Using Soil Apparent Electrical Conductivity to Optimize Sampling of Soil Penetration Resistance and to Improve the Estimations of Spatial Patterns of Soil Compaction

Siqueira, Glécio Machado; Dafonte, Jorge Dafonte; Bueno, Javier; Armesto, Montserrat Valcárcel; Silva, Ênio Farias de França e

doi:10.1155/2014/269480

Cited by 12 publications

(12 citation statements)

References 31 publications

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“…The measurement results were shown similarity with the abovementioned literatures for silty-clay soils [25,27,30]. In addition to our results, for the obtained apparent soil ER values to be more significant, soil penetration resistance should be simultaneously measured and correlated with soil moisture content and bulk density [35][36][37][38][39][40][41][42]. No faults were detected in the electromechanical, data acquisition, and software parts of the system during field operation.…”

Section: Discussionsupporting

confidence: 87%

Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications

Ünal

Kabaş

Sözer

2020

Sensors

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Soil electrical resistivity (ER) is an important indicator to indirectly determine soil physical and chemical properties such as moisture, salinity, porosity, organic matter level, bulk density, and soil texture. In this study, real-time ER measurement system has been developed with the help of an autonomous robot. The aim of this study is to provide rapid measurement of the ER in large areas using the Wenner four-probe measurement method for precision farming applications. The ER measurement platform consists of the Wenner probes, a y-axis shifter driven by a DC motor through a gear reducer, all installed on a steel-frame that mount to an autonomous robot. An embedded industrial computer and differential global positioning system (DGPS) were used to assist in real-time measuring, recording, mapping, and displaying the ER and the robot position during the field operation. The data acquisition software was codded in Microsoft Visual Basic.NET. Field experiments were carried out in a 1.2 ha farmland soil. ER and DGPS values were stored in Microsoft SQL Server 2005 database, an ordinary Kriging interpolation technique by ArcGIS was used and the average ER values were mapped for the soil depth between 0 and 50 cm. As a result, ER values were observed to be between 30.757 and 70.732 ohm-m. In conclusion, the experimental results showed that the designed system works quite well in the field and the ER measurement platform is a practical tool for providing real-time soil ER measurements.

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Section: Discussionsupporting

confidence: 87%

Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications

Ünal

Kabaş

Sözer

2020

Sensors

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“…Sensing of a single parameter of any environmental feature is not sufficient for the modeling due to the complex spatial structure and interactions. For example, Siqueira et al (2014) used two different soil EM probes (Veris P3000, Veris Technologies, Salina, KS, USA and EMD38 Dual Dipole Version, Geonics, Ontario, Canada, 2005) to generate optimal soil sampling designs and improve the spatial estimation of soil resistance to penetration (PR) on the field studied.…”

Section: Introductionmentioning

confidence: 99%

Georeferenced tractor wheel slip data for prediction of spatial variability in soil physical properties

et al. 2021

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The upcoming technological breakthrough in the cropping system will offer a more detailed insight into soil-to-plant, man-to-soil, and man-to-plant impacts, thus improving the forecasting and ensuring more efficient in-field management. This study presents various onthe-go sensing procedures which were conducted in order to evaluate the quality of spatial estimations of soil physical properties such as soil compaction, soil moisture content, bulk density and texture. Standard statistical tools showed high positive correlations between soil specific resistance and soil compaction (R 2 = .75), soil electromagnetic conductivity and moisture content (R 2 = .72) and tractor wheel slip and soil compaction (R 2 = .64). Variogram modeling of spatial autocorrelation gave the highest prediction error for tillage resistance (9.85%), followed by cone index (4.49%), moisture content (3.7%), bulk density (1.39%), clay + silt content (.98%), soil electromagnetic conductivity (.95%) and the least error was obtained for tractor wheel slip (.58%). The Central Composite Design (CCD) analysis confirmed significant contribution of soil compaction in the modeling of the specific soil resistance and tractor wheel slip, while soil moisture content and fine particle (clay + silt) content had a major impact on soil electromagnetic conductivity measurement. Soil bulk density had considerable importance in CCD modeling of tractor wheel slip.

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“…Soil drainage and drainage classes (including hydraulic conductivity)Rhoades et al (1997),Kravchenko et al (2002),Triantafilis et al (2004),Vervoort and Annen (2006),Liu et al (2008), aWeaver et al (2013), andRezaei et al (2016) Soil resistance to penetrationSiqueira et al (2014) a Data fusion: use of EC a (either ER or EMI) and 1 or more other proximal or satellite sensors (e.g., gamma-ray spectrometry, hyperspectral reflectance, synthetic aperture radar, LiDAR) or aerial photos.Definitions: EC a ¼ apparent soil electrical conductivity; ER ¼ electrical resistivity; EMI ¼ electromagnetic induction. Modified from Corwin, D.L., Lesch, S.M., 2005a.…”

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confidence: 99%

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Corwin

Scudiero

2019

Advances in Agronomy

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Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field-to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (EC a ) and multi-and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of EC a -directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field-to regional-scale salinity assessment. Highlights• A review of multi-scale soil salinity assessment using proximal and remote sensors is presented.

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Using Soil Apparent Electrical Conductivity to Optimize Sampling of Soil Penetration Resistance and to Improve the Estimations of Spatial Patterns of Soil Compaction

Cited by 12 publications

References 31 publications

Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications

Real-Time Electrical Resistivity Measurement and Mapping Platform of the Soils with an Autonomous Robot for Precision Farming Applications

Georeferenced tractor wheel slip data for prediction of spatial variability in soil physical properties

Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

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