Data Sheet 1_Integrated modelling of shading effects on alfalfa growth across different agrivoltaic systems.docx

IntroductionAgrivoltaic systems (AVS) combine agricultural production with solar energy generation on the same land. However, the spatiotemporal variability in light availability caused by panel shading presents a critical challenge for accurately predicting impacts on crop growth and yield.
MethodsThis study introduces a novel modeling framework that integrates a three-dimensional radiative model with a process-based crop growth model, implemented in the GroIMP platform, to simulate the performance of alfalfa (Medicago sativa L.) under contrasting AVS conditions. The model accounts for dynamic light interception, canopy temperature variation, and soil water availability. Field experiments were conducted in northern and central Italy under three conditions: open field (Site A), fixed-panel AVS (Site B), and bi-axial tracking AVS (Site C).
Results and discussionThe model was, the model was calibrated and validated using field data on leaf area index (LAI) (R² ≥ 0.79, RMSE ≤ 48.61), dry matter yield (R² ≥ 0.82, RMSE ≤ 48.6 g m⁻²) and canopy temperature (R² = 0.83, RMSE = 1.24 °C), demonstrating strong agreement with observations. The validated model enabled a detailed assessment of how different panel configurations influence microclimatic conditions, which in turn significantly affected alfalfa growth and biomass production. From this perspective, simulations revealed pronounced spatial gradients driven by shading intensity, system layout, and seasonal dynamics, emphasizing the critical role of AVS design in determining crop performance. In particular, yield differences among treatments reflected microclimatic modifications induced by the panels, with shading and rainfall redistribution likely affecting canopy temperature, soil moisture dynamics, and associated plant water relations.
ConclusionsThe proposed integrated modeling framework thus provides a robust and scalable tool for AVS design and management, supporting both agronomic planning and the optimization of structural configurations tailored to site-specific climatic conditions. By doing so, it may effectively contribute to the development of more adaptive, efficient, and sustainable agri-energy systems capable of balancing agricultural productivity with renewable energy generation.