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Computers and Electronics in Agriculture 46 (2005) 1–10

Editorial

Applications of apparent soil electrical conductivity

in precision agriculture

Abstract

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 of Computers 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.

doi:10.1016/j.compag.2004.10.004

2 Editorial / 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

equipment.

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

Erickson, 2000).

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) 1–10 3

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

points.

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

colleagues.

4 Editorial / 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

by Corwin 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) 1–10 5

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 by Lesch (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

experiments.

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 by Jaynes 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 by Kaffka 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

production.

The application of geospatial measurements ofECa as an agricultural management tool in

arid zone soils is discussed by Lesch et al. (2005). Three distinct applications are presented

that demonstrate the flexibility of ECa: salinity mapping, soil texture mapping, and tile line

location.

The final paper by Horney 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

1 Mention 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) 1–10 7

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

of reclamation.

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

variation.

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

8 Editorial / 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

spatial resolution.

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.

Acknowledgments

The authors thank Dr. Dan Schmoldt, Editor-in-Chief for the Americas of Computers

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 of Computers and Electronics

in Agriculture for 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) 1–10 9

tributors is reflected in a timely collection of review and technical papers. It was a pleasure

to work with each contributor.

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D.L. Corwin

USDA-ARS, George E. Brown Jr. Salinity Laboratory

450 West Big Springs Road, Riverside, CA 92507-4617, USA

Corresponding author. Tel.: [phone]; fax: [phone]

E-mail address: [e-mail]

R.E. Plant

Department of Agronomy and Range Science, University of California

Davis, CA 95616-8515, USA

Tel.: [phone]; fax: [phone]

E-mail address: [e-mail]