Source: UNIVERSITY OF NEBRASKA submitted to
FROM GENE TO GLOBAL HYDROCLIMATIC CONTROLS ON HYBRID PERFORMANCE PREDICTABILITY
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
EXTENDED
Funding Source
Reporting Frequency
Annual
Accession No.
1015252
Grant No.
2018-67013-27594
Project No.
NEB-21-176
Proposal No.
2017-07752
Multistate No.
(N/A)
Program Code
A1141
Project Start Date
Feb 15, 2018
Project End Date
Feb 14, 2022
Grant Year
2018
Project Director
Munoz-Arriola, F.
Recipient Organization
UNIVERSITY OF NEBRASKA
(N/A)
LINCOLN,NE 68583
Performing Department
Biological Systems Engineering
Non Technical Summary
A current challenge for the global community is to secure food provision for the decades to come. The goal of this proposal is to develop a conceptual model to predict hybrid performance in response to hydroclimatic changes. Historically, genetic progresses in maize production have responded to breeding activities. To pursue future successful and sustainable crop production we propose to develop a Genomics-by-Environment model. To implement such hybrid statistical modeling approach, possible sources of predictability of corn hybrid performance thorough changing climate forcings will be investigated and implemented in a conceptual framework as follows: (1) Develop a data management test bed to collect, standardize and integrate data; (2) Characterize spatiotemporal hydroclimatic controls and the associated uncertainties across scales; (3) Develop a conceptual Genetic-, Multi-trait-, and Hydroclimatic-sensitive Model; (4) Perform hydroclimatic-driven hybrid performance forecasts based on (a) the spatial regionalization of phenotypic and environmental data and (b) the temporal influence of EHCEs on phenotypic expressions under standardized indices and absolute values of environmental variables; and (5) Develop a conceptual framework for operational rapid-response hybrid performance forecasts. Ultimately, "simulated" successful hybrids in response to droughts may be obtained by integrating the geospatial expansion of genes at field-scale and the syntheses of global-scale hydroclimatic processes.
Animal Health Component
0%
Research Effort Categories
Basic
20%
Applied
40%
Developmental
40%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
2027310108120%
1320430108150%
4020420108130%
Goals / Objectives
GoalDevelop a conceptual model to predict hybrid performance in response to hydroclimatic forcingsGeneral ObjectiveDevelop and implement a multi-trait, multi-site expression to integrate hydroclimatic controls on the predictability of hybrids in response to EHCEsParticular ObjectivesDevelop a data management test bed to collect, standardize and integrate data.Characterize spatiotemporal hydroclimatic controls and the associated uncertainties across scales.Develop a conceptual Genetic-, Multi-trait-, and Hydroclimate-sensitive Model.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.Develop a conceptual framework for operational rapid-response hybrid performance forecasts.
Project Methods
I) Develop a data management test-bed to collect, standardize, and integrate dataPhenotypic, genomic and environmental data will be retrieved from G2F consortia. Complementary environmental information will be obtained from complex satellite and field network information systems. Since the G2F project was launched in 2014 at the beginning of this research, information is available for three years. In average, close to 28 locations were tested each year, and most of these were observed during the three years. Nearly 1,000 hybrids were tested, most of them during the three years. To allow connectivity between locations, researchers observed an average of 250 hybrids at each location in 2014, whereas in subsequent years, they observed close to 500 hybrids at each site. A portable weather station able to record information on at least eight weather conditions was used in each trial. As this research is an ongoing experiment, data generated in coming years will be integrated in the database as well. Gridded and station data from observations as well as remote sensing will be collected and standardized.Observed DataThe initial dataset includes precipitation, maximum temperature, minimum temperature, and wind speed at 1/16th degree resolution. The dataset is derived from approximately 20,000 cumulative National Climatic Data Center (NCDC) Cooperative Observer (COOP) stations across the United States, as well as stations across Canada and Mexico. Data will be merged with G2F precipitation, minimum, and maximum temperature. The synergraphic mapping system (SYMAP) algorithm (Shepard, 1984) will be used for gridding the temperature and precipitation at resolutions up to 1km to test suitability. The wind dataset was taken from the National Centers for Environmental Prediction (NCEP) National Center for Atmospheric Research (NCAR) reanalysis (Kalnay et al., 1996) and gridded using linear interpolation. Further methodology details can be found in Livneh et al. (2015) and Maurer et al. (2002). Daymet and MODIS data will also be used to calibrate/validate the approach above, as well as testing purposes.Data IntegrationThree years of G2F environmental (G2F-E), Genotypic (G2F -G) and Phenotypic (G2F -Y) data will be available by the time this project could start. The proposed series of experiments will allow the group to use a total of six years of data for the spring-summer season from 2014-2019. The ultimate goal in this section is to have a series of vectors and matrices with the same dimensions to serve as inputs for Equation 4. Dimensions of vectors and matrices as well as number of trails will be determined by the G2F data availability. The use of linear algebra in our approach will allow us to adapt the interannual changes that have already occurred. Environmental variables will be disaggregated using a fixed subdaily distribution; for example, co-op station data is available daily and G2F-E is available subdaily. Additional phenotypic data such as greening and dormancy periods based on MODIS-LAI (Tang et al., 2012) will be tested and used as complements to current G2F-Y. G2F-G will be used as it is provided by the source standardized to the same format. Standards for visualization will be developed for all G2F-data. This will facilitate data delivery, analytics and synthesis.II) Characterize spatiotemporal hydroclimatic controls and the associated uncertainties across scales.These analytics represented by metrics of environmental extremes will be used as G2F-E as well as G2F-Y in some cases (e.g., greening and dormancy obtained from MODIS-LAI; Figures 2b and 2c). An approach to identifying and assessing extremes is to measure the degree of deviation from normal; however, anomalies in absolute terms reflect different severities in different parts of the domain. These differences may be due to climate, soils, vegetation, or other factors specific to that region. An alternative approach is percentiles, which allow direct comparisons of extreme wet and dry events across the domain, helping identify extremes (Andreadis & Lettenmaier, 2006; Andreadis et al., 2005; Sheffield et al., 2009). The meaning of percentile can be attained by stating that the pth percentile of a distribution correlates to the value where approximately p percent of the values in the distribution are equal or less than that value. Monthly and daily percentiles will be calculated for each grid cell and G2F stations based on the climatology of the 64-year period (1950-2013) and the 14-year period (2000-2013) using MODIS-LAI availability. Total soil moisture was represented as percentiles (using the Gamma distribution) relative to all simulated values for a given grid cell and month. With this method, seasonal variations are removed, extremes can be more easily identified, and the intensity of events is better represented throughout the domain. Additionally, percentiles allow convenience due to their ordinal range from zero to one. The Gamma distribution has been identified as a good fit for the soil moisture data. Additional distribution functions will be tested (e.g., Weibull).III) Develop a conceptual Genetic-, Multi-trait-, and Hydroclimatic-sensitive Model.Genomic selection methodology potentially (upon data availability) could be applied to four different problems that breeders face on fields: (i) predict observed lines in observed environments [some lines tested in some environments but not in others]; (ii) predict unobserved lines in observed environments [new developed lines that have not been observed in any trial]; (iii) predict observed lines in unobserved environments [new locations]; and (iv) predict unobserved lines in unobserved locations [new lines that have never been tested would be tested in new environments].IV) Perform hydroclimatic-driven hybrid performance forecastsSpatial PatternsData in obtained in the section above will be used to address objective IV. Regionalization of the areas of interest will be based on gridded and G2F-Environmental data. This analysis will be implemented for each of the G2F-E (see section II) to characterize the sensitivity of hybrid performances to hydroclimate forcings.Temporal PatternsOnce the areas under water deficit in SM and precipitation have been selected (relative, as a function of the SPI, and absolute, as a function of their magnitude), hybrid performance simulations will be run for those lines under each scenario of water deficit. Selected hybrids under such conditions will be tested in other domains based on the regionalization of the previous section but also on the very site. The main purpose is to characterize hybrids' performance under dry and drought conditions in other sites of G2F. Hypothetical scenarios will be integrated with the genetics of dry and drought conditions throughout the G2F domain.V) Develop a conceptual framework for operational rapid-response hybrid performance forecast.Data-retrieval, experiments, and forecasting conceptual frameworks will be integrated in an architecture Sources of data and user needs vary across the G2D-domain. The development of an interoperability/information system will use an interoperation interface to cope with diversity and management of data to pursue a wider and more open accessibility of information. The system will collect data from the G2Fnetworks, the ACIS-High Plains Climate Center-CoOP climatological and meteorological stations, and remote and proximal sensing products to produce: 1) Standardized and multi-source G,E, and Y variables; 2) user-friendly access to historical G, E, and Y data; and ultimately 3) meaningful representations of integrated climate-, water- and G-E-Y-related information for users. The prototype of a system will be tested against un-integrated experiments. Information display in the appropriate forms and formats will be a key part of the system's legacy and future operational tests and activities.

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