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
Research Effort Categories
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.
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.