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Registro Completo |
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Biblioteca(s): |
Embrapa Agricultura Digital. |
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Data corrente: |
17/03/2014 |
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Data da última atualização: |
20/05/2014 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Autoria: |
MARIN, F. R.; JONES, J. W. |
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Afiliação: |
FABIO RICARDO MARIN, CNPTIA; JAMES W. JONES, University of Florida. |
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Título: |
Process-based simple model for simulating sugarcane growth and production. |
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Ano de publicação: |
2014 |
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Fonte/Imprenta: |
Scientia agrícola, Piracicaba, v. 71, n. 1, p. 1-16, Jan./Feb. 2014. |
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Idioma: |
Inglês |
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Conteúdo: |
ABSTRACT: Dynamic simulation models can increase research efficiency and improve risk management of agriculture. Crop models are still little used for sugarcane (Saccharum spp.) because the lack of understanding of their capabilities and limitations, lack of experience in calibrating them, difficulties in evaluating and using models, and a general lack of model credibility. This pa- per describes the biophysics and shows a statistical evaluation of a simple sugarcane process-based model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fitted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evalu- ated using data from five growing seasons at four locations in Brazil, where crops received adequate nutrients and good weed control. Thirteen of the 27 parameters were optimized using a Generalized Likelihood Uncertainty Estimation algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index, stalk and aerial dry mass, and sucrose content, using bias, root mean squared error, modeling efficiency, correlation coefficient and agreement index. The model well simulated the sugarcane crop in Southern Brazil, using the parameterization reported here. Predictions were best for stalk dry mass, followed by leaf area index and then sucrose content in stalk fresh mass. |
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Palavras-Chave: |
Cana-de-açúcar; Modelos de simulação. |
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Thesaurus Nal: |
Simulation models; Sugarcane. |
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Categoria do assunto: |
X Pesquisa, Tecnologia e Engenharia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/item/99365/1/Modelo-Stocropcane.pdf
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Marc: |
LEADER 02049naa a2200181 a 4500
001 1982543
005 2014-05-20
008 2014 bl uuuu u00u1 u #d
100 1 $aMARIN, F. R.
245 $aProcess-based simple model for simulating sugarcane growth and production.$h[electronic resource]
260 $c2014
520 $aABSTRACT: Dynamic simulation models can increase research efficiency and improve risk management of agriculture. Crop models are still little used for sugarcane (Saccharum spp.) because the lack of understanding of their capabilities and limitations, lack of experience in calibrating them, difficulties in evaluating and using models, and a general lack of model credibility. This pa- per describes the biophysics and shows a statistical evaluation of a simple sugarcane process-based model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fitted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evalu- ated using data from five growing seasons at four locations in Brazil, where crops received adequate nutrients and good weed control. Thirteen of the 27 parameters were optimized using a Generalized Likelihood Uncertainty Estimation algorithm using the leave-one-out cross-validation technique. Model predictions were evaluated using measured data of leaf area index, stalk and aerial dry mass, and sucrose content, using bias, root mean squared error, modeling efficiency, correlation coefficient and agreement index. The model well simulated the sugarcane crop in Southern Brazil, using the parameterization reported here. Predictions were best for stalk dry mass, followed by leaf area index and then sucrose content in stalk fresh mass.
650 $aSimulation models
650 $aSugarcane
653 $aCana-de-açúcar
653 $aModelos de simulação
700 1 $aJONES, J. W.
773 $tScientia agrícola, Piracicaba$gv. 71, n. 1, p. 1-16, Jan./Feb. 2014.
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Embrapa Agricultura Digital (CNPTIA) |
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