Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza
Development and testing of reliable tools for simulating rice production in salt-affected areas are presented in this paper. New functions were implemented in existing crop models ORYZA v3 and the cropping systems modelling framework APSIM. Field experiments covering two years, two different sites,...
| Autores principales: | , , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2018
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/164793 |
| _version_ | 1855535888492134400 |
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| author | Radanielson, A.M. Gaydon, D.S. Li, T. Angeles, O. Roth, C.H. |
| author_browse | Angeles, O. Gaydon, D.S. Li, T. Radanielson, A.M. Roth, C.H. |
| author_facet | Radanielson, A.M. Gaydon, D.S. Li, T. Angeles, O. Roth, C.H. |
| author_sort | Radanielson, A.M. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Development and testing of reliable tools for simulating rice production in salt-affected areas are presented in this paper. New functions were implemented in existing crop models ORYZA v3 and the cropping systems modelling framework APSIM. Field experiments covering two years, two different sites, and three varieties were used to validate both improved models. We used the salt balance module in the systems model APSIM to simulate the observed daily soil salinity with acceptable accuracy (RMSEn <35%), whereas ORYZA v3 used measured soil salinity at a given interval of days as a model input. Both models presented similarly good accuracy in simulating aboveground biomass, leaf area index, and grain yield for IR64 over a gradient of salinity conditions. The model index of agreement ranged from 0.86 to 0.99. Variability of yield under stressed and non-stressed conditions was simulated with a RMSE, of 191 kg ha−1 and 222 kg ha−1, respectively, for ORYZA v3 and APSIM-Oryza, corresponding to an RMSEn of 14.8% and 17.3%. These values are within the bounds of experimental error, therefore indicating acceptable model performance. The model test simulating genotypic variability of rice crop responses resulted in similar levels of acceptable model performance with RMSEn ranging from 11.3 to 39.9% for observed total above ground biomass for IR64 and panicle biomass for IR29, respectively. With the improved models, more reliable tools are now available for use in risk assessment and evaluation of suitable management options for rice production in salt-affected areas. The approach presented may also be applied in improving other non-rice crop models to integrate a response to soil salinity − particularly in process-based models which capture stage-related stress tolerance variability and resource use efficiency. |
| format | Journal Article |
| id | CGSpace164793 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2018 |
| publishDateRange | 2018 |
| publishDateSort | 2018 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1647932024-12-19T14:13:19Z Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza Radanielson, A.M. Gaydon, D.S. Li, T. Angeles, O. Roth, C.H. cropping systems grain yield growth models resource use efficiency salinity salt tolerance soil chemicophysical properties yields Development and testing of reliable tools for simulating rice production in salt-affected areas are presented in this paper. New functions were implemented in existing crop models ORYZA v3 and the cropping systems modelling framework APSIM. Field experiments covering two years, two different sites, and three varieties were used to validate both improved models. We used the salt balance module in the systems model APSIM to simulate the observed daily soil salinity with acceptable accuracy (RMSEn <35%), whereas ORYZA v3 used measured soil salinity at a given interval of days as a model input. Both models presented similarly good accuracy in simulating aboveground biomass, leaf area index, and grain yield for IR64 over a gradient of salinity conditions. The model index of agreement ranged from 0.86 to 0.99. Variability of yield under stressed and non-stressed conditions was simulated with a RMSE, of 191 kg ha−1 and 222 kg ha−1, respectively, for ORYZA v3 and APSIM-Oryza, corresponding to an RMSEn of 14.8% and 17.3%. These values are within the bounds of experimental error, therefore indicating acceptable model performance. The model test simulating genotypic variability of rice crop responses resulted in similar levels of acceptable model performance with RMSEn ranging from 11.3 to 39.9% for observed total above ground biomass for IR64 and panicle biomass for IR29, respectively. With the improved models, more reliable tools are now available for use in risk assessment and evaluation of suitable management options for rice production in salt-affected areas. The approach presented may also be applied in improving other non-rice crop models to integrate a response to soil salinity − particularly in process-based models which capture stage-related stress tolerance variability and resource use efficiency. 2018-10 2024-12-19T12:54:19Z 2024-12-19T12:54:19Z Journal Article https://hdl.handle.net/10568/164793 en Open Access Elsevier Radanielson, A.M.; Gaydon, D.S.; Li, T.; Angeles, O. and Roth, C.H. 2018. Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza. European Journal of Agronomy, Volume 100 p. 44-55 |
| spellingShingle | cropping systems grain yield growth models resource use efficiency salinity salt tolerance soil chemicophysical properties yields Radanielson, A.M. Gaydon, D.S. Li, T. Angeles, O. Roth, C.H. Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza |
| title | Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza |
| title_full | Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza |
| title_fullStr | Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza |
| title_full_unstemmed | Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza |
| title_short | Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza |
| title_sort | modeling salinity effect on rice growth and grain yield with oryza v3 and apsim oryza |
| topic | cropping systems grain yield growth models resource use efficiency salinity salt tolerance soil chemicophysical properties yields |
| url | https://hdl.handle.net/10568/164793 |
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