Raw Data for the Integrative Morphophysiological Assessment of Soybean Lines in Evaluating Leaf Rust Resistance

This dataset contains raw data from an experiment evaluating soybean genotypes for leaf rust resistance (Phakopsora pachyrhizi) through an integrative morphophysiological approach. The study assessed 36 soybean genotypes (30 hybrid lines, 6 parental lines) under field conditions using a Randomized Complete Block Design (RCBD) to identify key traits contributing to rust resistance.

The dataset includes morphophysiological parameters such as:

Morphological traits: Leaf area, trichome density, leaf epidermis thickness, stomatal density

Physiological traits: Chlorophyll content, lignin accumulation

Disease severity assessment: Rust severity scoring based on the International Working Group on Soybean Rust (IWGSR) system

Resistance classification: Based on Area Under the Disease Progress Curve (AUDPC) values and multivariate analysis

The data were analyzed using ANOVA, Principal Component Analysis (PCA), and hierarchical clustering to classify genotypes into resistant, moderately resistant, moderately susceptible, and susceptible categories. This dataset supports precision breeding strategies for developing rust-resistant, high-yielding soybean cultivars.