Process-based simple model for simulating sugarcane growth and production
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 eva...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidade de São Paulo
2014-02-01
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| Series: | Scientia Agricola |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100001&lng=en&tlng=en |
| _version_ | 1818937715844972544 |
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| author | Fábio R. Marin James W. Jones |
| author_facet | Fábio R. Marin James W. Jones |
| author_sort | Fábio R. Marin |
| collection | DOAJ |
| description | 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 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 fitted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated 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. |
| first_indexed | 2024-12-20T05:56:22Z |
| format | Article |
| id | doaj.art-96dd6baf1f134e189f57a15c8cecb1ff |
| institution | Directory Open Access Journal |
| issn | 1678-992X |
| language | English |
| last_indexed | 2024-12-20T05:56:22Z |
| publishDate | 2014-02-01 |
| publisher | Universidade de São Paulo |
| record_format | Article |
| series | Scientia Agricola |
| spelling | doaj.art-96dd6baf1f134e189f57a15c8cecb1ff2022-12-21T19:51:00ZengUniversidade de São PauloScientia Agricola1678-992X2014-02-0171111610.1590/S0103-90162014000100001S0103-90162014000100001Process-based simple model for simulating sugarcane growth and productionFábio R. Marin0James W. Jones1EmbrapaEmbrapaDynamic 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 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 fitted algorithms for simulating sucrose accumulation and leaf development driven by a source-sink approach were proposed. The model was evaluated 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.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100001&lng=en&tlng=en |
| spellingShingle | Fábio R. Marin James W. Jones Process-based simple model for simulating sugarcane growth and production Scientia Agricola |
| title | Process-based simple model for simulating sugarcane growth and production |
| title_full | Process-based simple model for simulating sugarcane growth and production |
| title_fullStr | Process-based simple model for simulating sugarcane growth and production |
| title_full_unstemmed | Process-based simple model for simulating sugarcane growth and production |
| title_short | Process-based simple model for simulating sugarcane growth and production |
| title_sort | process based simple model for simulating sugarcane growth and production |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0103-90162014000100001&lng=en&tlng=en |
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