摘要
本文选用2010~2020年的省级面板数据,以广东农村人口老龄化,广东农业生产总值,广东农业精准化发展及农业产业升级作为研究对象,运用GM(1,1)模型对农村人口老龄化进行预测分析,运用熵权秩和比模型构建精准农业程度指标与农业产业升级程度指标;运用VAR模型分析各变量内在影响。研究发现:第一,农村人口老龄化对农业精准化有长期的负向影响,这是由于老龄农户较难掌握先进信息技术,农村人口老龄化对农业产业升级具有正向影响,这是由于老龄化推动了农业朝组织化和规模化发展;第二,农村人口老龄化对农业生产总值存在较大的正向影响,老龄化倒逼了农业依靠生产高效、经营创新来发展;第三,从长期来看,农业精准化发展和产业升级会推动农业发展。根据结果,本文提出利用老年人红利,提高农业劳动力结构水平;完善精准化农业的基础设施,建立信息化平台;建立农业产业升级的相关机构等建议。
This paper selects the provincial panel data from 2010 to 2020 and takes the aging of the rural population in Guangdong, the GDP of agriculture in Guangdong, the precision development of agriculture in Guangdong, and the upgrading of the agricultural industry as the research objects. We use the GM(1,1) model to predict the aging population in Guangdong’s rural areas, use the entropy weight rank sum ratio model to construct Degree of precision agriculture Index and Degree of agricultural industry upgrading index, and then use the VAR model to analyze the intrinsic impact of each variables. The research results show that: firstly, the aging of the rural population has a long-term negative impact on agricultural precision because it is difficult for elderly farmers to master advanced information technology. At the same time, the aging of the rural population has a positive effect on the upgrading of the agricultural industry because aging promotes the development of agriculture towards organization and scale. Secondly, the aging of the rural population has a great positive impact on the agricultural GDP because aging forces agriculture to rely on production efficiency and business innovation. Thirdly, in the long run, the precision development of agriculture and the upgrading of the agricultural industry can effectively promote the development of agriculture. According to the research results, this paper proposes to make full use of the dividends of the elderly to improve the age structure of agricultural production, improve the infrastructure of precision agriculture in Guangdong, establish relevant information platforms and institutions, and put forward suggestions on policies and regulations.
出处
《数据挖掘》
2023年第1期83-97,共15页
Hans Journal of Data Mining