Coffee crop yield estimate using an agrometeorological‑spectral model

• Autor(a) principal: Rosa, Viviane Gomes Cardoso da
• Data de Publicação: 2011
• Outros Autores: Moreira, Mauricio Alves, Rudorff, Bernardo Friedrich Theodor, Adami, Marcos
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Pesquisa Agropecuária Brasileira (Online)
• Texto Completo: https://seer.sct.embrapa.br/index.php/pab/article/view/8623
• topic: Coffea; agricultural statistics; leaf area index; modeling; remote sensing; Coffea; estatísticas agrícolas; índice de área foliar; modelagem; sensoriamento remoto

The objective of this work was to evaluate an agrometeorological-spectral model to estimate coffee crop yield. Images from the MODIS sensor and meteorological data from the ETA regional weather forecast model were used to provide input variables to the agrometeorological-spectral model, in the South-Southeast region of Minas Gerais State, Brazil, for crop years 2003/2004 to 2007/2008. The input spectral variable of the spectral-agrometeorological model, the leaf area index (LAI), used in the determination of the maximum yield, was estimated with the normalized-difference vegetation index (NDVI) obtained from MODIS images. Other input variables for the model were: meteorological data generated by the ETA model and the soil available water capacity. Comparing 0.4; 3.0; 5.3; 1.5 and 8.5% for crop years 2003/2004, 2004/2005, 2005/2006, 2006/2007 and 2007/2008, respectively. The agrometeorological-spectral model, based on Doorenbos & Kassan model, was as efficient as the IBGE official model to estimate the coffee crop yield. Furthermore, it was possible to present the spatial variation of coffee crop yield loss and to predict 80% of final yield by the first fortight of February before the harvest.