한국농림기상학회지, 제 4권 제1호(2002) (pISSN 1229-5671, eISSN 2288-1859)
Korean Journal of Agricultural and Forest Meteorology, Vol. 4, No. 1, (2002), pp. 23~28
ⓒ Author(s) 2014. CC Attribution 3.0 License.


결로시간 예측을 위한 경험모형의 최적 기상변수

김광수(1), S, E, Taylor(1), M, L, Gleason(2) and K, J, Koehler(3)
(1)미국 아이오와 주립대학 농학과, (2)미국 아이오와 주립대학 식물병리학과
(3)미국 아이오와 주립대학 통계학과

(2001년 12월 21일 접수; 2002년 02월 18일 수락)

Optimal Weather Variables for Estimation of Leaf Wetness Duration
Using an Empirical Method

K. S. Kim(1), S. E. Taylor(1), M. L. Gleason(2), K. J. Koehler(3)
(1)Department of Agronomy, Iowa State University, Ames 50011, USA
(2)Department of Plant Pathology, Iowa State University, Ames 50011, USA
(3)Department of Statistics, Iowa State University, Ames 50011, USA

(Received December 21, 2001; Accepted February 18, 2002)

ABSTRACT
Sets of weather variables for estimation of LWD were evaluated using CART(Classification And Regression Tree) models. Input variables were sets of hourly observations of air temperature at 0.3-m and 1.5-m height, relative humidity(RH), and wind speed that were obtained from May to September in 1997, 1998, and 1999 at 15 weather stations in iowa, Illinois, and Nebraska, USA. A model that included air temperature at 0.3-m height, RH, and wind speed showed the lowest misidentification rate for wetness. The model estimated presence or absence of wetness more accurately (85.5%) than the CART/SLD model (84.7%) proposed by Gleason et al. (1994). This slight improvement, however, was insufficient to justify the use of our model, which requires additional measurements, in preference to the CART/SLD model. This study demonstrated that the use of measurements of temperature, humidity, and wind from automated stations was sufficient to make LWD estimations of reasonable accuracy when the CART/SLD model was used. Therefore, implementation of crop disease-warning systems may be facilitated by application of the CART/SLD model that inputs readily obtainable weather observations.

Keyword: Disease warning system, integrated pest management, weather models

MAIN

적요

CART(Classification and Regression Tree) 모형을 이용해서 결로시간 예측에 필요한 기상변수들을 평가하였다. 입력 기상 변수들은 0.3m와 1.5m에서 측정된 기온, 상대습도, 풍속의 시간별 측정값으로서 이 관측 값들은 1997년부터 1999년 5월에서 9월 사이에 미국의 Iowa, Illinois 및 Nebraska주에 위치한 15개 자동 기상 관측소에서 관측된 것이다. 0.3 m에서 측정된 기온, 상대습도, 그리고 풍속을 이용해서 얻어진 모형이 가장 높은 결로시간의 예측 적중율(85.5%)을 보였으며, 이 모형은 Gleason 등(1994)의 CART/SLD 모형의 적중률(84.7%) 보다 다소 높았다. 그러나 새로운 변수를 추가한 경우에 정확도의 향상이 다소 있었으나 CART/SLD 모형을 대체할 정도는 아니었다. 따라서, 기온, 상대습도, 풍속들의 종관 기상관측값들을 입력변수로 사용하는 CART/SLD 모형이 종관 기상관측 자료 이외의 추가적인 자료를 필요로 하는 모형으로 결로시간을 예측하는 것보다 합리적일 것으로 보인다.

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