TY  - JOUR
TI  - Spatio-temporal topsoil organic carbon mapping of a semi-arid Mediterranean region: The role of land use, soil texture, topographic indices and the influence of remote sensing data to modelling
UR  - https://doi.org/10.1016/j.scitotenv.2017.05.239
Y1  - 2017/06/02/
A1  - Schillaci, C
A1  - Acutis, M
A1  - Lombardo, L
A1  - Lipani, A
A1  - Fantappie, M
A1  - Maerker, M
A1  - Saia, S
N2  - SOC is the most important indicator of soil fertility and monitoring its space-time changes is a prerequisite to establish strategies to reduce soil loss and preserve its quality. Here we modelled the topsoil (0?0.3 m) SOC concentration of the cultivated area of Sicily in 1993 and 2008. Sicily is an extremely variable region with a high number of ecosystems, soils, and microclimates. We studied the role of time and land use in the modelling of SOC, and assessed the role of remote sensing (RS) covariates in the boosted regression trees modelling. The models obtained showed a high pseudo-R2 (0.63?0.69) and low uncertainty (s.d. < 0.76 g C kg? 1 with RS, and < 1.25 g C kg? 1 without RS). These outputs allowed depicting a time variation of SOC at 1 arcsec. SOC estimation strongly depended on the soil texture, land use, rainfall and topographic indices related to erosion and deposition. RS indices captured one fifth of the total variance explained, slightly changed the ranking of variance explained by the non-RS predictors, and reduced the variability of the model replicates. During the study period, SOC decreased in the areas with relatively high initial SOC, and increased in the area with high temperature and low rainfall, dominated by arables. This was likely due to the compulsory application of some Good Agricultural and Environmental practices. These results confirm that the importance of texture and land use in short-term SOC variation is comparable to climate. The present results call for agronomic and policy intervention at the district level to maintain fertility and yield potential. In addition, the present results suggest that the application of RS covariates enhanced the modelling performance.
SP  - 821
PB  - ELSEVIER SCIENCE BV
VL  - 601
SN  - 1879-1026
JF  - Science of The Total Environment
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
EP  - 832
KW  - Science & Technology
KW  -  Life Sciences & Biomedicine
KW  -  Environmental Sciences
KW  -  Environmental Sciences & Ecology
KW  -  SOC mapping
KW  -  Space-time SOC variation
KW  -  Agro-ecosystems
KW  -  R programming
KW  -  Digital soil mapping
KW  -  Legacy dataset
KW  -  SPATIAL-DISTRIBUTION
KW  -  CLIMATE-CHANGE
KW  -  AGRICULTURAL SOILS
KW  -  ECOSYSTEM SERVICES
KW  -  NITROGEN UPTAKE
KW  -  DECISION TREES
KW  -  EROSION RISK
KW  -  STOCKS
KW  -  SEQUESTRATION
KW  -  SCALE
AV  - public
ID  - discovery10059995
ER  -