Modeling and mapping of soil organic carbon stock in the state of Rondônia
Authors:   , Santos Cássio Marques Moquedace 
Published: July 2024

Climate changes caused by anthropic activities have been one of the main environmental issues around the world. The soil can act as a moderator of these changes, as it stores two thirds of all terrestrial carbon and small changes in this stock can increase the concentrations of greenhouse gases (GHG) and aggravate global warming. Therefore, the objective was to model and map the soil organic carbon stock in the state of Rondônia, at present and in future scenarios until 2100. Data from almost three thousand soil profiles from the study were used for the second approximation of the Socioeconomic Ecological Zoning of Rondônia. SOC stock were stratified at depths of 0-5, 5-15, 15-30, 30-60 and 60-100 cm and for the selection of variables two methods were adopted, one by correlation |< 0.95 | and another by importance (recursive feature elimination - RFE). Seven different machine learning algorithms were tested, and the selection and modeling process (training, validation, testing, and prediction) was performed 100 times for each depth with different subsets of samples. For the predictions of changes in the SOC stock between the years 2020 and 2100, we used the climate change data projected in contrasting CMIP6 scenarios (ssp126 and ssp585), predicted by the CanESM5 and MIROC6 models. At the end, average final maps and uncertainty maps were elaborated. The algorithm with the best performance and selected for the prediction was Random Forest (rf). The better performance of rf is explained by its ability to detect non-linear and hierarchical relationships between SOC stock and environmental predictors. The variable selection process reduced by 50% the number of predictors per correlation (r > |0.95|), reducing processing time. Soil class was one of the predictors that most influenced SOC stock, especially in the superficial layers (0-5, 5-15 and 15-30 cm). Furthermore, climate was another important predictor, especially in the subsurface layers (30-60, 60-100 cm). More than half of the 2,530.91 Tg of SOC present in one meter of depth are concentrated in the first 30 cm of soil. The largest SOC stock are in the southern region of the state, at low altitudes and places with seasonal flooding dynamics, and the smallest were found in the central region of the state, especially on the surface. The lower SOC values are associated with the high weathering of the Latossolos and the deep anthropization of the area. The highest density of SOC is found in Gleissolos and Organossolos, a consequence of their genesis and limitation of decomposition by hydromorphism. Higher altitude regions will suffer a high reduction in SOC stock in future scenarios, especially in the ssp585 scenario. However, in areas where SOC stock is limited, projections indicate an increase. In the two climate models (CanESM5 and MIROC6) the differences in impact on the SOC will be in areas with more carbon, with CanESM5 with the greatest variations in climate and in SOC demonstrating how much the SOC of Rondônia can be climate dependent. The dispersion of observed and predicted points of the adjusted model compared to other studies at national and international level, shows the importance of modeling in smaller extensions to achieve greater precision. Although there is a strong relationship between climate predictors and the SOC, caution is recommended when interpreting the results, especially in areas where the SOC tends to increase in future projections. Rondônia is in a region with low climatic amplitude, therefore, the adjusted model made predictions in scenarios beyond its training range. Keywords: machine learning. Amazon. climate changes. random forest. tropical soil. pedometrics