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Measurement, regional disparities, and dynamic evolution of food crop breeding technology innovation in China
1School of Economics and Management, Shandong Agricultural University, Tai'an, China.
Abstract:
Food crop breeding technology innovation is the cornerstone of national food security and a critical pathway to building a strong agricultural nation. Despite significant progress, China still lags behind developed countries in unit yield of major food crops, with the gap in breeding innovation and market application translating into substantial productivity disparities. While existing literature has explored crop breeding innovation from various dimensions, comprehensive evaluation frameworks based on the innovation chain perspective remain underdeveloped, limiting the comparability and policy relevance of research findings.This study constructs a food crop breeding technology innovation evaluation system from the innovation chain perspective, encompassing four interconnected stages: basic research, applied research, new variety development, and market application. Using panel data from 31 Chinese provinces (excluding Hong Kong, Macao, and Taiwan) from 2013 to 2023, we employ the entropy weight method to measure innovation levels, the Dagum Gini coefficient to decompose regional disparities, Kernel density estimation to analyze distribution dynamics, and Markov chain analysis to examine dynamic evolution characteristics with and without spatial effects.China's food crop breeding technology innovation demonstrates an upward trend amidst fluctuations, with main sales areas outperforming main production areas and balanced production-marketing areas. Regional disparities are significant but narrowing, with inter-regional differences constituting the primary source of overall inequality. The distribution of innovation levels shows increasing dispersion with gradually diminishing polarization. Traditional Markov chain analysis reveals a gradual, path-dependent development process with pronounced Matthew effects. Spatial Markov chain analysis confirms significant spatial spillover effects that intensify over time, where proximity to high-level regions enhances upward mobility probability.
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