Global Journal of Human-Social Science, B: Geography, Environmental Science and Disaster Management, Volume 23 Issue 6

Volume XXIII Issue VI Version I 16 ( ) Global Journal of Human Social Science - Year 2023 © 2023 Global Journals B Evaluating Soil Carbon Efflux Responses to Soil Moisture and Temperature Variations in Brazilian Biomes The environmental variables that were part of all the models were Ts and , which are possibly the main drivers of the efflux. In previous studies, it has been reported that these variables control soil respiration ( Davidson et al., 1998; Davidson et al., 2006; Liu et al., 2019 ); however, in this study, it appeared that the soil efflux further depended on other controlling variables, such as LAI and S, except for Pantanal, for which data on LAI was not available for the same period; thus, the combination of LAI and S was not possible. This possibly had an effect on the accuracy of the regression model (R² = 0.745), which was substantially lower for this site compared with the Ecotone (R² = 0.832) and Cerrado (R² = 0.916), respectively. Additionally, for Pantanal and the Ecotone (Figure 2) the regression model underestimated the monthly efflux after January given that the magnitude of their dependent variables (Ts and ) did not change sufficiently to capture the efflux that primarily increased from the month of March (Figure 2). Figure 2: Monthly measured and modeled carbon soil efflux for Cerrado-Amazonian Ecotone (Gray), Pantanal (white) and Cerrado (Black). In general, all the models had a high goodness- of-fit for efflux, and captured the dynamics and magnitude of observed efflux data. The models results suggest that the litter supply associated with an increase in ventilation, incident radiation, and soil evaporation, and a decrease in LAI, influence the soil carbon efflux. Thus, they can contribute to enhancing our understating of the efflux dynamics in tropical regions, based on the results of previous studies (Valentini et al., 2008; Lathuillière et al., 2017 ). Additionally, the regression models accurately described most of the efflux variations throughout the months for all the study sites, and could also perform additional analyses, such as the determination of the sensitivity of the efflux to the variables, Ts and . c) Regression models and analysis of efflux sensitivity to Ts and The sensitivity analysis tested the effect of the relevant dependent variables that were included in the regression model, i.e., Ts and , on the efflux (Table 2).