1. Application of CarboSOIL model to predict the effects of climate change on soil organic carbon stocks in agro-silvo-pastoral Mediterranean management systems Miriam Muñoz-Rojas, Luca Doro, Luigi Ledda, and Rosa Francaviglia

2. CarboSOIL, a new component of the Agroecological Decision Support System MicroLEIS MicroLEIS DSS Land degradation risk Land suitability Land capability Carbon sequestration capacity CarboSOIL: a land evaluation tool for soil C assessment

3. MicroLEIS DSS Land degradation risk Land suitabilityLand capability Carbon sequestration capacity Climate regulation Soil structure Soil fertility Water holding capacity Infiltration capacity Water use efficiency Soil biological health Climate regulation Soil structure Soil fertility Water holding capacity Infiltration capacity Water use efficiency Soil biological health CarboSOIL: a land evaluation tool for soil C assessment

4. Soil N, pH, CEC Texture Bulk density Water Holding Capacity Land use 15 land use types Climate Winter Temperature Summer temperature Annual Rainfall Site Elevation Slope CarboSOIL

5. CarboSOIL model diagram SOIL ORGANIC CARBON 0-25, 25-50, 50-75 cm OUTPUTS Incorporation of residues MineralisationAereation Nutrient availability Biological activity Accumulation / lossRespiration SOIL PROCESSES CLIMATE TJJAPPT LAND USE LULC SITE ELEVSLOPSERODRAI SOIL FCAPSANDCLAYPHWANITROCEXCBULK INPUT FACTORS TDJF Source: Muñoz-Rojas, 2012 CarboSOIL: a land evaluation tool for soil C assessment

6. CarboSOIL structure Variables CarboSOIL 25CarboSOIL 50CarboSOIL 75 Coef Intercept774.691085.651150.92 Climate PRPT0.0030.000 TDJF1.4300.6150.637 TJJA-0.930-0.6870.067 Site ELEV0.0000.0030.001 SLOP0.0040.0050.001 DRAI ad--- df-2.078-1.502-0.210 ex1.887-2.391-4.076 SERO ne--- se-0.997-0.883-0.403 re-0.1590.449-0.466 ge-0.3331.216-0.879 Soil NITRO1.93426.3096.063 PHWA0.8370.0721.039 CEXC-0.0090.0000.029 SAND0.8031.0561.161 CLAY-1.192-1.597-1.687 BULK-493.990-686.455-746.993 FCAP0.018-0.0680.002 Variables CarboSOIL 25CarboSOIL 50CarboSOIL 75 Coef Intercep t 774.691085.651150.92 Land use LULC ot--- nr1.169-0.527-0.469 pr-1.4830.858-0.580 vn-0.210-3.229-1.816 fr-1.009-0.5251.068 ol1.481-0.232-0.313 cm-0.371-1.068-2.222 af-0.051-1.627-0.063 bf-0.335-2.4050.029 cf0.284-1.3670.697 mf6.329-1.3770.982 gr1.5110.409-0.526 sc1.358-0.635-2.021 wd1.396-3.3981.334 sm-2.465-3.064-3.576 y = a+ b 1 x 1 +b 2 x 2 +b 3 x 3 + ---+b n x n y= SOC ; a= Intercept; b= variables; x= coefficients CarboSOIL: a land evaluation tool for soil C assessment

7. Study area  Mean altitude -285 m  Climate- warm temperate with dry and hot summer. Mean annual rainfall of 623 mm (range 367–811) Mean annual temperature 15.0°C (13.8–16.4)  Soil type - Haplic Endoleptic Cambisols, Dystric (WRB)  Potential native vegetation - Cork oak forest (Quercus suber L.), converted to managed land with pastures and vineyards in recent years North-eastern Sardinia (Italy) 40°46’N, 9°10’E

8. Land use transformation Study area Six land uses with different levels of cropping intensification were compared: Tilled vineyards (TV) No-tilled grassed vineyards (GV) Hay crop (HC): oats, Italian ryegrass and annual clovers or vetch for 5 years and intercropped by spontaneous herbaceous vegetation in the 6 th year Pasture (PA): 5 years of spontaneous herbaceous vegetation, and 1 year of intercropping with oats, Italian ryegrass and annual clovers or vetch cultivated as a hay crop Cork oak forest (CO) Semi-natural systems (SN): natural re-vegetation of former vineyards (scrublands, Mediterranean maquis and Helichrysum meadows)

9. Land use transformation From left to right clockwise: no-tilled grassed vineyard (GV), grazed hay crop under oats land cover (HC), semi-natural systems with scrubs and Mediterranean maquis (SN), grazed pasture with spontaneous vegetation (PA). Study area

10. CarboSOIL application in agro-silvo-pastoral Mediterranean management systems Input data Climate (actual and future) Site Soil Land use Model test and validation Measured values vs. Predicted values Scenario analysis Actual scenario Climate change scenarios

11. Input data: variables description and sources VARIABLE TYPE VARIABLE NAMECODEUNITSOURCE AND REFERENCE Dependent variable Soil Organic CSOCCMg/ha University of Sassari Francaviglia et al. (2012;2014) ClimateTotal precipitationPRPTmm CRA elaborations from baseline data and GCMs Winter TemperatureTDJF oCoC Summer TemperatureTJJA oCoC SiteElevationELEVm University of Sassari SlopeSLOP% DrainageDRAI- Field surveys and lab analyses Francaviglia et al. (2012;2014) Soil ErosionSERO- SoilNitrogenNITROg/100g pHPHWA- Cation Exchange Capacity CEXCmeq/100g SandSANDg/100g ClayCLAYg/100g Bulk densityBULKg/cc Field capacityFCAPg/100g Land useLand use/land coverLULC- Field surveys Input data Model test and validation Scenario analysis CarboSOIL application in agro-silvo-pastoral Mediterranean management systems

12. Input data: climate change scenarios Global Climate Models GISS (Goddard Institute of Space Studies, USA) HadCM3 (Met Office, Hadley Centre, UK) Climate Change Horizons 2010-2039 (2020) 2040-2069 (2050) 2070-2099 (2080) IPCC Models A2 (high population growth, slow economic and energetic development) B2 (more emphasis on sustainability and efficient technologies Input data Model test and validation Scenario analysis CarboSOIL application in agro-silvo-pastoral Mediterranean management systems

13. Input data Model test and validation Model application Assessment of the model performance and comparison between measured and simulated values where O i and S i are observed and simulated SOC at i th value, Ō is the mean of the observed data and n is the number of the paired values. The lowest possible value of RMSE is zero, indicating that there is no difference between simulated and observed data. CarboSOIL application in agro-silvo-pastoral Mediterranean management systems EF compares simulations and observations on an average level, and can range from -  to 1, with the best performance at EF=1.

14. Input data Model test and validation Model application 0-25 cm measured SD=14.12 25-50 cm measured SD=19.53 50-75 cm measured SD=11.09 Regression coefficients are significant at p<0.001 and R 2 are high, the standard deviation of the measured values is higher than RMSE, and EF is very close to the optimum value. CarboSOIL application in agro-silvo-pastoral Mediterranean management systems

15. Input data Model test and validation Model application CarboSOIL model predicted an overall increase of SOC stocks in the 2020 climate scenarios in all the soil sections, with the higher increases in the 50-75 cm section, and the smaller in the 25-50 cm soil section. CarboSOIL application in agro-silvo-pastoral Mediterranean management systems

16. Input data Model test and validation Model application A SOC decrease is instead expected in the 2050 and 2080 scenarios in the 25-50 cm soil section, more marked in the vineyards in comparison with the other land uses. CarboSOIL application in agro-silvo-pastoral Mediterranean management systems

17. Input data Model test and validation Model application Oppositely, SOC increases are still expected in the 0-25 cm section and to a more extent in the 50-75 cm section, particularly evident in the vineyards. CarboSOIL application in agro-silvo-pastoral Mediterranean management systems

18. The model CarboSOIL has proved its ability to predict SOC stocks at different soil depths under different climate change scenarios. Climate change will have a negative impact on SOC stocks in the soil section 25-50 cm, in particular in a long term (2050 and 2080). Important decreases of SOC stocks were found in vineyards. The methodology developed in this research might be easily applied to other Mediterranean areas with available data on climate, site, soil and land use. Conclusions

19. Miriam Muñoz-Rojas, Luca Doro, Luigi Ledda, and Rosa Francaviglia Thank you!