GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting.

Autor: Araújo MS; Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil., Chaves SFS; Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil., Dias LAS; Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil., Ferreira FM; Department of Crop Science - College of Agricultural Sciences, São Paulo State University, Botucatu, São Paulo, Brazil., Pereira GR; Department of Agronomy, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil., Bezerra ARG; Limagrain Brazil S.A., Jataí, Goiás, Brazil., Alves RS; Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil., Heinemann AB; Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, Goiás, Brazil., Breseghello F; Brazilian Agricultural Research Corporation (Embrapa Rice and Beans), Santo Antônio de Goiás, Goiás, Brazil., Carneiro PCS; Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil., Krause MD; Department of Agronomy, Iowa State University, Ames, IA, USA., Costa-Neto G; Institute for Genomics Diversity, Cornell University, Ithaca, NY, USA., Dias KOG; Department of General Biology, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil. kaio.o.dias@ufv.br.
Jazyk: angličtina
Zdroj: TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik [Theor Appl Genet] 2024 Mar 12; Vol. 137 (4), pp. 80. Date of Electronic Publication: 2024 Mar 12.
DOI: 10.1007/s00122-024-04579-z
Abstrakt: Key Message: We propose an "enviromics" prediction model for recommending cultivars based on thematic maps aimed at 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, which 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 enables: (i) the prediction of the phenotypic performance of tested genotypes in untested environments, (ii) the selection of the best-ranking genotypes based on their overall performance and stability using the FA selection tools, and (iii) the creation of thematic maps showing overall or pairwise performance and stability for decision-making. We exemplify the usage of the 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 demonstrate superior predicted performance. It also facilitates and optimizes cultivar recommendations by utilizing thematic maps.
(© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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