Korean Journal of Agricultural and Forest Meteorology, Vol. 14, No. 4, (2012), pp. 269~276
ⓒ Author(s) 2014. CC Attribution 3.0 License.
(2012년 11월 08일 접수; 2012년 12월 07일 수정; 2012년 12월 10일 수락)
(Received November 08, 2012; Revised December 07, 2012; Accepted December 10, 2012)
작물모형 평가에 사용되거나 사용할 수 있는 9가지 지표를 소개하였으며 이들의 특징은 다음과 같다. efficiency of model (EF)와 index of agreement(d)은 dimension이 없고 관측수(n)에 의존적이지 않았으며, dimension에 대해서만 자유로운 것은 relative root mean square error (RRMSE), bias factor (Bf)와 accuracy factor (Af)이다. Root mean sqruar, mean error, mean absolute error들은 관측수와 dimension에 영향을 받기 때문에 판단 시 주의가 필요하다. 따라서 이들의 특징을 파악하여 목적에 맞게 모형의 성능을 파악하여야 한다.
Bellocchi, G., M. Acutis, G. Fila, and Donatelli, Marcello, 2002: An Indicator of Solar Radiation Model Performance based on a Fuzzy Expert System. Agronomy Journal 94, 1222-1233.
Bowman, B. and H. van Laar, 2006: Description and evaluation of the rice growth model ORYZA2000 under nitrogenlimited conditions. Agricultural Systems 87, 249-273.
Choi, K, J. Lee, N. Chung, and W. Yang, 2002: The effect of temperature and day-length on the heading of rice cultivars. Treatises of Crop Researches 3, 163-170.
Chung, U., K. Cho, and B. Lee, 2006: Evaluation of sitespecific potential for rice production in Korea under the changing climate. Korean Journal of Agricultural and Forest Meteorology 8, 229-241. (in Korean with English abstract)
Cui, R. X. and B. W. Lee, 2002: Spikelets number estimation model using nitrogen nutrition status and biomass at panicle initiation and heading stage of rice. Korean Journal of Crop Science 47, 390-394.
Shin, J. and M. Lee, 1995. Rice production in south Korea under current and future climates. Modeling the impact of climate change on rice production in Asia. Matthews R. M., Kroff M. J., Bachelet D. and van Larr H.H. (eds), IRRI, CAB International 199-213.
Lee, C., J. Shin, D. Kim, K. Choi, T. Park, and J. Kim, 2004a: Flowering response of rice varieties on photoperiod at different temperature regimens. Treatises of Crop Researches 5, 258-264. (in Korean with English abstract)
Lee, C., J. Shin, D. Kim, K. Choi, T. Park, and J. Kim, 2004b: Growth simulation of Ilpumbyeo under Korean environment using rice growth simulation model “Oryza 2000″. Treatises of Crop Researches 5, 250-257. (in Korean with English abstract)
Lee, C. 2008: Development and application of model for estimating grain weight and grain N content in rice. Seoul National University. Ph.D thesis 102-134. (in Korean with English abstract)
Loague, K. and R. E. Green, 1991: Statistical and graphical methods for evaluating solute transport models:overview and application. Journal of Contaminant Hydrology 7, 51-73.
Rinaldi, M., N. Losavio, and Z. Flagella, 2003: Evaluation and application of the OILCROP-SUN model for sunower in southern Italy. Agricultural Systems 78, 17-30.
Nash, J. and I. Sutcliffe, 1970: River flow forecasting through conceptual models Part1-A discussion of Principles. Journal of Hydrology 10, 282-290.
Nicolardot, B., S. Recous, and B. Mary, 2001: Simulation of C and N mineralisation during crop residue decomposition: A simple dynamic model based on the C:N ratio of the residues. Plant and Soil 228, 83-103.
Ross, T., 1996: Indices for performance evaluation of predictive models in food microbiology. Journal of Applied Bacteriology 81, 501-508.
Ross, T., P. Dalgaard, and S. Tienungoon, 2000: Predictive modelling of the growth and survival of Listeria in shery products. International Journal of Food Microbiology 62, 231-245.
Willmott, C., 1981: On the validation of models. Physical Geography 2(2), 184-194.
Willmott, C., 1982: Some comments on the evaluation of model performance. Bulletin American Meteorological Society 63(11), 1309-1313.