Ecosystem Services 12 (2015) 55–70
Contents lists available at ScienceDirect
Ecosystem Services
journal homepage: www.elsevier.com/locate/ecoser
Threats to food production and water quality in the Murray–Darling
Basin of Australia
Jonathan E. Holland a,n,1, Gary W. Luck b,2, C. Max Finlayson b
a
b
EH Graham Centre, Charles Sturt University, Wagga Wagga, NSW, Australia
Institute for Land, Water and Society, Charles Sturt University, Albury, NSW, Australia
art ic l e i nf o
a b s t r a c t
Article history:
Received 5 September 2014
Received in revised form
9 January 2015
Accepted 22 February 2015
We analyse how salinity, acidity and erosion threaten the ecosystem services of food production and the
regulation of water quality in the Murray–Darling Basin, Australia’s most important food producing
region. We used the Drivers-Pressures-State-Impact-Response (DPSIR) framework, to show that each of
these threats undermines the functioning of the Basin’s agro-ecosystems and the two major ecosystem
services (four other ecosystem services are briefly considered). These threats are driven by natural
processes (e.g. rainfall) and anthropogenic activity (e.g. land clearing), and this leads to pressures exerted
by hydrology, nutrient cycles and wind. Satisfactory information is available on the state of acidity and
wind erosion, but information on the state of water erosion and salinity is inadequate. The impact of
these threats on food production was primarily by reducing crop yield, while the impacts on water
quality were to increase sediment, salt and nutrient loads. Management responses were either adaptive
or mitigative; the former targets impacts while the latter focuses on drivers and pressures. Most
management responses involved trade-offs between ecosystem services, although some synergies were
found. Scale and spatial variability strongly influence the selection of responses. Understanding the
mechanisms underpinning land degrading threats and the associated relationships allows better
assessment on impacts to ecosystem services.
& 2015 Elsevier B.V. All rights reserved.
Keywords:
Acidification
Erosion
Salinity
Land degradation
Trade-offs
DPSIR
Contents
1.
2.
3.
n
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
Review study area and approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
2.1.
Study area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
2.2.
Ecosystem services approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.3.
The drivers-pressures-state-impacts-response (DPSIR) framework. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
2.4.
Management responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
Evaluation of threats to ecosystem services . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.1.
Salinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.1.1.
Salinity drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.1.2.
Salinity pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.1.3.
Salinity state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.1.4.
Salinity impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.2.
Acidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.2.1.
Acidity drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.2.2.
Acidity pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.2.3.
Acidity state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.2.4.
Acidity impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Correspondence to: 14 Dalton Street, Wagga Wagga 2650, NSW, Australia. Tel.: þ 61 2 6938 1948; fax: þ 61 2 6938 1809.
E-mail addresses: [email protected] (J.E. Holland), [email protected] (G.W. Luck), mfi[email protected] (C. Max Finlayson).
1
EH Graham Centre, Charles Sturt University, Locked Bag 588, Wagga Wagga 2678, NSW, Australia.
2
Institute for Land, Water and Society, Charles Sturt University, PO Box 789, Albury 2640, NSW, Australia.
http://dx.doi.org/10.1016/j.ecoser.2015.02.008
2212-0416/& 2015 Elsevier B.V. All rights reserved.
56
J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
3.3.
Erosion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.3.1.
Erosion drivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.3.2.
Erosion pressures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.3.3.
Erosion state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.3.4.
Erosion impacts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4. Management responses to land degrading threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
4.1.
Adaptive management responses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
4.2.
Mitigatory management responses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.1.
Trade-offs and synergies in ecosystem services from management responses for salinity, acidity and erosion. . . . . . . . . . . . . . . . . . . . . 65
5.2.
Future priorities for research of land degrading threats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
5.3.
Implications for policy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
1. Introduction
Degradation of the world’s ecosystems in addition to global
population growth will challenge humanity’s capacity to feed itself
(FAO, 2010). The maintenance of the essential ecosystem services (ES)
that support human wellbeing will also be threatened (MA, 2005;
Molden, 2007). Developing countries, in particular, suffer the consequences of food restrictions (ABARES, 2011) and the loss of ES
(Boelee et al., 2011). However, the responsibility of feeding humanity
and providing ES is shared by all nations; a responsibility that requires
substantial advances in ecosystem and agricultural sciences to support
policy (Spiertz, 2010).
Many agro-ecosystems are severely affected by acidification,
salinity and erosion. Globally, approximately 10% of irrigated land
is affected by salinity (Tanji and Kielen, 2002), up to 30% of topsoils
are affected by acidity (Sumner and Noble, 2003), and around 56%
(1100 M ha 1) and 28% (550 M ha 1) of soils are affected by water
and wind erosion, respectively (Oldeman, 1994). These processes
threaten crucial ES such as the provision of food and water
(Keating and Carberry, 2010). There is currently insufficient understanding of how these threats effect the delivery of ES in
agricultural landscapes (MA, 2005).
Australia’s Murray–Darling Basin (MDB) is globally important
for food production and is ecologically rich. The MDB produces
39% of Australia’s food and fibre (ABS, 2008) of which approximately 60% is exported (DAFF, 2010). The agricultural practices in
the MDB include extensive cattle and sheep grazing, dryland and
irrigated mixed farming, and intensive irrigated agriculture, dairying and horticulture. The MDB supports more than 30,000 wetlands of which 16 are listed as wetlands of international
importance (Ramsar sites) (DEWHA, 2008), and over 35 threatened or near threatened bird species (Garnett et al., 2011).
Agricultural activities dominate land use in the Basin and tensions
arise when agricultural practices impact on land or water
resources, or when environmental policies threaten agricultural
practices (Pittock and Finlayson, 2011). The MDB is therefore an
ideal location to study the threats that salinity, acidity and erosion
pose to ES.
In this review, we demonstrate how salinity, acidity and
erosion threaten food production and water quality in the MDB.
A Drivers-Pressures-State-Impacts-Response (DPSIR) framework
(Smeets and Weterings, 1999) was used to analyse the relationships between these threats and the provision of ES. The DPSIR
framework was chosen because it is suited to assess large-scale
environmental change (Feld et al., 2010). We describe the natural
and anthropogenic processes associated with each threat. The
DPSIR framework was used to identify biophysical links between
the causes of these threats, how they change the state of the
environment and the consequences of those changes. We show
that management responses to these threats often lead to tradeoffs between ES. Finally, we suggest how to assess threats to ES,
and outline requirements for further research on threats and
management responses.
2. Review study area and approach
2.1. Study area
The MDB occupies approximately 1 million km2 of south-eastern
Australia. It contains many different soils and landforms. Its climate
ranges from subtropical in the north (latitude 261S) to temperate in
the south (latitude 371S) (Fig. 1). Rainfall is summer dominant in the
north and winter dominant in the south, and varies from below
200 mm year 1 on the western plains to over 1000 mm year 1 close
to the Great Dividing Range in the east. The long-term average annual
rainfall in the MDB is 530,618 GL, but most (94%) evaporates or is
transpired (ABS, 2008). Temperatures have distinct seasonality, but are
generally cooler in the south and warmer in the north and west. The
basin contains Australia’s three longest rivers, which supply 65% of
Australia’s irrigated land and 2 million people with fresh water (ABS,
2008). Agriculture is a very large consumer of water in the MDB,
however the absolute amount of water use fluctuates greatly between
years. For example, in 2005/06 agriculture used 7720 GL which
corresponded to 23% of the long-term water availability (ABS, 2008),
while in comparison 3564 GL was consumed in 2009/10 (ABS, 2011).
The streams and rivers of the MDB are highly regulated; a series of
dams and weirs are used to manage water to meet the conflicting
needs of irrigators and the environment and to mitigate floods (ABS,
2011).
Land use within the MDB mirrors land capability. The intensity of
dryland agriculture is governed by rainfall; extensive sheep and cattle
grazing occurs on the northern and western plains while the centre of
the basin is dominated by broad-acre dryland mixed farming. Irrigated
agriculture (including cropping and dairy farming) occurs near rivers
on alluvial “river flats” and on the plains in areas supplied by canals
(Fig. 1). The economic return per hectare of irrigated land is much
greater than from dry land. In 2000/01, irrigated land occupied 1.7% of
the Basin and returned $AU 1.25 billion or $AU 674 ha 1, while
dryland agriculture returned only $AU 2.48 billion from 82% of land
area at just $AU 29 ha 1 (Bryan et al., 2009a).
The major change in land use in the MDB over the past 150 years
has been the clearing of native vegetation for agriculture. Such
changes in land use have an enduring effect, while less extensive
but significant changes are triggered by commodity market prices. For
example, the area of the MDB which was irrigated increased by 22%
between 1996/97 and 2000/01 (Bryan et al., 2009b), while the area of
fruit and nut production (including apples, oranges and almonds)
J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
57
Fig. 1. The location of the Murray–Darling Basin, Australia and the distribution of major land use activities. Data source: ABARE-BRS (2010).
Table 1
The association between management responses and specific DPSI components for salinity, acidity and erosion with the subsequent effect on ecosystem services.
Threat
Ecosystem
services
Management response
Management response
type
DPSI target
Effect on ecosystem services
Salinity
Salinity/
acidity
Acidity
Erosion
Salinity
Acidity
Erosion
Salinity/
erosion
Erosion
Salinity
Water quality
Food production
Planting trees
Growing tolerant plants
Mitigative
Adaptive
Food production
Water quality
Soil quality
Soil quality
Soil quality
Air quality
Applying lime
Restricted grazing
Protecting native vegetation
Applying selected manures
Adopting minimal tillage
Planting trees
Adaptive
Mitigative
Mitigative
Adaptive
Adaptive
Mitigative
Pressure (hydrology)
Impact (salt/acid
sensitivity)
State (soil pH)
Driver (land use)
Pressure (hydrology)
State (soil properties)
Impact (soil carbon)
Driver (land use)
Reducing salt load
Increasing plant growth for crops or
fodder
Increasing crop growth
Reducing turbidity
Reducing spread of salinization
Maintaining soil nutrient levels
Maintaining soil carbon levels
Increasing interception
Air quality
Timber
production
Timber
production
Genetic resources
Restricted grazing
Spatially planned reforestation
Adaptive
Mitigative
State (soil loss)
Pressure (hydrology)
Reducing dust storm index
Increased timber production
Spatially planned reforestation
Mitigative
Driver (land use)
Increased timber production
Establishing conservation areas
Mitigative
Driver (land use)
Adaptive
State (plant diversity)
Maintaining plant and animal genetic
diversity
Maintaining plant genetic diversity
Acidity/
erosion
Salinity/
erosion
Acidity
Genetic resources Utilisation of native grasses for
grazing
increased by 14%, and the area of cotton decreased by 37% between
2000/01 and 2005/06 (ABS, 2008; Watson et al., 2014).
2.2. Ecosystem services approach
The Millennium Ecosystem Assessment (MA, 2005) defined an
ecosystem as “a dynamic complex of plant, animal and microorganism communities and their non-living environment interacting
as a functional unit” (p. 27) and ES as “the benefits that humans obtain
from ecosystems” (p. 27). We adopt these definitions in this review.
The conceptual framework outlined by the UK National Ecosystem
Assessment (UK NEA, 2011) is used to define the ES that are delivered
within the MDB. This framework clearly separates ecosystem processes, intermediate services and final services that deliver goods for
human consumption (Fisher et al., 2008). For example, primary
production is an ecosystem process that includes pollination (an
intermediate service) in food production (a final ES) to produce stone
fruit (a good). We primarily focus on two ES - producing food
(a provisioning service) and maintaining water quality (a regulating
service). Four other ES are summarised in Table 1 because of their
58
J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
particular relevance to the threats covered in our study. These four
services are air quality and soil quality (regulating services), timber
and forest products (provisioning service), and genetic resources
(provisioning service). Our study also takes into account other ES
indirectly; for example, some aspects of hazard regulation are covered
because it is closely related to the erosion threat or the control of it,
while climate regulation, which is highly influential for each selected
threat, is one of the major drivers (Fig. 2). Limited information about
these services makes a more in-depth treatment difficult.
2.3. The drivers-pressures-state-impacts-response (DPSIR)
framework
The DPSIR framework (Smeets and Weterings, 1999) was applied
to assess how the selected threats affected the delivery of the ES.
DPSIR identifies cause-effect relationships, and evaluates the consequence of management actions or responses within an individual
component and on the whole system (Rounsevell et al., 2010). We
used the DPSIR framework because it allows more than one threat to
be evaluated at once. The drivers of environmental change may be
natural or anthropogenic and may act directly or indirectly (MA,
2005). The drivers are the source of threats and they develop
pressures on the state of a variable or variables that qualify and
quantify the condition of an ecosystem. The state of the ecosystem
changes in response to these pressures. Here, we define the state as an
indicator of “the abiotic condition of soil, air and water, as well as the
biotic condition (biodiversity) at ecosystem/habitat, species/community and genetic level” (EEA, 2007; p. 13). In turn, the ecosystem’s
altered state leads to impacts on the level or nature of the ES it
delivers. Ecosystems can react positively or negatively to impacts and
we define impacts as a direct consequence of a change in the state.
Response may be needed, as either a policy directive or changed
management to minimise negative impacts or maximise positive ones
(Rounsevell et al., 2010).
In Australia, the DPSIR framework has been used to assess how
pressures and impacts have threatened and disturbed wetlands in
Queensland (Lynch, 2011), and in State of the Environment
reporting by the Queensland and Victorian state governments
(CES, 2005; EPA Qld, 2008). However, its use in scientific studies of
Australian land degradation has been limited.
to change the state. It involves new activities carried out within the
prevailing state of the ecosystem. Examples of adaptive management
are growing salt or acid tolerant crops to improve yield or quality (see
solid arrows in Fig. 2, Table 1). An exception is the adaptive management of acidity by applying lime which alters the state (soil pH), but
not its drivers (see dashed arrow in Fig. 2) (Table 1).
On the other hand, mitigatory responses aim to ‘improve’ the
state of the environment to a level that will maintain or improve
ES. They target the drivers or pressures of change. For example, to
manage salinity by changing land use in salt donor catchments
from annual crops and pastures to trees significantly changes the
hydrology (acting as a pressure) and delivery of salt to streams
(Table 1). A management response to erosion might involve
modifying land use (a driver) such as restricted grazing management to improve water quality (Table 1). Mitigatory management
responses are undertaken with a long-term outlook and are
generally more expensive than adaptive responses. While the
original trigger for action may be an impact on food production
or water quality, mitigatory management practices are implemented to reduce impact indirectly via drivers or pressures (Fig. 2).
A variety of management responses are required to deal with
the threats from salinity, acidity and erosion to agro-ecosystems.
Nature-based responses such as planting trees or protecting native
vegetation may be suitable for several different ES (Table 1). We
present a broad range of ES and their associated management
response in Table 1 (including food production, timber and forest
products and genetic resources—provisioning services; and water
quality, air quality and soil quality—regulating services) to illustrate the wide-ranging impacts caused by these threats.
3. Evaluation of threats to ecosystem services
In this section, we present a DPSIR framework (Fig. 2) developed to describe the natural and anthropogenic drivers that exert
a range of pressures that change, by different mechanisms, the
state of agro-ecosystems. A change in state is measured as the
level of, or trend in some quantity (such as a biophysical indicator)
that indicates salinity, acidity and/or erosion. We then report the
impacts of these changes on the two selected ES.
2.4. Management responses
3.1. Salinity
We classify responses as either adaptive or mitigatory. Adaptation
is about coping with a threat while still providing an adequate level of
ES (Rounsevell et al., 2010). Adaptive management recognises that the
state of the environment has changed for the worse, but it does not try
3.1.1. Salinity drivers
The natural, biophysical drivers of salinity are climate and weather.
Salinity is also strongly driven by land use and anthropogenic land use
change (Fig. 2). These drivers operate over many temporal and spatial
Fig. 2. The DPSIR framework applied to salinity (S), acidity/acidification (A) and erosion (E) (NB. the solid arrows apply to all threats, but the dashed arrow only applies
to acidity).
J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
scales. Annual and seasonal variability in weather affects transient
salinity locally. However, at longer time frames, it has been found that
changes in the amount of groundwater recharge can largely be
explained by the amount of rainfall received (Petheram et al., 2002).
The major anthropogenic driver for salinity in Australia has been the
introduction of farming systems that use less water than the native
vegetation they replaced; increasing recharge to groundwater, salt
mobilisation, and therefore, salinity (Haron and Dragovich, 2010;
Zhang et al., 2001). This is because most agricultural crops are annuals
while native vegetation was largely perennial. Annual plant species
have highly seasonal water requirements, but native woody species
use water throughout the year (Dunin, 2002). Land use changes have
driven the salinisation of rainfed (Charman and Murphy, 2007) and
irrigated land where reduced evapotranspiration is exacerbated by
adding irrigation water (Tanji and Kielen, 2002). The area or proportion of changed land use is a convenient indicator of the anthropogenic driver of salinity. The consideration of the driving effects from
different land-use types is important, although this should not ignore
the potentially strong effect of the management regime that is in
place. In our study area (the MDB), the dominant biophysical factor
influencing the management of salinity is plant water use. For
example, Crosbie et al. (2007) showed that the strategic establishment
and placement of a tree belt can reduce the volume of saline
discharges. Additional discussion on the role and effect of management responses is provided in Section 4.
3.1.2. Salinity pressures
Pressures related to salinity are predominantly hydrological
(Fig. 2). Introducing irrigation or imposing annual dryland farming
systems can exert local pressures with the water table rising and
mobilising stored salt (Petheram et al., 2002). These pressures can
be transmitted over long distances; therefore, irrigated land can be
salinised by salt imported in the irrigation water that originated
elsewhere (Tanji and Kielen, 2002). The hydraulic loading of the
land surface, measured as the amount of rainfall, flooding or
irrigation, can dramatically change the hydrology and thus the
pressure on salinity to develop. The drought experienced in eastern Australia over the 10 years from 1999 is one reason for the fall
in salinity levels in the MDB (MDBA, 2010a). For example, in
September 2008 the salinity level at Morgan, South Australia, a
key river salinity monitoring point, fell below the target level for
the first time since records began. Rainfall and flooding from 2010
through to 2012 across the MDB was higher than in previous years
(Giles, 2012) and is predicted to increase the risk of flood recession
salinity where accumulated salt is mobilised following flooding
(MDBA, 2010a). Salinity pressures vary significantly in time and
space; however, hydrological changes can be used to approximate
the state of salinity.
3.1.3. Salinity state
The state of salinity can be measured as the Electrical Conductivity
(EC) of streams or groundwater (Fig. 2). The area of the MDB affected
by salinity has not been measured recently, although previous surveys
provide an indication of the extent of saline conditions. Groundwater
quality was last assessed in the MDB in 1988. Broad classes of fresh,
brackish or salty showed that almost half (48%) of the MDB’s
groundwater was saline (Fig. 3) (Williams et al., 1994). In 1999, more
than 5 million tonnes of salt were mobilised over the MDB (MDBC,
1999). Trends in stream salinity between 1985 and 1994 were used to
calculate the salt output/input ratio for all catchments across the MDB
(Jolly et al., 2001). Jolly et al. showed that salt was mobilised in the
upper catchment areas and stored in mid-catchment irrigation
districts from where it could be remobilised later. The state of soil
salinity is best measured by the electrical conductivity (EC) of its
saturated extract (ECse) since it accounts for the moisture holding
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properties of the soil (Hazelton and Murphy, 2007). Despite sparse
knowledge of the state of salinity in the MDB, there is a good
understanding of the likely impacts from salinity on ES.
3.1.4. Salinity impacts
The impact of salinity on food production occurs via reduced
plant growth, and the impact on water quality through substantial
increases in salt content (Fig. 2). Salt may reduce water uptake by
plants through an osmotic effect or injure transpiring leaves;
although some plants can tolerate salinity better than others
(Munns, 2005). The specific impact of salinity differs between
crops, but it is well established that annual crops such as wheat
are susceptible to salinity resulting in reduced grain yields (Nuttall
et al., 2003). The threshold level at which yield or productivity
starts to decline and the rate of decline with increasing salinity
above this threshold has been measured for common agricultural
plants (Maas and Hoffman, 1977). These relationships were developed under artificial conditions in ‘sand tanks’ and did not account
for additional stresses from waterlogging or drought. This combination of stresses is important in pastures (Bennett et al., 2009b).
Horticultural crops have a similarly variable tolerance to salinity
depending on variety and rootstock. Not only the quantity but also
quality of products may be affected by salinity; both positive and
negative impacts may occur simultaneously; for example, increasing salinity reduced citrus yield and juice content, but increased
fruit sugar levels (Grieve et al., 2007). It is more common for a
general decline in quality with increasing salinity; for example,
irrigating grape vines with saline water increased the pH and
sodium content of grape juice, which reduces wine quality
(Stevens et al., 2011).
Some salts in the stream waters of the MDB are more damaging
than others (NaCl is more negative than Ca2 þ or SO24 salts). The
source of the most damaging salts may be found by measuring
changes in stream chemistry in space and time or relating stream
chemistry to hydrology (Conyers et al., 2008). Perhaps the most
difficult aspect of predicting salinity impacts is the variable time
lag after changes in pressures (e.g. rainfall) (Rancic et al., 2009).
3.2. Acidity
3.2.1. Acidity drivers
Hydrology and land use change are the most significant drivers
of soil acidification (Fig. 2). Climate has been recognised as a soil
forming factor (Jenny, 1941), which drives weathering processes
that acidify soils. Climate also drives natural acidifying processes
such as the leaching of HCO3 , the replacement of CO2 with organic
acids and the net gain in H þ ions from the transformation of N.
Rainfall is the most important driving component of climate. A link
between the rainfall and soil acidity has been observed in southern NSW (Chartres et al., 1990; Scott et al., 2007). Clear rainfall-soil
pH trends have also been found across the whole of Australia
(de Caritat et al., 2011). Such spatial relationships also correspond
with gradients in vegetation cover, N fixation and nitrate leaching,
all of which are associated with acidification processes (Kemmitt
et al., 2006). Overall, it is climates with the highest rainfall that
most strongly drive acidification. Changing land use can also drive
acidity strongly. Converting native vegetation to agricultural use,
particularly to pasture, is the change most associated with acidification (Scott et al., 2000). Land use is never a solitary driving
factor and past (and current) management practices play a role in
determining the importance of land use as a driver. For instance,
the application of limestone is a common management response
to increase agricultural productivity (Conyers et al., 2003). Furthermore, the drivers of climate and land use change often operate
simultaneously to form acidifying pressures.
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J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
Fig. 3. The area of groundwater salinity within the Murray–Darling Basin classified as fresh, brackish and saline using data from Williams et al. (1994).
3.2.2. Acidity pressures
Acidification pressures are applied through the major nutrient
cycling pathways (Fig. 2). Any change to the C, N and S cycles
significantly affect the rate of soil acidification (Bolan and Hedley,
2003). Overall, the cycling of N is more important than that of other
nutrients (such as C and S) on land used for agricultural activities in
terms of acidifying potential (Helyar et al., 1990). Each land use is
associated with a particular level of consumption and flow of
nutrients. The nature and level of nutrient cycling determines the
pressure that is applied. In contrast to salinity, all these nutrient
cycling pressures are in-situ and not external to where acidification
develops. Mineralization and oxidation produce protons (Hþ ions)
and increase acidifying pressure. Examples include the mineralization
of organic N to nitrate and adding N fertilizers to crop or pasture
production systems leading to oxidation of NH4þ to NO3 (which
generates H þ ions). Removing agricultural products (e.g. hay or grain)
also removes nutrients and transfer anions; the net effect is to
uncouple the H þ balance. Likewise, increased acidification pressure
can come from grazing and the deposition of manure transfers anions
to “stock camps” which change the distribution of nutrients (Condon
et al., 2004). Other acidifying land use factors include stocking rate and
pasture species (especially a high proportion of legumes) (Scott et al.,
2000). Increasing levels of soil organic matter cause an acidifying
pressure as the breakdown of organic N releases protons when
carbonic acid is formed from CO2 (Bolan and Hedley, 2003). Applying
fertilizer does not always increase pressure, using calcium nitrate as
the source of nitrogen can moderate acidification (Bolan and Hedley,
2003). Hence, the selection of fertilizer type and its rate of application
will influence the level of pressure applied.
3.2.3. Acidity state
The state for acidity is reflected in the soil pH, soil nutrient
deficiencies/toxicities and the nitrate content of water (Fig. 2). In
2001, it was estimated that between 18 and 19 million ha of land in
NSW had acidic soil (Dolling et al., 2001). In soil the critical level is
typically defined as a pH (CaCl2) o5.5 for the top soil 0–10 cm depth.
Over time, soils across NSW have become more acidic (Fenton and
Helyar, 2007), and within the MDB soils have acidified as more land
has been used for agriculture (Lockwood et al., 2003). Acidity affects
up to half of Australia’s agriculturally productive soils (McKenzie and
Dixon, 2006). Within the MDB there is a significant area (114,350 km2
or 11%) of acidic soils where the surface soil field pH is o5.5 (Fig. 4;
de Caritat and Cooper, 2010). Indicators of acidic soil include elevated
J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
61
Fig. 4. The soil field pH for surface soils (0 to 10 cm) in the Murray–Darling Basin. Data from de Caritat and Cooper (2010).
levels of Al and Mn, while it is common that plant available levels of
soil P are lower (Conyers et al., 1991). Soil acidification is increasing in
several catchments within the MDB and is considered to be widespread in the farmlands of southern Australia (SoE, 2011). On
agricultural soils acidification processes have been observed to be
associated with nitrification and nitrate leaching which pose a risk to
water quality. Consequently, the nitrate content of water in streams or
groundwater supplies has been found to increase as a result of
acidification and nitrate-N can also be used as another state measure
of acidity (Ridley et al., 2003). The ANZECC (2000) water quality
guidelines have set the maximum trigger value at 30 μg L 1 for
nitrate-N in freshwater ecosystems such as rivers and wetlands.
3.2.4. Acidity impacts
Soil acidity is a global issue reducing crop yields and constraining agricultural production, and affects 17% of natural ecosystems
(Bouwman et al., 2002). The major impacts of acidity are on crop
yield/quality for food production and nutrient content for water
quality (Fig. 2). In the MDB, several studies have reported negative
effects of soil acidity on crop (food) production, particularly in
southern NSW. For example, acidic soil significantly reduces the
yield of wheat (Conyers et al., 2003) and pasture (Li et al., 2006).
Increasing soil acidity on the South West Slopes of NSW reduced
the number of palatable species and feed quality of pastures
(Li et al., 2003). Grain quality as measured by protein content is
lower on acidic soils (Mullen et al., 2006).
Ridley et al. (2001) found that annual pastures grown on acidic
soils receiving no N fertilizer lost up to 33 kg N ha 1 year 1 in
both surface runoff and subsurface flow. Given the large area of
acidic soils (i.e. pH o5.5) within the MDB (see Fig. 4) there is
significant potential for acidity to have a major effect on water
quality. Since acidity reduces plant growth and cover in specific
areas, other impacts have been reported such as increased sedimentation (Cumming and Walker, 1981). Thus, soil acidification
can reduce water quality by two different means; (i) by increasing
turbidity, and (ii) by increasing nutrient concentrations (such as P
and N) (Scott et al., 2000). There are a number of impacts that arise
from soil acidity with negative effects on food production and
water quality within the MDB (as described above) and many
other agro-ecosystems across the globe.
3.3. Erosion
3.3.1. Erosion drivers
Erosion, is “the wearing away of the earth’s surface” (Rosewell
et al., 2007), p. 14), and shares the drivers of climate and land use
with salinity and acidity (Fig. 2). Water and wind erosion are
separate physical processes but share common drivers in climate
and specific combinations of weather conditions, particularly the
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interaction between rainfall intensity and hydrological balance.
Soil type and its parent material (geology) and the slope of land
strongly influence water erosion processes (Morgan, 2005). The
effect of rainfall on the level of wind erosion has been observed in
the MDB; for example, from 1971 to 1980 low wind erosion
activity corresponded with greater than average rainfall, while
from 2000 to 2005 there was active wind erosion during drought
conditions (McTainsh et al., 2007). The negative relationship
between wind erosion and rainfall is largely due to the effect that
moisture levels have on vegetation cover, but other factors such as
stocking rate are also important.
Changes in land use are major drivers of erosion, especially
activities that involve reducing ground cover (e.g. forest clearing)
(Prosser and Abernethy, 1999). Intense agricultural activity is a major
land use driver of erosion; for example, the total suspended sediments
from horticulture ( 3000 mg L 1) were 10 times greater than from
the grazing of native pastures ( 300 mg L 1) (Bartley et al., 2012).
Cropping land has, in general, much higher erosion rates (up to 84
times greater) than pasture (Edwards and Zierholz, 2007; Wallbrink
et al., 2003). The type and density of vegetation affects soil stability
and is equally important for water and wind erosion, especially on
susceptible soils (Rosewell et al., 2007). Consequently, the effect of a
given land-use type as a driver is dependent upon the management of
a given area of land. How, when and where vegetation cover is
managed, such as via stocking rate (Lilley and Moore, 2009), strongly
influences the nature of land use as a driver (see Section 4).
3.3.2. Erosion pressures
Erosion pressures are applied through hydrology and/or the wind
(Fig. 2). The main hydrological process is runoff and this is strongly
influenced by rainfall pressure. There are many hydrological factors
that determine sediment production and deposition including antecedent soil water content, raindrop impact and soil infiltration rate
(Rose, 2004). Dry soils are also more susceptible to the detachment
and lifting of soil particles by wind (saltation). The temporal and
spatial patterns of wind erosion correspond with those in rainfall,
which governs not only soil water content but plant growth and
ground cover too. The hydrological condition influences rainfall so that
dry periods increase the erosion pressure, while wet or moist
conditions reduce the pressure. Hydrology is not completely dominant
and works with wind to exert pressure on where and when wind
erosion occurs.
3.3.3. Erosion state
The state of water and wind erosion can both be measured directly
by the amount of soil lost, indirectly as sediment yield or as the
consequence of that loss either as amount of soil in the water (its
turbidity) or in the air (the dust storm index) (Fig. 2). Water erosion
varies throughout the MDB (Lu et al., 2006). For example, the Northern
Tablelands of NSW yield between 10–25 t sediment ha 1 year 1
which is much greater than the central and western parts of the
MDB where rates are o0.1 t sediment ha 1 year 1. The large spatial
variation in the hill-slope sediment delivery ratio modelled by Bui et al.
(2011) means that it is difficult to estimate the state of water erosion
for the whole of the MDB. Water erosion studies have produced widely
ranging estimates of the change in sediment flux caused by European
settlement (and the associated changes in land use). For example, in
the Upper Murrumbidgee catchment Olley and Wasson (2003) report
that current sediment yield estimates are 250,000 t year 1 which is
100 times higher than before European settlement (and the introduction of intensive grazing), but estimated to be half the sediment
yield during the 1850–1900 period.
Stream turbidity may identify where and when erosion is occurring, but not to quantify it. Estimating the change in the state of
erosion is difficult, as increased erosion may be disconnected from
sediment load. For example, an increase in erosion by 370% resulted
in only 150% more sediment in the Murrumbidgee catchment
(Verstraeten and Prosser, 2008). The turbitity of the MDB’s waterways
have been recently rated as ‘very poor’ and ‘poor’ following national
guidelines (Sinclair Knight Merz, 2011).
Wind erosion occurs in the western, more arid and flatter parts of
the MDB, and large areas have been identified where wind erosion is
severe (Smith and Leys, 2009). This is confirmed by modelled wind
erosion estimates from 2009 (Fig. 5) (Butler and Pudmenzky, 2011).
Observations of wind erosion for 430 years have shown that there
have been active periods where visibility is significantly restricted (this
may be 4640 g ha 1 year 1) and relatively inactive periods when no
wind erosion has been recorded (McTainsh et al., 2010). Regular
observations of dust in the MDB have been made to inform modelling
of wind erosion activity in DustWatch, which is a national monitoring
program (DustWatch, 2012). State measures of erosion (such as
sediment yield or the dust storm index) describe the area where
and the time when a threat is active, which can assist in the estimation
of the impact caused.
3.3.4. Erosion impacts
Soil loss diminishes the resource base to grow plants to produce
food, provide shelter for stock and habitat for native animals. Erosion
has had significant negative effects on agricultural production and the
environment in the MDB, especially during the later stages of the 19th
century and first half of the 20th century (Scott, 2001). The impact of
erosion on crop productivity is greatest in the early stages of erosion
(for all soil types), however the long-term effects vary with soil type
and location (Bui et al., 2011). The degree of impact also depends on
the depth of the top soil and the continuing erosion rate. Erosion
reduces crop production because organic carbon and nutrients are lost
with the soil loss (e.g. P bound to sediment) (Rosewell et al., 2007).
Edwards (1993) reported that crop yields were reduced by up to 52%
in the Central West of NSW owing to erosion and that losses were
greater on land used for summer crops than for winter crops.
The off-site impacts of erosion are from the flow of sediments
which reduce water quality or in the case of wind erosion, air quality.
Soil water erosion transports sediment with nutrients (Bui et al., 2011;
Edwards, 1993) and the transport of nutrients (e.g. N and P) bound to
eroded sediments is well documented in the MDB (Lane et al., 2008;
Smith et al., 2010; Wallbrink et al., 2003). Hence, the loss of top soil (i.
e. 0–5 cm depth) has a significant impact on both soil fertility and
water quality. The ecological impact of sediment in the Murrumbidgee
river is reduced production (photosynthesis) of organisms such as
phytoplankton, because of lower light transmission and smothering
highly productive biofilm algae (Lovett et al., 2007). In addition, Lovett
et al. (2007) reported that sediment transport often increases nutrient
loads which foster eutrophication or algal blooms. This significant
water quality impact has implications for the food web of riverine
ecosystems and illustrates the broader effect of erosion within agroecosystems.
4. Management responses to land degrading threats
Management responses are the final component of the DPSIR
framework and provide options to cope with the impact of threats
(Fig. 2). A sound response requires understanding of the interactions
between drivers, pressures and impacts, and can include policies or
government programs. This review focuses on just the biophysical
mechanisms to facilitate change to maintain the ES of food production
and water quality; policies to enable these changes are not considered.
Due to the widespread nature of salinity, acidity and erosion across the
MDB, there is a particular need to know when, where and which
management practice(s) should be implemented. The incidence and
severity of these threats varies greatly in time and space, however
J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
63
Fig. 5. The modelled wind erosion rate (mg m 1 s 1) of the soil surface in 2009 for the Murray–Darling Basin. Data were sourced from Butler and Pudmenzky (2011).
continuous measurement is very expensive. Consequently, the state
can be assessed by indicators or direct measurement taken less
frequently and following when/where a significant impact occurs, or
at a set time interval. It is recognised that for all of the threats
considered there is a maximum level to which adaptive management
responses can be applied, and a range of conditions where adaptive
responses perform well; however, above a given level (e.g. very high
salinity) a mitigative response is required. Because adaptive responses
are targeted at the state or impacts (Fig. 2), they typically require
significantly lower levels of resources (capital, labour) compared to
mitigatory responses which are directed to pressures or drivers
(Fig. 2). Adaptive responses are more nature-based interventions than
mitigatory responses and aim to allow the ecosystem to function
closer to its’ existing status. Furthermore, the consideration of the
scale and spatial variability of the threat is critical for optimal selection
and application for each management response. Thus, while a risk of
mismatch between the scale of the threat and management response
exists (Pelosi et al., 2010), the use of Fig. 2 to clearly identify the driver,
pressure, state or impact in the decision making process should
improve the selection of the management responses listed below.
4.1. Adaptive management responses
Adaptive management strategies employed to cope with increasing
salinity include the use of salt tolerant plants (halophytic plants such
as saltbush) (Collard et al., 2011) and crop varieties with high salt
tolerance (Munns, 2005). Alternatively, changing to annual crops with
higher salinity tolerance (e.g. from wheat to barley) may be sufficient
to deal with salinity on a given parcel of land. As a response to salinity,
these adaptive practices offer opportunities to maintain food production, but are unlikely to improve water quality. Modified irrigation
practices offer the possibility to manage salinity by leaching salts from
the soil as a means to maintain food production (Duncan et al., 2008).
Targeting the salinity threat is more than just identifying the potential
responses, and the best results require careful spatial placement of
management actions. Strategically locating management responses
can improve their effect on the impact target (i.e. water quality)
(Nordblom et al., 2010).
The simplest and most common adaptive management for acidity
is to grow acid tolerant species (Hayes et al., 2008). Some native plants
can adapt to acidic conditions using a mechanism known as ‘biological
pumping’ where cations from the subsoil are balanced by anions from
a plant; Tighe et al. (2009) found field evidence of this mechanism in a
semi-arid region with invasive native scrub. Liming is the most
common way to manage the acidity of agricultural soils and it differs
from other adaptive responses because it changes the state (Fig. 2).
Applying lime raises the soil pH by changing the nutrient cycles; with
the amount of lime required determined by the soil’s buffering
capacity (White, 2006). The liming of acid soil can increase cereal
grain yields (Conyers et al., 2003) and both pasture and crop
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production benefit from liming (Evans and Scott, 2007). While liming
can maintain or increase food production there is evidence that liming
pastures has a negative impact on water quality (Ridley et al., 2001).
Because of concerns about the environmental impacts of soil acidification, the Soil Conservation Service of NSW increased the scale of lime
application by providing farmer subsidies (Scott et al., 2000). Typically
lime application is done on a field by field basis, but these subsidies
were provided in order to encourage more widespread use of lime and
thus to minimise siltation of the new Pejar reservoir (Cumming and
Walker, 1981).
Adaptive management of erosion is based on protecting the soil
surface, largely through modifying vegetation cover. The choice of crop
type influences the likelihood and severity of soil water erosion. For
example, cereal crops are superior to peas (Malinda, 1995) in minimising erosion and the loss of soil. In grazed landscapes, vegetation
cover can be managed through manipulating stocking rates (Lilley and
Moore, 2009). Conservation farming practices have been credited with
reducing erosion from cropping land (Thomas et al., 2007). Freebairn
et al. (2009) reported that the increased adoption of conservation
tillage over an 18 year long study in southern Queensland corresponded with reduced sediment losses. Other practices which can also
influence sediment deposition include vehicular traffic (Li et al., 2008),
mulching (Dorahy et al., 2008) and the amount of stubble retained
(Packer et al., 1992), because of the potential erosion pressure which
can be caused. This is due to the effect on the soil surface (traffic) or
through the protection of the soil surface (mulch, stubble). A thorough
consideration of the scale and source of the erosion threat is required
for applying adaptative management responses over a large study area
such as the MDB. For instance, Bowmer (2011) reported that the
localised benefits from stubble farming (in southern regions) are likely
to be exceeded by erosion from gullies. Thus, the potential benefits
from erosion management (in terms of reducing sediment losses) of
gullies (a relatively small area) may be more important than an area
protected by stubble (i.e. a larger cropped area). For wind erosion,
there are few responses that are specifically targeted at this threat,
however windbreaks are recommended in South Australia on light
sandy soil types to protect crops against sandblasting (Bennell et al.,
2007). The effectiveness and longevity of the adaptation management
practices described above are limited and strongly relate to the land
use; this in turn affects the extent that food production is maintained
or water quality is protected.
4.2. Mitigatory management responses
Salinity mitigation has focused largely on reducing groundwater
rise and/or stream salt load through changing catchment hydrology (a
pressure) by reducing recharge or via an engineering approach.
Changes to catchment hydrology can be achieved by establishing land
uses (drivers) that mimic the water-use patterns of pre-European
settlement vegetation (Eberbach, 2003). In the MDB, land uses have
been promoted that include trees or other woody perennials planted
as alleys within crops or pastures, strategically located vegetation
blocks or evenly spaced ‘woodlands’, and perennial pastures. The
hydrological pressures associated with salinity involve complex
upstream-downstream interactions. A catchment analysis of potential
land-use changes on salt load by Nordblom et al. (2010) highlighted
the significance of spatial placement for tree plantations (as a
management response) and evaluated the subsequent effectiveness
in mitigating against salinity.
Engineering solutions implemented in irrigation areas include
sub-surface drainage, groundwater pumping and aquifer depressurisation. These can minimise widespread or ‘diffuse’ salinisation;
for example, the use of subsurface drainage to control the height of
water tables (Hornbuckle et al., 2005). Alternative approaches deal
with the disposal of dilute saline effluent. The practices involve
effluent reuse on farms (Smith et al., 1996), evaporation basins at
farm (SPPAC, 1989) or district scale (Ahmed et al., 2000) or the
serial biological concentration (SBC) of salts which can be applied
to treat discrete or point sources of salt (Khan et al., 2007). SBC
involves the collection of water with relatively low salinity, which
is then used to irrigate a series of increasingly salt tolerant plants.
Drainage past the roots at each stage is collected and applied to
the next, more salt-tolerant stage. Thus a large volume of dilute,
difficult to dispose of effluent, is converted to a small volume of
highly concentrated brine, which could be converted to salt. The
SBC response involves a major intervention to land use (drivers)
and hydrology (pressure) which is problematic from a practical
and financial viewpoint. For example, Connor (2008) analysed the
financial cost of an engineering project to pump saline water to
evaporation ponds, and found that it would potentially take
several decades until the benefits exceeded the costs. Major
hydrological interventions that change the flow regime of a
catchment such as dilution or environmental flows have been
proposed as a mitigatory response to target salinity in the MDB
(Goss, 2003; MDBA, 2010b).
While mitigatory salinity management practices typically target land use (driver) or hydrology (pressures) (Fig. 2), at times they
are also adaptive. Actions such as planting salt tolerant saltbush
are an adaption to the enduring saline state (Seddon et al., 2009),
but also have a mitigating effect by changing hydrology/water use.
Lucerne and other perennial plant species have been used to
manage the risk of salinity in pasture due to a hydrological change
(Lefroy et al., 2005). Likewise, opportunistic summer forage cropping has been found to be effective in reducing deep drainage
(Wang et al., 2008). It should be noted that both lucerne and
summer forage cropping are financially beneficial to the farmer as
they produce income. For all the mitigatory management
responses given above, the best results will occur where the scale
of the salinity threat is well matched to the drivers or pressures
being targeted.
Acidity is usually managed by adaptation; the mitigation of acidity
has received much less attention. Land use and acidification rates are
linked; land use intensity and net acid addition rate may predict areas
at risk of acidification (Wilson et al., 2009). This was confirmed in
southern NSW where cropping land and pasture land had different
levels of surface soil acidity (Scott et al., 2007). While other drivers of
acidity such as rainfall were important, this study concluded that land
use dominated regional patterns of soil acidity. Consequently, farmers
and land managers can change land use as a management response to
mitigate acidification. In some locations, changing the land use to
forestry is recommended, while elsewhere land retirement for recreational use (such as non-commercial land holdings or “hobby farms”)
or crop rotations may be the best option (Scott et al., 2000). In a survey
of crop rotations across southern Australia, Coventry et al. (2003)
found that nutrient cycles, specifically either the N or C cycle, were an
associated part of crop rotations that have the potential to increase the
acidifying pressure. Modifying crop rotations can be considered as
land use change; rotations which add excessive nitrogen will have an
acidifying effect. There are several practices that can be applied to
target nutrient cycle pressures, especially N losses. Responses include
practices which reduce nitrate leaching, minimise product removal
and maintain a sound nutrient balance (Peters et al., 2011). Nitrate
leaching can be managed by reducing the amount of N fertilizer
applied and by optimising the timing of application (Di and Cameron,
2002). Grazing management and in particular reducing the stocking
rate can reduce acidification rates (Scott et al., 2000). In summary,
mitigatory responses for acidity are focused on making changes to
land use (a driver) or the nutrient cycle (a pressure). These practices
aim to protect water quality or to maintain food production at a
modified level. Management responses for acidity (both adaptative
and mitigatory) are most typically introduced at the field scale. It
remains a challenge to apply large scale responses (i.e. across many
J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
landowners) that target acidity. Management responses that limit
water erosion by hydrological means use engineering approaches
such as terraces and waterways or bio-engineering with shelterbelts
(Morgan, 2005). Changing tillage practices to increase infiltration and
surface roughness can reduce runoff and erosion. Adopting no-tillage
and conservation farming practices have reduced erosion and the
associated soil loss from crop production systems (Freebairn et al.,
2009; Thomas et al., 2007).
Increasing vegetation cover is one way to mitigate both water and
wind erosion. Vegetation cover can be maintained by halting the
conversion of native vegetation to crop or pasture (Thornton et al.,
2007), growing cover crops between the rows of grapes in vineyards
(White, 2009) and stablizing vulnerable slopes with vegetative hedges
(Truong and Scattini, 1990). Minor changes at the farm level may lead
to significant benefits over a greater scale. A rotation could be changed
to increase the duration of vegetation cover by growing a greater
proportion of perennial pastures. Land use suitability criteria could be
used to decide to not grow crops on land with a high risk of erosion.
5. Discussion
We have reviewed the causes (drivers, pressures) and effects (state,
impacts) of three land degrading threats (salinity, acidity and erosion)
to the ES of providing food and managing water quality. We also
examined options or solutions to respond to these threats. The causeeffect relationships and management responses we describe are
generic and may be applied globally. We now evaluate the relationships and interactions between management responses and the two
selected ES which can result in either trade-offs or synergies. Tradeoffs between ES occur when a management response increases the
provision of one service while decreasing the provision of another
service (Bennett et al., 2009a). In contrast, synergies develop when a
management response results in the increased provision of two ES. It
is inevitable that ‘winners’ or ‘losers’ emerge when attempting to deal
with either salinity, acidity, erosion or other threatening processes.
Finally, we discuss future priorities for developing a better understanding of threats to ES provision and indicate critical implications for
related policy.
5.1. Trade-offs and synergies in ecosystem services from
management responses for salinity, acidity and erosion
Following the approach of Bennett et al. (2009a), we evaluated
the nature of the relationships between management responses
and key ES (Fig. 6). This allows detection of important attributes of
these relationships: (i) the relative strength of the connection
between the management response with the ES; (ii) the extent
and type of the interaction between the ES; (iii) how both ES are
directly affected; and (iv) whether a trade-off or synergy is likely
to develop between the ES. To represent the ES in Fig. 6, we have
given different impacts for food production and correspondingly
different states for water quality.
In sector 1 of Fig. 6 salt tolerant crops are grown on saline land.
There is a direct positive effect on crop production, but no effect on
either land salinity or water quality. This management response does
not result in either a trade-off or synergy, it can be described as having
a win-benign effect. A similar management response to acidity is the
growing of acid tolerant crops. An example of a trade-off occurs with
the application of limestone (CaCO3) to acidic soils, whereby pasture
production may increase, but water quality decreases (Sector 2, Fig. 6).
Because there is no interaction between the ES for Sectors 1 and 2 in
Fig. 6, the ES connections are weak. Here, the dynamic nature of the
response in biophysical agro-ecosystems ought to be considered; thus,
determining the long-term residual benefits of applying limestone is
critical (Conyers et al., 2003). Likewise, the state of the soil (see Section
65
3.2.3.) may be managed according to the desired level of service
regulation (i.e. acceptable water quality), type of service provision (i.e.
crop grown) and their combination.
The strategic use of tree belts as a response to salinity
illustrates the importance of understanding the interactions
between the ES (Sector 3). Crosbie et al. (2007) reported that tree
belts can have significant hydrological implications (a pressure)
which in turn change the movement of salt. Here the management
response has a single direct, positive relationship with the water
quality ES and has the potential to increase crop production via a
hydrological means (i.e. by reducing the extent of watertable rise).
In this example, there are also positive effects on biodiversity (via
landscape connectivity); thus Fig. 6 could be expanded to include
other ES. This management response results in a synergy and has a
win:win effect on the selected ES. An example of better-connected
ES is given in Sector 4 where a modified irrigation (with a reduced
leaching fraction) response is shown (Fig. 6). The strength of
connection from crop production to salt load depends on the
elapse of time: in the short term there are stronger effects on
water quantity, while the water quality (e.g. salt load) effects
develop over the long term (Herron et al., 2003). Moreover, in
sector 3 and 4 of Fig. 6, we provide examples of how different
management responses to the same threat result in contrasting ES
interactions, although both responses are focused on influencing
the pressure (hydrology).
The nexus between water quality and quantity (water flow) has
been described as the common hydrological trade-off for salinity
management (Cocks, 2003). However, we show that this need not be
the case. If irrigation is managed to minimise the leaching fraction and
salt delivery to groundwater it is possible to increase crop production
while reducing salt load (Sector 4, Fig. 6) (Thayalakumaran et al.,
2007). Unfortunately, irrigation practices in the MDB often increased
salt mobilisation and resulted in a trade-off with both crop production
and salt load increasing (Duncan et al., 2008). Increased connections
between ES occur in Sectors 5 and 6 (Fig. 6). The minimum tillage
management response decreases both the erosion threat and the
sediment load. There is a positive two-way interaction between the ES
which retains more soil and increases crop production; thus, in the
growing season the crop provides biomass which in turn enhances
erosion control (Freebairn et al., 2009). Serial Biological Concentration
(SBC) is an engineering solution that realises a synergy between the
competing ES of crop production and water quality (Sector 6, Fig. 6).
Of all the examples given, the SBC management response contains the
most interactions between the ES and the response; it is the most
complex and highest risk example given. It is difficult to successfully
achieve SBC of salts, as either an incorrect choice of crop or hydrological leakage would turn the synergy (Table 2) to a trade-off
between ES. It is worth noting that Khan et al. (2007) discussed
hydrology and water table fluctuations in their assessment of SBC.
Evaluating the MDB with the two selected ES is a useful manner in
which to gauge the functional performance of this agro-ecosystem.
5.2. Future priorities for research of land degrading threats
To better understand how the selected threats relate to ES,
more attention must be paid to monitoring and assessment, and
whole-of-systems research.
Monitoring and assessment: There is a great need for improved
assessment of the state of salinity and water erosion in the MDB.
New data would enable the effect of these threats on ES to be
better understood and also to assess the effectiveness of recent
management responses. In comparison, the available information
on acidity (soil pH) and wind erosion is better. Despite the amount
of attention on salinity, the most recent state data are 410 years
old (Jolly et al., 2001; MDBC, 1999; Williams et al., 1994). New
surveys and data are needed on stream salinity (including salt
66
J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
Fig. 6. Examples of the relationships between management responses with two key ecosystem services (food production and water quality) and the connections between
these ecosystem services for the threats of salinity (S), acidity (A) and erosion (E). (Solid arrows are positive effects, dashed arrows are negative effects and dotted arrows are
positive interactions; with the circles for the ES the vertical arrows indicate an increasing (pointing up) or decreasing (pointing down) response, while the horizontal arrows
indicate very little or no change. Crop production/yield and pasture production are used to indicate food production; salt load, nitrate level and sediment load are all
measures of water quality.)
Table 2
Management practice response examples, their corresponding trade-offs or synergies and ecosystem service interactions for the threats of salinity, acidity and erosion with
key references.
Sector Management response
Threats targeted
Trade-off or
synergy
Interactions between ecosystem
services
Reference
1a
1b
2a
3a
3b
4a
4b
Growing tolerant crops
Growing tolerant crops
Liming
Perennial vegetation (tree belts)
Mulching
Modified irrigation
Change crop type
None
None
Trade-off
Synergy
Synergy
Synergy
Synergy
None
None
None
Unidirectional,
Unidirectional,
Unidirectional,
Unidirectional,
Collard et al. (2011)
Hayes et al. (2008)
Conyers et al. (2003)
Crosbie et al. (2007)
Dorahy et al. (2008)
Duncan et al. (2008)
Carroll et al. (1997)
5a
6a
Synergy
Synergy
Bidirectional, positive
Bidirectional, positive
Freebairn et al. (2009)
Khan et al. (2007)
6b
Minimum tillage
Engineering works (serial biological
concentration)
Controlled grazing
Salinity
Acidity
Acidity
Salinity
Erosion
Salinity
Salinity, acidity,
erosion
Erosion
Salinity
Erosion, acidity
Synergy
Bidirectional, positive
6b
Stubble retention
Erosion
Synergy
Bidirectional, positive
Lilley and Moore (2009), Scott et al.
(2000)
Packer et al. (1992)
a
b
positive
positive
positive
positive
These examples are illustrated in Fig. 6.
These sector numbers would apply to these examples and have the same type of ES relationships/interactions.
composition), groundwater salinity and the area of salinised soil.
For water erosion, more data on sediment deposition and the
erosion rates for different land use types across the MDB are
required. Additional observations on sediment yields and associated nutrient loads would be useful to update and improve upon
modelled estimates (Lu et al., 2006). Continued monitoring and
assessment is required to provide up-to-date data to better understand the spatial and temporal changes from threats on the state
and their associated impacts on key ES in the MDB. At the global
level there is a great need to gather new data on the state for
salinity, acidity and erosion to determine any changes since
previous surveys or estimates (Oldeman, 1994; Sumner and
Noble, 2003; Tanji and Kielen, 2002). This will allow the effect
that these threats have on international food security to be
assessed and any effects on the status of other vital ES to be
realised. Furthermore, the application of DPSIR at any scale (from
country to basin or catchment) enables the basis for a risk
assessment to be developed of multiple threats for ES such as
water quality, food or others.
Whole-of-systems research: The commonality of and interaction
between the drivers and pressures of the threats studied here is
clearly shown by the DPSIR framework (Fig. 2). There have been
very few studies in Australia that have investigated these interactions. The hypothesis of Conyers et al. (2008) linking soil acidity
and the mobilisation of salts in the mid-Murrumbidgee river
catchment provides a strong indication that major threats interact
J.E. Holland et al. / Ecosystem Services 12 (2015) 55–70
at the landscape scale, but this requires further validation. In
addition the relationship between soil acidity and erosion
observed by Cumming and Walker (1981) has not been thoroughly
studied over widespread areas of the MDB. This suggests that
fundamental understanding on the causes of land degrading
threats is limited. We found that a large number of management
responses are well documented, but consider only a single threat
or a single land use change. Interactions between management
practices are poorly understood, especially where two or more
threats are present. Research needs to consider the external effects
of farm scale action and vice verse regional solutions need to
consider the farm scale consequences of the land use changes they
call for.
Many studies that report on the impacts of threats have limited
scope, (e.g. the effects of soil acidity on wool production (Li et al.,
2006b), while providing no indication of the wider impacts on
other aspects of production or the environment (including water
quality and other ES). Hence, there is uncertainty on the aggregate
impact of each threat at a large scale such as for the MDB. Also,
there is a need to know the broader impact of salinity, acidity and
erosion on other ES, especially climate regulating services (e.g.
evapotranspiration which controls the amount of water vapour
entering the atmosphere (UK NEA, 2011). It follows that the
impacts on all ES at both the regional and global level need to
be regularly (at least once per decade) determined due to
significant recent changes in the drivers. The brevity of most
studies misses the large temporal changes which drive agroecosystems. Long-term studies are needed to better quantify the
drivers, pressures and state of salinity, acidity and erosion. Wholeof-systems research is invaluable for determining the impact on ES
from specific and multiple threats and also unravelling the complexity of ES trade-offs. A whole systems approach is also advocated (called ‘ecological intensification’) by Hochman et al. (2013),
with the aim of improving resource use efficiency and reducing
the negative impacts from agricultural land use in Australia. Future
studies on threats should be undertaken at a large scale over the
long-term to improve the prediction of impacts on ES.
67
address multiple impacts are encouraged, although the spatial
variability of land use makes this difficult. Nevertheless, some
management responses such as controlled grazing or changing
crop type (Table 2) can be implemented in different ways to target
several different threats. Implementation of management
responses should be encouraged where and when synergies are
most likely, while the risk of trade-offs should be identified and
decisions made according to the competing interests. In many
cases trade-offs can not be avoided, but as this study has shown,
by applying the DPSIR framework, relationships can be identified
to estimate trade-offs or synergies. Bryan and Crossman (2013)
outline the complexity in achieving efficient and desirable policy
outcomes and concluded there is a need to account for the
interactions between multiple ES for the management of agricultural land use. Nevertheless, when it comes to options that protect
water quality there is often a need for incentives to change
behaviour and for community involvement (Bowmer, 2014). The
spatial distribution of different types of land managers is a
significant challenge to policy development. Land tenure differences across the MDB create difficulties in accommodating the
varied objectives with regard to ES. ES relationships with management responses should be used for the development of policy that
accounts for ES provision at a range of scales. Further elucidation
of ES relationships and interactions will ensure that policies are
developed that encourage synergies rather than trade-offs. The
assessment by Jones et al. (2013) of nitrogen impacts on ES show
the importance of determining the net benefit in ES even though
there may be uncertainty or difficulty with this calculation. In the
same way policy measures to address landscape-scale threats such
as salinity, acidity or erosion ought to work towards overall net
benefits for ES and not try to pick single winners. Therefore, policy
development for these threats must thoroughly account for the
drivers, pressures and state (Fig. 2) and ES impacts, while
acknowledging the temporal and spatial variability that characterise the agro-ecosystem.
Acknowledgements
5.3. Implications for policy
Previous programs to manage the agricultural and natural
resource bases of the MDB have often been focused on a single
threat e.g. Acid Soil Action (NSW Agriculture, 2000) or National
Action Plan for Salinity and Water Quality (Commonwealth of
Australia, 2000). We clearly show that threats interact and have
multiple impacts on ES. Policy can focus on ES rather than on
threats. However, multiple ES and their interactions must be
considered. Policy should target specific end effects or impacts
rather than selectively focusing on just some ES. For example, the
MDB Basin Plan (MDBA, 2011) has focused on water supply as a
provisioning ES at the expense of water quality as a regulating
service. This leads to an unbalanced treatment of ES, which may
not reflect the importance level of each ES. The DPSIR framework
in Fig. 2 shows the shared drivers, pressures and impacts for
salinity, acidity and erosion. Because there are two common
drivers (climate and land use) between these threats, policy
should recognise there are strong inter-relationships at the landscape scale and not just pay attention to a single threat. Awareness
of the large variability that exists in the time scale over which
different degradation processes operate is also needed. Nutrient
cycling pressures are typically much faster than hydrological
pressures. Thus, policy makers ought to note that the greatest
difficulty and uncertainty lies in making estimates of future
impacts from land degrading threats. Management responses vary
significantly in their rate, effectiveness, cost and scale in targeting
impacts. Thus, policies will be more effective if responses that
The senior author is grateful for helpful discussions and advice
from Dr Mark Conyers, Mr Albert Oates, Dr Tom Nordblom and Dr
Iain Hume (NSW DPI). We would like to thank Ms. Deanna Duffy
(Spatial Data Analysis Unit, CSU) for creating the maps in this
paper. We are grateful for financial support provided for the senior
author to undertake this research. This support was provided by
Prof. S. Thomas (DVC-Research, Charles Sturt University), Prof. M.
Finlayson (ILWS, Charles Sturt University) and Prof. D. Lemerle (E.
H. Graham Centre, Charles Sturt University). The contribution of
Gary Luck was supported by Australian Research Council Future
Fellowship FT0990436G.
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