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

2017 ◽  
Vol 31 (4) ◽  
pp. 491-498 ◽  
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.

scholarly journals Influence of ryegrass managements on the physical properties of a Haplohumox

2018 ◽  
Vol 53 (8) ◽  
pp. 952-960 ◽  
Author(s):  
Bruno Vizioli ◽  
Karina Maria Vieira Cavalieri-Polizeli ◽  
Gabriel Barth
Keyword(s):  
Organic Carbon ◽  
Physical Properties ◽  
Total Organic Carbon ◽  
Bulk Density ◽  
Block Design ◽  
Soil Bulk Density ◽  
Pedotransfer Functions ◽  
Root Penetration ◽  
Organic Carbon Content ◽  
No Tillage

Abstract: The objective of this work was to evaluate the influence of ryegrass (Lolium multiflorum) managements on the physical properties of a Haplohumox, and on the yields of corn and of ryegrass cultivated in succession to corn. The experiment was carried out in a randomized complete block design, with three treatments and three replicates, in which treatments were the different managements of ryegrass under no-tillage for silage, soil cover, and grazing. After nine years of management, samples were collected at 0.00-0.05, 0.05-0.10, 0.10-0.20, and 0.20-0.30-m soil depths, to determine the following soil properties: texture, total organic carbon, soil bulk density, macroporosity, microporosity, total porosity, and resistance to root penetration. The index of structural stability was estimated from texture and total organic carbon data. Maximum soil bulk density and permanent wilting point were also estimated from pedotransfer functions. Corn and ryegrass dry matter yields were determined from plants harvested inside the plot area. Total organic carbon content increased as depth increased. The ryegrass managements in no-tillage system, in succession to corn, does not influence the soil physical properties of a Haplohumox, and maintains high corn and ryegrass yields.

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.

scholarly journals Physical Soil Quality of Semi - Arid Savanna as Influenced by Acacia senegalensis in Desert Research Experimental Plot Yobe State University Damaturu, Northern Nigeria

2021 ◽  
pp. 7-17
Author(s):  
Hamza Haruna ◽  
Galal H.G. Hussein ◽  
Mohammed B
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Bulk Density ◽  
Soil Quality ◽  
Minimum Data Set ◽  
Organic Carbon Content ◽  
Data Set ◽  
Minimum Data ◽  
Lower Bulk Density ◽  
Semi Arid

Soil is a living and dynamic natural reservoir and source of plant nutrients that play numerous key roles in terrestrial ecosystems. This study investigated the impact of three adjacent land use systems (Acacia senegalensis plantation (ACP), pilostigma raticulatum plantation (PRP) and Ground nut field (GNF) on selected soil physical quality indicators in a Northern Nigeria semi- arid Savanna. Minimum data set for assessing soil quality (Prime quality agricultural land) in this study include bulk density, organic carbon content, total nitrogen, carbon stock, available phosphorus and pH values obtained from DRMCC research field. Mean values of the data set were arranged and scored to obtain totals among the minimum data set (MDS). Soil quality is considered a key element for evaluating the sustainability of land management practices. Data generated were analyzed using ANOVA and significant means were determined using Duncan multiple range test (DMRT). ACP had significantly higher organic carbon content (9.37 gkg-1) and lower bulk density (2.16 gkg-1) than pilostigma and GNF respectively. The lower bulk density (ρb) and high organic carbon in ACP might be due to high leaf shading by acacia while the lower bulk density in ground nut field aided by trampling induced compaction resulted in its high relative field capacity (RFC), permanent wilting point (PWP) and micro-p ore spaces (PMIC) tillage in ground nut field created loose soil in the plough layer (<20 cm) which turn out to its low bulk density (ρb). Acacia plantation contained highest total nitrogen value (1.23 gkg-1); perhaps resulting Acacia leaf litter is known to have a high decomposition rate. Pilostigma plantation contained (1.22 gkg-1) nitrogen, while the least nitrogen content was obtained under ground nut field. On scoring the land use types and depth against the minimum data set, the least total was that under acacia plantation, followed by pilostigma plantation then ground nut field. Therefore, soils under acacia plantation were ranked best quality (SQ1) for cultivation purposes at 0-10 cm, followed by pilostigma land use type that were ranked SQ2. Ground nut field soils were ranked least (SQ6) in quality for use in crop production at depth of 10-20 cm.

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.

scholarly journals Injection of liquid swine slurry and effects on properties of a Nitossolo Vermelho

2018 ◽  
Vol 53 (4) ◽  
pp. 518-521 ◽  
Author(s):  
Cleber Rech ◽  
Jackson Adriano Albuquerque ◽  
Juliano Corulli Corrêa ◽  
Alvaro Luiz Mafra ◽  
Diego Bortolini
Keyword(s):  
Organic Carbon ◽  
Physical Properties ◽  
Carbon Content ◽  
Bulk Density ◽  
Soil Bulk Density ◽  
Organic Carbon Content ◽  
No Tillage ◽  
Swine Slurry ◽  
Tillage System ◽  
Liquid Slurry

Abstract: The objective of this work was to evaluate the superficial and injected applications of swine slurry and urea to the soil, regarding their effects on the physical properties of a Nitossolo Vermelho distroférrico under a no-tillage system. The treatments were: injected slurry into the groove with a liquid swine slurry injector (LSSI); slurry on surface, applied on the lines by the LSSI kept raised; urea injected by opening the groove with the LSSI and distributed manually; and corn, under no-tillage, as a control. Sowing and the injection of liquid slurry or urea do not modify the organic carbon content, pH, and aggregation, but alter the soil bulk density and porosity in the mobilized line, and increase the macropores.

A generally applicable pedotransfer function that estimates field capacity and permanent wilting point from soil texture and bulk density

2008 ◽  
Vol 88 (5) ◽  
pp. 761-774 ◽  
Author(s):  
J. A. P. Pollacco
Keyword(s):  
Organic Matter ◽  
Soil Moisture ◽  
Bulk Density ◽  
Soil Texture ◽  
The United States ◽  
Pedotransfer Functions ◽  
Pedotransfer Function ◽  
Data Set ◽  
Wide Range ◽  
Fitting Parameters

Hydrological models require the determination of fitting parameters that are tedious and time consuming to acquire. A rapid alternative method of estimating the fitting parameters is to use pedotransfer functions. This paper proposes a reliable method to estimate soil moisture at -33 and -1500 kPa from soil texture and bulk density. This method reduces the saturated moisture content by multiplying it with two non-linear functions depending on sand and clay contents. The novel pedotransfer function has no restrictions on the range of the texture predictors and gives reasonable predictions for soils with bulk density that varies from 0.25 to 2.16 g cm-3. These pedotransfer functions require only five parameters for each pressure head. It is generally accepted that the introduction of organic matter as a predictor improves the outcomes; however it was found by using a porosity based pedotransfer model, using organic matter as a predictor only modestly improves the accuracy. The model was developed employing 18 559 samples from the IGBP-DIS soil data set for pedotransfer function development (Data and Information System of the International Geosphere Biosphere Programme) database that embodies all major soils across the United States of America. The function is reliable and performs well for a wide range of soils occurring in very dry to very wet climates. Climatical grouping of the IGBP-DIS soils was proposed (aquic, tropical, cryic, aridic), but the results show that only tropical soils require specific grouping. Among many other different non-climatical soil groups tested, only humic and vitric soils were found to require specific grouping. The reliability of the pedotransfer function was further demonstrated with an independent database from Northern Italy having heterogeneous soils, and was found to be comparable or better than the accuracy of other pedotransfer functions found in the literature. Key words: Pedotransfer functions, soil moisture, soil texture, bulk density, organic matter, grouping

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.

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