ArticleScientific reports2026
Measurement, regional disparities, and dynamic evolution of food crop breeding technology innovation in China.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.