| 丁艳喜,宋文博,孟庆香,郭子龙,康凯,王杨.河南省农用地集约利用影响因素计量分析——基于遗传算法-BP神经网络、广义脉冲响应函数的实证研究[J].干旱地区农业研究,2015,33(3):224~230 |
| 河南省农用地集约利用影响因素计量分析——基于遗传算法-BP神经网络、广义脉冲响应函数的实证研究 |
| Econometric analysis on influencing factors of intensive use of agricultural land in Henan Province——An empirical research based on GA-BP and Generalized Impulse Response Function |
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| DOI：10.7606/j.issn.1000-7601.2015.03.36 |
| 中文关键词: 农用地 集约利用 影响因素 计量分析 河南省 |
| 英文关键词:agricultural land intensive use influencing factors econometric analysis Henan Province |
| 基金项目:中国地质调查局项目（12120113007300） |
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| 摘要点击次数: 1848 |
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| 中文摘要: |
| 基于河南省1978—2012年相关时间序列数据，构建遗传算法-BP神经网络模型，对研究期间河南省农用地集约利用水平进行测算。在此基础上，应用协整理论、误差修正模型、广义脉冲响应函数和方差分解，研究了河南省农用地集约利用与其影响因素的动态响应关系。结果表明，农用地集约利用综合指数和农民人均收入、农业总产值、人均耕地、政策法规存在长期均衡关系；农民人均收入、农业总产值和政策法规对农用地集约利用推动作用长期内更为显著；农民人均收入和政策法规是农用地集约利用预测方差的主要来源，总贡献保持在52%以上，而农业总产值和人均耕地对农用地集约利用预测方差的贡献不足14%。从整体看，河南省农民人均收入和政策法规是影响农用地集约利用的主要因素。最后根据研究结论提出了相关政策建议。 |
| 英文摘要: |
| Based on the related time series data from 1978 to 2012 in Henan Province, constructed the genetic algorithm-BP neural network model and measured the intensive use of agricultural land. On this basis, using the cointegration theory, error correction model, general impulse response function and variance decomposition, researched the dynamic response relationship between intensive use of agricultural land and its influencing factors. The results showed that: There were a long-term equilibrium relationship between the comprehensive index of intensive use of agricultural land and per capita income of farmers, total value of farm output, per capital arable land and policy and rules. The promoting role to the intensive use of agricultural land by the per capita income of farmers, total value of farm output, policy and rules were more significant in long term. The per capita income of farmers and policy and rules was the major source for forecasting variance of intensive use of agricultural land. The total contribution was kept above 52%. But the contribution of total value of farm output and per capital arable land to forecasting variance of intensive use of agricultural land was below 14%. On the whole, the per capita income of farmers and policy and rules were the major factor to affec the intensive use of agricultural land. Finally, according to the research conclusion, put forward the related policy and suggestions. |
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