A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders,Belgium) |
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| Authors: | J Meersmans F De Ridder F Canters S De Baets M Van Molle |
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| Institution: | 1. INRA, US1106 Unité Infosol, F-45000 Orléans, France;2. Faculty of Agriculture and Environment, The University of Sydney, 1 Central Avenue, Australia Technology Park, Eveleigh, NSW 2015, Australia;3. Department of Geography, College of Life and Environmental Sciences, University of Exeter, Amory building — room 431, Rennes Drive, EX4 4RJ Exeter, UK;1. George Lemaître Centre for Earth and Climate Research, Earth & Life Institute, Université catholique de Louvain, Place Louis Pasteur 3, 1348 Louvain-la-Neuve, Belgium;2. School of Geography, College of Life and Environmental Sciences, Hatherly Laboratory, University of Exeter, Exeter, Devon EX4 4PS, United Kingdom;3. Fonds de la Recherche Scientifique — FNRS, Belgium;1. State Key Laboratory Base of Eco-hydraulic Engineering in Arid Area, Xi''an University of Technology, Xi'' an, Shaanxi 710048, PR China;2. State Key Laboratory of Soil Erosion and Dry-land Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, PR China;3. School of Civil Engineering and Architecture, Xi''an University of Technology, Xi'' an, Shaanxi 710048, PR China;1. Department of Agricultural Chemistry and Soil Science, Faculty of Science, Agrifood Campus of International Excellence - ceiA3, University of Cordoba, Cordoba, Spain;2. Department of Botany, Ecology and Plant physiology, Faculty of Science, Campus of International Excellence - ceiA3, University of Cordoba, Cordoba, Spain;3. Department of Biochemistry and Molecular Biology, Faculty of Science, Campus of International Excellence - ceiA3, University of Cordoba, Cordoba, Spain;4. Sustainable Use and Management of Soils (SUMAS) Research Group, Spain;1. Key Laboratory of Ecohydrology of Inland River Basin, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China;2. University of Chinese Academy of Sciences, Beijing 100049, China;3. Research Institute of Forestry Science of Bai Long Jiang Forestry Management Bureau, Lanzhou 730070, China;4. College of Geography and Environment Science, Northwest Normal University, Lanzhou 730070, China |
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| Abstract: | Estimates of the amount of Soil Organic Carbon (SOC) at the regional scale are important to better understand the role of the SOC reservoir in global climate and environmental issues. This study presents a method for estimating the total SOC stock using data from Flanders (Belgium). More than 6900 SOC measurements from the national soil survey (database ‘Aardewerk’) are combined with a digital land use map and a digital soil map of Flanders. The spatial distribution of the SOC stock is studied in its relation to factors such as soil texture, soil moisture (drainage class) and land use. The resulting map with a resolution of 15 m consists of different classes forming a combination of these environmental factors. The results show that the lowest SOC amount (kg m? 2) is stored under cropland whereas the highest amount is found under grassland. Regarding the effect of soil properties, a significant correlation between SOC stock and depth of the ground water table is observed. Sandy loam soils stock the lowest SOC amount (kg m? 2), whereas clay soils retain the highest SOC amount. First, the mean SOC amounts of the land use–soil type classes are calculated and assigned to the corresponding cells in order to obtain a total SOC stock with its spatial distribution for Flanders. Then, a multiple regression model is applied to predict the SOC value of a particular land use–soil type class on the map. This model is based on the observed relationships between SOC and land use–soil type characteristics, using the entire dataset. The first approach does not allow to obtain a (reliable) SOC value for all land use–soil type classes due to a lack of samples in some classes. A major advantage of the regression model approach is the attribution of class specific SOC values to each land use–soil type class, regardless of the number of observations in the classes. Consequently, by applying the model approach instead of the mean approach, the area for which a reliable SOC estimate could be obtained increased by 8.1% (from 9420 km2 to 10179 km2) and the total predicted SOC stock increased by 10.1% (from 88.7 ± 5.6 Mt C to 97.6 ± 1.1 Mt C). |
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