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

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

Grassland and

Matherials and methodsThe site

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Arable crops71%

Grassland and pasture

13%

Permanent crops16%

6° Censimento Agricoltura 2010, ISTAT)

The model RothC10N

Organicinput

DPM

CO2

IOM

DPM/RPM for most crop 1.44 (59% DPM and 41% % RPM, fordeciduous 0.25 (20% DPM and 80% RPM)

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RPM BIO

HUM

HUM

BIO

CO2

DPM= Decomposable Plant MaterialRPM= Resistant Plant MaterialBIO= Microbial BiomassHUM= Humified organic matterIOM= Inert Organic Matter

GSOC17, 21-23 March 2017, FAO HQ Rome

The datasets

20yrs crop succession in 6827 landparcels

AGRIT/RICA/ISTAT

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Climate dataAGRI4CAST

Soil database CREA280 profiles

Set-up of a harmonised spatially explicitdatabase assembled in a GIS

GIS Database

Final SOC and CO2 for each landparcel after 20 years

RothC10N simulations

RothCIS tool

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Spatial interpolation with EBK

parcel after 20 years

SOC stock assessment

Validation

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 ofdata (78 profiles collected in 2013)

GSOC17, 21-23 March 2017, FAO HQ Rome

By overlaying the maps (land use and EBK SOC stock) in a GIS environment weestimated the SOC stock for each land use category

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Empirical Bayesian Kriging (EBK) final spatialization of SOC stock in the agriculturalland 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

• soil C level in rainfed arable systems in the Foggia province, withthe current practices, are almost at steady state possibleoptions to increase SOC sequestration are reduced soildisturbance and diversification of crops in rotation (+legumes) toincrease net productivity

• summer irrigated arable crops showed important losses of C

Discussion

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summer irrigated arable crops showed important losses of Cpossible options to increase C sequestration rates are

reduction of irrigation volumes (deficit irrigation), application oforganic fertilizers, use of minimum tillage

• Vines and olive groves present a high level of C accumulation

GSOC17, 21-23 March 2017, FAO HQ Rome

The proposed methodology (i.e. linking a biophysicalmodel 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 dynamicsin 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 forC sequestration of the different land uses

GSOC17, 21-23 March 2017, FAO HQ Rome

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

Thank you

for your attention

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