Spatiotemporal differentiation and evolution of associated networks of China's agricultural innovation ecosystem

Exploring the spatiotemporal patterns and evolution of spatial association networks of agricultural innovation ecosystems is crucial for coordinated regional agricultural development. Currently, its development exhibits regional imbalance, and the overall level needs further improvement. Based on China's provincial data from 2012 to 2023, an evaluation index system is constructed to explore the spatiotemporal pattern evolution, centroid migration, dispersion trends, and characteristics of spatial association networks of agricultural innovation ecosystems. Methods including game theory-based combined weighting, kernel density estimation, standard deviation ellipse, modified gravity model, and social network analysis are adopted. Temporally, indices at national and four regional levels show fluctuating upward trends; inter-provincial growth gaps expand, with the eastern region at the highest level, while the western and northeastern regions have similar levels with balanced intra-regional development. Spatially, overall levels improve with narrowing regional differences, showing a gradient decrease from southeast to northwest; the centroid shifts from northwest to southeast, tending toward spatial convergence. Association networks present complex multi-line structures with good connectivity, with increasing network density but declining hierarchy and efficiency. In spatial clustering, eastern and southern regions are mostly net beneficiary areas, western regions are mainly net spillover areas, the central region plays an intermediary role, and inter-block effects have significantly improved. Theoretically, it enriches quantitative research paradigms, fills gaps in spatiotemporal evolution and association network mechanisms, and deepens theoretical cognition from a complex system perspective. Practically, it provides a basis for improving overall levels, formulating differentiated strategies, and optimizing resource allocation, helping address regional imbalance and offering references for global agricultural development. • China's agricultural innovation ecosystem index rises yearly. • Inter-provincial gap narrows, showing a gradient distribution from SE to NW. • Spatial association network has good connectivity. • Inter-block linkages and spillover effects improve significantly. • It provides the evolution of spatial theory and policy implications.