Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/45494
Título: Simulating tropical forage growth and biomass accumulation: an overview of model development and application
Palavras-chave: Agricultural production systems simulator
Crop models
Grass
Pasture
Plantas forrageiras - Crescimento
Biomassa
Simulador de sistemas de produção agrícola
Modelos de cultivo
Gramíneas tropicais
Pastagens
Data do documento: 2016
Editor: John Wiley & Sons, Ltd.
Citação: ANDRADE, A. S. et al. Simulating tropical forage growth and biomass accumulation: an overview of model development and application. Grass And Forage Science, [S.I.], v. 71, n. 1, p. 54-65, 2016. DOI: http://dx.doi.org/10.1111/gfs.12177.
Resumo: Crop models can aid the synthesis and application of knowledge, planning of experiments and forecasting in agricultural systems. Few studies have reviewed the uses and applications of these models for tropical forages. The purpose of this study was to review the information available in this scientific area, highlighting the main models, their applications and limitations. Several empirical models have been developed to predict the growth and biomass accumulation of tropical forages, especially for the genera Cynodon, Paspalum, Panicum and Brachiaria. Their application, however, is often location or region specific. The adaptation of mechanistic models to accurately predict biomass accumulation in tropical grasses is still limited. Recent advances have been made on the plot-scale and farm-scale process-based models ALMANAC, CROPGRO Perennial Forage and agricultural production systems simulator (APSIM), with promising results. In addition, global-scale process-based models, such as the Century Agroecosystem Model and the Orchidee Grassland Management Model, have been tested for tropical grassland areas. A greater number of region-specific calibrations of empirical models can enhance their use, and improved databases and model parameterizations for a wide range of tropical grasses will enable the continuous improvement of mechanistic models.
URI: http://dx.doi.org/10.1111/gfs.12177
http://repositorio.ufla.br/jspui/handle/1/45494
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