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Application of Gray Metabolic GM (1,1) Model in Prediction of Annual Total Yields of Chinese Aquatic Products

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  • Huang, Songqian
  • Wang, Weimin
  • Zeng, Cong
  • Hao, Shuang
  • Cao, Xiaojuan

Abstract

To predict the annual total yields of Chinese aquatic products in future five years (2011-2015), based on the theory and method of gray system, this paper firstly establishes a conventional GM (1,1) model and a gray metabolic GM (1,1) model respectively to predict the annual total yields of Chinese aquatic products in 2006-2009 and compare the prediction accuracy between these two models. Then, it selects the model with higher accuracy to predict the annual total yields of Chinese aquatic products in future five years. The comparison indicates that gray metabolic GM (1,1) model has higher prediction accuracy and smaller error, thus it is more suitable for prediction of annual total yields of aquatic products. Therefore, it adopts the gray metabolic GM (1,1) model to predict annual total yields of Chinese aquatic products in 2011-2015. The prediction results of annual total yields are 55.32, 57.46, 59.72, 62.02 and 64.43 million tons respectively in future five years with annual average increase rate of about 3.7%, much higher than the objective of 2.2% specified in the Twelfth Five-Year Plan of the National Fishery Development (2011 to 2015). The results of this research show that the gray metabolic GM (1,1) model is suitable for prediction of yields of aquatic products and the total yields of Chinese aquatic products in 2011-2015 will totally be able to realize the objective of the Twelfth Five-Year Plan.

Suggested Citation

  • Huang, Songqian & Wang, Weimin & Zeng, Cong & Hao, Shuang & Cao, Xiaojuan, 2013. "Application of Gray Metabolic GM (1,1) Model in Prediction of Annual Total Yields of Chinese Aquatic Products," Asian Agricultural Research, USA-China Science and Culture Media Corporation, vol. 5(03), pages 1-5, March.
  • Handle: RePEc:ags:asagre:147793
    DOI: 10.22004/ag.econ.147793
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    Agribusiness;

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