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Measured and modelled forest soil respiration fluxes and controlling factors at global to local scales

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Title: Measured and modelled forest soil respiration fluxes and controlling factors at global to local scales
Author(s): Laakkonen, Antti
Contributor: University of Helsinki, Faculty of Agriculture and Forestry
Degree program: Master's Programme in Forest Sciences
Specialisation: Forest Ecology and Management
Language: English
Acceptance year: 2020
Abstract:
Understanding soil respiration behaviour in different environments is one of the most crucial research questions currently in environmental sciences, since it is a major component of the carbon cycle. It can be divided into many source components, them being litter decomposition, soil organic matter, root respiration and respiration in the rhizosphere. Many biotic and abiotic factors control soil respiration through complicated relationship networks. Strong controlling factors being soil temperature, soil moisture, substrate supply and quality, soil nitrogen content, soil acidity and soil texture. As these relationships are biome-specific, they must be understood in order to produce more accurate assessments worldwide. In this study annual soil respiration rates and its controlling factors were investigated globally in unmanaged and natural mature forest biomes. Observed values were extracted from Soil respiration database (SRDB) v.5, and it was complemented with spatially and temporally linked data from remotely sensed and modelled databases to produce variables for forest productivity, meteorological conditions and soil properties. Furthermore, empirical soil respiration models and machine learning algorithms, as well as previous estimates, were compared to each other. Locally, monthly manual soil respiration measurements from boreal forest site in Hyytiälä, Finland from the years 2010-2011, with environmental, soil temperature and soil water conditions were investigated to identify seasonal differences in controlling factors of soil respiration rate. Soil respiration controls were found to differ between biomes. Furthermore, the Artificial Neural Network algorithm used was observed to outperform empirical models and previous estimates, when biome specific modelling was implemented with the continental division. Artificial neural networks and other algorithms could produce more accurate estimates globally. Locally soil respiration rates were observed to differ seasonally, with soil temperature controls being stronger during the growing season and when snow depth exceeded 30 cm, soil water conditions, controlled soil respiration strongly.
Keyword(s): soil respiration biome controls mature forest unmanaged natural forest ANN modelling


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