논문 상세보기
pp.509-518
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.
1. 서 론
2. 연구 방법론
2.1. 역거리가중법
2.2. 일반화가법모형
2.3. 베이지안 공간선형모형
2.4. 모형평가
3. 연구 자료
4. 연구 결과
4.1. 지구통계모형 예측성능 평가
4.2. 지구통계모형에 따른 공간분포 예측사례
4.3. 양파 재배지 농업기상정보 생성
5. 결 론
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