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Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

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Weighted kernels improve multi-environment genomic prediction

PDF) Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials

Global Change Biology, Environmental Change Journal

Predictive accuracy of single-step best linear unbiased

Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize, BMC Plant Biology

Multi-environment Genomic Selection in Rice Elite Breeding Lines, Rice

Multiple-trait analyses improved the accuracy of genomic prediction and the power of genome-wide association of productivity and climate change-adaptive traits in lodgepole pine, BMC Genomics

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction - ScienceDirect

PDF) Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize