Supporting land degradation neutrality assessment by soil organic carbon stock mapping in Hungary

2021 ◽  
Author(s):  
Annamária Laborczi ◽  
Gábor Szatmári ◽  
János Mészáros ◽  
Sándor Koós ◽  
Béla Pirkó ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Degradation ◽  
Carbon Stocks ◽  
Conversion Factors ◽  
Land Use Conversion ◽  
Soc Stock ◽  
Default Data ◽  
Stock Values

<p>‘Strategic objective 1’ of the United Nations Convention to Combat Desertification (UNCCD) aims to improve conditions of affected ecosystems, combat desertification/land degradation, promote sustainable land management, and contribute to land degradation neutrality. The indicator ‘Proportion of land that is degraded over total land area’ (SO1) is compiled from three sub-indicators: ‘Trends in land cover’ (SO1-1), ‘Trends in land productivity or functioning of the land’ (SO1-2), ‘Trends in carbon stocks above and below ground’ (SO1-3).</p><p>Soil organic carbon (SOC) stock can be adopted as the metric of SO1-3, until globally accepted methods for estimating the total terrestrial system carbon stocks will be elaborated. SOC can be considered as one of the most important properties of soil, which shows not just spatial but temporal variability. According to our previous results in the topic, UNCCD default data of SOC stock for Hungary is strongly recommended to be replaced with country specific estimation of SOC stock.</p><p>SOC stock maps were compiled in the framework of DOSoReMI.hu (Digital, Optimized, Soil Related Maps and Information in Hungary) initiative, predicted by proper digital soil mapping (DSM) method. Reference soil data were derived from a countrywide monitoring system. The selection of environmental covariates was based on the SCORPAN model. The elaborated SOC stock mapping methodology have two components: (1) point support modelling, where SOC stock is computed at the level of soil profile, and (2) spatial modelling (quantile regression forest), where spatial prediction and uncertainty quantification are carried out using the computed SOC stock values.</p><p>We analyzed how SOC stock changed between 1998 and 2016.  Nationwide SOC stock predictions were compiled for the years 1998, 2010, 2013, and 2016. For the intermediate years, we do not recommend to calculate SOC stock values, because we have no information on the dynamics of change in the intervening years. Based on the 1998 SOC stock prediction, we compiled a SOC stock map for 2018, using only land use conversion factors, according to the default data conversion values.</p><p>According to the elaborated scheme during the respective period, significant changes cannot be detected, only tendentious SOC stock changes appear. Based on our results, we recommend to use spatially predicted layers for all years when data are available, rather than calculating SOC stock change based on land use conversion factors.</p><p><strong>Acknowledgment:</strong> Our research was supported by the Hungarian National Research, Development and Innovation Office (NKFIH; K-131820) and by the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (Gábor Szatmári).</p>

scholarly journals Impact of land use conversion on soil organic carbon stocks in an agro-pastoral ecotone of Inner Mongolia

2017 ◽  
Vol 27 (8) ◽  
pp. 999-1010 ◽  
Author(s):  
Wei Zhao ◽  
Zhongmin Hu ◽  
Shenggong Li ◽  
Qun Guo ◽  
Hao Yang ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Inner Mongolia ◽  
Carbon Stocks ◽  
Land Use Conversion ◽  
Soil Organic Carbon Stocks

scholarly journals On the rebound: soil organic carbon stocks can bounce back to near forest levels when agroforests replace agriculture in southern India

2015 ◽  
Vol 2 (2) ◽  
pp. 871-902 ◽  
Author(s):  
H. C. Hombegowda ◽  
O. van Straaten ◽  
M. Köhler ◽  
D. Hölscher
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Tree Species ◽  
Regional Scale ◽  
Clay Fraction ◽  
Carbon Stocks ◽  
Southern India ◽  
Reference Plot ◽  
Soc Stock ◽  
Soc Stocks

Abstract. Tropical agroforestry has an enormous potential to sequester carbon while simultaneously producing agricultural yields and tree products. The amount of soil organic carbon (SOC) sequestered is however influenced by the type of the agroforestry system established, the soil and climatic conditions and management. In this regional scale study, we utilized a chronosequence approach to investigate how SOC stocks changed when the original forests are converted to agriculture, and then subsequently to four different agroforestry systems (AFSs): homegarden, coffee, coconut and mango. In total we established 224 plots in 56 plot clusters across four climate zones in southern India. Each plot cluster consisted of four plots: a natural forest reference plot, an agriculture reference and two of the same AFS types of two ages (30–60 years and > 60 years). The conversion of forest to agriculture resulted in a large loss the original SOC stock (50–61 %) in the top meter of soil depending on the climate zone. The establishment of homegarden and coffee AFSs on agriculture land caused SOC stocks to rebound to near forest levels, while in mango and coconut AFSs the SOC stock increased only slightly above the agriculture stock. The most important variable regulating SOC stocks and its changes was tree basal area, possibly indicative of organic matter inputs. Furthermore, climatic variables such as temperature and precipitation, and soil variables such as clay fraction and soil pH were likewise all important regulators of SOC and SOC stock changes. Lastly, we found a strong correlation between tree species diversity in homegarden and coffee AFSs and SOC stocks, highlighting possibilities to increase carbon stocks by proper tree species assemblies.

scholarly journals Effects of land use changes and conservation measures on land degradation under a Mediterranean climate

2015 ◽  
Vol 7 (1) ◽  
pp. 115-145 ◽  
Author(s):  
Y. Mohawesh ◽  
A. Taimeh ◽  
F. Ziadat
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Land Degradation ◽  
Soil Conservation ◽  
Land Use Changes ◽  
Conservation Measures ◽  
Stone Walls ◽  
Effects Of Land Use ◽  
Soil Conservation Measures

Abstract. Land degradation resulting from improper land use and management is a major cause of declined productivity in the arid environment. The objectives of this study were to examine the effects of a sequence of land use changes, soil conservation measures, and the time since their implementation on the degradation of selected soil properties. The climate for the selected 105 km2 watershed varies from semi-arid sub-tropical to Mediterranean sub-humid. Land use changes were detected using aerial photographs acquired in 1953, 1978, and 2008. A total of 218 samples were collected from 40 sites in three different rainfall zones to represent different land use changes and different lengths of time since the construction of stone walls. Analyses of variance were used to test the differences between the sequences of land use changes (interchangeable sequences of forest, orchards, field crops, and range), the time since the implementation of soil conservation measures, and rainfall on the thickness of the A-horizon, soil organic carbon content, and texture. Soil organic carbon reacts actively with different combinations and sequences of land use changes. The time since stone walls were constructed showed significant impacts on soil organic carbon and the thickness of the surface horizon. The effects of changing the land use and whether the changes were associated with the construction of stone walls, varied according to the annual rainfall. The results help in understanding the effects of land use changes on land degradation processes and carbon sequestration potential and in formulating sound soil conservation plans.

Soil organic carbon stocks in southeast Germany (Bavaria) as affected by land use, soil type and sampling depth

2012 ◽  
Vol 18 (7) ◽  
pp. 2233-2245 ◽  
Author(s):  
Martin Wiesmeier ◽  
Peter Spörlein ◽  
Uwe Geuß ◽  
Edzard Hangen ◽  
Stephan Haug ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Type ◽  
Carbon Stocks ◽  
Sampling Depth ◽  
Soil Organic Carbon Stocks

Random sampling of stony and non-stony soils for testing a national soil carbon monitoring system

Soil Research ◽  
2012 ◽  
Vol 50 (1) ◽  
pp. 18 ◽  
Author(s):  
C. B. Hedley ◽  
I. J. Payton ◽  
I. H. Lynn ◽  
S. T. Carrick ◽  
T. H. Webb ◽  
...  
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Random Sampling ◽  
Model Calibration ◽  
Clay Mineralogy ◽  
Carbon Stocks ◽  
Calibration Dataset ◽  
Soil Organic Carbon Stocks ◽  
Field Estimation

The New Zealand Soil Carbon Monitoring System (Soil CMS) was designed, and has been used, to account for soil organic carbon change under land-use change, during New Zealand’s first Commitment Period (2008–2012) to the Kyoto Protocol. The efficacy of the Soil CMS model has been tested for assessing soil organic carbon stocks in a selected climate–land-use–soil grouping (cell). The cell selected for this test represents an area of 709 683 ha and contains soils with a high-activity clay mineralogy promoting long-term stabilisation of organic matter, and is under low-producing grassland in a dry temperate New Zealand climate. These soils have been sampled at randomly selected positions to assess total soil organic carbon stocks to 0.3 m, and to compare with the modelled value. Results show no significant difference between the field estimation (67 ± 30 Mg C/ha), the mean value of the model calibration dataset (79 ± 28 Mg C/ha), and the value predicted by the model (101 ± 41 Mg C/ha), although all estimates have large uncertainties associated with them. The model predicts national soil organic carbon stocks as a function of soil texture, clay mineralogy, land use, climate class, and a slope–rainfall erosivity product. Components of uncertainty within the model include the size and distribution of the calibration dataset, and lack of representativeness of the calibration soil samples, which were sampled for other reasons, e.g. soil survey and forest mensuration. Our study has shown that major components of uncertainty in our field estimation of soil organic carbon stocks (investigated using the indices reproducibility, RP; and coefficient of variation, CV) are short-range (within-plot) and regional (between-sites) spatial variability. Soil organic carbon stocks vary within our selected climate–land-use–soil cell due to varying stoniness (stony soils RP 44%, CV 21%; non-stony soils RP 27%, CV 13%), soil depth, slope position, and climatic effects. When one outlier soil was removed from the model calibration dataset, and the three very stony sites were removed from the randomly selected field validation set, the model calibration dataset and the field dataset means agreed well (78 ± 26 and 78 ± 21 Mg C/ha, respectively). The higher modelled value, before removal of the outlier, is likely to reflect a bias in the model dataset towards conventionally selected modal profiles containing less stony soils than those encountered by the random sampling strategy of our field campaign. Therefore, our results indicate (1) that the Soil CMS provides an adequate estimation of soil organic carbon for the selected cell, and (2) ongoing refinements are required to reduce the uncertainty of prediction.

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