基于典范对应分析的郴州市农业机械化发展研究

为明确郴州市农业机械化发展的现状及驱动因素,基于典范对应分析方法,以郴州市2017年农业机械化发展水平数据及其影响数据为依托,对郴州市11个县(市、区)的农业机械化发展格局的空间差异进行分析。结果表明,典范对应分析中的前4个排序轴的排序总惯量为0.544,累计方差贡献率为64.1%,说明典范对应分析能较好地应用于农业机械化发展的研究。郴州市农业机械化发展格局存在明显的区域分异现象。其中发展水平较高的地区集中在郴州市的西北部,地理生产条件较好,种植结构较为合理;而发展水平较低的地区集中在郴州市的东南部,受地理因素制约较大,农业规模化作业条件较差,经济发展较为落后。今后,郴州市应注重因地制宜地发展农业机械化,加强经济发展,保证合理的农业劳动力。

Research on the development of agricultural mechanization in Chenzhou City based on canonical correspondence analysis

In order to clarify the development status and driving factors of Chenzhou s agricultural mechanization development,it was analyzed the spatial differences in the development pattern of agricultural mechanization in 11 counties(cities,districts)of Chenzhou City based on Chenzhou s 2017 agricultural mechanization development data and agricultural mechanization development impact data by using canonical correspondence analysis(CCA)method.The results showed that the sorting total inertia of the first four sorting axes in the canonical correspondence analysis was 0.544,and the cumulative variance contribution rate was 64.1%,it was indicated that the canonical correspondence analysis could be applied to the research of agricultural mechanization.The development pattern of agricultural mechanization in Chenzhou had obvious regional differentiation.On the whole,the areas with higher development level of agricultural mechanization were concentrated in the northwest of Chenzhou City,which had better production conditions and more reasonable planting structure.However,the areas with low development level were concentrated in the southeast of Chenzhou City,which was greatly restricted by geographical factors,with poor agricultural scale operation conditions and bad economic development.In the future,Chenzhou City should pay attention to the development of agricultural mechanization according to local conditions,improve the awareness of the limitations of agricultural mechanization by relevant personnel,and strengthen the economic development while ensuring appropriate agricultural labor force.