Journal of Integrative Agriculture ›› 2021, Vol. 20 ›› Issue (7): 1996-2008.DOI: 10.1016/S2095-3119(20)63439-6

所属专题： 农业经济与管理合辑Agricultural Economics and Management

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  • 收稿日期:2020-04-22 出版日期:2021-07-01 发布日期:2021-06-02

Mechanization and efficiency in rice production in China

SHI Min1, Krishna P. PAUDEL2, CHEN Feng-bo1
  

  1. 1 College of Economics & Management, South China Agricultural University, Guangzhou 510642, P.R.China
    2 Department of Agricultural Economics and Agribusiness, Louisiana State University (LSU) and LSU Agricultural Center, Baton Rouge, Louisiana 70803, USA
  • Received:2020-04-22 Online:2021-07-01 Published:2021-06-02
  • Contact: Correspondence Krishna P. PAUDEL, E-mail: kpaudel@agcenter.lsu.edu
  • About author:SHI Min, E-mail: shimin@scau.edu.cn;
  • Supported by:
    The authors acknowledge the financial support from the National Social Science Foundation of China (14BGL094), the Rice Research System in Guangdong Province, China (2019KJ105) and the EU Project H2020 Program (822730). Paudel’s time on this paper was supported by the United States Department of Agriculture (USDA), National Institute of Food and Agriculture (NIFA) funded Hatch projects (#94382 and #94483).

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摘要：

中国农业机械及其社会化服务发展迅速，必然会影响水稻生产效率。2015年我们在中国五个主要的水稻生产省份进行调研，获得450个农户家庭情况及其3096地块农业生产情况相关数据。利用这些问卷调查数据，我们计算了技术效率、配置效率和规模效率。我们利用“一步法”随机前沿模型，计算技术效率，并利用技术效率值对表征社会人口、土地自然特征的因素进行回归分析，找到技术效率的影响因素。接着，比对分析三个水稻生产环节的技术效率影响因素。为解决内生性问题和选择偏误，我们还利用Heckman选择模型来计算技术效率。研究发现：（1）使用“一步法”随机前沿模型计算获得的技术效率平均值为0.74。使用Heckman选择模型考虑自选择偏误时，技术效率平均值为0.80。（2）化学农药施用机械化对技术效率有正向影响，但耕作和收割环节机械化对技术效率的影响不显著。（3）相对于土地和劳动力而言，机械化投入偏高。过高的机械化成本投入到较小的耕地面积，导致配置效率较低，规模不经济。（4）稻农绝大多数以次优的土地规模进行农业生产。未来的政策应侧重于鼓励土地流转以实现规模效率和分配效率，同时在水稻的化学农药施用环节采用机械化以提高技术效率。


Abstract:

Agricultural mechanization and custom machine services have developed rapidly in China, which can influence rice production efficiency in the future.  We calculate technical efficiency, allocative efficiency, and scale efficiency using data collected in 2015 from a face-to-face interview survey of 450 households that cultivated 3 096 plots located in the five major rice-producing provinces of China.  We use a one-step stochastic frontier model to calculate technical efficiency and regress the efficiency scores on socio-demographic and physical land characteristics to find the influencing variables.  Variables influencing technical efficiency are compared at three different phases of rice cultivation.  We also calculate technical efficiency by using the Heckman Selection Model, which addresses technological heterogeneity and self-selection bias.  Results indicate that: (1) the average value of technical efficiency using a one-step stochastic frontier model was found to be 0.74.  When self-selection bias is accounted for using the Heckman Selection Model, the average value of the technical efficiency increases to 0.80; (2) mechanization at the chemical application phase has a positive effect on technical efficiency, but mechanization does not affect efficiency at the plowing and harvesting phases; (3) machines are overused relative to both land and labor, and high machine input use on the small size of landholding has resulted in allocative inefficiency; (4) rice farmers are overwhelmingly operating at a sub-optimal scale.  Future policies should focus on encouraging farmland transfer in rural areas to achieve scale efficiency and allocative efficiency while promoting mechanization at the chemical application phase of rice cultivation to improve technical efficiency. 

Key words: technical efficiency ,  allocative efficiency ,  scale efficiency ,  rice farmers ,  stochastic frontier function