Balancing crop production and electricity generation in rice agrivoltaic systems: A multi-modeling approach

Context and objective Agrivoltaics is an integrated system that addresses food and energy security by combining crop production with photovoltaic energy generation. However, consistently meeting government crop yield requirements under variable weather conditions remains a major challenge. Process-based models may be useful to modify the agrivoltaic design to improve crop yield stability. Our objectives were to assess the long-term yield variability in a lowland rice agrivoltaic system and to simultaneously simulate the impact of photovoltaic panel tilt angles on crop production and energy generation. Methods We independently calibrated three popular models using on-farm data from Japan and integrated them to quantify the food–energy balance: the models were for irradiance (Rhinoceros), crop growth (ORYZA v3), and the photovoltaic component (PVsyst). We used 30 years of daily weather data (1994–2023) for the study site in scenario analysis to estimate the effects of the tilt angle on solar radiation under the agrivoltaic system, the crop yield, and the electrical energy that was generated. Results and conclusions The relative yield (agrivoltaics / control) ranged from 0.66 to 0.94, but Japan’s governmental requirement is 0.80. Mean daily solar radiation during the cropping season was closely correlated with relative yield ( r = 0.67, p < 0.001). The scenario analysis indicated that adjusting tilt angles from the default by 7° and 30° towards the vertical led to 80% and 90% probabilities, respectively, of meeting the government’s yield requirement, while decreasing the annual electricity generation by 0.2% and 3.2%. The results demonstrated the usefulness of our research concept: a multi-modeling approach that combines irradiance, crop, and photovoltaic models can identify suitable agrivoltaic designs in the context of the food–energy nexus. Significance The relationships between crop yield and electricity generation in agrivoltaic systems depend on the insolation conditions at each location. A multi-modeling approach offers a useful framework for estimating potential photovoltaic energy generation and creating suitability maps of agrivoltaics.