Simulation and prediction of soybean growth and development under field conditions
Thermal unit is often used as the main driving force in crop simulation models. However, simulation models built with this approach often do not lead to a satisfactory accuracy of prediction when it regards to soybean; mainly due to strong photoperiod influence on soybean and complicated interactio...
| Autores principales: | , , , |
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| Formato: | Journal Article |
| Lenguaje: | Inglés |
| Publicado: |
2010
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/89075 |
| _version_ | 1855530921976922112 |
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| author | Zhang, L. Zhang, J. Kyei-Boahen, S. Zhang, M. |
| author_browse | Kyei-Boahen, S. Zhang, J. Zhang, L. Zhang, M. |
| author_facet | Zhang, L. Zhang, J. Kyei-Boahen, S. Zhang, M. |
| author_sort | Zhang, L. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Thermal unit is often used as the main driving force in crop simulation models. However, simulation
models built with this approach often do not lead to a satisfactory accuracy of prediction when it regards to soybean; mainly due to strong photoperiod influence on soybean and complicated interactions between
photoperiod and temperature. This study tried to simulate and predict soybean phenological growth using
calendar-day based approach. Field experiments were conducted at the Delta Research and Extension Center, Stoneville, Mississippi, USA. Five year (1998-2002) field data were used with 24 sowing dates from maturity groups (MG) III to MG VI soybean varieties. Three methods, artificial neural network (ANN), k- nearest neighbor (kNN) and regression were used to construct prediction models. Vegetative and reproductive growth stages were modeled separately. Results indicated that calendar-based prediction model in soybean growth calculation is a feasible approach. All three methods achieved the acceptable prediction accuracy. On average, prediction errors of ANN, kNN and Regression methods were 3.6, 2.8 and 3.6 days for vegetative stage and 4.4, 3.5 and 4.7 days for reproductive stages, respectively. |
| format | Journal Article |
| id | CGSpace89075 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2010 |
| publishDateRange | 2010 |
| publishDateSort | 2010 |
| record_format | dspace |
| spelling | CGSpace890752023-06-12T17:12:42Z Simulation and prediction of soybean growth and development under field conditions Zhang, L. Zhang, J. Kyei-Boahen, S. Zhang, M. soybeans phenology prediction model regression united states of america artificial neural network simulation Thermal unit is often used as the main driving force in crop simulation models. However, simulation models built with this approach often do not lead to a satisfactory accuracy of prediction when it regards to soybean; mainly due to strong photoperiod influence on soybean and complicated interactions between photoperiod and temperature. This study tried to simulate and predict soybean phenological growth using calendar-day based approach. Field experiments were conducted at the Delta Research and Extension Center, Stoneville, Mississippi, USA. Five year (1998-2002) field data were used with 24 sowing dates from maturity groups (MG) III to MG VI soybean varieties. Three methods, artificial neural network (ANN), k- nearest neighbor (kNN) and regression were used to construct prediction models. Vegetative and reproductive growth stages were modeled separately. Results indicated that calendar-based prediction model in soybean growth calculation is a feasible approach. All three methods achieved the acceptable prediction accuracy. On average, prediction errors of ANN, kNN and Regression methods were 3.6, 2.8 and 3.6 days for vegetative stage and 4.4, 3.5 and 4.7 days for reproductive stages, respectively. 2010 2017-10-20T09:14:00Z 2017-10-20T09:14:00Z Journal Article https://hdl.handle.net/10568/89075 en Limited Access Zhang, L., Zhang, J., Kyei-Boahen, S. & Zhang, M. (2010). Simulation and prediction of soybean growth and development under field conditions. American-Eurasian Journal of Agricultural and Environmental Sciences, 7(4), 374-385. |
| spellingShingle | soybeans phenology prediction model regression united states of america artificial neural network simulation Zhang, L. Zhang, J. Kyei-Boahen, S. Zhang, M. Simulation and prediction of soybean growth and development under field conditions |
| title | Simulation and prediction of soybean growth and development under field conditions |
| title_full | Simulation and prediction of soybean growth and development under field conditions |
| title_fullStr | Simulation and prediction of soybean growth and development under field conditions |
| title_full_unstemmed | Simulation and prediction of soybean growth and development under field conditions |
| title_short | Simulation and prediction of soybean growth and development under field conditions |
| title_sort | simulation and prediction of soybean growth and development under field conditions |
| topic | soybeans phenology prediction model regression united states of america artificial neural network simulation |
| url | https://hdl.handle.net/10568/89075 |
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