Computers and Electronics in Agriculture 46 (2005) 1–10
Applications of apparent soil electrical conductivity
in precision agriculture
Sustainable agriculture is considered the most viable means of meeting future food needs for the
world’s increasing population through its goal of delicately balancing crop productivity, profitability,
natural resource utilization, sustainability of the soil–plant–water environment and environmental impacts.
Precision agriculture is a proposed approach for achieving sustainable agriculture. Site-specific
crop management (or site-specific management, SSM) refers to the application of precision agriculture
to crop production. Site-specific crop management utilizes rapidly evolving information and
electronic technologies to modify the management of soils, pests and crops in a site-specific manner as
conditions within a field change spatially and temporarily. Geospatial measurements of apparent soil
electrical conductivity (ECa) are the most reliable and frequently used measurements to characterize
within-field variability of edaphic properties for application to SSM. The collection of papers that
comprises this special issue ofComputers and Electronics in Agriculture provides a review of the
current technology and understanding of geospatial measurements of ECa and current approaches for
their application in SSM. The objective of this preface is to run a thread through the papers to show
their interrelationship and to identify significant points. The spectrum of topics covered by the papers
include: (i) a review of the use of ECa measurements in agriculture, (ii) multi-dimensional ECa modeling
and inversion, (iii) theory and principles elucidating the edaphic properties that influence the
ECa measurement, (iv) ECa survey protocols for characterizing spatial variability, (v) ECa-directed response
surface sampling design, (vi) designing and evaluating field-scale experiments using geospatial
ECa measurements, (vii) mapping of soil properties with ECa, (viii) spatially characterizing ECa and
water content with time domain reflectometry (TDR), (ix) delineating productivity and SSM zones
and (x) SSM methods for reclaiming salt-affected soils. The greatest potential for the application of
geospatial measurements of ECa in SSM is to provide reliable spatial information for directing soil
sampling to identify and characterize the spatial variability of edaphic properties influencing crop
Abbreviations:CEC, cation exchange capacity; ECa, apparent soil electrical conductivity; EM, electromagnetic
induction; EMh, electromagnetic induction measurement in the horizontal coil-mode configuration; EMv,
electromagnetic induction measurement in the vertical coil-mode configuration; ER, electrical resistivity; FKMe,
fuzzy k-means; GIS, geographic information system; GPS, global positioning system; HSR, hierarchical spatial
regression; RTK, real time kinematic; TDR, time domain reflectometry
0168-1699/$ – see front matter. Published by Elsevier B.V.
2Editorial / Computers and Electronics in Agriculture 46 (2005) 1–10
yield. This in turn can be used to delineate SSM units, which are key components of SSM. The future
of SSM depends upon the continued development and integration of information and electronic technologies
that can identify and characterize, both temporally and spatially, not only edaphic properties
but also topographical, biological, meteorological and anthropogenic factors influencing within-field
variations in crop productivity. The implementation of global positioning system (GPS)-controlled
variable-rate equipment will need spatial information to effectively determine input application rates.
Because of their reliability, ease of measurement and flexibility, geospatial ECa data will undoubtedly
contribute a significant portion of the spatial soils-related information needed to direct variable-rate
Published by Elsevier B.V.
Keywords:ECa; Site-specific management units; Spatial variability; Soil quality 1. Introduction
The prospect of meeting the world’s food demand for an additional three billion people
over the next three to five decades is a formidable, but not insurmountable challenge.
Barring unexpected technological breakthroughs, sustainable agriculture is one agronomic
means of meeting this challenge. The concept of sustainable agriculture is predicated on
a delicate balance of maximizing crop productivity and maintaining economic stability,
while minimizing the utilization of finite natural resources and detrimental environmental
impacts (Corwin et al., 1999). One of the key techniques for attaining sustainable agriculture
is site-specific crop management by means of precision agriculture (Lowenberg-DeBoer and
Site-specific crop management (or site-specific management, SSM) is a technologically
driven concept that relies upon information and electronic technologies to modify the management
of soils, pests and crops in a site-specific manner as conditions within a field change
spatially and temporarily. The technological pieces crucial to the development of SSM first
became commercially available in the 1980s and have just recently fallen into place with the
maturation of the global positioning system (GPS) and geographical information systems
(GIS). Many of the early stumbling blocks in the development of SSM were related to the
unfulfilled promises of satellite imagery, which was perceived to be the primary sensor from
which cause-and-effect variations in agricultural fields would be determined and managed.
It became quickly apparent that satellite imagery was only one piece to a technologically
complex puzzle that is just now being pieced together.
Crucial aspects of SSM are (i) quantification of yield variability in small areas of the
field, (ii) quantification of the spatial variability of soil properties influencing yield and (iii)
adjustment of inputs such as fertilizers, pesticides and seeding rates based on knowledge of
soil and yield variability (Atherton et al., 1999). Bullock and Bullock (2000) point out that
efficient methods for accurately measuring within-field variations in soil physico-chemical
properties are needed to make SSM a reality. The geospatial measurement of apparent soil
electrical conductivity (ECa) is one of the ground-based sensing technologies that is helping
to bring SSM from a concept to a reality.
Editorial / Computers and Electronics in Agriculture 46 (2005) 1–103
The earliest applications of ECa measurements in agriculture, primarily conducted by
Rhoades and colleagues in the 1970s at the USDA-ARS Salinity Laboratory, were for the
measurement of soil salinity. Due to its ease of measurement and reliability, ECa has over the
past 30 years evolved into one of the most frequently used technologies to characterize field
variability for application inSSM(Corwin and Lesch, 2003). It is the goal of the special issue
to provide readers with a detailed understanding of the techniques for measuring ECa, the
theory and principles of the measurement of ECa in soil to elucidate those physico-chemical
properties that influence ECa and a comprehensive background of how the measurement
of ECa has been used in the past and how it is currently being applied for SSM. The
objective of this preface is to run a thread through the papers comprising the special issue
to show their interrelationship within a SSM context and to identify their most significant
2. Special issue organization
The special issue is organized into three sections: background information, ECa-directed
sampling and experimental design, and SSM applications. The sections consist of five, two
and eight papers, respectively.
The section concerning background information consists of three papers by Corwin and
Lesch, one by Friedman, and one by Pellerin and Wannamaker. The first paper by Corwin
and Lesch sets the stage by covering historical development, basic principles and an
overview of current applications of ECa in agriculture. The Friedman paper concentrates
on the factors influencing ECa measurement, while the paper by Pellerin and Wannamaker
provides a review of multi-dimensional electromagnetic modeling and inversion. The second
and third papers by Corwin and Lesch (parts I and II) outline protocols for conducting
an ECa field survey to characterize spatial variability and demonstrate the use of the protocols
to characterize the spatial variability of physico-chemical properties in a soil quality
assessment of a saline-sodic soil, respectively.
The second section addresses ECa-directed sampling and experimental design. The paper
by Lesch addresses the use of the response-surface sampling design to direct soil sampling
from geospatial ECa data, while the paper by Johnson and colleagues uses geospatial ECa
data to direct and evaluate field-scale experimental design.
The final section is a compendium of papers demonstrating a variety of applications
of geo-referenced ECa data to SSM. Wraith and colleagues use time domain reflectometry
to spatially characterize water content. Papers by Kitchen and colleagues and by
Jaynes and colleagues deal with the delineation of productivity zones. The relationship
of soil properties to ECa and the subbasin-scale distribution of clay content based on
geospatial ECa measurements is addressed in papers by Sudduth and colleagues and by
Triantafilis and Lesch, respectively. Lesch and colleagues explore the use of geospatial
measurements of ECa for salinity mapping, soil texture mapping and the location
of drainage tile lines. Kaffka and colleagues analyze the relationship between ECa, soil
properties and sugar beet yield to derive SSM information and profitability implications.
A methodology for site-specific soil amendment application is presented by Horney and
4Editorial / Computers and Electronics in Agriculture 46 (2005) 1–10
2.1. Background: fundamental principles, theory and modeling, and survey protocols
As an introduction to the collection of papers in this special issue,Corwin and Lesch
(2005a)provide a comprehensive review of the historical development of ECa measurements
in agriculture as well as a discussion of the basic principles, including general theory
and factors influencing the ECa measurement; different geophysical techniques for measuring
ECa; mobilized ECa measurement equipment and applications to SSM. Friedman
(2005)delves more deeply into the factors influencing ECa measurement with a discussion
of (i) how and to what extent various soil and environmental attributes affect the measurement,
(ii) the physical theoretical problem, its limitations and different concepts for its
analysis and (iii) the experimental and theoretical findings regarding the effects of soil and
environmental attributes on the ECa of saturated and unsaturated soils. From this discussion,
Friedman (2005)provides a clear picture of the roles of the various geometrical and
interfacial attributes of soil and soil solution in determining ECa.
Pellerin andWannamaker (2005)reviewthe state-of-the-art in electromagnetic modeling
and inversion of 1-D, 2-D and 3-D earth conductivity structures to reveal the complex
relationship between actual conductivity structure and geophysical data measured near the
surface. Issues that are addressed include capabilities and limitations of the various common
field-measurement systems, methods to predict the geophysical response and incremental
response sensitivity to earth structure, as well as techniques for iteratively estimating an
earth model that maximizes resolution without sacrificing model stability.Pellerin and
Wannamaker (2005)conclude that 11
2-D inversion, where resistivities and depths of local 1-
Dmodels are laterally constrained with respect to neighboring values or a large-scale average
structure, may have the greatest potential for interpreting SSM datasets. They caution that
lateral heterogeneity is usually more serious than researchers expect; consequently, “explicit
multi-dimensional analysis is avoided only at one’s peril.”
Wraith et al. (2005)provide a comprehensive reviewof time domain reflectometry (TDR)
to measure ECa and demonstrate its application to map water content. The advantage of
TDR is that it permits a direct measure of both ECa and water content, where other methods
such as electromagnetic induction (EM) or electrical resistivity (ER) do not. Although,
the application of TDR to field- and landscape-scale characterization of ECa and water
content is at present not as practical for obtaining intensive spatial data as mobile EM and
ER, vehicle-based TDR units are under study. Truly “on-the-fly” TDR measurements for
field-scale applications may be feasible in the near future.
Protocols for conducting an ECa survey to characterize soil spatial variability are outlined
byCorwin and Lesch (2005b). The protocols consist of eight stages: (i) site description and
ECa survey design, (ii) ECa data collection with mobile GPS-based equipment, (iii) soil
sampling design, (iv) soil core sampling, (v) laboratory analysis, (vi) calibration of ECa
to ECe, (vii) spatial statistical analysis and (viii) GIS database development and graphic
display. To demonstrate their application, the ECa survey protocols were followed for a soil
quality assessment of a 32.4 ha field in California’s San Joaquin Valley (Corwin and Lesch,
2005c). The results clearly demonstrate the utility of geospatial ECa measurements for
characterizing the spatial variability of certain soil properties at field scales. The protocols
provide guidelines to assure the reliability, consistency and compatibility of ECa survey
measurements and their interpretation.
Editorial / Computers and Electronics in Agriculture 46 (2005) 1–105
2.2. ECa-directed sampling and experimental design
The second section deals with sampling and experimental design strategies based on
geospatial ECa measurements. These strategies have a far-reaching effect on the characterization
of soil spatial variability and on field-scale experimental design and evaluation. Both
papers in this section provide applications with relevance beyond that of SSM to include a
variety of landscape-scale studies that must characterize spatial variability or account for
its influence in sampling or experimental design.
The first paper byLesch (2005) is a statistically rigorous discussion of model-based
response-surface sample design strategy based on geospatial ECa measurements to direct
soil sampling. ECa-directed soil sampling provides a means of characterizing the spatial
variability of soil properties correlated to ECa with a significant reduction in the number of
sample locations as compared to grid sampling. This has widespread application to a variety
of landscape-scale issues outside SSM, including soil quality assessment and modeling of
non-point source pollutants in the vadose zone.
Johnson et al. (2005)detail the use of geospatial measurements of ECa to design
and evaluate non-replicated field-scale experiments. Field-scale experiments do not
lend themselves to traditional experimental design concepts of replication and blocking.
A comparison of the mean square errors for several soil properties and surface
residue mass at a field-scale site and nearby plot-scale experiment shows that ECaclassified
within-field variance approximates plot-scale experimental error. This supports
the use of within-field ECa-classified variance as a surrogate for experimental plot error
and provides a means for evaluating treatment differences in non-replicated field-scale
2.3. Applications of geospatial ECa measurements related to SSM
The remaining papers delve into disparate applications of geospatial ECa measurements
in SSM. Each of the technical research papers in this section provides an additional piece
to the SSM puzzle.
Kitchen et al. (2005)address the issue of whether productivity zones can be delineated
using ECa and elevation measurements on Missouri claypan soil fields. Productivity zones
are zones of similar yield and are of use to a producer to make management decisions based
upon reliable estimates of expected yield. Unsupervised fuzzy-c means clustering is used
on yield data to delineate ground-truth productivity zones and on combinations of ECa and
elevation data to delineate hypothetical productivity zones. A comparison of the groundtruth
and hypothetical productivity zones using an overall accuracy statistic and the Kappa
coefficient reveals that there is a 60–70% agreement when combined ECa and elevation
data are used.
Productivity zones are also addressed byJaynes et al. (2005). The delineation of productivity
zones is based on a series of profiling steps in conjunction with cluster analysis
to determine the relationship between yield clusters and easily measured field properties
of elevation, simple terrain attribute data (e.g., slope, aspect, etc.) and ECa. Apparent soil
electrical conductivity and the terrain attributes of slope, plane curvature, aspect and depth
of depression are effective in identifying soybean yield clusters. This allows easily mea6
Editorial / Computers and Electronics in Agriculture 46 (2005) 1–10
sured field attributes to be used to approximate soybean productivity zones in similar fields
where yield data may not be available.
Sudduth et al. (2005)look at the relationship of ECa to various soil physico-chemical
properties across a wide range of soil types, management practices and climatic conditions.
The study is an impressive inventory of 12 fields covering six north-central states in the USA.
For all fields clay content and cation exchange capacity (CEC) presented the highest correlation
with ECa. The implication of thiswork is that it may be feasible to develop relationships
between ECa and clay and CEC that are applicable across a wide range of soil and climatic
conditions.Sudduth et al. (2005) also compare two commercial ECa-sensing systems (i.e.,
Geonics EM-38 and Veris 31001) on diverse soil landscape. Differences between the instruments
are attributed to differences between depth-weighted response functions coupled
with differences between the degree of soil profile layering from one site to the next.
Response-surface sampling design, fuzzy k-means (FKMe) classification, hierarchical
spatial regression (HSR) modeling and spatial ECa measurements are used by Triantafilis
and Lesch (2005)to develop a map of the spatial distribution of clay content (averaged over
the top 7m) based on measurements taken with Geonics1 EM34 and EM38 conductivity meters
for the lower Macquarie Valley of New SouthWales, Australia. The final map provides
spatial information about subsurface clay variability over an area of roughly 19,000 ha and
demonstrates the utility of spatial ECa measurements in characterizing spatial variability at
a scale well beyond that of a single field. Results indicate that if a grid of 250 or 125m had
been used, rather than the 0.5 km grid, a significant decrease in the predicted variance of
interpolated values from the HSR model would have resulted.
The relationship between ECa measurements, soil properties and sugar beet yields in saltaffected
soil from the San Joaquin and Imperial Valleys is studied byKaffka et al. (2005).
Sugar beet yield in the San JoaquinValley field, where drainagewas impaired, is most highly
correlated with saturation percentage suggesting that yield is texturally driven, while in the
Imperial Valley field, where tile drainage has been effective, yield is most correlated with
salinity. This work further demonstrates the utility of using ECa measurements to establish
the relationship between soil properties and crop yield for the purpose of answering resource
input questions of how much, when and where, which are crucial to direct SSM. With the
derived SSM information, the paper delves into the relationship between yield and profit to
consider the option of taking those areas of land that are a net loss to the producer out of
The application of geospatial measurements ofECa as an agricultural management tool in
arid zone soils is discussed byLesch et al. (2005). Three distinct applications are presented
that demonstrate the flexibility of ECa: salinity mapping, soil texture mapping, and tile line
The final paper byHorney et al. (2005) proposes a methodology based on ECa-directed
soil sampling to guide site-specific soil amendment application. The steps include: (1)
generation of an ECa map, (2) directed soil sampling for salinity, (3) determination of the
estimated amendment requirement as a function of location in the field and (4) integration
1Mention of trademark or proprietary products does not constitute an endorsement or guarantee/warranty of the
product by the U.S. Department of Agriculture and does not imply its approval to the exclusion of other products
that may also be available.
Editorial / Computers and Electronics in Agriculture 46 (2005) 1–107
of the individual amendment requirements into a practical spatial pattern for amendment
application. Preliminary results for saline-sodic fields in central California do not show any
statistically significant alterations in soil condition at this point. The spatial complexity of
the sites and the short-term evaluation have masked any statistically perceptible evidence
3. Future directions
The greatest agronomic potential for ECa in the short term is for directing soil sampling
to characterize soil spatial variability. Characterization of soil spatial variability is
a fundamental component of a variety of applications, such as soil quality assessment,
landscape-scale solute transport in the vadose zone and SSM. Each of these applications
is interrelated. Site-specific crop management depends on the quantification of the spatial
variability of soil properties influencing crop productivity, which is basically an assessment
of the variation of soil quality within a field where the associated management goal is crop
productivity. Similarly, landscape-scale solute transport in the vadose zone is a key aspect
of SSM because an intended outcome of SSM is to minimize detrimental environmental
impacts to soil and water resources. These are often most easily assessed through model
simulations of solute transport to determine the fate and distribution of environmental contaminants.
When geospatial measurements of ECa are spatially correlated with geo-referenced yield
data, their combined use provides an excellent tool for identifying edaphic factors that influence
crop yield, which can, in turn, be used to delineate SSM units (Corwin et al., 2003;
Corwin and Lesch, 2005a). The delineation of productivity zones from geospatial measurements
of ECa provides another approach to SSM (Kitchen et al., 2005; Jaynes et al.,
2005). Even so, an understanding of the soil-related factors influencing yield or the identification
of productivity zones does not provide the whole picture for SSM because yield
is influenced by a complex interaction of topographical (elevation, aspect, etc.), meteorological
(humidity, temperature, etc.), biological (e.g., pests), anthropogenic (management
related) and edaphic (soil related) factors. Moreover, the precise manner in which these
factors influence the dynamic process of plant growth and reproduction is not always well
understood. To be able to manage within-field variation in yield it is necessary to have an
understanding within a spatial context of the relationship of all dominant factors causing the
Past research has shown at times that yield and ECa do not necessarily correlate. In
those instances, soil-related factors measured by ECa were not influencing yield, but rather
yield was influenced by soil factors that either were not measured by ECa or were nonedaphic
factors. For this reason, spatial knowledge of yield-influencing non-edaphic or
non-ECa-correlated factors is needed. The combined use of multiple sensors (e.g., EM,
multispectral imagery, hyperspectral imagery, ground penetrating radar, Doppler radar, Xray
tomography, advanced very high resolution radiometry, aerial photography, magnetic
resonance imaging, microwaves and thermal infrared) is needed to obtain the full spectrum
of spatial data necessary to pinpoint the topographic, meterologic, biologic, anthropogenic
and edaphic factors influencing yield. Of these, the use of hyperspectral imagery and EM
8Editorial / Computers and Electronics in Agriculture 46 (2005) 1–10
measurements of ECa combined with real-time kinematic (RTK) GPS probably have the
greatest potential from a cost-benefit perspective.
Remotely sensed imagery and EM measurements of ECa provide complementary information.
Remotely sensed imagery is generally best suited for spatially characterizing
dynamic properties associated directly with plant vegetative development, while ECa measurements
are best suited for spatially characterizing static soil properties such as texture,
water table depth and steady-state salinity. Remotely sensed imagery is particularly well
suited for obtaining spatial crop information during the maturation of a crop. Geospatial
measurements of ECa are most reliable for measuring static soil properties that may influence
crop yield because of the associated soil sampling that is required for ground truth to
establish what soil property or properties are influencing ECa at a given point of measurement.
Soil sampling and analysis is time and labor intensive, making the measurement of
dynamic soil properties using ECa generally untenable. Ground truth for remotely sensed
imagery is also necessary, but (i) wide-coverage real-time remote images are generally easier
to obtain than spatially comparable real-time ECa data unless ECa is measured from a airborne
platform and (ii) calibrations are often faster since soil sampling for ECa can involve
several depth increments and numerous soil properties. Conventional mobilized groundbased
ECa platforms cannot begin to compete with satellite or airborne imagery from the
perspective of extent of coverage of real-time data. Nonetheless, ground-based ECa surveys
at field scales have their place because they allow greater control and potentially increased
There is no question that geospatial measurements of ECa have found a niche in SSM
research and practice and will likely continue to serve a significant role in the future.
This special issue provides further proof that this contention is not overstated. However,
additional spatial information is needed to fill gaps in the database necessary for SSM
including, (i) the need for integrated spatial data of topographic, meteorologic, biologic,
anthropogenic and edaphic factors influencing yield; (ii) the need for real-time data and
rapid processing/analysis to enable temporal as well as spatial management decisions and
(iii) the need for sensors that can measure dynamic soil properties and crop responses to
those properties. The integrated use of multiple remote and ground-based sensors is the
future direction that SSM will likely take to obtain the extensive spatial data that will be
needed to direct variable-rate technologies. Variable-rate technologies driven by a networkcentric
system of multiple sensors will ultimately take SSM from a drawing board concept
to a reality.
The authors thank Dr. Dan Schmoldt, Editor-in-Chief for the Americas ofComputers
and Electronics in Agriculture, for the invitation to serve as guest editors for the special
issue entitled “Applications of ECa Measurements in Precision Agriculture.” The authors
wish to extend their gratitude to Dr. Schmoldt and the staff ofComputers and Electronics
in Agriculturefor their assistance in bringing this special issue to publication. Their professionalism
and dedication is greatly appreciated. The authors also extend their appreciation
to the invited contributors to the special issue. The time and effort spent by each of the conEditorial
/ Computers and Electronics in Agriculture 46 (2005) 1–109
tributors is reflected in a timely collection of review and technical papers. It was a pleasure
to work with each contributor.
Atherton, B.C., Morgan, M.T., Shearere, S.A., Stombawgh, T.S., Ward, A.D., 1999. Site-specific farming: a
perspective on information needs, benefits and limitations. J. Soil Water Conserv. 54 (2), 455–461.
Bullock, D.S., Bullock, D.G., 2000. Economic optimality of input application rates in precision farming. Prec.
Agric. 2, 71–101.
Corwin, D.L., Lesch, S.M., 2003. Application of soil electrical conductivity to precision agriculture: theory,
principles, and guidelines. Agron. J. 95 (3), 455–471.
Corwin, D.L., Lesch, S.M., Shouse, P.J., Soppe, R., Ayars, J.E., 2003. Identifying soil properties that influence
cotton yield using soil sampling directed by apparent soil electrical conductivity. Agron. J. 95 (2), 352–
Corwin, D.L., Lesch, S.M., 2005a. Apparent soil electrical conductivity measurements in agriculture. Comp.
Electron. Agric. 46, 11–43.
Corwin, D.L., Lesch, S.M., 2005b. Characterizing soil spatial variability with apparent soil electrical conductivity:
I. survey protocols. Comp. Electron. Agric. 46, 103–133.
Corwin, D.L., Lesch, S.M., 2005c. Characterizing soil spatial variability with apparent soil electrical conductivity:
II. case study. Comp. Electron. Agric. 46, 135–152.
Corwin, D.L., Loague, K., Ellsworth, T.R., 1999. Assessing non-point source pollution in the vadose zone with
advanced information technologies. In: Corwin, D.L., Loague, K., Ellsworth, T.R. (Eds.), Assessment of Nonpoint
Source Pollution in the Vadose Zone. Geophysical Monogr. 108. AGU, Washington, D.C., USA, pp.
Friedman, S.P., 2005. Soil properties influencing apparent electrical conductivity: a review. Comp. Electron. Agric.
Horney, R.D., Taylor, B., Munk, D.S., Roberts, B.A., Lesch, S.M., Plant, R.E., 2005. Development of practical
site-specific management methods for reclaiming salt-affected soil. Comp. Electron. Agric. 46, 379–
Jaynes, D.B., Colvin, T.S., Kaspar, T.C., 2005. Identifying potential soybean management zones from multi-year
yield data. Comp. Electron. Agric. 46, 309–327.
Johnson, C.K., Eskridge, K.M., Corwin, D.L., 2005. Apparent soil electrical conductivity: Applications for designing
and evaluating field-scale experiments. Comp. Electron. Agric. 46, 181–202.
Kaffka, S.R., Lesch, S.M., Bali, K.M., Corwin, D.L., 2005. Site-specific management in salt-affected sugar beet
fields using electromagnetic induction. Comp. Electron. Agric. 46, 329–350.
Kitchen, N.R., Sudduth, K.A., Myers, D.B., Drummond, S.T., Hong, S.Y., 2005. Delineating productivity
zones on claypan soil fields using apparent soil electrical conductivity. Comp. Electron. Agric. 46, 285–
Lesch, S.M., 2005. Sensor-directed response surface sampling designs for characterizing spatial variation in soil
properties. Comp. Electron. Agric. 46, 153–179.
Lesch, S.M., Corwin, D.L., Robinson, D.A., 2005. Apparent soil electrical conductivity mapping as an agricultural
management tool in arid zone soils. Comp. Electron. Agric. 46, 351–378.
Lowenberg-DeBoer, J., Erickson, K., 2000. Precision Farming Profitability. Purdue University, West Lafayette,
Pellerin, L.,Wannamaker, P.E., 2005. Multi-dimensional electromagnetic modeling and inversion with application
to near-surface earth investigations. Comp. Electron. Agric. 46, 71–102.
Sudduth, K.A., Kitchen, N.R., Wiebold, W.J., Batchelor, W.D., Bollero, G.A., Bullock, D.G., Clay, D.E., Palm,
H.L., Pierce, F.J., Schuler, R.T., Thelen, K.D., 2005. Relating apparent electrical conductivity to soil properties
across the North-Central USA. Comp. Electron. Agric. 46, 263–283.
Triantafilis, J., Lesch, S.M., 2005. Mapping clay content variation using electromagnetic induction techniques.
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10Editorial / Computers and Electronics in Agriculture 46 (2005) 1–10
Wraith, J.M., Robinson, D.A., Jones, S.B., Long, D.S., 2005. Spatially characterizing apparent electrical conductivity
and water content of surface soils with time domain reflectometry. Comp. Electron. Agric. 46, 239–
USDA-ARS, George E. Brown Jr. Salinity Laboratory
450 West Big Springs Road, Riverside, CA 92507-4617, USA
Corresponding author. Tel.: +1 951 369 4819; fax: +1 951 342 4962
Department of Agronomy and Range Science, University of California
Davis, CA 95616-8515, USA
Tel.: +1 530 752 1705; fax: +1 530 752 4361