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中国种植业专业化演化及其影响因素

Research on Evolution and Influence Factors of Planting Industry in China
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摘要 通过构建中国种植业的地方专业化系数FR_(i),阐释了1978—2017年中国种植业的地方专业化演化特征,并运用面板数据回归模型等方法进行实证研究。结果表明:(1)中国各省区市种植业的地方专业化系数FR_(i)增长速度不一,相对正常状态的年份数约占65.08%,要远多于相对高值状态和相对低值状态;(2)进出口水平和农用化肥施用量对地区的种植业地方专业化水平提升分别具有较大和较小的正向促进作用;第一产业增加值占GDP的比重、财政支出占GDP的比重以及商品价格指数的提升不利于种植业地方专业化水平的提高;农业人口人均农业机械动力、地区GDP占全国的比重、农民人均纯收入水平、第一产业投资水平以及农业从业人口数量的增加则没有显著的影响。 This paper described the evolutionary characters of local planting industry in China from 1978 to 2017 by building the indexes of local specialization coefficient of planting industry(FR_(i)),and conducted an empirical study by the method of panel data regression model.The main contents and results are summed up as follows.(1)The FR_(i) of Chinese provinces,autonomous regions and municipalities keep a different growth.In general,the number of years in the relative normal value state of FR_(i) accounts about 65.08%,far more than the relative high value state and the relative low value state in China’s provinces,autonomous regions and municipalities.(2)The import and export level promotes the specialization of planting industry greatly,while less promotion by the applying quantity of chemical fertilizer in farmland.The growth in the proportion of primary industry,the proportion of fiscal expenditure to GDP and the commodity price index are unfavourable for the specialization of planting industry with different degrees.The agricultural population per capita agricultural machinery power,proportion of regional GDP,farmer per capita net income,investment on the primary industry and growth in the farming population have no significant influence on the specialization of planting industry.
作者 叶青 王武林 YE Qing;WANG Wu-lin(Ocean College,Minjiang University,Fuzhou 350108,China;College of Environment and Safety Engineering,Fuzhou University,Fuzhou 350108,China)
出处 《湖南师范大学自然科学学报》 CAS 北大核心 2022年第2期94-102,共9页 Journal of Natural Science of Hunan Normal University
基金 国家自然科学基金项目(41701118)。
关键词 种植业 专业化 影响因素 面板数据 中国 planting industry specialization influence factors panel data China
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