Results of the summarizing process for the arXiv paper: 1708.03859v1

The estimation of Soil Organic Carbon (SOC) is crucial for managing both natural and anthropic ecosystems, particularly in light of its relationship with greenhouse gas emissions. While statistical applications have dominated SOC stock mapping at a regional scale, the use of Quantile Regression (QR) to spatially predict SOC distribution has been largely unexplored by the scientific community. In this study, QR was tested to estimate SOC stock (0-30 cm depth) in agricultural areas within a highly variable semi-arid region (Sicily, Italy), covering approximately 25,000 km2. Topographic and remotely sensed predictors were used, and results were compared with available SOC stock measurements. The QR models produced robust performances and allowed for the recognition of dominant effects among predictors with respect to the considered quantile. This information suggests that QR can discern predictor influences on SOC stock at specific sub-domains of each predictor. The predictive map generated at the median showed lower errors than those from Joint Research Centre and International Soil Reference and Information Centre benchmarks. These results suggest that QR is a comprehensive and effective method for mapping SOC using legacy data in agro-ecosystems. Additionally, R code scripted for QR is included in this study providing a valuable resource for future research efforts in this field. Overall, this study highlights the potential benefits of utilizing QR as an alternative approach to traditional statistical methods when predicting SOC distribution in agricultural areas.

• Soil Organic Carbon (SOC) estimation is important for managing natural and anthropic ecosystems
• SOC has a relationship with greenhouse gas emissions
• Quantile Regression (QR) can be used to spatially predict SOC distribution in agricultural areas
• QR models were tested in a highly variable semi-arid region in Sicily, Italy
• Topographic and remotely sensed predictors were used to estimate SOC stock (0-30 cm depth)
• QR models produced robust performances and allowed for the recognition of dominant effects among predictors with respect to the considered quantile
• The predictive map generated at the median showed lower errors than those from Joint Research Centre and International Soil Reference and Information Centre benchmarks
• QR is an effective method for mapping SOC using legacy data in agro-ecosystems
• R code scripted for QR is included in this study providing a valuable resource for future research efforts in this field.

Soil Organic Carbon (SOC) is important for managing natural and human-made environments. SOC is related to greenhouse gas emissions, which can harm the environment. Scientists used a special method called Quantile Regression (QR) to predict where SOC is in agricultural areas in Sicily, Italy. They used information about the land and pictures from far away to make their predictions. The QR method worked well and made better maps than other methods scientists have used before. This study also includes a helpful computer program that other scientists can use in the future." Definitions- Soil Organic Carbon (SOC): carbon found in soil that comes from dead plants and animals - Greenhouse gas emissions: gases released into the atmosphere that trap heat and contribute to global warming - Quantile Regression (QR): a statistical method used to predict values at different points of a distribution - Spatially predict: predicting values across space or location - Remotely sensed predictors: using images taken from satellites or aircrafts to gather information about an area

Exploring Quantile Regression to Predict Soil Organic Carbon Stock in Agricultural Areas

Soil organic carbon (SOC) is a key factor in managing both natural and anthropic ecosystems. It has a direct relationship with greenhouse gas emissions, making it essential for researchers to understand how SOC behaves and where it can be found. While statistical applications have been the primary method used to map SOC stock at regional scales, the use of quantile regression (QR) as an alternative approach has not been explored extensively by the scientific community.

In this study, QR was tested to estimate SOC stock in agricultural areas within Sicily, Italy – a semi-arid region covering approximately 25,000 km2. Topographic and remotely sensed predictors were used, and results were compared with available SOC stock measurements. The aim of this research was to determine if QR could provide more accurate predictions than traditional methods when mapping SOC distribution in agro-ecosystems.

Testing Quantile Regression

The first step of this research was to test whether or not QR could accurately predict soil organic carbon stock (0-30 cm depth). To do so, topographic and remotely sensed predictors were applied using R code scripted specifically for QR models. These models produced robust performances that allowed for recognition of dominant effects among predictors with respect to the considered quantiles. This information suggests that QR can discern predictor influences on SOC stock at specific sub-domains of each predictor – something that traditional statistical methods cannot do as effectively.

Comparing Results

Once the predictive maps generated by QR had been created, they were compared against benchmarks from Joint Research Centre and International Soil Reference and Information Centre datasets. The results showed lower errors than those from these benchmarks when predicting median values – suggesting that QR is indeed a comprehensive and effective method for mapping SOC using legacy data in agro-ecosystems.

Conclusion

Overall, this study highlights the potential benefits of utilizing QR as an alternative approach to traditional statistical methods when predicting soil organic carbon distribution in agricultural areas such as Sicily’s semi-arid region. Additionally, R code scripted for QR is included in this study providing a valuable resource for future research efforts in this field