Prediction Triple Effect on Soybean: Responses to Production and Grain Quality Under Elevated CO₂, High Temperature, and Drought

Soybean (Glycine max) is a major crop for food, feed, and bioenergy, yet its productivity and nutritional quality are threatened by climate change factors such as elevated CO₂ (eCO₂), high temperature, and drought. Here, we integrate experimental data with predictive modeling to evaluate the individual and combined impacts of these stressors—the “Triple Effect”—on soybean yield and seed composition. Generalized linear models (GLMs) were used to estimate grain production and quality traits from biomass at 60 days, while machine learning models (XGBoost, CatBoost) predicted responses under multifactorial stress. Model accuracy was assessed using root mean square error (RMSE). eCO₂ increased grain production by 142%, whereas high temperature reduced yield by 91%. In combination, eCO₂ and high temperature enhanced yield by 143%, but drought mitigated these benefits, leading to a 60% reduction. Triple Effect predictions revealed increases in grain production (50%), soluble sugars (35%), and amino acids (175%), accompanied by decreases in starch (20%) and protein (6%). These shifts indicate a metabolic reallocation that boosts productivity at the expense of nutritional quality. Our findings highlight the need for breeding climate-resilient soybean cultivars that balance yield and quality under multifactorial stress.