Relating apparent electrical conductivity to soil properties across the north-central USA

Sudduth, K. A.; Kitchen, Newell R.; Wiebold, William J.; Batchelor, William D.; Bollero, Germán A.; Bullock, D. G.; Clay, David E.; Palm, H. L.; Pierce, Francis J.; Schuler, Ronald T.; Thelen, Kurt D.

doi:10.1016/j.compag.2004.11.010

Cited by 310 publications

(197 citation statements)

References 24 publications

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“…Elevation was negatively correlated to ECa when all basins were combine together and our results were in agreement with results observed by Peralta and Costa [49]. Results from a multi-state dataset collected by Sudduth et al [6] on ECa and soil properties showed weak to moderate correlations of ECa with clay content and CEC across most fields. Correlations of ECa with other soil properties including soil moisture, silt, sand, organic C and paste EC were lower and more variable for the fields across North-central USA [6].…”

Section: Correlation Of Eca With Soil Properties At Three Topographicsupporting

confidence: 92%

“…Geophysical methods such as electromagnetic induction (EMI) can be used to study the spatial variability of soil physical, chemical, and hydrological properties at the field to the small catchment scales [1][2][3][4][5][6]. The EMI technique can measure apparent soil electrical conductivity (ECa) by inducing an electrical current in the soil [7].…”

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 Topographicsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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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“…Farzamian et al (2015) combined electrical resistivity tomography (ERT) and EM38 to measure moisture content variations and estimate saturated hydraulic conductivity in natural conditions. Sudduth et al (2005) used ECa to reflect soil physical and chemical properties. Researchers found that electromagnetic methods are fast and reliable to delineate soil properties.…”

Section: Introductionmentioning

confidence: 99%

GIS-mapping spatial distribution of soil salinity for Eco-restoring the Yellow River Delta in combination with Electromagnetic Induction

Liu

Zhang

et al. 2016

Ecological Engineering

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a b s t r a c tSoil salinization is one of serious ecological problems around the world, which seriously restricts the stability of ecosystem and the economic development of agriculture. Mapping and monitoring spatial distribution of soil salinity is important for management of ecology and agriculture. This study was carried out to explore the spatial distribution of soil salinity in the Yellow River Delta using the portable device EM38-MK2 with geostatistical analysis. Apparent soil electrical conductivities were measured under four kinds of measurement modes (0.5H, 0.5 V, 1.0H and 1.0 V, respectively). The results revealed that electrical conductivity of 1:5 soil to water extract (ECe) varied from 0.965 to 1.872 dS m −1 for all sampled soil profiles, and the salinity of topsoil was the highest, which indicated that soil soluble salts accumulated to the surface. The salinity in the top layer showed strong spatial variability while salinities of other layers were moderate. Soil salinity displayed a significant zonal distribution, gradually decreasing with the increase of distance to the coastline. The regions with high ECa values were located in the north and the east of the study area, whereas regions with low ECa values were located in the south and the west parts. The correlation coefficient showed that salinities of adjacent two soil layers reached a significant level of 0.01, and gradually decreased with increasing soil depth. The linear interpretation models with ECa as independent variables and ECe as dependent variables for each depth were with R 2 between 0.828 and 0.919. The interpretation models, taking ECa and ECe of 0-15 cm depth as independent variables, and ECe of each layer in 15-100 cm depth as dependent variables, were with higher R 2 between 0.930 and 0.953. The mean error (ME) showed that there was small positive deviation in 40-100 cm whereas a high positive deviation in the topsoil (0-40 cm). The scatter plots also indicated that the models had better accuracy of salinity estimation in the top soil layers (0-80 cm). The results provides solid basis for eco-restoring in the Yellow River delta.

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“…This methodology can improve the characterization of the spatial pattern of edaphic properties that influence the nutrient content of the soil, which in turn can be used to define SSM units (Moral et al, 2010). However, the EC a applications in HMZ showed weak and inconsistent relationships between EC a and soil characteristics (Corwin and Lesch, 2003;Sudduth et al, 2005). These inconsistent relationships may be generated by the potentially complex interrelationships between EC a and soil characteristics (soil properties and nutrient levels).…”

Section: Introductionmentioning

confidence: 99%

Delineation of management zones with soil apparent electrical conductivity to improve nutrient management

Peralta

Costa

2013

Computers and Electronics in Agriculture

122

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a b s t r a c tSite-specific management demands the identification of subfield regions with homogeneous characteristics (management zones). However, determining subfield areas is difficult because of complex correlations and the spatial variability of soil properties and nutrient concentrations, responsible for variations in crop yields within the field. We evaluated whether apparent electrical conductivity (EC a ) is a potential estimator of soil properties and nutrients, and a tool for the delimitation of homogeneous zones. Two field sites with several soil series were studied in southeastern Cordoba Province, Argentina. Soil properties and nutrient concentrations were compared with EC a using principal components (PC)-stepwise regression and ANOVA. The PC-stepwise regression showed that soil properties (pH, EC 1:2.5 , CEC, SOM) and nutrients (Na +2 , Mg +2 , Mn +2 , Cu +2 , Ca +2 , Zn +2 , Fe +2 ) are key loading factors to explain the EC a (R 2 > 0.90). In contrast, K + , P, NO À 3 -N and SO À2 4 -S), content were not able to explain the EC a . The ANOVA showed that EC a measurements successfully delimited two homogeneous soil zones associated with the spatial distribution of soil properties and some nutrients (Na +2 , Mg +2 , Mn +2 , Cu +2 , Ca +2 , Zn +2 , Fe +2 ). These results suggest that field-scale EC a maps have the potential to design sampling zones to implement site-specific management strategies.

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Relating apparent electrical conductivity to soil properties across the north-central USA

Cited by 310 publications

References 24 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

GIS-mapping spatial distribution of soil salinity for Eco-restoring the Yellow River Delta in combination with Electromagnetic Induction

Delineation of management zones with soil apparent electrical conductivity to improve nutrient management

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