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Comparing Simulated and Measured Values Using Mean Squared Deviation and its Components

^a National Institute of Agro-Environmental Sciences, 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8604, Japan
^b Rice FACE Project, Japan Science and Technology Corp.–National Institute of Agro-Environmental Sciences, 3-1-1 Kannondai, Tsukuba, Ibaraki 305-8604, Japan

View larger version (41K): [in a new window]   Fig. 1 Comparison of the correlation between the simulated and measured maize yields at nine locations in different states of the USA. The left vertical axis is the lack of fit of the regression (1 - r2), and the right vertical axis is the correlation coefficient (r). Results from (A) CERES-Maize model and (B) ALMANAC model are shown for locations in the states of Minnesota (MN), New York (NY), Iowa (IA), Illinois (IL), Nebraska (NE), Missouri (MO), Kansas (KS), Louisiana (LA), and Texas (TX). Data are from Kiniry et al. (1997)

View larger version (39K): [in a new window]   Fig. 2 Comparison of the mean squared deviation (MSD) and its components, lack of correlation weighted by the standard deviations (LCS), squared difference between standard deviations (SDSD), and squared bias (SB), for the (A) CERES-Maize model and (B) ALMANAC model for nine USA locations. Locations are in the states of Minnesota (MN), New York (NY), Iowa (IA), Illinois (IL), Nebraska (NE), Missouri (MO), Kansas (KS), Louisiana (LA), and Texas (TX). Data are from Kiniry et al. (1997)

View larger version (24K): [in a new window]   Fig. 3 Mean squared deviation (MSD) and its components, lack of correlation weighted by the standard deviations (LCS), squared difference between standard deviations (SDSD), and squared bias (SB), in a comparison of five wheat models in simulating biomass dry weight under different irrigation regimes in a field experiment in New Zealand. Data are from Jamieson et al. (1998)

View larger version (22K): [in a new window]   Fig. 4 Mean squared deviation (MSD) and its components, lack of correlation weighted by the standard deviations (LCS), squared difference between standard deviations (SDSD), and squared bias (SB), in a comparison of five wheat models in simulating grain yield under different irrigation regimes in a field experiment in New Zealand. Data are from Jamieson et al. (1998)

View larger version (22K): [in a new window]   MFig. A-1 Ratio of squared difference between standard deviations (SDSD) to lack of correlation weighted by the standard deviations (LCS) as a function of standard deviation of the simulation (SDs)/standard deviation of the measurement (SDm) and correlation coefficient (r). Indicates SDs/SDm