Bünemann et al. (2018). Soil quality – A critical review

Soil Biology and Biochemistry 120 (2018) 105–125

bl.org

(E.K. Bünemann).

hydrology may in

ﬂ

uence potential values of soil properties to such a

degree (

Fig. 1

) that it is impossible to establish universal target values,

at least not in absolute terms. Soil quality assessment thus needs to
include baseline or reference values in order to enable identi

ﬁ

cation of

management e

ﬀ

ects. Soils often react slowly to changes in land use and

management, and for that reason it can be more di

ﬃ

cult to detect

changes in soil quality before non-reversible damage has occurred than
for the quality of water and air (

Nortcli

Blume and Leinweber (2004)

, 2002

). Therefore, an im-

portant component of soil quality assessment is the identi

ﬁ

cation of a

set of sensitive soil attributes that re

ﬂ

ect the capacity of a soil to

function and can be used as indicators of soil quality. Because man-
agement usually has only limited short-term e

ﬀ

ects on inherent prop-

erties such as texture and mineralogy, other indicators, including bio-
logical ones, are needed. The distinction between inherent (static) and
manageable (dynamic) attributes, however, is not absolute and also
context-dependent (

Schwilch et al., 2016

). For example, stoniness as an

inherent property is nevertheless manageable, e.g. by removal of stones
from an area to facilitate tillage and to build separating walls between

ﬁ

elds, or by addition of gravel and stones to improve friability, to ac-

celerate soil warming in spring or decrease evaporation. Soil manage-
ment by humans has even given rise to separate classes in the soil
taxonomic system, such as Plaggic anthrosols, the plaggen soils of
northwestern Europe (e.g.,

), and Terric

anthrosols, the Amazonian Dark Earths, also known as Terra Preta de
Índio (

Glaser and Birk, 2012

Letey et al., 2003; Sojka and Upchurch,

The history of the concept of soil quality shows that it is rooted in

two di

ﬀ

erent approaches that either put more emphasis on the inherent

soil properties or on the e

ﬀ

ects of human management. The oldest

mention in the scienti

ﬁ

c literature is by

Mausel (1971)

who de

ﬁ

ned soil

quality as

“

the ability of soils to yield corn, soybeans and wheat under

conditions of high-level management. The choice of these crops to re-

ﬂ

ect soil quality in Illinois is due to their overwhelming agricultural

economic dominance.

”

This de

ﬁ

nition emphasises agricultural pro-

duction and is linked to land evaluation (see below). A similar de-
scription was provided by SSSA (1987; cited in

Doran and Parkin, 1994

)

as the

“

inherent attributes of soils that are inferred from soil char-

acteristics or indirect observations

”

. This de

ﬁ

nition is comparable to

the more recent term soil capability, de

ﬁ

ned as the intrinsic capacity of

a soil to contribute to ecosystem services, including biomass production

(

Bouma et al., 2017

). The emphasis on inherent, more static soil

properties was closely connected to soil taxonomy. It also took man-
agement for granted (

“

under conditions of high-level management

”

),

without specifying those conditions.

Larson and Pierce (1991)

ex-

pressed uneasiness with the focus on agricultural productivity and
proposed to disconnect soil quality from productivity.

Doran and Parkin

(1994)

observed that de

ﬁ

nitions of soil quality included the capacity of

soils to function sustainably, but likewise considered the focus on
production to be too restrictive. They wanted a de

ﬁ

nition of soil quality

to stress the main issues of concern regarding soil use. Besides pro-
ductivity, they therefore included the ability of soils to contribute to
environmental quality and to promote plant, animal and human health
in their de

ﬁ

nition as cited above.

The concept of soil quality by

Doran and Parkin (1994)

was heavily

criticized in a series of papers (

1999; Sojka et al., 2003

). That criticism contained various elements.

First, these authors claimed that the concept of soil quality could
transform soil science from a value-neutral science into a value system
and even referred to soil quality as promoting ideas of a politically
correct soil. Second, they expressed discontent with the idea of a uni-
versal soil quality index, to which they referred as institutionalizing soil
quality. Third, they criticized the concept because of its bias towards
certain soil types as a consequence of the focus on intrinsic properties.
And

ﬁ

nally, they criticized the de

ﬁ

nition because in its original form it

puts too much emphasis and value on a limited number of annual crops
that provide cheap food and that are heavily subsidized. Their proposal
to replace the term soil quality management by the term quality soil
management did not

ﬁ

nd support, but their criticisms did in

ﬂ

uence the

further development of an operational concept of soil quality, in which
management has become the central issue: agricultural productivity
does not hold a privileged position any longer, trade-o

ﬀ

s are explicitly

recognized at the expense of a universally applicable index, and the role
of soil scientists in relation to societal stakeholders who manage soils
(farmers, owners of land for nature conservation, policy makers, etc.)
has changed. A particular recommendation of Sojka and co-authors was
to speak of soil use rather than soil functions, so that the responsibility
to maintain the quality of the soil can be clearly assigned to the user of
the soil. Soil quality assessment then provides the scienti

ﬁ

c tools for

evaluation of the management of soil resources, considering also the

Fig. 1.

Abiotic and biotic factors constituting soil quality in the soils of the world (modi

ﬁ

ed from

Brussaard (2012)

). Reproduced with permission from Oxford University Press (

www.

oup.com

).

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

106

societal demands of the various bene

ﬁ

ts that soils, if managed well, can

provide to humankind. The valuation of soil quality hence becomes
connected to the valuation of the ecosystem services provided by soils.
A further bene

ﬁ

t of such a soil quality concept is that it raises awareness

and enhances communication between stakeholders regarding the im-
portance of soil resources (

Karlen et al., 2001

). Recently, there has been

renewed interest in this educational aspect, either by focusing more on
visual soil assessment (

Ball et al., 2013

) or by proposing interactive soil

quality assessment tools, such as LandPKS (

https://www.landpotential.

org/

) and the app currently being developed in the EU Horizon-2020

project

‘

Interactive Soil Quality Assessment in Europe and China for

Agricultural Productivity and Environmental Resilience (iSQAPER -

http://www.isqaper-project.eu/

In this paper, we aim to critically review soil quality publications

and assessment tools, especially with respect to soil quality indicators,
in terms of commonalities, meaningful di

ﬀ

erences and omissions. To

this end, the relevant de

ﬁ

nitions and terminologies are introduced in

section

2

, followed by an overview of approaches to soil quality as-

sessment in section

3

. The focus of this review is on analytical mea-

surements. The most important approaches using visual soil evaluation
in the

ﬁ

eld are only brie

ﬂ

y presented, since visual soil assessments have

been reviewed recently (

Emmet-Booth et al., 2016

). In section

4

, the

choice of soil quality indicators is discussed in-depth with respect to
requirements of indicators and methods to select a minimum dataset. A
compilation of the most frequently proposed indicators is followed by
paragraphs on novel soil quality indicators with potential added value
and on the interpretation of indicator values, including the potential
aggregation into an operational soil quality index and its disadvantages.
In the conclusions (section

5

), we propose the crucial steps to be taken

for successful soil quality assessment and analyze to what extent these
have been implemented so far. Finally, fostering soil quality is con-
sidered in the wider context of enhancing environmental quality, em-
bedded in an interactive process of co-creation of knowledge by sci-
entists and other actors in urgent transitions towards sustainable use
and management of natural resources (section

6

Barrera-Bassols and Zinck, 2003

2. Concepts related to soil assessment

2.1. Soil fertility, land quality, soil capability, soil quality and soil health

Various forms of soil assessment are encapsulated in di

ﬀ

erent con-

cepts. Apart from mining minerals, the main interest in soil has tradi-
tionally been in its potential for agricultural production. Assessments of
the suitability of soil for crop growth may have been made even before
the evidence of written records. Documentation can be found in ancient
Chinese books such as

“

Yugong

”

and

“

Zhouli

”

, written during the Xia

(2070

–

1600 BCE) and Zhou (1048

–

256 BCE) dynasty, respectively

(

Harrison et al., 2010

), and in the work of Roman authors such as

Columella (

Warkentin, 1995

). Ethnopedology also provides several

examples of indigenous soil classi

ﬁ

cations that focus on indicators that

allow judgement of the suitability of particular soils for various crops
(e.g.,

). The suitability of soil for agri-

cultural production is captured in the concept of

soil fertility

, originating

from the German literature on

“

Bodenfruchtbarkeit

”

that is pre-

dominantly aligned to crop yields (

Patzel et al., 2000

). Accordingly, the

FAO describes soil fertility as

“

the ability of the soil to supply essential

plant nutrients and soil water in adequate amounts and proportions for
plant growth and reproduction in the absence of toxic substances which
may inhibit plant growth

”

Carter et al., 1997; Dumanski and Pieri, 2000

www.fao.org

).

Mäder et al. (2002)

extend

that scope in proposing that a fertile soil

“

provides essential nutrients

for crop plant growth, supports a diverse and active biotic community,
exhibits a typical soil structure and allows for an undisturbed decom-
position

”

. Nevertheless, the concept of soil fertility is generally oper-

ationalized chemically and partly physically in terms of the provision to
crops of nutrients and water only.

To address physical and/or biological characteristics of soil, other

concepts are more commonly used. One of the earliest is

land quality

,

which integrates characteristics of soil, water, climate, topography and
vegetation (

) in the con-

text of land evaluation, which aims to assess the use potential of land,
based on its attributes (

Rossiter, 1996

). An early comprehensive ela-

boration of the concept is the FAO Framework for Land Evaluation
(

FAO, 1976

). Soil survey is part of land quality assessment for land

evaluation. It is done once or only repeated over large time intervals,
relying heavily on

ﬁ

eld observations, supplemented with very few

measured parameters (

Huber et al., 2001

). Land evaluation anticipates

decisions on the optimal allocation of land for various uses and is,
hence, the

ﬁ

rst step to sustainable land management. In countries with

low population densities, the main purpose of land evaluation in the
past was to identify fertile land for agricultural production, whereas in
more densely populated regions such as Europe it was more targeted at
identifying de

ﬁ

cient factors in agriculture that could be remedied, in

particular by manuring (

van Diepen et al., 1991

). However, land eva-

luation has also been used as part of a strategy to assess broader land
use options (

van Latesteijn, 1995

). Similarly,

soil capability

, i.e. the in-

trinsic capacity of a soil to contribute to ecosystem services (

Bouma

et al., 2017

), provides a neutral assessment of what soils can do and

how their potential can be reached.

Since

Mausel (1971)

introduced the term

soil quality

, it has some-

times been used in the context of land quality and land evaluation (e.g.

Eswaran et al., 1997

). Whereas land quality and land evaluation pri-

marily address the inherent soil properties that do not change easily
and are often assessed for the entire pro

ﬁ

le, soil quality is more focused

on the dynamic soil properties that can be strongly in

ﬂ

uenced by

management and are mainly monitored in the surface horizon
(0

–

25 cm) of the soil (

Karlen et al., 2003

). However, when studying

direct impacts of soil quality on water quality it is imperative that in-
herent soil properties in deeper parts of the soil pro

ﬁ

le are included in

the assessment.

Typically, the concept of soil quality is considered to transcend the

productivity of soils (

Larson and Pierce, 1991; Parr et al., 1992

) to

explicitly include the interactions between humans and soil, and to
encompass ecosystem sustainability as the basis for the bene

ﬁ

ts that

humans derive from soils as well as the intrinsic values of soil as being
irreplaceable and unique (

Carter et al., 1997

). The term soil quality in

this broader sense was already used by

Warkentin and Fletcher (1977)

.

Recently, soil quality assessment is increasingly incorporated in land
evaluation, as land evaluation procedures are now used in many dif-
ferent ways and for a range of purposes, including sustainable land
management (

Hurni et al., 2015

), environmental risk assessments,

monitoring of environmental change (

Sonneveld et al., 2010

) and land

restoration (

Schwilch et al., 2012

). In the land-potential knowledge

system LandPKS, general management options are based on long-term
land potential (depending on climate, topography and inherent soil
properties) and can be modi

ﬁ

ed according to weather conditions and

dynamic soil properties (

Herrick et al., 2016

). The integration of soil

quality and land evaluation goes as far as developing soil natural capital
accounting systems, stressing the importance of soils for human well-
being (

Robinson et al., 2017

).

In a program to assess and monitor soil quality in Canada (

Acton

and Gregorich, 1995

), the term soil quality was used interchangeably

with

soil health

and, in spite of the wider context in which it was pre-

sented, de

ﬁ

ned primarily from an agricultural perspective as

“

the soil's

ﬁ

tness to support crop growth without becoming degraded or otherwise

harming the environment

”

. The term soil health originates from the

observation that soil quality in

ﬂ

uences the health of animals and hu-

mans via the quality of crops (e.g.

Warkentin, 1995

). Indeed, linkages

to plant health are common, as in the case of disease-suppressive soils
(

Almario et al., 2014

). Soil health has also been illustrated via the

analogy to the health of an organism or a community (

Doran and

Parkin, 1994; Larson and Pierce, 1991

The debate about soil quality vs. soil health arose quickly after the

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

107

concept of soil quality was criticized in the 1990s. In contrast to soil
quality, soil health would

“

capture the ecological attributes of the soil

which have implications beyond its quality or capacity to produce a
particular crop. These attributes are chie

ﬂ

y those associated with the

soil biota; its biodiversity, its food web structure, its activity and the
range of functions it performs

”

Moebius-Clune et al. (2016)

Pankhurst et al., 1997

). These authors

further consider

“

that the term soil health encompasses the living and

dynamic nature of soil, and that this di

ﬀ

erentiates it from soil quality

”

.

They therefore

“

adopt the view that although the concepts of soil

quality and soil health overlap to a major degree and that in many
instances the two terms are used synonymously (

…

.), soil quality fo-

cuses more on the soil's capacity to meet de

ﬁ

ned human needs such as

the growth of a particular crop, whilst soil health focuses more on the
soil's continued capacity to sustain plant growth and maintain its
functions

”

. Meanwhile, the debate subsided and partly changed focus.

For example,

consider that soil quality

includes both inherent and dynamic soil properties, and that soil health
is equivalent to dynamic soil quality. The di

ﬀ

erential usage may also

link to the observation of

Romig et al. (1996)

, that, whereas soil quality

is the preferred term of researchers, soil health is often preferred by
farmers.

The di

ﬀ

erences between land quality and soil quality observed by

Karlen et al. (2003)

and between soil quality and soil health observed

by

Pankhurst et al. (1997)

and

Moebius-Clune et al. (2016)

can be

summarized in a transition in focus from land quality to soil quality and
soil health going from inherent to dynamic soil properties. The website
of the Natural Resources Conservation Service, USA (

http://www.nrcs.

usda.gov/wps/portal/nrcs/main/soils/health/

) states that

“

soil health,

also referred to as soil quality, is de

ﬁ

ned as the continued capacity of

soil to function as a vital living ecosystem that sustains plants, animals,
and humans

”

. We conclude that the distinction between soil quality and

soil health developed from a matter of principle to a matter of pre-
ference and we therefore consider the terms equivalent. We further
express this by explicitly including the soil biota/biodiversity and re-
lated soil functions and soil-based ecosystem services in

Figs. 1

Kibblewhite et al., 2008a

3

.

Like in land quality assessment and land evaluation, approaches to

soil quality and soil health go beyond the reductionist approach of

measuring (indicators of) soil properties and processes. Although such
measurements

remain

important

from

a

practical

perspective

(

), the concepts of soil quality and soil health

also include the capacity for emergent system properties such as the
self-organization of soils, e.g. feedbacks between soil organisms and soil
structure (

Lavelle et al., 2006

), and the adaptability to changing con-

ditions.

2.2. Linking soil quality to soil functions and ecosystem services

Ecosystem services are de

ﬁ

ned as

“

the bene

ﬁ

ts which humans de-

rive from ecosystems

”

Costanza et al., 1997

). With the early concept

developed by

Doran and Sa

Baveye et al., 2016; Glenk et al., 2012

ey (1997)

, soil quality was addressing not

only one ecosystem service such as provision of food, but also trying to
represent and balance the multi-functionality of soil. This has recently
been further embedded in the development of

“

functional land man-

agement

”

, which assesses both the bene

ﬁ

ts and trade-o

ﬀ

s of a multi-

functional system for managing soil-based ecosystem services in agri-
culture (

Schulte et al., 2014

) and a wider range of land uses (

Coyle

et al., 2016

).

Among scientists, the concept of ecosystem services is often used in

connection with the concept of soil functions.

‘

Function

’

is, however,

variably used as a synonym for 1) process, 2) function

ing

, 3) role and 4)

service (

). Therefore,

Schwilch

et al. (2016)

advise against using the term, but

Baveye et al. (2016)

note

that function

“

in a narrow and well-de

ﬁ

ned context (

…

) has been used

in connection with soils for over 50 years, and has served as a con-
ceptual foundation for an appreciable body of research and signi

ﬁ

cant

policy making, at least in Europe

”

(e.g., the Soil Thematic Strategy of

the

European Commission, 2006

). Therefore, we concur with

Baveye

et al. (2016)

that

“

it makes sense to try to retain both

“

function

”

and

“

service

”

terminologies, as long as they can be articulated (

…

) with

respect to soil properties and processes

”

. In their seminal paper re-

constructing how the notion that nature meets, or gets in the way, of the
needs of people has pervaded concepts and theory in ecology vs. soil
science,

Baveye et al. (2016)

argue that mainstream ecology, by its

emphasis on organisms, tended to neglect the soil, in particular the non-

Fig. 2.

Linkages between soil threats, soil functions and soil-based ecosystem services. Further developed from the scheme presented by

Kibblewhite et al. (2008a)

and modi

ﬁ

ed by

Brussaard (2012)

.

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

108

living soil, whereas mainstream soil science tended to avoid the term
ecosystem, emphasizing the importance of soil properties and processes
in landscape terms. In accordance with

Glenk et al. (2012)

, we de

ﬁ

ne

soil functions as (bundles of) soil processes that underpin the delivery of
ecosystem services. This de

ﬁ

nition will su

ﬃ

ce for all practical purposes

related to manageable soil functions, which can be used to address the
gap between

“

what is

”

and

“

what can be

”

, based on soil capability, i.e.

“

what soils can do

”

(

Bouma et al., 2017

), which is, in the context of this

review, what living soils can do. Complementary to this bottom-up
approach, soil functions can be used in a top-down approach when
identifying the gap between what is currently measured in soil assess-
ment schemes and what should be measured in view of assessing the
soil functions that are impacted by, or to be managed in view of current
and upcoming policies (

van Leeuwen et al., 2017

), possibly through the

use of environmental accounting systems increasingly adopted by pol-
icymakers, such as the soil natural capital accounting system proposed
by

Robinson et al. (2017)

.

Just as ecosystem services are in

ﬂ

uenced by (bundles of) soil pro-

cesses, the latter are in turn a

ﬀ

ected by soil threats. The EU Soil

Thematic Strategy identi

ﬁ

ed the main threats to soil quality in Europe

as soil erosion, organic matter decline, contamination, sealing, com-
paction, soil biodiversity loss, salinization,

ﬂ

ooding and landslides

(

EuropeanCommission, 2002; Montanarella, 2002

). Soil threats have

been emphasized in order to inform risk assessment exercises indicating
(geographical) areas where soil functioning is potentially hampered
(

van Beek et al., 2010

). Di

ﬀ

erent schemes linking soil-based ecosystem

services and soil functions have been developed (

Haygarth and Ritz,

2009; Kibblewhite et al., 2008a; Tóth et al., 2013

), but none of them

includes soil threats. The scheme presented by

Kibblewhite et al.

(2008a)

and modi

ﬁ

ed by

Brussaard (2012)

was developed as a con-

ceptual basis for the iSQAPER project, including soil threats as a

ﬀ

ecting

the various soil functions and associated ecosystem services (

Fig. 2

).

The soil functions in

Fig. 2

equate almost entirely to the

“

intermediate

services

”

de

ﬁ

ned by

Bennett et al. (2010)

, which are similar to the soil

processes presented by

Schwilch et al. (2016)

. The ecosystem services

in this scheme can be seen as a soil-related sub-set of the ecosystem
services mentioned in the Common International Classi

ﬁ

cation of

Ecosystem Services (CICES -

http://biodiversity.europa.eu/maes/

common-international-classi

cation-of-ecosystem-services-cices-

classi

cation-version-4.3

), currently elaborated in the Mapping and

Assessment of Soil Ecosystems and their Services (MAES-Soil) Pilot
project

https://webgate.ec.europa.eu/fp

s/wikis/display/MAESSoil/

MAES+Soil+Pilot

Baveye et al., 2016; Bouma, 2014; Sojka and Upchurch,

It has been argued that soil quality can indeed only be assessed in

relation to one or several soil functions, ecosystem services or soil
threats (e.g.

1999; Volchko et al., 2013

). Therefore, clear de

ﬁ

nitions of these terms

as well as

ﬁ

rmly established associations with soil quality indicators are

the basis of any functional soil quality concept.

As soil quality plays a role in decision-making in the face of soil

threats, the DPSIR (driver

–

pressure

–

state

–

impact

–

response) framework

(

EuropeanEnvironmentAgency, 1998

) has frequently been adopted for

use in EU policy to support decision-making and as a means to bridge
the science-policy gap (

Tscherning et al., 2012

). Applying the DPSIR

framework to soil (

Fig. 3

),

“

drivers

”

are pedoclimatic conditions and

land use policies, while

“

pressures

”

are land use and management and

the associated soil threats. Pressures and drivers and their variabilities
and interactions determine the

“

state

”

of the soil, with subsequent

“

impact

”

on soil and ecosystem functioning, and the

“

response

”

in

terms of the delivery of ecosystem goods and services. Subsequent
adaptive management may be re-active to observed deterioration of soil
functioning or pro-active to reach transitions to newly desired soil
functioning. To assess any changes in the status of soil quality, assess-
ment tools are needed, and these are the subject of sections

3 and 4

.

3. Approaches to soil quality assessment

A plethora of soil quality assessment and monitoring tools have

become available since the 1990s. Here, we give an overview of the
main developments in di

ﬀ

erent countries, before addressing aspects of

soil quality indicators in more depth in section

4

.

3.1. Analytical approaches to soil quality

National assessments of soil quality are often based primarily on

analytical approaches (

). One of the earliest national programs

to assess and monitor soil quality was started in Canada in 1988 (

Acton

and Gregorich, 1995

), using benchmark sites to assess changes in soil

quality over time, especially in relation to the soil threats erosion,
compaction, organic matter loss, acidi

ﬁ

cation and salinization (

Wang

et al., 1997

). While the Canadian soil quality monitoring program as

such was not consistently continued, the data are still partly used in the
assessment of agri-environmental indicators that cover soil, water and
air quality (

Clearwater et al., 2016

). At a coarser scale, a GIS-based

approach to characterize primarily inherent soil quality was presented
by

Macdonald et al. (1998)

.

Two major soil quality assessment approaches focusing at the plot

scale were developed in the USA (

http://www.soilquality.org

). The Soil Management

Fig. 3.

The Driver-Pressure-State-Impact-Response framework applied to soil. Modi

ﬁ

ed from

Brussaard et al. (2007)

. Permission for reproduction granted by Elsevier.

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

109

Table

1

Major

soil

quality

assessment

approaches

based

on

analytical

indicators

according

to

geographic

origin

(North

America,

Europe,

China):

Objective

s,

target

group,

scale,

interpretation

approach,

website.

Country

Reference(s)

Name

(if

any)

Objectives

Target

group

(assumed)

Spatial

scale

Interpretation

Website

(if

any)

Canada

(

)

National

soil

quality

monitoring

program

Assess

status

of

and

trends

in

soil

health

Not

stated

(policy)

23

benchmark

sites

(5

–

10

ha

each)

across

Canada

Mainly

trend

analysis

(

)

Assess

inherent

soil

quality

and

susceptibility

to

change

Not

stated

(policy)

Regional

and

national

Rating

procedures

with

respect

to

4

soil

functions

USA

(

)

Soil

management

assessment

framework

(SMAF)

Evaluate

management

practices,

educate

about

soil

quality

Land

managers,

advisors,

general

public

Plot

scale

Scoring

curves,

additive

index

(

)

Cornell

Soil

Health

Test

Assess

soil

health,

address

soil

degradation,

increase

productivity

Farmers

Plot

scale

Scoring

curves,

overall

score

http://soilhealth.cals.cornell.

edu

Australia

(

)

Soil

Quality

Website

Benchmark

sites,

soil

quality

monitoring

and

education

Farmers

National

and

regional

Target

values;

threshold

values

wherever

possible

http://soilquality.org.au/

New

Zealand

(

)

“

500

soils

project

”

,

soil

indicator

assessment

(Sindi)

Assess

soil

quality

for

environmental

reporting

Government;

Sindi:

regional

council

sta

ﬀ

,

landowners

511

sites

across

New

Zealand,

x

soil

types,

10

land

uses

Comparative

(compared

to

database)

or

according

to

target

ranges

https://sindi.

landcareresearch.co.nz

France

(

)

Observatoire

de

la

Qualité

des

Sols

(OQS),

Réseau

des

mesures

de

la

qualité

des

sols

(RMQS)

Assess

soil

quality

for

environmental

protection,

food

security

and

sustainable

management

practices

Not

stated

(policy)

11

sites

(OQS)

2000

sites

(RMQS)

Mainly

trend

analysis

UK

(

)

Assess

soil

function

of

environmental

interaction

Policy

National

Trigger

values

Ireland

(

)

Soil

quality

assessment

research

project

(SQARE)

Assessment

of

soil

functions

Farmers

Plot

(38

farms)

https://www.teagasc.ie/

environment/soil/research/

square/

The

Netherlands

(

)

National

Soil

Quality

Monitoring

Network

Assess

soil

quality

and

land-use

e

ﬀ

ects

Not

stated

(policy)

200

locations

Target

values

EU

(

)

European

Soil

Monitoring

and

Assessment

framework

Provide

objective,

reliable

and

comparable

information

at

European

level

Policy

(

)

ENVASSO,

RECARE

Assess

soil

degradation

Policy

http://esdac.jrc.ec.europa.

eu/projects/envasso

http://www.recare-project.

eu/

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

110

Assessment Framework (SMAF) developed at the Soil Quality Institute
(

Andrews and Carroll, 2001; Andrews et al., 2004; Karlen et al., 2001;

Wienhold et al., 2004, 2009

) is rather unique in its

ﬂ

exibility in the

selection of indicators. Based on a clear de

ﬁ

nition of the main eco-

system service(s) or management objective(s) to be addressed, a set of
indicators is selected out of 81 potential indicators using selection rules.
The user can disregard or alter the proposed minimum dataset as de-
sired, although that limits comparability between sites. The inter-
pretation of an indicator value is based on scoring curves and an ad-
ditive soil quality index can be derived. The Cornell Soil Health Test
(

Idowu et al., 2008; Moebius-Clune et al., 2016

) is much more stan-

dardized and targeted directly at land users, o

ﬀ

ering various soil health

testing packages for farmers, landscape managers and others, and
supplying them with management advice together with the results.

In New Zealand, a nationwide survey of seven soil quality indicators

at 511 sites aimed at establishing benchmark values across all major
soil types and land-uses (

Lilburne et al., 2002, 2004; Sparling and

Schipper, 2004; Sparling and Schipper, 2002

). Based on these data, an

online tool called Sindi (soil indicator assessment) was developed
(

Lilburne et al., 2002

) that allows the comparison of measurements of

soil properties in a given soil type with the information in the database.

In Australia, a consortium of public and private partners provides

fact sheets and regional, soil type-speci

ﬁ

c critical threshold values of a

range of soil quality indicators for impact on agricultural production,
supplemented by land use-speci

ﬁ

c distributions of measured indicator

values (

Gonzalez-Quiñones et al., 2015

). Hence, individual farmers can

compare their own data for every indicator with the range of values
known for similar circumstances in the region. Supplementary general
information is also provided that can be used to modify management
for environmental goals such as carbon sequestration and minimizing
nutrient losses to the environment.

In Europe, many national approaches to soil quality assessment

were developed. Those focusing on soil biodiversity rather than on
general soil quality were reviewed by

Pulleman et al. (2012)

. The

French

“

soil quality observatory

”

was started in 1986 and included 11

sites (

Martin et al., 1998

). The more recent soil quality monitoring

system (RMQS) program is based on a 16 × 16 km grid of the French
territory and feeds into the French Information System on soils (

Antoni

et al., 2007; Arrouays et al., 2003

). In the UK, the

ﬁ

rst approach to soil

quality monitoring (

Loveland and Thompson, 2002

) had a focus on

forestry and semi-natural soils. After further elaboration, a minimum
dataset of only seven measurements was proposed (

Merrington, 2006

).

In addition, Countryside Survey has been monitoring a few soil prop-
erties such as pH, soil organic carbon and some aspects of soil biodi-
versity (

Black et al., 2003

) since 1978 (

http://www.countrysidesurvey.

org.uk

). In Ireland, recent work on the assessment of soil functions at

grassland farms combines a full soil pro

ﬁ

le description and visual soil

assessment with determination of a suite of analytical indicators (

Bondi

et al., 2017

). In The Netherlands, a set of indicators for soil ecosystem

services developed by RIVM (National Institute for Public Health and
the Environment) was used in two

ﬁ

ve-year measurement cycles in 200

sites of the Dutch soil quality monitoring network (

Wattel-Koekkoek

et al., 2012

). Target values and ranges for agronomic land use are based

on median values of the monitoring network and on judgement of a
group of soil experts. Also in the Netherlands, a large Public Private
Partnership

‘

Sustainable Soil

’

is developing a soil quality assessment

system in which a set of soil chemical, physical and biological in-
dicators is related to target values and ranges for integral advice on soil
management (

www.beterbodembeheer.nl

Kibblewhite et al., 2008b

Given the plethora of soil monitoring programs in Europe, a

common European soil monitoring framework was proposed (

Huber

et al., 2001

), which was based as much as possible on existing mon-

itoring activities. Subsequently, the EU-FP6 project ENVASSO (EN-
Vironmental ASsessment of Soil for mOnitoring) aimed at de

ﬁ

ning and

documenting a soil monitoring system for implementation in support of
a European Soil Framework Directive (

),

focused on the assessment of soil threats, which however never mate-
rialized. Nevertheless, three priority indicators for each soil threat
(

Huber et al., 2008

) were identi

ﬁ

ed, and this list was further revised

and amended by the EU-FP7 project RECARE (Preventing and Re-
mediating Degradation of Soils in Europe through Land Care) as shown
in

Supplementary Table 1

.

The history of soil quality assessment in China was reviewed for an

international readership by

Teng et al. (2014)

. Due to increasing

pressure to maintain and improve soil quality in China, the Chinese
government in 2008 established the China Soil Quality Standardisation
& Technology Committee (SAC/TC 404) that has been responsible for
formulating and modifying soil quality standards in China, including
terminology, indicators, criteria, soil sampling methods, analytical
methods, standards for soil quality assessment, and remediation of
contaminated soils (

Chen et al., 2011

). By 2010, 141 soil quality-related

standards had been set up, partly adopted from ISO.

The

ﬂ

exible and context-speci

ﬁ

c approach to soil quality assessment

of the SMAF as described above has inspired several recent studies that
apply multivariate statistical methods to select the most relevant in-
dicators, often based on assumed but not assessed connections between
indicators and soil functions, and utilize scoring functions to arrive at a
soil quality index geared to the speci

ﬁ

c conditions (

Armenise et al.,

2013; Askari and Holden, 2015; Congreves et al., 2015; de Paul Obade
and Lal, 2016; Lima et al., 2013; Swanepoel et al., 2014; Tesfahunegn,
2014; Velasquez et al., 2007

). The drawback of such

ﬂ

exible ap-

proaches lies in the limited comparability between studies, even more
than between di

ﬀ

erent applications of the SMAF.

The compilation of major soil quality assessment approaches in

2015; Mueller et al., 2013

shows the variation in objectives, target groups (though often

not explicitly stated) and spatial scales. Most of these approaches re-
main at the plot/

ﬁ

eld/site scale. Recently developed sensor-based ap-

proaches show promise to expand soil quality assessment to the land-
scape level (e.g.

Vågen et al., 2013

). Importantly, explicit evaluation of

soil quality with respect to speci

ﬁ

c soil threats, functions and ecosystem

services has rarely been implemented, and few approaches provide
clear interpretation schemes of measured indicator values. This limits
their adoption by land managers as well as policy.

3.2. Visual assessment approaches to soil quality

The above approaches to soil quality assessment typically require

analytical laboratory facilities. Approaches targeting farmers and
stressing the educational aspect bene

ﬁ

t from more empirical, qualita-

tive indicators that can be easily assessed in the

ﬁ

eld, deliver immediate

results, and facilitate communication between farmers and scientists
(

Beare et al., 1997

).

In the Wisconsin Soil Health Program, for example, a soil health

score card was developed that collects farmers

’

observations on soil and

plants, and includes a few questions on animal health and water quality
(

Romig et al., 1996

). In Europe, the GROW Observatory (

http://

growobservatory.org/

) was established in 2016, which is developing

simple tools to support soil management for farmers and soil stake-
holders, such as simple

ﬁ

eld-based assessments and educational tools.

Visual soil assessment (VSA) approaches have been developed in dif-
ferent parts of the world (

Table 2

). Most of these methods target mainly

soil structure, sometimes in relation to productivity (

Abdollahi et al.,

). The methods vary in material and time

requirements, with spade methods being generally faster to perform
than pro

ﬁ

le methods and thus being more suitable for farmers (

Boizard

et al., 2005

). The method developed by

Peerlkamp (1959)

, which was

used in the Netherlands for 40 years, has recently been improved by
simpli

ﬁ

cation of the scoring scheme and inclusion of a visual key (

Ball

et al., 2007; Guimaraes et al., 2011

) to further support the use of the

method by non-experts of soil science. Straightforward interpretation is
certainly an asset of visual soil quality assessment, but visual soil as-
sessment alone cannot evaluate the status of ecosystem services driven

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

111

by biological and chemical soil processes (

Ball et al., 2017

). Because

visual soil assessment provides di

ﬀ

erent information than laboratory

approaches (

Emmet-Booth et al., 2016

) the combination of both would

be advantageous (

Pulido Moncada et al., 2014

). Ultimately, the in-

creased use of visual soil assessment is considered to be important in
yield gap analysis and land management programs (

McKenzie et al.,

2015

).

4. Soil quality indicators

4.1. Requirements for soil quality indicators

Various requirements for soil quality indicators have been identi

ﬁ

ed

in some (but by far not all) approaches to assessing soil quality
(

Doran and Parkin, 1996; Doran and Sa

). All publications that list such requirements mention at least

one conceptual condition such as that a chosen indicator must be re-
lated to a given soil threat, function or ecosystem service and be re-
levant. However, this is not of great use if soil quality assessment is not
targeting a speci

ﬁ

c soil threat, function or ecosystem service.

Of the practical requirements, ease of sampling and measurement is

almost always mentioned, and reliability and cost are also considered
important. Practical considerations such as the disadvantage of in-
dicators requiring undisturbed samples often play an important role in

discarding otherwise suitable soil quality indicators (

Idowu et al.,

2008

), which is a serious limitation from a scienti

ﬁ

c perspective. Where

the measurement of a speci

ﬁ

c soil indicator is considered too expensive,

too di

ﬃ

cult or not possible (e.g. bulk density, due to the stoniness of the

soil), pedotransfer functions may provide a proxy value through the
measurement of other properties, for example carbon and texture for
bulk density (

Reidy et al., 2016

). The application of pedotransfer

functions was already considered useful in early soil quality publica-
tions (

Saxton and Rawls, 2006; Toth et al., 2015

ey, 1997; Larson and

Pierce, 1994

) and has again been advocated more recently (

Bone et al.,

2010

), especially for complex soil properties such as hydrologic char-

acteristics (

). However, the

inaccuracy of pedotransfer functions needs to be clearly stated.

Sensitivity to changes in management is mentioned frequently

(

), but there may be trade-o

ﬀ

s with robustness to seasonal

variation. Regarding the interpretation of the obtained values, com-
parability to data from other sampling campaigns is often desired.
However, some indicators such as organic carbon (or soil organic
matter) content and pH are often measured, whereas others such as
bulk density or earthworm diversity are rarely assessed (

Morvan et al.,

2008

). Moreover, the requirement to have clear (absolute) interpreta-

tion schemes for a given indicator is mentioned in only half of the
publications (

Roger-Estrade
et al. (2004)

), even though assessment of soil quality cannot be

Table 2

Comparison of major visual soil assessment methods (X signi

ﬁ

es required material or performed observations).

Country

Australia

France

Australia

UK

New Zealand

Brazil/UK

Germany

Reference

McKenzie (2001)

McGarry (2006)

Ball et al.
(2007)

Shepherd et al.
(2008)

Guimaraes et al.
(2011)

Mueller et al. (2014)

Stated objectives

(assessment of

…

)

soil structure,
suitability for root
growth

soil structure

land degradation

soil structure

soil quality

soil structure

soil properties with
respect to yield potential

Method name

SOILpak

Pro

ﬁ

l cultural

VS-Fast

Peerlkamp

VSA

Principle

spade

trench

spade

pit

Material

spade

X

plastic basin

X

hard square board

X

plastic bag or sheet

X

knife

X

auger

X

water bottle

X

tape measure or ruler

X

Time needed (min)

25

–

90

60

–

180

?

5

–

15

25

5

–

15

10

–

40

General observations

soil layers, A-horizon

X

surface crusting or cover

X

surface ponding

X

slope

X

soil erosion

X

Soil physical properties

soil texture

X

soil structure

X

soil consistence

X

aggregate size distrib.

X

aggregate shape

X

slaking/dispersion

X

soil porosity

X

soil colour

X

soil mottles (no., colour)

X

available water

X

water in

ﬁ

ltration

X

Soil chemical properties

soil pH

X

labile organic C

X

Soil biological properties

earthworms (no., size)

X

potential rooting depth

X

root development

X

a

Visual evaluation of soil structure.

b

Muencheberg Soil Quality Rating.

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

112

Table

3

Considerations

and

criteria

for

soil

quality

indicators

mentioned

in

various

publications.

Criteria

and

considerations

Conceptual

soil

function

and/or

ecosystem

processes;

xx

x

Relevance,

representation

of

key

variables

controlling

soil

quality,

correlated

to

long-

term

response,

allow

evaluation

of

assessment

criteria

xx

x

Signi

ﬁ

cance

at

the

appropriate

scale

x

Integrate

soil

physical,

chemical,

biological

properties

xx

Allow

estimation

of

soil

properties

or

functions

which

are

more

di

ﬃ

cult

to

measure

directly

x

Practical

Ease

of

sampling

and

measurement

(simplicity,

practicality,

single

or

repeated

sampling

and

measurement,

provide

information

in

short

timeframe)

xx

x

High

throughput

of

analysis,

wide

applicability

xx

Amount

of

soil

needed

x

Sample

storage

before

analysis

x

Reliability

and

reproducibility

of

measurement

xx

x

Existence

of

a

standard

method

of

estimation

(standard

operating

procedure)

xx

Availability

of

reference

material

for

quality

control

x

Cost

(sampling,

hardware,

analysis,

labour)

xx

x

Sensitivity

Spatial

variation

x

Temporal

variation

(not

in

ﬂ

uenced

by

short-term

weather

patterns)

xx

x

Sensitivity

to

changes

in

management,

or

land

use,

response

to

perturbation

as

well

as

corrective

measures

xx

x

Interpretation

Comparability

with

routine

sampling

and

monitoring

programs

(context

data

available);

part

of

standard

tests;

baseline

available

xx

x

Ease

of

interpretation,

interpretation

criteria

available

xx

x

Archivability,

capable

of

continuous

assessment

x

Mappable

trend

indicators

x

Generic

or

diagnostic

value

x

Not

redundant

x

a

As

cited

in

.

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

113

put into practice without it.

Finally, indications to what extent soil quality indicators actually

ful

ﬁ

ll the requirements listed in

Andrews and Carroll, 2001; Lima et al., 2013; Schipper

are often missing but would be

needed to make informed choices in soil quality assessment programs.

4.2. Methods for selecting a minimum dataset

Increasing the number of indicators can increase collinearity as well

as the complexity of the relationships between indicators and man-
agement options. Moreover, costs of measurements easily become
prohibitive, especially if detailed soil biological parameters are in-
cluded (

O'Sullivan et al., 2017

). For these reasons, the number of soil

quality indicators that is actually analyzed on a given set of samples
needs to be reduced to a minimum dataset.

In the

ﬁ

rst proposed minimum datasets, this selection was based on

expert judgement (e.g.

Doran and Parkin, 1994

). Subsequently, statis-

tical data reduction by multivariate techniques such as principal com-
ponent analysis (PCA), redundancy analysis (RDA) and discriminant
analysis (e.g.

and Sparling, 2000; Shukla et al., 2006

), and multiple regression

(

Kosmas et al., 2014

) became more common. After this initial data

reduction, simple or multiple correlation analysis can further decrease
the number of indicators (

Andrews and Carroll, 2001; Kosmas et al.,

2014

), sometimes followed by the use of expert judgement for choosing

only one out of two or more highly correlated soil properties (

Sparling

and Schipper, 2002

). With these techniques, the number of indicators

ﬁ

nally selected typically ranges between 6 and 8. Because soil proper-

ties that are relevant for soil functioning but do not show much

variation in a given study will not be included in the minimum dataset,
validation of the minimum dataset is important, for example by testing
its relation to prede

ﬁ

ned and independently measured management

goals (

Andrews and Carroll, 2001

).

A participatory approach of selecting soil biological indicators from

a long list of potential indicators was presented by

Ritz et al. (2009)

.

Potential indicators were scored by scientists and end-users in a

“

lo-

gical-sieve

”

approach, which allowed several iterations. The di

ﬀ

erent

requirements for an indicator (

et al., 2005; Ritz et al., 2009

) were weighted: reproducibility

was considered absolutely essential, whereas the existence of a standard
protocol had the lowest weight. A modi

ﬁ

ed version of this method was

applied by

Stone et al. (2016a)

to establish the top 10 biodiversity in-

dicators of soil quality (de

ﬁ

ned as the ability to perform key soil pro-

cesses) across the agricultural area of European member states for use
in future monitoring.

Finally, the most important soil quality indicators can also be in-

ferred from participatory conceptualization of how complex systems
function. For example,

Troldborg et al. (2013)

and

Aalders et al. (2011)

established a Bayesian Belief network de

ﬁ

ning which factors are most

in

ﬂ

uential in determining the risk of compaction and erosion, respec-

tively.

Hence, the selection of a minimum dataset derived from a larger set

of soil quality indicators is a necessary step in soil quality assessments
because of

ﬁ

nancial and time limitations and to avoid collinearity.

Methodological transparency is imperative to allow wide application of
minimum dataset selection.

Fig. 4.

Frequency of di

ﬀ

erent indicators (min.

10%) in all reviewed soil quality assessment ap-
proaches (n = 65). Soil biological, chemical and
physical indicators shown in green, red and blue,
respectively. For further details on indicators see

Supplementary Table 3

. Publications dealing ex-

clusively with forest soils (e.g.

Schoenholtz et al.,

2000; Zhang, 1992

) or focusing on biological in-

dicators only, without also looking at chemical
and/or physical indicators (

Filip, 2002; Parisi

), were not included

in this compilation. If the same authors proposed
the same set of indicators in more than one
publication, then only the

ﬁ

rst was considered. In

two publications (

Andrews et al., 2002; Biswas

et al., 2017

), two di

ﬀ

erent sets of indicator were

proposed. Thus, the total number of reviewed
publications was 62 while the total number of
indicator sets was 65.

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

114

Table

4

Soil

biological

indicators,

methodologies,

soil

functions,

and

advantages/disadvantages

at

di

ﬀ

erent

scales.

Table

compiled

from

(

).

Indicator

Methodology

Main

soil

functions

Main

pros

Main

cons

Individual,

population

and

community

level

Presence,

richness,

abundance

of

individual

soil

organisms

(for

details

see

Supplementary

Table

6

)

Traditional

handsorting

and

microscopic

methods;

molecular

quantitation

(qPCR).

Element,

organic

matter

and

water

cycling,

biological

population

regulation,

soil

structure

maintenance

Taxonomic

and

functional

level.

Not

always

linked

directly

with

functions.

Di

ﬃ

cult

to

apply

to

fauna,

e.g.

protozoa,

mites

and

collembola.

Microbial

biomass

and

fungal

biomass,

fungal:bacteria

ratio

Direct

counting,

chloroform

fumigation

extraction,

SIR,

PLFA,

molecular

quantitation.

Element

and

organic

matter

cycling,

decomposition,

soil

structure

maintenance

Sensitive

and

well

other

soil

quality

indicators.

Spatially

variable,

di

ﬃ

cult

interpretation,

contradictory

results.

Unclear

direct

link

to

functionality.

Indices

based

on

faunal

communities

(e.g.

Maturity

Index,

Enrichment

Index,

Channel

Index,

Structural

Index

for

nematodes)

Counting

and

identi

ﬁ

cation

of

speci

ﬁ

c

groups

of

organisms

Element

and

organic

matter

cycling,

biological

population

regulation,

decomposition

Sensitive.

Taxonomic

and

functional

level.

Time-consuming

and

costly.

Specialist

required

for

morphological

identi

ﬁ

cation.

Community

composition

Manual

counting

and

identi

ﬁ

cation

Element

and

organic

matter

cycling,

biological

population

regulation,

habitat

provision,

decomposition,

soil

structure

maintenance

Division

in

functional

groups

can

give

an

indication

of

functions.

Time-consuming,

expertise

required.

Not

indicative

of

active

biota.

PLFA

Correlated

with

other

measurements.

Good

indicator

of

active

microbial

biomass.

Integrated

information

on

the

microbial

community.

Time-consuming.

No

direct

link

with

functions.

Coarse

resolution.

Fingerprinting

methods

(e.g.

DGEE,

T-RFLP,

A-RISA,

ARDRA,

TGGE),

microarrays

Greater

phylogenetic

resolution.

No

direct

link

with

function.

Di

ﬃ

cult

comparison

between

studies

due

to

great

variety

in

methods.

Di

ﬃ

culties

to

extract

and

amplify

DNA.

Sequencing

(metabarcoding)

Detailed

view

of

diversity.

Enormous

amounts

of

data.

Detects

less

abundant

organisms.

Permits

discovery

of

new

diversity.

Taxonomic

genes

no

direct

link

with

functions.

Di

ﬃ

culties

to

extract

and

amplify

DNA.

Costly.

Problems

handling

of

large

datasets

and

analyses.

Dependent

on

libraries.

No

standard

methodology.

Community

Level

Physiological

Pro

ﬁ

ling

(Biolog

™

,

MicroResp

™

)

Element

and

organic

matter

cycling,

decomposition,

habitat

provision

Insight

into

functionality

of

the

community.

MicroResp

™

closer

to

in

situ

conditions,

shorter

time

of

measurements.

Many

replicates

needed

because

of

variability.

Ecosystem

level

Soil

respiration,

nitrogen

mineralization,

denitri

ﬁ

cation,

nitri

ﬁ

cation

CO

2

evolution,

N

2

O

emission,

NO

3

produced.

Element,

organic

matter

and

water

cycling,

decomposition,

habitat

provision

Sensitive

and

ecologically

relevant.

Highly

variable

and

ﬂ

uctuating.

Relatively

laborious.

Potentially

mineralizable

nitrogen

Anaerobic

incubation.

Good

correlation

with

MB

and

total

soil

N.

Relatively

laborious.

Metabolic

quotient

(qCO

2

),

microbial

quotient

(MicrC/SoilC)

Sensitive,

simple

and

inexpensive.

Di

ﬃ

cult

interpretation:

confounds

disturbance

with

stress.

DNA

and

protein

synthesis.

Thymidine

and

leucine

DNA

incorporation.

Re

ﬂ

ection

of

active

microbial

biomass.

No

standardized

procedure.

Enzymatic

activities

Extraction

of

enzymes

in

the

soil

and

incubation

with

various

substrates.

Element

and

organic

matter

cycling,

decomposition,,

biological

population

regulation

Closely

important

soil

quality

parameters.

Very

sensitive.

Simple

and

inexpensive

methods.

Standard

procedure

not

available.

Contradictory

results,

complex

behaviour

and

variable

for

each

enzymes.

Potential

activity.

Functional

genes

and

transcripts

FISH,

Microarrays,

meta

-

transcriptomic,

qPCR,

metagenome

analysis.

Closer

link

to

functionality.

FISH

and

microarrays

can

give

an

idea

of

active

microorganisms.

High

sensitivity

and

throughput.

Restricted

to

known

gene

sequences.

Genes

and

transcripts

might

not

be

expressed.

Di

ﬃ

culties

linked

with

RNA

extraction.

Costly.

Metabolomics

and

metaproteomics

Assessment

and

quantitation

of

metabolites

and

proteins

in

the

soil.

Element

and

organic

matter

cycling,

decomposition,,

biological

population

regulation,

soil

structure

maintenance

Closer

link

to

functionality.

Field

in

development.

Di

ﬃ

cult

extraction

of

metabolites

and

proteins.

Stable

isotope

probing

Incorporation

of

13

C-

or

15

N-

labelled

substrates

into

DNA,

RNA,

PLFA,

proteins

Element

and

organic

matter

cycling,

decomposition

Permit

to

establish

link

between

biodiversity

and

functions.

Allow

in

situ

analysis

of

active

microbial

population.

Field

in

development.

Time

involved

in

the

assimilation

of

the

substrates.

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

115

4.3. Frequently proposed soil quality indicators

To identify the most frequently proposed (combinations of) soil

quality indicators, we summarized 62 publications (

Supplementary

Table 2

) in which 65 minimum datasets of measured soil properties

have been proposed. Due to the plethora of methods and terms, a cer-
tain aggregation of measured indicators into categories was required,
e.g. aggregate stability, shear strength, tilth and friability, structure,
consistence and slake test were merged in a category called structural
stability (

Supplementary Table 3

). We included both peer-reviewed

journal articles on soil quality assessment approaches and reports on
national monitoring programs, aiming at global coverage. Considering
that soil quality assessment includes many steps, from the de

ﬁ

nition of

objectives via the selection of indicators to the interpretation of ob-
tained indicator values, we only included studies that address more
than one of these steps and thus have a certain conceptual and gen-
eralizable nature. Consequently, studies that are entirely limited to the
comparison of a set of indicators in di

ﬀ

erent management systems were

excluded. Even though we may have missed some publications, espe-
cially from national assessment schemes, we noted that increasing the
number of evaluated datasets from 45 to 65 during the compilation
hardly changed the outcome. Therefore, we are con

ﬁ

dent that our

evaluation shows a valid picture of which soil quality indicators are
most used.

Total organic matter/carbon and pH are the most frequently pro-

posed soil quality indicators (

Fig. 4

), followed by available phosphorus,

various indicators of water storage and bulk density (all mentioned
in > 50% of reviewed indicator sets). Texture, available potassium and
total nitrogen are also frequently used (> 40%). The average number of
proposed indicators is 11 (

Supplementary Tables 4 and 5

), which is

probably more than is feasible from a practical as well as a

ﬁ

nancial

viewpoint under most circumstances. Therefore, a trend towards
smaller indicator sets in recent years can be seen. However, the de-
velopment of novel indicators, which can be applied on a high number
of samples in a fast and cheap way, could change the picture in the
future.

In most publications, at least one indicator of each category (phy-

sical, chemical and biological) is included. These categories are typi-
cally represented automatically when all soil functions or soil-based
ecosystem services are addressed. However, soil biological indicators
were missing from 40% of the reviewed minimum datasets.

Soil physical indicators, especially those related to water storage,

were frequently proposed in the early assessment schemes and again in
the last 5 years, while they were less common in between
(

Supplementary Table 4

). Among the soil chemical indicators, soil or-

ganic carbon content, pH, available P and K, total N, electrical con-
ductivity, cation exchange capacity, and mineral N were proposed more
often than all other indicators. Likewise, soil respiration, microbial
biomass, N mineralization and earthworm density were more frequent
among the biological indicators than the other 10 indicators that have
been proposed at least once (

Supplementary Table 5

).

The explicit mentioning of extrinsic factors (

Supplementary Table 5

)

such as climate, management or site data is surprisingly rare. In par-
ticular, yield, plant nutrient status and other measures of ecosystem
services are very often not included. This means that soil quality as-
sessment is typically not explicitly linked to ecosystem services or soil
threats. An example of how to establish linkages between soil proper-
ties, soil functions and ecosystem services via correlations can be found
in

van Eekeren et al. (2010)

. Recent publications advocate indicators

that are applicable to several soil processes (

Bone et al., 2010

). In

Lima

et al. (2013)

, for example, earthworms serve as indicators for both

water and nutrient cycling. However, many of the other publications
lack a clear conceptual and/or mechanistic relationship between in-
dicators and soil functions and ecosystem services.

4.4. Novel soil quality indicators

Adoption of additional or novel soil quality indicators into

minimum datasets is of interest if they have clear added value from the
perspective of the management goals for a particular situation. Recent
developments in soil science, especially in soil biology, but also in
spectroscopy and other

ﬁ

elds, hold promise for future soil quality as-

sessment schemes. Below, we brie

ﬂ

y review these developments, from

biological and biochemical indicators to data capture and high-
throughput approaches that have the potential to change soil quality
assessment approaches quite substantially.

Soil

organisms

play

a

central

role

in

soil

functioning

(

Supplementary Table 6

). Therefore, adding biological and biochemical

indicators can greatly improve soil quality assessments (

Barrios, 2007

Bastida et al., 2008; Bone et al.,

Moreover, the assessment of biological indicators of soil quality is re-
quired to connect abiotic soil properties to (changes in) soil functions in
terms of biochemical and biophysical transformations and (potential)
aboveground vegetation performance (

Lehman et al., 2015

). Never-

theless, soil biological indicators are still underrepresented in soil
quality assessments and mostly limited to black-box measurements such
as microbial biomass and soil respiration (

Fig. 4

,

Table 4

). Despite clear

potential, more speci

ﬁ

c indicators such as those based on nematodes

(

Stone et al., 2016b

), (micro) arthropods (

Rüdisser et al., 2015

) or a

suite of soil biota (

Velasquez et al., 2007

) have rarely been suggested,

possibly because they require speci

ﬁ

c knowledge and skills. This si-

tuation is unfortunate because soil biota are considered the most sen-
sitive indicators of soil quality due to their high responsiveness to
changes in environmental conditions (

2010; Kibblewhite et al., 2008a; Nielsen and Winding, 2002

). In par-

ticular, there is an urgent need for indicators of soil

–

borne diseases

(

Kyselková et al., 2014; Liu et al., 2016; Trivedi et al., 2017

). In this

context, soil suppressiveness, de

ﬁ

ned as the property of a soil to natu-

rally reduce plant disease incidence (

Hornby, 1983

), is of interest.

Speci

ﬁ

c soil suppressiveness is the result of the presence of speci

ﬁ

c

antagonists to pathogens, while general soil suppressiveness is based on
the collective capacity of soil and plant microbiomes to act com-
plementarily against pathogens (

Schlatter et al., 2017

). Both combined

are governing soil suppressiveness as a whole (

Yadav et al., 2015

Janvier et al., 2007; Wu et al., 2015

Several soil abiotic and biotic parameters have been suggested to un-
derlie suppressiveness, such as soil pH, speci

ﬁ

c cations such as Mg and

K, soil total N content, microbial biomass and activity, diversity and
structure of microbial communities and speci

ﬁ

c microbial taxa in the

case of speci

ﬁ

c suppressiveness (

),

but without validation.

Recent rapid developments in soil biology have prompted the fea-

sibility of indicators based on genotypic and phenotypic community
diversity (

Hartmann et al., 2015; Kumari et al., 2017; Nielsen and

Winding, 2002; Ritz et al., 2009

). Molecular methods focusing on DNA

and RNA hold great potential to perform faster, cheaper and more in-
formative measurements of soil biota and soil processes than conven-
tional methods (

Hartmann et al., 2015; Hermans et al., 2017

). Consequently, they may yield

novel indicators that could substitute or complement existing biological
and biochemical soil quality indicators in regular monitoring programs
(

). In the participatory

approach used by

Stone et al. (2016a)

, seven out of ten selected in-

dicators were indeed based on molecular methods, with

‘

molecular

bacteria and archaea diversity

’

on top. In addition, recent data analysis

approaches such as network analysis, structural equation modelling and
machine learning could facilitate the establishment of links between
indicators and functions (

Allan et al., 2015; Creamer et al., 2016

). For

example,

Karimi et al. (2017)

proposed microbial networks as in-

tegrated indicators of environmental quality that can overcome the lack
of sensitivity and speci

ﬁ

city of taxonomic diversity indicators. How-

ever, the prediction of process rates from the presence and quantity of

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

116

genes and transcripts is yet to be clearly established (

Rocca et al.,

2015

). Results gathered with these molecular techniques are also faced

with biases introduced by sample contamination, PCR reaction, choice
of primers and OTU de

ﬁ

nition and taxonomic assignment techniques

(

Abdelfattah et al., 2017; Hugerth and Andersson, 2017; Schloter et al.,

2018

). The analysis of the

“

big data

”

generated with sequencing also

poses a serious challenge in terms of time, computing capacities and
interpretation, since a large proportion of soil organisms yet remains to
be characterized in taxonomic and functional terms (

Schloter et al.,

2018; Bouchez et al., 2016

). Other molecular techniques such as me-

tabolomics (

Vestergaard et al., 2017

) and metaproteomics (

Simon and

Daniel, 2011

) may yield potentially suitable soil quality indicators be-

cause the measurements are directly linked to ecosystem processes
(

). These technologies have bene

ﬁ

ts but are limited

in their application by the di

ﬃ

culty to extract metabolites and proteins

from soil and to choose representative samples (

Wang et al., 2015; Watzinger, 2015

).

Stable Isotope Probing (SIP) in conjunction with phospholipid fatty acid
analysis (PLFA) and DNA probing could also help to link soil biodi-
versity to soil processes (

). Finally,

for a meaningful integration of indicators based on molecular methods
into soil quality assessments, standardized techniques and a reference
system are still lacking and will have to be established (

Bouchez et al.,

2016

).

Although total soil organic matter is ubiquitous as a soil quality

indicator (

Fig. 4

), changes in response to management and land use are

di

ﬃ

cult to detect since the total pool is large (

Haynes, 2005

). Moreover,

due to the structural and functional heterogeneity of total soil organic
matter, its relevance in soil processes is not unequivocal. Therefore,
qualitative information on soil organic matter may be more informative
in soil quality assessments. Pools of soil organic matter such as labile or
active carbon are typically more sensitive to disturbance than total soil
organic matter and can give a better indication about soil processes
(

Gregorich et al., 1994

). Suggestions to measure this fraction include:

particulate organic matter (

Cambardella and Elliott, 1992

), perman-

ganate-oxidizable carbon (

Weil et al., 2003

), hot water-extractable

carbon (

Ghani et al., 2003

) and water-soluble carbon, also called dis-

solved organic carbon (

Filep et al., 2015

). Despite their sensitivity to

management and strong correlations to other parameters that are more
di

ﬃ

cult to measure, their relationship with soil processes is not well

understood, partly because it is not clear which part of the organic
matter they represent. Other methods to characterize (quality and
quantity) of total soil organic matter such as thermal and spectroscopic
methods are rapidly developing (

Clemente et al., 2012; Derenne and

Quénéa, 2015; Mouazen et al., 2016

) and hold promise for soil quality

assessments.

Additionally, soil sensing approaches such as spectroscopic techni-

ques, e.g. near-infrared spectroscopy and remote sensing, o

ﬀ

er the

opportunity to measure various soil chemical, physical and biological
parameters in a fast and inexpensive way (e.g.

Cecillon et al., 2009;

Gandariasbeitia et al., 2017; Kinoshita et al., 2012; Paz-Kagan et al.,
2014

). Sensors can be used directly in the

ﬁ

eld or in the laboratory

(

McKenzie et al., 2003

), and commercial providers increasingly o

ﬀ

er

spectroscopy-based analyses (e.g.

,

). Combining laboratory-based visible and near-infrared spectro-

scopy with

in situ

measurements such as electrical conductivity and

penetration resistance may be particularly useful (

Veum et al., 2017

).

Spectroscopic techniques, however, also face limitations that hamper
their routine use in soil quality assessment. First, when applied to the
soil surface in the

ﬁ

eld, information is gained only about the

ﬁ

rst

millimeters of the soil. Second, sample characteristics such as moisture
content, particle size distribution and roughness of the soil surface can
in

ﬂ

uence the outcome of the analysis (

Baveye and Laba, 2015; Stenberg

et al., 2010

). Third, a calibration step is used to relate the spectral in-

formation to soil characteristics (

Gandariasbeitia et al., 2017

) and the

prediction is as good as the calibration data set. Several studies showed
that calibration e

ﬃ

ciency varies between studies and parameters con-

sidered (

Islam et al., 2003

);

Kinoshita et al., 2012

). Through their

nature, spectroscopic estimates are always less precise than traditional
analytical methods (

Islam et al., 2003

). Creation of freely-available

databases that can be used for proper calibration and prediction of soil
properties are essential for realizing the full potential of these techni-
ques. These databases should involve both NIR spectra and results from
wet chemistry and biological methods.

X-ray tomography is another non-destructive technique that can be

used for soil structural analysis and can shed light on processes in-
tegrating soil physical and biological properties (

Helliwell et al., 2013

Ge et al., 2011; Viscarra

It avoids some drawbacks of spectroscopic techniques, namely the fact
that it scans a 3D image of the soil instead of only scanning its surface.
Nevertheless, this technique is still a long way from routine application
for soil quality assessment.

Such novel indicators potentially allow a more detailed assessment

of soil processes. At the same time, some of the techniques may be
developed into high-throughput soil analysis to shed light on the spatial
and temporal variability of soil parameters and determine soil quality
across di

ﬀ

erent scales for application in precision agriculture, mon-

itoring programs and life cycle assessments (

Rossel et al., 2017

). The rapid evolution of these techniques and the

decreasing costs associated with them will facilitate this development.
However, the practical operability of these indicators by di

ﬀ

erent sta-

keholders needs to be taken into account. The various limitations de-
scribed above still seriously hamper application of such novel indicators
in routine soil quality assessments. In addition, the absence of standard
operating procedures (SOPs) and accepted threshold values, especially
for molecular methods, make the comparison and the interpretation of
the results challenging (

Callahan et al., 2016

). The

ﬁ

nal and most im-

portant limitation to the interpretation of these novel soil quality in-
dicators is the lack of functional linkages with soil processes and
management implications.

Although use of novel indicators directly by farmers would be an

advantage, most farmers are willing to send samples to the laboratory
as long as the analyzed indicators are meaningful and responsive to
management (

). For policy makers operating or

setting up soil quality monitoring schemes, the introduction of novel
indicators would also be aided by relating them to existing ones that
may be phased out when performance (or cost-e

ﬃ

ciency) of novel in-

dicators is superior. At the moment, however, most novel soil quality
indicators still belong to the research domain, and many technological,
practical and interpretation related issues need to be overcome.

4.5. Interpretation of indicator values

An indicator is only useful if its value can be unequivocally inter-

preted and reference values are available. Reference values for a given
indicator could be either those of a native soil, which may however not
be suitable for agricultural production, or of a soil with maximum
production and/or environmental performance (

Doran and Parkin,

1994

). In the Netherlands, for example, ten reference soils for good soil

biological quality were selected out of 285 sites that had been mon-
itored for over ten years (

Rutgers et al., 2008

). These reference soils

represent speci

ﬁ

c combinations of soil type and land-use (e.g. arable

land on clay soil). Soil quality indicators at a given site could thus be
compared to those at the reference site as well as to the mean value, and
5% and 95% percentiles of all sites under a given land-use, with the
percentiles given as a means to express the frequency distribution. An
important drawback of this approach is that the reference may not be at
an optimum in all parameters (

Rutgers et al., 2012

Loveland and Thompson, 2002

Acceptable values for an indicator can also be de

ﬁ

ned as those at

which there is no loss or signi

ﬁ

cant impairment of functioning

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

117

(

). In the context of pollution, thresholds

of contamination are often used (

Chen, 1999

). Likewise,

Arshad and

Martin (2002)

list threshold levels for soil quality indicators, but this is

rarely found in other publications on soil quality assessment. For plant
nutrients, most agricultural advisory services use thresholds of avail-
able reserves below which plant production may become nutrient-
limited, while maximum values are related to the risk of losses (

Allen

et al., 2006; Schoumans et al., 2014

). Indicator thresholds for other soil

functions are absent from most soil quality assessment approaches.

A more advanced way to evaluate soil quality indicators is the es-

tablishment of standard non-linear scoring functions, which typically
have the shapes i) more is better, ii) optimum range, iii) less is better, or
iv) undesirable range, with i-iii being most common in soil science. The
shape of such curves is established based on a combination of literature
values and expert judgement (

Andrews et al., 2004

). When scoring

curves are based on regional data, such as in the Cornell Soil Health
Assessment (

Moebius-Clune et al., 2016

), then scores are relative to

measured values in the respective region. Each indicator measurement
is transformed to a value between 0 and 1 (or 0 and 100) using a scoring
algorithm (

Karlen and Stott, 1994

), with a score of 0 being the poorest

(lower threshold) and a score of 1 (or 100) the best (upper threshold).
The baseline value equals the midpoint between threshold values. Va-
lidation of scoring curves is possible if datasets with measurements of
the given soil quality indicator and a related soil process are available.

Obviously, acceptable target ranges of soil quality indicators need to

be soil- and land use-speci

ﬁ

c, and they depend not only on targeted soil

functions, but also on both spatial and temporal scale of soil quality
assessments, with regional target ranges typically being narrower than
national ones (

Lilburne et al., 2004; Wienhold et al., 2009

). In addition,

acceptable ranges of a soil quality indicator for one property or process
are often highly dependent on the value of another soil property or
process, e.g. dependence of microbial biomass or soil organic carbon on
soil texture (

Candinas et al., 2002; Johannes et al., 2017

).

It has been claimed that the interpretation of soil quality indicators,

i.e. the establishment of target or workable ranges, will always remain
contentious, which is partly due to a lack of data, partly due to the
curvilinear pattern that many indicators follow and partly because the
use of expert judgement is contentious itself (

Merrington, 2006

). A

comparative approach in which indicator values or scores of a given
sampling point are put in relation to other sampling points may be the
most intuitive and

ﬂ

exible basis for interpretation, since it gives a re-

lative assessment (e.g. top 25%) and allows continuing evolution of the
system. This approach is being implemented in the iSQAPER project,
where the variation in soil quality indicator values within pedo-climatic
zones is determined. Ranges are de

ﬁ

ned for speci

ﬁ

c land uses (e.g.

arable land, grassland), and benchmark scores based on relative

frequency are given. This approach may also introduce modular ex-
tensions of indicators that are only relevant in speci

ﬁ

c contexts, where

stakeholders can relate to them. Decision trees based on environmental
conditions, management systems and relevance of ecosystem services
can guide the selection of speci

ﬁ

c indicators.

4.6. Deriving a soil quality index and alternatives

Many studies on soil quality have searched for a way to aggregate

the information obtained for each soil quality indicator into a single soil
quality index, even though this was deemed impossible by

Sojka and

Upchurch (1999)

. For example,

Velasquez et al. (2007)

summed the

contributions of each of

ﬁ

ve sub-indicators (hydraulic properties, che-

mical fertility, aggregation, organic matter and biodiversity) to derive
the general indicator of soil quality (GISQ). In the SMAF, an additive
index yields a number between 1 and 10 (

Andrews et al., 2004

McGarry, 2006; Mueller et al., 2014; Shepherd et al.,

However, if assessed soil functions or ecosystem services rank very
di

ﬀ

erently in importance, then some kind of weighting is mandatory.

For example, in the recent Canadian monitoring of soil quality

within the agri-environmental indicator assessment, a soil quality
compound index is calculated as the weighted average of the perfor-
mance indices for erosion, soil organic carbon content, trace elements
and soil salinization (

Clearwater et al., 2016

). Another example is the

multi-objective approach based on principles of systems engineering
proposed by

Karlen and Stott (1994)

. The main soil functions are

weighted according to their importance for the overall goal in soil
quality management at a given site, and an overall rating of soil quality
with respect to the prede

ﬁ

ned goal is obtained by summing the

weighted soil functions. An exemplary application of this approach can
be found in

Lima et al. (2013)

, who used SIMOQS (Sistema de Mon-

itoramento da Qualidade do Solo) software developed in Brazil to cal-
culate a soil quality index (

Table 5

).

Visual soil assessments are also often summarized in an overall soil

quality rating (

2008

). Typically, the scores for the di

ﬀ

erent indicators are summed up,

with some weighting applied. In the Muencheberg Soil Quality Rating,
the weighted sum of the basic indicators is multiplied with values for
hazard indicators such as contamination, acidi

ﬁ

cation and

ﬂ

ooding

(

Mueller et al., 2014

).

Instead of deriving an overall soil quality index, colour coding for

di

ﬀ

erent indicators alone or aggregated according to soil functions is

more meaningful. For example, in the outputs the Cornell soil health
test, in Sindi, and in the Australian soil quality monitoring framework a
tra

ﬃ

c light system of 3

–

5 colours indicates low, adequate or excessive

values for a given indicator. Other graphical presentations such as
amoeba diagrams (or spider diagrams) can likewise convey more

Table 5

Example of weighting of soil functions and associated indicators (

Lima et al., 2013

Rutgers et al., 2009, 2012

Soil function

Weight

Indicator level 1

Weight

Indicator level 2

Weight

Water in

ﬁ

ltration, storage and supply

0.33

Available water

0.25

Mean weight diameter

0.25

Earthworms

0.25

Correlated indicators

0.25

Soil organic matter

0.50

Bulk density

0.50

Nutrient storage, supply and cycling

0.33

Available water

0.25

Earthworms

0.25

Soil organic matter

0.25

Micronutrients

0.25

Manganese

0.33

Copper

0.33

Zn

0.33

Sustain biological activity

0.33

Soil organic matter

0.50

Earthworms

0.50

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

118

information on trade-o

ﬀ

s and synergies than a single number or index

(

).

The ultimate purpose of a soil quality index is to inform farmers and

other land managers about the e

ﬀ

ect of soil management on soil

functionality. An aggregated presentation of the outcome of soil quality
assessments, especially by graphical means, can indeed be useful also
for educational purposes and for communicating to society as a whole
the consequences that human decisions can have on soil-based eco-
system services.

4.7. Stakeholder involvement

Because the reviewed literature is often not clear (enough) on who

were the main developers and who are the main end users of the soil
quality assessment schemes (

work-package/work-package-1/

,

Table 2

), we asked (by e-mail) 17

scientists who stood at the cradle of such schemes, or can currently act
as spokespersons for them, to answer the following questions:

1 Who were the three main stakeholders, in order of importance, who

were

involved in the development

of the soil quality assessment

scheme?

2 Who are the three main stakeholders, in order of importance,

using

the soil quality assessment scheme?

3 Can you guide us to published or internet-accessible information (if

any) on the extent of use and on user feedback?

We received answers from 11 countries: Australia (2 programs),

Brazil, Canada, China, England, France, Germany, the Netherlands,
New Zealand, Scotland and USA. The main

developers

of soil quality

assessment schemes turned out to be scientists (8x) and government
agencies (3x), while farmer organizations were top-ranked only once.
The second position was taken by a mix of scientists (3x), (regional)
government agencies (3x) and agricultural advisors (2x). Third posi-
tions were

ﬁ

lled in only 5x, with various stakeholders. When it comes to

end users

, government agencies and consultants/agricultural advisors

are top-ranked (each 4x), and farmers 2x. In second position are sci-
entists (4x), (regional) authorities (3x), farmers/land managers (2x)
and students (1x). Hence, not unexpectedly, scientists play a leading
role in the development of soil quality assessment schemes.
Remarkably,

however,

farmers/land

managers,

consultants/agri-

cultural advisors and other stakeholders usually play an insigni

ﬁ

cant

role in development, whereas they turn out to be important end users of
the schemes. Quantitative data on the use of the assessment schemes is
available in only four cases and user feedback data are equally scarce.

5. Conclusions

Our review has revealed how soil quality assessment has changed

through time (

Fig. 5

) in terms of objectives, tools and methods, and

overall approach. A number of steps are to be taken in soil quality as-
sessment (

Fig. 6

), elements of which are addressed to very di

ﬀ

erent

degrees in the large number of approaches that have been developed
during the past three decades and reviewed in this article. An ele-
mentary start is a clear de

ﬁ

nition of the

objectives

, i.e. whether soil

assessment is meant as a basis for management recommendations, seen
as an educational tool, or as part of a monitoring program. Likewise,

target users

should be named and involved from the beginning in order

to increase adoption of the developed assessment approach. Such ap-
proach has been taken in the Horizon 2020 project LANDMARK, where
the assessment of soil functions and indicators has in the

ﬁ

rst place been

derived through stakeholder workshops (

http://landmark2020.eu/

). The application of stakeholder-

based assessment requires di

ﬀ

erent tools for di

ﬀ

erent knowledge. For

example, visual soil assessment tools are targeted at farmers for un-
derstanding the status of soil structure in the

ﬁ

eld, whereas more de-

tailed knowledge on productivity requires laboratory measurements,
which are, e.g., o

ﬀ

ered to farmers in the Cornell soil health assessment

(

Moebius-Clune et al., 2016

) and by recently developed commercial soil

testing services based on spectroscopic methods (see section

4

The

selection of soil quality indicators

needs to be based on

mechanistic linkages between indicators and soil functions or eco-
system services that have sometimes been proposed (

Creamer et al.,

2016

) but rarely established

ﬁ

rmly through experimental validation

(e.g.

van Eekeren et al., 2010

). A clear de

ﬁ

nition of the targeted soil

function(s) will determine the soil depth that is to be evaluated, since
some soil functions are mainly related to the topsoil, whereas others are
related to the entire soil pro

ﬁ

le. An asset of a novel soil quality fra-

mework would be the possibility to choose indicators based on the
targeted soil threats, soil functions and ecosystem services, which is
deemed possible by using the logical-sieve method (

Stone et al., 2016a

Norton et al., 2016; Schulte et al., 2015

Conceptually, soil threats, functions and ecosystem services are all
linked (

Fig. 2

), and concepts focusing on either of these can thus be

reconciled, if it is recognized that the targeted soil function or eco-
system service and associated choice of indicators are scale-dependent
(

). (Multi-) functionality should

clearly be integrated in future approaches to soil quality, such as that of
functional land management (

Schulte et al., 2015

) applied in the

LANDMARK project.

The possibility to choose between

substitute or proxy indicators

(

Fig. 6

) would be highly bene

ﬁ

cial but is so far rarely o

ﬀ

ered. The use of

Fig. 5.

Main objectives, tools and approaches of soil quality assessment through history.

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

119

parallel independent lines of evidence in ecological risk assessment
(

Rutgers and Jensen, 2011

) and the inclusion of both qualitative and

quantitative information in classical land evaluation (

Sonneveld et al.,

2010

) could be models for that. Besides soil indicators, whether ob-

tained using

ﬁ

eld assessments, analytical methods, high-throughput

approaches or pedotransfer functions, also non-soil factors such as cli-
matic and site conditions and non-soil indicators such as plant perfor-
mance and aboveground biodiversity, landscape and socio-economic
indicators (e.g.

Culman et al., 2010; Jackson et al., 2012

) should be

considered.

The

interpretation

of the values of the proposed soil quality in-

dicators needs to be well-de

ﬁ

ned. If no system for interpretation is

provided, the indicators cannot be used in practice. For many soil
properties, texture-dependent scoring curves need to be developed,
which is possibly one of the greatest challenges. The increased avail-
ability of digital soil maps and soil survey data such as the LUCAS soil
data available from the Joint Research Centre (

http://esdac.jrc.ec.

europa.eu/content/lucas-2009-topsoil-data

) or global soil grids in

250 M

(

https://soilgrids.org/#/?zoom=2&layer=geonode:taxnwrb_

250m

) provides an opportunity to establish such scoring curves or

target values more easily from frequency distributions of a given soil
property. However, if soils in a region are badly managed or were so in
the past, such a frequency distribution may not include the optimum
state. In this case, the principle of identifying reference sites with ac-
knowledged good soil quality (

Rutgers et al., 2008, 2012

) would be

more suitable, or could be combined with the scoring curve approach.
Reference or threshold values are required both to use soil quality in-
dicators to their full potential and to translate the interpretation into
appropriate management and policy advice. The assessment of the (dis)
agreement of results obtained from di

ﬀ

erent lines of evidence (e.g. sets

of indicators based on physical, chemical or biological parameters; see
e.g.

Velasquez et al., 2007

) can be adopted from mathematical proce-

dures developed in ecological risk assessment (

Karlen et al., 2001;

Rutgers and Jensen, 2011

isric.org/explore/soilinfo

An overall

soil quality index

is often desired but actually not very

meaningful, since soil quality is best assessed in relation to speci

ﬁ

c soil

functions. Rather than calculating an overall index, a graphical re-
presentation of how well a given soil ful

ﬁ

ls its various functions is much

more e

ﬀ

ective in communicating with stakeholders, target users and

the general public. In practice, di

ﬀ

erent sets of soil quality indicators

will be used with di

ﬀ

erent weightings, depending on the set of soil

threats and ecosystem services at stake according to the

“

stake-

holders

”

.

Future soil quality assessment and monitoring can bene

ﬁ

t from re-

cent technological developments such as the SoilInfo App (

http://www.

), mobile data capture including photographs

and big-data approaches which are both used in the proposed LandPKS
tool (

www.landpotential.org

), and high-throughput soil analysis ap-

proaches, such as visual and near-infrared spectroscopy. Future tools
promise to be truly

interactive

, such as the soil quality assessment tool

(SQAPP) that is being developed within the EU iSQAPER project.

Finally, soil quality assessment can become e

ﬀ

ective to improve the

state of our soils only with inclusion of

management or policy advice

.

6. Outlook

Science plays an important part in the search, under prevailing

pedo-climatic conditions (

Fig. 1

), for indicators of the structural and

process aspects of soil functioning that mediate the delivery of soil-
based ecosystem services deemed important by actors and other sta-
keholders who exert(ed) pressures on the soil through land use and soil
threats. The key terms here are

‘

actors

’

and

‘

stakeholders

’

. Terms such

as

‘

soil function

’

,

‘

ecosystem service

’

and, indeed,

‘

soil quality

’

, are

boundary concepts, i.e. concepts that enable researchers from di

ﬀ

erent

disciplines, policy-makers, and other stakeholders to develop a common
language and integrate and derive knowledge relevant to their

ﬁ

eld

(

Schleyer et al., 2017

). Beyond scientists, those who have an immediate

stake in soil quality are land managers, i.e. farmers, managers of nature
conservation areas, roadsides, banks of waterways and urban green
areas, and the public at large. As soil quality management is also about
societal negotiation in the face of unavoidable trade-o

ﬀ

s between var-

ious soil uses, the very development of soil quality indicator schemes
will bene

ﬁ

t from the involvement of actors and other stakeholders with

a view to implement adaptive land use and management (

Barrios et al.,

2006, 2012

).

Although, clearly, soil quality is not merely a natural science topic,

in most of the reviewed assessment schemes farmers/land managers did
not play a leading role. We suggest that intimate involvement of end
users is a major point of attention, but it may still not lead to full im-
plementation of the results. For example, in the Illinois Soil Quality

Fig. 6.

Main steps in the development of a soil quality assessment approach.

E.K. Bünemann et al.

Soil Biology and Biochemistry 120 (2018) 105–125

120

Initiative, where farmers were involved in the development of soil
quality assessment schemes, they were constrained in the necessary
implementation of the results by socio-economic factors (

Wander et al.,

2002

). Clearly, other actors play an important part. Industries that ul-

timately also depend on the soil, will be (come) important actors, too,
such as food,

ﬁ

bre and fuel industries, and electricity production,

manufacturing and fashion industries (

Davies, 2017

). Their interest is

in sustained resource supply, which is at stake because of ongoing loss
of soil functionality and increased variability in harvests and water
supply associated with global climate change, partly induced by un-
sustainable land use and management. Land managers, industries and,
indeed, investors and insurance companies and the public sector at
large are increasingly aware of the associated monetary and societal
costs and,

vice versa

, they understand the urgency of adaptive land

management and re-design in the framework of food systems
(

Foresight, 2011

) and a fossil-free and circular economy (

Rockström

et al., 2016

Mäder et al., 2002; Seufert and Ramankutty, 2017

To be part of such urgent transitions, soil scientists are challenged to

engage as

‘

honest brokers

’

of knowledge who increase the decision

space of actors (

Pielke, 2007

). This engagement of soil (quality) re-

searchers should take into account the following points:

First, we should consider (fostering) soil quality an integral part of

(enhancing) environmental quality in general, as argued by

Döring

et al. (2015)

. We should not consider soil quality in isolation, but as

part of quality assessment and adaptation of systems, e.g. of agricultural
systems such as mainstream

vs.

integrated

vs.

conservation agriculture

(

Stavi et al., 2016

) or mainstream

vs.

integrated

vs.

organic agriculture

(

). This requires en-

gagement with farmers of di

ﬀ

erent philosophies from purely organic to

industrialized, and with other players in food systems.

Second, we should recognize that the radical changes in agricultural

practices, summarized as

‘

smart farming

’

(

Walter et al., 2017

), require

novel soil quality assessment tools, both in de-intensifying mainstream
agriculture and in intensifying ecological agriculture (

Struik et al.,

2014

).

Third, our focus should not just be on informing adaptive land

management in existing agricultural systems, but also on fundamental
system re-design, summarized as regenerative agriculture (

Rhodes,

2017

), in the framework of the circular economy.

Fourth, engaging with societal goals such as the UN Sustainable

Development Goals (SDGs) is not only important in itself, but strategic
in stressing the importance of soil (quality) knowledge for society
(

Bouma, 2014

). In turn, monitoring progress towards the SDGs will

require soil quality monitoring too, e.g. through the UNCCD Land De-
gradation Neutrality goals and associated reporting mechanism
(

Akhtar-Schuster et al., 2017

Finally, awareness of the power relationships in the context of sci-

enti

ﬁ

c support to stakeholders is essential. Generally, existing institu-

tions and power relations resist innovation. Hence, the challenge is to
associate with initiatives and policies that can create a greater space for
innovation and system re-design and strengthen actors

’

in

ﬂ

uence from

lower up to higher levels (

Giller et al., 2008

Ockleford et al., 2017; Römbke et al., 2016

The engagement we make a plea for may require painstaking e

ﬀ

orts,

from gradual but consistent improvements within existing legislative
frameworks (e.g.

) to de-

veloping fundamental alternatives to current land use practices (e.g.

Montgomery, 2017; Rhodes, 2017

). Such engagement will at the same

time require unquestionable scienti

ﬁ

c independence in the co-creation

of knowledge (

Mauser et al., 2013

). We suggest that such engagement is

necessary for the improvement of existing schemes and the develop-
ment of novel schemes for assessment and monitoring of soil quality, as
well as for the evaluation of their use and usefulness for all actors in-
volved.

Authorship statement

EKB, PM and LB conceptualized the manuscript and EKB, GB and LB

wrote it. ZB, RC, GDD, RdG, LF, VG, TWK, PM, MP, WS and JWvG
contributed suggestions and text on speci

ﬁ

c components of the manu-

script.

Con

ﬂ

icts of interest

None.

Acknowledgements

We gratefully acknowledge funding by:
- the EU FP7 project Ecological Function and Biodiversity Indicators

in European Soils (Eco

ﬁ

nders), grant no. 264465, for LB and RC.

- the EU Horizon 2020 project Interactive Soil Quality Assessment in

Europe and China for agricultural productivity and environmental re-
silience (iSQAPER), grant no. 635750, for EKB, GB, LB, ZB, RdG, LF,
VG, PM and WS (mediated through the Swiss State Secretariat for
Education, Research and Innovation in the case of EKB, PM and, partly,
GB).

- the EU Horizon 2020 project Land Management: Assessment,

Research, Knowledge base (LANDMARK), grant no. 635201, for RC.

We thank the iSQAPER consortium for discussions and feedback

during various workshops. For answering a questionnaire regarding
stakeholder involvement in soil quality assessment and use, we are
grateful to Drs. Bruce Ball, Lucy Clearwater, Jean-Roger Estrade, Rachel
Guimaraes, David McKenzie, Graham Merrington, Lothar Müller,
Daniel Murphy, Bryan Stevenson, Harold van Es and Esther Wattel.
Thanks are also due to Dr. Paolo Barberi for useful suggestions at an
early stage of writing this review. Two anonymous reviewers greatly
helped to improve the manuscript through their critical and con-
structive comments.

Appendix A. Supplementary data

Supplementary data related to this article can be found at

http://dx.

doi.org/10.1016/j.soilbio.2018.01.030

Aalders, I., Hough, R.L., Towers, W., 2011. Risk of erosion in peat soils - an investigation

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