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This article in AJ

  1. Vol. 103 No. 2, p. 304-315
     
    Received: July 9, 2010
    Published: Mar, 2011


    * Corresponding author(s): marin@cnptia.embrapa.br
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doi:10.2134/agronj2010.0302

Parameterization and Evaluation of Predictions of DSSAT/CANEGRO for Brazilian Sugarcane

  1. Fabio R. Marin *a,
  2. James W. Jonesb,
  3. Frederick Royceb,
  4. Carlos Suguitanib,
  5. Jorge L. Donzelib,
  6. Wander J. Pallone Filhoc and
  7. Daniel S.P. Nassifd
  1. a Embrapa Agriculture Informatics, Av. André Tosello, 209- Barão Geraldo, CP 6041-13083-886- Campinas, SP
    b Dep. of Agricultural and Biological Engineering, Museum Rd., P.O. Box 110570, Univ. of Florida, Gainesville, FL 32611
    c Sugarcane Technology Center, Fazenda Santo Antônio S/N, Bairro Santo Antônio, Piracicaba–SP, Brazil, CEP 13400-970, Cp. 162
    d University of Sao Paulo, College of Agriculture “Luiz de Queiroz”, Piracicaba, SP, Av.: Pádua Dias, 11 CP 9, Piracicaba–SP, Brazil, 13418-900

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 cross-validation 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 = 0.122 mm) than for irrigated treatment (mean RMSE = 0.214 mm). 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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Copyright © 2011. American Society of AgronomyCopyright © 2011 by the American Society of Agronomy

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