Annals of Forest Science · 2022 · 19 citations · 46 references
AgricultureForestrySoil SciencesForest ManagementSoil FertilitySoil Organic MatterBiomass Resource AssessmentCarbon SequestrationCarbon Stock AssessmentCarbon Stock EstimationResidue ManagementResidue RetentionForest Biomass InventoryForest Residue Biomass AssessmentForest ResiduesHarvesting Residue UtilizationResidue Biomass ManagementSoil Nutrient DepletionSoil Organic CarbonSoil Organic Carbon Content Measurement
Abstract Key message Managing forest residues according to the carbon content of the soil helps to minimize the ecological footprint of their removal. Context In Mediterranean mountain ecosystems, unsustainable harvesting of wood residues might contribute to land degradation, carbon, and nutrient depletion in forest soils. Aims This study aimed to assess the amount of forest biomass residues that should be left on-site to minimize the depletion of soil fertility. Methods We estimated the availability of biomass residues in the public forest land of the Basilicata region of Southern Italy by collecting stand-scale inventory attributes from forest management plans. Subsequently, we quantified the amount of forest biomass residue released by implementing a scenario-based approach. Results Approximately 5800 m 3 year −1 of forest residues could be potentially available for bio-based industries at the regional scale within the next 10 years. Such residues mainly belong to broadleaved forest types, having a high variability in their soil organic stock (228.5–705.8 Mg C ha −1 ) and altitudinally spanning from 400 to 1500 m a.s.l. In these forests, the simulated scenarios displayed a wide range of average harvestable residues from 2.5 to 5.5 m 3 ha −1 , containing approximately 1.1 to 2.1 Mg ha −1 of organic carbon. Conclusion Our study suggests that forest management plans are a useful source of information to estimate the available forest biomass residues consistently. In southern Mediterranean mountain forests, the management of forest residues according to soil carbon content helps to minimize the environmental impact and increase their sustainability.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
SoilGrids250m: Global gridded soil information based on machine learning
Tomislav Hengl, Jorge Mendes de Jesus, G.B.M. Heuvelink et al. · PLoS ONE · 2017 · 4.7K citations · Full text