Résumé
Introduction: The demand for sustainable agriculture has led to increased interest in intercropping as a way to reduce the use of chemicals and maximize production. However, accurately predicting the benefits of intercropping is challenging due to the complex interactions between different plant species, the environment, and agricultural practices. While soil-crop models are useful for understanding these interactions throughout the growing season, there are few models that can accurately simulate intercropping systems. A first version of STICS was designed to simulate bi-specific intercrops (Brisson et al., 2004). Main: In this study, we present a set of simple and versatile formalisms for designing a model that can simulate key interactions in bi-specific intercropping systems, including processes such as plant development, light interception, plant growth, nitrogen and water balance, and yield formation, considering factors like management practices, soil conditions, and climate. We evaluated the effectiveness of these formalisms by integrating them into the STICS soil-crop model and comparing the results with observed data from various intercropping systems involving cereal and legume mixtures, such as Faba bean-Wheat, Pea- Barley, Soybean-Sunflower, and Wheat-Pea mixtures. Our findings demonstrate that the proposed equations and guidelinesoffer a comprehensive simulation of soil-plant interactions in different types of bi-specific intercrops. The new STICS model consistently performed well and proved to be versatile across a range of spring and winter intercrops of arable crops, with prediction errors of 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for grain yield (Vezy et al., accepted in ASD). Conclusion : The complete set of formalisms has been developed and published for simulating bi-specific intercropping systems and integrated into the STICS soil-crop model (Vezy et al. 2021; Vezy et al. accepted in ASD). The new version of STICS improved for intercropping, with its emphasis on being versatile, accurate, simple, and easy to parameterize, is well-suited for assisting researchers in virtually assessing sustainable intercrop systems that are suitable for local conditions, thus contributing to the agroecological transition