HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Figures Only Full Text Free Full Text (PDF) Free Alert me when this article is cited Alert me if a correction is posted Services Similar articles in this journal Similar articles in ISI Web of Science Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via ISI Web of Science (27) Citing Articles via Google Scholar Google Scholar Articles by Gauch, H. G. Articles by Fick, G. W. Search for Related Content PubMed Articles by Gauch, H. G., Jr. Articles by Fick, G. W. Agricola Articles by Gauch, H. G. Articles by Fick, G. W. Related Collections Other Approaches Statistics

MODELING

Model Evaluation by Comparison of Model-Based Predictions and Measured Values

^a Crop and Soil Sciences, Cornell Univ., Ithaca, NY 14853
^b Dep. of Mathematics and Dep. of Statistical Science, Cornell Univ., Ithaca, NY 14853

^* Corresponding author (gwf2{at}cornell.edu).

Received for publication February 12, 2002. The appropriateness of a statistical analysis for evaluating a model depends on the model's purpose. A common purpose for models in agricultural research and environmental management is accurate prediction. In this context, correlation and linear regression are frequently used to test or compare models, including tests of intercept a = 0 and slope b = 1, but unfortunately such results are related only obliquely to the specific matter of predictive success. The mean squared deviation (MSD) between model predictions X and measured values Y has been proposed as a directly relevant measure of predictive success, with MSD partitioned into three components to gain further insight into model performance. This paper proposes a different and better partitioning of MSD: squared bias (SB), nonunity slope (NU), and lack of correlation (LC). These MSD components are distinct and additive, they have straightforward geometric and analysis of variance (ANOVA) interpretations, and they relate transparently to regression parameters. Our MSD components are illustrated using several models for wheat (Triticum aestivum L.) yield. The MSD statistic and its components nicely complement correlation and linear regression in evaluating the predictive accuracy of models.

Abbreviations: ANOVA, analysis of variance • LC, lack of correlation • LCS, lack of positive correlation weighted by the standard deviations of the measurements and simulations • MSD, mean squared deviation • MSEP, mean squared error of prediction • NU, nonunity slope • SB, squared bias • SDSD, difference in the magnitudes of fluctuation between the measurements and simulations

This article has been cited by other articles:

				R. H. Skinner, M. S. Corson, and T. G. Gilmanov Simulating Gross Primary Productivity of Humid-Temperate Pastures Agron. J., May 7, 2008; 100(3): 801 - 807. [Abstract] [Full Text] [PDF]

				J. I. Lizaso, A. E. Fonseca, and M. E. Westgate Simulating Source-Limited and Sink-Limited Kernel Set with CERES-Maize Crop Sci., September 1, 2007; 47(5): 2078 - 2088. [Abstract] [Full Text] [PDF]

				H. J. Causarano, J. N. Shaw, A. J. Franzluebbers, D. W. Reeves, R. L. Raper, K. S. Balkcom, M. L. Norfleet, and R. C. Izaurralde Simulating Field-Scale Soil Organic Carbon Dynamics Using EPIC Soil Sci. Soc. Am. J., June 8, 2007; 71(4): 1174 - 1185. [Abstract] [Full Text] [PDF]

				T. H. Waiser, C. L. S. Morgan, D. J. Brown, and C. T. Hallmark In Situ Characterization of Soil Clay Content with Visible Near-Infrared Diffuse Reflectance Spectroscopy Soil Sci. Soc. Am. J., March 12, 2007; 71(2): 389 - 396. [Abstract] [Full Text] [PDF]

				D. Parsons, J. H. Cherney, and H. G. Gauch Jr. Alfalfa Fiber Estimation in Mixed Stands and Its Relationship to Plant Morphology Crop Sci., October 2, 2006; 46(6): 2446 - 2452. [Abstract] [Full Text] [PDF]

				D. Parsons, J. H. Cherney, and H. G. Gauch Estimation of Preharvest Fiber Content of Mixed Alfalfa-Grass Stands in New York Agron. J., June 27, 2006; 98(4): 1081 - 1089. [Abstract] [Full Text] [PDF]

				K. Kobayashi Comments on another way of partitioning mean squared deviation proposed by Gauch et al. (2003) Agron. J., July 1, 2004; 96(4): 1206 - 1207. [Full Text] [PDF]