Production of Agrometeorological Information in Onion Fields using Geostatistical Models

지구 통계 모형을 이용한 양파 재배지 농업기상정보 생성 방법

  • Im, Jieun (Department of Statistics, Daegu University) ;
  • Yoon, Sanghoo (Division of Mathematics and Big Data Science, Daegu University)
  • 임지은 (대구대학교 통계학과) ;
  • 윤상후 (대구대학교 수리빅데이터학부)
  • Received : 2018.02.07
  • Accepted : 2018.04.17
  • Published : 2018.07.31


Weather is the most influential factor for crop cultivation. Weather information for cultivated areas is necessary for growth and production forecasting of agricultural crops. However, there are limitations in the meteorological observations in cultivated areas because weather equipment is not installed. This study tested methods of predicting the daily mean temperature in onion fields using geostatistical models. Three models were considered: inverse distance weight method, generalized additive model, and Bayesian spatial linear model. Data were collected from the AWS (automatic weather system), ASOS (automated synoptic observing system), and an agricultural weather station between 2013 and 2016. To evaluate the prediction performance, data from AWS and ASOS were used as the modeling data, and data from the agricultural weather station were used as the validation data. It was found that the Bayesian spatial linear regression performed better than other models. Consequently, high-resolution maps of the daily mean temperature of Jeonnam were generated using all observed weather information.


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