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The emission of CO2 from the soil surface, known as soil carbon dioxide efflux (FCO2) is key to the compression of the global carbon cycle. FCO2 has a direct relationship with soil physicochemical attributes such as SOM quantity and quality, microbial biomass and community composition, pH and texture. Our objective was to develop a technique based on reflectance spectroscopy products, digital soil mapping and machine learning techniques, to create and spatialize a model of soil FCO2 for the entire Brazilian territory. We obtained maps with a spatial resolution of 30 m and the FCO2 was fitted to a multivariate regression model with an R2 of 0.56. The most important and significant variables correspond to sand, asymmetric CH2, urease and beta-glucosidase enzymatic activity, and CH. This FCO2 map can be used as a general approximation of soil CO2 emission potential and contribute to soil management in response to global climate change.
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