Exploring the spatial relations between soil physical properties and apparent electrical conductivity

Carroll, Z.L.; Oliver, M. A.

doi:10.1016/j.geoderma.2005.03.008

Cited by 84 publications

(57 citation statements)

References 13 publications

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“…Clay content was not significantly correlated to ECa except for ECa-H-1 and ECa-V-1 at 0-15 cm depth on shoulder position at our research site. In another study, conducted by Carroll and Oliver [13] moderate and moderate to strong correlation were observed between ECa and soil textural fractions (sand, silt and clay) at two separate research sites. The relations between ECa and the topsoil properties were stronger than those for the subsoil.…”

Section: Correlation Of Eca With Soil Properties At Three Topographicmentioning

confidence: 85%

“…The relations between ECa and the topsoil properties were stronger than those for the subsoil. A negative correlation was observed for sand and bulk density with ECa as large bulk densities were associated with sandy soil [13]. Heil and Schmidhalter [55] investigated spatial distribution of clay, silt, and sand/gravel on highly variable landscape in Germany and concluded that clay and sand/gravel were most closely related to the ECa, and observed R 2 values ranging between 0.67 and 0.76.…”

Section: Correlation Of Eca With Soil Properties At Three Topographicmentioning

confidence: 98%

“…Brevik et al [7] conducted a study on soils with different water content and found that ECa changes temporally and is highly dependent on soil moisture. Many studies have shown correlation of ECa with soil properties important for plant growth and crop yields including soil compaction, depth of claypan, soil water content, soil texture, drainage, TC, TN, soluble salts, extractable P and soil pH [3,[12][13][14]. Drier and coarser soils are less electrically conductive than wetter and finer soils [15].…”

Section: Introductionmentioning

confidence: 99%

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Spatial Relation of Apparent Soil Electrical Conductivity with Crop Yields and Soil Properties at Different Topographic Positions in a Small Agricultural Watershed

2016

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Use of electromagnetic induction (EMI) sensors along with geospatial modeling provide a better opportunity for understanding spatial distribution of soil properties and crop yields on a landscape level and to map site-specific management zones. The first objective of this research was to evaluate the relationship of crop yields, soil properties and apparent electrical conductivity (ECa) at different topographic positions (shoulder, backslope, and deposition slope). The second objective was to examine whether the correlation of ECa with soil properties and crop yields on a watershed scale can be improved by considering topography in modeling ECa and soil properties compared to a whole field scale with no topographic separation. This study was conducted in two headwater agricultural watersheds in southern Illinois, USA. The experimental design consisted of three basins per watershed and each basin was divided into three topographic positions (shoulder, backslope and deposition) using the Slope Position Classification model in ESRI ArcMap. A combine harvester equipped with a GPS-based recording system was used for yield monitoring and mapping from 2012 to 2015. Soil samples were taken at depths from 0-15 cm and 15-30 cm from 54 locations in the two watersheds in fall 2015 and analyzed for physical and chemical properties. The ECa was measured using EMI device, EM38-MK2, which provides four dipole readings ECa-H-0.5, ECa-H-1, ECa-V-0.5, and ECa-V-1. Soybean and corn yields at depositional position were 38% and 62% lower than the shoulder position in 2014 and 2015, respectively. Soil pH, total carbon (TC), total nitrogen (TN), Mehlich-3 Phosphorus (P), Bray-1 P and ECa at depositional positions were significantly higher compared to shoulder positions. Corn and soybeans yields were weakly to moderately (<±0.75) correlated with ECa. At the deposition position at the 0-15 cm depth ECa-H-0.5 was weakly correlated (r < ±0.50) with soil pH and was moderately correlated (r = ±0.50-±0.75) with organic matter (OM), calcium (Ca) and sulfur (S). Slope variation from 1%-20% at the research site had a strong influence on soil properties at watershed scale. When data from all topographic positions were combined together in all basins spatial interpolation between Mehlich-3 P and ECa-H-0.5 resulted in a larger cross validation RMSE compared to individual shoulder and backslope positions. Results demonstrated that topographic position should be considered while making correlations of ECa with soil properties. Methods of delineating topography positions presented in this paper can easily be replicated on other fields with similar landscape characteristics and EMI sensor based survey techniques can certainly improve and help in making detailed prediction maps of soil properties.

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Section: Correlation Of Eca With Soil Properties At Three Topographicmentioning

confidence: 85%

Section: Correlation Of Eca With Soil Properties At Three Topographicmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

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Spatial Relation of Apparent Soil Electrical Conductivity with Crop Yields and Soil Properties at Different Topographic Positions in a Small Agricultural Watershed

2016

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“…Geostatistical methods, some of which are described in detail in [18][19][20][21][22], have wide applications in geology, environmental studies, mining research, and petroleum engineering [23][24][25][26][27][28]. More recently, Olea et al [29] have developed a formulation of a correlated variables methodology and co-simulation for assessment of gas resources in Woodford shale play, Arkoma basin, in eastern Oklahoma.…”

Section: Introductionmentioning

confidence: 99%

Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes

Karacan¹,

Olea²

2013

International Journal of Rock Mechanics and Mining Sciences

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Gob gas ventholes (GGVs) are used to control methane inflows into a longwall mining operation by capturing the gas within the overlying fractured strata before it enters the work environment. Using geostatistical co-simulation techniques, this paper maps the parameters of their rate decline behaviors across the study area, a longwall mine in the Northern Appalachian basin. Geostatistical gas-in-place (GIP) simulations were performed, using data from 64 exploration boreholes, and GIP data were mapped within the fractured zone of the study area. In addition, methane flowrates monitored from 10 GGVs were analyzed using decline curve analyses (DCA) techniques to determine parameters of decline rates. Surface elevation showed the most influence on methane production from GGVs and thus was used to investigate its relation with DCA parameters using correlation techniques on normal-scored data. Geostatistical analysis was pursued using sequential Gaussian co-simulation with surface elevation as the secondary variable and with DCA parameters as the primary variables. The primary DCA variables were effective percentage decline rate, rate at production start, rate at the beginning of forecast period, and production end duration. Cosimulation results were presented to visualize decline parameters at an area-wide scale. Wells located at lower elevations, i.e., at the bottom of valleys, tend to perform better in terms of their rate declines compared to those at higher elevations. These results were used to calculate drainage radii of GGVs using GIP realizations. The calculated drainage radii are close to ones predicted by pressure transient tests.

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“…They concluded that optimum gridding, spatial correlation, and modeling can generate more accurate and realistic fades and coal property reconstructions. In addition to coal research, geostatistics have been used to assess spatial uncertainty of soil water content (Delbari et al, 2009), to explore the spatial relations between soil physical properties and electrical conductivity (Carroll and Oliver, 2005), and to conduct spatial analyses of metal contents in sediments of the Yangtze River basin (Zhang and Selinus, 1997).…”

Section: Introductionmentioning

confidence: 99%

Geostatistical modeling of the gas emission zone and its in-place gas content for Pittsburgh-seam mines using sequential Gaussian simulation

Karacan

Olea²,

Goodman

2012

International Journal of Coal Geology

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Determination of the size of the gas emission zone, the locations of gas sources within, and especially the amount of gas retained in those zones is one of the most important steps for designing a successful methane control strategy and an efficient ventilation system in longwall coal mining. The formation of the gas emission zone and the potential amount of gas-in-place (GIP) that might be available for migration into a mine are factors of local geology and rock properties that usually show spatial variability in continuity and may also show geometric anisotropy. Geostatistical methods are used here for modeling and prediction of gas amounts and for assessing their associated uncertainty in gas emission zones of longwall mines for methane control.This study used core data obtained from 276 vertical exploration boreholes drilled from the surface to the bottom of the Pittsburgh coal seam in a mining district in the Northern Appalachian basin. After identifying important coal and non-coal layers for the gas emission zone, univariate statistical and semivariogram analyses were conducted for data from different formations to define the distribution and continuity of various attributes. Sequential simulations performed stochastic assessment of these attributes, such as gas content, strata thickness, and strata displacement. These analyses were followed by calculations of gas-in-place and their uncertainties in the Pittsburgh seam caved zone and fractured zone of longwall mines in this mining district. Grid blanking was used to isolate the volume over the actual panels from the entire modeled district and to calculate gas amounts that were directly related to the emissions in longwall mines.Results indicated that gas-in-place in the Pittsburgh seam, in the caved zone and in the fractured zone, as well as displacements in major rock units, showed spatial correlations that could be modeled and estimated using geostatistical methods. This study showed that GIP volumes may change up to 3 MMscf per acre and, in a multi-panel district, may total 9 Bcf of methane within the gas emission zone. Therefore, ventilation and gas capture systems should be designed accordingly. In addition, rock displacements within the gas emission zone are spatially distributed.

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Exploring the spatial relations between soil physical properties and apparent electrical conductivity

Cited by 84 publications

References 13 publications

Spatial Relation of Apparent Soil Electrical Conductivity with Crop Yields and Soil Properties at Different Topographic Positions in a Small Agricultural Watershed

Spatial Relation of Apparent Soil Electrical Conductivity with Crop Yields and Soil Properties at Different Topographic Positions in a Small Agricultural Watershed

Sequential Gaussian co-simulation of rate decline parameters of longwall gob gas ventholes

Geostatistical modeling of the gas emission zone and its in-place gas content for Pittsburgh-seam mines using sequential Gaussian simulation

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