Abstract Intercropping offers a sustainable path to yield stability and resource efficiency, but its success hinges on managing species‐specific trade‐offs. This study introduces a machine‐learning framework to decode the complex interactions in a pea ( Pisum sativum L.) and cucumber ( Cucumis sativus L.) intercropping system. By applying Random Forest regression, Neural Network sensitivity analysis, and Pareto ranking to a comprehensive dataset, we identified the primary agronomic drivers. Our analysis revealed that crop biomass, nitrogen dynamics, and soil health were the most influential predictors of system performance. A key finding was the competitive asymmetry: pea emerged as the dominant species, as quantified by compatibility indices (competitive ratio, Aggressivity aggressivity index), yet this competition slightly reduced cucumber yield compared to its sole crop. Despite this, the system achieved a significant land‐use advantage, with a land equivalent ratio >1, demonstrating that the overall synergy and yield benefit for pea result in superior resource use efficiency at the system level. Sensitivity analysis further highlighted crop water content and pest management as critical, manageable factors for optimizing outcomes. This work demonstrates the power of machine learning to move beyond trial‐and‐error, providing a data‐driven blueprint for designing efficient and sustainable vegetable intercropping systems.
Ikram et al. (Thu,) studied this question.