Forecasting and Analysis the Demand of Agricultural Mechanization for Economic Development

Abstract:

The development of agricultural mechanization not only has to consider its development speed, but also should coordinate with economic development. Therefore, taking economic development as the independent variable, and agricultural mechanization development as the dependent variable, the nonlinear relationship model was established. Then, on the basis of forecasting GDP which on behalf of the economic development level, the demands of agricultural mechanization for economic development was predicted. Given the limitations of single forecast model, the nonlinear combination forecast models based on BP neural network was established to forecast the development relationship between economic and agricultural mechanization. The predicted results show that the fitting mean absolute percentage error is 2.61% for the relationship of economic development with agricultural mechanization development, and the fitting mean absolute percentage error is 2.14% for the GDP, which are all far less than the fitting error of traditional forecast models. The validation forecast was carried out; the results show that the combined forecast model can effectively improve the prediction accuracy. The demand of agricultural mechanization for economic development was forecasted from 2012 to 2020 in China using the established nonlinear combined forecast model based on BP neural network. The results show that the demand of total power of agricultural machinery for economic will be 1232298.2 MW by 2015 and 1560579.6 MW by 2020.

• Periodical: Advanced Materials Research (Volumes 694-697)
• Pages: 3512-3515
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.694-697.3512
• Online since: May 2013
• Authors: Jin Yan Ju, Rong Xin Zhu, Lei Geng
• Keywords: BP Neural Network (BPNN), Combined Forecast, Economic, Total Power of Agricultural Machinery

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DOI: 10.1007/s10614-006-9041-7

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