclimate, genetics, and phenotype by developing and implementing data analytics and software engineering. This project targeted an audience formed by geneticists, agronomists, and statisticians to understand better local to regional maize responses to climate and the relevance of this information to farmers, advisers, and scientists. The formidable agronomic and digital resources provided by the G2F initiative represented an opportunity to accomplish all the Objectives listed and explained above and create products for multiple target audiences. Four project outputs were created and made available to the scientific and agronomic breeding communities. The outputs were: 1.The analytics of deep learning to fulfill environmental data gaps and the consequent enhancement of the G2F multi-dimensional OMICS database. 2.The creation of the CLIM4OMICS database and analytics for data quality control and classification. 3.The development of the coupling GSA-GxE; and 4.The creation of the software GEnomics-by-ENvironment phenotype predictive system (GEEN; www.thegeen.com). The four products described above allowed us to reach three relevant conclusions for the breeding community, and we believe we will inspire more collaborations with the Earth System community. The conclusions are: The connection between climate and OMICS enhanced data with improvements in maize yield predictability, The consistent regional maize yields in the aftermath of the 2019 flooding event in the Northern High Plains and The local-to-sub-continental sensitivity of maize yields to climate variables. This project remains relevant to communities aiming to advance the science and technology of fusing breeding and climate digital resources and information technologies to address some of the challenges climate change imposes on agriculture. Also, the outputs and outcomes open new opportunities to understand better and tackle complexity in breeding success under a changing climate. The open access to these products will allow breeding and Earth System communities to harness the G2F operational digital resources. Changes/Problems:The delays that preclude the conclusion of this project in the last stage are related to (1) once the students graduate, the reviews of manuscripts slowdown, and (2) journals and journals' reviewers have taken an unexpectedly long time. For example, in the manuscript "CLIM4OMICS: a geospatially comprehensive climate and multi- OMICS database for Maize phenotype predictability in the U.S. and Canada," the review process of the latter will take at least two months. The other submissions have taken more than two months since the initial submission. We appreciate the flexibility and the support during the regular and extended time for this project. What opportunities for training and professional development has the project provided?The project provided various opportunities for the PIs and the students involved. The opportunities include leveraging and pursuing local to federal funding. Funded projects or activities by the University of Nebraska included "System Science," "Quantitative Life Sciences Initiative Summer Graduate Student Support Competition," a graduate research assistantship from the School of Natural Resources, and a Senior Design project from the College of Computing. At the federal level, we received funding from the USDA-NIFA CPS Dig, Sip, Breathe: Automated Monitoring of Carbon and Water Cycles in Agriculture and the USGS-CESU OpenET Science Team support and eeMETRIC development, operation, and review. We leveraged the funding provided by the NSF Research Training Program on Resilient Complex Landscapes. Two Ph.D. and one Master's students received their degrees--in addition to nine undergraduate students and four Ph.D. Students were partially funded. Co-PI Jarquin and three Ph.D. students reach postdoctoral, faculty, or Scientist positions at the end of this project. All undergraduates in Computer Science and non-computer Science majors currently work in the private sector, and at least half of them have listed the PIs as their recommendation referees. The remaining partially funded graduate students are pursuing a Ph.D. in Data Science for Agriculture and Natural Resources. PIs and students presented the progress and acknowledged the USDA-NIFA funding for this project. The PIs participated in multiple educational and curricular activities. Seven graduate and undergraduate-level courses (thirteen-semester courses) involving the environment, climate, water, agriculture, and ecosystems were taught at the University of Nebraska-Lincoln. All included at least a lecture about the present project and acknowledging the funding source. How have the results been disseminated to communities of interest?Regional and international conferences allowed us to connect the Breeding and Earth System's communities. This "research bridge," powered by the GxE model, allowed us to publish about weather and climate patterns and their effects on the predictability of phenotypes in Agricultural and Biological journals. We published in Earth System and Environmental journals on how genomics enhances the simulation and geospatial representation of crop responses to climate. We published twelve peer-reviewed scientific articles and six proceedings. Furthermore, we published seven databases and software architectures following the FAIR principles. Also, we disseminated our findings nationally or internationally through four courses, one visit, and eighteen invited talks. All the intellectual contributions listed above reached communities of interest in breeding and climate and acknowledged the funding provided by the USDA-NIFA Plant Breeding for Agricultural Production Program. What do you plan to do during the next reporting period to accomplish the goals?
Nothing Reported
Impacts
What was accomplished under these goals?
MAMajor Activities;DCData Collected;SMSummary Statistics;KOKey Outcomes OBJ 1 MA: We designed and built the data management module as a stand-alone architecture of software or as a module of the software delivered in Objective 5. This module provides a centralized database of all data types used in the GxE model. It has detailed information on different phenotypes and metadata related to all projects. It provides graphic interfaces and APIs for creating, retrieving, updating, and deleting operations. Users can also perform different types of analytics using logic and conditional operators. For development purposes the APIs interface is open access while the user interface is restricted to developers. The visualization module has been integrated and has undergoing continues additions and improvements. Additionally, we developed cosegmentation algorithms and databases for phenotyping and analytics for UAV data integration. DC: The team developed the analytics to correct inconsistencies in the G2F database using data driven models. The artificial neural network used integrated G2F and environmental data for Rainfall, minimum and maximum temperature, wind speed, and relative humidity. SM: The most significant impact has been training six undergraduate, one Master's, and two Ph.D. students to develop environmental analytics for phenotype diagnostics and predictability. We shared our progress and data with the community, leading to successful internal support for and national awards for the students in this project at national and international conferences. We published six peer-reviewed publications, four databases, one architecture of software, ten conference presentations, and one USDA-NIFA and one USGS grants. KO: Students presented at venues relevant to the OMICS and Climate communities. We published in similar venues highlighting the efforts of USDA-NIFA and the G2F initiative, bringing climate analytics and analyses to the OMICS community and analytics and analyses for OMICS in the Earth Science community. The activities above fostered the improvement of the G2F database by enhancing environmental data using deep learning techniques and three publicly available data sources. CLIM4OMICS, contains the improved database and digital tools following the principles of findability, accessibility, interoperability, and reusability. OBJ 2 MA: We evidenced how precipitation is one of the key hydroclimate drivers of maize yield across the G2F initiative area of study. To assess the spatial distribution of climate drivers of maize yield predictability, we developed, implemented, and tested the coupling of GxE and global sensitivity analysis (GSA) analytics. The coupled GxE-GSA analytics also represent a novel effort toward quantifying uncertainty in climate data and its propagation into the predictions of yields or other phenotypes. DC: Algorithms for the metrics of EHCEs have been developed. These algorithms incorporated by the WMO for EHCEs were connected to create spatiotemporal aggregates of environmental data, coupled with phenotype assessments, and incorporated into presentations, publications, and developed software. We developed algorithms for planting date metrics and growing season duration. SM: This objective resulted in three peer-reviewed journal articles, three conference presentations, one conference proceeding article, and one PhD graduate. This objective has resulted in two NASA grants funded. KO: Coupled GxE-GSA could lead to the identification of the main patterns of climate variability responsible for the prediction of maize yields. As for now, some of the key findings indicate that ranked Rainfall, followed by Solar Radiation, Relative humidity, and temperature were predictors of maize yields in the G2F stations. SR is the aggregated source of predictability in maize yields in the region. OBJ 3 MA: A model to perform predictions based on covariance structures has been implemented for analyzing the updated environmental data. A multi-trait model was used to analyze experiments containing multiple phenotypic covariates randomly selected and organized by environmental attributions. We studied how the selection of superior genotypes is driven by the yield, evidenced in testing single-trait and multi-trait genomic predictions. DC: We increased the number of environments by more than 30% between 2014 and 2018. We are exploring the addition of the years 2019 to 2021. We created data quality control and classification analytics for the enhancement of the G2F initiative. SM: We published four scientific articles and present our research advancements in four venues. KO: One of the key findings evidenced, when some genotypes were fully or partially tested, that phenotype predictions using an alternative single-trait model outperformed the multi-trait model. This experimental design also guided us to identify the contributions of randomly increased environmental data availability and the emerging patterns of rainfall variability. The second key finding illustrated how combining OMICS data and enhanced climate data improved the predictability of maize yield. OBJ 4 MA: We estimated the contribution of the climate and OMICS database enhancement to the predictability of maize yields via environmental covariance structures using G×E modeling. We quantified the predictability of yields using trial selection, such as randomization, ranking, and precipitation gradients. Finally, we developed a framework for predicting crop risks before planting. DC: The data collected and data collection algorithms for the climate and OMICS database enhancement are described ABOVE. We collected remote sensing data from multiple platforms and integrated Sentinel-2 NDVI and SMAP soil water content using Google Earth. SM: Three peer-reviewed articles were published, ten conference papers presented, and one Ph.D. student graduated. KO:We proved that the predictability of maize yields was associated with the variability of climate variables such as precipitation. Also, that the main drivers of maize predictability were unevenly distributed across the study area. The designed climate crop-risk framework evidenced the association between climate extremes such as floods and the collective selection of hybrids in the Northern High Plains. This predictive framework also assisted in the identification of the growing season time windows used in objective 3. OBJ 5 MA: The progress made on Objectives 1-4 can be synthesized by the contributions above and how we leveraged them to consolidate the GEnomics by ENvironment phenotype predictive system (GeEn; www.thegeen.com). The analytics for climate data improvement, which enabled the enhancement of G2F's OMICS database, were implemented and tested. We used Amazon's Active Web Services (AWS) for the enhanced database (Obj. 1). We are developed a module with quality control and classification features, which has helped to improve G2F data curation and other large-scale experiments in need of climate data. We tested the analytics to quantify climate data uncertainty and their propagation to phenotype simulations (Obj. 2). We tested a model for the simulation of phenotypes using multiple stations across the US. GEEN is operational and new modules are added regularly. DC: Data and software were continuously collected, published, and used. A running prototype was tested, and results on software stability, robustness, and parsimony showed its reliability. A manuscript and the respective tests are under review. SM: This objective resulted in a prototype of software architecture, two conference presentations, and two undergraduate software development experiences. We added two Ph.D. students in Computer Sciences to work on software development and climate analytics, and funded by UNL's School of Natural Resources and a USDA-NIFA project funded last year. KO: Prototype of the architecture of software GeEn in Amazon's AWS.?
Publications
|
Progress 02/15/21 to 02/14/22
Outputs
Target Audience:Climate change has become a subject of concern for food security and agriculture's sustainability because of the increasing intensity, frequency, and complexity of extreme hydrometeorological and climate events. We call compound climate events those formed by extreme and average conditions leading to environmental disasters such as floods and droughts. However, the complexity of such events remains unclear for basic research and management practices. The ongoing drought across the West US evidence that crop production, farmers' livelihoods, and our supply chain are unprepared, vulnerable, and poorly resilient to unclearly evolving phenomena. This project remains relevant to advance the science and technology of fusing breeding and information technologies to address some of the challenges referred to above. Also, this approach opens new opportunities to better understand and tackle complexity in breeding success in a changing climate. Our goal is to develop a conceptual model to predict hybrid performance in response to hydroclimatic changes. Our premise is that by harnessing hydroclimate, genetic, and phenotype through the development and implementation of data analytics and software engineering, we will address some of the challenges described above. We designed and constructed an architecture of the software containing hybrid statistical and deep learning modeling methodologies built to find sources of predictability of corn hybrid performance in response to hydroclimate changes. The architecture of software GEnomics-by-ENvironment phenotype predictive system (GEEN; www.thegeen.com): (1) contains a data management testbed that collects, standardize and fuse multiple sources of environmental data; (2) integrates data for the characterization of spatiotemporal hydroclimatic controls, and the associated uncertainties across scales; (3) operates a conceptual Genetic-, Multi-trait-, and Hydroclimatic- sensitive Model; and we are (4) creating the analytics and software architecture to perform hydroclimatic-driven hybrid diagnostics and forecasts, and we are (5) consolidating a conceptual framework for an operational rapid-response hybrid performance forecasts. While we request a second extension to complete the project, we are on track to accomplish the proposed objectives and deliver the respective outcomes before the end of the project.? Changes/Problems:The COVID-19 pandemic has delayed some activities that require interactions among people and traveling. Expressions of such actions have been teamwork for software development, proposal development, and industry visits. We will deliver what we proposed initially. What opportunities for training and professional development has the project provided?PI- Munoz-Arriola taught two formal courses (Complexity Science and Hydroclimate data applications and synthesis). Research and advise an undergraduate intern and a Ph.D. student in the Department of Computer Sciences and Engineering; two Ph.D. students in the Department of Biological Systems Engineering and the School of Natural Resources; two undergraduate research experiences in the School of Natural Resources. In addition to the reported activities, PI Munoz-Arriola contributed and led activities toward the recruitment and retention of students and faculty of historically underrepresented and minority groups. The PI continued his involvement with the National Science Foundation Research Training program on Resilient Complex Agricultural Landscapes. Rubi Quñones is funded by this program and collaborates on the project. The School of Natural Resources is currently supporting Hasnat Aslam, a Ph.D. student working on software engineering. Co-PI-Diego Jarquin recently obtained a tenure track faculty in the AgronomyDepartment at the University of Florida. We will be developing his research and teaching program in the following months. How have the results been disseminated to communities of interest?PI- Munoz-Arriola: member of the American Meteorological Society (AMS) Water Resources Committee and co-chair of the 2022 AMS co-chaired the Food Security and Water and Food Security Themes (two of five themes on environmental security). This activity led to the organization of two presidential panes and the release of a blog on food security. Also, a collaboration with the Indian Institutes of Technology Bombay and Roorkee has led to co-advice two Ph.D. students (one graduated and the other nominated Ph.D. candidate). These efforts indicate the international community's interest in the study and development of climate analytics for predictive adaptive systems. Three scientific papers were published, two submitted, and four are under development, acknowledging the support of this project. Also, eight works were presented in national and international venues by students involved in the project. Co-PI. Diego Jarquin led and coauthored at least 7 manuscripts . Some of these results have been presented in internationalconferences as keynote and invited speaker. What do you plan to do during the next reporting period to accomplish the goals?PI- Munoz-Arriola. For the last year of the project, we expect to finish testing the phenotype predictive tool GEEN and release it publicly. Also, we will submit and publish four scientific articles, conclude the training of undergraduate and graduate students in this project, and disseminate our findings to agricultural, weather and climate, and Earth sciences communities. In the first case, we will conclude the user and developer documentation for GEEN; release the data analytics for OMICS and climate data enhancement. We will publish three papers on phenotype predictability and two articles on image processing. In terms of student training, we will graduate two Ph.D. students, retain one Ph.D. student, and recruit one Master's student. The Ph.D. and Master's students will provide continuity to the research activities that emerged from our project (i.e., the introduction of remote and proximal sensing products and artificial intelligence on phenotype predictability). We plan to present our findings at the 2022 Annual Maize Genetics, the 2022 American Geophysical Union meetings, and the 2023 American Meteorological Society annual conference. PI-Munoz-Arriola expects to continue to strengthen communication and collaboration with the G2F initiative and private companies, exploring new synergies with such sectors. We will transform these efforts into funding opportunities for the continuity of this research. Co-PI Diego Jarquin will continue the model development stage for evolving the current model and allow it to perform predictions for multiple traits under stress conditions and for different windows of time for different weather covariates affecting crop responses. A student will be continuously trained to understand, develop, modify, and utilize the new implementations including the extension of model to deal with different traits.
Impacts
What was accomplished under these goals?
MA= Major Activities; DC=Data Collected; SM=Summary Statistics; KO=Key Outcomes Objective 1.Develop a data management test bed to collect, standardize and integrate data. MA: Wequantified improvements in phenotype predictability by enhancing environmental data. We mapped the spatial distribution of improvements in model predictability, probing that climate data enhancements were the main drivers of maize yield predictions. We consolidated the G2F database (genetic, phenotypic, and environmental) through deep learning algorithms. We expanded the recorded climate data, enabling the use of additional OMICS data available (Sarzaeim et al. 2022a). We designed a geospatial visualization and data management module for the GEEN software. In particular, this module is an alternative to the collection, visualization, and delivery of data in the GEEN software. Also, this module is aimed to provide an open-source platform for the OMICS community interested in fostering climate data discovery. Other products that support these efforts include cosegmentation algorithms and databases for phenotyping (Quñones et al. 2021a, b) and analytics for UAV data integration (Rico et al. 2021a,b). SM: This objective has resulted in two publications, two databases, five conference presentations, and the design of an architecture of software containing the analytics for the testing of improved environmental data. One MS student graduated, and two Ph.D. projects are progressing. Some of the ideas led to 1 funded project by UNL as co-PI. KO: We trainedthree undergraduate, one MS, and two Ph.D. students to develop environmental analytics for phenotype diagnostics and predictability. We shared our progress with the community, leading to successful internal support for and national awards at national and international conferences for the students in this project. Students presented five oral presentations (ASABE 2021, American Meteorological Society 2022, and PHENOME 2022). Today, two manuscripts have been published, one is ready for submission, and three more are under development. Objective 2.Characterize spatiotemporal hydroclimatic controls and the associated uncertainties across scales. MA: We developed and implemented the coupling of GxE and global sensitivity analysis (GSA) analytics. The coupled GxE-GSA analytics represent a novel effort toward quantifying uncertainty in climate data and its propagation into the predictions of yields or other phenotypes. Key findings indicate that solar radiation followed by rainfall and temperature are the primary sources of error in the predictability of maize yields in the US. Yet, rainfall is the predominant source of error in phenotype predictability in most stations across the country. SM: One manuscript will be submitted in May 2022. KO: We have been successful in developingand testing of the GxE-GSA analytics and its addition to the GEEN software. Objective 3.Develop a conceptual Genetic-, Multi-trait-, and Hydroclimate-sensitive Model. MA: We developed and tested a model to perform predictions based on covariance structures. We tested potential sources of phenotype predictability associated with the enhancement of climate and OMICS data. Our findings demonstrate that combining OMICs data and climate data improves the predictability of maize yield. Some of the results illustrate how predictability improvement varies across the US. The associated publication will be published shortly after submitting the project's fourth-year report (Sarzaeim et al. 2022b). DC: We increased the number of environments by more than 30% between 2014 and 2018. We are exploring the addition of the years 2019 to 2021. We expect to deliver data quality control and classification analytics and software to the G2F initiative and the phenotyping community. SM: We continue facing delays and difficulties because of COVID-19 traveling restrictions. Again, this reporting year aimed to visit the industries to identify the suitability of the tools we are developing. However, it has not been possible. Instead, we submitted a couple of proposals to BAYER, exploring potential collaborations in software co-development, but both were rejected. Traveling is possible for most of the project members, which will allow us to resume our efforts in this area. KO: The analytics for GxE modeling have been tested, evidencing the accomplishment of this objective. Objective 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 MA: We published our first scientific publication on identifying and testing metrics of risks to floods. This publication presents a framework for the prediction of crop risks before planting. Such an approach will contribute to helping the selection of hybrids. Furthermore, this predictive framework will assist in identifying the growing-season time windows used in objective 3. DC: We collected remote sensing data from multiple platforms and integrated Sentinel-2 NDVI and SMAP soil water content using Google Earth. SM: One peer-reviewed proceeding was published, and a journal article is under development. Two 2 Ph.D. and one master's student are involved (1 Ph.D. funded by this project). KO: One of the PIs was co-chair of the 2022 American Meteorological Society annual conference on the themes Food Security and Water and Food security. Objective 5.Develop a conceptual framework for operational rapid-response hybrid performance forecasts MA: As in the previous report, the progress made on Objectives 1-4 can be synthesized by the contributions above and how we leveraged them to consolidate the GEnomics by ENvironment phenotype predictive system (GeEn; www.thegeen.com). The analytics for climate data improvement, which enabled the enhancement of G2F's OMICS database, were implemented and tested. We used Amazon's Active Web Services (AWS) for the enhanced database (Obj. 1). We are developing a module with quality control and classification features, which will be helpful to improve G2F data curation and other large-scale experiments in need of climate data. We tested the analytics to quantify climate data uncertainty and their propagation to phenotype simulations (Obj. 2). We tested a model for the simulation of phenotypes using multiple stations across the US. We probed that improvements in climate data can improve the predictability of maize yields (Obj. 3). We created a climate-risk framework for identifying and selecting growing-season time windows in phenotype simulations (Obj. 4). The construction of GeEn represents the integration of activities 1-4. GEEN is in a testing stage, and new modules have been added since the previous report. For example, we added modules for data quality control, classification, and uncertainty quantification. We continue the training of undergraduate and graduate students from the Computer Sciences and Engineering and Biological Systems Engineering departments and the School of Natural Resources. Two Ph.D. and two undergraduate students will graduate this May. Two graduate students will be partially funded by the project to work during the final year. DC: Data and software are continuously collected, published, and used. A running prototype is being tested, and results on software stability, robustness, and parsimony will be available during the fall. SM: This objective has resulted in a prototype of software architecture, two conference presentations, and two undergraduate software development experiences. We added one Ph.D. and one MS student to work on software development and climate analytics. These students are funded by the University of Nebraska-Lincoln's School of Natural Resources and the project. KO: Prototype of the architecture of software GeEn in Amazon's AWS.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Qui�ones, R., F. Mu�oz-Arriola, S. Das Chouhdury, and A. Samal (2021). Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping. PLOS-One. https://doi.org/10.1371/journal.pone.0257001.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Romanelli, T, F. Mu�oz-Arriola, and A. F. Cola�o (2022). Conceptual framework to integrate economic drivers of decision-making for technology adoption in agriculture. Engineering Proceedings, 43 (9): pp 1-5. https://doi.org/10.3390/ engproc2021009043.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Rico, D.A., Carrick Detweiler, and Francisco Mu�oz-Arriola (2020). Power-over-Tether UAS Leveraged for Nearly-Indefinite Meteorological Data Acquisition. 2020 ASABE Annual International Meeting, Paper No. 1345. DOI: https://doi.org/10.13031/aim.202001345.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Sarzaeim, P., W. Ou, L. Alves de Oliveira, and Francisco Munoz-Arriola (2021). Flood-Risk Analytics for Climate-Resilient Agriculture Using Remote Sensing in the Northern High Plains. In Geo-Extreme 2021, pp. 234-244. https://doi.org/10.1061/9780784483695.023.
- Type:
Other
Status:
Published
Year Published:
2021
Citation:
Qui�ones, R., F. Mu�oz-Arriola, S. Das Chouhdury, and A. Samal (2021). Cosegmentation for Plant Phenotyping (CosegPP) Data Repository Collected Via a High-Throughput Imaging System. https://doi. org/10.5281/zenodo.5117176.
- Type:
Other
Status:
Published
Year Published:
2021
Citation:
Carrillo, C.M. and F. Munoz-Arriola (2021). Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains. DOI 10.5072/zenodo.983162.
- Type:
Other
Status:
Published
Year Published:
2022
Citation:
Sarzaeim, P., F. Mu�oz-Arriola, and D. Jarquin (2022). Large-scale and Multi-dimensional Climate, Genetics, and Phenotypes Database for Maize Yield Predictability in the U.S. and Canada. https://doi.org/10.5281/zenodo.6299090.
- Type:
Journal Articles
Status:
Accepted
Year Published:
2022
Citation:
Sarzaeim, P., F. Mu�oz-Arriola, and D. Jarquin (2022). Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability. Journal of Experimental Botany. DOI: 10.1093/jxb/erac146.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Qui�ones, R., F. Munoz-Arriola, S. Das, and A. Samal Accurate Co-Segmentation in High-Throughput and High Dimensional Plant Image Sequences NAPPN 2022 (North American Plant Phenotyping Network). 2-25 February 2022. Athens, GA. Graduate student award.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Qui�ones, R., F. Munoz-Arriola, S. Das, and A. Samal. Advancing Detection of Dynamic Environmental Effects on Plants Using Computer Vision Analytics in High-Throughput Phenotyping Facilities. 102nd American Meteorological Society Annual Meeting, Virtual Meeting. January 24, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Aslam, H. and F. Munoz-Arriola. Climate Visualization Analytics for the Performance GxE Modeling (Phenotype Predictability Software) Large-Scale Experiment. 102nd American Meteorological Society Annual Meeting, Virtual Meeting. January 24, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2022
Citation:
Sarzaeim, P., H. Aslam1, R. Qui�ones1, and F. Munoz-Arriola. Development of Climatic Spatiotemporal and Analytical Visualization for Maize Response to Climate. 102nd American Meteorological Society Annual Meeting, Virtual Meeting. January 24, 2022.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Sarzaeim, P, W. Ou, L. Alves de Oliveira, and Francisco Munoz-Arriola (2021). Flood-Risk Analytics for Climate-Resilient Agriculture Using Remote Sensing in the Northern High Plains. Geo-Extreme 2021conference, November 8, 2021.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Munoz-Arriola, F., W. Ou, J. Jain, H. Aslam, and A. Zarco. Towards a Conceptual Framework to Predict Physical Vulnerability of Major Crops to Winter Floods in The Northern High Plains. 2021 ASABE Annual International Meeting. Virtual and On Demand, July 13-15, 2021(Invited).
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2021
Citation:
Rico, D.A., Carrick Detweiler, Francisco Mu�oz-Arriola. The Tethered Aircraft Unmanned System (TAUS) Operational Performance Validation for a Reliable Deployment Sensing Atmospheric Variables. 2021 ASABE Annual International Meeting. Virtual and On Demand, July 13-15, 2021.
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Persa R., A., Jarquin D*. Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi- trait Approaches in Soybeans. Agriculture. doi: 10.3390/agriculture10080308
- Type:
Journal Articles
Status:
Published
Year Published:
2021
Citation:
Persa R., Iwata H., Jarquin D*. Use of family structure information in interaction with environments for leveraging genomic prediction models. The Crop Journal. https://doi.org/10.1016/j.cj.2020.06.004
|
Progress 02/15/20 to 02/14/21
Outputs
Target Audience:Hydrometeorological and climate events have become a subject of concern in the agricultural sector because of the increasing intensity, frequency, and complexity. We call complex climate events those formed by extreme and average conditions leading to environmental disasters such as floods and droughts.However, the complexity of such events remains unclear for basic research and management practices. The ongoing COVID-19 pandemic or the 2021 Artic Outbreak in Texas evidence that crop production, farmers' livelihoods, and our supply chain are unprepared, vulnerable, and poorly resilient to unclearly evolving phenomena. This project remains relevant to advance the science and technology of fusing breeding and information technologies to address some of the challenges referred to above. Also, this approach opens new opportunities to better understand and tackle complexity in breeding success in a changing climate. Our goal is to develop a conceptual model to predict hybrid performance in response to hydroclimatic changes. Our premise is that by harnessing hydroclimate, genetic, and phenotype through the development and implementation of data analytics and software engineering, we will address some of the challenges described above. We designed and constructed an architecture of the software containing hybrid statistical and deep learning modeling methodologies built to find sources of predictability of corn hybrid performance in response to hydroclimate changes. The architecture of software GEnomics-by-ENvironment phenotype predictive system (GEEN; www.thegeen.com): (1) contains a data management testbed that collects, standardize and fuse multiple sources of environmental data; (2) integrates data for the characterization of spatiotemporal hydroclimatic controls, and the associated uncertainties across scales; (3) operates a conceptual Genetic-, Multi-trait-, and Hydroclimatic-sensitive Model; and we are (4) creating the analytics and software architecture to perform hydroclimatic-driven hybrid diagnostics and forecasts, and we are (5) consolidating a conceptual framework for an operational rapid-response hybrid performance forecasts. While we request an extension to complete the project, we are on track to accomplish the proposed objectives and deliver the respective outcomes before February 2022.? Changes/Problems:We experienced delays in achieving the objectives and deliver the proposed products due to COVID-19. COVID-19 impacted our group in various forms, our abilities to meet and progress on the coding and the conceptualization of analytics. Also, inhabilitated our mobility since the university established a strict traveling policy, which affected our visits to the field and companies. We proposed to extend the life of the project for one more year without additional funding. The extension was granted, and we continue working toward achieving the proposed goals. What opportunities for training and professional development has the project provided?PI- Munoz-Arriola taught two formal courses (Hydroclimatology, Soil, and Water Resources Engineering, and advise a Senior Design Project in the Department of Computer Sciences and Engineering). In addition to the reported activities last year, the PI Francisco Munoz-Arriola developed a course on decision-making in water and agricultural systems and the University of Sao Paulo's Biosystems Engineering Department. A group of five undergraduate students (Hallie Hohbein, Anna Zhang, Zoe Trautman, David Recic, and Joe Carter) finished their work on developing the Genomics by Environment phenotype predictive system (GeEn). PIs Francisco Munoz-Arriola and Diego Jarquin were involved in the design and development of the architecture of software together with Dr. Byrav Ramamurthy (CSE faculty), Parisa Sarzaeim (current Ph.D. in Biological Systems Engineering funded by the project), Rubi Quñones (Ph.D. student in the Department of Computer Sciences and Engineering). Joseph Carter was hired as an intern in the project and is the main software developer. Another two interns were hired to support our software engineering activities, but their participation was brief due to COVID-19 complications. The undergraduate research experience Garret Williams (Sophomore student in BSE) was hired to work on "Quantifying environmental effects on maize yield by hybrid using G2F data". The PI continued his involvement with the National Science Foundation Research Training program on Resilient Complex Agricultural Landscapes. Rubi Quñones is funded by this program and is also a collaborator in the project. Additionally, three Ph.D. students (one part-time and one fully funded) have been involved in the project in year three during the spring and part of the summer of 2020.A master student Daniel Rico (Computer Sciences and Engineering), has participated in the project developing analytics to collect environmental data by UAV monitoring platforms. Co-PI-Diego Jarquin developed an online five-week course for introducing genotype-by-environment interaction in genomic selection applications for Ph.D. students in the Department of Agronomy and Horticulture (AGRO-816E). In addition, Co-PI Diego Jarquin developed an introduction to statistics course for undergraduate students at Nebraska Wesleyan University (two sections - 28 students each). Advising six students, three Ph.D. level and (2 Department of Statistics, 1 Department of Agronomy and Horticulture) and three MS (two Department of Statistics, one Crop Science Department at the University of Illinois at Urbana Champaign). Currently, Co-PI Jarquin is hosting a Ph.D. visiting student from the University of Vicosa, Brazil. How have the results been disseminated to communities of interest?PI- Munoz-Arriolais member of the American Meteorological Society (AMS) Water Resources Committee and co-chair of the 2022 AMS was recently nominated co-chair for the Food Security Theme (one of the five main themes of the Annual Conference early next year). We will co-chair with the Ph.D. listed above the session "Climate Data Analytics and Software for Food Security". We have consolidated the international research collaborative programs on Hydroinformatics and Integrated Hydrology with the Delft Institute for Water Education (The Netherlands); and the Indian Institutes of Technology Bombay and Roorkee (co-advising two Ph. D students). Five additional posters were presented in regional and international venues by students involved in the project. Co-PI. Diego Jarquin has led and coauthored at least ten manuscripts and one book chapter. Some of these results have been presented in 5 internationals invited (International Quantitative Genetics Conference 6, IRRI, VS. BASF, Universidade Federal de Lavras, XXV Scientific Meeting of the Argentinian Group of Biometry) and four local conferences (Nebraska Plant Breeding Symposium, Department of Agronomy and Horticulture Seminar Series at UNL, Corteva, Department of Statistics Seminar Series at UNL). Diego Jarquin had numerous online meetings with scientists in the Laboratory of Biometry and Bioinformatics in the Department of Agricultural and Environmental Biology at the University of Tokyo, the Crops Sciences Department at the University of Illinois at Urbana Champaign, Cornell University, ITAA, IRRI, CENICANA, Kansas State University, Missouri University for discussing the development and implementation of models similar those here proposed. What do you plan to do during the next reporting period to accomplish the goals?PI- Munoz-Arriola. While the narrative below has minor changes concerning the previous year,we expect to reach the proposed goals and deliver what we initially proposed. We expect to implement the operation of the software (GEnomix by ENvironment phenotype predictive tool; GeEn) developed in years 1, 2, and 3. In particular, the operation of pipelines, the implementation of geospatial and predictive analytics of GxE in the software, the feedback with the academic, public, and private sectors, and publication of findings in scientific journals. We investigated the geospatial, temporal, and process-based complexities of extreme precipitation and drought in agricultural areas. Leveraging resources and inspiring students (not directly affiliated with this project), we worked on the geospatial attributions of floods in maize and soybean cropping systems. Such activities will "pay off" next year with the publication and submission of three research articles. PI-Munoz-Arriola expects to continue to strengthen communication and collaboration with the G2F initiative and private companies, exploring new synergies with such sectors. Two PhDs will continue working as part of the work proposed for year 4. 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 current project. We expect this could be translated into funding opportunities for graduate students. The impact of the pandemic this year delayed some of our activities but allow us to prepare the next stage of the project. This was possible because of the Department of Computer Sciences and Engineering's contributions and the School of Natural Resources. We will submit three articles (geospatial analytics and controls of climate in phenotype prediction, sources and propagation of hydroclimate uncertainties in phenotype forecasts, and portable software for phenotype predictability) and present three papers at the AMS meeting in January 2022. In year 4, we will hire two computer scientists (at the undergraduate level) as interns in the project. Both students have worked with us as part of their Senior Design capstone project. Co-PI Diego Jarquin. Continue the model development stage for evolving the current model and perform predictions for multiple traits under stress conditions and different time windows for additional weather covariates affecting crop responses. A student will be continuously trained to understand, develop, modify and utilize the new implementations, including the extension of the model to deal with different traits.
Impacts
What was accomplished under these goals?
Objective 1.Develop a data management test bed to collect, standardize and integrate data. MA: Theteam tested the impacts of improved environmental data on the predictability of phenotypes. We used the consolidated G2F database (composed of genetic, phenotypic, and environmental data), which has been enhanced by applying deep learning algorithms to fulfill environmental data gaps. We explored the collection of data from unmanned aerial vehicles to assess the impact of drone trajectories on the collection of environmental data. This approach incorporates the growing availability of UAV data within the G2F project. We explored the mechanisms to simplify complex environmental variations on the predictability of phenotypes. We used an environmental index that can integrate the environmental complexity into a single holistic metric, which can be integrated into the GxE model as a potential addition of predictability and computational simplification. SM: This objective has resulted in 2 conference proceedings, three conference presentations, 3algorithms containing the analytics for the testing of improved environmental data, the simplification of environmental complexity, and the sampling of environmental data in UAVs. One undergraduate, 1 Master's, and 1 Ph.D. project. Some ideas have led to 1 additional federal grant application (NSF-NIT, currently pending) Objective 2.Characterize spatiotemporal hydroclimatic controls and the associated uncertainties across scales. MA: We developed an algorithm that propagates the error of environmental variables into the creation of environmental matrices (Global Sensitivity Analysis). These matrices are the analytical elements of the solution of the GxE model through covariance matrices. The first stage of the coupling of GSA and GxE has been achieved. By the end of the summer, we will have an algorithm that identifies the sources of uncertainty in environmental variables and their propagation into the predictions of phenotypes. SM: This objective has resulted in 1 internal grant funded by UNL to mitigate the impacts of the COVID-19 pandemic on the student's research. KO: To date, the most significant impact has been a change in knowledge with the presentations in the ASABE 2020. The presented work brought the attention of the agricultural engineering, water, and remote sensing sectors because of the relevance of the proposed applications to producers, water, and agricultural managers. A proceeding for the ASABE 2020 has been published about the global sensitivity analysis and its application in phenotype predictability. Objective 3. Develop a conceptual Genetic-, Multi-trait-, and Hydroclimate-sensitive Model. MA: A model to perform predictions based on covariance structures has been implemented for analyzing the updated environmental data. A multi-trait model was used to analyze experiments containing multiple phenotypic covariates randomly selected and organized by environmental attributions. The experimental design will allow us to identify the contributions of increasing the availability of environmental data randomly and possible precipitation patterns. This model is being tested on the G2F data. Last year we reported, "... a simulation study was conducted to assess the contribution of the current environments on improving the predictability of phenotypes." We hypothesized that a larger number of environments would benefit the performance of the GxE model. We have found that the use of more than 60 environments/sites leads to a marginal improvement in the predictability of phenotypes. This year, we are exploring the regionalization of sites and the attribution of environmental variables to phenotype predictability by region (more information is provided in Obj4) DC: We increased the number of environments by 30% between 2014 and 2017. We are exploring the addition of years 2018-2020. SM: We faced some delays and difficulties because of COVID-19 traveling restrictions. This reporting year, we were planning to visit the industries developing breeding programs and predictive phenotype software. Unfortunately, that was not possible. We plan to do some visits this summer if the traveling restrictions and the public health are improving. KO: The analytics for GxE modeling have been integrated into the software described below. Objective 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 MA: We implemented the analytics to identify the risks faced by corn and soybean crops to flooding events, including some conceptualizations to determine how these events may affect the predictability of phenotypes. We integrated remote sensing data and USDA's NASS database, introducing a novel risk analysis framework. DC: Implement routines to predict the risks crops face of determined planting dates and growing season duration. These algorithms have been developed testing Sentinel-2-NDVI using Google Earth. Additionally, the analytics for regionalization using principal component analysis and K-clustering have been created. SM: In 2020, this objective resulted in 1 accepted proceeding paper, the involvement of two Ph.D. students, and one postdoc (1 Ph.D. funded by this project). This objective has resulted in one internal grant funded by UNL's "Quantitative Life Sciences Initiative Summer Graduate Student Support competition" to mitigate the impacts of the COVID-19 pandemic on the student's research. KO: One of the PIs is convenor of the GEO-Extreme 2021, chairing the session "Design, management, and innovation for climate-resilient water-for-agriculture infrastructure" at an ASCE meeting. Objective 5. Develop a conceptual framework for operational rapid-response hybrid performance forecasts. MA: The achievements in this section respond to the achievements in Objectives 1-4.In particular, the progress toward constructing the Genomics by ENvironment phenotype predictive system (GeEn; www.thegeen.com). We have built the analytics and databases in Amazon's Active Web Services (AWS) (Obj. 1). We tested the analytics to collect geospatial data. We used those data to characterize extreme events and their impact on crops at a regional scale (Obj. 2). We have constructed a basic model for the simulation of phenotypes using multiple stations (with spatial and climate attributions; Obj. 3). We tested off-line phenotype simulations to assess the sensitivity of phenotype simulations to improved and geospatially distributed environmental data (Obj. 4). The design of GeEn and its ongoing construction represents the integration of activities 1-4 and the leverage of multiple digital and intellectual resources. We trained five students from the Department of Computer Sciences and Engineering who were working on the construction of GeEn during the Fall 2019 and the Spring of 2020. We hired an intern who has been part of the team since the Fall of 2019. DC: Integration of analytics into online software is expected to be ready by the end of the spring. A running prototype is expected by the end of the summer, together with the performance of phenotype simulations. SM: This objective has resulted in 1 beta-version of software architecture, one conference presentation, eight undergraduate software development experiences. We added two Ph.D. students: one in computer sciences and engineering and a second student in climate analytics to strengthen software engineering and statistical innovation, respectively. These students are funded by the National Science Foundation and the University of Nebraska-Lincoln's School of Natural resources. KO: Preliminary version of the architecture of software GeEn in Amazon's AWS.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Jarquin D., Kajiya-Kanegae H. Taishen C., Persa R.,Yabe S., Iwata H. Coupling Day Length Data and Genomic Prediction tools for Predicting Time-Related Traits under Complex Scenarios. Scientific Reports. Sci Rep 10, 13382 (2020). https://doi.org/10.1038/s41598-020-70267-9.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Persa R., Bernardeli A., Jarquin D*. Prediction Strategies for Leveraging Information of Associated Traits under Single- and Multi- trait Approaches in Soybeans. Agriculture. doi: 10.3390/agriculture10080308.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Persa R., Iwata H., Jarquin D. Use of family structure information in interaction with environments for leveraging genomic prediction models. The Crop Journal, Volume 8 , Issue 5 : 843-854(2020) https://doi.org/10.1016/j.cj.2020.06.004.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Bernardeli A., Santos de Carvalho Rocha J., Borem A., Lorenzoni R., Aguiar R., Nayara J., Silva B., Delmond Bueno R., Silva Alves R., Jarquin D., Ribeiro C., Dal?Bianco M. (2020) Modeling spatial trends and enhancing genetic selection: An approach to soybean seed composition breeding. Cropscience. https://doi.org/10.1002/csc2.20364
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Adhikari A., Basnet B*., Jarquin D., Crossa J. (2020) Genomic prediction for anther extrusion in CIMMYT hybrid wheat breeding program via modelling pedigree, genomic and environmental relationships. Front. Genet., 08 December 2020 | https://doi.org/10.3389/fgene.2020.586687.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Pandey M., Jarquin D., Chaidhari S., Janila P., Crossa J., Bhat R., Radhakrishnan R., Hickey R., Varsney R*. Genome-based prediction of groundnut traits of multi-environment breeding trials including genomic � environment interaction. Submitted in Theoretical and Applied Genetics 133, 31013117 (2020). https://doi.org/10.1007/s00122-020-03658-1
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Jarquin D., Howard R., Crossa J., Beyene Y., Gowda M., Martini J. W.R., Covarrubias G, Burgueno, J., Pacheco A., Grondona M., Wimmer V., Prasanna B.M. (2020) Genomic Prediction Enhanced Sparse Testing for Multi-Environment Trials. Submitted in G3: Genes, Genomes, Genetics. Early online June 13, 2020. https://doi.org/10.1534/g3.120.401349.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
McFarland B.A., Aikhalifah N., Bohn M., Bubert J., Buckler E.S., Ciampitti I., Edwards J., Ertl D., Gage J.L., Falcon C.M., Flint-Garcia S., Gore M.A., Graham C., Hirsch C.N., Holland J.B., Hood E., Hooker D., Jarquin D., Kaeppler S.M., Knoll J., Kruger G., Lauter N., Lee E.C., Lima D.C., Lorenz A., Lynch J.P., McKay J., Miller N.D., Moose S.P., Murray S.C., Nelson R., Poudyal C., Rocheford T., Rodriguez O., Romay M.C., Schnable J.C., Schnable P.S., Scully B., Sekhon R., Silverstein K., Singh M., Smith M., Spalding E.P., Springer N., Thelen K., Thomison P., Tuinstra M., Wallace J., Walls R., Wills D., Wisser R.J., Xu W., Yeh C.T., de Leon N*. (2020) Maize Genomes to Fields (G2F): 2014-2017 Field Seasons: Genotype, Phenotype, Climatic, Soil, and Inbred Ear Image Datasets. BMC Research Notes, 13: 71.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Hussain W., Campbell M., Jarquin D., Walia H., Morota G*. (2020) Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions. The Plant Genome; e20011. https://doi.org/10.1002/tpg2.20011.
- Type:
Book Chapters
Status:
Published
Year Published:
2020
Citation:
Diego Jarquin, Reka Howard, Alencar Xavier, Sruti Das Choudhury. Predicting Yield by Modeling Interactions between Canopy Coverage Image Data, Genotypic and Environmental Information for Soybeans. Intelligent Image Analysis for Plant Phenotyping, CRC Press, 2020. 267-286
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Development of a genomic selection pipeline using large matrices (3K genotypes and 14 million markers) in chickpea. XXV Scientific Meeting of the Argentinian Group of Biometry. Tandil Argentina. November 11.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Recent developments for embracing GxE in breeding applications. XXIV International Symposium Genotype x Environment interactions: novelties, challenges and opportunities. Virtual edition. Universidade Federal de Lavras. October 7.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Improving genomic prediction of target hybrids in unobserved environments using geospatial assessment of predictive analytics derived from machine learning techniques. International Quantitative Genetics Conference 6 (virtual). Brisbane, Australia. November 6.
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Amaranto, A., F. Pianosi, D. Solomatine, G. Corzo-Perez, and F. Munoz-Arriola (2020). Sensitivity Analysis of Hydroclimatic Controls of Data-driven Groundwater Forecast in Irrigated Croplands. Journal of Hydrology. https://doi.org/10.1016/j.jhydrol.2020.124957
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Alves de Oliveira, L.2, B. L. Woodbury, J. H. de Miranda, and F. Munoz-Arriola (2020). Geospatial upscaling of atrazines transport using electromagnetic induction across point to field scale (2020). ASABE Annual International Meeting, Paper No. 884. DOI: https://doi.org/10.13031/aim.202001165
- Type:
Books
Status:
Published
Year Published:
2020
Citation:
Kumar, M., F. Munoz-Arriola, H. Furumai, and T Chaminda (2020) RESILIENCE, RESPONSE, AND RISK IN WATER SYSTEMS: SHIFTS IN NATURAL FORCINGS AND MANAGEMENT PARADIGMS. Springer Transactions in Civil and Environmental Engineering. ISBN#978-981-15-4667-9: 395pp
- Type:
Journal Articles
Status:
Published
Year Published:
2020
Citation:
Pandey, V., P. K. Srivastava, R. K. Mall, F. Munoz-Arriola, and D. Han (2020). Multi-Satellite Precipitation Products for Meteorological Drought Assessment and Forecasting in Bundelkhand region of Central India. Geocarto Internacional. https://doi.org/10.1080/10106049.2020.1801862
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Rico, D.A., Carrick Detweiler, and Francisco Mu�oz-Arriola (2020). Power-over-Tether UAS Leveraged for Nearly Indefinite Meteorological Data Acquisition. 2020 ASABE Annual International Meeting, Paper No. 1345. DOI: https://doi.org/10.13031/aim.202001345.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Sarzaeim, P., D. Jarquin, and F. Mu�oz-Arriola (2020). Analytics for climate-uncertainty estimation and propagation in maize-phenotype predictions. 2020 ASABE Annual International Meeting, Paper No. 1165. DOI: https://doi.org/10.13031/aim. 202020884.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Garret Williams, Parisa Sarzaeim1, and Francisco Mu�oz-Arriola (2020). Simplification of Complex Environmental Variations on Maize-Phenotype Predictability. 2020 ASABE Annual International Meeting, Paper No. 1291. DOI: https://doi.org/10.13031/aim.202001291.
- Type:
Other
Status:
Other
Year Published:
2020
Citation:
Hohbein, H., A. Zhang, Z. Trautman, D. Brecic, and J. Carter. P. Sarzaeim, D. Jarquin, and F. Munoz-Arriola (2020). Prototype of the GEnetics by ENvironment (GEEN): A Phenotype Predictive System.
- Type:
Websites
Status:
Published
Year Published:
2020
Citation:
Prototype of the GEnetics by ENvironment (GEEN): A Phenotype Predictive System.
https://www.thegeen.com/#/
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Sarzaeim, P1., D. Jarquin4, and F. Mu�oz-Arriola. Analytics for climate-uncertainty estimation and propagation in maize-phenotype predictions. 2020 ASABE 2020 Annual International Meeting. Virtual and On Demand, July 13-15, 2020.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Williams, G5. P. Sarzaeim1, F. Mu�oz-Arriola. Simplification of Complex Environmental Variations on Maize-Phenotype Predictability. 2020 ASABE 2020 Annual International Meeting. Virtual and On Demand, July 13-15, 2020
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Carter, J. P. Sarzaeim, D. Jarquin, R. Quinones, E. Tanghanwaye, and F. Munoz-Arriola. The GEnetic by Environment (GEEN) Phenotype Predictive System
Software Development. NAPPN Annual Conference. February 17, 2021.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Wilson, A. M., R. Cifelli, F. Munoz-Arriola, J. Giovannettone, J. Vano, T. Parzybok, A. Dufour, J. Jasperse, K. Mahoney, and B. McCormick. Efforts to Build Infrastructure Resiliency to Future Hydroclimate Extremes. 101st American Meteorological Society Annual Meeting, Virtual Meeting. January 11, 2021
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Jain, J1., D. Khare, and F. Munoz-Arriola. Mapping Attributions between Flood Vulnerabilities and Risk Management Policies in India. 101st American Meteorological Society Annual Meeting, Virtual Meeting. January 11, 2021
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Ntaganda5, P., M. Shyaka5, and F. Munoz-Arriola. Rwanda's Hydroclimate across Urban and Agricultural Landscapes. 101st American Meteorological Society Annual Meeting, Virtual Meeting. January 11, 2021
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Wilson, A. M., R. Cifelli, F. Munoz-Arriola, J. Giovannettone, J. Vano, T. Parzybok, A. Dufour, J. Jasperse, K. Mahoney, and B. McCormick. Efforts to Build Infrastructure Resiliency to Future Hydroclimate Extremes. American Geophysical Union, Fall Conference, Virtually. December 9, 2020.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Rico, D.A.1, Carrick Detweiler, Francisco Mu�oz-Arriola. Power-over-Tether UAS Leveraged for Nearly Indefinite Meteorological Data Acquisition. 2020 ASABE 2020 Annual International Meeting. Virtual and On Demand, July 13-15, 2020.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2020
Citation:
Alves de Oliveira2, L., F. Mu�oz-Arriola, D. Martin, C.R. Allen. Crafting an irrigation sustainability framework in the Northern High Plains (USA) using stakeholders opinions. 2020 ASABE
|
Progress 02/15/19 to 02/14/20
Outputs
Target Audience:The team reached again the communities of scientists and practitioners this year at the National Association Plant Breeders Conference and the Genomes to Fields and PHENOME 2020 annual conference and other venues. We expect further to outreach the academic, public and private sector, starting with those that work directly with the G2F. COVID-19 may challenge this effort since we may undergo some logistic drawbacks during the growing season of 2020. Our objective is to communicate about the software created and the statistical and AI activities developed as part of this project. Recruitment activities were focused on training "Digital Breeders." We worked with one Ph.D. student in Biological Systems engineer and lost one Ph.D. student in Statistics, but we were able to work with a Ph.D. student in remote sensing, five undergraduate students in computer sciences, and one in biological systems engineering, all contributing to achieving the goal of the project. Again, this year, our commitment to support women and minority groups in STEM education was reflected in this process. Changes/Problems:We lost a Ph.D. student after ten months of funding. This loss did not impact the deliverables for year two since we were able to leverage some student support. For example, classroom activities in the School of Natural Resources and the Department of Biological Systems Engineering led to the development of geospatial analytics for remote sensing products. Also, in the Department of Computer Sciences and Engineering, five undergraduate students selected our project as their final senior-design capstone project. What opportunities for training and professional development has the project provided?PI- Munoz-Arriola taught two formal courses (Hydroclimatology, Soil, and Water Resources Engineering, and advise a Senior Design Project in the Department of Computer Sciences and Engineering). In addition to the reported activities last year, the PI Francisco Munoz-Arriola introduced a Final Project opportunity in the course of Hydroclimatology related to the crop responses to flooding events in the Northern High Plains. The Final Project, "Spatiotemporal diagnostics of major crops' vulnerability in the Northern High Plains," was presented by the students at the 2020 American Meteorological Society meeting. Starting in September 2019, a group of five undergraduate students (Hallie Hohbein, Anna Zhang, Zoe Trautman, David Recic, and Joe Carter) began to work on the development of the Genomics by Environment phenotype predictive system (GeEn). PIs Francisco Munoz-Arriola and Diego Jarquin were involved in the design and development of the architecture of software together with Dr. Byrav Ramamurthy (CSE faculty) and Parisa Sarzaeim (current Ph.D. in Biological Systems Engineering funded by the project). The undergraduate research experience Garret Williams (Sophomore student in BSE) was hired to work on "Quantifying environmental effects on maize yield by hybrid using G2F data". The PI continued his involvement with the National Science Foundation Research Training program on Resilient Complex Agricultural Landscapes. Two Ph.D. students (one part-time and one fully funded) have been involved in the project in year 2. Co-PI-Diego Diego Jarquin taught two short courses in Brazil (Unversity of Sao Paulo and the University of Vicosa) to around 120 attendees (including students and faculty, and researchers from the industry). The core of this course was focused on the study of the genotype-by-environment interaction, including multi-trait predictions for Genomic Selection. The project fully funded one Ph.D. student during part of year 2. However, she left the program after ten months of funding. Additionally, three students at the Ph.D. level and one at MS level were advised during this term; the MS-level student graduated and is currently pursuing a Ph.D. in the Department of Agronomy and Horticulture studying the GxE interaction in multi traits in wheat. Also, I advise two students at the University of Vicosa, who visited the UNL this year. How have the results been disseminated to communities of interest?PI- Munoz-Arriola: A key finding this year was that current G2F sites might need to expand to improve the predictabilities of phenotypes in response to the environment and the genetics of maize. Team members are looking for forms to strengthen such a hypothesis by sharing our findings to the communities of agricultural engineers, agronomists, and geoscientists at national to international venues. In particular, we have started to interact with a broader G2F-community and communities of maize geneticists in Mexico. The team is confident that leveraging data, predictive analytics, and hydroclimate information will lead the project to identify sources and mechanisms that enhance the predictability of phenotypes. One article and four presentations evidence our commitment to sharing nationally and internationally our findings and the support of this program. Co-PI. Diego Jarquin led and co-authored 9 manuscripts and 3 book chapters. Some of these results have been presented in 2 international conferences as invited speaker (at the CSSA meeting in San Antonio and the Plant Breeding symposium in Vicosa Brazil). Co-PI Jarquin has an on-going scientist at the Laboratory of Biometry and Bioinformatics in the Department of Agricultural and Environmental Biology at the University of Tokyo, Japan, to work on the development and implementation of models similar to those here proposed. What do you plan to do during the next reporting period to accomplish the goals?PI- Munoz-Arriola expects to implement the operation of the software (GEnomix by ENvironment phenotype predictive tool; GeEn) developed in years 1 and 2. In particular, the operation of pipelines, the implementation of geospatial and predictive analytics of GxE in the software, the feedback with the academic, public and private sectors, and publication of findings in scientific journals. We investigated the geospatial, temporal, and process-based complexities of extreme precipitation and drought in agricultural areas. Leveraging resources and inspiring students (not directly affiliated with this project), we worked on the geospatial attributions of floods in maize and soybean cropping systems. Such activities will "pay off" next year with the publication of one research article and two proceedings to be presented at the ASABE conference. 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. One PhDs will continue working as part of the work proposed for year 3. 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. The impact of the Ph.D. student lost this year was minor since we were able to leverage contributions from the Department of Computer Sciences and Engineering. We will submit three articles (geospatial analytics and controls of climate in phenotype prediction, sources and propagation of hydroclimate uncertainties in phenotype forecasts, and portable software for phenotype predictability) and present three papers in the ASABE meeting in July 2020. In year 3, we will hire two computer scientists (at the undergraduate level) as interns in the project. Both students have worked with us as part of their Senior Design capstone project. Additionally, we will recruit an Agronomists or Statistician at the Master level to support the activities of Objectives 3 and 4. Co-PI Diego Jarquin. Continue the model development stage for evolving the current model and allow it to perform predictions for multiple traits under stress conditions once Objective 3 is accomplished. The current Ph.D. student funded by this project and other students contributing to the project will be continuously trained to understand, develop, and utilize the new implementations.
Impacts
What was accomplished under these goals?
MA= Major Activities; DC=Data Collected; SM=Summary Statistics; KO=Key Outcomes Objective 1. MA: We (the team) implemented a pipeline to retrieve phenotypic, genomic, and environmental data from the Genomes to Fields initiative for years 2014-2017and additional were integrated from public sources including remote sensing, gridded and station-based networks. We identified that G2F data requires a robust quality control approach founded on computational and physical principles. We developed such an approach but needed additional work to become a stand-alone and automatic procedure given the inconsistencies in the source data. DC: The implemented predictive analytics for data-driven models to improve the environmental database was consolidated. SM: This objective has resulted in 1 peer-reviewed publications, 4 conference presentations, 2 extension presentation (in the format of short courses to students in two Universities in Brazil, the University of Sao Paulo and the University of Vicosa), 1 undergraduate project, and 1 Ph.D. projects. Some of the ideas have led to 1 additional federal grant application (USDA-FACT, currently pending). KO: To date, the most significant impact has been the increase in the interest in OMICS database development and the application of Artificial Intelligence. Our research activities have introduced new concepts and knowledge to a scientific community that is rapidly adopting some of these applications. We presented our progresses in international conferences. Two oral presentations, two posters, and two invited talks indicate the level of interest in the intersection of OMICS, Environment, and data science (2019 National Association Plant Breeders Conference, ASABE 2019, G2F collaborators meeting at the PHENOME 2020, and the EGU 2018). Today, the study and integration of the GxE in commercial breeding is a skill set in high demand. We envision that the foreseen developments will have a significant impact in the near future. The dissemination of the knowledge through short courses has allowed us to equip young scientists to provide the newest developments and concepts in the study of the GxE interaction. This topic is evolving rapidly in the public and private sectors. The presentations and courses have allowed us to meet with scientists that share similar research interests, opening opportunities for future collaborations. Objective 2. MA: While we developed analytics to identify extreme precipitation and drought, the occurrence of the 2019 flood in the Northern High Plains led us to re-evaluate the role of extremes in this project. This re-assessment highlighted the challenges faced by producers due to floods, constraining the growing season. Thus, the predictability of rapid-response hybrids during years with floods become a complementary element for the spatiotemporal characterization of hydroclimate controls on phenotypes. We are using remote sensing products from Sentinel 2 and MODIS to identify the spatial distribution of planting dates and growing seasons duration in corn at regional scale. The analytics to process these data can be useful for USDA's NASS since their assessment of planting dates is based on empirical approaches. This project will allow us to characterize both, wet and dry extremes geospatially. DC: Development of algorithms for the metrics for planting dates and growing season duration. These algorithms have been developed testing platforms such a Google Earth for MODIS-NDVI. However, we found that the best resource to use because of its spatial resolution and the sampling frequency is Sentinel II (Sentinel I was launched in 2014). Thus, data from the constellation of Sentinel products will be useful to characterize the areas subject to dry and wet spells, complementing the hydroclimate analytics and geospatial assessments. SM: In 2019, this objective resulted in 3 conference presentations and 1 Ph.D. project (partially funded). This objective has resulted in 1 internal grant funded. KO: To date, the most significant impact has been a change in knowledge with the presentations in the ASABE 2019 and the AMS 2020. The presented work brought the attention of the water and remote sensing sectors because of the relevance of the proposed applications to producers as well as water and agricultural managers. Objective 3. MA: A baseline model to perform predictions based on covariance structures have been implemented for analyzing the updated environmental data. Specifically, a multi-trait model was used to analyze experiments containing multiple phenotypic covariates. Currently, this model is being tested on soybean data sets comprising seven traits. This development will be implemented this year with the maize data once the testing stage is done. Also, a simulation study was conducted to assess the contribution of the current environments on improving the predictability of phenotypes. We found that a larger number of environments is desirable to obtain a more accurate prediction. The team is pursuing a series of tests to evaluate if need the number of samples might be responsible for the poor improvement. Thus, the addition of two more years of data, which is likely to happen, will allow us to evaluate whether we are close to reaching the plateau of predictability, or perhaps more sampling spots are needed. DC: As mentioned above, we added the year 2017 of G2F data and improved the environmental data with NWS and NSRDB data. SM: Preliminary results have been analyzed. At the ASABE 2019 annual conference. We also had hitches since we lost one Ph.D. student in statistics after ten months of funding. KO: An off-line pipeline for running the analysis and predictions was developed. Objective 4. MA: Work in progress since data obtained in Obj. 2 is needed to launch these activities. Objective 5. MA: The progress made on Objectives 1-4 can be synthesized here to highlight their contributions to the design and construction of the Genomics by ENvironment phenotype predictive system (GeEn; www.thegeen.com). We have constructed the analytics and databases needed to run an off-line simulation of phenotypes using variable hydroclimate conditions (Obj. 1). We built the analytics for the collection of geospatial data to characterize extreme events and their impact of crops at a regional scale (Obj. 2). We have designed a basic conceptual framework for the simulation of phenotypes using multiple stations (with no spatial and climate attributions yet; Obj. 3). We have run a basic off-line phenotype simulation for the assessment of the suitability of environmental data (Obj. 4). The design of GeEn and its on-going construction represents the integration of activities 1-4, as well as the leverage of multiple digital and intellectual resources. For example, five students from the Department of Computer Sciences and Engineering were working on the construction of GeEn during the Fall and Spring semesters. Also, two students from the Hydroclimatology course taught by one of the PIs did their final project on the geospatial analytics for the identification of planting dates and growing season length. The Co-PI actively collaborated in the development of the online implementation that will allow stakeholders to perform the analysis using their configurations. Such configurations will provide the user the opportunity to select the environments they are interested in, the specific environmental covariable to consider in the analysis, and particular periods for considering the environmental data. These alternatives might match with specific phenological stages of plant development. DC: Integration of analytics into an off-line conceptual model for the simulation of phenotypes associated with large-scale experiments. SM: This objective has resulted in 1 beta-version of the architecture of software, 1 conference presentations, 5 undergraduate projects. KO: Preliminary version of the architecture of software GeEn.
Publications
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Amaranto1, A., F. Munoz-Arriola, G. Corzo-Perez, and D. Solomatine (2019). A Spatially enhanced data-driven multi-model to improve semi-seasonal groundwater forecasts in the High Plains aquifer, USA. Water Resources Research. DOI:10.1029/2018WR024301.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Ashish Kumar1, RAAJ Ramsankaran3, Luca Brocca, Francisco Munoz-Arriola (2019). A Machine learning approach for improving near-real-time satellite-based rainfall estimates by integrating soil moisture. Remote Sensing. doi:10.3390/rs11192221.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Munoz-Arriola, F., C. Wunderlin, P. Sarzaeim, M. Khan, W. Ou, and P. Greer. Decoupling the Hydro-climatological condition before and during the recent flooding event in the Missouri River Basin. 100th American Meteorological Society Annual Meeting, Boston, MA. January 15, 2020.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Sarzaeim, P., W. Ou, Khan, L. Alves, and F. Munoz-Arriola. Spatiotemporal diagnostics of major crops vulnerability in the Northern High Plains. 100th American Meteorological Society Annual Meeting, Boston, MA. January 15, 2020.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Sarzaeim, P., F. Munoz-Arriola, A. Amaranto, D. Jarquin, and D. Bradford. Geospatial assessment of phenotype predictive analytics using machine learning techniques. ASABE 2019 Annual conference. Boston, MA. July 8th, 2019.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Munoz-Arriola, F., P. Sarzaeim, A. Amaranto, D. Jarquin, and D. Bradford. Geospatial assessment of phenotype predictive analytics using machine learning techniques. European Geosciences Union General Assembly 2018. Vienna, Austria. April 8th 2019.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Khan, M., C. Wunderlin, P. Sarzaeim, W. Ou, and F. Munoz-Arriola. Decoupling the Hydro-climatological condition before and during the recent flooding event in the Missouri River Basin. 100th American Meteorological Society Annual Meeting, Boston, MA. January 13, 2020
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Jarquin, D., F. Munoz-Arriola, P. Sarzaeim, and A. Amaranto. Geospatial assessment of phenotype predictive analytics using machine learning techniques and genome information. 2019 National Association Plant Breeders Conference. Pine Mountain, GA. August 27th, 2019.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Rico, D. A., C. Detweiler, and F. Munoz-Arriola. Power Tethered UAS Network for Automated Indefinite Data Acquisition to Assist Agricultural Management and Production. ASABE 2019 Annual conference. Boston, MA. July 9th, 2019.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2020
Citation:
Genomes to Fields initiative. Plugin-based architecture of software to predict corn phenotypes. 2020 G2F GxE Collaborators Meeting at the Phenome Meeting. February 24, 2020.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Liu C., Sukumaran S., Jarquin D., Crossa J., Dreisigacker S., Sansaloni S., Reynolds M. (2019) Comparison of Array- and Sequencing-based Markers for Genome Wide Association Mapping and Genomic Prediction in Spring Wheat. CropScience. doi: 10.2135/cropsci2019.06.0377
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Crossa, J., Martini, J., Gianola, D., Montesinos-Lopez, O.A., Cuevas, J., Perez-Rodriguez, P., Jarquin, D., and Juliana, P. (2019) DEEP KERNEL AND DEEP LEARNING FOR GENOME-BASED PREDICTION OF SINGLE TRAITS IN MULTI-ENVIRONMENT BREEDING TRIALS. Frontiers Genetics. Evolutionary and Population Genetics.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Falcon, C., Kaeppler, S., Spalding, E., Miller, N., Haase, N., AlKhalifah, N., Bohn, M., Buckler, E., Campbell, D., Ciampitti, I., Coffey, L., Edwards, J., Ertl, D., Flint-Garcia, S., Gore, M.A., Graham, C., Hirsch, C., Holland, J., Jarquin, D., Knoll, J., Lauter, N., Lawrence-Dill, C., Lee, E., Lorenz, A.J., Lynch, J., Murray, S., Nelson, R., Romay, C., Rocheford, T., Schnable, P., Scully, B.T., Smith, M., Springer, N., Tuinstra, M., Walton, R., Weldekidan, T., Wisser, R., Xu, W., and de Leon, N. (2019) RELATIVE UTILITY OF AGRONOMIC, PHENOLOGICAL, AND MORPHOLOGICAL TRAITS FOR ASSESSING GENOTYPE-BY-ENVIRONMENT INTERACTION IN MAIZE INBREDS. Cropscience. doi: 10.2135/cropsci2019.05.0294
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Singh, A., Li, G., Brohammer, A., Jarquin, D., Hirsch, C., Alfan, J., and Lorenz, A.J. (2019) Genome-Wide Association and Gene Co-expression Network Analyses Reveal Complex Genetics of Resistance to Gosss Wilt of Maize G3: Genes, Genomes, Genetics October 1, 2019 vol. 9 no. 10 3139-3152; https://doi.org/10.1534/g3.119.400347
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Howard, R., Gianola, D., Montesinos-L�pez, O.A., Juliana, P., Singh, R., Poland, J., Shrestha, S., P�rez-Rodr�guez, P., Crossa, J., and Jarqu�n, D. (2019) Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments G3: Genes, Genomes, Genetics September 1, 2019 vol. 9 no. 9 2925-2934; https://doi.org/10.1534/g3.119.400508
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Howard, R., & Jarquin, D. (2019). Genomic Prediction Using Canopy Coverage Image and Genotypic Information in Soybean via a Hybrid Model. Evolutionary Bioinformatics. https://doi.org/10.1177/1176934319840026
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Morota, G., Jarquin, D., Campbell, M.T., Iwata, H. (2019) Statistical methods for the quantitative genetic analysis of high-throughput phenotyping data. arXiv:1904.12341 [q-bio.GN]
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Genomic selection models for predicting end-use quality traits in CIMMYT spring bread wheat. Jarquin D., Howard R., Crossa J., Battenfield S., Poland J., Fritz A., Guzman C. LACC/IGW. 13th International Gluten Workshop.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2019
Citation:
Improving genomic-enabled prediction accuracy by modeling the genotype-by-environment interaction for quality traits in Kansas wheat Howard R., Jarquin D., Crossa J., Battenfield S., Poland J., Fritz A., Miller R., Guzman C. LACC/IGW. 13th International Gluten Workshop.
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Jarquin D., Howard R., Liang Z., Gupta SK., Schnable JC., Crossa J. (2019) Enhancing hybrid prediction in pearl millet using genomic and/or multi-environment phenotypic information of inbreds. Frontiers. doi: 10.3389/fgene.2019.01294
- Type:
Journal Articles
Status:
Published
Year Published:
2019
Citation:
Jarqu�n, D., Howard, R., Graef, G., & Lorenz, A. (2019). Response Surface Analysis of Genomic Prediction Accuracy Values Using Quality Control Covariates in Soybean. Evolutionary Bioinformatics. https://doi.org/10.1177/1176934319831307
|
Progress 02/15/18 to 02/14/19
Outputs
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:
Nothing Reported
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.
Impacts
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.
Publications
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