02097naa a2200301 a 450000100080000000500110000800800410001902400460006010000230010624501220012926000090025152011710026065000190143165300180145065300280146865300160149665300240151265300240153665300210156065300260158165300180160765300250162570000230165070000200167370000170169370000170171077300680172721515952023-02-10       2023    bl uuuu     u00u1 u    #d7 a10.5752/p.2318-2962.2023v33n.72p.1102DOI1 aSILVA, L. A. P. da  aProjection of climate change impacts on net primary productivity of the legal Amazon - Brazil.h[electronic resource]  c2023  aAbstract. The Amazon Rainforest is one of the main carbon sinks (CO2) on Earth. However, recently, owing to anthropogenic activities and climate change, it has lost its stability in CO2 absorption. Therefore, understanding the dynamics of future climate change scenarios is essential. We assessed the influence of future climate change scenarios on NPP (biomass) levels in the Amazon Forest using ML models. The tested models were Bayesian, linear, and random forest models. The current scenario was evaluated using 19 bioclimatic covariates (WorldClim dataset). Future scenarios were based on RCPs 2.6 and 8.5 (based on the MIROC5 and HadGEM2-ES models). Random Forest had the best performance statistics (R² = 0.71 in training and 0.68 in the holdout-test). These climate change scenarios imply an increase in the average NPP for the Amazon forest, especially with the greater intensification in RCP 2.6 (10 and 12 % for the HadGEM2-ES and MIROC5 models, respectively). Forests (evergreen broadleaf forest areas) will have a greater carbon fixation capacity. In general, the Amazon forest will have an increased carbon fixation capacity by the end of the century.  aClimate change  aAmazon Forest  aAprendizado de máquina  aCarbon sink  aFloresta aleatória  aFloresta Amazônica  aMachine Learning  aMudanças climáticas  aRandom Forest  aSumidouro de carbono1 aSOUZA, C. M. P. de1 aSILVA, C. R. da1 aBOLFE, E. L.1 aROCHA, A. M.  tCaderno de Geografiagv. 33, n. 72, p. 110-130, jan./mar. 2023.