Organic carbon, organic matter and bulk density relationships in boreal forest soils

2008 ◽  
Vol 88 (3) ◽  
pp. 315-325 ◽  
Author(s):  
Catherine Périé ◽  
Rock Ouimet
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Bulk Density ◽  
Forest Soils ◽  
Sandy Loam ◽  
Measurement Techniques ◽  
Complete Recovery ◽  
Soil Bulk Density ◽  
Dry Combustion

Relationships between soil organic carbon (SOC), organic matter (SOM), and bulk density (BD) were established in acidic loamy to sandy loam fine fractions of forest soils in Quebec (Canada). The interest of such relationships rests with the possibility of using simple and rapid techniques to estimate SOC and BD. It is also a crucial step in establishing the correspondence among several databases when SOC data are obtained using different measurement techniques. In this study, SOC was measuredby dry combustion (SOCNDC) and wet digestion (SOCWD) methods, and organic matter by loss-on-ignition (LOI). Our results suggest that, in these soils: (1) LOI can be used for estimating SOC (r2 = 0.95, RMSEP = 16%) and SOCDC/SOM significantly decreased with increasing depth from 0.49 to 0.27; (2) SOCDC and SOCWD were highly correlated. Even if SOCWD provided near complete recovery of SOCDC, dry combustion remains the preferred method for SOC analysis since recovery decreased with increasing depth from 100 to 83%. (3) BD was also strongly related to SOM(r2 = 0.81). We recommend using the organic density approach to estimate BD from SOM because it allows BD to be predicted without significant bias and with a degree of accuracy of 14%. Key words: Forest soils, soil organic carbon, soil organic matter, soil bulk density

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

2006 ◽  
Vol 28 (2) ◽  
pp. 115 ◽  
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.

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

2016 ◽  
Vol 20 (9) ◽  
pp. 3859-3872 ◽  
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.

Soils under Shorea robusta in foot hills of the Eastern Himalaya - their characteristics and carbon sequestration potential

2020 ◽  
Vol 43 (4) ◽  
pp. 295-301
Author(s):  
Samar Gangopadhyay ◽  
◽  
Samar Banerjee ◽  
Avinash Jain ◽  
Saikat Banerjee ◽  
...  
Keyword(s):  
Organic Matter ◽  
Carbon Sequestration ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Bulk Density ◽  
Clay Content ◽  
Carbon Sequestration Potential ◽  
Shorea Robusta ◽  
Sequestration Potential ◽  
Study Sites

Forest soils supporting Sal-Shorea robusta (Roxb. ex Gaertn. f.) plantations in the foot hills of Darjeeling and Kurseong Divisions in West Bengal were studied for their physicochemical characteristics and carbon sequestration potential. Soils are acidic, high in organic carbon and clay content but low in soil reaction (pH) and bulk density (BD). Thick deposit of leaf litter and its decomposition products increase the soil organic carbon (SOC). Significant amount of clay content also increases the moisture content which helps in decomposing the organic matter, reducing the bulk density of soil and reduces erosion. Soil organic matter tends to concentrate with roughly more than half of the soil organic carbon in the upper soil horizons (0-30cm) at all the study sites. Among the study sites, Samardanga block registers lowest SOC while Bamanpukuri block shows highest SOC stock.

scholarly journals Soil organic carbon in no-tillage systems of different ages in Southwest Mato Grosso, Brazil

2021 ◽  
Vol 25 (4) ◽  
pp. 250-255
Author(s):  
Renata M. Severiano ◽  
Maria A. P. Pierangeli ◽  
Nilton de S. Santos ◽  
Vinícius Xavier
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Bulk Density ◽  
Soil Bulk Density ◽  
Carbon Stocks ◽  
Native Vegetation ◽  
No Tillage ◽  
Tillage Systems ◽  
Mato Grosso ◽  
Tillage System

ABSTRACT The objectives of this study were to evaluate the effect of the no-tillage system on soil bulk density, soil organic carbon, and carbon stocks in Plinthic subgroups and Oxisols, located in Pontes and Lacerda, State of Mato Grosso, Brazil. The treatments were native vegetation and no-tillage systems established for 3, 8, 10, and 12 years. To analyse soil organic carbon, soils were sampled in each area, with three repetitions, at layers of 0-0.05; 0.05-0.10; 0.10-0.20; 0.20-0.40; 0.40-0.60; 0.60-1.00; 1.00-1.50 and 1.50-2.00 m. For soil bulk density, undisturbed samples were collected at layers of 0-0.20 and 0.20-0.40 m. Compared with areas of native vegetation, soil bulk density values after 12 years increased by 25% in Oxisols and 30% in the Plinthic subgroups. In Oxisols and Plinthic subgroups, respectively, organic carbon concentration was, on average, 20.57, 25.04 g kg-1 under native vegetation; 16.82, 16.59 g kg-1 after 3 years of no-tillage; 13.31, 4.96 g kg-1 after 8 years; 16.52, 14.39 g kg-1 after 10 years; and 17.97, 18.53 g kg-1 after 12 years. In both soils, the no-tillage system contributed to an increase in carbon stocks over the years, but not at depth, being generally limited to the top 0.20 m of the soils. Compared to native vegetation, after 12 years of no-tillage, carbon stocks decreased at a rate of 0.075 Mg ha-1 year-1 in the Plinthic subgroups and increased by 2.3 Mg ha-1 year-1 in Oxisols.

Deriving regional pedotransfer functions to estimate soil bulk density in Austria

2020 ◽  
Vol 71 (4) ◽  
pp. 241-252
Author(s):  
Cecilie Foldal ◽  
Robert Jandl ◽  
Andreas Bohner ◽  
Ambros Berger
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Bulk Density ◽  
Linear Models ◽  
Soil Depth ◽  
Soil Bulk Density ◽  
Pedotransfer Functions ◽  
Undisturbed Soil ◽  
The Impact

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.

A pedo-transfer function (PTF) for estimating soil bulk density from basic soil data and its comparison with existing PTFs

Soil Research ◽  
2002 ◽  
Vol 40 (5) ◽  
pp. 847 ◽  
Author(s):  
Ravinder Kaur ◽  
Sanjeev Kumar ◽  
H. P. Gurung
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Carbon Content ◽  
Bulk Density ◽  
Soil Texture ◽  
Soil Bulk Density ◽  
Organic Carbon Content ◽  
Data Set ◽  
The Absolute ◽  
Relative Errors

Collection of non-destructive soil core samples for determination of bulk densities is costly, difficult, time- consuming, and often impractical. To overcome this difficulty, several attempts have been made in the past to estimate soil bulk densities through pedo-transfer functions (PTFs), requiring soil texture and organic carbon (OC) content data. Although many studies have shown that both organic carbon and texture predominantly determine soil bulk density, a majority of the PTFs developed so far are a function only of organic matter (OM)/OC. In addition, no attempts have been made to test and compare the applicability of these PTFs on an independent soil data set. Thus, through this study efforts have been made not only to develop a robust soil bulk density estimating PTF, based on both soil texture and organic carbon content data, but also to compare its predictive potential with the existing PTFs on an independent soil data set from 4 ecologically diverse micro-watersheds in Almora district of Uttaranchal State in India. Effects of varying levels of soil particle size distributions and/or OC/OM contents on the absolute relative errors associated with these PTFs were also analysed for assessing their applicability to the independent soil data set. Amongst the existing PTFs, Curtis and Post, Adams, Federer, and Huntington-A methods were found to be associated with positive bias or mean errors (ME) and root mean square prediction differences (RMSPD) ranging between 0.10 and 0.38, and between 0.23 and 0.45, respectively, whereas Alexander-A, Alexander-B, Manrique and Jones-A, Manrique and Jones-B, and Rawls methods were found to be associated with negative ME and RMSPD values ranging between -0.08 and -0.15, and 0.18 and 0.23, respectively. In contrast, Bernoux, Huntington-B, and Tomasella and Hodnett-PTFs, with RMSPD values ranging between 0.18 and 0.20, were the only methods associated with little or no bias. However, on comparing the predictive potential of the existing PTFs, in terms of their 1 : 1 relationships between the observed and predicted soil bulk densities and ME and RMSPD values, only Manrique and Jones-B (ME: -0.08; RMSPD: 0.18), Alexander-A (ME: -0.08; RMSPD: 0.19), and Rawls (ME: -0.11; RMSPD: 0.22) methods were observed to give somewhat more realistic soil bulk density estimations. The study revealed very limited predictive potential of the existing PTFs, due to their development on specific soils and/or ecosystems, use of an indirectly computed organic matter (instead of directly measured organic carbon) content as a predictor variable, poor predictive potential of developed regression model(s), and/or subjective errors. In contrast to this, the new soil bulk density estimating PTF was found to be associated with far better 1 : 1 relationship between the observed and predicted soil bulk densities and zero ME (or bias) and lowest (0.15 g/cm3) RMSPD values. The absolute relative errors associated with both the new and the existing soil OC/OM and texture-dependent PTFs were observed to be almost insensitive to the varying levels of silt and clay. However, compared with the existing PTFs, these errors associated with the new PTF were observed to be much more insensitive to the varying levels of OC/OM, thereby indicating the applicability of the new PTF to a wide range of soil types.

Potential use of underseeded barley to increase carbon inputs to a loam soil in the New Brunswick potato belt

1999 ◽  
Vol 79 (1) ◽  
pp. 211-216 ◽  
Author(s):  
H. W. Rees ◽  
T. L. Chow ◽  
D. F. Walker ◽  
A. O. M. Smith
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
New Brunswick ◽  
Bulk Density ◽  
Crop Residues ◽  
Potato Production ◽  
Soil Bulk Density ◽  
Barley Grain ◽  
Italian Ryegrass ◽  
Total Biomass

Organic carbon depletion as a result of intensive potato production is a serious concern in northwestern New Brunswick, Canada. Underseeding of rotational grain crops with selected grasses and legumes is being used by farmers to increase crop residues in order to maintain soil organic carbon levels. The objectives of this study were to quantify changes in selected soil propertiesm, effects on grain yield, and increases in total biomass production resulting from different underseeding treatments on a Caribou fine-loamy Brunisolic Gray Luvisol. Clovers, ryegrasses and a mixture of timothy, Alsike and red clover were underseeded in Chapais barley plots. Underseeding did little to improve soil bulk density, macroporosity and saturated hydraulic conductivity of the upper plow layer (4–12 cm depth) over one growing season. Averaged over a 3-yr period, underseeding did not significantly affect barley grain yields; however, annual reductions of up to 17% were experienced with some of the more aggressive companion crops, such as Lemtal Italian ryegrass. Underseeding significantly increased total biomass contributions (residues) to the upper 20 cm of the soil by 40 to 90%, making the year in grain production a potential net organic carbon supplier rather than an organic carbon depletor. Key words: Companion crop, roots, soil organic carbon, soil bulk density, soil permeability, crop residue

scholarly journals Critical Analysis of the van Bemmelen Conversion Factor used to Convert Soil Organic Matter Data to Soil Organic Carbon Data: Comparative Analyses in a UK Loamy Sand Soil

2016 ◽  
Vol 6 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Lee Heaton ◽  
Michael A. Fullen ◽  
Ranjan Bhattacharyya
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Conversion Factor ◽  
Soil Samples ◽  
Loamy Sand ◽  
Soil C ◽  
Loamy Sand Soil ◽  
Dry Combustion

Converting soil organic matter (SOM) data to soil organic carbon (SOC) data usually uses the van Bemmelen factor of 0.58 (or in reverse its reciprocal of 1.724) as a universal conversion factor. The accuracy of this conversion factor has been questioned. Under the Kyoto Protocol (1997) dry combustion is recommended to provide reproducible analyses to measure soil carbon stocks. However, dry combustion equipment is expensive and entails high maintenance. For rapid and inexpensive measurements, loss-on-ignition (LOI) is often used. A total of 278 loamy sand topsoil (0-5 cm depth) samples were taken during three soil sampling sessions (9 January 2007, 22 January 2009 and 10 October 2011) from runoff plots, splash erosion plots and grassed/cultivated plots on the Hilton Experimental Site, Shropshire, UK. A total of 124 soil samples were collected from both runoff and splash plots in both 2007 and 2009 (Bhattacharyya et al., 2011a). Some 22 of the collected samples in 2011 were from grassland (Ah horizon) and eight from cultivated soils (Ap horizon). Homogenized soil samples were split and SOM was determined on oven-dried samples by LOI and total SOC was determined by dry combustion. A conversion factor of 0.845 was used to obtain SOC from total soil C, following Rawlins et al. (2011). Results showed strong associations (R² = 0.70, P 0.001, n = 278) between SOM and SOC data. For all data, SOM to SOC conversion factors varied between 0.36-0.98, with a mean value of 0.66 (SD = 0.105). The mean values of the conversion factor were 0.64, 0.69 and 0.56, respectively, for the samples collected in 2007, 2009 and 2011. Results indicate the van Bemmelen factor (0.58) is a reasonable predictor, but both temporal and spatial variations occur around it within a specific soil type. Thus, caution should be exercised in SOM/SOC data conversions using the van Bemmelen factor.

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

2016 ◽  
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 (CRNP). 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 standing biomass. 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 using globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the 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/cm3, 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 RSME ~0.035 cm3/cm3 at a SWC = 0.40 cm3/cm3). In terms of vegetation, fast growing crops (i.e. maize and soybeans) contribute to the CRNP signal primarily through the water within their biomass and this signal must be minimized 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/m2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments.

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