| dc.contributor.author | Zhang, L. |
| dc.contributor.author | Zhang, J. |
| dc.contributor.author | Kyei-Boahen, S. |
| dc.contributor.author | Zhang, M. |
| dc.date.accessioned | 2019-12-04T11:10:47Z |
| dc.date.available | 2019-12-04T11:10:47Z |
| dc.date.issued | 2010 |
| dc.identifier.citation | 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. |
| dc.identifier.issn | 1818-6769 |
| dc.identifier.uri | https://hdl.handle.net/20.500.12478/2266 |
| dc.description.abstract | 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. |
| dc.format.extent | 374-385 |
| dc.language.iso | en |
| dc.subject | Soybeans |
| dc.subject | Phenology |
| dc.subject | Prediction |
| dc.subject | Model |
| dc.subject | Regression |
| dc.subject | United States Of America |
| dc.subject | Artificial Neural Network |
| dc.subject | Simulation |
| dc.title | Simulation and prediction of soybean growth and development under field conditions |
| dc.type | Journal Article |
| dc.description.version | Peer Review |
| cg.contributor.affiliation | Mississippi State University |
| cg.contributor.affiliation | Chinese Academy of Agricultural Sciences |
| cg.contributor.affiliation | International Institute of Tropical Agriculture |
| cg.contributor.affiliation | University of California |
| cg.coverage.region | Acp |
| cg.coverage.region | North America |
| cg.coverage.country | United States |
| cg.identifier.url | https://www.researchgate.net/publication/228434899 |
| cg.authorship.types | CGIAR and developing country institute |
| cg.iitasubject | Grain Legumes |
| cg.iitasubject | Plant Breeding |
| cg.iitasubject | Plant Production |
| cg.iitasubject | Soybean |
| cg.journal | American-Eurasian Journal of Agricultural and Environmental Sciences |
| cg.howpublished | Formally Published |
| cg.accessibilitystatus | Limited Access |
| local.dspaceid | 91836 |
| cg.targetaudience | Scientists |
