This paper focuses on the sustainable development dilemma of agricultural production in China under the pattern of intensive management, which is seriously challenged by agricultural non-point source pollution. The key to effectively break through the dilemma is to promote the co-governance of agricultural non-point source pollution control by stakeholders including local governments, new agricultural operators and traditional farmers. Accordingly, this paper discusses the interactive decision-making relationships between new agricultural operators and traditional farmers under the guidance of local governments, by constructing a trilateral evolutionary game model, as well as analyzing evolutionary cooperative stability strategies and realizing the simulation of evolution processes in different scenarios by MATLAB. The results show that new agricultural operators play a leading role in agricultural non-point source pollution control, whose strategies have effects such as technology spillover. The rewards from the superior government will support local governments in taking proactive action in the co-governance of agricultural non-point source pollution control, and then local governments can offer technical support and subsidies to new agricultural operators and traditional farmers for reducing their costs. Furthermore, this paper also finds that there are green synergy effects among the groups, where the variations of parameters and strategies by one group would affect the two others. Additionally, agricultural land operation rights transfers would cause traditional farmers to take more time to cooperate in the co-governance of agricultural non-point source pollution control. In order to promote the multi-agent co-governance of agricultural non-point source pollution control under intensive management pattern, this paper suggests that it should be necessary to reduce their costs and improve incentives, as well as to increase the common interests among groups and enhance their green synergy effects.

The diffusion of green agricultural production under intensive management pattern is an interactive process of strategy comparison and learning on complex networks among traditional farmers and new agricultural operation entities. Based on the theory of evolutionary game and complex networks, we construct evolutionary game models on the scale-free networks to simulate the evolution process of green agricultural production under the market mechanism and the government guidance mechanism, respectively. The comparison analysis results in different scenarios show that the stable state of the green agricultural production network is determined by interactions among the subjects. Detailed experimental results indicate that the double-score system under government guidance mechanism has a significant effect on the diffusion of the green agricultural production, of which the extra reward or penalty obtained from government is crucial. Besides, the diffusion of the green agricultural production under the market mechanism is mostly affected by the net profit of green agricultural production. These results are of great significance for increasing efficiency of government’s incentive and promoting the initiatives of traditional farmers and new agricultural operation entities in the green agricultural production.

We recruited 211 new generation employees in research and development teams to examine how internal and external locus of control (LOC) are related to innovative behavior, both directly and indirectly, and to examine the moderated mediation roles of innovation climate and work engagement in this relationship. Results show that internal (vs. external) LOC had direct and indirect positive (vs. negative) effects on innovative behavior. Further, work engagement mediated the LOC–innovative behavior relationship, and innovation climate strengthened the internal LOC–innovative behavior relationship. These results shed light on the personality antecedents of innovative behavior and show how individual differences shape work engagement, and how innovation climate influences innovative behavior. Theoretical and practical implications are discussed.