Natural Resources Conservation Service National Soil Survey Center 11 Campus Blvd., Suite 200 Phone: (610) 557-4233 Newtown Square, PA 19073 FAX: (610) 557-4136 _________________________________________________________________________________________________________

SUBJECT: SOI – Geophysical Assistance June 24, 2015 TO: John Warner File Code: 330-7

USDA-NRCS Soil Survey Region 5 760 S. Broadway Salina, KS 67401-4642

Purpose: To conduct electromagnetic inductions (EMI) surveys in support of field studies being carried out in Kansas and to provide exposure and training on the use and operation of the EM38-MK2 meter to soil scientists. Participants: Laura Bricknell, Soil Scientist, USDA-NRCS, Hays, KS Gene Campbell, Supervisory Soil Scientist, USDA-NRCS, Clinton, MO Jim Doolittle, Research Soil Scientist, USDA-NRCS-NSSC, Newtown Square, PA Jeffrey Hellerich, Soil Scientist, USDA-NRCS, Manhattan, KS Tyler Labenz, Resource Soil Scientist, USDA-NRCS, Hutchinson, KS Tyson Morley, MLRA Project leader, USDA-NRCS, Altus, OK Lance Ohnmacht, Engineering (Civil) Technician, USDA-NRCS, Hutchinson, KS Lee Perkins, Soil Scientist, USDA-NRCS, Springfield, MO Ryan Still, Resource Soil Scientist, USDA-NRCS, Hays, KS John Warner, Soil Data Quality Specialist, USDA-NRCS Soil Survey Region 5, Salina, KS Activities: All activities were completed during the period of June 15 to 18, 2015. Summary:

1. In general, at the sodium-affected soils (SAS) sites in Chautauqua and Cherokee Counties, values of apparent conductivity (ECa) increased with increasing soil depth. This vertical trend reflects variations in water, clay and soluble salt contents. The patchy spatial ECa patterns evident in this study is attributed to variations in soluble salt concentrations alone. These patterns are a reflection of the prevailing hydropedologic conditions and processes (e.g., soil structure, depth to the water table, seepage, leaching, and evaporative discharge).

2. At each of the SAS sites, spatial ECa patterns are very complex and have a highly uneven distribution pattern that is speckled or spotted in appearance. While this complex and spotted distribution pattern is commonly associated with sodicity and salinity, without ground-truth verification and some sampling, explanations are speculative.

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3. All ECa data that were collected on SAS sites have been entered into an Excel spreadsheet. The ECa data and plots contained in this report can be used to identify sampling locations for sampling and characterization.

4. A high-intensity EMI survey, which was conducted across an area of Yaggy fine sandy loam, 0 to 1 % slopes, in Reno County, revealed highly complex and intricate spatial ECa patterns over short distances. The band-like patterns are assumed to reflect contrasting soils and alluvial deposits that change rapidly (both horizontally and vertically) in physical properties. Order-two soil maps and descriptions are inadequate in describing this variability.

5. This is my last field assignment as a soil scientist. I have dug my last hole and surveyed my last field. I deeply appreciate this opportunity to work with my friends and associates in Kansas. This is my goodbye.

With kind regards, Jim Doolittle Research Soil Scientist Gene Campbell, Supervisory Soil Scientist, USDA-NRCS, Clinton, MO Luis Hernandez, Acting Director, Soil Science Division, USDA-NRCS, Washington, DC Tyson Morley, MLRA Project leader, USDA-NRCS, Altus, OK Susan Picking, Administrative Support Assistant, SSR-5, Salina, KS Philip J. Schoeneberger, Research Soil Scientist/Liaison SSR5, Soil Survey Research & Laboratory,

NSSC- USDA-NRCS, Lincoln, NE Ryan Still, Soil Scientist, USDA-NRCS, Hays, KS Wes Tuttle, Soil Scientist (Geophysical), USDA-NRCS-NSSC, Wilkesboro, NC Douglas Wysocki, National Leader, Soil Survey Research & Laboratory, NSSC-USDA-NRCS, Lincoln,

NE

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Background: The last three decades have witnessed the development and growth of site-specific management (also referred to as precision agriculture). Site-specific management adjusts farming practices to measured variations in soil properties by dividing farmlands into management zones that have different seeding and chemical requirements (Mulla, 1993). Site-specific management requires the use of global-positioning system (GPS) and the creation of high-intensity maps. Management zones are identified on georeferenced maps by variations in crop yields and/or soil properties. To divide farmlands into management zones, site-specific management relies on detailed maps showing the location, size, and distribution of soils and soil properties within fields. Electromagnetic induction (EMI) has been used as an accurate, fast and inexpensive means of mapping soils and soil properties at a level of resolution that is comparable with the requirements of site-specific management (Corwin and Lesch, 2003; Bianchini and Mallarino, 2002; Lund and Christy, 1998; Jaynes, 1995 and 1996; Sudduth et al., 1995). As large, high-resolution data sets can be collected from mobile EMI platforms equipped with GPS, EMI is well suited to intermediate- or field-scale surveys (Adamchuk et al., 2004; Freeland et al., 2002) Advantages of EMI are its portability, speed of operation, flexible observation depths, and moderate resolution of subsurface features. Electromagnetic induction uses electromagnetic energy to measure the apparent conductivity (ECa) of earthen materials. Apparent conductivity is a weighted, average conductivity for a column of earthen materials (Greenhouse and Slaine, 1983). Variations in ECa are produced by differences in the electrical conductivity of earthen materials. Electrical conductivity is principally influenced by volumetric water content, type and concentration of ions in solution, temperature and phase of the soil water, and amount and type of clays in the soil matrix (McNeill, 1980). The ECa of earthen materials increases with increased soluble salt, water, and clay contents (Kachanoski et al., 1988; Rhoades et al., 1976). Electromagnetic induction provides benefits to soil surveys and soil interpretations. Being quick and non-invasive, measurements can be collected with EMI at a level of resolution not practical with traditional soil coring methods and tools. Spatial ECa patterns can provide improved resolution to existing soil maps (Hedley et al., 2004; Doolittle et al, 2008). A major contribution of EMI to soil surveys has been the identification and delineation of small included areas of dissimilar soils within soil polygons (Fenton and Lauterbach, 1999). In this role, differences in ECa are linked to different soils having dissimilar physiochemical properties (Farahani and Flynn, 2007; King et al., 2005; Brevik and Fenton, 2003; Anderson-Cook et al., 2002) and hydrologic processes (Kravchenko, 2008; Waine et al., 2000; Kachanoski et al., 1988). In Kansas, as elsewhere, concerns exist over the interpretation, classification, and mapping of sodium-affected soils [SAS]. Levels of soil sodicity varies considerably across landscapes and within soil delineations. While areas of high sodicity and salinity are easily detected in fields, most soil delineations contain soils that vary in sodium and other soluble salt concentrations, and range from non-sodic to sodic soils. As a consequence, soil sodicity is difficult to measure, characterize, map, and manage. Soil sodicity is determined by laboratory measurements of exchangeable sodium percentage (ESP), sodium adsorption ratio (SAR), and by field pH measurements and observations on the physical appearance of soils. Laboratory measurements are relatively time-consuming and costly, and are therefore limited in number. Surrogate field measures, such as ECa, have the potential to improve the mapping and management of SAS. As a field-scale approach, EMI methods is more expedient, complete, and economical than conventional laboratory determinations of ESP, SAR, pH, and electrical conductivity of the saturated paste (ECe). With EMI, comprehensive and detailed surveys of fields can be conducted and the spatial variability of soil sodicity can be more adequately assessed. However, in order to establish relationships between ECa and sodicity and/or salinity (ECe), and to develop predictive equations, EMI requires the collection of a small number of soil samples for laboratory analysis.

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Over the years, EMI has been used to study sodic soils (Amezketa, 2007; Corwin et al., 2003; Nelson et al., 2002; Nettleton et al., 1994, Ammons et al., 1989). Ammons et al. (1989) used EMI to distinguish Natraqualfs in Tennessee. Nettleton et al. (1994) observed relatively strong relationships between ECa and ESP and ECe. These researchers concluded that EMI provides a rapid and accurate method for describing the composition of SAS map units in south-central Illinois. Sodium-affected soils in Illinois are typically non-saline and lack significant concentrations of soluble salts. In California, a study conducted by Corwin et al. (2003) on saline-sodic soils resulted in correlation coefficients that ranged from 0.70 to 0.82 between ECa and ECe, SAR, SO4, Na and Mg. However, correlation coefficients were noticeably lower (0.31 to 0.32) between ECa and ESP. In Spain, in a study conducted by Amezketa (2007), a strong and significant correlation (r = 0.91) was observed between SAR and ECa. However, the strength of this correlation was attributed to a significant cross-correlation between SAR and ECe. The large cross-correlation between ECe and SAR was attributed to “evapo-concentration processes that occur in saline-sodic soils” (Amezketa, 2007). Significant cross-correlation between ECa and SAR were also reported in studies conducted by Corwin et al. (2003) and Nelson et al. (2002). In studies conducted by Nelson et al. (2002) on saline-sodic soils in Australia, ECa measurements were reasonably correlated with ESP. However, on nonsaline-sodic soils, no relationships were observed between ECa and ESP. Contrary to the findings of Nelson et al. (2002), Nettleton et al. (1994) observed significant correlations between ECa and ESP on nonsaline-sodic soils.

Figure 1. Gene Campbell conducts an EMI survey with an EM38-MK2 meter, Allegro field

computer and Trimble GPS receiver across a corn field in Cherokee County, Kansas. Equipment: An EM38-MK2-2 meter (Geonics Limited; Mississauga, Ontario), was used in this investigation.1 The EM38-MK2-2 meter requires no ground contact and only one person to operate (Figure 1). The EM38-MK2-2 meter operates at a frequency of 14,500 Hz and weighs about 5.4 kg (11.9 lbs.). The meter has

1 Manufacturer's names are provided for specific information; use does not constitute endorsement.

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one transmitter coil and two receiver coils. The receiver coils are separated from the transmitter coil by distances of either 100 or 50 cm. This configuration provides nominal penetration depths of about 150 and 75 cm in the vertical dipole orientation (VDO), and about 75 and 38 cm in the horizontal dipole orientation (HDO). In either dipole orientation, the EM38-MK2-2 meter provides measurements of both the quadrature-phase (ECa) and in-phase (susceptibility) components within the two depth ranges. Apparent conductivity (ECa) is typically expressed in milliSiemens/meter (mS/m). Susceptibility is expressed parts per thousand (ppt). Operating procedures for the EM38-MK2-2 meter are described by Geonics Limited (2007). A Trimble AgGPS 114 L-band DGPS (differential GPS) antenna (Trimble, Sunnyvale, CA) was used to georeferenced the EMI data.2 Using the RTmap38 program (Geomar Software, Inc., Mississauga, Ontario), both GPS and ECa data were simultaneously recorded and displayed on an Allegro CX field computer (Juniper Systems, North Logan, UT). 2 To help summarize the results of the EMI surveys, the SURFER for Windows (version 10.0) software (Golden Software, Inc., Golden, CO) was used to construct the two-dimensional simulations shown in this report.2 Grids were created using kriging methods with an octant search. Study Site: Chautauqua County site: The Chautauqua County site (37.1284o N latitude, 96.0978o W longitude) is located about 4.5 km north of Peru and 7.3 km east of Sedan, Kansas. The site is located on the floodplain of Possum Trot Creek. Figure 2 is a soil map of the study site from the Web Soil Survey3. The study site is located within an area that is mapped as Osage-Drummond complex, occasionally flooded (8204) and Dennis silt loam, 1 to 3 % slopes (8679). The very deep, poorly drained Osage (fine, smectitic, thermic Typic Epiaquerts) soils formed in thick clayey alluvium on flood plains. The deep, somewhat poorly drained Drummond (fine, mixed, superactive, thermic Mollic Natrustalfs) soils formed in material weathered from loamy and clayey alluvium predominantly from Permian red beds on flood plains. Drummond has a sodium absorption ratio (SAR) ranging from 0 to 15, and salinity of less than 1 to 16 dS/m. The very deep, somewhat poorly drained Dennis (fine, mixed, active, thermic Aquic Argiudolls) soils formed in materials weathered from shale on the Cherokee Prairies (MLRA 112). Cherokee County site: The Cherokee County site (37.1346o N latitude, 94.6846o W longitude) is located about 4.3 km southeast of Crestline and 5.2 km southwest of Badger, Kansas. Figure 3 is a soil map of the study site from the Web Soil Survey. The study site is located within an area that is mapped as Dennis silt loam, 1 to 3 % slopes (8679) and Taloka silt loam, 0 to 1 % slopes (8927). The very deep, somewhat poorly drained Taloka (fine, mixed, active, thermic Mollic Albaqualfs) soils formed in loamy and clayey material weathered from colluvium and alluvium over interbedded shales and sandstone on the Cherokee Prairies. This small, rectangular study area crossed two of the drainage ditches that can be seen in lower, central portion of the image shown in Figure 3.

2 Manufacturer's names are provided for specific information; use does not constitute endorsement. 3 Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. Web Soil Survey. Available online at http://websoilsurvey.nrcs.usda.gov/. Accessed [06/20/2015].

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Figure 2. This soil map of the Chautauqua County site is from the Web Soil Survey.

Figure 3. This soil map of the Cherokee County site is from the Web Soil Survey.

Reno County site: The Reno County site (38.1600o N latitude, 98.1375o W longitude) is located to the immediate north of the Arkansas River about 4.8 km west-northwest of Nickerson, Kansas. Figure 4 is a soil map of the study site from the Web Soil Survey. The study site is located within an area that is mapped as Yaggy fine sandy loam, 0 to 1 % slopes (5845). The very deep, somewhat poorly drained Yaggy (sandy, mixed, mesic Oxyaquic Ustifluvents) soils formed in stratified loamy alluvium over sands on flood plains in river valleys of the Great Bend Sand Plains (MLRA 79).

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Figure 4. This soil map of the Cherokee County site is from the Web Soil Survey. The Arkansas River forms

the water body in the lower portion of this image.

Figure 5. Tyson Morley (center) discuss survey procedures using a specially designed canister to house the

EM38-MK2 meter for mobile survey with an ATV. Survey procedures: Surveys were completed across the sites located in Chautauqua and Cherokee Counties by walking with the EM38-MK2-2 held above the ground surface in the VDO (see Figure 1). The long axis of the meter was orientated parallel to the direction of advance. A mobile survey was completed in Reno County by driving an ATV at a uniform speed in a back and forth manner across the study site. For this survey, the

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EM38-MK2 meter was housed in a canister developed by Geonics Limited and towed behind an ATV (see Figure 5). The ECa data were not temperature corrected to a standard temperature. Results: Chautauqua County site: A detailed EMI survey was conducted across the Chautauqua County site with the EM38-MK2 meter. Based on 870 ECa measurements, for the 0 to 75 cm depth interval, ECa averaged 36.4 mS/m with a range of 11.6 to 109.2 mS/m. However, one-half of these measurements were between 22.8 and 43.8 mS/m. For the 0 to 150 cm depth interval, ECa averaged 51.2 mS/m with a range of 17.8 to 133.1 mS/m. One-half of these recorded measurements were between values of 32.4 and 64.1 mS/m. In general, across the Chautauqua site, ECa increases with increasing depth. This trend can be associated with increased moisture, clay and soluble salt contents at lower soil. Figure 6 contains two, two-dimensional plot of the Chautauqua County site showing the spatial variation in the ECa measured with the EM38-MK2 meter. In these plots, the soil boundary line was imported from the Web Soil Survey, and is shown in black and separates a map unit dominated by Dennis soil from one of Osage and Drummond soils. The spatial ECa patterns evident in these plots are very complex and have a highly uneven, speckled or spotted distribution pattern. While this complex and spotted distribution pattern is commonly associated with sodicity, no apparent causal explanation can be provided at this time for these patterns.

Figure 6. These plots show the spatial ECa patterns obtained from data collected with an EM38-MK2 meter

operated in the 50-cm (left-hand plot) and 100-cm (right-hand plot) intercoil spacings at the Chautauqua County site. The soil line and map unit names are from the Web Soil Survey.

Cherokee County site: A detailed EMI survey was conducted across the Cherokee County site with the EM38-MK2 meter. Based on 404 ECa measurements, for the 0 to 75 cm depth interval, ECa averaged 72.1 mS/m with a range of 27.3 to 177.4 mS/m. One-half of these measurements were between 27.3 and 82.2 mS/m. For the 0 to

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150 cm depth interval, ECa averaged 83.5 mS/m with a range of 40.4 to 162.1 mS/m. One-half of these recorded measurements were between values of 38.1 and 96.0 mS/m. As at the Chautauqua County site, ECa increases with increasing depth. This vertical trend is associated with increased moisture, clay and soluble salt contents at lower soil. Figure 7 contains two, two-dimensional plot of the Cherokee County site showing the spatial distribution of ECa measured with the EM38-MK2 meter. In each plot, the soil boundary line was imported from the Web Soil Survey, and is shown in black and separates a map unit dominated by Dennis and Taloka soils. In each plot, values of ECa are not uniformly distributed across the site, but appears to form distinct interconnected clusters that display speckled or pockmarked patterns. The same color ramp and scale has been used in Figure 7 (Cherokee County site) as in Figure 6 (Chautauqua County site). A comparison of the plots shown in Figures 6 and 7 reveals that ECa is higher and more variable at the Cherokee County site than at the Chautauqua County sites. Soils at the two sites are closely similar in textured and drainage. If higher ECa is attributed to higher levels of sodicity and soluble salts, the contradiction is that sodium-affected Drummond soils are recognized only at the site with the lower ECa.

Figure 7. These plots show the spatial ECa patterns obtained from data collected with an EM38-MK2 meter operated in the 50-cm (upper plot) and 100-cm (lower plot) intercoil spacings at the Cherokee County site.

The soil line and map unit names are from the Web Soil Survey. Reno County site: A detailed EMI survey was conducted across an area of Yaggy fine sandy loam, 0 to 1 % slopes, in Reno County with the EM38-MK2 meter. Based on 4702 ECa measurements, for the 0 to 75 cm depth interval, ECa averaged 1.9 mS/m with a range of -39.4 to 25.3 mS/m. One-half of these measurements were between -3.8 and 8.7 mS/m. The large number of negative values is believed to reflect principally errors in calibration. For the 0 to 150 cm depth interval, ECa averaged 19.9 mS/m with a range of -34.4 to 43.9 mS/m. One-half of these recorded measurements were between values of 14.1 and 26.1 mS/m. The low ECa is associated with the low soluble salt and clay contents of Yaggy soils. Negative values may reflect the presence of metallic artifacts scattered across the site. Figure 8 is a two-dimensional plot of the Reno County site showing the spatial distribution of ECa measured with the 100 cm intercoil spacing of the EM38-MK2 meter. The spatial patterns are

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exceedingly intricate and reflect channel deposits that change rapidly in properties. As noted by Daniels and Hammer (1992), soil variability often results from material variability. On floodplains, predictions of soils and soil materials are exceedingly difficult as a result of the abrupt vertical and horizontal textural changes (Daniels and Hammer, 1992). The meandering stream systems of the Arkansas River deposited sediments as channel, channel-margin, and overbank deposits. Lateral sedimentation and vertical accretion cause changes in sediment types and properties. Sediments vary in texture vertically and over short distances. These processes produce highly complex and variable soils and stratigraphic layers.

Figure 8. This plot shows the spatial ECa patterns obtained from data collected with an EM38-MK2 meter

operated in the 100-cm intercoil spacings at the Reno County site. The plot of spatial ECa patterns shown in Figure 8 demonstrates the complexity of soils within a mapped unit of Yaggy fine sandy loam, 0 to 1 % slopes. Yaggy soil is simply described as consisting of a vertical sequence of loamy alluvium over sandy alluvium. No description is adequate to describe the horizontal variability shown in Figure 8. Alternating bands of higher and lower conductivity reflect changes in soil texture and structure. Areas of lower ECa reflect thicker layers of sands, while bands of higher conductivity reflect the presence of strata with higher silts and clay contents. References: Adamchuk, V.I., J.W. Hummel, M.T. Morgan, and S.K. Upadhyaya, 2004. On-the-go soil sensors for precision agriculture. Computers and Electronic in Agriculture 44: 71-91. Amezketa, E., 2007. Use of electromagnetic techniques to determine sodicity in saline-sodic soils. Soil Use and Management 23: 278-285.

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Ammons, J.T., M.E. Timpson, and D.L. Newton, 1989. Application of aboveground electromagnetic conductivity meter to separate Natraqualfs and Ochraqualfs in Gibson County, Tennessee. Soil Survey Horizons 40: 66-70. Anderson-Cook, C.M., M.M. Alley, J.K.F. Roygard, R. Khosla, R.B. Noble and J.A. Doolittle, 2002. Differentiating soil types using electromagnetic conductivity and crop yield maps. Soil Science Society of America Journal 66(5): 1562-1570. Bianchini, A.A., and A.P. Mallarino, 2002. Soil-sampling alternatives and variable-rate liming for a soybean-corn rotation. Agronomy Journal 94:1355-1366. Brevik, E.C., and T.E. Fenton, 2003. Use of the Geonics EM-38 to delineate soil in a loess over till landscape, southwestern Iowa. Soil Survey Horizons 44: 16-24. Corwin, D.L., S.R. Kaffka, J.W. Hopmans, Y. Mori, J.W. Van Groenigen, C. Van Kessel, S M. Lesch, and J.D. Oster, 2003. Assessment and field-scale mapping of soil quality properties of a saline-sodic soil. Geoderma 114 (3-4): 231-259. Corwin, D. L., S. M. Lesch, J. D. Oster and S. R. Kaffka. 2006. Monitoring management-induced spatio-temporal changes in soil quality through soil sampling directed by apparent electrical conductivity. Geoderma 131: 369-387. Corwin, D.L. and S.M. Lesch, 2003. Application of soil electrical conductivity to precision agriculture: theory, principles, and guidelines. Agronomy Journal 95:455-471. Daniels, R.B., and R.D. Hammer, 1992. Soil Geomorphology. John Wiley & Sons, New York. Doolittle, J.A., R.D. Windhorn, D.L. Withers, S.E, Zwicker, F.E. Heisner and B.L. McLeese, 2008. Soil Scientists Revisit a High-Intensity Soil Survey in Northwest Illinois with Electromagnetic Induction and Tradition Methods. Soil Survey Horizons 49(4):102-108. Farahani H.J., and R.L. Flynn. 2007. Map quality and zone delineation as affected by width of parallel swaths with mobile agricultural sensors. Biosystems Engineering 96(2): 151-159. Fenton, T.E., and M.A. Lauterbach, 1999. Soil map unit composition and scale of mapping related to interpretations for precision soil and crop management in Iowa. In: Proceeding of the 4th International Conference on Precision Agriculture. 239-251 pp. American Society of Agronomy, Madison, Wisconsin. Freeland, R.S., R.E. Yoder, J.T. Ammons, and L. L. Leonard, 2002. Mobilized surveying of soil conductivity using electromagnetic induction. American Society of Agricultural Engineers. Applied Engineering in Agriculture 18(1): 121-126. Geonics Limited, 2007. EM38-MK2 ground conductivity meter operating manual. Geonics Ltd., Mississauga, Ontario. Greenhouse, J.P., and D.D. Slaine, 1983. The use of reconnaissance electromagnetic methods to map contaminant migration. Ground Water Monitoring Review 3(2): 47-59.

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Hedley, C.B., I.J. Yule, C.R. Eastwood, T.G. Sheperd, and G. Arnold, 2004. Rapid identification of soil textural and management zones using electromagnetic induction sensing in soils. Australian Journal of Soil Research 42: 389-400. Jaynes, D.B., 1995. Electromagnetic induction as a mapping aid for precision farming. In: Clean Water, Clean Environment, 21st Century: Team Agriculture. Working to Protect Water Resources, 153-156 pp. 5 to 8 March 1995, Kansas City, Missouri. Jaynes, D.B., 1996. Improved Soil Mapping using Electromagnetic Induction Surveys. Proc. 3rd Int. Conf. on Precision Agriculture, Minneapolis, June 23-26, 1996. ASA-CSSA-SSSA. Madison, Wisconsin. Kachanoski, R.G., E.G. Gregorich, and I.J. Van Wesenbeeck, 1988. Estimating spatial variations of soil water content using noncontacting electromagnetic inductive methods. Canadian Journal of Soil Science 68:715-722. King, J.A., P.M.R. Dampney, R.M. Lark, H.C. Wheeler, R.I. Bradley, and T.R. Mayr, 2005. Mapping potential crop management zones within fields: Use of yield-map series and patterns of soil physical properties identified by electromagnetic induction sensing. Precision Agriculture 6: 167-181. Kravchenko, A.N., 2008. Mapping soil drainage classes using topographic and soil electrical conductivity. In: Handbook of Agricultural Geophysics, Allred, B. J., J .J. Daniels and M. R. Ehsani (editors), 255-261 pp. CRC Press, Taylor and Francis Group, Boca Raton, Florida. Lund, E.D. and C.D. Christy, 1998. Using electrical conductivity to provide answers to precision farming. I-327 to I-334 pp. In: First International Conference on Geospatial Information in Agriculture and Forestry. ERIM International, Ann Arbor, MI. McNeill, J.D., 1980. Electrical Conductivity of soils and rocks. Technical Note TN-5. Geonics Ltd., Mississauga, Ontario. Mulla, D.J., 1993. Mapping and managing spatial patterns in soil fertility and crop yields. In: Proceedings of Second International Conference on Precision Management for Agricultural Systems. Robert, P.C., R.H. Rust, and W.E. Larson (editors), 15-26 pp. March 27-30, 1994, Minneapolis, Minnesota. American Society of Agronomy, Madison, Wisconsin. Nelson, P.N., A T. Lawer, and G.J. Ham, 2002. Evaluation of methods for field diagnosis of sodicity in soil and irrigation water in the sugarcane growing districts of Queensland, Australia. Australian Journal of Soil Research 40 (8): 1249-1265. Nettleton, W.D., L. Bushue, J A. Doolittle, T.J. Endres, and S J. Indorante, 1994. Sodium-affected soil identification in south-central Illinois by electromagnetic induction. Soil Science Society of America Journal 58:1190-1193. Rhoades, J.D., P.A. Raats, and R.J. Prather, 1976. Effects of liquid-phase electrical conductivity, water content, and surface conductivity on bulk soil electrical conductivity. Soil Science Society of America Journal 40:651-655. Sudduth, K.A., N.R. Kitchen, D.H. Hughes, and S.T. Drummond, 1995. Electromagnetic induction sensing as an indicator of productivity on claypan soils. 671-681 pp. In: Robert, P. C., R.H. Rust, and

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W.E. Larson (editors). Proceedings of Second International Conference on Precision Management for Agricultural Systems. Minneapolis, Minnesota. March 27-30, 1994. American Society of Agronomy, Madison, Wisconsin. Waine, T.W., B.S. Blackmore, and R.J. Godwin, 2000. Mapping available water content and estimating soil textural class using electromagnetic induction. EurAgEng Paper No. 00-SW-044, Warwick, UK.