Envirotype approach for soybean genotype selection through the integration of georeferenced climate and genetic data using artificial neural networks

• Published in: Euphytica Vol. 220; no. 1; p. 8
• Main Authors: Leichtweis, Bruno Grespan, de Faria Silva, Letícia, Peixoto, Marco Antônio, Peternelli, Luiz Alexandre, da Silva, Felipe Lopes
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 2024; Springer Nature B.V
• Subjects: Artificial neural networks; Biomedical and Life Sciences; Biotechnology; Climatic conditions; Climatic data; Discriminant analysis; Environmental changes; Gene mapping; Genotype & phenotype; Genotype-environment interactions; Genotypes; Geographic information systems; Life Sciences; Neural networks; Neurons; Norms; Plant Genetics and Genomics; Plant Pathology; Plant Physiology; Plant Sciences; Polynomials; Principal components analysis; Regression models; Self organizing maps; Soybeans; Statistical analysis; Variance; Environment types; Reaction norms; Self-organizing maps; Genotype × environment interaction
• ISSN: 0014-2336; 1573-5060
• DOI: 10.1007/s10681-023-03267-1

The selection of better-evaluated genotypes for a target region depends on the characterization of the climate conditions of the environment. With the advancement of computer technology and daily available information about the weather, integrating such information in selection and interaction genotype × environment studies has become a challenge. This article presents the use of the technique of artificial neural networks associated with reaction norms for the processing of climate and georeferenced data for the study of genetic behaviors and the genotype × environment interaction of soybean genotypes. The technique of self-organizing maps (SOM) consists of competitive learning between two layers of neurons; one is the input, which transfers the data to the map, and the other is the output, where the topological structure formed by the competition generates weights, which represent the dissimilarity between the neural units. The methodologies used to classify these neurons and form the target populations of environments (TPE) were the discriminant analysis (DA) and the principal component analysis (PCA). To study soybean genetic behavior within these TPE, the random regression model was adopted to estimate the components of variance, and the reaction norms were adjusted through the Legendre polynomials. The SOM methodology allowed for an explanation of 99% of the variance of the climate data and the formation of well-structured TPE, with the membership probability of the regions within the TPE above 80%. The formation of these TPE allowed us to identify and quantify the response of the genotypes to sensitive changes in the environment.