Research on the Efficiency of Green Agricultural Science and Technology Innovation Resource Allocation Based on a Three-Stage DEA Model-A Case Study of Anhui Province, China

Study on the influence mechanism of adoption of smart agriculture technology behavior

Smart agricultural (SA) technology has become a technological support for modern agriculture. By exploring the decision-making process and psychological motivation of farmers in adopting SA technology, it is conducive to achieving the popularisation of SA technology and promoting the modernisation of agriculture. Based on microscopic research data, a Structural Equation Model (SEM) is used to analyse the influencing factors and extent of cotton farmers' adoption of SA technologies, using Deconstructive Theory of Planned Behavior (DTPB) as the analytical framework. This was combined with in-depth interviews to further reveal the motivations and influencing mechanisms of cotton farmers' adoption of SA technologies. The results show that under the behavioural belief dimension, cotton farmers value the positive effect of perceived usefulness even though the risk of the technology itself has a dampening effect on adoption intentions. Under the normative belief dimension, superior influence influenced the willingness to adopt SA technologies to a greater extent than peer influence. Under the control belief dimension, factors such as self-efficacy and information channels influence willingness to adopt technology and behaviour. In addition, behavioural attitudes, subjective norms, and perceived behavioural control all contribute to cotton farmers' willingness to adopt SA technologies, and can also influence behaviour directly or indirectly through willingness to adopt. Policy and technology satisfaction positively moderate the transition from willingness to behaviour. Therefore, preferential policies are proposed to reduce the cost of adopting SA technologies; to continuously improve the level of SA technologies; to establish SA technology test plots to provide a reference base; and to increase knowledge training on SA and expand access to information.

Does Agricultural Credit Input Promote Agricultural Green Total Factor Productivity? Evidence from Spatial Panel Data of 30 Provinces in China

Improving agricultural green total factor productivity is crucial to promoting high-quality agricultural development. This paper selects the panel data of 30 provinces in China from 2009 to 2020 and uses the super-efficiency SBM model with undesirable outputs to measure the agricultural green total factor productivity of all regions in China. On this basis, this paper uses the panel data fixed-effect model and spatial Durbin model to empirically discuss the impact of agricultural credit input on agricultural green total factor productivity and its spatial spillover effect. The main conclusions are as follows: First, from 2009 to 2020, the average values of agricultural green total factor productivity in national, eastern, central, and western regions are 0.8909, 0.9977, 0.9231, and 0.8068, respectively, and the agricultural green total factor productivity needs to be further improved. Second, the agricultural green total factor productivity presents a significant and positive spatial correlation, and the spatial distribution of agricultural green total factor productivity is not random and irregular. Third, agricultural credit input can significantly promote agricultural green total factor productivity in the local region, but it hinders the improvement of agricultural green total factor productivity in the adjacent regions. Fourth, the impact of agricultural credit input on the agricultural green total factor productivity and its spillover effect has a significant regional heterogeneity. This paper believes that paying attention to the spatial spillover effect of agricultural total factor productivity, optimizing the structure and scale of agricultural credit input, and formulating reasonable agricultural credit policies can improve agricultural green total factor productivity.