Climate and genetic data enhancement using deep learning analytics to improve maize yield predictability.

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Abstract

Despite the efforts to collect genomics and phenomics (OMICs) and environmental data, spatiotemporal availability and access to digital resources still limit our ability to predict plants’ response to changes in climate. Our goal is to quantify the improvement in the predictability of maize yields by enhancing climate data. Large-scale experiments like the Genomes to Fields (G2F) are an opportunity to provide access to OMICs and climate data. Here, the objectives are to: (1) improve the G2F OMICs and environmental database by reducing the gaps of climate data using deep neural networks, (2) estimate the contribution of climate and genetic database enhancement to the predictability of maize yields via environmental covariance structures in Genetic by Environment (GxE) modeling, and (3) quantify the predictability of yields resultant from the enhancement of climate data, the implementation of the GxE model, and the application of three trial selection schemes (e.g., randomization, ranking, and precipitation gradient). The results show a 12.1% increase in predictability due to climate and OMICs database enhancement. The consequent enhancement of covariance structures evidenced in all train-test schemes indicated an increase in maize yields predictability. The largest improvement is observed in “random-based” approach, which adds environmental variability to the model.© The Author(s) 2022. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: [email protected].