Table_1_Soil C Storage Potential of Exogenous Organic Matter at Regional Level (Italy) Under Climate Change Simulated by RothC Model Modified for Amen.DOCX (43.76 kB)

Table_1_Soil C Storage Potential of Exogenous Organic Matter at Regional Level (Italy) Under Climate Change Simulated by RothC Model Modified for Amended Soils.DOCX

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posted on 29.11.2018, 04:09 by Claudio Mondini, Maria Luz Cayuela, Tania Sinicco, Flavio Fornasier, Antonia Galvez, Miguel Angel Sánchez-Monedero

Soil amendment with exogenous organic matter (EOM) represents an effective option for sustainable management of organic residues and enhancement of soil organic C (SOC) content. Optimization of soil amendment is hampered by the high variability in EOM quality and pedoclimatic conditions. A possible solution to this problem could be represented by spatially explicit soil C modeling. The aim of this study was the evaluation at regional level of the long term C storage potential of EOM added to the soil under climate change by using a modified version of the RothC specifically developed for C simulation in amended soil. To achieve this goal a spatially explicit version of the modified RothC model was deployed to assess at a national scale the potential for C storage of agricultural soils amended with different EOMs. Long term model simulations of continuous amendment (100 years) indicated that EOMs greatly differ for their soil C sequestration potential (range 0.110–0.385 t C ha−1 y−1), mainly depending to their degree of stabilization. Spatial explicit modeling of amended soil, taking into account the different combinations of EOMs and application sites, indicated a high variability in the potential of SOC accumulation at the national level (range: 0.06–0.62 t C ha−1 y−1). EOM quality showed a larger impact on long term SOC accumulation than variability in pedoclimatic conditions. Model simulations predicted that the contribution of soil amendment in tackling greenhouse gas (GHG) emissions is limited: soil C sequestration potential of compost applied to all Italian agricultural land corresponded to 5.3% of the total annual GHG emissions in Italy. Large scale modeling enables areas with the largest potential for EOM accumulation to be identified, therefore suggesting ways for optimizing resources. The spatially explicit version of the modified RothC model improves the predictive power of SOC modeling at regional scale in amended soils, because it takes into account, besides variability in pedoclimatic conditions, the large differences in EOMs quality.

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