Identifying key factors influencing maize stalk lodging resistance through wind tunnel simulations with machine learning algorithms

Abstract

Climate change has intensified maize stalk lodging, severely impacting global maize production. While numerous traits influence stalk lodging resistance, their relative importance remains unclear, hindering breeding efforts. This study introduces an combining wind tunnel testing with machine learning algorithms to quantitatively evaluate stalk lodging resistance traits. Through extensive field experiments and literature review, we identified and measured 74 phenotypic traits encompassing plant morphology, biomass, and anatomical characteristics in maize plants. Correlation analysis revealed a median linear correlation coefficient of 0.497 among these traits, with 15.1 % of correlations exceeding 0.8. Principal component analysis showed that the first five components explained 90 % of the total variance, indicating significant trait interactions. Through feature engineering and gradient boosting regression, we developed a high-precision wind speed-ear displacement prediction model (R² = 0.93) and identified 29 key traits critical for stalk lodging resistance. Sensitivity analysis revealed plant height as the most influential factor (sensitivity coefficient: −3.87), followed by traits of the 7th internode including epidermis layer thickness (0.62), pith area (−0.60), and lignin content (0.35). Our methodological framework not only provides quantitative insights into maize stalk lodging resistance mechanisms but also establishes a systematic approach for trait evaluation. The findings offer practical guidance for breeding programs focused on enhancing stalk lodging resistance and yield stability under climate change conditions, with potential applications in agronomic practice optimization and breeding strategy development.