scholarly journals Pedoclimatic zone-based three-dimensional soil organic carbon mapping in China

Geoderma ◽  
2020 ◽  
Vol 363 ◽  
pp. 114145 ◽  
Author(s):  
Xiao-Dong Song ◽  
Hua-Yong Wu ◽  
Bing Ju ◽  
Feng Liu ◽  
Fei Yang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Three Dimensional ◽  
Carbon Mapping

Evaluating the capability of a UAV ‐borne spectrometer for soil organic carbon mapping in bare croplands

2021 ◽  
Author(s):  
He Zhang ◽  
Pu Shi ◽  
Giacomo Crucil ◽  
Bas Wesemael ◽  
Quentin Limbourg ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Mapping

Application of a combinatorial approach for soil organic carbon mapping in hills

2021 ◽  
Vol 300 ◽  
pp. 113718
Author(s):  
Youlin Luo ◽  
Kai Wang ◽  
Huanxiu Li ◽  
Changquan Wang ◽  
Qiquan Li
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Combinatorial Approach ◽  
Carbon Mapping

scholarly journals Soil Organic Carbon Stocks in Terrestrial Ecosystems of China: Revised Estimation on Three-Dimensional Surfaces

2016 ◽  
Vol 8 (10) ◽  
pp. 1003 ◽  
Author(s):  
Rui Zhou ◽  
Xianzhang Pan ◽  
Hongxu Wei ◽  
Xianli Xie ◽  
Changkun Wang ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Three Dimensional ◽  
Terrestrial Ecosystems ◽  
Carbon Stocks ◽  
Soil Organic Carbon Stocks

scholarly journals Microspectroscopic visualization of how biochar elevates the soil organic carbon ceiling

2021 ◽  
Author(s):  
Zhe (Han) Weng ◽  
Lukas Van Zwieten ◽  
Michael Rose ◽  
Bhupinder Pal Singh ◽  
Ehsan Tavakkoli ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Molecular Diversity ◽  
Three Dimensional ◽  
Specific Enzyme ◽  
Eucalyptus Saligna ◽  
Soil C ◽  
Decadal Scale ◽  
Use Efficiency ◽  
Microbial C

Abstract The soil carbon saturation concept suggests an upper limit to store soil organic carbon (SOC), set by the mechanisms that protect soil organic matter from decomposition. Biochar has the capacity to protect new C including rhizodeposits and microbial necromass. However, the decadal scale mechanisms by which biochar influences the molecular diversity, spatial heterogeneity, and temporal changes of SOC persistence remain unresolved. Here we show that the soil C saturation ceiling of a Ferralsol under subtropical pasture could be elevated by 2 Mg (new) C ha-1 by the application of Eucalyptus saligna biochar 8.2 years after the first application. Using one, two-, and three-dimensional analyses, significant increases were observed in the spatial distribution of root-derived 13C in microaggregates (53-250 µm, 11 %) and new C protected in mineral fractions (<53 µm, 5 %). Microbial C-use efficiency was concomitantly improved by lowering specific enzyme activities, contributing to the decreased mineralization of native SOC by 18 %. We provide evidence that the global SOC ceiling can be elevated using biochar in Ferralsols by 0.01-0.1 Pg new C yr-1.

Volumetric soil quality modelling with machine learning in a diverse agricultural landscape in Andalusia, Spain

2020 ◽  
Author(s):  
Tobias Rentschler ◽  
Martin Bartelheim ◽  
Marta Díaz-Zorita Bonilla ◽  
Philipp Gries ◽  
Thomas Scholten ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Properties ◽  
Soil Quality ◽  
Three Dimensional ◽  
Soil Mapping ◽  
Soil Profiles ◽  
Soil Functions ◽  
Depth Functions

&lt;p&gt;Soils and soil functions are recognized as a key resource for human well-being throughout time. In an agricultural and forestry perspective, soil functions contribute to food and timber production. Other soil functions are related to freshwater security and energy provisioning. In general, the capacity of a soil to function within specific boundaries is summarised as soil quality. Knowledge about the spatial distribution of soil quality is crucial for sustainable land use and the protection of soils and their functions. This spatial knowledge can be obtained with accurate and efficient machine-learning-based soil mapping approaches, which allow the estimation of the soil quality at distinct locations. However, the vertical distribution of soil properties is usually neglected when assessing soil quality at distinct locations. To overcome such limitations, the depth function of soil properties needs to be incorporated in the modelling. This is not only important to get a better estimation of the overall soil quality throughout the rooting zone, but also to identify factors that limit plant growth, such as strong acidity or alkalinity, and the water holding capacity. Thus, the objective of this study was to model and map the soil quality indicators pH, soil organic carbon, sand, silt and clay content as a volumetric entity. The study area is located in southern Spain in the Province of Seville at the Guadalquivir river. It covers 1,000&amp;#160;km&lt;sup&gt;2&lt;/sup&gt; of farmland, citrus and olive plantations, pastures and wood pasture (Dehesa) in the Sierra Morena mountain range, at the Guadalquivir flood plain and tertiary terraces. Soil samples were taken at 130 soil profiles in five depths (or less at shallow soils). The profiles were randomly stratified depending on slope position and land cover. We used a subset of 99 samples from representative soil profiles to assess the overall 513 samples with FT-IR spectroscopy and machine learning methods to model equal-area spline, polynomial and exponential depth functions for each soil quality indicator at each of the 130 profiles. These depth functions were modelled and predicted spatially with a comprehensive set of environmental covariates from remote sensing data, multi-scale terrain analysis and geological maps. By solving the spatially predicted depth functions with a vertical resolution of 5&amp;#160;cm, we obtained a volumetric, i.e. three-dimensional, map of pH, soil organic carbon content and soil texture. Preliminary results are promising for volumetric soil mapping and the estimation of soil quality and limiting factors in three-dimensional space.&lt;/p&gt;

Use of Ecosystem Information to Improve Soil Organic Carbon Mapping of a Mediterranean Island

2007 ◽  
Vol 36 (1) ◽  
pp. 262-271 ◽  
Author(s):  
Luigi P. D'Acqui ◽  
Carolina A. Santi ◽  
Fabio Maselli
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Mediterranean Island ◽  
Carbon Mapping
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