Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks

TL;DR

This study applies Quantile Regression to map soil organic carbon in semi-arid agricultural regions, revealing predictor effects at different quantiles and outperforming existing benchmarks in predictive accuracy.

Contribution

It introduces the novel application of Quantile Regression for spatial SOC prediction, providing detailed insights into predictor influences across quantiles.

Findings

QR models achieved robust performance in SOC estimation.

Predictor effects vary across different quantiles, revealing nuanced influences.

The median-based predictive map outperforms existing benchmarks.

Abstract

Soil Organic Carbon (SOC) estimation is crucial to manage both natural and anthropic ecosystems and has recently been put under the magnifying glass after the Paris agreement 2016 due to its relationship with greenhouse gas. Statistical applications have dominated the SOC stock mapping at regional scale so far. However, the community has hardly ever attempted to implement Quantile Regression (QR) to spatially predict the SOC distribution. In this contribution, we test QR to estimate SOC stock (0-30 $cm$ depth) in the agricultural areas of a highly variable semi-arid region (Sicily, Italy, around 25,000 $km2$) by using topographic and remotely sensed predictors. We also compare the results with those from available SOC stock measurement. The QR models produced robust performances and allowed to recognize dominant effects among the predictors with respect to the considered quantile. This information, currently lacking, suggests that QR can discern predictor influences on SOC stock at specific sub-domains of each predictors. In this work, the predictive map generated at the median shows lower errors than those of the Joint Research Centre and International Soil Reference, and Information Centre benchmarks. The results suggest the use of QR as a comprehensive and effective method to map SOC using legacy data in agro-ecosystems. The R code scripted in this study for QR is included.