Factor analytic selection tools and environmental feature-integration enable holistic decision-making in Eucalyptus breeding

• Published in: Scientific reports Vol. 14; no. 1; pp. 18429 - 14
• Main Authors: Chaves, Saulo F. S., Damacena, Michelle B., Dias, Kaio Olimpio G., de Almada Oliveira, Caio Varonill, Bhering, Leonardo L.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08-08-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/208/8; 631/449/711; Air temperature; Brazil; Clonal selection; Decision Making; Environment; environmental features; Eucalyptus - genetics; Factor analytic mixed models; Gene-Environment Interaction; Genotype; Genotype-by-environment interaction; Genotype-environment interactions; Genotypes; Humanities and Social Sciences; multidisciplinary; Plant Breeding - methods; Principal components analysis; reliability; Reproducibility of Results; Science; Science (multidisciplinary); Silviculture; Soil characteristics; Soil temperature; tree breeding; Brazil; Genotype-by-environment interaction; tree breeding; environmental features; reliability; Factor analytic mixed models
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-69299-2

Understanding the genotype-by-environment interaction (GEI) and considering it in the selection process is a
sine qua non
condition for the expansion of Brazilian eucalyptus silviculture. This study’s objective is to select high-performance and stable eucalyptus clones based on a novel selection index that considers the Factor Analytic Selection Tools (FAST) and the clone’s reliability. The investigation explores the nuances interplay of GEI and extends its insights by scrutinizing the relationship between latent factors and real environmental features. The analysis, conducted across seven trials in five Brazilian states involving 78 clones, employs FAST. The clonal selection was performed using an extended FAST index weighted by the clone’s reliability. Further insights about GEI emerge from the integration of factor loadings with 25 environmental features through a principal component analysis. Ten clones, distinguished by high performance, stability, and reliability, have been selected across the target population of environments. The environmental features most closely associated with factor loadings, encompassing air temperature, radiation, and soil characteristics, emerge as pivotal drivers of GEI within this dataset. This study contributes insights to eucalyptus breeders, equipping them to enhance decision-making by harnessing a holistic understanding-from the genotypes under evaluation to the diverse environments anticipated in commercial plantations.