Differences in pedotransfer functions of bulk density lead to high uncertainty in soil organic carbon estimation at regional scales: Evidence from Chinese terrestrial ecosystems

Summary Soil bulk density is a required variable for quantifying stocks of elements in soils and is therefore instrumental for the evaluation of land-use related climate change mitigation measures. Our motivation was to derive a set of pedotransfer functions for soil bulk densities usable to accommodate different levels of data availabilities. We derived sets of linear equations for bulk density that are appropriate for different forms of land-use. After introducing uncertainty factors for measured parameters, we ran the linear models repeatedly in a Monte Carlo simulation in order to test the impact of inaccuracy. The reliability of the models was evaluated by a cross-validation. The single best predictor of soil bulk density is the content of soil organic carbon, yielding estimates with an adjusted R2 of approximately 0.5. A slight improvement of the estimate is possible when additionally, soil texture and soil depth are known. Residual analysis advocated the derivation of land-use specific models. Using transformed variables and assessing land-use specific pedotransfer functions, the determination coefficient (adjusted R2) of the multiple linear models ranged from 0.43 in cropland up to 0.65 for grassland soils. Compared to pedotransfer function, from the literature, the performance of the linear modes were similar but more accurate. Taking into account the likely inaccuracies when measuring soil organic carbon, the soil bulk density can be estimated with an accuracy of +/− 9 to 25% depending on land-use. We recommend measuring soil bulk density by standardized sampling of undisturbed soil cores, followed by post-processing of the samples in the lab by internationally harmonized protocols. Our pedotransfer functions are accurately and transparently presented, and derived from well-documented and high-quality soil data sets. We therefore consider them particularly useful in Austria, where the measured values for soil bulk densities are not available.

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Importance of suberin biopolymer in plant function, contributions to soil organic carbon and in the production of bio-derived energy and materials

Biotechnology for Biofuels ◽

10.1186/s13068-021-01892-3 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Anne E. Harman-Ware ◽

Samuel Sparks ◽

Bennett Addison ◽

Udaya C. Kalluri

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Biomass Conversion ◽

Terrestrial Ecosystems ◽

Bioenergy Crops ◽

System Management ◽

Performance Improvements ◽

Future Technologies ◽

Analysis Models ◽

Analytical Approaches

AbstractSuberin is a hydrophobic biopolymer of significance in the production of biomass-derived materials and in biogeochemical cycling in terrestrial ecosystems. Here, we describe suberin structure and biosynthesis, and its importance in biological (i.e., plant bark and roots), ecological (soil organic carbon) and economic (biomass conversion to bioproducts) contexts. Furthermore, we highlight the genomics and analytical approaches currently available and explore opportunities for future technologies to study suberin in quantitative and/or high-throughput platforms in bioenergy crops. A greater understanding of suberin structure and production in lignocellulosic biomass can be leveraged to improve representation in life cycle analysis and techno-economic analysis models and enable performance improvements in plant biosystems as well as informed crop system management to achieve economic and environmental co-benefits.

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

Sustainability ◽

10.3390/su8101003 ◽

2016 ◽

Vol 8 (10) ◽

pp. 1003 ◽

Cited By ~ 2

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

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Interactive comment on „Soil organic carbon stocks are systematically overestimated by misuse of the parameters bulk density and stone content“ by Christopher Poeplau et al.

10.5194/soil-2016-78-sc1 ◽

2017 ◽

Author(s):

Matthias Hunziker

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Bulk Density ◽

Carbon Stocks ◽

Soil Organic Carbon Stocks

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Soil organic carbon stocks are systematically overestimated by misuse of the parameters bulk density and stone content

10.5194/soil-2016-78 ◽

2016 ◽

Author(s):

Christopher Poeplau ◽

Cora Vos ◽

Axel Don

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Bulk Density ◽

Agricultural Soils ◽

Soil Layer ◽

Future Studies ◽

Soil Organic Carbon Stocks ◽

Soc Stock ◽

The Mean ◽

Soc Stocks

Abstract. Estimation of soil organic carbon (SOC) stocks requires estimates of the carbon content, bulk density, stone content and depth of a respective soil layer. However, different application of these parameters could introduce a considerable bias. Here, we explain why three out of four frequently applied methods overestimate SOC stocks. In stone rich soils (> 30 Vol. %), SOC stocks could be overestimated by more than 100 %, as revealed by using German Agricultural Soil Inventory data. Due to relatively low stone content, the mean systematic overestimation for German agricultural soils was 2.1–10.1 % for three different commonly used equations. The equation ensemble as re-formulated here might help to unify SOC stock determination and avoid overestimation in future studies.

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Sampling probes affect bulk density and soil organic carbon measurements

ael ◽

10.1002/ael2.20005 ◽

2020 ◽

Vol 5 (1) ◽

Author(s):

Sumit Sharma ◽

Tracy Wilson ◽

Tyson Ochsner ◽

Jason G. Warren

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Bulk Density

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Organic carbon partitioning in soil and litter in subtropical woodlands and open forests: a case study from the Brigalow Belt, Queensland

The Rangeland Journal ◽

10.1071/rj05015 ◽

2006 ◽

Vol 28 (2) ◽

pp. 115 ◽

Cited By ~ 12

Author(s):

S. H. Roxburgh ◽

B. G. Mackey ◽

C. Dean ◽

L. Randall ◽

A. Lee ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Carbon ◽

Bulk Density ◽

Soil Surface ◽

Soil Bulk Density ◽

Carbon Stocks ◽

Soil Carbon Stocks ◽

Landscape Scales ◽

Open Forests

A woodland–open forest landscape within the Brigalow Belt South bioregion of Queensland, Australia, was surveyed for soil organic carbon, soil bulk density and soil-surface fine-litter carbon. Soil carbon stocks to 30 cm depth across 14 sites, spanning a range of soil and vegetation complexes, ranged from 10.7 to 61.8 t C/ha, with an overall mean of 36.2 t C/ha. Soil carbon stocks to 100 cm depth ranged from 19.4 to 150.5 t C/ha, with an overall mean of 72.9 t C/ha. The standing stock of fine litter ranged from 1.0 to 7.0 t C/ha, with a mean of 2.6 t C/ha, and soil bulk density averaged 1.4 g/cm3 at the soil surface, and 1.6 g/cm3 at 1 m depth. These results contribute to the currently sparse database of soil organic carbon and bulk density measurements in uncultivated soils within Australian open forests and woodlands. The estimates of total soil organic carbon stock calculated to 30 cm depth were further partitioned into resistant plant material (RPM), humus (HUM), and inert organic matter (IOM) pools using diffuse mid-infrared (MIR) analysis. Prediction of the HUM and RPM pools using the RothC soil carbon model agreed well with the MIR measurements, confirming the suitability of RothC for modelling soil organic carbon in these soils. Methods for quantifying soil organic carbon at landscape scales were also explored, and a new regression-based technique for estimating soil carbon stocks from simple field-measured soil attributes has been proposed. The results of this study are discussed with particular reference to the difficulties encountered in the collection of the data, their limitations, and opportunities for the further development of methods for quantifying soil organic carbon at landscape scales.

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Land use and management influences on surface soil organic carbon in Tasmania

Soil Research ◽

10.1071/sr12251 ◽

2013 ◽

Vol 51 (8) ◽

pp. 615 ◽

Cited By ~ 15

Author(s):

W. E. Cotching ◽

G. Oliver ◽

M. Downie ◽

R. Corkrey ◽

R. B. Doyle

Keyword(s):

Land Use ◽

Organic Carbon ◽

Soil Organic Carbon ◽

Soil Carbon ◽

Bulk Density ◽

Management Practices ◽

Carbon Stocks ◽

Carbon Accounting ◽

Continuous Cropping ◽

Texture Contrast

The effects of environmental parameters, land-use history, and management practices on soil organic carbon (SOC) concentrations, nitrogen, and bulk density were determined in agricultural soils of four soil types in Tasmania. The sites sampled were Dermosols, Vertosols, Ferrosols, and a group of texture-contrast soils (Chromosol and Sodosol) each with a 10-year management history ranging from permanent perennial pasture to continuous cropping. Rainfall, Soil Order, and land use were all strong explanatory variables for differences in SOC, soil carbon stock, total nitrogen, and bulk density. Cropping sites had 29–35% less SOC in surface soils (0–0.1 m) than pasture sites as well as greater bulk densities. Clay-rich soils contained the greatest carbon stocks to 0.3 m depth under pasture, with Ferrosols containing a mean of 158 Mg C ha–1, Vertosols 112 Mg C ha–1, and Dermosols 107 Mg C ha–1. Texture-contrast soils with sandier textured topsoils under pasture had a mean of 69 Mg C ha–1. The range of values in soil carbon stocks indicates considerable uncertainty in baseline values for use in soil carbon accounting. Farmers can influence SOC more by their choice of land use than their day-to-day soil management. Although the influence of management is not as great as other inherent site variables, farmers can still select practices for their ability to retain more SOC.

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Incorporation of globally available datasets into the roving cosmic-ray neutron probe method for estimating field-scale soil water content

Hydrology and Earth System Sciences ◽

10.5194/hess-20-3859-2016 ◽

2016 ◽

Vol 20 (9) ◽

pp. 3859-3872 ◽

Cited By ~ 27

Author(s):

William Alexander Avery ◽

Catherine Finkenbiner ◽

Trenton E. Franz ◽

Tiejun Wang ◽

Anthony L. Nguy-Robertson ◽

...

Keyword(s):

Organic Carbon ◽

Soil Organic Carbon ◽

Water Content ◽

Bulk Density ◽

Soil Water Content ◽

Cosmic Ray ◽

Soil Bulk Density ◽

Local Soil ◽

Calibration Parameters

Abstract. The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the Earth's terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNPs). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of vegetation. However, determining the calibration parameters for this equation is labor- and time-intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the roving CRNP. Here, we develop a 1 km product of soil lattice water over the continental United States (CONUS) using a database of in situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in situ samples of clay percent (RMSE  =  5.45 wt %, R2  =  0.68), soil bulk density (RMSE  =  0.173 g cm−3, R2  =  0.203), and soil organic carbon (RMSE  =  1.47 wt %, R2  =  0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RMSE ∼  0.035 cm3 cm−3 at a SWC  =  0.40 cm3 cm−3). In terms of vegetation, fast-growing crops (i.e., maize and soybeans), grasslands, and forests contribute to the CRNP signal primarily through the water within their biomass and this signal must be accounted for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSE  <  1 kg m−2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments.

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Comparison of different models for predicting soil bulk density. Case study – Slovakian agricultural soils

International Agrophysics ◽

10.1515/intag-2016-0079 ◽

2017 ◽

Vol 31 (4) ◽

pp. 491-498 ◽

Cited By ~ 2

Author(s):

Jarmila Makovníková ◽

Miloš Širáň ◽

Beata Houšková ◽

Boris Pálka ◽

Arwyn Jones

Keyword(s):

Organic Carbon ◽

Bulk Density ◽

Monitoring System ◽

Soil Bulk Density ◽

Pedotransfer Functions ◽

Research Centre ◽

Organic Carbon Content ◽

Joint Research ◽

Data Set ◽

Sustainable Resources

Abstract Soil bulk density is one of the main direct indicators of soil health, and is an important aspect of models for determining agroecosystem services potential. By way of applying multi-regression methods, we have created a distributed prediction of soil bulk density used subsequently for topsoil carbon stock estimation. The soil data used for this study were from the Slovakian partial monitoring system-soil database. In our work, two models of soil bulk density in an equilibrium state, with different combinations of input parameters (soil particle size distribution and soil organic carbon content in %), have been created, and subsequently validated using a data set from 15 principal sampling sites of Slovakian partial monitoring system-soil, that were different from those used to generate the bulk density equations. We have made a comparison of measured bulk density data and data calculated by the pedotransfer equations against soil bulk density calculated according to equations recommended by Joint Research Centre Sustainable Resources for Europe. The differences between measured soil bulk density and the model values vary from −0.144 to 0.135 g cm−3 in the verification data set. Furthermore, all models based on pedotransfer functions give moderately lower values. The soil bulk density model was then applied to generate a first approximation of soil bulk density map for Slovakia using texture information from 17 523 sampling sites, and was subsequently utilised for topsoil organic carbon estimation.

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