Temporal and spatial variability of soil bulk density and near-saturated hydraulic conductivity under two contrasted tillage management systems

Alletto, Lionel; Coquet, Yves

doi:10.1016/j.geoderma.2009.05.023

Cited by 187 publications

(147 citation statements)

References 43 publications

Supporting

Mentioning

115

Contrasting

Unclassified

Order By: Relevance

“…Soil bulk density is a dynamic soil property, as it varies in time and space. It is influenced by crop and land management practices, as well as by natural processes such as the climate conditions that affect soil cover, organic matter levels, soil structure or porosity (Alletto and Coquet, 2009;Dam et al, 2005;Husnjack, 2002;Kumar et al, 2012;Logsdon, 2012;Norman et al, 2016;Timm et al, 2006;Veiga et al, 2008). Reversible processes in soils always tend to achieve an equilibrium state of soil bulk density , thus, at field capacity, and lacking external degradation influences, the bulk density of soil varies around the equilibrium.…”

Section: Introductionmentioning

confidence: 99%

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

Makovníková¹,

Širáň²,

Houšková³

et al. 2017

International Agrophysics

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Makovníková¹,

Širáň²,

Houšková³

et al. 2017

International Agrophysics

View full text Add to dashboard Cite

show abstract

“…The transport parameters, the hydraulic conductivity, the other soil properties and the relations among them are highly spatially variable following a structural pattern overlapped by an erratic component, also referred as structured variation (Alletto & Coquet, 2009;Goovaerts, 1997;Isaaks & Srivastava, 1989;Mulla & Mc Bratney, 2002;Trangmar et al, 1986). The spatial variability of soil properties might be studied at the centimeter scale, as well as at a regional scale since the soil heterogeneity is present in all scales (Chapuis et al, 2005;DeGroot & Baecher, 1993;Lacasse & Nadim, 1996;Søvik & Aagaard, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…The study of the spatial variability in soil science is commonly performed using geostatistics (Alletto & Coquet, 2009;Erşahin et al, 2017;Gwenzi, Hinz, Holmes, Phillips, & Mullins, 2011;Marín-Castro, Geissert, Negrete-Yankelevich, & Gómez-Tagle Chávez, 2016). This technique is based on the random function model assumption, where variables are modeled as random variables usually spatially correlated.…”

Section: Introductionmentioning

confidence: 99%

“…Geostatistics has been widely used to study the spatial variability of several soil properties (Alletto & Coquet, 2009;Brocca, Morbidelli, Melone, & Moramarco, 2007;Goovaerts, 1998;Grego, Vieira, Antonio, & Della Rosa, 2006;Iqbal, Thomasson, Jenkins, Owens, & Whisler, 2005;Mbagwu, 1995;Tesfahunegn, Tamene, & Vlek, 2011;Vieira, 1997;Y. Q. Wang & Shao, 2013;Zhao et al, 2011) and specifically of the hydraulic conductivity (Bohling et al, 2012;Gwenzi et al, 2011;Hu, Shao, Wang, Fan, & Reichardt, 2008;L.…”

Section: Introductionmentioning

confidence: 99%

“…But the value of these parameters can change according to the measurement support, and when that change is not considered, the reliability of the predictions may be compromised . The dependence of parameter values on measurement support is called scale effect, it is a result of the parameters spatial variability (Alletto & Coquet, 2009;Mulla & Mc Bratney, 2002;, and it has been subject of many studies (Chapuis et al, 2005;Deng et al, 2013;Hristopulos & Christakos, 1997;Neuman, 1994;Niemann & Rovey, 2000;Rovey & Cherkauer, 1995;Singh, Singh, & Gamage, 2016; X.-H. Yang, Liu, & Tang, 2017;). …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Upscaling of water flow and mass transport in a tropical soil: numerical, laboratory and field studies

Godoy¹

View full text Add to dashboard Cite

Numerical models are becoming fundamental tools to predict a range of complex problems faced by geotechnical and geo-environmental engineers. However, to render the model reliable for future predictions, the model input parameters must be determined with consideration of the scale effects. If there is a difference of scales between the observation and the model scales there are two possible ways to consider it: or models are constructed with elements of a size similar to that at which the data were measured, or some upscaling rules must be defined. In this context, this thesis focuses on upscaling of water flow and mass transport in a tropical soil by means of numerical, laboratory and field studies. This thesis is organized in four parts.First, the heterogeneity, correlation and cross-correlation between solute transport parameters (dispersivity, α, and partition coefficient, Kd) and soil properties are studied in detail. In this part, it is verified that the hydraulic conductivity (K) and solute transport parameters are highly heterogeneous, while soil properties are not. Spatial correlation of α, K, and statistically significant variables are studied, and it would probably improve the estimation only in a small-scale study, since the spatial correlation are only observed up to 2.5 m. This study is a first attempt to evaluate the spatial variation in the correlation coefficient of transport parameters of a reactive and a nonreactive solute, indicating the more relevant variables and the one that should be included in future studies.In the second part, scale effect on K, dispersivity and partition coefficient of potassium and chloride are studied experimentally by means of laboratory and field experiments. The purpose is to contribute to the discussion about scale effects on K, α and Kd and understanding how these parameters behave with the change in the scale of measurement. Results show that K values increases with scale, regardless of the method of measurement, except for the results obtained from double-ring infiltrometer tests. Dispersivity trends to increase exponentially with the sample height.Partition coefficient tends to increase with sample length, diameter and volume. These differences in the parameters according to the scale of measurement must be considered when these observations are later used as input to numerical models, otherwise the responses can be misrepresented.x Third, stochastic analysis of three-dimensional hydraulic conductivity upscaling is performed using a simple average and the Laplacian-with-skin methods for a variety of block sizes based on real K measurements. In this part it is demonstrated the errors that can be introduced by using a deterministic upscaling using simple averages of the measured K without accounting for the spatial correlation. Results show that K heterogeneity can be incorporated in the daily practice of the geotechnical modeler.The aspects to consider when performing the upscaling are also discussed. Finally, the dependence of the exponent of the p-n...

show abstract

Soil organic matter and water retention

Lal

2020

Agronomy Journal

189

View full text Add to dashboard Cite

The current and projected anthropogenic global warming and the attendant increase in the severity and extent of soil degradation may exacerbate the intensity and duration of drought occurrence in agroecosystems. Restoration of the soil organic matter (SOM) content of degraded/depleted soils can increase soil water retention (SWR) more at field capacity (FC) than that at the permanent wilting point (PWP), and thus increase the plant available water capacity (PAWC). The magnitude of increase in PAWC may depend on soil texture and the initial SOM content. Thus, restoration of the SOM content of degraded/depleted soils can make them as well as agroecosystems climate-resilient. Management practices which enhance soil health by restoring SOM content include conservation agriculture, cover cropping, residue mulching, and complex farming systems involving integration of crops with trees and livestock. Such technologies must be fine-tuned under site-specific conditions. Additional research is needed to establish the cause-effect relationship between increase in SOM content and PAWC and the ameliorative effect on drought-resilience for diverse crops and cropping systems. 1 INTRODUCTION Global warming has already reached 1 • C above the preindustrial levels (Lindsey & Dahlman, 2020; Sánchez-Lugo, Berrisford, Morice, & Nicolas, 2018), which in turn has aggravated the severity of drought caused by soil moisture deficit (Berg & Sheffield, 2018). Additionally, the positive net ecosystem exchange (NEE), caused by soilwater deficit, can make soil a net source of CO 2 (Green et al., 2019), especially following some repeated summer droughts (Sowerby, Emmett, Tietema, & Beier, 2008). Further, anthropogenic perturbations may even destabilize the

show abstract

Temporal and spatial variability of soil bulk density and near-saturated hydraulic conductivity under two contrasted tillage management systems

Cited by 187 publications

References 43 publications

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

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

Upscaling of water flow and mass transport in a tropical soil: numerical, laboratory and field studies

Soil organic matter and water retention

Contact Info

Product

Resources

About