Forecasting and Coupled Coordination Analysis of Supply and Demand for Sustainable Talent in Chinese Agriculture

Abstract

In recent years, China has achieved notable results with its poverty alleviation program, the focus of which is shifting toward the comprehensive promotion of rural revitalization. The role played by sustainable human resources in agriculture is becoming increasingly prominent. In this context, China’s sustainable talent in agriculture is used as the research object, and a neural network analysis method is applied to construct a prediction model of sustainable agricultural talent to forecast its supply and demand. The prediction aims to provide a scientific basis for the strategic planning of talent development for rural revitalization. Based on the forecast results by region and province, we analyzed the level of coordinated development of talent supply and demand to provide a reference for the coordinated development of supply and demand of sustainable talent in agriculture in China. The results showed that a large sustainable agricultural talent demand gap exists in China. The overall talent supply and demand coupling coordination level is low; we found significant differences among different regions and provinces, characterized by decreasing order of the northeast, central, west, and east. According to the socio-economic development level, agricultural economic foundation, and other factors, we divided the provinces into six types for analysis. To promote the coordinated development of sustainable human agricultural resources, talent policy support at the national level is required to reduce the loss of human resources to other countries; at the regional level, the talent environment for rural revitalization should be optimized to increase the attraction of talent. At the provincial level, agricultural and forestry education resources should be created to increase the supply of sustainable agricultural talent.