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http://dx.doi.org/10.5532/KJAFM.2021.23.4.366

Setting Criteria of Suitable Site for Southern-type Garlic Using Non-linear Regression Model  

Choi, Won Jun (Climate change Assessment Division, National Institute of Agricultural Sciences)
Kim, Yong Seok (Climate change Assessment Division, National Institute of Agricultural Sciences)
Shim, Kyo Moon (Climate change Assessment Division, National Institute of Agricultural Sciences)
Hur, Jina (Climate change Assessment Division, National Institute of Agricultural Sciences)
Jo, Sera (Climate change Assessment Division, National Institute of Agricultural Sciences)
Kang, Mingu (Climate change Assessment Division, National Institute of Agricultural Sciences)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.23, no.4, 2021 , pp. 366-373 More about this Journal
Abstract
This study attempted to establish a field data-based write analysis standard by analyzing field observation data, which is non-linear data of southern garlic. Five regions, including Goheung, Namhae, Sinan, Changnyeong, and Haenam, were selected for analysis. Observation values for each observation station were extracted from the temperature data of farmland in the region through inverse distance weighted. Southern-type garlic production and temperature data were collected for 10 years, from 2010 to 2019. Local regression analysis (Kernel) of the obtained data was performed, and growth temperatures were analyzed, such as 0.8 (18.781℃), 0.9 (18.930℃), 1.0 (19.542℃), 1.1 (20.165℃), and 1.2 (21.042℃) depending on the bandwidth. The analyzed optimum temperature and the grown temperature (4℃/25℃) were applied to extract the growth temperature for each temperature by using the temperature response model analysis. Regression analysis and correlation analysis were performed between the analyzed growth temperature and production data. The coefficient of determination(R2) was analyzed as 0.325 to 0.438, and in the correlation analysis, the correlation coefficient of 0.57 to 0.66 was analyzed at the significance probability 0.001 level. Overall, as the bandwidth increased, the coefficient of determination was higher. However, in all analyses except bandwidth 1.0, it was analyzed that all variables were not used due to bias. The purpose of this study is to accommodate all data through non-linear data. It was analyzed that bandwidth 1.0 with a high coefficient of determination while accepting modeling as a whole is the most suitable.
Keywords
TRM(Temperature Response Model); Local regression; Kernel regression; Cultivation areas; Optimum temperature;
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