Summary
The conservation and improvement of forest soil resources is essential for achieving the UN Sustainable Development Goals and the EU Green Deal. Policy-makers and land managers need detailed information -quantitative and spatially explicit- on the effects of management strategies on soil condition and capability for supplying ecosystem services. The rise of digital soil mapping (DSM) and integrative modelling approaches have brought powerful tools for generating data-driven knowledge that can guide the decision-making process. SELVANS will implement an innovative DSM framework for assessing the effects of contemporary forest management on soil change, expressed as biogeochemical processes and microbial functions. The hypothesis is that the response of indicators of soil condition to management practices will differ by soil class depending on the biophysical environment, inherent soil properties and the legacy of land use history. Furthermore, SELVANS will investigate the links between anthropedogenesis and soil microbial functional diversity, and its effects on carbon and nutrient cycling. Soil classes and the degree of anthropogenic pressures on soil will be generated using data-mining algorithms, environmental covariates, time series of satellite imagery, national forestry inventory data and legacy soil data. Soil condition will be characterized with a comprehensive set of physical, chemical and biological soil properties (e.g., soil organic carbon, nutrients, soil microbial diversity and functioning) originating from completed and ongoing projects evaluating the functioning of forest soils. SELVANS' outputs will identify thresholds for decline in soil condition and risk of soil degradation, with an impact for forest management and conservation of soil resources. Finally, the capability of soils for delivering ecosystem services will be quantified applying the integrative modelling framework ARIES.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101063363 |
| Start date: | 15-05-2023 |
| End date: | 14-05-2025 |
| Total budget - Public funding: | - 181 152,00 Euro |
Cordis data
Original description
The conservation and improvement of forest soil resources is essential for achieving the UN Sustainable Development Goals and the EU Green Deal. Policy-makers and land managers need detailed information -quantitative and spatially explicit- on the effects of management strategies on soil condition and capability for supplying ecosystem services. The rise of digital soil mapping (DSM) and integrative modelling approaches have brought powerful tools for generating data-driven knowledge that can guide the decision-making process. SELVANS will implement an innovative DSM framework for assessing the effects of contemporary forest management on soil change, expressed as biogeochemical processes and microbial functions. The hypothesis is that the response of indicators of soil condition to management practices will differ by soil class depending on the biophysical environment, inherent soil properties and the legacy of land use history. Furthermore, SELVANS will investigate the links between anthropedogenesis and soil microbial functional diversity, and its effects on carbon and nutrient cycling. Soil classes and the degree of anthropogenic pressures on soil will be generated using data-mining algorithms, environmental covariates, time series of satellite imagery, national forestry inventory data and legacy soil data. Soil condition will be characterized with a comprehensive set of physical, chemical and biological soil properties (e.g., soil organic carbon, nutrients, soil microbial diversity and functioning) originating from completed and ongoing projects evaluating the functioning of forest soils. SELVANS' outputs will identify thresholds for decline in soil condition and risk of soil degradation, with an impact for forest management and conservation of soil resources. Finally, the capability of soils for delivering ecosystem services will be quantified applying the integrative modelling framework ARIES.Status
SIGNEDCall topic
HORIZON-MSCA-2021-PF-01-01Update Date
09-02-2023
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