Advanced SearchSearch Tips
Garlic yields estimation using climate data
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Garlic yields estimation using climate data
Choi, Sungchun; Baek, Jangsun;
  PDF(new window)
Climate change affects the growth of crops which were planted especially in fields, and it becomes more important to use climate data to predict the yields of the major vagetables. The variation of the crop products caused by climate change is one of the significant factors for the discrepancy of the demand and supply, and leads to the price instability. In this paper, using a panel regression model, we predicted the garlic yields with the weather conditions of different regions. More specifically we used the panel data of the several climate variables for 15 main garlic production areas from 2006 to 2015. Seven variables (average temperature, average maximum temperature, average minimum temperature, average surface temperature, cumulative precipitation, average relative humidity, cumulative duration time of sunshine) for each month were considered, and most significant 7 variables were selected from the total 84 variables by the stepwise regression. The random effects model was chosen by the Hausman test. The average maximum temperature (January), the cumulative precipitation (March, October), the cumulative duration time of sunshine (April, October) were chosen among the variables as the significant climate variables of the model
Garlic yields;meteorological data;panel analysis;
 Cited by
스마트팜 데이터를 이용한 토마토 최적인자에 관한 연구,나명환;박유하;조완현;

Journal of the Korean Data and Information Science Society, 2017. vol.28. 6, pp.1427-1435 crossref(new window)
Baltagi B. H. (2005). Econometric Analysis of panel Data, 3th Ed., John Wiley & Sons, New York.

Chang, S. H. (2000). Study on the prediction models for the productions of major food crops. Journal of the Korean Data & Information Science Society, 11, 47-55.

Choi, S. and Baek, J. (2016). Onion yields estimation using spatial panel regression model. The Korean Journal of Applied Statistics, submitted.

Croissant, Y. and Millo, G. (2008). Panel data econometrics in R : The plm package. Journal of Statistical Software, 27, Issue 2.

Greene, W. H. (2012). Econometric Analysis, 7th Ed., pearson, London.

Kim, M. R. and Kim, S. G. (2014). Examining impact of weather factors on apple yield. Korean Journal of Agricultural and Forest Meteorology, 16, 274-284. crossref(new window)

Lee, J. W. (1996). Analysis of major production factor for pepper, garlic and onion. Journal of Rural Development, 19, 27-50.

Lee, H. Y. and Noh, S. C. (2013). Advanced Statistical Analytics. Moonwoosa, Goyang.

Min, I. S. and Choi, P. S. (2012). STATA Panel Data Analysis, The Korean Association of STATA, Seoul.

Rural Development Administration. (2014). Garlic : 2014,

Shin, D. H. and Lee, M. S. and Park, J. H. and Lee, Y. S. (2015). A meta analysis of the climate change impact on rice yield in south korea. Journal of the Korean Data & Information Science Society, 26, 355-365. crossref(new window)

Son, J. H. (2003). A study on th cropping systems of garlics and onions produced at the main farming fields, Chonnam National University, Gwangju.

Yang, H., Choi, J. S., Han, J. T. and Jeong, J. (2015). Influence of tuition and scholarship on the stop-out rate: an empirical analysis using panel regression model. Journal of the Korean Data & Information Science Society, 26, 631-638 crossref(new window)