dc.contributor.author Lombardo, Luigi dc.contributor.author Saia, Sergio dc.contributor.author Schillaci, Calogero dc.contributor.author Mai, Paul Martin dc.contributor.author Huser, Raphaël dc.date.accessioned 2017-12-28T07:32:14Z dc.date.available 2017-12-28T07:32:14Z dc.date.issued 2017-08-13 dc.identifier.uri http://hdl.handle.net/10754/626517.1 dc.description.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. dc.publisher arXiv dc.relation.url http://arxiv.org/abs/1708.03859v1 dc.relation.url http://arxiv.org/pdf/1708.03859v1 dc.rights Archived with thanks to arXiv dc.title Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks dc.type Preprint dc.contributor.department Computational Earthquake Seismology (CES) Research Group dc.contributor.department Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division dc.contributor.department Earth Science and Engineering Program dc.contributor.department Physical Science and Engineering (PSE) Division dc.contributor.department Statistics Program dc.eprint.version Pre-print dc.contributor.institution Council for Agricultural Research and Economics (CREA), Cereal and Industrial Crops Research Centre (CREA-CI), Foggia, Italy dc.contributor.institution Department of Agricultural and Environmental Science, University of Milan, Italy dc.identifier.arxivid arXiv:1708.03859 kaust.person Lombardo, Luigi kaust.person Mai, Paul Martin kaust.person Huser, Raphaël
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