FOR A GREATER EFFICIENCY OF THE BRAZILIAN PASTURES:

Abstract

Climate change, induced by human activities, is already a reality in the most diverse environments on Earth. Modelling forage capacity for herds has become an important management strategy, aiming to reduce environmental impacts and increase efficiency in meat production. This work aimed to estimate the Gross Primary Productivity (GPP) in pasture areas in savanna environments (locally known as Cerrado) of the state of Goiás, Brazil, with specific parameterization data for Brachiaria species. The experiment was carried out in pasture areas in the Rio Vermelho hydrographic basin (BHRV), in the western portion of Goiás, using Landsat 8 OLI / TIRS sensor satellite images, in which the variation in the GPP was recorded in 22 images in the period from October 2014 to May 2018. This parameter was estimated by coupling the SEBAL algorithms to estimate evapotranspiration, combined with the CASA model, which, together with surface data, calculates the GPP. Furthermore, for this same area, an adaptation of the GPP product methodology obtained by MOD17A2H was also carried out for Landsat 8 images to understand better the variation in GPP in medium spatial resolution images (30 m). Among the results, the SEBAL / CASA method proved to be more efficient among the methods applied in this research, following the climatic seasonality of the region and its influences on the pasture areas, with a variation of 0.10 to 5 g C m-2. Therefore, the estimate of the GPP aiming at a reading of the pasture and local climatic data presented better results with the calibration of the models with specific data.