Regression-based evaluation of ecophysiological models
Ecophysiological models are increasingly used as research and decision support tools in agriculture, but it is often difficult to assess how suitable a model is for a particular application. Model evaluations usually involve bivariate linear regression between observed and simulated values, which as...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Wiley
2007
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/44030 |
| _version_ | 1855515281952079872 |
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| author | White, Jeffrey W. Boote, Kenneth J. Hoogenboom, Gerrit Jones, Peter G. |
| author_browse | Boote, Kenneth J. Hoogenboom, Gerrit Jones, Peter G. White, Jeffrey W. |
| author_facet | White, Jeffrey W. Boote, Kenneth J. Hoogenboom, Gerrit Jones, Peter G. |
| author_sort | White, Jeffrey W. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Ecophysiological models are increasingly used as research and decision support tools in agriculture, but it is often difficult to assess how suitable a model is for a particular application. Model evaluations usually involve bivariate linear regression between observed and simulated values, which assumes statistical independence among observed values. However, observed data often have dependencies if they originate from series of experiments or involve experiments using nested designs (e.g., with split plots). By representing experiments, cultivars, or other variables as factors, linear regression models can specify expected dependencies, permitting analyses that are statistically more rigorous and provide more insights into model performance. This study evaluated the Cropping System Model (CSM)-CROPGRO-Soybean model using regressions that included environment and cultivars as factors as well as continuous variables such as temperature or daylength. When applied to 28 data sets for soybean [Glycine max (L.) Merr.], representing 113 treatment combinations, the regressions showed that the model simulated days to anthesis and grain yield well for a wide range of environments. Differences among environments represented a larger portion of unexplained variation than did differences among cultivars. Further improvements thus might be sought in modeling crop response to environment rather than in representing cultivar differences, or alternatively, in characterizing soil profiles or daily weather rather than cultivars. A submodel for photosynthesis that scaled leaf-level values to canopy simulated grain yield more accurately than a simpler submodel. Multiple regressions provided much more information on model performance than simple bivariate comparisons. |
| format | Journal Article |
| id | CGSpace44030 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2007 |
| publishDateRange | 2007 |
| publishDateSort | 2007 |
| publisher | Wiley |
| publisherStr | Wiley |
| record_format | dspace |
| spelling | CGSpace440302024-08-27T10:34:50Z Regression-based evaluation of ecophysiological models White, Jeffrey W. Boote, Kenneth J. Hoogenboom, Gerrit Jones, Peter G. cropping systems plant ecology mathematical models regression analysis sistemas de cultivo ecología vegetal modelos matemáticos análisis de regresión Ecophysiological models are increasingly used as research and decision support tools in agriculture, but it is often difficult to assess how suitable a model is for a particular application. Model evaluations usually involve bivariate linear regression between observed and simulated values, which assumes statistical independence among observed values. However, observed data often have dependencies if they originate from series of experiments or involve experiments using nested designs (e.g., with split plots). By representing experiments, cultivars, or other variables as factors, linear regression models can specify expected dependencies, permitting analyses that are statistically more rigorous and provide more insights into model performance. This study evaluated the Cropping System Model (CSM)-CROPGRO-Soybean model using regressions that included environment and cultivars as factors as well as continuous variables such as temperature or daylength. When applied to 28 data sets for soybean [Glycine max (L.) Merr.], representing 113 treatment combinations, the regressions showed that the model simulated days to anthesis and grain yield well for a wide range of environments. Differences among environments represented a larger portion of unexplained variation than did differences among cultivars. Further improvements thus might be sought in modeling crop response to environment rather than in representing cultivar differences, or alternatively, in characterizing soil profiles or daily weather rather than cultivars. A submodel for photosynthesis that scaled leaf-level values to canopy simulated grain yield more accurately than a simpler submodel. Multiple regressions provided much more information on model performance than simple bivariate comparisons. 2007-03 2014-10-02T08:33:07Z 2014-10-02T08:33:07Z Journal Article https://hdl.handle.net/10568/44030 en Open Access Wiley |
| spellingShingle | cropping systems plant ecology mathematical models regression analysis sistemas de cultivo ecología vegetal modelos matemáticos análisis de regresión White, Jeffrey W. Boote, Kenneth J. Hoogenboom, Gerrit Jones, Peter G. Regression-based evaluation of ecophysiological models |
| title | Regression-based evaluation of ecophysiological models |
| title_full | Regression-based evaluation of ecophysiological models |
| title_fullStr | Regression-based evaluation of ecophysiological models |
| title_full_unstemmed | Regression-based evaluation of ecophysiological models |
| title_short | Regression-based evaluation of ecophysiological models |
| title_sort | regression based evaluation of ecophysiological models |
| topic | cropping systems plant ecology mathematical models regression analysis sistemas de cultivo ecología vegetal modelos matemáticos análisis de regresión |
| url | https://hdl.handle.net/10568/44030 |
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