Process-based simple model for simulating sugarcane growth and production.

MARIN, F. R.; JONES, J. W.

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Campo DC	Valor	Idioma
dc.contributor.author	MARIN, F. R.	pt_BR
dc.contributor.author	JONES, J. W.	pt_BR
dc.date.accessioned	2014-03-17T11:11:11Z	pt_BR
dc.date.available	2014-03-17T11:11:11Z	pt_BR
dc.date.created	2014-03-17	pt_BR
dc.date.issued	2014	pt_BR
dc.identifier.citation	Scientia agrícola, Piracicaba, v. 71, n. 1, p. 1-16, Jan./Feb. 2014.	pt_BR
dc.identifier.uri	http://www.alice.cnptia.embrapa.br/alice/handle/doc/982543	pt_BR
dc.description	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.	pt_BR
dc.language.iso	eng	eng
dc.rights	openAccess	eng
dc.subject	Modelos de simulação	pt_BR
dc.subject	Cana-de-açúcar	pt_BR
dc.title	Process-based simple model for simulating sugarcane growth and production.	pt_BR
dc.type	Artigo de periódico	pt_BR
dc.date.updated	2014-05-20T11:11:11Z	pt_BR
dc.subject.nalthesaurus	Simulation models	pt_BR
dc.subject.nalthesaurus	Sugarcane	pt_BR
riaa.ainfo.id	982543	pt_BR
riaa.ainfo.lastupdate	2014-05-20	pt_BR
dc.contributor.institution	FABIO RICARDO MARIN, CNPTIA; JAMES W. JONES, University of Florida.	pt_BR
Aparece nas coleções:	Artigo em periódico indexado (CNPTIA)

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