GIS-FA: An approach to integrate thematic maps, factor-analytic and envirotyping for cultivar targeting

Key message: We propose an enviromics prediction model for cultivar recommendation based on thematic maps for decision-makers. Parsimonious methods that capture genotype-by-environment interaction (GEI) in multi-environment trials (MET) are important in breeding programs. Understanding the causes and factors of GEI allows the utilization of genotype adaptations in the target population of environments through environmental features and Factor-Analytic (FA) models. Here, we present a novel predictive breeding approach called GIS-FA that integrates geographic information systems (GIS) techniques, FA models, Partial Least Squares (PLS) regression, and Enviromics to predict phenotypic performance in untested environments. The GIS-FA approach allows: (i) predict the phenotypic performance of tested genotypes in untested environments; (ii) select the best-ranking genotypes based on their over-all performance and stability using the FA selection tools; (iii) draw thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of GIS-FA approach using two datasets of rice [Oryza sativa (L.)] and soybean [Glycine max (L.) Merr.] in MET spread over tropical areas. In summary, our novel predictive method allows the identification of new breeding scenarios by pinpointing groups of environments where genotypes have superior predicted performance and facilitates/optimizes the cultivar recommendation by utilizing thematic maps.