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 | Acesso ao texto completo restrito à biblioteca da Embrapa Agricultura Digital. Para informações adicionais entre em contato com cnptia.biblioteca@embrapa.br. |
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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
04/10/2011 |
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Data da última atualização: |
13/01/2020 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MARIN, F. R.; JONES, J. W.; ROYCE, F.; SUGUITANI, C.; DONZELI, J. L.; PALLONE FILHO, W. J.; NASSIF, D. S. P. |
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Afiliação: |
FABIO RICARDO MARIN, CNPTIA; JAMES W. JONES, University of Florida; FREDERICK ROYCE, University of Florida; CARLOS SUGUITANI, Sugarcane Technology Center; JORGE L. DONZELI, Sugarcane Technology Center; WANDER J. PALLONE FILHO, Sugarcane Technology Center; DANIEL S. P. NASSIF, ESALQ/USP. |
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Título: |
Parameterization and evaluation of predictions of DSSAT/CANEGRO for Brazilian sugarcane. |
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Ano de publicação: |
2011 |
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Fonte/Imprenta: |
Agronomy Journal, Madison, v. 103, n. 2, p. 304- 315, Dec. 2011. |
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DOI: |
10.2134/agronj2010.0302 |
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Idioma: |
Inglês |
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Conteúdo: |
Abstract - The DSSAT/CANEGRO model was parameterized and its predictions evaluated using data from five sugarcane (Saccharum spp.) experiments conducted in southern Brazil. The data used are from two of the most important Brazilian cultivars. Some parameters whose values were either directly measured or considered to be well known were not adjusted. Ten of the 20 parameters were optimized using a Generalized Likelihood Uncertainty Estimation (GLUE) algorithm using the leave-one-out crossvalidation technique. Model predictions were evaluated using measured data of leaf area index (LAI), stalk and aerial dry mass, sucrose content, and soil water content, using bias, root mean squared error (RMSE), modeling efficiency (Eff), correlation coefficient, and agreement index. The Decision Support System for Agrotechnology Transfer (DSSAT)/CANEGRO model simulated the sugarcane crop in southern Brazil well, using the parameterization reported here. The soil water content predictions were better for rainfed (mean RMSE = 0.122mm) than for irrigated treatment (mean RMSE = 0.214mm). Predictions were best for aerial dry mass (Eff = 0.850), followed by stalk dry mass (Eff = 0.765) and then sucrose mass (Eff = 0.170). Number of green leaves showed the worst fit (Eff = -2.300).The cross-validation technique permits using multiple datasets that would have limited use if used independently because of the heterogeneity of measures and measurement strategies. |
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Palavras-Chave: |
Cana-de-açúcar; Modelo DSSAT/CANEGRO; Parametrização. |
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Thesagro: |
Simulação. |
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Thesaurus Nal: |
Models; Saccharum; Simulation models; Sugarcane. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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Marc: |
LEADER 02329naa a2200301 a 4500
001 1902350
005 2020-01-13
008 2011 bl uuuu u00u1 u #d
024 7 $a10.2134/agronj2010.0302$2DOI
100 1 $aMARIN, F. R.
245 $aParameterization and evaluation of predictions of DSSAT/CANEGRO for Brazilian sugarcane.$h[electronic resource]
260 $c2011
520 $aAbstract - The DSSAT/CANEGRO model was parameterized and its predictions evaluated using data from five sugarcane (Saccharum spp.) experiments conducted in southern Brazil. The data used are from two of the most important Brazilian cultivars. Some parameters whose values were either directly measured or considered to be well known were not adjusted. Ten of the 20 parameters were optimized using a Generalized Likelihood Uncertainty Estimation (GLUE) algorithm using the leave-one-out crossvalidation technique. Model predictions were evaluated using measured data of leaf area index (LAI), stalk and aerial dry mass, sucrose content, and soil water content, using bias, root mean squared error (RMSE), modeling efficiency (Eff), correlation coefficient, and agreement index. The Decision Support System for Agrotechnology Transfer (DSSAT)/CANEGRO model simulated the sugarcane crop in southern Brazil well, using the parameterization reported here. The soil water content predictions were better for rainfed (mean RMSE = 0.122mm) than for irrigated treatment (mean RMSE = 0.214mm). Predictions were best for aerial dry mass (Eff = 0.850), followed by stalk dry mass (Eff = 0.765) and then sucrose mass (Eff = 0.170). Number of green leaves showed the worst fit (Eff = -2.300).The cross-validation technique permits using multiple datasets that would have limited use if used independently because of the heterogeneity of measures and measurement strategies.
650 $aModels
650 $aSaccharum
650 $aSimulation models
650 $aSugarcane
650 $aSimulação
653 $aCana-de-açúcar
653 $aModelo DSSAT/CANEGRO
653 $aParametrização
700 1 $aJONES, J. W.
700 1 $aROYCE, F.
700 1 $aSUGUITANI, C.
700 1 $aDONZELI, J. L.
700 1 $aPALLONE FILHO, W. J.
700 1 $aNASSIF, D. S. P.
773 $tAgronomy Journal, Madison$gv. 103, n. 2, p. 304- 315, Dec. 2011.
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