The Enviromic marker

Abstract We formalize “enviromic markers” as modeling units parallel to DNA markers, but herein for genotype-environment (G × E) prediction. Four operational premises (linearity; site potential; heterogeneous favorability; and envirotypic covariates (ECs)-genotype-dependence) are presented to enable their use in linear mixed models and also to motivate four construction strategies: (i) using raw environmental covariates as linear markers; (ii) applying transformations to capture mild nonlinearities; (iii) deriving ecophysiological functions; and (iv) engineering markers with Artificial Intelligence (AI) models which learn nonlinear environment → phenotype mappings for linear downstream use. Environmental data quality control is detailed, including checks of spatial coverage and resolution, variance within the TPE, collinearity control, and spatial/temporal validation without leakage. Envirome data are linked with GIS to compute environmental kernels, quantify covariate shifts, and deliver pixel-level predictions with uncertainty diagnostics. The framework clarifies assumptions and standardizes the use of enviromic markers for predictive breeding analyses.