A modelling assessment of the maize crop growth, yield and soil water dynamics in the Northeast of Brazil
The present study aims to evaluate the APSIM-Maize model performance to use it as a decision-making tool to help improve production rates, reduce production costs and assess the potential impacts of climate change on crop yields in the Northeast of Brazil. The crop, soil and weather data used in the...
| Autores: | , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/221521 |
| Acceso en línea: | http://dx.doi.org/10.21475/ajcs.20.14.06.p1410 http://hdl.handle.net/11449/221521 |
| Access Level: | acceso abierto |
| Palabra clave: | Agricultural systems APSIM Crop simulation model Field experiment Sowing date Zea mays l |
| Sumario: | The present study aims to evaluate the APSIM-Maize model performance to use it as a decision-making tool to help improve production rates, reduce production costs and assess the potential impacts of climate change on crop yields in the Northeast of Brazil. The crop, soil and weather data used in the simulations were obtained from field experiments carried out in maize crops in 2008 and 2011 in two different edaphoclimatic regions in Alagoas State, Northeast Brazil. The approach we used explored the ability of APSIM to simulate growth variables and soil water dynamics of a maize variety (AL Bandeirante). During parametrization, we made some adjustments regarding the variety and soil organic matter to attain a better representation of the growth and soil water dynamics, respectively. The APSIM-Maize model predicted the leaf area index with a RMSE (Root Mean Square Error) ranging between 0.14 and 1.06 cm2 cm-2 and the biomass production with an RMSE between 2.30 and 3.34 Mg ha-1. The volumetric soil water content was satisfactorily predicted with RMSE ranging between 0.02 and 0.08 mm mm-1. Results showed that this model is a useful tool for decision-making, which can be potentially used as a support in climate risk management and policies, aiming to improve regional production, provided it has been previously validated with independent datasets. |
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