The Government–Farmer Cooperation Mechanism and Its Implementation Path to Realize the Goals of Optimizing Grain Planting Structure

Abstract

In order to alleviate the grain supply–demand structural contradictions and ensure the realization of grain planting structure optimization goals, it is necessary to clarify the interactive relationship between multiple entities, establish a cooperation mechanism, and explore its implementation paths. To this end, a differential game model is built to compare and analyze the optimal strategies, optimal benefits, and overall system outcomes for both the government and farmers under three scenarios: the Nash non-cooperative game, the Stackelberg game, and the collaborative cooperation game. Then, key factors and their influencing mechanisms that affect the government–farmer cooperation mechanism are revealed. Finally, the csQCA model is used to explore the implementation paths for different stakeholders to ensure the sound operation of the cooperation mechanism. The results show the following: (1) The government–farmer cooperation mechanism should consist of an inner core system with the government–farmer interaction as the core and an outer system comprising the market environment, cooperation environment, and institutional environment. These two systems should coordinate with each other, respond to each other, and drive progress together. (2) The cooperation mechanism can optimize behavioral enthusiasm, resulting in individual and overall benefits for both the government and farmers. However, its scientific and orderly implementation is affected by factors such as the cost coefficient. Additionally, subsidies serve as a powerful policy tool to enhance farmers’ enthusiasm, thereby increasing the benefits for both parties and maximizing the effectiveness of the cooperation mechanism. (3) There are three implementation paths corresponding to large-scale farmers, rural elites, and small-scale farmers: being led by external policy tools, linkage guidance between decision-making environment and willing subjects, and factor allocation and environmentally driven decision-making. These findings can provide theoretical support and case reference for marginal farmland management and planting structure optimization management in underdeveloped areas.