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Research On Member Selection Of Agricultural Industrialization Consortium Under Urban-rural Integration

Posted on:2023-02-09Degree:MasterType:Thesis
Country:ChinaCandidate:Z W XueFull Text:PDF
GTID:2569306821452124Subject:Business management
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In the report of the 19 th National Congress of the Communist Party of China,regarding the issue of "agriculture,rural areas and farmers",it creatively proposed the implementation of the rural revitalization strategy and proposed a new path for the integrated development of urban and rural areas.Provinces should quickly deploy work around solving the "three rural" issues,accurately control the development direction of agricultural industrialization,emphasize key points such as leading enterprises,connection mechanisms,industrial integration,and large-scale agricultural investment promotion,and focus on building an integrated industrial chain of research,production and sales.Improve the construction of agricultural industrialization complexes.Nowadays,it is faced with problems such as the lack of obvious leading role in member selection of leading enterprises,the imperfect member selection mechanism,the imperfect internal management system of members,and the lack of simulation of evaluation methods.Most of the current research is aimed at the process,current situation and countermeasures of agricultural industrialization consortium,which makes the research on the selection of agricultural industrialization consortium members relatively blank.To better enhance the overall efficiency of the agricultural industrialization consortium and select the best member enterprises of the agricultural industrialization consortium are the key measures to enhance the development of agricultural and rural economy and promote the integrated development of urban and rural areas.Therefore,this paper studies the selection of the members of the agricultural industrialization consortium under the background of urban-rural integration.First of all,this paper refers to a large number of relevant literature and theoretical knowledge of agricultural industrialization consortium,consults the data and analyzes the current situation of my country’s agricultural industrialization consortium under the background of urban-rural integration,as well as the problems existing in the selection of members of my country’s consortium under the stage,and discusses the members in depth.The optimal choice is necessary to promote the integrated development of urban and rural areas.Secondly,through the evaluation and analysis of the existing evaluation and selection methods,a suitable method for the selection of members of the agricultural industrialization consortium is determined,and a brand-new member selection mechanism is formulated.mechanism.Then,the operation of the consortium is divided into three stages: R&D production,transportation and marketing.According to the characteristics of the member enterprises of the consortium required in each stage,an evaluation index system is constructed,and the principal component analysis method is used to optimize the indicators.Combined with the BP neural network construction Member selection model.Through a large number of reading a large number of policy documents,field research,expert scoring and other methods to collect data,and use Matlab software to process the data as a model input sample,build an agricultural industrialization consortium member selection model,and then help leading enterprises to better Formulate member selection strategies to enhance the operational capabilities of agricultural industrialization consortia.It can better provide reference for urban-rural integration,and provide suggestions and basis for the construction of new urbanization.Finally,the real case is substituted into the model for application,which proves that the constructed agricultural industrialization consortium member selection model is feasible and has certain validity and rationality.
Keywords/Search Tags:urban-rural integration, agricultural industrialization consortium, member selection, BP neural network
PDF Full Text Request
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