Articles | Volume 15, issue 9
https://doi.org/10.5194/essd-15-3963-2023
https://doi.org/10.5194/essd-15-3963-2023
Data description paper
 | 
06 Sep 2023
Data description paper |  | 06 Sep 2023

CLIM4OMICS: a geospatially comprehensive climate and multi-OMICS database for maize phenotype predictability in the United States and Canada

Parisa Sarzaeim, Francisco Muñoz-Arriola, Diego Jarquin, Hasnat Aslam, and Natalia De Leon Gatti

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Cited articles

Agricultural Model Intercomparison and Improvement Project (AgMIP): https://agmip.org/, last access: 31 December 2022. 
Amaranto, A., Munoz-Arriola, F., Corzo, G., Solomatine, D. P., and Meyer, G.: Semi-seasonal groundwater forecast using multiple data-driven models in an irrigated cropland, J. Hydroinform., 20, 1227–1246, https://doi.org/10.2166/hydro.2018.002, 2018. 
Amaranto, A., Munoz-Arriola, F., Solomatine, D. P., and Corzo, G.: A Spatially Enhanced Data-Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA, Water Resour. Res., 55, 5941–5961, https://doi.org/10.1029/2018WR024301, 2019. 
Amaranto, A., Pianosi, F., Solomatine, D., Corzo, G., and Muñoz-Arriola, F.: Sensitivity analysis of data-driven groundwater forecasts to hydroclimatic controls in irrigated croplands, J. Hydrol., 587, 124957, https://doi.org/10.1016/j.jhydrol.2020.124957, 2020. 
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A genomic, phenomic, and climate database for maize phenotype predictability in the US and Canada is introduced. The database encompasses climate from multiple sources and OMICS from the Genomes to Fields initiative (G2F) data from 2014 to 2021, including codes for input data quality and consistency controls. Earth system modelers and breeders can use CLIM4OMICS since it interconnects the climate and biological system sciences. CLIM4OMICS is designed to foster phenotype predictability.
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