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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Large-scale and Multi-dimensional Climate, Genetics, and Phenotypes Database for Maize Yield Predictability in the U.S. and Canada

Authors: Sarzaeim, Parisa; Munoz-Arriola, Francisco; Jarquín, Diego;

Large-scale and Multi-dimensional Climate, Genetics, and Phenotypes Database for Maize Yield Predictability in the U.S. and Canada

Abstract

Improved G2F is a data repository consisting of OMICs (genetic and phenotypic) and environmental data for maize yield predictability across 84 experimental fields in the U.S. and province of ON in Canada between 2014-2017. The G2F genetic dataset contains numerical genotypes of 376 tested maize lines. The G2F phenotypic dataset provides the 8,271 observations of maize yield measurements in the experimental fields. The G2F environmental dataset contains the minimum temperature (Tmin), average temperature (Tmean), maximum temperature (Tmax), minimum dew point (DPmin), average dew point (DPmean), maximum dew point (DPmax), minimum relative humidity (RHmin), average relative humidity (RHmean), maximum relative humidity (RHmax), minimum solar radiation (SRmin), average solar radiation (SRmean), maximum solar radiation (SRmax), accumulative rainfall (Racc), average wind speed (WSmean), and average wind direction (WDmean) time series for 84 experiments. Acknowledgement The authors acknowledge the support provided by the Agriculture and Food Research Initiative Grant number NEB-21-176 and NEB-21-166 from the USDA National Institute of Food and Agriculture, Plant Health and Production and Plant Products: Plant Breeding for Agricultural Production. In addition, we thank the Genomes to Fields (G2F) Initiative for providing the database. We also acknowledge the support from Quantifying Life Sciences Initiative at the University of Nebraska-Lincoln and Holland Computing Center of the University of Nebraska.

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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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