Research On Optimal Allocation Of Agricultural S&T Resources Based On Complex Adaptive Theory

The No.1 paper of the central document in 2017 pointed out that the "three rural issues" is a fundamental issue concerning the national economy and the people’s livelihood.Without the modernization of agriculture and rural areas,there would be no modernization of the country.As we all know,the fundamental way out for the development of agricultural modernization lies in scientific and technological progress,while scientific and technological progress relies on the effective support of agricultural S&T resources.China’s agriculture is at a critical stage of accelerating the transformation of traditional agriculture and accelerating its modernization.The structural S&T innovation and structural optimization of agricultural S&T resource allocation have become a strong driving force for agricultural development.Based on this,this paper takes the agricultural scientific research institution as the research object,and based on the complex adaptive system theory,uses the nuclear density estimation function to analyze the dynamic evolution of the agricultural S&T resource input structure.Through the construction of agricultural S&T resources optimization configuration model,and using MATLAB software multi-objective genetic algorithm to solve the optimal allocation of agricultural S&T resources,and analyze the results.Based on the conclusions obtained,the corresponding policy recommendations for future investment in agricultural S&T financial resources are proposed.This study includes the following five aspects:The first part,through the systematic review of agricultural S&T resources input,agricultural S&T resource allocation structure and optimization configuration,defines relevant concepts from agricultural S&T human resources and financial resources to explain the conceptual framework of agricultural S&T resource allocation system.The second part describes the basic theory,core ideas,characteristic mechanisms and theoretical characteristics of complex adaptive systems,and analyzes the complexity and CAS characteristics of agricultural S&T resource systems.The third part describes and analyzes the general situation of agricultural S&T resource allocation structure,based on industries such as planting,animal husbandry,fishery,farmland and agricultural mechanization,basic research,experimental development,applied research and different stages,as well as agronomy,animal husbandry and veterinary.The data of disciplines of science and aquaculture use the Gini coefficient and the nuclear density function method to analyze the regional differences of agricultural S&T input and the dynamic evolution of distribution.In the fourth part,based on the theory of complex adaptive system,the optimal allocation model of agricultural S&T resources is constructed,the initialization of the model is described,and the boundaries of the constraints in the model are determined.The regression coefficients of the model are tested by Eviews to determine the establishment of agricultural S&T resources.After the feasibility of the model is configured,the multi-objective genetic algorithm in MATLAB software is used to calculate the optimal allocation model of agricultural S&T.The fifth part,The results show that the total investment in agricultural S&T resources among the various entities is significantly increased.The analysis proves that increasing investment in experimental development funds and reducing the investment in the number of applied researchers and basic research funds will help to optimize agricultural S&T output.Therefore,while fully exploiting the superior resources according to local conditions,it is possible to quickly establish regional agricultural characteristics by improving the educational level and comprehensive quality of agricultural researchers.Effectively integrate various types of agricultural S&T resources to improve the allocation efficiency and allocation capacity of agricultural S&T resources,and fundamentally solve the problem of agricultural S&T resource allocation.