scholarly journals Mapping soil organic carbon stock change by soil monitoring and digital soil mapping at the landscape scale

Geoderma ◽  
2019 ◽  
Vol 351 ◽  
pp. 1-8 ◽  
Author(s):  
Yosra Ellili ◽  
Christian Walter ◽  
Didier Michot ◽  
Pascal Pichelin ◽  
Blandine Lemercier
Keyword(s):  
Organic Carbon ◽  
Carbon Stock ◽  
Landscape Scale ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Monitoring ◽  
Soil Organic Carbon Stock ◽  
Carbon Stock Change ◽  
Organic Carbon Stock ◽  
Stock Change

Comparison of uncertainty quantification methods on the example of soil organic carbon stock mapping in Hungary

2020 ◽  
Author(s):  
Gábor Szatmári ◽  
László Pásztor
Keyword(s):  
Decision Making ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Uncertainty Quantification ◽  
Carbon Stock ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Soil Organic Carbon Stock ◽  
Wide Range ◽  
Organic Carbon Stock

<p>Digital soil mapping (DSM) aims to provide spatial soil information for a wide range of studies (e.g. agro-environmental management, nature conservation, rural development, water and food security etc.). For this purpose, advanced statistical methods are in use for inferring the spatial variations of soil. Nowadays, there is a heap of evidences that researchers and stakeholders are not just interested in the maps of soil properties, functions and/or services but in their uncertainties as well. This is indispensable to support decision making process. In DSM various uncertainty quantification methods are in use, however, only a few studies have addressed the issue of comparing them. In this study, we compared the suitability of several commonly applied digital soil mapping methods to quantify uncertainty with regard to a survey of soil organic carbon stock in Hungary. To fairly represent the wide range of DSM methods, the followings were selected: universal kriging (UK), sequential Gaussian simulation (SGS), random forest plus kriging (RFK) and quantile regression forest (QRF). For RFK two uncertainty quantification methods were adopted based on kriging variance (RFK-1) and bootstrapping (RFK-2). We used a control dataset consisting of 200 independent SOC stock observations for validating not just the spatial predictions but their uncertainty quantifications as well. For validating the uncertainty quantifications we applied accuracy plots (a.k.a. prediction interval coverage probability plots) and a modified version of G-statistics. According to our results, QRF and SGS provided the best quantifications of uncertainty. UK and RFK-2 overestimated whereas RFK-1 underestimated the uncertainty. Based on our results we could draw a conclusion that there is a need to validate the uncertainty quantifications before using them for decision making. Furthermore, special attention should be paid to the assumptions made in uncertainty quantification.</p><p> </p><p>Acknowledgment: Our research was supported by the Hungarian National Research, Development and Innovation Office (NRDI; Grant No: KH126725) and the Premium Postdoctoral Scholarship of the Hungarian Academy of Sciences (PREMIUM-2019-390) (Gábor Szatmári).</p>

Uncertainty analysis of soil organic carbon stock change in Canadian cropland from 1991 to 2001

2004 ◽  
Vol 10 (6) ◽  
pp. 983-994 ◽  
Author(s):  
A. J. VandenBygaart ◽  
E. G. Gregorich ◽  
D. A. Angers ◽  
U. F. Stoklas
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Uncertainty Analysis ◽  
Carbon Stock ◽  
Soil Organic Carbon Stock ◽  
Carbon Stock Change ◽  
Organic Carbon Stock ◽  
Stock Change

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 ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Carbon Stock ◽  
Carbon Stock Change ◽  
Spatio Temporal ◽  
Organic Carbon Stock ◽  
Stock Change ◽  
Temporal Assessment

Grazing exclosures increase soil organic carbon stock at a rate greater than “4 per 1000” per year across agricultural landscapes in Northern Ethiopia

2021 ◽  
Vol 782 ◽  
pp. 146821
Author(s):  
Florent Noulèkoun ◽  
Emiru Birhane ◽  
Habtemariam Kassa ◽  
Alemayehu Berhe ◽  
Zefere Mulaw Gebremichael ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Stock ◽  
Agricultural Landscapes ◽  
Northern Ethiopia ◽  
Soil Organic Carbon Stock ◽  
Organic Carbon Stock

Assessment of soil organic carbon stock in the upper Yangtze River basin

2013 ◽  
Vol 10 (5) ◽  
pp. 866-872 ◽  
Author(s):  
Xiao-guo Wang ◽  
Bo Zhu ◽  
Ke-ke Hua ◽  
Yong Luo ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
River Basin ◽  
Yangtze River ◽  
Carbon Stock ◽  
Yangtze River Basin ◽  
Soil Organic Carbon Stock ◽  
Upper Yangtze River ◽  
Organic Carbon Stock ◽  
The Upper Yangtze River

Soil organic carbon stock in different urban land uses: high stock evidence in urban parks

2019 ◽  
Vol 23 (1) ◽  
pp. 159-171 ◽  
Author(s):  
Claudia Canedoli ◽  
Chiara Ferrè ◽  
Davide Abu El Khair ◽  
Emilio Padoa-Schioppa ◽  
Roberto Comolli
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Stock ◽  
Urban Parks ◽  
Urban Land ◽  
Land Uses ◽  
Soil Organic Carbon Stock ◽  
Organic Carbon Stock

scholarly journals Assessment of Soil Organic Carbon Stock of Temperate Coniferous Forests in Northern Kashmir

2015 ◽  
Vol 4 (1) ◽  
pp. 161-178
Author(s):  
Davood A. Dar ◽  
Bhawana Pathak ◽  
M. H. Fulekar
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Quality ◽  
Carbon Stock ◽  
Temperate Forest ◽  
Temperate Forests ◽  
Forest Site ◽  
Soil Organic Carbon Stock ◽  
Organic Carbon Stock

 Soil organic carbon (SOC) estimation in temperate forests of the Himalaya is important to estimate their contribution to regional, national and global carbon stocks. Physico chemical properties of soil were quantified to assess soil organic carbon density (SOC) and SOC CO2 mitigation density at two soil depths (0-10 and 10-20 cms) under temperate forest in the Northern region of Kashmir Himalayas India. The results indicate that conductance, moisture content, organic carbon and organic matter were significantly higher while as pH and bulk density were lower at Gulmarg forest site. SOC % was ranging from 2.31± 0.96 at Gulmarg meadow site to 2.31 ± 0.26 in Gulmarg forest site. SOC stocks in these temperate forests were from 36.39 ±15.40 to 50.09 ± 15.51 Mg C ha-1. The present study reveals that natural vegetation is the main contributor of soil quality as it maintained the soil organic carbon stock. In addition, organic matter is an important indicator of soil quality and environmental parameters such as soil moisture and soil biological activity change soil carbon sequestration potential in temperate forest ecosystems.DOI: http://dx.doi.org/10.3126/ije.v4i1.12186International Journal of Environment Volume-4, Issue-1, Dec-Feb 2014/15; page: 161-178

Sign in / Sign up

Export Citation Format

Share Document