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http://dx.doi.org/10.5762/KAIS.2018.19.5.480

Development of Examination Model of Weather Factors on Garlic Yield Using Big Data Analysis  

Kim, Shinkon (Division of Business, Kwangwoon University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.19, no.5, 2018 , pp. 480-488 More about this Journal
Abstract
The development of information and communication technology has been carried out actively in the field of agriculture to generate valuable information from large amounts of data and apply big data technology to utilize it. Crops and their varieties are determined by the influence of the natural environment such as temperature, precipitation, and sunshine hours. This paper derives the climatic factors affecting the production of crops using the garlic growth process and daily meteorological variables. A prediction model was also developed for the production of garlic per unit area. A big data analysis technique considering the growth stage of garlic was used. In the exploratory data analysis process, various agricultural production data, such as the production volume, wholesale market load, and growth data were provided from the National Statistical Office, the Rural Development Administration, and Korea Rural Economic Institute. Various meteorological data, such as AWS, ASOS, and special status data, were collected and utilized from the Korea Meteorological Agency. The correlation analysis process was designed by comparing the prediction power of the models and fitness of models derived from the variable selection, candidate model derivation, model diagnosis, and scenario prediction. Numerous weather factor variables were selected as descriptive variables by factor analysis to reduce the dimensions. Using this method, it was possible to effectively control the multicollinearity and low degree of freedom that can occur in regression analysis and improve the fitness and predictive power of regression analysis.
Keywords
Big Data; Weather; Garlic Production; Prediction; Multicollinearity;
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1 Korea Institute for Industrial Economics & Trade, http://www.kiet.go.kr/servlet/isearch, 2014.
2 J. K. Koh, J. H. Kim, "Have local officials recognized the importance of adaptive policy," Journal of the Korean Urban Management Association, vol. 24, no. 3, pp. 51-72, 2011.
3 O. S. Kwon, H. K. Cho, E. B. Cho, K. S. Roh, "Climate Variables and Rice Productivity: A Semiparametric Analysis Using Panel Regional Date," Korean Journal of Agricultural Economics, vol. 54, no. 3, pp. 71-94, 2013.
4 L. You, M. W. Rosegrant, S. Wood, D. Sun, "Impact of growing season temperature on wheat productivity in china," Agricultural for Meteorology, vol. 149, pp. 1009-1014, 2009. DOI: https://doi.org/10.1016/j.agrformet.2008.12.004   DOI
5 S. H. Han, B. H. Lee, M. S. Park, J. h. Seoung, H. S. Yang, S. C. Shin, "A Study of building Crop Yield Forecasting Model Considering Meteorological elements," Korea Rural Economic Institute, p.152, 2011.
6 T. B. John, C. H. Yu, "Exploratory Data Analysis," Wiley, 2003.
7 G. Ciaburro, "Regression Analysis with R: Design and develop statistical nodes to identify unique relationships within data at scale", Jan 31, 2018.
8 Y. Kano, H. Akira, "Stepwise variable selection in factor analysis," Psychometrika, vol. 65, no. 1, pp. 7-22, 2000. DOI: https://doi.org/10.1007/BF02294182   DOI
9 T. A. Brown, "Confirmatory Factor Analysis for Applied Research", 2nd Edi. (Methodology in the Social Sciences), Jan 8, 2015.
10 R. L. Gorsuch, "Factor Analysis: Classic Edition", Psychology Press, Dec 24, 2014.
11 L. D. Schroeder and D. L. Sjoquist, "Understanding Regression Analysis: An Introductory Guide", Quantitative Applications in the Social Sciences, Nov 24, 2016.
12 D. E. Farrar and R. R Glauber, "Multicollinearity in Regression Analysis; the Problem Revisited", Aug 24, 2017.
13 Wikipedia contributors, "Focus On: Regression Analysis: Dependent and independent Variables, Multicollinearity, Simple linear Regression, Heteroscedasticity, Lasso (statistics), ... Estimation, Errors and Residuals, etc.", Feb 22, 2018.