The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Model Evaluation by Comparison of Model‐Based Predictions and Measured Values
Abstract
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.
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Cites
Statistical analysis of regional yield trials : AMMI analysis of factorial designs
Gauch, H. G. (1992) · Elsevier eBooks · Cited by 717
Self-citationComparing Simulated and Measured Values Using Mean Squared Deviation and its Components
Kobayashi, K., & Salam, M. U. (2000) · Agronomy Journal · doi:10.2134/agronj2000.922345x · Cited by 591
Gauch Jr, H. G. (2002) · doi:10.1017/cbo9780511815034 · Cited by 385
Mean squared error of prediction as a criterion for evaluating and comparing system models
Wallach, D., & Goffinet, B. (1989) · Ecological Modelling · doi:10.1016/0304-3800(89)90035-5 · Cited by 315
Jamieson, P. D., Porter, J. R., Goudriaan, J., Ritchie, J. T., van Keulen, H., & Stol, W. (1998) · Field Crops Research · doi:10.1016/s0378-4290(97)00060-9 · Cited by 262
Ewert, F., Rodriguez, D., Jamieson, P., Semenov, M. A., Mitchell, R. A. C., Goudriaan, J., Porter, J. R., Kimball, B. A., Pinter, P. J., Manderscheid, R., et al. (2002) · Agriculture, Ecosystems & Environment · doi:10.1016/s0167-8809(01)00352-8 · Cited by 195
Prediction, Parsimony and Noise
Gauch, H. G. (1993) · American Scientist · Cited by 113
Self-citation
Cited by
How to evaluate models: Observed vs. predicted or predicted vs. observed?
Piñeiro, G., Perelman, S., Guerschman, J. P., & Paruelo, J. M. (2008) · Ecological Modelling · doi:10.1016/j.ecolmodel.2008.05.006 · Cited by 891
Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions
Bouman, B. A. M., & van Laar, H. H. (2006) · Agricultural Systems · doi:10.1016/j.agsy.2004.09.011 · Cited by 305
Performance of CERES-Rice and CERES-Wheat models in rice–wheat systems: A review
Timsina, J., & Humphreys, E. (2006) · Agricultural Systems · doi:10.1016/j.agsy.2005.11.007 · Cited by 272
McNally, S. R., Beare, M. H., Curtin, D., Meenken, E. D., Kelliher, F. M., Calvelo Pereira, R., Shen, Q., & Baldock, J. (2017) · Global Change Biology · doi:10.1111/gcb.13720 · Cited by 139
Simulating Field‐Scale Soil Organic Carbon Dynamics Using EPIC
Causarano, H. J., Shaw, J. N., Franzluebbers, A. J., Reeves, D. W., Raper, R. L., Balkcom, K. S., Norfleet, M. L., & Izaurralde, R. C. (2007) · Soil Science Society of America Journal · doi:10.2136/sssaj2006.0356 · Cited by 50
Metadata
- Authors
- Gauch, H. G., Hwang, J. T. G., & Fick, G. W.
- Venue
- Agronomy Journal
- Publisher
- Wiley
- Published
- 2003-11
- Volume
- 95
- Issue
- 6
- Pages
- 1442-1446
- ISSN
- 0002-1962 (ISSN-L)
- 1435-0645
- OpenAlex
- W2067823858
- Cited by
- 317
- References
- 7
- FWCI
- 8.89
- Type
- journal article
- Language
- en
- Open access
- closed
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- Gauch, H. G., Hwang, J. T. G., & Fick, G. W. (2003). Model Evaluation by Comparison of Model‐Based Predictions and Measured Values. Agronomy Journal. https://doi.org/10.2134/agronj2003.1442
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
