Estimation of Sugar Cane Yield in the Northeast of Thailand with MLR Model
Abstract
This study investigated the significant explanatory variables influenced to the sugar cane yield in the northeast of Thailand with MLR (multiple linear regression) model. Best subsets and stepwise techniques were applied to gain the suitable MLR equation compiling with assumption of regression. The result of study revealed there were 5 important independent variables; Cultivated area, sugar cane quantity sent to factories, average price of sugar cane, minimum temperature and number of rainy days, selected in the estimated regression equation with 14.7412 for standard error of estimation. The performance of MLR model was verified with the root mean square error (RMSE). It indicated that the MLR model efficiently estimated the sugar cane yield with the small value of RMSE (12.7802). Keywords : sugar cane yield, MLR model, best subsets, stepwise regressionReferences
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Michael, H. K., John, N., Christopher, J. N. & William Li. (2005). Applied Linear Regression Models, 5th Edition. New York: McGraw-Hill.
National Statistics Organization of Thailand. (2014). Rainfalls and Temperatures Statistics at Meteorological Department. Retrieve April 10, 2015 from web site: http://service.nso.go.th/nso/web/statseries /statseries27.html. (in Thai)
Office of Agricultural Economics. (2015). Commercial price of sugarcane. Retrieved March 30, 2015 from web site: http://www.oae.go.th/main.php?filename=index. (in Thai)
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Xu, Y. C., Shen, S. Q. & Chen, Z. (2010). Comparative Study of Sugarcane Average Unit Yield Prediction with Genetic BP Neural Network Algorithm. China: Department of Computer Science, Guangdong Polytechnic Institute Guangzhou, College of Engineering, South China Agricultural University.
Bukate, O. & Seresangtakul, P. (2013). Sugarcane Production Forecasting Model of the Northeastern by Artificial
Neural Network. KKU Sci. J., 41(1), 213-225. (in Thai)
Chimnarong, V. (2009). The relationship between climatic parameters and sugarcane yields: the case study of Mitr Phukieo Sugarcane Plantations. Thesis, Master of Environmental Science, Khon Kaen University.
(in Thai)
Durbin, J. & Watson, G. S. (1951). Testing for Serial Correlation in Least Squares Regresiion II. Biometrika, 38(2),
159-177.
Food and Agriculture Organization of the United Nations. (2015). Crop production. Retrieved January 27, 2015 from web site: http://faostat3.fao.org/faostat-gateway/go/to/download/Q/QC/E
Kapetch, P. & Pannangpetch, K. (2012). The Effects of Climate Change on Sugarcane Production in Northeast of Thailand: A case Study in Kalasin Province. Khon Kaen Agr. J., 40, 83-91. (in Thai)
Michael, H. K., John, N., Christopher, J. N. & William Li. (2005). Applied Linear Regression Models, 5th Edition. New York: McGraw-Hill.
National Statistics Organization of Thailand. (2014). Rainfalls and Temperatures Statistics at Meteorological Department. Retrieve April 10, 2015 from web site: http://service.nso.go.th/nso/web/statseries /statseries27.html. (in Thai)
Office of Agricultural Economics. (2015). Commercial price of sugarcane. Retrieved March 30, 2015 from web site: http://www.oae.go.th/main.php?filename=index. (in Thai)
Office of the Cane and Sugar Board. (2011). Report of sugarcane cultivated area in Thailand. Retrieved 29 March, 2015 from web site: http://www.ocsb.go.th/th/cms/ detail.php?ID=923&SystemModuleKey=journal
Romeu, J. L. (2003). Anderson-Darling: a goodness of fit test for small samples assumptions. Selected Topics in Assurance Related Technologies. 10(5), 1-6.
Xu, Y. C., Shen, S. Q. & Chen, Z. (2010). Comparative Study of Sugarcane Average Unit Yield Prediction with Genetic BP Neural Network Algorithm. China: Department of Computer Science, Guangdong Polytechnic Institute Guangzhou, College of Engineering, South China Agricultural University.
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Published
2017-06-22
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Research Article
