Soil organic carbon in Italian forests and agroecosystems: Estimating current stock and future changes with a spatial modelling approach

Caddeo, Antonello; Marras, Serena; Sallustio, Lorenzo; Spano, Donatella; Sirca, Costantino

doi:10.1016/j.agrformet.2019.107654

Cited by 22 publications

(8 citation statements)

References 64 publications

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“…This approach will provide cost-effective fuel related information and maps providing many advantages for land and resource management analysis, e.g., fuel hazard assessment, preventive fire management measures, strategic fire management planning, through the prioritization of areas and resource allocation for fuel treatment to mitigate potential fire danger, risk, and fire emission and model carbon-cycle and air quality. Finally, spatially-explicit information coupled with future climate (Caddeo et al 2019) and land use change scenarios (Martellozzo et al 2018) would offer a valuable support towards more comprehensive landscape planning able to minimize risk related disturbances such as wildfires , as well as maximize ecosystem services provisioning and their trade-offs at the landscape level (Vizzarri et al 2017).…”

Section: Discussionmentioning

confidence: 99%

Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management

Ascoli¹,

Vacchiano²,

Scarpa³

et al. 2020

iForest

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Section: Discussionmentioning

confidence: 99%

Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management

Ascoli¹,

Vacchiano²,

Scarpa³

et al. 2020

iForest

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“…Similar method was also employed by Gray et al [14] and Adhikari et al [24]. However, it should be noted that although land use change was also an important variable in modelling SOC dynamics in previous studies [16,21,47], we did not include it in the model for this study. This was mainly because the anthropogenic disturbances were relatively limited after the National Nature Reserve of the Qilian Mountains was established in 1988, and the human-induced land use change was not intense [48].…”

Section: Soc Predictionmentioning

confidence: 99%

“…Most soil carbon models were suitable for single-point or typical ecosystem simulations, and could be used to simulate higher-resolution SOC dynamics at regional or watershed scales with the help of GIS techniques. This was the case in Caddeo et al [16], who simulated the SOC dynamics of Italian forest and farmland ecosystems under the Representative Concentration Pathway (RCP) 4.5 (low-to-moderate emission pathway) and RCP8.5 (high emission pathway) scenarios from the 2000s to the 2100s by the CENTURY 5.0 model and GIS techniques at a 500 m resolution, and they found that the SOC was projected to experience a moderate carbon loss. In general, the mechanisms of process-based models are clear, and thus the simulation accuracy at site level is quite high [17].…”

Section: Introductionmentioning

confidence: 96%

Assessing Soil Organic Carbon Stock Dynamics under Future Climate Change Scenarios in the Middle Qilian Mountains

Liu

Zhu

et al. 2021

Forests

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Soil organic carbon (SOC) simply cannot be managed if its amounts, changes and locations are not well known. Thus, evaluations of the spatio-temporal dynamics of SOC stock under future climate change are crucial for the adaptive management of regional carbon sequestration. Here, we evaluated the dynamics of SOC stock to a 60 cm depth in the middle Qilian Mountains (1755–5051 m a.s.l.) by combining systematic measurements from 138 sampling sites with a machine learning model. Our results reveal that the combination of systematic measurements with the machine learning model allowed spatially explicit estimates of SOC change to be made. The average SOC stock in the middle Qilian Mountains was expected to decrease under future climate change, while the size and direction of SOC stock changes seemed to be elevation-dependent. Specifically, in comparison with the 2000s, the mean annual precipitation was projected to increase by 18.37, 19.80 and 30.80 mm, and the mean annual temperature was projected to increase by 1.9, 2.4 and 2.9 °C under the Representative Concentration Pathway (RCP) 2.6 (low-emissions pathway), RCP4.5 (low-to-moderate-emissions pathway), and RCP8.5 (high-emissions pathway) scenarios by the 2050s, respectively. Accordingly, the area-weighted SOC stock and total storage for the whole study area were estimated to decrease by 0.43, 0.63 and 1.01 kg m–2 and 4.55, 6.66 and 10.62 Tg under the RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively. In addition, the mid-elevation zones (3100–3900 m), especially the subalpine shrub-meadow Mollic Leptosols, were projected to experience the most intense carbon loss. However, the higher elevation zones (>3900 m), especially the alpine desert zone, were characterized by significant carbon accumulation. As for the low-elevation zones (<2900 m), SOC was projected to be less varied under future climate change scenarios. Thus, the mid-elevation zones, especially the subalpine shrub-meadows and Mollic Leptosols, should be given priority in terms of reducing CO2 emissions in the Qilian Mountains.

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“…Globally, forest ecosystems cover approximately more than 4 billion hectares (FAOSTAT, 2020) and the most recent estimates reported that they store about 650 Pg of C, of which 44% in the biomass, 11% in dead wood and litter, and 45% into the soil (FAO, 2010). In Italy, forested areas amount for about 10 million hectares (FAOSTAT, 2020) and their soils store from 80 to 130 Mg C ha −1 to 1 m depth (Caddeo et al, 2019). Italian forests are mainly distributed in mountainous areas (Cesaro & Romano, 2017), where soils are often characterized to be poorly developed, shallow and skeletal due to limited pedogenic processes and high soil erosion rates (Alewell et al, 2008; Guerra et al, 2020; Romeo et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In Italy, forested areas amount for about 10 million hectares (FAOSTAT, 2020) and their soils store from 80 to 130 Mg C ha À1 to 1 m depth (Caddeo et al, 2019). Italian forests are mainly distributed in mountainous areas (Cesaro & Romano, 2017), where soils are often characterized to be poorly developed, shallow and skeletal due to limited pedogenic processes and high soil erosion rates (Alewell et al, 2008;Guerra et al, 2020;Romeo et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

et al. 2022

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In soil organic carbon (SOC) survey, the role of sampling approach is crucial and should not be underestimated. In this sense, the appropriateness of soil sampling by pedogenic horizons (PGH) and fixed depth layers (FDL) in forest is still a field of debate. The present work aimed to: (a) study the spatial variability of SOC concentrations and C stocks through PGH and FDL samplings in uneven-aged sweet chestnut, European beech and Norway spruce forests of the Apennine chain (Italy); (b) clarify the major advantages and drawbacks of sampling by PGH and FDL. In a representative area (18 Â 18 m) of each forest, a soil profile was investigated and eight additional sampling points were selected. The sampling was performed both by PGH and FDL (0-15 and 15-30 cm). For each forest, no SOC content and C stock differences in 0-30 cm soil depth were found between PGH (58.8 ± 5.3 g kg À1 and 103 ± 7 g ha À1 , respectively) and FDL (52.7 ± 4.3 g kg À1 and 117 ± 9 Mg ha À1 , respectively) sampling. However, under Norway spruce, PGH sampling pointed-out that C was mainly stored in Oe and Oa horizons (51.1 vs. 146 Mg ha À1 in the 0-30 cm layer). The higher coefficient of variation obtained when soil horizons were considered (from 19.2% to 72.8% for PGH and from 16.5% to 25.7% for FDL) suggested that PGH sampling preserved the information about the spatial variability within an ecosystem. The information loss about SOC vertical and spatial distribution would indicate the inability of FDL sampling to support decision-making plans addressed for sustainable use of soil resource.

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Soil organic carbon in Italian forests and agroecosystems: Estimating current stock and future changes with a spatial modelling approach

Cited by 22 publications

References 64 publications

Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management

Harmonized dataset of surface fuels under Alpine, temperate and Mediterranean conditions in Italy. A synthesis supporting fire management

Assessing Soil Organic Carbon Stock Dynamics under Future Climate Change Scenarios in the Middle Qilian Mountains

Soil organic carbon stock assessment in forest ecosystems through pedogenic horizons and fixed depth layers sampling: What's the best one?

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