Soil CO2 emission and carbon stocks in agricultural areas and planted forests in the cerrado region of Mato Grosso do Sul

Abstract

In recent decades, the concentrations of greenhouse gases (GHGs) have increased in the atmosphere, mainly due to human activities that act directly on natural biogeochemical cycles. This increase in the concentration of greenhouse gases has caused worrying changes in the global climate. Carbon dioxide (CO2) represents about 60% of GHG total emissions in the planet, being the soil organic carbon one of the main terrestrial reservoirs for storage and exchange of atmospheric C. The aim of this study is to evaluate the spatiotemporal variability of CO2 emissions (FCO2) and the stock of soil carbon in areas of planted forest and agricultural area in the Cerrado in Mato Grosso do Sul. The assessments are being conducted in areas of eucalyptus, pine and riparian forest planted using native species. Subsequently, new evaluations will be conducted in native Cerrado forest area, areas of integration of forest, livestock and pasture, and corn production area (conventional tillage). In addition to determining FCO2 through two dynamic systems LI-8100, analysis about soil temperature and its moisture, as well as about the soil oxygen (O2) capture rate, will be carried out. The chemical and physical properties of the soil and humidification index of soil organic matter will also be obtained. Initially, the results will be presented in terms of descriptive statistics. The temporal variability of FCO2, soil temperature and moisture, and O2 capture in all the studied areas will be evaluated through the analysis of repeated measures in time using mixed models. The spatial variability of the variables will be studied using geostatistical techniques (semivariogram analysis, interpolation by ordinary kriging and sequential Gaussian simulation, followed by uncertainty analysis). Exploratory multivariate analysis (principal component analysis, factor analysis and structural equations analysis) and mathematical modeling will describe the relationships between the analysed variables. (AU)