TY - JOUR
T1 - Modeling soil organic carbon with Quantile Regression
T2 - Dissecting predictors’ effects on carbon stocks
AU - Lombardo, Luigi
AU - Saia, Sergio
AU - Schillaci, Calogero
AU - Mai, P. Martin
AU - Huser, Raphaël
PY - 2018/5/15
Y1 - 2018/5/15
N2 - Soil organic carbon (SOC) estimation is crucial to manage natural and anthropic ecosystems. Many modeling procedures have been tested in the literature, however, most of them do not provide information on predictors’ behavior at specific sub-domains of the SOC stock. Here, we implement Quantile Regression (QR) to spatially predict the SOC stock and gain insight on the role of predictors (topographic and remotely sensed) at varying SOC stock (0–30cm depth) in the agricultural areas of an extremely variable semi-arid region (Sicily, Italy, around 25,000km2). QR produces robust performances (maximum quantile loss = 0.49) and allows to recognize dominant effects among the predictors at varying quantiles. In particular, clay mostly contributes to maintain SOC stock at lower quantiles whereas rainfall and temperature influences are constantly positive and negative, respectively. This information, currently lacking, confirms that QR can discern predictor influences on SOC stock at specific SOC sub-domains. The QR map generated at the median shows a Mean Absolute Error of 17 t SOC ha- 1 with respect to the data collected at sampling locations. Such MAE is lower than those of the Joint Research Centre at Global (18 t SOC ha- 1) and at European (24 t SOC ha- 1) scales and of the International Soil Reference and Information Centre (23 t SOC ha- 1) while higher than the MAE reached in Schillaci et al. (2017b) (Geoderma, 2017, issue 286, page 35–45) using the same dataset (15 t SOC ha- 1). The results suggest the use of QR as a comprehensive method to map SOC stock using legacy data in agro-ecosystems and to investigate SOC and inherited uncertainty with respect to specific subdomains. The R code scripted in this study for QR is included.
AB - Soil organic carbon (SOC) estimation is crucial to manage natural and anthropic ecosystems. Many modeling procedures have been tested in the literature, however, most of them do not provide information on predictors’ behavior at specific sub-domains of the SOC stock. Here, we implement Quantile Regression (QR) to spatially predict the SOC stock and gain insight on the role of predictors (topographic and remotely sensed) at varying SOC stock (0–30cm depth) in the agricultural areas of an extremely variable semi-arid region (Sicily, Italy, around 25,000km2). QR produces robust performances (maximum quantile loss = 0.49) and allows to recognize dominant effects among the predictors at varying quantiles. In particular, clay mostly contributes to maintain SOC stock at lower quantiles whereas rainfall and temperature influences are constantly positive and negative, respectively. This information, currently lacking, confirms that QR can discern predictor influences on SOC stock at specific SOC sub-domains. The QR map generated at the median shows a Mean Absolute Error of 17 t SOC ha- 1 with respect to the data collected at sampling locations. Such MAE is lower than those of the Joint Research Centre at Global (18 t SOC ha- 1) and at European (24 t SOC ha- 1) scales and of the International Soil Reference and Information Centre (23 t SOC ha- 1) while higher than the MAE reached in Schillaci et al. (2017b) (Geoderma, 2017, issue 286, page 35–45) using the same dataset (15 t SOC ha- 1). The results suggest the use of QR as a comprehensive method to map SOC stock using legacy data in agro-ecosystems and to investigate SOC and inherited uncertainty with respect to specific subdomains. The R code scripted in this study for QR is included.
KW - Digital soil mapping
KW - Mediterranean agro-ecosystem
KW - Quantile Regression
KW - R coding
KW - Topsoil organic carbon
KW - ITC-ISI-JOURNAL-ARTICLE
UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1016/j.geoderma.2017.12.011
UR - https://ezproxy2.utwente.nl/login?url=https://webapps.itc.utwente.nl/library/2018/isi/lombardo_mod.pdf
U2 - 10.1016/j.geoderma.2017.12.011
DO - 10.1016/j.geoderma.2017.12.011
M3 - Article
AN - SCOPUS:85040234556
SN - 0016-7061
VL - 318
SP - 148
EP - 159
JO - Geoderma
JF - Geoderma
ER -