Abstract
Abstract
In the current era of global warming, soil organic content is one of the most important soil properties. The goal of the entire globe is for carbon neutrality to be achieved and regularly assessed. It is hoped that a dynamic, quick and effective soil organic carbon mapping method will be able to distribute the presence of soil organic carbon to support calculations for changes in carbon stocks and carbon sequestration so that carbon neutrality can be achieved. Digital Soil Mapping (DSM) recently has become the ultimate framework for accurately representing spatial distribution based on its quantitative result and uncertainty analysis. These advantages allow DSM to be replicated uniquely in each mapped area. Digital soil mapping requires input in the form of laboratory and field observation results that are spatially modeled using machine learning techniques. Field observations and laboratory data for Sumatra and Java Island from the Indonesian Center for Agricultural Land Resources Standard Testing (1970-2022) were used in this study, and the results were modeled using Quantile Regression Forests (QRF) in the R Software. Evaluation results from this model with 5738 observation points covering a 47.3 million-hectare-sized island of Sumatra and 3398 observation points covering a 12.8 million-hectare-sized island of Java show an RMSE value of 0.78 with a coefficient of determination (R2) of 0.31 for Sumatra Island and RMSE value of 0.68 with a coefficient of determination (R2) of 0.71 for Java Island. These findings indicate that the neighborhoods for the organic carbon content on the islands of Sumatra and Java differ quite noticeably. This may be due to the relatively wide range in some soils in the Sumatra region, which are peat soils with relatively high carbon content values compared to regions in Java where mineral soils predominate. In conclusion, the evaluation results for digital mapping with the QRF model for soil organic carbon content in Indonesia referring to these 2 large islands show good results with sufficient coefficients of determination in mineral soil areas and there is a need a different modeling approach in areas where peat soil predominates.