Genetic coefficients adjustments and tests of Cropgro-soybean simulation model for the northwestern of Paraná State, Brazil - DOI: 10.4025/actasciagron.v27i2.1857
Simulation models are tools that generate sceneries, considering the several combinations of factors that influence on crop productivity. Cropgrowith models provide with questions involving research, crop handling and planning. This is helpful to understand the genetic, physiologic and environmental...
| Autores: | , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2008 |
| País: | Brasil |
| Institución: | Universidade Estadual de Maringá (UEM) |
| Repositorio: | Acta Scientiarum. Agronomy (Online) |
| Idioma: | portugués |
| OAI Identifier: | oai:periodicos.uem.br/ojs:article/1857 |
| Acceso en línea: | http://www.periodicos.uem.br/ojs/index.php/ActaSciAgron/article/view/1857 |
| Access Level: | acceso abierto |
| Palabra clave: | simulação soja Cropgro-soybean modelos agrometeorológicos 5.03.00.00-8 Engenharia Agrícola |
| Sumario: | Simulation models are tools that generate sceneries, considering the several combinations of factors that influence on crop productivity. Cropgrowith models provide with questions involving research, crop handling and planning. This is helpful to understand the genetic, physiologic and environmental interactions. It is also useful at decisions involving cultural practices before and during the field stage of crop, as fertilizer application and irrigation. This study was developed for Palotina region, Paraná state, (24º17' S of latitude, 53º50'30'' of longitude and 333 m of altitude). To simulate growth and development of soybean crop (Glycine max (L) Merrill), Cropgro-soybean simulation model was utilized. Genetic coefficients calibration was done to CD 202, CD 204, CD 206 and CD 210 soybean cultivars, cultivated in Palotina region, Paraná state. After calibration, a simulation of soybean productivity for sowing in November 1st was carried out. The aim was to test the calibrated coefficients in the 25 harvests between 1974 and 1999. According to the analysis, the model presented high sensibility to genetic coefficient variation of the studied cultivars. Rainfall distribution is one of the factors that mostly influenced the Leaf Area Index and productivity. This means that the model strongly penalizes in water deficit conditions. Maximum productivity potential of the four cultivars, for Palotina region, was approximately 3573 kg ha-1. |
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