Search

The performance of numerical, statistical, and data-driven diagnostic and predictive crop production modeling relies heavily on data quality for input and calibration or validation processes. This study presents a comprehensive database and the analytics used to consolidate it as a homogeneous, consistent, multidimensional genotype, phenotypic, and environmental database for maize phenotype modeling, diagnostics, and prediction. The data used are obtained from the Genomes to Fields (G2F) initiative, which provides multiyear genomic (G), environmental (E), and phenotypic (P) datasets that can be used to train and test crop growth models to understand the genotype by environment (GxE) interaction phenomenon. A particular advantage of the G2F database is its diverse set of maize genotype DNA sequences (G2F-G), phenotypic measurements (G2F-P), station-based environmental time series (mainly climatic data) observations collected during the maize-growing season (G2F-E), and metadata for each field trial (G2F-M) across the United States (US), the province of Ontario in Canada, and the state of Lower Saxony in Germany. The construction of this comprehensive climate and genomic database incorporates the analytics for data quality control (QC) and consistency control (CC) to consolidate the digital representation of geospatially distributed environmental and genomic data required for phenotype predictive analytics and modeling of the GxE interaction. The two-phase QC–CC preprocessing algorithm also includes a module to estimate environmental uncertainties. Generally, this data pipeline collects raw files, checks their formats, corrects data structures, and identifies and cures or imputes missing data. This pipeline uses machine-learning techniques to fill the environmental time series gaps, quantifies the uncertainty introduced by using other data sources for gap imputation in G2F-E, discards the missing values in G2F-P, and removes rare variants in G2F-G. Finally, an integrated and enhanced multidimensional database was generated. The analytics for improving the G2F database and the improved database called Climate for OMICS (CLIM4OMICS) follow findability, accessibility, interoperability, and reusability (FAIR) principles, and all data and codes are available at 10.5281/zenodo.8002909 (Aslam et al., 2023a) and 10.5281/zenodo.8161662 (Aslam et al., 2023b), respectively. [ABSTRACT FROM AUTHOR]

The performance of numerical, statistical, and data-driven diagnostic and predictive crop production modeling heavily relies on data quality for input and calibration/validation processes. This study presents a comprehensive database and the analytics used to consolidate it as a homogeneous, consistent, and multi-dimensional genotype, phenotypic, and environmental database for maize phenotype modeling, diagnostics, and prediction. The data used is obtained from the Genomes to Fields (G2F) initiative, which provides multi-year genomic (G), environmental (E), and phenotypic (P) datasets that can be used to train and test crop growth models to understand the genotype by environment (GxE) interaction phenomenon. A particular advantage of the G2F database is its diverse set of maize genotype DNA sequences (G2F-G), phenotypic measurements (G2F-P), station-based environmental time series (mainly, climatic data) observations collected during the maize growing season (G2F-E), and metadata for each field trials (G2F-M) across the U.S. and the province of Ontario in Canada. The construction of this comprehensive climate and genomic database incorporates the analytics for data quality control (QC) and consistency control (CC) to consolidate the digital representation of geospatially distributed environmental and genomic data required for phenotype predictive analytics and modeling the GxE interaction. The two-phase QC-CC pre-processing algorithm also includes a module to estimate environmental uncertainties. Generally, this data pipeline collects raw files, checks their formats, corrects data structures, and identifies and cures/imputes missing data. This pipeline uses machine learning techniques to fulfill the environmental time series gaps and quantifies the uncertainty introduced by using other data sources for gaps imputation in G2F-E, discards the missing values in G2F-P, and removes rare variants in G2F-G. Finally, an integrated and enhanced multi-dimensional database is generated. The analytics for improving the G2F database and the improved database called "CLIM4OMICS" follows the FAIR principles, and all the digital resources are available at http://doi.org/10.5281/zenodo.7490246 (Sarzaeim, et al., 2023). [ABSTRACT FROM AUTHOR]