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http://dx.doi.org/10.5532/KJAFM.2021.23.1.34

Utilization Evaluation of Numerical forest Soil Map to Predict the Weather in Upland Crops  

Kang, Dayoung (Division of Mathematics and Big Data Science, Daegu University)
Hwang, Yeongeun (Division of Mathematics and Big Data Science, Daegu University)
Yoon, Sanghoo (Division of Mathematics and Big Data Science, Daegu University)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.23, no.1, 2021 , pp. 34-45 More about this Journal
Abstract
Weather is one of the important factors in the agricultural industry as it affects the price, production, and quality of crops. Upland crops are directly exposed to the natural environment because they are mainly grown in mountainous areas. Therefore, it is necessary to provide accurate weather for upland crops. This study examined the effectiveness of 12 forest soil factors to interpolate the weather in mountainous areas. The daily temperature and precipitation were collected by the Korea Meteorological Administration between January 2009 and December 2018. The Generalized Additive Model (GAM), Kriging, and Random Forest (RF) were considered to interpolate. For evaluating the interpolation performance, automatic weather stations were used as training data and automated synoptic observing systems were used as test data for cross-validation. Unfortunately, the forest soil factors were not significant to interpolate the weather in the mountainous areas. GAM with only geography aspects showed that it can interpolate well in terms of root mean squared error and mean absolute error. The significance of the factors was tested at the 5% significance level in GAM, and the climate zone code (CLZN_CD) and soil water code B (SIBFLR_LAR) were identified as relatively important factors. It has shown that CLZN_CD could help to interpolate the daily average and minimum daily temperature for upland crops.
Keywords
Forest soil map; Generalized addictive model; Kriging; Randomforest; Upland crops;
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Times Cited By KSCI : 3  (Citation Analysis)
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