This paper investigates the time lags effects of innovation input on output in the national innovation systems (NIS). Firstly, we analyze the intrinsic properties of China’s NIS based on the conceptual framework and the causal loop diagram. Secondly, we construct a time lags distribution calculation model and employ the main innovation input indicators to measure the specific characteristics of time lags effects of innovation input on output for China in the period 2000–2012. The results indicate that there are considerable time lags in the four major feedback loops, and the distribution of time lags is various according to the characteristics of innovation input and influencing factors in the internal transformation. The trends of time lags from R&D personnel and industry‐academia‐research collaboration show steady growth, and the trends of time lags from R&D expenditure and government’s macro‐control take inverted U‐shaped pattern. Finally, we combine the estimation results of lags distribution calculation model with the analysis results of the causal loop diagram (including positive loops and negative loops) to provide some innovation policy suggestions. This study provides important implications for our understanding of the long‐term complex lags effects of innovation input on output as well as for policy‐makers designing and implementing the innovation strategies.

Synergy among the various components of national agricultural innovation systems (AISs) promotes agricultural development. This paper investigated the innovation synergy among the various innovation elements of national AISs. First, we developed a synergy analysis model consisting of three innovation variables (innovation allocation, innovation output, and innovation potentiality) and one control variable (government policy supports). Secondly, a broad set of innovation indicators was selected to describe the innovation variables and the control variable, and the solutions of the order parameter equation were then calculated to investigate the self-organized synergistic patterns of a panel of the Group of Twenty (G20) countries. The empirical results indicated the following. (1) All of the G20 countries’ national AISs had the potential to evolve into more advanced self-organized synergistic states under current government policy support. Furthermore, all of the developing countries were in the active period of synergy, showing stronger synergistic rising powers. However, most of the developed countries were in the stable or general period of synergy, in which synergistic rising powers were relatively weaker; (2) Stronger government policy supports played a positive role in promoting the interaction and collaboration among innovation elements and promoted the national AIS to evolve into a more advanced self-organized synergistic state. This study has important implications for understanding the complex innovation synergy of national AISs, as well as for the design and implementation of agricultural innovation strategies for policy-makers.

This study is based on the data from co-authored papers, collaborative patents, and jointly authored varieties involving Chinese agricultural enterprises, universities, and research institutions from 2011 to 2023. We construct a three-dimensional dynamic equation system to model the agricultural industry–university–research (I-U-R) collaborative innovation network operation mechanism. Inspired by the Belousov–Zhabotinsky (B-Z) reaction, we model a three-variable oscillator with the state variables (network structure embeddedness, partner heterogeneity, and collaborative innovation output) to represent three primary substances in the chemical oscillators. This study investigates the network’s operational patterns and its determinants. Findings reveal that the patent network operates more efficiently than the paper and variety networks. Dependence on external government support increases with innovation complexity, coordination difficulty, and social value. Although a “structural optimization–resource agglomeration–output explosion” state is theoretically attainable under threshold conditions, the observed reality reflects “marginal structural optimization–continuous resource depletion–zero output growth”. Among the entities, eighteen are active leaders, forty-two constitute a stable but low-dynamism backbone, and ninety are general participants with limited innovation capacity. Significant structural contradictions highlight the need for targeted policy interventions to guide the network toward a more advanced and orderly state.