| 基于MIV和GA-BP模型的农业机械化水平影响因素实证分析 |
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| 引用本文： | 张永礼,陆刚,武建章.基于MIV和GA-BP模型的农业机械化水平影响因素实证分析[J].农业现代化研究,2015,36(6):1026-1031. |
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| 作者姓名： | 张永礼 陆刚 武建章 |
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| 作者单位： | （石家庄经济学院管理科学与工程学院，河北 石家庄 050031）,（石家庄经济学院管理科学与工程学院，河北 石家庄 050031）,（石家庄经济学院管理科学与工程学院，河北 石家庄 050031） |
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| 基金项目： | 国家自然科学基金项目（71201110）；2015年度河北省社会科学发展研究课题（2015041229）。 |
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| 摘 要： | 农业机械化是农业现代化的前提和标志。利用2007-2012年31个省(市)面板数据,建立GA-BP神经网络模型,计算MIV值,对我国农业机械化水平影响因素进行了实证分析。结果表明,农村劳动力转移率、农村居民收入水平和农作物种植结构对农业机械化水平的影响较大,土地经营规模影响较小;户均人口数、水稻播种面积比重、农村居民家庭经营山地面积有负向影响,其它因素有正向影响。从区域来看,农村劳动力转移率是华北、东北、华东和中南地区农业机械化水平主要影响因素,户均人口数在华东、中南和东北地区具有负向影响,但在西北和西南地区具有正向影响,农业机械化水平总体呈现"东部缺地,西部缺人"的现状。从趋势来看,农村劳动力转移率、玉米播种面积比重和农村居民家庭人均纯收入的正向影响在不断增强,农村居民家庭经营山地面积、水稻播种面积比重、户均人口数的负向影响在波动中逐年减弱。为提高农业机械化水平,应加快农村剩余劳动力转移,突破稻作农机技术瓶颈,发展适合山区作业的中小型农业机械,同步推进农机专业服务市场发展和农业适度规模化经营。
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| 关 键 词： | 农业机械化水平 影响因素 MIV 遗传算法 BP神经网络 |
| 收稿时间： | 2015/1/17 0:00:00 |
| 修稿时间： | 2015/5/22 0:00:00 |
The empirical analysis on the influencing factors of agricultural mechanization level based on MIV and GA-BP neural network model |
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| ZHANG Yong-li,LU Gang and WU Jian-zhang.The empirical analysis on the influencing factors of agricultural mechanization level based on MIV and GA-BP neural network model[J].Research of Agricultural Modernization,2015,36(6):1026-1031. |
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| Authors: | ZHANG Yong-li LU Gang and WU Jian-zhang |
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| Abstract: | Agricultural mechanization is a precondition and also a character of agricultural modernization. Based on the panel data of 31 provinces from 2007 to 2012, this paper established a GA-BP neural network model and applied the MIV algorithm method to analyze the influencing factors of agricultural mechanization level in China. Results showed that rural labor force transfer rate, farmers' income and planting structure had significant impacts; while land management scale had weak impacts on agricultural mechanization leve. Family size, rice planting acreage, and mountain area had negative impacts, other factors had positive impacts. From a regional perspective, rural labor force transfer rate was the main influencing factor in North, Northeast, East and Central-South regions. Family size had negative impact in East, Central-South and Northeast regions, but had positive impact in Northwest and Southwest regions. Agricultural mechanization level was also affected by land shortage in eastern region and labor shortage in western region. By examining the trends, this paper found that the positive impacts of rural labor force transfer rate, corn planting acreage, and farmers' income on agricultural mechanization level had been increasing, the negative impacts of mountain area, rice planting acreage, and family size on agricultural mechanization level had been decreasing with some fluctuations. In order to improve agricultural mechanization level, this paper provides the following suggestions: 1) to accelerate the transfer of rural surplus labors; 2) to break the technology bottleneck of agricultural machinery in rice production; 3) to develop small and medium-sized agricultural machinery suitable for mountainous areas; and 4) to promote agricultural machinery service market and moderate scale agriculture together. |
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| Keywords: | agricultural mechanization level influencing factors MIV genetic algorithm BP neural network model |
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