Abstract
The global agro-industry is changing, with a shift toward high-tech, data-driven systems to tackle environmental challenges and resource shortages. This study examines how digital and energy transitions are occurring together within the agricultural innovation ecosystem, driven by global demand for sustainability. Using a dataset of more than 2 million patents filed from 2000 to 2019, the research maps how technology spreads with Main Path Analysis (MPA) and explores the factors behind this process using an Exponential Random Graph Model (ERGM). The MPA identifies 12 main technology paths, starting with GPS-based management and progressing to advanced, energy-efficient uses of AI, IoT, and blockchain. ERGM results show that the innovation network forms selective, trust-based clusters and follows a “Matthew Effect,” where leading technologies attract more investment. The analysis finds that aligning policy frameworks is the most important factor in building these networks, even more than traditional R&D spending. Environmental sustainability also has a strong positive effect, showing that the ecosystem favors technologies that lower carbon emissions and energy costs. While individual farmers do not yet have much influence on the larger innovation network, including them is important for fair technology sharing and system resilience. These results provide policymakers and agribusinesses with a strategic guide, showing that a successful digital and energy transition requires a balanced approach that combines market forces, robust regulations, and inclusive innovation.