scholarly journals Spatio-temporal assessment of topsoil organic carbon stock change in Hungary

2019 ◽  
Vol 195 ◽  
pp. 104410 ◽  
Author(s):  
Gábor Szatmári ◽  
Béla Pirkó ◽  
Sándor Koós ◽  
Annamária Laborczi ◽  
Zsófia Bakacsi ◽  
...  
Geoderma ◽  
2019 ◽  
Vol 351 ◽  
pp. 1-8 ◽  
Author(s):  
Yosra Ellili ◽  
Christian Walter ◽  
Didier Michot ◽  
Pascal Pichelin ◽  
Blandine Lemercier

2020 ◽  
Author(s):  
László Pásztor ◽  
Annamária Laborczi ◽  
Gábor Szatmári

<p>The minimum set of indicators recommended for tracking progress towards LDN against a baseline are: land cover, land productivity and carbon stocks above and below ground. While land cover and its change can be and actually is operatively monitored by Earth Observation in a relatively straightforward manner, spatio-temporal assessment of the two other, soil related indicators poses challenges.</p><p>Soil organic carbon (SOC) stock in Hungary was first mapped in the frame of Global Soil Organic Carbon Map initiative. The Hungarian Soil Information and Monitoring System was used to create the GSOC product with quantile regression forest, which made the assessment of local uncertainty possible.  The map was produced with 500 meter spatial resolution and aggregated for the predefined 1 km grid. Since it used data collected in the first field campaign, in 1994, consequently its estimates represent that year’s state.</p><p>In 2018 a national report was expected by UNCCD on LDN firstly quantifying trends in carbon stocks above and below the ground. Based on global databases (ESA Climate Change Initiative Land Cover Dataset, SoilGrids250) default values were assigned to countries, which were asked about its acceptance or providing more accurate estimations based on national datasets. Similarly to the global initiative, SOC change estimation was not based on soil reference data dating from two distinct dates, but on the only available spatial prediction and changes of SOC were exclusively attributed to changes in land cover. Corine Land Cover Change maps were used to derive the GSOC estimations for the base year (2000) as well as for the target year (2012) from the original SOC map (representing 1994) according to Trends.Earth tool guidelines. SOC change between 2000 and 2012 was estimated by the difference of the two predictions.</p><p>In the next step, the SOM measurements on the samples collected in 2010 in the frame of Hungarian Soil Information and Monitoring System became available to map soil organic carbon stock in the topsoils (0-30 cm) of Hungary for the year 2010. New modelling was carried out based on the experiences of GSOC estimations, the map was produced with 100 m resolution using quantile regression forest for both years. 10-fold cross-validation was used for checking the accuracy of the spatial predictions and uncertainty quantifications. The performance of the spatial predictions and uncertainty quantifications was appropriate, which was verified by the computed biases, the root mean square errors, accuracy plots and the G statistics. Based on the compiled SOC stock maps, we assessed the spatial and temporal changes of SOC stocks on the whole area of Hungary except artificial surfaces and water bodies. The total SOC stock in the topsoil increased by 27.18 Tg over the respective period. We compared our estimate with others provided by global and continental SOC stock inventories. The comparison pointed out that a SOC stock map compiled by a given country can provide more accurate estimates at national level. We recommend applying the SOC stock map of 1992 as baseline to track and assess SOC stock change in Hungary.</p>


Forests ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 394
Author(s):  
Xinhui Xu ◽  
Zhenkai Sun ◽  
Zezhou Hao ◽  
Qi Bian ◽  
Kaiyue Wei ◽  
...  

Forests can affect soil organic carbon (SOC) quality and distribution through forest types and traits. However, much less is known about the influence of urban forests on SOC, especially in the effects of different forest types, such as coniferous and broadleaved forests. Our objectives were to assess the effects of urban forest types on the variability of SOC content (SOC concentration (SOCC) and SOC density (SOCD)) and determine the key forest traits influencing SOC. Data from 168 urban forest plots of coniferous or broadleaved forests located in the Beijing urban area were used to predict the effects of forest types and traits on SOC in three different soil layers, 0–10 cm, 10–20 cm, and 20–30 cm. The analysis of variance and multiple comparisons were used to test the differences in SOC between forest types or layers. Partial least squares regression (PLSR) was used to explain the influence of forest traits on SOC and select the significant predictors. Our results showed that in urban forests, the SOCC and SOCD values of the coniferous forest group were both significantly higher than those of the broadleaved group. The SOCC of the surface soil was significantly higher than those of the following two deep layers. In PLSR models, 42.07% of the SOCC variance and 35.83% of the SOCD variance were explained by forest traits. Diameter at breast height was selected as the best predictor variable by comparing variable importance in projection (VIP) scores in the models. The results suggest that forest types and traits could be used as an optional approach to assess the organic carbon stock in urban forest soils. This study found substantial effects of urban forest types and traits on soil organic carbon sequestration, which provides important data support for urban forest planning and management.


2013 ◽  
Vol 10 (5) ◽  
pp. 866-872 ◽  
Author(s):  
Xiao-guo Wang ◽  
Bo Zhu ◽  
Ke-ke Hua ◽  
Yong Luo ◽  
Jian Zhang ◽  
...  

2019 ◽  
Vol 23 (1) ◽  
pp. 159-171 ◽  
Author(s):  
Claudia Canedoli ◽  
Chiara Ferrè ◽  
Davide Abu El Khair ◽  
Emilio Padoa-Schioppa ◽  
Roberto Comolli

2017 ◽  
Vol 39 (2) ◽  
pp. 169 ◽  
Author(s):  
Heyun Wang ◽  
Zhi Dong ◽  
Jianying Guo ◽  
Hongli Li ◽  
Jinrong Li ◽  
...  

Grassland ecosystems, an important component of the terrestrial environment, play an essential role in the global carbon cycle and balance. We considered four different grazing intensities on a Stipa breviflora desert steppe: heavy grazing (HG), moderate grazing (MG), light grazing (LG), and an area fenced to exclude livestock grazing as the Control (CK). The analyses of the aboveground biomass, litter, belowground biomass, soil organic carbon and soil light fraction organic carbon were utilised to study the organic carbon stock characteristics in the S. breviflora desert steppe under different grazing intensities. This is important to reveal the mechanisms of grazing impact on carbon processes in the desert steppe, and can provide a theoretical basis for conservation and utilisation of grassland resources. Results showed that the carbon stock was 11.98–44.51 g m–2 in aboveground biomass, 10.43–36.12 g m–2 in plant litters, and 502.30–804.31 g m–2 in belowground biomass (0–40 cm). It was significantly higher in CK than in MG and HG. The carbon stock at 0–40-cm soil depth was 7817.43–9694.16 g m–2, and it was significantly higher in LG than in CK and HG. The total carbon stock in the vegetation-soil system was 8342.14–10494.80 g m–2 under different grazing intensities, with the largest value in LG, followed by MG, CK, and HG. About 90.54–93.71% of the total carbon in grassland ecosystem was reserved in soil. The LG and MG intensities were beneficial to the accumulation of soil organic carbon stock. The soil light fraction organic carbon stock was 484.20–654.62 g m–2 and was the highest under LG intensity. The LG and MG intensities were beneficial for soil nutrient accumulation in the desert steppe.


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