›› 2013, Vol. 15 ›› Issue (2): 45-53.DOI: doi:10.3969/j.issn.1008-0864.2013.02.08

• 农业创新论坛 • 上一篇    下一篇

甘肃省粮食生产影响因素分析

郝振华,叶得明*   

  1. (甘肃农业大学经济管理学院, 兰州 730070)
  • 收稿日期:2012-09-11 修回日期:2012-12-20 出版日期:2013-04-15 发布日期:2013-04-16
  • 通讯作者: 叶得明,副教授,硕士生导师,主要从事农业经济管理方面的研究和教学。E-mail:yedeming@gsau.edu.cn
  • 作者简介:郝振华,硕士研究生,研究方向为农业与农村经济。E-mail:haozhenhua2006@163.com。

Analysis of Influential Factors on Grain Production in Gansu Province

HAO Zhen-hua, YE De-ming*   

  1. (Economics and Management College of Gansu Agricultural University|Lanzhou 730070, China)
  • Received:2012-09-11 Revised:2012-12-20 Online:2013-04-15 Published:2013-04-16

摘要:

明确粮食生产能力,对确保区域粮食安全具有重要意义。以1990-2010年甘肃省粮食生产的相关资料为样本,选取九项对粮食生产有较大影响的指标,建立多元线性回归模型,并利用SPSS软件进行计量分析。结果表明:影响甘肃省粮食生产的主要因素为粮食播种面积、化肥施用量、农村用电量、成灾面积,影响程度分别为92.0%、24.8%、16.9%、-22.2%。结合甘肃省的实际情况,提出稳定粮食生产的基本对策是:在保证粮食播种面积的前提下,进一步提高化肥利用效率;加强电力资源管理、农业基础设施建设,特别是农田水利建设;并加快农业科技创新。

关键词: 粮食生产;多元回归;影响因素;甘肃省

Abstract:

It is of great significance for regional grain security to make clear the grain production capacity. Taking relevant grain production data from 1990 to 2011 in Gansu Province as sample, this article selected 9 indicators which had greater influence on grain production, and established a multiple linear regression model,and then analyzed the influential factors of grain production in Gansu Province by SPSS software. The results showed that those major influential factors were sowing areas of grain, applying quantity of chemical fertilizer, electric power consumption in rural areas, and areas affected by disasters. Their influence degree were 92.0%、24.8%、16.9%、-22.2%. According to the actual situation of Gansu Province, this paper put forward basic countermeasures to maintain the stability of grain production as following: in the premise of guaranteeing stable grain sowing areas, to further improve chemical fertilizer utilization efficiency; to strengthen power resources management and infrastructure construction of farm land, especially irrigation and water conservancy construction; and to speed up the innovation of agricultural science and technology.

Key words: grain production, multiple regression, influential factors, Gansu Province

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