Better estimates of soil carbon from geographical data: a revised global approach
Soils hold the largest pool of organic carbon (C) on Earth; yet, soil organic carbon (SOC) reservoirs are not well represented in climate change mitigation strategies because our database for ecosystems where human impacts are minimal is still fragmentary. Here, we provide a tool for generating a gl...
| Published in: | Mitigation and Adaptation Strategies for Global Change |
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| Main Authors: | , , , , , , , , , , , , , , , , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.12123/2925 https://link.springer.com/article/10.1007/s11027-018-9815-y https://doi.org/10.1007/s11027-018-9815-y |
| Summary: | Soils hold the largest pool of organic carbon (C) on Earth; yet, soil organic carbon (SOC) reservoirs are not well represented in climate change mitigation strategies because our database for ecosystems where human impacts are minimal is still fragmentary. Here, we provide a tool for generating a global baseline of SOC stocks. We used partial least square (PLS) regression and available geographic datasets that describe SOC, climate, organisms, relief, parent material and time. The accuracy of the model was determined by the root mean square deviation (RMSD) of predicted SOC against 100 independent measurements. The best predictors were related to primary productivity, climate, topography, biome classification, and soil type. The largest C stocks for the top 1 m were found in boreal forests (254 ± 14.3 t ha−1) and tundra (310 ± 15.3 t ha−1). Deserts had the lowest C stocks (53.2 ± 6.3 t ha−1) and statistically similar C stocks were found for temperate and Mediterranean forests (142 - 221 t ha−1), tropical and subtropical forests (94 - 143 t ha−1) and grasslands (99-104 t ha−1). Solar radiation, evapotranspiration, and annual mean temperature were negatively correlated with SOC, whereas soil water content was positively correlated with SOC. Our model explained 49% of SOC variability, with RMSD (0.68) representing approximately 14% of observed C stock variance, overestimating extremely low and underestimating extremely high stocks, respectively. Our baseline PLS predictions of SOC stocks can be used for estimating the maximum amount of C that may be sequestered in soils across biomes. EEA Santa Cruz Fil: Duarte Guardia, Sandra. Universidad Nacional de la Patagonia Austral; Argentina Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Amelung, Wulf. University of Bonn. Soil Science and Soil Ecology. Institute of Crop Science and Resource Conservation (INRES); Alemania Fil: Sheil, Douglas. Norwegian University of Life Sciences. Faculty of Environmental Sciences and Natural Resource Management; Noruega. Jalan Cifor Rawajaha. Center for International Forestry Research (CIFOR); Indonesia Fil: Borchard, Nils. Forschungszentrum Jülich GmbH. Agrosphere Institute (IBG-3); Alemania. Jalan Cifor Rawajaha. Center for International Forestry Research (CIFOR); Indonesia. Ruhr-University Bochum, Institute of Geography, Soil Science/Soil Ecology; Alemania. Plant Production Natural Resources Institute Finland (Luke); Finlandia Fil: Laffan, Shawn W. University of New South Wales. School of Biological, Earth and Environmental Sciences; Australia Fil: Bird, Michael I. James Cook University. College of Science, Technology and Engineering and Centre for Tropical Environmental and Sustainability Science; Australia Fil: Dieleman, Wouter. James Cook University. College of Science, Technology and Engineering and Centre for Tropical Environmental and Sustainability Science; Australia Fil: Pepper, David A. University of New South Wales. School of Biological, Earth and Environmental Sciences; Australia. University of Canberra. Institute for Applied Ecology; Australia Fil: Zutta, Brian. Perú. Ministerio del Ambiente. Programa Nacional de Conservación de Bosques; Perú Fil: Jobbagy Gampel, Esteban Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Matemática Aplicada de San Luis "Prof. Ezio Marchi". Universidad Nacional de San Luis. Facultad de Ciencias Físico, Matemáticas y Naturales. Instituto de Matemática Aplicada de San Luis; Argentina Fil: Silva, Lucas C. R. University of Oregon. Institute of Ecology & Evolution. Department of Geography. Environmental Studies Program; Estados Unidos Fil: Bonser, Stephen P. University of New South Wales. School of Biological, Earth and Environmental Sciences. Evolution and Ecology Research Centre; Australia Fil: Berhongaray, Gonzalo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Litoral.Facultad de Ciencias Agrarias; Argentina Fil: Piñeiro, Gervasio. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Ecología. Laboratorio de Análisis Regional y Teledetección; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de la República. Facultad de Agronomia; Uruguay Fil: Martinez, Maria Jose. Universidad Científica del Sur. Escuela de Agroforestería; Perú Fil: Cowie, Annette L. NSW Department of Primary Industries; Australia. University of New England. School of Environmental and Rural Science; Australia Fil: Ladd, Brenton. Universidad Científica del Sur. Escuela de Agroforestería; Peru. UNSW Australia. School of Biological. Earth and Environmental Sciences, Evolution and Ecology Research Centre; Australia |
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