This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Ms. Roberta Farina, from CREA - Italy, in FAO Hq, Rome

1. Towards a Tier 3 approach to estimate SOC
stocks at sub-regional scale in Southern Italy
20/03/2017 0GSOC17, 21-23 March 2017, FAO HQ Rome
Roberta Farina, Claudia Di Bene, Rosa Francaviglia, Rosario Napoli, Alessandro Marchetti
CREA, Consiglio per la ricerca e l’analisi dell’economia agraria, Rome

2. The assessment of the spatial and temporal dynamics
of Soil Organic Carbon (SOC) influenced by land use
and soil type
The approach was based on a bio-physical model
Objective
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The approach was based on a bio-physical model
(RothC10N*) combined with a spatially explicit
database including soil, land use and climate
*Farina et al., 2013
GSOC17, 21-23 March 2017, FAO HQ Rome

3. Grassland and
Matherials and methods
The site
20/03/2017 2GSOC17, 21-23 March 2017, FAO HQ Rome
Arable crops
71%
Grassland and
pasture
13%
Permanent crops
16%
6° Censimento Agricoltura 2010, ISTAT)

4. The model RothC10N
Organic
input
DPM
CO2
IOM
DPM/RPM
for most crop 1.44 (59% DPM and 41% % RPM, for
deciduous 0.25 (20% DPM and 80% RPM)
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RPM BIO
HUM
HUM
BIO
CO2
DPM= Decomposable Plant Material
RPM= Resistant Plant Material
BIO= Microbial Biomass
HUM= Humified organic matter
IOM= Inert Organic Matter
GSOC17, 21-23 March 2017, FAO HQ Rome

5. The datasets
20yrs crop succession in 6827 land
parcels
AGRIT/RICA/ISTAT
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Climate data
AGRI4CAST
Soil database CREA
280 profiles
Set-up of a harmonised spatially explicit
database assembled in a GIS

6. GIS Database
Final SOC and CO2 for each land
parcel after 20 years
RothC10N
simulations
RothCIS tool
20/03/2017 5GSOC17, 21-23 March 2017, FAO HQ Rome
Spatial interpolation with EBK
parcel after 20 years
SOC stock assessment
Validation

7. ResultsFinal regional SOC stock (Mg C ha-1) obtained
spatializing the RothC10N output by the EBK
procedure in Foggia Province in 2013.
Total agricultural area
427,665 ha
EBK Total SOC stock
19.0 Tg C
EBK SOC stock 42,6 Mg C ha-1
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ERRORS*
SE=-0.3 Mg C ha-1
SRMSE=1.01 Mg C ha-1
SOC stock change
(1994-2013)
0,3 Tg C
*validation with an independent set of
data (78 profiles collected in 2013)
GSOC17, 21-23 March 2017, FAO HQ Rome

8. By overlaying the maps (land use and EBK SOC stock) in a GIS environment we
estimated the SOC stock for each land use category
20/03/2017 7GSOC17, 21-23 March 2017, FAO HQ Rome

9. Empirical Bayesian Kriging (EBK) final spatialization of SOC stock in the agricultural
land use categories, in Foggia Province (Apulia Region, Italy).
EBK spatialization
Land use Area* (ha)
Mean SOC stock
(Mg ha-1)
SD
Amount of
SOC (Tg)
Arable crops
Rainfed rotations 261,000 45.38 6.41 11.85
Irrigated rotations 105,245 43.86 4.95 4.62
Woody crops
Results
20/03/2017 8GSOC17, 21-23 March 2017, FAO HQ Rome
Woody crops
Vines 31,408 39.33 5.70 1.24
Olives 23,365 42.27 7.51 0.99
Grasslands
Pastures 6,342 44.95 5.76 0.29
Land use change
A2P 200 42.55 5.70 0.01
P2A 105 39.50 5.50 0.004
Total 427,665 42.55 5.93 18.98
*Source: CORINE land cover 2012 map

10. • soil C level in rainfed arable systems in the Foggia province, with
the current practices, are almost at steady state possible
options to increase SOC sequestration are reduced soil
disturbance and diversification of crops in rotation (+legumes) to
increase net productivity
• summer irrigated arable crops showed important losses of C
Discussion
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summer irrigated arable crops showed important losses of C
possible options to increase C sequestration rates are
reduction of irrigation volumes (deficit irrigation), application of
organic fertilizers, use of minimum tillage
• Vines and olive groves present a high level of C accumulation
GSOC17, 21-23 March 2017, FAO HQ Rome

11.  The proposed methodology (i.e. linking a biophysical
model with and EBK spatial interpolation in a a GIS
evironment) can be applied in other regions with the
same data availability
 RothC10N showed to predict accurately the C dynamics
in the systems considered
Conclusions
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in the systems considered
 The accuracy of estimation was greatly improved by the
use of cropping sequences on a annual base
 Spatial predictions allowed to identify the potential for
C sequestration of the different land uses
GSOC17, 21-23 March 2017, FAO HQ Rome

12. Improvements
1) Data
• Availability of more precise data for management
• Availability of more detailed productivity data (farm
accounting or remote sensing data)
2) Modeling
Conclusions
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2) Modeling
• Include/link to a crop growth module
• Possibility to simulate conservation practices (no
tillage) or more than one typology of exogenous C
input (manure, organic fertilizers, plant residues,
digestate)
GSOC17, 21-23 March 2017, FAO HQ Rome

13. Thank you
for your attention
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