MULTIVARIATE ANALYSIS OF SOYBEAN AGRONOMIC TRAITS UNDER DIFFERENT PLANT STRUCTURE MANAGEMENT PRACTICES
DOI:
https://doi.org/10.66104/069gp719Keywords:
Foliar fertilization; MGIDI; multivariate analysis; plant growth modulators; soybean yield.Abstract
Soybean is a major crop in global agriculture, and the application of plant growth modulators offers a promising approach to improve yield; this study aimed to evaluate their effects on agronomic traits and yield components to identify the most effective management strategy and the primary determinants of grain yield using multivariate analytical techniques. The experiment was conducted in Mineiros, Goiás, Brazil, using a randomized block design with eight treatments and four replications under field conditions. Treatments involved the application of plant growth modulators PR136 (Prime), StoppingGo (Valence), Equaliza (Master), Stimulate (Stoller), Biozyme (UPL), MaxCel (Sumitomo), and StartStop (Blucorp) at the V4 growth stage, plus an untreated Control, and agronomic traits related to vegetative and reproductive growth were analyzed using multivariate analysis of variance (MANOVA), principal component analysis (PCA), linear mixed models, path analysis, and the Multi-Trait Genotype-Ideotype Distance Index (MGIDI). MANOVA detected significant differences among treatments (p < 0.05), PCA revealed consistent covariance patterns among traits with the first two principal components explaining a substantial portion of the total variance, and path analysis identified the number of pods per plant and stem base diameter as the main positive direct determinants of grain yield, whereas plant height and the number of lateral branches had negative effects. The MGIDI, applied with a 15% selection intensity, exclusively selected the Stimulate treatment as the closest to the ideal ideotype. Multivariate techniques proved effective for synthesizing information from multiple traits and supporting management decisions in soybean, highlighting Stimulate as the treatment with the most balanced multivariate performance and StoppingGo as particularly effective in optimizing plant architecture and yield components.
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Copyright (c) 2026 Amarildo Borges da Silva, Ivan Ricardo Carvalho, Diego Oliveira Ribeiro, Glicélia Pereira Silva, Alexandre Igor de Azevedo Pereira, Carmen Rosa da Silva Curvelo, Rodrigo Vieira da Silva, Deborah Amorim Martins, Luiz Leonardo Ferreira
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