Modelling sugarcane production systems I. Development and performance of the sugarcane module

Keating, Brian; Robertson, Michael; Muchow, R.C.; Huth, Neil

doi:10.1016/s0378-4290(98)00167-1

Cited by 261 publications

(207 citation statements)

References 22 publications

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“…Dynamic simulation models can increase research effi ciency by allowing the analyst to search for strategies and analyze system performance, improve risk management, and interpret fi eld experiments that deal with crop responses to soil, management, genetic or environmental factors (Keating et al, 1999).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…is of major social and economic importance in Brazil. Worldwide, there have been several models developed specifi cally for sugarcane crop simulation (Pereira and Machado, 1986;Jones et al, 1989;Langellier and Martine, 2007;Keating et al, 1999;Thorburn et al, 2005;Inman-Bamber, 1991;Singels et al, 2008).Some of the physiological development and growth parameters that appear in the functions vary among sugarcane cultivars, meaning that they have to be estimated from data in order to predict growth and yield. Some of the parameters cannot be measured directly in typical experiments; instead, they have to be estimated based on data that are measured in experiments.…”

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Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

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Dynamic simulation models can increase research effi ciency 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, diffi culties in evaluating and using models, and a general lack of model credibility. This paper describes the biophysics and shows a statistical evaluation of a simple sugarcane processbased model coupled with a routine for model calibration. Classical crop model approaches were used as a framework for this model, and fi tted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated using data from fi ve 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 effi ciency, correlation coeffi cient 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. IntroductionDynamic simulation models can increase research effi ciency by allowing the analyst to search for strategies and analyze system performance, improve risk management, and interpret fi eld experiments that deal with crop responses to soil, management, genetic or environmental factors (Keating et al., 1999).Sugarcane (Saccharum spp.) is of major social and economic importance in Brazil. Worldwide, there have been several models developed specifi cally for sugarcane crop simulation (Pereira and Machado, 1986;Jones et al., 1989;Langellier and Martine, 2007;Keating et al., 1999;Thorburn et al., 2005;Inman-Bamber, 1991;Singels et al., 2008).Some of the physiological development and growth parameters that appear in the functions vary among sugarcane cultivars, meaning that they have to be estimated from data in order to predict growth and yield. Some of the parameters cannot be measured directly in typical experiments; instead, they have to be estimated based on data that are measured in experiments. Recent literature contains relatively little work on parameter estimation for crop models (Ahuja and Ma, 2011; Makowski et al., 2006). Makowski et al. (2006) point out the importance of raising the quality of calibration in crop models with automatic procedures for parameter adjustment. This would help ensure that the data are always used appropriately and in the same way for parameter estimation (Wallach et al., 2001), but such procedures are not available for direct use with existing sugarcane models.A new sugarcane model was developed ...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Process-based simple model for simulating sugarcane growth and production

Marin

Jones

2014

Sci. agric. (Piracicaba, Braz.)

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“…Existem diversos modelos de simulação de crescimento da cana-de-açúcar em uso atualmente, como: Auscane (Jones et al, 1988), QCane (Liu & Kingston, 1995), APSIM (Keating et al, 1999), Mosicas (Martiné, 2003) e Casupro (Villegas et al, 2005). Entre esses modelos, o DSSAT/Canegro (Inman-Bamber, 1991;Singels et al, 2008) é um dos principais.…”

Section: Introductionunclassified

Parametrização e avaliação do modelo DSSAT/Canegro para variedades brasileiras de cana-de-açúcar

Nassif

Marin

Filho

et al. 2012

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi parametrizar e avaliar o modelo DSSAT/Canegro para cinco variedades brasileiras de cana-de-açúcar. A parametrização foi realizada a partir do uso de dados biométricos e de crescimento das variedades CTC 4, CTC 7, CTC 20, RB 86-7515 e RB 83-5486, obtidos em cinco localidades brasileiras. Foi realizada análise de sensibilidade local para os principais parâmetros. A parametrização do modelo foi feita por meio da técnica de estimativa da incerteza de probabilidade generalizada ("generalized likelihood uncertainty estimation", Glue). Para a avaliação das predições, foram utilizados, como indicadores estatísticos, o coeficiente de determinação (R 2 ), o índice D de Willmott e a raiz quadrada do erro-médio (RMSE). As variedades CTC apresentaram índice D entre 0,870 e 0,944, para índice de área foliar, altura de colmo, perfilhamento e teor de sacarose. A variedade RB 83-5486 apresentou resultados similares para teor de sacarose e massa de matéria fresca do colmo, enquanto a variedade RB 86-7515 apresentou valores entre 0,665 e 0,873, para as variáveis avaliadas.Termos para indexação: Saccharum, biometria, calibração, modelagem, validação. Parameterization and evaluation of the DSSAT/Canegro model for Brazilian sugarcane varietiesAbstract -The objective of this work was to parameterize and to evaluate the DSSAT/Canegro model for five Brazilian sugarcane varieties. The parameterization was done using biometric and growth data from the varieties CTC 4, CTC 7, CTC 20, RB 86-7515, and RB 83-5486, obtained in five Brazilian locations. Local sensitivity analysis was performed for the main parameters. Model parameterization was done using the generalized likelihood uncertainty estimation (Glue) method. The predictions were evaluated using the coefficient of determination (R²), Willmott's D-index, and the root mean square error (RMSE) as statistical indexes. CTC varieties showed D values ranging from 0.870 to 0.944 for leaf area index, stalk height, tillering, and sucrose content. The RB 83-5486 variety showed similar results for sucrose content and fresh matter mass of stalks, whereas the RB 86-7515 variety showed values ranging from 0.665 to 0.873 for all variables analyzed.

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“…BARBIERI (1993) utilizou um valor fixo de 0,58. O modelo APSIM -Sugarcane (KEATING et al, 1999) simula a fixação de carbono da atmosfera utilizando a PAR variável com a temperatura e com os teores de nitrogênio desvinculada da eficiência de transpiração. Valores fixos para coeficiente de extinção e transpiração são usados para cana-planta e socas.…”

Section: Introductionunclassified