Integrative multi-trait phenotyping reveals coordinated root and antioxidant responses underlying drought tolerance in soybean

Abstract

Drought stress markedly constrains soybean yields; however, progress in breeding has been limited by reliance on single-trait selection. Effective drought adaptation likely requires coordinated responses across root architecture, antioxidant defense, and osmotic adjustment, yet the relationships among these traits complexes across diverse germplasm remain poorly understood. A comprehensive assessment of drought tolerance was conducted, evaluating 301 soybean genotypes over 2 years under both well-watered and drought-stressed conditions at 40% field capacity, using 11 morphological and physiological traits. Genetic variation was characterized through analysis of variance, broad-sense heritability estimation, and genetic and phenotypic coefficients of variation. Multivariate approaches, including principal component analysis, hierarchical clustering, and five machine learning classifiers, were used to identify trait interactions and classify drought responses. The Multi-trait Genotype-Ideotype Distance Index (MGIDI) and Stress Tolerance Index (STI) were applied for multi-trait genotype selection. Root traits demonstrated high broad-sense heritability (0.74–0.77), indicating substantial genetic potential for selection. Drought conditions reduced root parameters by 10–20%, whereas catalase activity increased by 24.3%. Proline content exhibited extreme genotypic variation (50–1,500 µg g⁻¹), with the highest accumulation associated with the poorest performance, characterized by severe membrane damage and limited root development, a pattern consistent with a stress-injury profile rather than effective tolerance. Hierarchical clustering revealed that superior genotypes attained drought tolerance through coordinated, moderate responses maintaining extensive root systems (total root length 1,239 mm) and high catalase activity (156 µmol min⁻¹ g⁻¹) rather than through extreme expression of individual traits. Machine learning variable importance analysis identified catalase activity, total root length, malondialdehyde, and proline as the most discriminative traits. Six elite genotypes (Jiyu 92; Dengke No. 1; Kennong 57; Mengdou 28; Ronda 130; Jinong SB 2012 − 136) were consistently identified as top performers across MGIDI, STI, cluster membership, and drought-to-control trait ratios. Effective drought adaptation necessitates balanced, multi-trait coordination rather than the maximization of individual traits. Concentrating early-stage phenotyping on the four most important traits identified by machine learning would substantially reduce per-genotype resource requirements while retaining 70–80% of the discriminative information. The six identified elite genotypes are high-priority candidates for field-based validation and potential incorporation into drought-resilient soybean breeding programs.