FOOD AND AGRICULTURE ORGANIZATION OF
THE UNITED NATIONS (FAO)
LAND DEGRADATION ASSESSMENT IN
DRYLANDS (LADA)
Technical Report 2
BIOPHYSICAL INDICATOR TOOLBOX
(Pressure/State)
April 2007
TABLE OF CONTENTS
1.
2.
3.
4.
5.
Page
2
3
Background
Framework for developing indicators/ conceptual model to guide indicator
development
2.1. PSR
2.2. DPSIR
2.3. Shortcomings of the DPSIR framework
Definitions
3.1. Land
3.2. Drylands
3.3. Land degradation
3.4. Indicators
Indicator toolbox
4.1. Levels
4.2. Stratification/mapping
4.3. Categories
4.4. Aggregation
4.5. Selection of indicators
4.5.1. Climate resources
4.5.2. Soil resources
4.5.3. Terrain resources
4.5.4. Water resources
4.5.5. Vegetation resources
4.6. Proposed pressure and state indicators (Table)
References
5.1. Documents
5.2. Websites
3
3
6
6
6
7
7
8
9
9
9
9
10
13
13
14
15
16
17
18
19
19
22
ANNEX 1: Assessment of individual biophysical soil parameters for the aggregation
into one single soil health indicator
ANNEX 2: Physical and biological soil crusts (background information)
ANNEX 3: Comments on Visual Soil-Field Assessment Tool
ANNEX 4: Pressure indicators
ANNEX 5: State indicators
1
24
30
33
34
51
FOREWORD
This paper has been prepared in the context of the work carried out by the LADA project
in order to identify a set of indicators for the assessment of land degradation. After the email consultation realized in 2004, it has become apparent that a huge number of
potential indicators can be identified at every level of scale. Although that would allow
considerable flexibility when implementing an assessment exercise, it would also results
in a multiplication of different and scattered datasets, often not comparable among each
other. This situation would have been not compatible with the internal consistency
required by the project activity.
For this reasons, the project has asked to a consultant, Mr. Frank Berding, to analyze this
situation and produce a selection of indicators, capable to give a comprehensive overview
of the degradation phenomenon but at the same time remaining within a manageable
number of data to be collected and analyzed.
Furthermore, the scope of this work has been limited to only biophysical indicators, and
only those that would fit into the “pressure” and “state” categories of the DPSIR
framework.
The criteria that have been followed in the selection of the indicators presented in this
paper are:
- SMART. The indicators should comply with the requirements of the SMART concept,
so they should be Specific, Measurable, Achievable, Relevant, Time-bound
- In order to limit the number of indicators, aggregate indicators taking into account
several parameters have been created whenever deemed appropriate
The cost of achieving and measuring the indicators has been taken into account, in order
to avoid the creation of an unreachable “wishing list”.
2
ACKNOWLEDGEMENTS
This report is largely based on the work of Mr. Frank Berding, FAO Consultant for the
LADA project.
3
1. Background
Increasing international concern about environmental degradation in drylands resulted in
1977 in the UN Conference on Desertification (UNCOD) and a Plan of Action to Combat
Desertification (PACD). Unfortunately, despite this and other efforts, the UNEP
concluded in 1991 that the problem of land degradation in arid, semi-arid and dry subhumid areas had intensified, although there were “local examples of success”
(www.unccd.int/convention).
One major effort, conducted between 1987 and 1990, was the Global Assessment of
Human-Induced Soil Degradation (GLASOD) project, commissioned by UNEP and
carried out by ISRIC (www.isric.org). The GLASOD project produced a world map of
the status of human-induced soil degradation, based mainly on expert judgment. The
GLASOD criteria for degrees of land degradation tried to specify resilience, i.e. the
capacity of the land to recover quickly to former levels of productivity – or to resume the
trend to increased productivity – after an adverse influence such as drought, floods, or
human abandonment or mismanagement (FAO, 1997).
The UN Conference on Environment and Development (UNCED), which was held in Rio
de Janeiro in 1992 supported a new, integrated approach to the problem, emphasizing
action to promote sustainable development at the community level. Its Agenda 21 called
for the development of indicators of sustainable development. It also called on the UN
General Assembly to establish an Intergovernmental Negotiating Committee to prepare a
Convention to Combat Desertification, particularly in Africa. The Convention was
adopted in Paris on 17 June 1994 and entered into force on 26 December 1996.
Work on the development of environmental indicators had already started in the late
1980s (Canadian and Dutch Governments, OECD, WRI). After 1992 the number of
conferences and workshops on environmental or sustainable development indicators as
well as the number of organizations pursuing indicator work grew substantially.
The LADA project was officially launched in 2002 (although preliminary work, e.g.
workshops, started already in December 2000). It is funded by the GEF in association
with UNEP and the Global Mechanism and is executed by the FAO. It responds to the
need to strengthen support to combat land degradation as foreseen by the UNCCD.
During the first phase of the project (2002-2004), LADA aims to generate up-to-date
ecological, social, economic and technical information, including a combination of
traditional knowledge and modern science, to guide integrated and across-sector
management in drylands. The principal objective of the LADA project is to develop tools
and methods to assess and quantify the nature, extent, severity and impacts of land
degradation on ecosystems, river basins and carbon storage in drylands at a range of
spatial and temporal scales (Nachtergaele, Ispra Workshop, 2002). One of the tools to be
prepared is a set of land degradation indicators that can be used at various levels.
4
2. Framework for developing indicators/conceptual model to guide
indicator development
2.1. PSR
The PSR (Pressure, State, Response) framework, following a cause – effect – social
response logic, was developed by the OECD from earlier work by the Canadian
Government. The PSR framework arises from a simple set of questions (WRI, 1995):
1. What is happening to the state of the environment or natural resources? The answer is
supposed to be given by state indicators (changes or trends in the physical or
biological state of the natural world)
2. Why is it happening? The answer is to be given by pressure indicators (stresses or
pressures from human activities that cause environmental change)
3. What is being done about it? The answer is to be given by response indicators
(measures of the policy adopted in response to environmental problems)
The PSR framework, later also called DSR framework (Driving force, State, Response)
by OECD, was accepted by many environmental agencies in the early 1990s and became
widely used for identifying a cohesive and balanced set of environmental indicators. One
of the main problems with the PSR framework has been trying to differentiate between
pressure and state indicators, and the need to expand the framework to deal more
specifically with the needs for describing sustainable development. For compatibility
reasons and for a better description of those economic and social factors and trends that
are the primary source of environmental problems, the environmental indicator
community has formulated the DPSIR model.
2.2. DPSIR
In 1999 the European Environmental Agency (EEA), on the basis of PSR, developed the
DPSIR framework. DPSIR stands for Driving forces, Pressures, States, Impacts and
Responses and offers a basis for analyzing the inter-related factors that impact on the
environment.
Table 1: Differences between PSR and DPSIR (www.ens.gu.edu.au/aes1161; WRI, 1995)
Framework
PSR
Driving force
-
Pressure
DPSIR
Basic sectoral trends; e.g. energy generation,
transport, industry, agriculture, tourism.
Indicators: underlying factors influencing a
variety of relevant variables; e.g. the number
of cars per inhabitant, total industrial
production, GDP
Underlying factors or forces such as population Human activities directly affecting the
growth, consumption or poverty.
environment; e.g. CO2 emissions.
From a policy viewpoint pressures are often
considered the starting point for tackling
5
State
Impact
Response
environmental issues
Indicators: describe/measure stresses or
pressures from human activities that cause
environmental change; show the causes of
environmental problems: depletion of natural
resources through extraction or overharvesting,
releases of pollutants or wastes into the
environment, and interventions such as
infrastructure development or the conversion of
natural ecosystems to other uses.
Are perhaps more readily available for analysis
since they can be derived from socio-economic,
environmental and other databases
Condition of the environment that results from
the above pressures, e.g. the levels of air
pollution, land degradation or deforestation.
The state of the environment will in turn affect
human health and well-being as well as the
socio-economic fabric of society
Indicators: describe/measure changes or trends
in the physical or biological state of the natural
world; measure the quality or “state” of the
environment, particularly declines attributable
to human activities; e.g. measures of
stratospheric ozone concentrations, of urban air
quality or of stocks of fish
Indicators of state should be designed to be
responsive to the pressures and at the same
time facilitate corrective action
-
Actions taken by society either individually or
collectively, that are designed to ease or
prevent negative environmental impacts, to
correct existing damage, or to conserve natural
resources.
Indicators: describe measures of the policy
adopted in response to environmental
problems; gauge efforts taken by society or by
a given institution to improve the environment
or mitigate degradation; measure how policies
are implemented by tracking treaty agreements,
budget commitments, research, regulatory
compliance, introduction of financial
incentives, or voluntary behavioural changes
Indicators: toxic emissions noise caused by
traffic, parking space required by cars etc
Observable changes of the environment; e.g.
rising global temperature
Indicators: show current condition of the
environment; e.g. concentration of lead in
urban areas, noise levels near main roads,
global mean temperature
Effects of a changed environment; e.g.
decrease in agricultural production,
hurricanes, floods
Indicators: describe the ultimate effects od
changes of state; e.g. the number of children
suffering from lead-induced health problems,
mortality due to noise-induced heart attacks,
number of people starving due to climatechange induced crop losses
Response of society to solve the problem; e.g.
research on solar energy, energy taxes
Indicators: demonstrate the efforts of society
(i.e. politicians, decision-makers) to solve the
problems; e.g. percentage of cars with
catalytic converters, maximum allowed noise
levels for cars; price level of gasoline; budget
spent for solar energy research
6
Conceptual example, based on use of pesticides and impacts on groundwater
Pressure on the environment caused by application of pesticides and one result of this is
an impact on the levels of pesticides in groundwater. These are monitored by
measurement against agreed quality standards. The response to increasing levels of
chemical residues in groundwater is to use the financial instrument of taxation to modify
the levels of pesticide use that are responsible for the pressure. Continued monitoring of
the situation is necessary.
2.2.1. Driving forces/indirect causes
Driving forces are underlying factors influencing a variety of relevant variables. They
represent basic sectoral trends such as energy generation, industry, transport, agriculture,
tourism etc, (www.ens.gu.edu.au/aes1161) which correspond to human activities aimed at
satisfying demands from society. There are also natural driving forces such as global
climate (climate change) and the geological or geophysical setting. Indicators are not
very responsive to policy actions, e.g. road traffic is driven by powerful economic forces
and cannot be expected to change drastically in the future.
2.2.2. Pressures/direct causative factors
Pressures are variables which directly cause (or may cause) environmental (land
degradation) problems. They are exerted by human activities that directly affect the
environment such as carbon dioxide or methane emissions, inappropriate or overintensive land use/land management, livestock grazing in high densities and with static
grazing patterns, fire, whether natural or induced, encroachment of urban centres and
human settlements (increased demand for services such as water, sanitation and waste
disposal; creation of impervious surfaces by paving and compaction of the soil) or by
natural factors such as natural disasters conditioned by the geological setting
(earthquakes, volcanic eruptions, tsunamis) or extreme climatic events (triggered by
climate change). Pressure indicators are essential for measuring the effectiveness of the
responses actuated, together with the attainment of the goals set at the stage when the
action is planned (www.ens.gu.edu.au/aes1161).
2.2.3. State (condition of the land)
State indicators show the current condition of the environment. State indicators such as
the concentration of pollutants (e.g. the concentration of lead in urban areas or the degree
of soil loss) mirror the effects of the pressures exerted on an area as well as being the
final indicators of the effectiveness of the responses adopted. Indicators of state are
generally slow to respond to variations in environmental pressure and so cannot be used
to assess the validity of the policies in the short term (www.ens.gu.edu.au/aes1161).
2.2.4. Impacts
Impact indicators describe the ultimate effects of changes of state (e.g. the percentage of
children suffering from lead-induced health problems; the number of people starving due
7
to soil degradation-induced crop losses. These indicators are still slower than state
indicators to respond to variations in pressure (www.ens.gu.edu.au/aes1161).
2.2.5. Responses
Response indicators demonstrate the efforts of society (i.e. politicians, decision-makers)
to solve the problems (e.g. the percentage of cars with catalytic converters, the price level
of gasoline, the budget spent for solar energy research etc) (www.ens.gu.edu.au/aes1161).
Direct or indirect actions taken by land users and managers because of the impacts on
their livelihood caused by state, pressures and driving forces; such responses may
manifest themselves as possible remediation actions; the experience of land users
themselves, who run informal experiments with nature accrue knowledge and experience
(indigenous or traditional knowledge) about remediation actions (knowledge transfer,
WOCAT!)
2.3. Shortcomings of the DPSIR framework
The framework is perhaps unnecessarily complicated if one keeps in mind the basic
questions: what is happening, why is it happening, what is being done about it? The
illusion is created that every aspect of land degradation can be categorized, i.e. put neatly
into one of the 5 categories, but in reality a driving force may also be considered a
pressure or a state may be considered an impact (see population growth and land
productivity decline respectively, p. 20, Ponce-Hernandez and Koohafkan, 2004). Cause
and effect may not always manifest themselves in the perceived order but may be
interchangeable. This is confirmed by G. Van Lynden (FAO, 2003) who wrote that “it is
often difficult to separate, for instance, driving force from pressure or state from impact;
this leads to confusion and ambiguities”.
Nevertheless the framework helps to keep the focus on issues and process links and to put
order into the multitude of data and interrelationships. This is the reason why the DPSIR
approach is the mechanism used for the LADA framework for the integration of the biophysical to the social, economic, cultural and policy factors of land degradation, and is
applied in the context of the interplay and trade-offs between the five capitals: natural,
social, financial, physical and human (Ponce Hernandez and Koohafkan, FAO, 2004).
3. Definitions
LADA is about land degradation assessment in drylands. Therefore it is essential to have
a definition of land, drylands and land degradation. Furthermore it is important to have a
clear definition of an indicator for LADA purposes as well.
3.1. Land
The holistic concept of land was already recognized in the Framework for Land
Evaluation (FAO, 1976), repeated implicitly in UNCED’s chapter 10 of 1993, and
8
formally described in FAO 1995 (W. Sombroek, Land resources evaluation and the role
of land-related indicators, FAO Land and Water Bulletin 5, 1997):
“Land is a delineable area of the earth’s terrestrial surface, encompassing all attributes of
the biosphere immediately above or below this surface, including those of the nearsurface climate, the soil and terrain forms, the surface hydrology (including shallow
lakes, rivers, marshes and swamps), the near-surface sedimentary layers and associated
groundwater reserve, the plant and animal populations, the human settlement pattern and
physical results of past and present human activity (terracing, water storage or drainage
structures, roads, buildings, etc.”
Ponce Hernandez and Koohafkan (FAO, 2004) provide a thorough list of definitions.
Regarding land they adopt an abbreviated version of the above definition for LADA:
“Land is a delineable area encompassing all attributes of the biosphere immediately
above or below the earth surface, including the soil, terrain, surface hydrology, the nearsurface climate, sediments and associated groundwater reserve, the biological resource,
as well as the human settlements pattern and infrastructure resulting from human
activity”.
3.2. Drylands
Areas having a ratio of P/PET between 0.05 and 0.65, where P is precipitation and PET
is potential evapo-transpiration. A further breakdown of this range yields definitions of
“hyper-arid” (P/PET < 0.05), “arid” (0.05 < P/PET < 0.20), “semi-arid” (0.20 < P/PET <
0.50) and “dry sub-humid” (0.50 < P/PET < 0.65). “Susceptible drylands” are considered
the arid, semi-arid and dry sub-humid regions of the earth (Van Lynden, G.W.J. and
Kuhlmann, T., 2002: Review of degradation assessment methods.
www.fao.org/ag/agl/agll/lada/emailconf.stm)
The definition is important in the sense that it provides a clear delineation of the type of
land (determined by climate) that LADA has to focus on.
3.3. Land degradation
Ponce-Hernandez and Koohafkan (FAO 2004) define land degradation as a complex set
of processes of impoverishment of terrestrial ecosystems under the impact of human
activities. Land degradation can be understood as the gradual or permanent loss of
productivity of the land resulting from human activities, mainly from the mismatch
between land quality and the intensity of activities as part of the actual land use.
UNCCD defines land degradation as a natural process or a human activity that causes the
land no longer being able to sustain properly its economic functions or the original
ecological functions (quoted in FAO, 1998: Terminology for integrated resources
planning and management. www.fao.org/ag/agl/aglhomep.stm).
The UNCCD definition seems more complete as it includes both human activities and
natural processes that may cause land degradation. The distinction is important for
adequately structuring driving forces and pressures (see below).
Over time the concept of land degradation has evolved and today’s view is that it is the
capacity of the land to perform ecosystem functions and services that matters most.
9
Therefore FAO’s LADA team has adopted the following definition: land degradation is
the reduction in the capacity of the land to perform ecosystem functions and services that
support society and development (LADA team, FAO, 2006: Reflections on indicators for
land degradation assessment. Draft).
3.4. Indicators
An indicator quantifies and simplifies phenomena and helps understand complex
realities. An indicator tells us something about changes in a system
(www.ens.gu.edu.au/aes1161).
Whether an indicator is useful or not is very much dependent on a particular context. A
quoted example is the rate of soil loss which is an important indicator of environmental
stability in for example, the North American prairies, but which is probably less
important or even possibly unhelpful if used north of the Arctic Circle.
Indicators are selected to provide information about the functioning of a specific system,
for a specific purpose – to support decision making and management. An indicator
quantifies and aggregates data that can be measured and monitored to determine whether
change is taking place. The indicator needs to help decision makers realize why change is
taking place.
Indicators are statistics or measures that relate to a condition, change of quality, or
change in state of something valued. They provide information and describe the state of
the phenomena of interest, but with a significance beyond that directly associated with an
individual parameter (OECD, 1993, quoted by Dumanski and Pieri, FAO, 1997)
According to WRI (1995) the goal of environmental indicators is to communicate
information about the environment – and about human activities that affect it – in ways
that highlight emerging problems and draw attention to the effectiveness of current
policies. Indicators must tell us, in short, whether things are getting better or worse.
From the above we may conclude that (land degradation) indicators are carriers of
information that should help us understand complex phenomena (thus facilitating
decisions) and should tell us about changes in a system. As such they are part of
monitoring land degradation. Monitoring is the repeated assessment of land condition
over time so that inferences can be made on stability or changes in land conditions. The
selection of relevant indicators is a crucial step in the provision of measurable criteria for
monitoring, management and policy support (G.W.J. Van Lynden et al, FAO, 2004).
Therefore an indicator can only tell something about change if there is at least one earlier
measurement (reference) to which the present one can be compared.
Indicators are used at various levels (global, national/regional and local). They are
selected according to their relevance for the methodological framework DPSIR and
classified as driving force indicators, pressure indicators, state indicators, impact
indicators and response indicators. According to W.E.H. Blum (2002) the assessment of
the state of land degradation and its classification into different categories alone, is not
meaningful, because we need to know exactly why and how the state has happened,
which means what are the pressures and the driving forces behind on one side, and on the
other side we need to know the direct and indirect impacts of the state and the answer,
10
how to cope with land degradation, giving responses in order to alleviate or mitigate
negative effects.
4. Indicator toolbox
The LADA project is developing an indicator toolbox containing a minimum set of
‘universal’ indicators that can be measured at local and global scale and which allow for
extrapolation at these different scales. The indicators in the toolbox are relatively easy to
measure or obtain, and in general are related to several conditions of the land, in such a
way that the set of indicators, even if not exhaustive, can describe the system in a cost
effective way (LADA team, FAO, Rome, 2006)
With this document an attempt is made to propose a minimum set of pressure and state
indicators as a contribution to the above-mentioned toolbox.
4.1. Levels
LADA is about assessing land degradation at a global scale and a pilot country scale. In
order to assess and to monitor land degradation indicators are needed to follow historical
trends (mainly at the global level) and to establish a baseline for indicators at local level
as historical trends are non-existent in most countries (F. Nachtergaele, pers. commun.
05.07.06). At the same time these indicators need to be linked in a causal scheme for
which LADA has adopted the DPSIR scheme (see chapter 2). This paper is mainly about
indicators to serve at the local level but which can be extrapolated to the national level.
4.2. Stratification/mapping
An essential prerequisite for any cost-effective assessment is to build in from the start
specific key factors that can be used to extrapolate results to nearby areas where no such
detailed investigation can be undertaken because of the high costs involved. Stratification
in this respect is useful because by superimposing different attributes of land in a map
units can be obtained that can be considered useful and homogeneous and capture the
main factors that may influence land degradation (LADA team, FAO, Rome, 2006).
Human activities that encroach upon or degrade land and the ability of land to recover
from degradation vary significantly from one area to another. For these reasons,
indications of land degradation (collected at local/subnational level) of use to
policymakers may be impossible to compile unless they are based on spatially referenced
data – essentially digital maps.
4.3. Categories
The definition of land (see paragraph 3.1) implies that land degradation assessment has to
consider processes and/or issues that affect climate, soil, terrain, water and vegetation
resources. The proposed pressure and state indicators will therefore be classified
according to these 5 categories.
11
Biodiversity loss would rather be considered under ‘Impact’ of land degradation
processes; the assessment of biodiversity decline or loss of biodiversity, is a professional
field of its own and is better handled separately by the specialists (see for instance the
efforts by UNEP/Convention on biological diversity (www.biodiv.org/default.shtml) , the
OECD/Agri-Biodiversity indicators (www1.oecd.org/agr/biodiversity/), the Global
Change and Terrestrial Ecosystem/GCTE (www.gtce.org) which is a Core project of the
International Geosphere-Biosphere Programme/IGBP (www.igbp.kva.se).
4.4. Aggregation
Example of aggregation from literature (WRI, 1995)
To keep indicators as simple as possible, a single measure is usually selected for each
major issue. Often a considerable degree of aggregation is required. This is illustrated by
the example of climate change, described by the World Resources Institute (WRI, 1995):
Climate change is seen as an issue. Greenhouse gas emissions are seen as the pressure
while concentrations in the atmosphere reflect the state of the atmosphere. The main
greenhouse gases released by human activities are carbon dioxide, methane, nitrous
oxide, chlorofluorocarbons and halons. Without aggregation there would be one indicator
(indicating the concentration) for each of these gases. However, because decision makers
(who generally are non specialists) need more compact information, the indicators for
each of the GHGs are aggregated to form one indicator for climate change. This is done
as follows:
a) quantities (emissions) of carbon dioxide, methane, nitrous oxide,
chlorofluorocarbons (CFCs) and halons are measured;
b) two factors are combined in the Global Warming Potential (GWP) of each gas:
how long do they remain in the atmosphere and how well do they absorb the heat
radiated by the earth?
c) the GWP is used as a weighting factor for emissions of that gas;
d) the weighted summation of the GHGs, expressed as CO2 equivalents, forms the
indicator for climate change.
The result is a single indicator of “equivalent” emissions (WRI, 1995).
WRI (1995) provides a table with environmental issues plus indicators corresponding to
pressure, state and response. Below only part of the table is reproduced. The purpose is to
show that soil degradation is considered an environmental issue and that land use changes
(indicator for pressure) trigger top soil loss (state indicator) while measures of
rehabilitation and protection reflect response indicators.
Table 2: Matrix of environmental indicators
Issues
Pressure
State
Climate change
Greenhouse gas emissions
Ozone depletion
Halocarbon emissions;
production
Response
Concentrations
Energy intensity; environmental
measures
(Chlorine) concentrations; Protocol sign.; CFC recovery;
O3 column
Fund contribution
Eutrophication
12
Acidification
Toxic
contamination
Urban env. quality
Biodiversity
Waste
Water resources
Forest resources
Fish resources
Soil degradation
Oceans/coastal
Zones
Land use changes
Top soil loss
Rehabilitation/protection
The point is that land degradation, when seen in a broader perspective such as the
environment as a whole, may be represented by only one indicator. However, from the
LADA point of view more detail is required although aggregation remains imperative.
Example of aggregation of soil characteristics into one single indicator
The assessment of soil characteristics on farmer fields is preferably done without the help
(or with a minimum) of instruments to avoid costs. Several authors have made valuable
contributions to the field assessment of land degradation in the framework of LADA
(M.A. Stocking and N. Murnaghan, 2001; Des McGarry, 2005). Stocking and Murnaghan
propose a field assessment method for soil loss and for production constraints whereas
McGarry proposes a field assessment methodology for biophysical soil characteristics
(Visual Soil-Field Assessment Tool). The proposed procedure, which is partly based on
McGarry’s methodology, partly on other publications (Stocking & Murnaghan, Tongway,
Kosmas et al./Medalus project etc), considers ten parameters to be assessed and then
aggregated into one single soil health indicator. The idea behind is 1. a single indicator,
representing only one soil characteristic, may be unreliable but with all, or the majority
of, individual indicators pointing into the same direction enough circumstantial evidence
is gathered to have confidence in the aggregated or combined indicator; 2. the principle
of aggregation should meet the concern expressed by some (e.g. M. Stocking in FAO,
2003) that, “when asked for indicators, soil scientists (especially) come up with long lists
of standard soil variables”.
The ten biophysical soil parameters are:
1. Soil depth
2. Structure
3. Tillage pan/compaction
4. Texture
5. Coarse fragments
6. Rooting conditions
7. Organic matter/soil colour
8. Biological activity
9. Surface crust (9a: physical crust or 9b: biological crust)
10. Sodicity
13
Site selection and the digging of a small hole (preferably at least 40 cm deep) is done
according to the VS-Fast system (McGarry, 2005).
Soil depth is assessed as follows (classes, range, rating):
Class
1. Very shallow
2. Shallow
3. Moderately deep
4. Deep
Range
< 25 cm
25 – 50 cm
50 – 100 cm
> 100 cm
Rating/Score
0
1
2
3
The other parameters are assessed similarly by creating classes and assigning a score to
each class. See ANNEX 1 for a more detailed description of the individual assessments.
The aggregation is done by adding the scores per class and comparing the sums to the
table below. A provisional rating factor is suggested because not all parameters can be
considered of equal importance for soil health (soil depth for instance is given a
weighting factor of 2 which means that in the table below the original rating is multiplied
by 2).
Summary
Soil property
1. Soil depth
2. Structure
3. Tillage pan/compaction
4. Texture
5. Coarse fragments
6. Rooting condition
7. Org. matter/colour
8. Biological activity
9. Surface crust
9a. Physical or
9b. Biological
10. Sodicity
Maximum score
Rating of constraint
Severe
Moderate
Slight
None
0
0
0
0
0
0
0
0
2
1
1.5
2
1.5
1
1.5
1
4
2
3
4
3
2
3
2
6
3
4.5
6
4.5
3
4.5
3
0
0
0
0
1.5
1
1.5
14 (13.5)
3
2
3
28 (27)
4.5
3
4.5
42 (40.5)
Suggested
weighting
factor*
2
1
1.5
2
1.5
1
1.5
1
1.5
1
1.5
Soil health indicator (field level)
Score of >35: very good (No severe or moderate constraints)
Score of 29 – 35: good (No severe constraints)
Score of 21 – 28: medium
Score of 11 – 20: poor
Score of ≤ 10: very poor
14
Another example of aggregation is represented by the soil loss indicator (see ANNEX 5,
state indicator no 7). The field assessment for water erosion phenomena is mainly based
on Stocking and Murnaghan (2001), the field assessment for wind erosion phenomena is
mainly based on the Guidelines for agricultural land use planning in Botswana (LUPSAD
project, FAO/UNDP, 1995).
4.5. Selection of indicators
4.5.1. Climate resources
The degradation of the climate resource is neither easy to demonstrate nor easy to capture
with one or a few indicators. This is so because real climate change, if any, takes place
over decades or even centuries. Therefore observed short term trends are difficult to
judge and to evaluate (F. Nachtergaele, 2002) while their relevance for possible long term
change is often debatable. Nevertheless the global warming trend appears difficult to
deny and is most probably caused by the emission of green house gases (GHGs). The
IPPC and the WMO are the competent bodies to monitor and assess the phenomenon
which, in the LADA context, acts as a driving force in the sense that global warming is
suspected to be responsible for an increase of the intensity of extreme climatic events
such as droughts, heavy rains and strong winds.
Pressure indicators: The extreme climatic events are seen as a pressure. Indicators that
would capture that pressure, are either of a direct nature: statistics reflecting the
frequency of extreme climatic events (Natural calamities indicator) or of an indirect
nature: land cover and land use changes (Land cover/land use change indicator). The
statistical information required for the natural calamities indicator is recorded by each
country and compiled at continental or global level by several institutions (Note: natural
calamities are not limited to extreme climatic events but also comprise calamities
conditioned by physiography such as volcanic eruptions and earthquakes/tsunamis; the
indicator is therefore also used for the Terrain resources category, see below, and may
require subindicators and some form of aggregation). Often extreme climatic events have
a very strong local, sometimes lasting, impact but the indicator is certainly not a ‘local’
indicator. It is typically an indicator that may allow detecting trends at national level or
rather at subcontinental, continental and global level. The same is true for the land
cover/land use change indicator (Note: land cover/land use change is in fact a complex
issue which covers many land degradation aspects; it may be necessary to subdivide the
indicator into subindicators for e.g. gullies, landslides, cover by wind-blown sediments
etc, followed by some form of aggregation) for which the information, especially the
areas affected and the type of phenomenon, needs to be compiled with the help of
satellite imagery interpretation. See ANNEX 4 for a detailed description of the proposed
pressure indicators.
State indicators: the most important climatic parameter is precipitation. Therefore all
three proposed state indicators revolve around water: aridity index, rainfall variability and
soil moisture. The Aridity index determines in the first place the climatic zones that
LADA is dealing with (arid, semi-arid and dry subhumid, see paragraph 3.2). Information
for calculating the indicator is collected at local level (meteorological stations). At local
level it may be possible to detect a trend (towards more aridity or less aridity) if a
15
sufficiently long time series is available but such a trend may only be validated if more
stations point into the same direction and this needs to be done by compilation at national
level and beyond. The same is true for Rainfall variability. This indicator, instead of
using the mere standard deviation of annual rainfall, is calculated by dividing the
standard deviation of annual rainfall by the average annual rainfall (coefficient of
variation); it expresses the standard deviation of annual totals as a percentage of average
annual rainfall; the higher the coefficient, the more variable the rainfall is from year to
year. The advantage is that the coefficient of variation allows one to compare the
variability of rainfall at any location, regardless of mean precipitation. Both indicators
may be linked to agro-ecological zones through stratification.
The Soil moisture index is based on remote sensing techniques. In most cases the
indicator may reflect differences at subnational level to national level, depending on the
resolution of the satellite imagery.
The above-mentioned agrometeorology or statistics related indicators are relatively easy
and cheap to compile. The remote sensing related indicators (land cover/land use change,
soil moisture) require interpretation skills and may be relatively expensive to compile,
especially if field checks are required. See ANNEX 5 for a detailed description of the
proposed state indicators.
4.5.2. Soil resources
Soil degradation is defined as a decline in soil qualities commonly caused through
improper use by humans. This includes physical, chemical and/or biological
deterioration. Examples are loss of organic matter, decline in soil fertility, decline in
structural conditions, erosion, adverse changes in salinity, acidity or alkalinity and the
effects of toxic chemicals, pollutants or excessive flooding (ISSS, 1996, quoted by F.
Nachtergaele, 2002).
Soil degradation is a key factor affecting the sustainability of agricultural systems. Of the
chemical processes involved in soil degradation, nutrient depletion is one of the most
important. Because soil nutrient supply is vitally important for crop production,
unchecked nutrient depletion has major implications for the sustainability of agricultural
systems and future world food supplies (Syers et al., 2002).
Pressure indicators: Nutrient depletion (or nutrient mining) is considered as a pressure
and the proposed indicator that may represent that pressure is Nutrient balance. Overexploitation of soil resources may also lead to physical and biological deterioration, e.g.
because of mechanization, monocropping, irrigation etc. Land use is seen here as the
pressure. For instance the increase of land for agriculture may come from previously
forested areas or from marginal land or hilly areas which often leads to water and/or wind
erosion as well as decline of biophysical properties of the soil. The proposed indicator is
land cover/land use change which may be compiled with the help of satellite imagery
(determination of land cover and land use change in ha or percentage). The interpretation
of satellite imagery may be done with the help of sub-indicators (one per major land
cover/land use change), followed by some form of aggregation. Urbanization is seen as
the third pressure on soil resources because it leads to (almost) irreversible loss of soil
because of construction, roads etc. which in turn increases the pressure on the remaining
land resources. Although this pressure could be captured as a sub-indicator of the land
16
cover/land use indicator, preference is given to a separate indicator, Soil sealing, because
it can be easily understood by decision makers and land use planners. See ANNEX 4 for
a detailed description of the proposed pressure indicators.
State indicators: Nutrient depletion leads to a decline of soil fertility. For assessing the
state of soil fertility one may perform soil analysis which is relatively costly. Therefore a
proxy indicator, Soil fertility, is proposed which is largely based on the assessment of
production constraints in the field (Stocking and Murnaghan, 2001). Soil fertility is
evaluated with the help of field observations and farmers’ knowledge such as crop yield
(past and expected), crop growth characteristics, nutrient deficiencies incl. field testing (if
available) and fertilizer application routine. At the same time reference needs to be made
to existing soil surveys and associated soil analysis results (if available).
Over-exploitation of soil resources (over-intensification, cultivation of marginal and hilly
areas etc) leads to deterioration of the biophysical properties of the soil, erosion and,
especially in irrigated areas, to salinization and contamination. State indicators which
should reflect these deterioration processes are Soil health, soil loss, soil salinity and soil
contamination.
Soil health and Soil loss are both aggregated indicators, composed of 10 respectively 4
sub-indicators. The sub-indicators are assessed partly according to guidelines provided by
Stocking and Murnaghan (2001) and McGarry (2005), partly with the help of guidelines
from other publications. For the Soil health indicator weighting factors are used to
approximately express the prevalence of certain soil parameters over others. Soil salinity
is treated separately (not included in soil health) because salinization in agricultural areas
(irrigation schemes) is very often the result of water management, quality and scarcity,
and can be such a threat to agricultural production that decision makers need to be made
directly aware of the process.
See ANNEX 5 for a detailed description of the proposed state indicators.
4.5.3 Terrain resources
Pressure indicators: As mentioned above natural calamities are not limited to extreme
climatic events but also comprise calamities conditioned by physiography such as
volcanic eruptions and earthquakes/tsunamis; the indicator is therefore also used for the
terrain resources category, see below, and may require subindicators and some form of
aggregation. The Natural calamities indicator is supposed to capture the pressure exerted
by these calamities by indicating the frequency of the events while the Land cover/land
use change indicator would indicate the area affected and the nature of the phenomenon.
The statistical information required for the natural calamities indicator is recorded by
each country and compiled at continental or global level by several institutions while the
information for the land cover/land use change needs to be compiled with the help of
satellite imagery interpretation.
Another important pressure at the level of the terrain resources is the cultivation of
sloping land. This almost inevitably leads to soil erosion even if soil and moisture
measures are taken. A moderate amount of soil loss may be tolerable, especially when
resilient soils are concerned but on soils with low resilience even little erosion may have
a large impact on crop yields. Unfortunately it happens too often that no adequate
conservation measures are taken which means that accelerated erosion is given a chance
17
(e.g. gullies). Furthermore when the protecting forest cover has been removed slopes are
more prone to land slides, especially following periods of heavy rainfall. While the
Cultivated sloping land indicator will indicate the area cultivated per slope class, the land
cover and land use change indicator will indicate the changes of vegetation cover and
land use on slopes. See ANNEX 4 for a detailed description of the proposed pressure
indicators.
State indicators: No separate state indicators are proposed. The pressures associated with
the terrain resources category may all lead to the degradation of soils which is reflected
by the same indicators as for the soil resources category.
4.5.4. Water resources
Pressure indicators: The (fresh) water resources comprise surface water and ground
water. The resource is a limited one, especially countries within or sharing arid, semi-arid
and subhumid zones. Over-exploitation of the limited resource is seen as the main
pressure affecting the water resource category and to reflect that pressure the Water
consumption indicator is proposed. The water resources are also affected by pollution
which on the one hand may lead to public health problems while on the other it may
exacerbate the scarcity of the resource. As in the case of the soil resources, Emission of
contaminating substances is seen as the pressure and the proposed indicator is therefore
the same as mentioned under the soil resources. See ANNEX 4 for a detailed description
of the proposed pressure indicators.
State indicators: The main consequence of the over-exploitation of the limited water
resources is a decline of the quantity of the available fresh water for domestic or
industrial use. The proposed indicators which would reflect the declining available
quantities of fresh water are the Water availability indicator and the Ground water level
indicator. The over-exploitation of the limited water resource may lead to salinization.
The proposed indicator which would reflect this process is the Water salinity indicator.
See ANNEX 5 for a detailed description of the proposed state indicators.
4.5.5
Vegetation resources
Vegetation degradation implies reduction in biomass, decrease in species diversity, or
decline in quality in terms of nutritional value for livestock and wildlife (Eswaran et al,
2001, quoted by Nachtergaele, 2002). Clear and detailed criteria for evaluating vegetation
degradation are apparently not available yet, although punctual studies show a clear
relationship between floristic change and land degradation (CNEARC, 2002, quoted by
Nachtergaele, 2002). The major land cover category of natural and semi-natural
(terrestrial) vegetation (FAO, 2000) is composed of forests/woodlands, shrublands and
range lands (herbaceous). Most of the pressure on these land cover types is coming from
human activities such as deforestation, selective logging and overgrazing. These
pressures may be reflected by the Land cover/land use change indicator and the Stocking
rate indicator. The land cover/land use change indicator is supposed to quantify changes
18
(number of ha or square km) caused by fires (past or on-going), deforestation, denudation
through overgrazing. The stocking rate indicator should quantify the number of (tropical)
livestock units per square km (or vice versa) and expresses the pressure caused by overexploitation of range lands. See ANNEX 4 for a detailed description of the proposed
pressure indicators.
State indicators: only one state indicator associated with the vegetation resources
category is proposed, the Vegetation activity indicator. This indicator is designed to
monitor vegetation conditions over the years and detect trends. Given the fact that
deforestation, selective logging, overgrazing etc lead to a decline of the vegetation
diversity, fragmentation and destruction of habitat, which have as an impact the loss of
biodiversity, at least a second indicator would be needed to express the decline of
biological diversity. However the lack of information appears to make it very difficult to
apply such an indicator as yet. According to FAO, 2005 (Global Forest Resources
Assessment 2005) ‘Forest composition is a valuable indicator of biological diversity but
information is unavailable for many countries, which makes a detailed analysis of the
value of the indicator difficult’. See ANNEX 5 for a detailed description of the proposed
state indicator.
4.5.6
Contamination issues
Emission of pollutants and the consequent soil and water contamination are important
processes that can be regarded as pressures (the emission) or state (the contamination).
Environmental pollution and the emission of contaminating substances such as persistent
organic pollutants (POPs) and heavy metals in particular constitute a pressure on the
environment that may lead to land degradation. The proposed pressure indicator is
Emission of contaminating substances which is the result of an aggregation of subindicators for heavy metals and for POPs.
Pollution of the environment by emission of persistent organic pollutants (POPs), N, P
and S compounds and heavy metals may lead to soil contamination. In agricultural areas
the application of excess fertilizer may cause soil and water eutrophication but, because
excess N and P compounds in soils usually also end up in ground and surface water,
eutrophication is incorporated in the water contamination indicator (see below). The Soil
contamination indicator therefore only represents contamination by POPs and heavy
metals (aggregated indicator).
The emission of contaminating substances also may lead to water contamination. The
proposed indicator which would reflect this process is the Water contamination indicator.
These indicators are described in a separated section (ANNEX 6) because of the
relatively high cost and complexity of their measurement. Also, they are likely to apply
only in limited and well defined areas, mostly in developed countries or in more
developed and industrial zone of developing countries.
19
4.6.
Proposed pressure and state indicators
Detailed description in ANNEX 4 (PRESSURE) and ANNEX 5 (STATE)
Degradation issue
(PRESSURE)
Climate resources
Climate extreme events (droughts,
heavy rains, strong winds) causing
landslides, gullies (heavy rains),
loss of land cover (long drought),
sedimentation (dust storms)
Soil resources
Nutrient depletion (mining)
Over-exploitation
Pollution of environment
Urbanization (absolute loss of soil
resources)
Terrain resources
Natural disasters (physiography):
sedimentation (volcanic eruptions),
salinization (tsunamis)
Cultivation of slopes: erosion,
landslides
Water resources
Over-exploitation/water scarcity
Pollution of environment
Vegetation resources
Deforestation
Selective logging
Rangeland/overgrazing
Pressure Indicator
Degradation issue
(STATE)
State indicator
Natural calamities
Land cover/land use
change
Reliability of rainfall
Increased pressure on
remaining resources
Aridity index
Rainfall variability
Soil moisture
Soil health
Soil loss
Soil salinity
Water salinity
Vegetation density
Nutrient balance
Land cover/land use
change
Soil productivity decline
Water pollution
Water/wind erosion
Emission of
contaminating substances
Soil sealing
Soil pollution
Soil fertility
Soil loss
Soil salinity
Soil contamination
Water salinity
Water contamination
Soil contamination
Water contamination
Soil health
Soil fertility
Soil loss
Natural calamities
Land cover/land use
change
Increased pressure on
remaining resources
Soil salinization
Cultivated sloping land
Land cover/land use
change
Erosion by water
Obliteration of land by
landslide material and
increased pressure on
remaining resources
Water consumption
Decline of available
quantity and quality of
freshwater resources
Emission of
contaminating substances
Water pollution
Land cover/land use
change
Loss of habitat
Erosion by water
Stocking rate
Land cover/land use
change
Soil compaction
Decline of forage quality
Erosion by water
Erosion by wind
Increased pressure on
remaining resources
Soil health
Soil loss
Soil fertility
Soil salinity
Soil loss
Soil health
Soil moisture
Rainfall variability
Water availability
Ground water level
Water salinity
Soil salinity
Water contamination
Soil contamination
Vegetation activity
Soil loss
Soil fertility
Soil health
Vegetation activity
Soil loss
Soil fertility
20
5. References
5.1. DOCUMENTS
AIDCCD, 2005. Local & Regional Desertification Indicators in a Global Perspective. Proceedings of the
International Seminar, Beijing, China, 16 – 18 May 2005, AIDCCD/EC-Research Directorate General,
Centro Interdipartementale di Ateneo – NRD Nucleo di Ricerca sulla Desertificazione, Sassari, Italy, 2005
AIDCCD, 2005. Report on the State of the art on existing indicators and CCD implementation in the
UNCCD Annexes, AIDCCD/EC-Research Directorate General, Centro Interdipartementale di Ateneo –
NRD Nucleo di Ricerca sulla Desertificazione, Sassari, Italy, 2005
Atlas of Namibia, Ministry of Environment and Tourism of Namibia, Windhoek, 2002 (Rainfall variability)
AQUASTAT Information System on Water and Agriculture: Review of world water resources by country
Baret, F. and G. Guyot. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment.
Remote Sensing of Environment 35: 161-173
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Bergsma, E. et al., Terminology for soil erosion and conservation. ISSS, ITC, ISRIC, 1996
Blum, W. E. H., Land degradation, Setting the frame. ISPRA workshop, Italy, 2002
Clarke, G.R., The study of the soil in the field, 5th edition, Clarendon Press, Oxford,1971
Di Gregorio, A. and L. J. M. Jansen, Land cover classification system, FAO, Rome, 2000
Douglas, M., Guidelines for the monitoring and evaluation of better land husbandry, 1997
Dumanski J. et al., Indicators of land quality and sustainable management. An Annotated Bibliography.
The World Bank, Washington, 1998
Euroconsult, 1989. Agricultural Compendium for Rural Development in the Tropics and Subtropics.
Elsevier, Amsterdam, The Netherlands
FAO, 1989. Radioactive fallout in soils, crops and food. FAO Soils Bulletin 61, FAO, Rome, 1989
FAO/UNDP LUPSAD Project, 1995. Guidelines for agricultural land use planning in Botswana. Fourth
draft by LUPSAD Project working group. BOT/91/001(FAO/UNDP)-Field document 10, October 1995
FAO, 1997. Land quality indicators and their use in sustainable agriculture and rural development. Land
and water Bulletin 5, FAO, Rome, 1997
FAO, 1998. Terminology for integrated resources planning and management. FAO, Rome, 1998
(www.fao.org/ag/agl/aglhomep.stm)
FAO, 2000. Manual on integrated soil management and conservation practices, Land and Water Bulletin 8,
FAO, Rome, 2000
FAO, 2001. Lecture notes on the major soils of the world. World Soil Resources Report 94, FAO, Rome, 2001
FAO, 2002. Crops and drops. Making the best use of water for agriculture, FAO, Rome, 2002.
(www.fao.org/DOCREP/005/Y3918E/Y3918E00.HTM)
21
FAO, 2003. Roy, R.N. et al., Assessment of soil nutrient balance. Approaches and methodologies. Fertilizer
and Plant Nutrition Bulletin 14, FAO, Rome, 2003 (www.fao.org/docrep/006/y5066e/y5066e00.htm) (Soil
nutrient balance)
FAO, 2003. Data sets, indicators and methods to assess land degradation in drylands. Report of the LADA
e-mail conference 9 October – 4 November 2002. World Soil Resources report 1000, FAO, Rome, 2003
FAO, 2005. Draft proposed indicators for Land Degradation Assessment in Drylands, FAO, 2005
FAO, 2005. FAO Legislative Study 86: Groundwater in international law. Compilation of treaties and other
legal instruments. S. Burchi and K. Mechlem. FAO/Unesco, Rome, 2005
FAO, 2005. Global Forest resources Assessment 2005. FAO Forestry Paper 147, Rome, 2005
FAO, 2006, Guidelines for soil description. Rome, Italy
FAO, 2006. World reference base for soil resources 2006. FAO World soil resources report 103, FAO,
2006, Rome, Italy
Farrell, E.F., The Sahel: Environmental Degradation and the Rise of Islamic Fundamentalism by 2025, MA
Thesis, American Graduate School of International relations and Diplomacy, Paris, 2005
Gordon, L., Does rainfall increase in the Sahel mask a degradation trend? Regime shifts, Ecological
management, Vulnerability, 2006
Hein, L. and N. de Ridder, Desertification in the Sahel: a reinterpretation. Global Change Biology, Vol. 12,
May 2006 (Vegetation activity)
Indicators of land quality and sustainable management. Satellite symposium, 16th Congress of Soil Science,
Montpellier, France, 1998
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Kosmas, C. et al., The Medalus project. Mediterranean desertification and land use. Manual on key
indicators of desertification and mapping environmentally sensitive areas to desertification. European
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degradation assessment. FAO, Rome, 2006
Lakshmi, V., Use of satellite remote sensing in hydrological predictions in ungaged basins, 20th
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Evaluation, 8, (1988) 161-165
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http://lada.virtualcentre.org/eims/download.asp?pub_id=92920
McGarry, Des, A methodology of a Visual Soil – Field Assessment Tool. Natural Resources Sciences,
Queensland Government, Australia, 2005
Montanarella, L., The EU thematic strategy on soil protection. ISPRA workshop, Italy, 2002
22
Nachtergaele, F., Trends in land use and implications for sustainable development, FAO, Rome, 2006
Nachtergaele, F., Land degradation assessment in drylands: the LADA project. ISPRA workshop, Italy,
2002
Namibia’s Third National report on the Implementation of the United Nations Convention to Combat
desertification. Desert Research Foundation of Namibia, Windhoek, October 2004
Parajka, J. et al., Assimilating scatterometer soil moisture data into conceptual hydrologic models at the
regional scale. Hydrol. Earth Syst. Sci., 10, 2006
Ponce Hernandez R. and P. Koohafkan, Methodological framework for Land Degradation Assessment in
Drylands (LADA) (simplified version), FAO, Rome, 2004
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Great Plains with ERTS’, Third ERTS Symposium, NASA SP-351 I, 309-317
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Schreiber, K.V., An approach to monitoring and assessment of desertification using integrated geospatial
techniques. Millersville University, Pennsylvania, USA, 2006
Smaling, E., An agro-ecological framework for integrated nutrient management (with special reference to
Kenya. Phd thesis, Wageningen, The Netherlands, 1993
Stocking, M.A. and N. Murnaghan, Handbook for the field assessment of land degradation, Earthscan
Publications Ltd, London, 2001
Stocking, M. A., Tropical soils and food security: the next 50 years. Vol 302, Science, 2003
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Svensson, L., Socio-economic indicators for causes and consequences of land degradation, 2005
Syers, K.J. et al., Nutrient balance changes as an indicator of sustainable agriculture. 17th WCSS, Thailand,
2002
Tongway, D., Rangeland soil condition assessment manual. CSIRO, Division of wildlife and ecology,
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focus on salinization, nutrient decline and soil pollution). ISRIC. AGL/MISC/36/2004, FAO, Rome, 2004
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Resources Institute, May 1995
23
5.2. WEBSITES
Climate
www.kcl.ac.uk/projects/desertlink/downloads.htm (Aridity index)
www.drought.noaa.gov/palmer.html (Aridity index)
http://www.ars.usda.gov/Research/docs.htm?docid=8974 (Soil moisture)
ftp://ftp.fao.org/agl/agll/ladadocs/activesensor.doc (Soil moisture)
http://tornado.sfsu.edu/geosciences/classes/m356/RainfallVariability/TempVar.htm (Rainfall variability)
Soils
http://soils.usda.gov/sqi/assessment/test_kit.html, USDA Soil Test Kit (Soil health)
www.soilcrust.org, An introduction to biological crusts (Soil health/biological crusts)
http://eduscapes.com/nature/cryptsoil/index1.htm, Cryptobiotic soil crusts (Soil health/biological crusts)
www.crcv.com.au (Soil health/structure)
www.mapjourney.com/sahel/conc/conc_004_.htm (RUE)
http://themes.eea.europa.eu/IMS/CSI →Terrestrial →Land take CSI014 (Soil sealing)
(www.fao.org/docrep/T0389E/To389E02.htm (Soil sealing)
www.fao.org/docrep/T0389E/To389E02.htm (Soil sealing)
www.ball.com/aerospace/quickbird.html (Soil sealing)
www.epa.nsw.gov.au/soe/issues/7_5.htm (Soil sealing)
www.kcl.ac.uk/projects/desertlinks/downloads.htm (Soil sealing)
http://en.wikipedia.org/wiki/Soil_contamination (Soil contamination)
Vegetation
http://rangeview.arizona.edu/glossary/ndvi.html (Vegetation activity)
http://e..wikipedia.org/wiki/Normalized_Difference_Vegetation_Index (Vegetation activity)
http://earthobservatory.nasa.gov/Library/MeasuringVegetation, J. Weier and D. Herring, Sept. 1999
(Vegetation activity)
http://www.africover.org (Land cover/land use change)
http://ipsnews.net/fao_magazine/environment.shtml (FAO, Earth Policy Institute and International Food
Policy Research Institute, 2002) (Stocking rate)
http://lead.virtualcentre.org/en/dec/toolbox/Index.htm (Stocking rate)
http://www.fao.org/docrep/R7488E/r7488e04.htm#3.4.2%20stocking%20rate%20and%20carrying%20cap
acity (Stocking rate)
http://lead.virtualcentre.org/en/dec/toolbox/Index.htm (Stocking rate)
http://www.creaf.uab.es/miramon/publicat/papers/lisboa98/for_fire.htm (Vegetation activity)
http://ag.arizona.edu/OALS/oals/ (Vegetation activity)
Water
http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.stm (Water availability)
www.mpu.agric.za (→State of the environment, →Water, →Total Surface Water Resources available per
capita) (Water availability)
http://esl.jrc.it/envind/met_sht/ms_we056.htm (Water consumption)
www.fao.org/docrep/008/y5739e/y5739e00.HTM (Ground water level)
www.iwmi.cgiar.org/pubs/wwvisn/GrWater.htm (Ground water level)
www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/dbase/index.stm (Ground water level)
http://earthtrends.wri.org/searchable_db/ → Water Resources and Freshwater Ecosystems (Ground water
level)
www.oecd.org →Statistics →Environment (Ground water level)
www.nwanews.com/adg/Business/167660 (Ground water level)
www.ramsar.org/forum/forum_jordan_azraq.htm (Ground water level)
www.iwmi.cgiar.org - The Global Groundwater Situation, IWMI, 2000 (Ground water level)
http://themes.eea.europa.eu/Specific_media/water/indicators → Water exploitation index (Water
availability)
http://arch.rivm.nl/environmentaldata/E_Environmenta_quality/E2_Surface_water_quality/ (Water contamination)
www.grid.unep.ch/product/publication/freshwater_europe/quality.php (Water contamination)
24
www.lehigh.cdu/kaf3/public/www-data/background/hvymt/2.html (Water contamination)
www.gemswater.org (Global Environment Monitoring System-The World of Water Quality) (Water
contamination)
http://www.lwr.kth.se/Forskningsprojekt/Salinity_Growth/Index.htm (Water salinity)
http://www.nwpg.gov.za/soer/FullReport/water.html (Water salinity)
http://themes.eea.europa.eu/Specific_media/water/indicators/WQ03b,2003.1001 (Water salinity)
http://soils.usda.gov/sqi/assessment/files/Chpt12.pdf (Water salinity, field kit)
http://www.fao.org/DOCREP/005/Y3918E/y3918e03.htm#P0_0 (Water salinity)
ftp://ftp.fao.org/agl/aglw/docs/agricfoodwater.pdf (Water salinity)
http://www.biu.ac.il/soc/besa/water/zaslavsky.html (Water salinity)
Terrain/Natural calamities
www.pcigeomatics.com/services/support_center/tech_papers/dem_aster.pdf (Cultivated sloping land)
www.epa.gov/climatechange/effects/extreme.html (Natural calamities)
www.grida.no/climate/ippc_tar/wg2/009.htm#tabspm1 (Natural calamities)
(www.ldeo.columbia.edu/chrr) Columbia Center for Hazards and Risk Research
(www.em-dat.net/disasters), Centre for Research on the Epidemiology of Disasters, The International
Disaster Database.
Contamination
http://esl.jrc.it/envind/meth_sht (Emission of contaminating substances, soil contamination, water
contamination, soil nutrient balance)
www.epa.gov (Emission of contaminating substances)
http://www.suwic.group.shef.ac.uk/proj-metal.htm (Emission of contaminating substances)
Miscellaneous
www.kuleuven.be/icto/bv/bvbank/bijlagen/v60_DPSIR (DPSIR framework)
www.unccd.int/convention (Background)
www.ens.gu.edu.au/aes1161 (DPSIR, Indicators)
http://earthtrends.wri.org/text/ (World Resources Institute: statistics per country per subsector)
www.prolinnova.net (since 2004 management and maintenance of the website (indigenous knowledge) is
in the hands of the International Institute of Rural Reconstruction (IRRR) of the Philippines in cooperation
with the Dutch NGO ETC Ecoculture)
http://edcsns17.cr.usgs.gov/glcc/globdoc2_0.html (Global land cover characteristics; U.S. Geological
Survey/USGS-National Center for Earth Resources Observation and Science/EROS)
25
ANNEX 1
Assessment of individual biophysical soil parameters for the
aggregation into one single soil health indicator
1. Soil depth
Effective soil depth is defined as the depth of the soil at which root growth of grasses or crops is
strongly inhibited. Rooting depth being plant specific, it is recommended that the representative
species of grasses and cereals are used to indicate the effective rooting depth of the soil. (FAO,
1990). This proposition is difficult to follow and, apart from obvious situations such as the
presence of a lithic contact, the estimation of effective soil depth is subject to individual
interpretation. This is probably the reason why in the most recent “Guidelines for soil
description” (FAO, 2006) (effective) soil depth is no longer among the characteristics to be
recorded. Nevertheless soil depth is still part of soil classification (e.g. Leptosols, Leptic and
Lithic qualifiers in WRB, FAO2006) in the form of the occurrence of continuous hard rock at
certain depths.
It is often not possible to establish soil depth correctly on the sole basis of a 40 cm deep hole. In
the case of continuous hard rock or a petric layer occurring at or within 40 cm, the evidence is
easy to record. In the absence of continuous hard rock or a petric layer evidence of greater soil
depth must be obtained from the surrounding area (e.g. roadcuts, well, existing pits, fallen trees),
farmer’s knowledge or, if possible, from auger observation.
The following classes and ratings are suggested:
Class
Range
1. Very shallow
< 25 cm
2. Shallow
25 – 50 cm
3. Moderately deep
50 – 100 cm
4. Deep
> 100 cm
Rating/Score
0
1
2
3
2. Soil structure
Soil structure is even more difficult to assess than effective soil depth. For fast soil assessment
purposes the complete description in terms of grade, size and type of aggregates (FAO, 2006)
requires too much experience or is too much subject to individual interpretation. Therefore only
the grade is considered.
The following classes and ratings are suggested:
Class
Range
1. None
Soil is single grain or massive
2. Weak
Poorly formed aggregates
3. Moderate
Well formed aggregates
4. Strong
Very well formed aggregates
Rating/Score
0
1
2
3
3. Tillage pan/compaction (porosity, consistence)
Tillage pans impede the movement of water, air and plant roots through the soil. The presence of
a tillage pan is both a negative indicator of soil condition and a symptom of unsustainable land
management practices. In paddy soils however, with an anthraquic horizon, a plough pan is a
desired characteristic and necessary for water stagnation and should therefore not necessarily be
interpreted negatively.
26
The following classes and ratings are suggested
Class
Range
1. None
No tillage pan, friable consistence*
(moist) and abundant pores/voids
throughout
2. Slight
Slightly developed tillage pan, friable to
firm consistence (moist) and many fine
pores throughout but with few large pores
2. Moderate
Moderately developed tillage pan, firm
consistence (moist) and moderate amount
of pores but very few large pores
3. Severe
Strongly developed tillage pan, with
massive structure, very firm to extremely
firm consistence (moist) and very few or
no pores
Rating/Score
3
2
1
0
* Consistence is highly dependent on current water content and also subject to individual interpretation.
4. Texture
Texture has important effects on a soil’s water holding capacity, aeration and porosity,
workability, hydraulic conductivity, compaction potential, resistance to root penetration, nutrient
holding capacity (CEC) and resistance to acidification.
The relative proportion of sand, silt and clay fractions making up the soil mass is called thesoil
texture. Texture is intimately related to the mineral composition, the specific surface area and the
soil pore space. It affects practically all of the factors governing plant growth. Soil texture
influences the movement and availability of soil moisture, aeration, nutrient availability and the
resistance of the soil to root penetration. It also influences physical properties related to the soil’s
susceptibility to soil degradation, such as aggregate stability (FAO, 2000).
The texture of a soil can be estimated in the field by taking one or two table-spoonfuls of soil in
one hand and adding water, drop by drop, to the soil as it is being worked in the hand until a
sticky consistence is reached. The soil is then rolled into a ball (if possible) and texture estimated
by reference to Table 1 below (the assessment is somewhat different from the one proposed in the
VS-Fast system (McGarry, 2005):
Texture group Textural class/plasticity
Sand
Loam
Feel/stickiness/polish
Sand: stays loose and separated; can be accumulated in the form
of a pyramid. Figure A
When rubbed, leaves no film on
fingers.
Loamy sand: can be given the shape of a ball which falls apart
easily. Figure B
When rubbed, leaves a slight film on
fingers.
Sandy loam: can be given the shape of a ball that can be taken
apart easily; the ball may be rolled into a cylinder (3-7 mm
diameter) but with difficulty. Figure B
Silt loam: the ball can be rolled into a small short cylinder (3-7
mm diameter) but can hardly be bent without breaking (includes
Silt which is rather rare as a texture). Figure C
Loam: contains roughly the same amount of sand and silt, (3050%), complemented by clay; the ball can be rolled into a
cylinder (3-7 mm diameter) that breaks when bent into a U.
Figure D
Sandy feel, moderately floury;
adheres somewhat to at least one
finger; not soapy or sticky
Smooth soapy feel; very floury;
adheres somewhat to at least one
finger; does not take a polish
No predominant feel; somewhat
floury, somewhat sticky, adheres to
fingers and thumb; does not take a
polish
27
Clayloam
Clay
Sandy clayloam: can usually be rolled into a cylinder (3-7 mm
diameter) that breaks or cracks when bent into a U.
Figure E
Silty clayloam: can be rolled into a cylinder (about 3 mm
diameter) that breaks or cracks when bent into a U.
Figure E
Clayloam: can be rolled into a cylinder (about 3 mm diameter)
that does not necessarily break when bent into a U, but breaks
when shaped into a ring (2-3 cm diameter). Figure E/Figure F
Sandy clay: can be rolled into a thin cylinder cylinder (about 3
mm diameter) that may break or show some cracks when shaped
into a ring (2-3 cm diameter). Figure F
Silty clay: can be rolled into a thin cylinder cylinder (about 3 mm
diameter) that may show some cracks when shaped into a ring (23 cm diameter). Figure F
Medium clay: can be rolled into a thin cylinder cylinder (about 3
mm diameter) that can be shaped into a ring (2-3 cm diameter)
that usually does not show any crack unless the texture is close to
silty clay,sandy clay or clayloam; is very plastic when
sufficiently moist and can be moulded rather easily into any
shape. Figure G
Heavy clay: can be rolled into a thin cylinder (about 3 mm
diameter) that can be shaped into a ring (2-3 cm diameter),
without showing any cracks; is extremely plastic and can easily
be moulded into any shape when sufficiently moist. Figure G
Sandy feel; distinctly sticky, adheres
to fingers and thumb; smears but too
sandy to take a polish
Somewhat smooth soapy feel;
adheres to fingers and thumb; smears
but does not take a polish
Between sandy and soapy feel;
adheres to fingers and thumb; smears
but does not take a polish
Very sticky but with sandy feel;
takes a polish but sand grains stand
out on surface
Very sticky but with soapy feel;
takes a polish.
Very sticky; with some care sand
grains can be detected; takes a clear
polish; fingerprints detectable
Extremely sticky; takes a high
polish; takes clear fingerprints.
Particle size classes: Sand: 0.063 – 2 mm; Silt: 2 – 63 m; Clay: < 2 m
The following ratings are suggested:
Class
Sand, loamy sand
Sandy loam, silt loam,
heavy clay
Medium clay, sandy
clayloam, silty clay, sandy
clay, silty clayloam
Loam, clayloam
Range
Low water and nutrient holding capacity*, good
workability, high to very high infiltration rate
Low to medium water and nutrient holding
capacity; good workability, moderate to high
infiltration rate (sandy loam and silt loam);
medium to high available water holding capacity,
very high nutrient holding capacity; poor
workability; very slow infiltration rate (heavy
clay)
Medium to high available water holding capacity;
high nutrient holding capacity; medium to poor
workability, moderate to slow infiltration rate
Very high water holding capacity, high nutrient
holding capacity, medium workability, moderate
infiltration rate
Rating/Score
0
1
2
3
* Water holding capacity and nutrient holding capacity not only depend on the quantity of clay but also on
the type of clay. Therefore the rating is a relative one, valid within a region with similar dominant clay
type.
28
5. Stones/gravel content
The presence of coarse fragments (diameter > 2 mm) influences the nutrient status, water
movement, rooting volume, use and management of the soil. The following classes and ratings
are suggested (charts for estimating proportions of coarse fragments should be used if available):
Class
1. None to common
2. Common to many
3. Many to abundant
4. Dominant
Range
0 – 15 %
15 – 40 %
40 – 80 %
> 80 %
Rating/Score
3
2
1
0
6. Rooting conditon
The ease of root penetration, the size and abundance, the local concentration and the sudden
change of orientation of roots are dependent on texture, coarse fragments, tillage pan, structure
etc. The root system (size, abundance, orientation etc) is examined as the soil on the spade is
gently broken up. The following classes of root condition and ratings are suggested:
Class
Good condition
Moderate condition
Poor condition
Very poor condition
Range
Unrestricted root development, many (<2mm, > 50/dm2;
> 2mm, > 5/dm2)
Horizontal and vertical root development somewhat
limited; more roots between coarse structural elements
than inside; common roots (<2mm, 50-200/dm2; >
2mm, > 5 - 20/dm2)
Horizontal and vertical root development clearly limited;
most roots concentrated in cracks between structural
units, almost no roots inside units; few roots (<2mm, 20
- 50/dm2; > 2mm, > 2 - 5/dm2)
Severe restriction of horizontal and vertical root
development; presence of L-shaped roots, overthickening of roots or roots squashed between coarse
structural units or concentrated above dense layer, no
roots inside units; none to very few roots
(<2mm, 0 - 20/dm2; > 2mm, 0 - 2/dm2)
Rating/Score
3
2
1
0
Source:
FAO 2006, Guidelines for soil description
McGarry 2005, A methodology of a Visual Soil-Field Assessment Tool
7. Organic matter/soil colour
The content of organic matter of mineral soils can be estimated from the colour of a soil. This
estimation is based on the assumption that the soil colour is due to a mixture of dark coloured
organic substances and light coloured minerals. Generally, the darker the soil the higher the
organic matter content. If the soil is dry it should be moistened. The best way to determine the
colour is to use a soil colour chart (e.g. Munsell) and to record the value (moist). The following
classes and ratings are suggested:
Organic matter class
1. Very low
1. Low
2. Medium
Colour/value/percentage range
White; value 8
Grey; value 5-7
Dark grey to black grey; value 3-4.5
Rating/Score
0
1
2
29
3. High
Black; value 2-2.5
3
Source: FAO 2006, Guidelines for soil description
8. Biological activity
Soil biota are the very “life” of the soil (McGarry, 2005). Their presence, in large numbers
throughout the soil profile is conducive to a good condition of the soil (porosity, hence aeration
and infiltration/water movement, soil fertility etc). If not visible directly their presence may be
inferred from biological features such as open or infilled large burrows (krotovinas), termite or
ant channels and burrows, other insect nests, worm casts and channels. The following classes and
ratings are suggested:
Biological activity class
1. None
1. Low
2. Medium
3. High
Description
No biological features, no earthworms
Few biological features or soil biota; 1- 4
earthworms counted in spadeful
Common biological features or biota; 4 – 8
earthworms counted in spadeful
Many biological features or biota; > 8
earthworms counted in spadeful
Rating/Score
0
1
2
3
9. Soil surface crusts
Two main categories of soil surface crusts can be distinguished: 1. Physical (and chemical) crusts
and 2. Biological soil crusts. Physical and chemical crusts are inorganic features such as platy
surface crusts or salt crusts. Biological soil crusts are formed by living organisms and their byproducts, creating a crust of soil particles bound together by organic materials. For a discussion of
physical and biological crusts see ANNEX 2.
Physical crusts are essentially a consequence of a change in land cover, usually man-induced, and
are as such not part of the original ecosystem, while biological crusts are part and parcel of the
semi-arid and arid ecosystems in which they develop and thrive.
Therefore the presence/appearance of physical soil crusts may be interpreted as a sign of land
degradation (negative effects on water infiltration) whereas in the case of biological crusts it is
rather their poor condition or disappearance (facilitating erosion) that is indicative of land
degradation. The evaluation of soil crusts needs to be part of any field assessment tool of land
degradation. The following classes and ratings are suggested:
Physical soil crusts
Class
1. None
2. Slight
3. Moderate
4. Severe
Description
No crust present
Thin to medium crust (1 – 5 mm) on up to 20
% of the surface
Thin to medium crust (1 – 5 mm) present on
20 - 50 % of the surface, thick crust (> 5 mm)
present in few patches
Thin, medium and thick crust present on
more than 50 % of the surface with common
patches of thick crust
Biological crust/cryptogam cover*
Class
Description
1. None
No cryptogam cover present (completely
Rating/Score
3
2
1
0
Rating/Score
0
30
1. Slight
2. Moderate
3. Extensive
destroyed)
Cryptogam cover present on less than 20 %
of the surface
Cryptogam cover present on 20 - 50 % of the
surface
Cryptogam cover present on more than 50 %
of the surface
1
2
3
* to be assessed in areas where biological crusts are (or are supposed to be) a natural part of the soil
surface environment. Source for assessment: D. Tongway, 1994. Rangeland soil condition assessment
manual. CSIRO, Division of wildlife and ecology, Canberra, Australia
10 Sodicity
Class
Description of sodicity signs
1. None
No signs of sodicity, also not in nearby areas, see below; depth of
groundwater > 2m
Sodicity: in shallow pit soil structure is weak; in close-by areas some
1. Slight
2. Moderate
3. Severe
puddles of surface water are coloured black by dispersed organic
colloids (slick spots); upon drying, black crusts are formed
Sodicity: waterlogging is a common surface feature; some puddles of
surface water are coloured black by dispersed organic colloids (slick
spots); upon drying, black crusts are formed; hardsetting surface, but
when worked soil becomes easily dusty when dry; corrosion of road
furniture such as steel posts, road signs, guard rail is an increasing problem;
tunnel/pipe erosion is visible in some degraded areas. Crops sensitive to
high sodium saturation (e.g. beans) perform poorly; tolerant crops (e.g.
cotton do not appear to be affected.
Sodicity: in shallow pit the top of the B-horizon is visible in the form of
well defined vertical columns or prisms, having a rounded top with lighter
colour and smooth, shiny and well defined sides; soil structure in topsoil is
poorly developed.; waterlogging is a common surface feature; puddles of
Rating/
Score
3
2
1
0
surface water are frequently coloured black by dispersed organic
colloids (slick spots); upon drying, black crusts are formed;
hardsetting surface, but when worked soil becomes very dusty when dry;
corrosion of road furniture such as steel posts, road signs, guard rail is a
severe problem; tunnel/pipe erosion is visible in some degraded areas.
31
ANNEX 2
Physical and biological soil crusts
Physical soil crusts
Surface sealing is used to describe crusts that develop at the soil surface after the topsoil
dries out (FAO, 2006). These crusts may inhibit seed germination, reduce water
infiltration and increase runoff. The attributes of surface sealing are the consistence,
when dry, and thickness of the crust. They usually have a platy structure (stratification of
very thin layers of finer and coarser material) and may show fine vesicular voids, formed
by compressed air (gas bubbles) in the slaking crust after heavy rainfall.
According to FAO (1993) two main types of physical soil crusts may be distinguished in
West Africa: structural crusts and depositional crusts. Structural crusts develop in situ,
depositional crusts are formed of particles which have been transported from their
original location. Structural crusts may be divided into slaking crusts and sieving crusts,
depending on the texture of the top layer. The slaking crusts consist of a layer made of
fine particles with rougher patches of partly broken down clods; they form when soils
contain enough clay (> 15-20%) to entrap and compress air during wetting so that
aggregates break down. Sieving crusts are formed of up to three thin layers: on top a
layer of loose coarse sand, in the middle a layer of fine densely packed grains with
vesicular pores and below a lower layer of finer material with reduced porosity which
causes the low infiltrability of the crust. The textural differentiation results from a sieving
process (downward movement of finer particles through the upper sandy layer and
accumulation in the underlying layers). A particular form of sieving crust is the pavement
crust where coarse rock fragments are embedded in the crust. Infiltrability is very poor.
These crusts range from the Sahara (desert pavement) to the dry savannah zone. Another
type of surface crust is the erosion crust which form from slaking crusts which have been
smoothed and enriched in fine particles, and from sieving crusts where the loose sandy
layers have been removed by overland flow or wind.
Depositional crusts may be divided into runoff depositional crusts and still depositional
crusts. Runoff depositional crusts consist of alternate very thin layers contrasting in
texture and can be up to a few cm thick. They usually overlie structural crusts with which
they have sharp boundaries and are often found between ridges or under furrow
irrigation. Still depositional crusts for in standing water and consist of densely packed
and well-sorted particles, the size of which increases with depth. When dry these crusts
often break up into curled-up plates.
On many cultivated or bare fields the soil infiltration ability is commonly limited by
surface crusting rather than by deeper profile conditions. As a result, rainwater falling on
bare ground cannot penetrate, and runs off sideways, even on very gentle slopes.
The rapid drop in infiltration rate (IR) of most of the bare soils during rainstorms is due
mainly to the surface seal. The seal is less permeable, by a few orders of magnitude, than
the subsurface horizon. Surface seal, as well as most of the other crust formations, results
from three processes (Agassi et al. 1981).
- Physical disintegration of soil aggregates and their compaction, caused by the impact
action of raindrops.
32
- Chemical dispersion of the clay particles. The low electrical conductivity of the
rainwater as well as the organo-chemical bonds between the primary particles of the
surface aggregates, control the rate and degree of dispersion.
- An interface suction force which arranges the once suspended clay particles into a
continuous dense layer. This almost impermeable layer can form at the very surface of
clay soil or in the immediate sub-surface washed-in layer, as discussed by McIntyre
(1958).
The above separation of the three processes is artificial. The marked reduction in
infiltration rate commonly observed depends on their combined action.
Reduced infiltration due to surface sealing (crust formation) can be a problem on
agricultural fields but even more so on the extensive gently sloping glacis connecting for
instance the foot of granitic inselbergs or ironstone controlled plateaus/mesas/buttes to
the local drainage system in the West African Sudanian and Sahelian pastoral zones,
where much of the original vegetation has disappeared, due to excessive grazing and
wood harvesting, and has left the soil surface unprotected and prone to surface sealing.
Infiltrability of crusted soils ranges from 0 to 25 mm/h depending on the crust type, the
cover, soil moisture and soil depth (FAO, 1993). On exposed soils infiltration can be as
little as 20% of the heaviest rainstorm. These gently sloping land forms are caught in a
vicious circle with the formerly ustic moisture regime gradually degrading towards a
drier type of soil moisture regime (e.g. aridic) with all the negative consequences for soil
life and vegetation.
On the other hand soil crusting can have some beneficial effects. In the northern fringe of
the Sahel, even where rainfall water infiltrates totally, it is still insufficient to grow any
crop. Here runoff from crustal basins (‘impluvia’) is collected naturally or artificially and
concentrated over a smaller surface, or in a river bed where conditions are generally more
favourable to infiltration. Without runoff from crusted soils, there would be many fewer
temporary ponds and soils with water tables which are so crucial for cattle breeding and
crop production in the arid and semi-arid regions (FAO, 1993).
Biological soil crusts
Biological soil crusts are also known as cryptogamic, microbiotic and cryptobiotic crusts.
Biolocial crusts are predominantly composed of cyanobacteria (formerly blue-green
algae), green and brown algae, mosses and lichens. Liverworts, fungi and bacteria can
also be important components. Such crusts are commonly found in semiarid and arid
environments throughout the world (www.soilcrust.org). The general appearance of the
crusts in terms of colour, surface topography and surficial coverage varies. They
generally cover all soil spaces not occupied by vascular plants and may constitute 70% or
more of the living cover. Cyanobacteria appear to compose the vast majority of the crust
structure in most semiarid and arid areas. Some more acidic soils are dominated by green
algae. Shifts between green algal and cyanobacterial dominance have been attributed to
changes in pH, with the decreasing alkalinity favouring green algae. More stable crusts
are dominated by lichens and/or mosses. The organism that dominates the crust is partly
determined by microclimate and may also represent different successional stages in crust
development.
33
Biological crusts play an important role in the environment. They form a spongy layer
that helps protect the soil from erosion, absorbs moisture, and provides nitrogen and other
nutrients for plant growth. When frozen, the biological crust uplifts and cracks. Cracks in
the layer can provide germination sites for seeds. Because they are concentrated in the
top 1 to 4 mm of the soil, they primarily affect processes that occur at the soil-air
interface. These include soil stability and erosion, atmospheric nitrogen fixation, nutrient
contributions to plants, soil-plant-water relations, infiltration, seedling germination and
plant growth. Many studies of how soil crusts affect plant health have shown increases in
survival and/or nutrient content in crust-covered environments as opposed to bare soil.
Although biological crusts are well adapted to severe growing conditions they are poorly
adapted to compressional disturbances such as domestic livestock grazing and
recreational or military activities. Disturbance to soil surfaces in arid regions can lead to
large soil losses: when the integrity of the crust is broken through trampling or other
means, the soil is more susceptible to wind and water erosion. Indirect disturbances
include 1. actions that increase shrub components such as excessive grazing with a
negative impact on crust functioning because of possible allelopathic effects on the
nitrogen-fixing capabilities of crusts and 2. burial by eroded soil material; crusts tolerate
shallow burial by extending sheaths to the surface to begin photosynthesis again but
deeper burial will kill it.
Biological crusts can alter water infiltration. Where crusts greatly increase surface
roughness (especially in regions where frost heaving plays a role), infiltration is generally
increased. Where crusts do not significantly increase surface roughness, infiltration is
generally reduced due to the presence of cyanobacterial filaments. They may also be
hydrophobic when dry but it appears that after even slight moistening the hydrophobic
character usually disappears fast. Differences in water infiltration are site-specific and
also related to soil texture and chemical properties of the soil.
34
ANNEX 3
Comments on Visual Soil-Field Assessment Tool
Visual soil assessment methodology is being developed for “use by farmers on-farm” (McGarry,
2005). The technical jargon may be difficult to understand without some formal education,
therefore the methodology would have to be adapted for small farmers in the developing world.
Small farmers in the developing world are very able to assess their soils, they generally have a
very good understanding of the productive qualities and the constraints of their soils but their
local knowledge is also embedded in the numerous local languages which means that the VSA
methodology needs to be made accessible to the small farmers.
An adapted version of VSA, in at least some important local languages, would have to be
prepared, perhaps consisting mainly of self-explanatory drawings and/or pictures. How then
apply the VSA methodology in the developing world where many if not most of the semiarid to
subhumid lands are being used for agriculture and grazing? The obvious answer seems to be with
the help of local extension agents (Government, NGO’s, private sector). However, even if enough
extension agents could be mobilized to assist small farmers with assessing their soils and possible
signs of land degradation, it is doubtful whether the results would be reliable enough to serve the
purpose of building local databases required for the formulation and monitoring of land
degradation indicators, because “the field assessor must have an open mind, observant eyes and
the qualities of a detective” (Stocking and Murnaghan, 2001). Not many extension agents are
likely to possess all of these qualities. Most agents would need thorough training, especially in
participatory techniques and open-mindedness, in order not to regard farmers as ignorant
labourers, who simply should do what they are told, but to see farmers as partners and as experts
in their own field and themselves as facilitators rather than conventional teachers. One of the best
ways national governments can assist and strengthen the existing extension network, apart from
improving field work conditions and organizing training/refresher courses, is to facilitate access
to internet for extension agents and promote national indigenous knowledge resource centres and
farmer field schools. These national centres would be affiliated with regional and global networks
(see Footnote).
VSA methodology could be coupled with local knowledge and edited in local languages by
national centres and tested in farmer field schools. The methodology would probably be limited
to one or more transect walks conducted by the farmer(s) and the extension agent, a participatory
field sketch (see Stocking and Murnaghan, 2001), digging one or several about 40 cm deep holes
on representative sites and recording the (simplified) visual soil descriptors (see below) including
the assessment of crust formation. Measurement of slaking and dispersion, soil pH, water
infiltration and labile fraction of soil organic matter would only be included if the means are
easily available, otherwise they could be limited to benchmark sites only. Ideally it would be
combined with the assessment of soil loss indicators and production constraint indicators as
described by Stocking and Murnaghan (2001) in order to obtain a more comprehensive
assessment of soil productivity.
Footnote: The Netherlands University Foundation for International Cooperation (NUFFIC) used to host
the Global Network of Indigenous Knowledge Resource Centres (CIRAN) and was responsible for
maintaining the website from 1992 until 2004. NUFFIC then transferred its activities in the field of
indigenous knowledge to a consortium of southern organizations. Since 2004 management and
maintenance of the website (www.prolinnova.net) is in the hands of the International Institute of Rural
Reconstruction (IRRR) of the Philippines in cooperation with the Dutch NGO ETC Ecoculture.
35
ANNEX 4 PRESSURE INDICATORS
1. CULTIVATED SLOPING LAND (PRESSURE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Cultivated sloping land
Percentage of land cultivated on slopes with gradient > 10%
Percentage
Local (e.g. watershed) to national
Annual
2. Position within the logical framework DPSIR
Type of indicator
Pressure
3. Target and political pertinence
Objective(s)
Importance with respect to
land degradation
International conventions
and agreements
To monitor the use of sloping land.
To map the percentage of cultivated land for different slope classes.
To guide towards appropriate measures to manage soil resources on sloping land.
Cultivation of sloping land, especially without adequate soil conservation measures,
leads to accelerated soil erosion and eventual permanent loss of the resource.
World Soil Charter , adopted at the 21st Session of the FAO Conference, in November
1981. The Charter establishes a set of principles for the optimum use of the world's land
resources, for the improvement of their productivity, and for their conservation for future
generations.
Many of the global conventions organized by the United Nations are responses to global
degradation:
1992: the United Nations Conference on Environment and Development (also known as
UNCED or the Rio Earth Summit); Agenda 21, Chapter 12
1994: the United Nations Convention to Combat Desertification (also known as
UNCCD); Article 4.2.
Millennium Development Goal (MDG) nr 7: ensure environmental sustainability
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
Slope gradient classes are defined in Guidelines for Soil Description (FAO, 2006)
10-15, 15-30, 30-60 and >60%).
Digital elevation models (DEMs) are increasingly used for visual and mathematical
analysis of topography, landscapes and landforms, as well as modeling of surface
processes. DEMs can be generated from stereo satellite data derived from electro-optic
scanners such as ASTER (Advanced Spaceborne Thermal Emission and Reflection
Radiometer). ASTER is a high-spatial-resolution, multispectral imaging system flying
aboard TERRA, a satellite launched in December 1999 as part of NASA’s Earth
Observing System (EOS)
(www.pcigeomatics.com/services/support_center/tech_papers/dem_aster.pdf).
A map of slope classes can be derived from the DEM and can be overlayed with a land
use map.
Alternatively a slope class map can be derived from a topographical map of sufficient
detail (1:50,000), either manually or after digitization and can be overlayed with a land
use map.
The indicator does not differentiate between land use types.
Soil loss, natural calamities, land cover/land use change
The indicator can be measured at local level (village area, watershed) up to national level
36
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Percentage cultivated land per slope class
Satellite imagery, topographic maps
Topographic maps (scale of 1:50,000) are available in most countries.
For availability of satellite imagery, see Lantieri, 2003:
(http://lada.virtualcentre.org/eims/download.asp?pub_id=92920).
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
FAO
-
7. Practical applications of the indicator (references to case studies)
See for instance: www.pcigeomatics.com/services/support_center/tech_papers/dem_aster.pdf
8. Additional information
-
2. LAND COVER/LAND USE CHANGE (PRESSURE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Land cover/land use change
Land cover is the observed (bio)physical cover on the earth’s surface (FAO, 2000).
Land cover change is defined as the alteration of the physical or biotic nature of a site,
e.g. the transformation of forest to grassland.
Land use is characterized by the arrangements, activities and inputs people undertake in a
certain land cover type to produce, change or maintain it (FAO, 2000).
Land use change may involve alterations in the human management of land, including
settlement, cultivation, pasture, rangeland and recreation (Meyer and Turner, 1994).
%, ha/year, grid cells
Landsat TM:
Landsat TM:
2. Position within the logical framework DPSIR
Type of indicator
Pressure
3. Target and political pertinence
Objectives
Importance with respect to
land degradation
International conventions
and agreements
To map land cover changes which reflect a land degradation process
To detect and map areas whose land cover has undergone negative change (land
degradation) or positive change (rehabilitation), often known respectively as hot spots
and bright spots
To map land degradation features directly from remote sensing images (e.g. wind
erosion, salinization, overgrazing, gully erosion, areas subject(ed) to forest/bushfires).
Land degradation is often a direct consequence of land cover/land use change in absolute
terms (e.g. forestry → agriculture with less biodiversity) or in terms of intensification
(e.g. subsistence agriculture → monoculture with high input of fertilizers and irrigation
→ pollution of ground water, water scarcity and salinization (F. Nachtergaele, 2006)).
Land cover types commonly subject to desertification processes can be identified and
located, and samples of each type more fully surveyed in the field. Land use changes can
be detected in comparisons of images taken over time, revealing locations of activities
associated with desertification. When combined with information on the physical
features of the landscape (slopes, drainage patterns), hotspots of high potential
degradation can be identified for more detailed study (Lantieri, 2003).
Millennium Development Goals (MDG) Poverty alleviation and environmental
sustainability
Agenda 21 Chapter 12 Combating desertification and drought
37
UNCCD Article 4.2 (a) commits Parties to “adopt an integrated approach addressing the
physical, biological and socio-economic aspects of the processes of desertification and
drought”
UNCCD Article 4.2 (c); to “integrate strategies for poverty eradication into efforts to
combat desertification and mitigate the effects of drought”
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
In practice, colour composite HR taken at two distant periods can be used to prepare land
cover change maps
n.a.
HR or VHR satellite imagery
Land cover mapping requires ground truthing, the level of which increases with the
complexity of the legend (D. Lantieri, 2003); the use of GPS helps considerably the
ground calibration, as it allows the localization of individual pixels of geo-corrected HR
imagery on the ground.
Some important land degradation features cannot seen on RS data: sheet erosion, rills,
fuelwood depletion on trees, loss of soil fertility are not visible on satellite imagery.
Vegetation activity (NDVI), soil sealing, stocking rate, soil loss, soil salinity, soil
moisture, rainfall variability, climate/physiography induced calamities
Mapping scales typically vary from 1:50,000 (local/subnational) to 1: 500,000
(national/regional/AEZ/global) (Lantieri, 2003)
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Time series of remote sensing data (aerial photographs, multispectral satellite images,
topographic maps).
All optical satellite data, but high resolution data (e.g. Landsat Thematic Mapper) are
particularly cost effective (Lantieri, 2003).
Landsat TM, SPOT; http://edcsns17.cr.usgs.gov/glcc/globdoc2_0.html (Global land
cover characteristics; U.S. Geological Survey/USGS-National Center for Earth
Resources Observation and Science/EROS)
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
FAO, UNEP
Cooperazione Italiana
7. Practical applications of the indicator (references to case studies)
Africover is a FAO remote sensing project, launched in 1994, aimed at mapping land cover of African countries at a
scale useful to land planning, resource management and environmental monitoring (http://www.africover.org). The
methodology results from a broad international consultation process and uses a hierarchical standardized a priori
classification method, called Land Cover Classification System (LCCS), designed to meet specified user requirements,
but independent of the scale or means used (see also 8. Additional information).
The FAO East Africa Project is the first operational module of the Africover programme. The main source of land
cover information is provided by Landsat 7 images (D. Lantieri, 2003).
8. Additional information
FAO, 2000: The Land cover classification system (LCCS) can be used as a reference classification system because its
diagnostic criteria also allow for correlation with existing classifications/legends. Land cover is defined very broadly as
the observed (bio)physical cover including the vegetation (natural or planted) and human constructions which cover the
earth’s surface. Water, ice, bare rock or sand surfaces count as land cover. Land Cover Classes are defined by the
combination of a set of independent diagnostic criteria, the so-called classifiers, which are hierarchically arranged to
assure a high degree of geographical accuracy. Because of the heterogeneity of land cover, the same set of classifiers
can not be used to define all land cover types. The hierarchical arrangement may differ from one land cover type to
another. Therefore, the classification is designed according to two main phases:
- a dichotomous phase where eight major land cover types are distinguished; and
38
- a modular-hierarchical phase where the set of classifiers and their hierarchically arrangement are tailored to the major
land cover type.
The LCCS has been assessed by the international community as probably one of the most advanced and comprehensive
classification systems available today to analyze land cover, and usable in all parts of the world. Several important
initiatives (e.g. GLC 2000, IGBP) have been using LCCS for their work and many institutions (UNEP, US agencies)
are considering the application of LCCS in their programmes.
D. Lantieri, 2003: In practice, HR taken at two distant periods can be used to prepare land cover changes maps or
databases. Among the features that can be shown on these maps and that are of prime interest to LADA, the following
should be mentioned:
- agricultural areas under intensification: these include areas where fields are increasing in number and perhaps in size,
and/or where crops are encroaching on lands (originally, forest or rangelands), or new irrigated areas;
- reduction of woody cover in particular in woodlands of medium to high crown cover. Intense deforestation, slash and
burn practices are highly visible on two date images.
- important reduction of grass cover in rangelands – grass biomass is seen well on HR imagery, in particular when the
data have been processed, using for example vegetation indexes (see vegetation activity indicator). Therefore, changes
in grass cover can be seen between two distant dates. However, great care should be taken to account for the rainfall
conditions at the time of the image acquisition, as changes in grass cover can be related more to rainfall availability
than to long-term degradation or desertification process.
3. NATURAL CALAMITIES (PRESSURE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Natural calamities
Climate induced calamities include land slides (due to heavy rains), sedimentation due to
dust storms and floods, salinization due to floods, loss of land cover due to long drought
aggravated by increased risk of fires.
Physiography induced calamities include volcanic eruptions (obliterating agricultural
land by lavaflows and volcanic ash rains) and earthquakes (triggering land slides and/or
tsunamis (causing salinization of coastal areas).
Area affected in km2
National
Annual
2. Position within the logical framework DPSIR
Type of indicator
Pressure
3. Target and political pertinence
Objective(s)
Importance with respect to
land degradation
International conventions
and agreements
To map and characterize (area affected, type of degradation) natural calamities
The indicator provides a yardstick for measuring the permanent or temporary (medium to
long-term) loss of land which causes increased pressure on the remaining resources with
increased risk of land degradation.
For the 21 century the IPPC expects climate change to express itself through higher
maximum and minimum temperatures (global warming), more intense precipitation,
increased wind intensities (storms, hurricanes), increased precipitation variability,
increased risks of droughts and floods. The consequences with regard to the natural
resources and human society comprise decreased water resources quantity, soil erosion,
land slides, decreased crop yields, increased risk of forest fires, rising sea level, increased
risk to human life and health.
(www.epa.gov/climatechange/effects/extreme.html)
(www.grida.no/climate/ippc_tar/wg2/009.htm#tabspm1)
Physiography induced calamities may obliterate vast tracts of land by lava flows and/or
volcanic ash (permanent or long-term loss of productive land) or cause salinization of
coastal areas by saline water floods such as tsunamis (medium-term loss of productive
land).
UNCED (1992): Agenda 21, chapter 12: combating desertification and drought
Millennium Development Goals (MDG): Poverty alleviation and environmental
39
sustainability
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
A landslide is a geological phenomenon which includes a wide range of ground
movement, such as rock falls, deep failure of slopes and shallow debris flows. Although
gravity acting on an over steepened slope is the primary reason for a landslide, there are
other contributing factors affecting the original slope stability. One important factor is
the saturation of steep rock and soil slopes by water (e.g. heavy rain). The consequence
is denudation of mountain/hill slopes, soil loss due to water erosion and obliteration of
agricultural land by the sliding mass of rocks, soil and vegetation debris.
A dust storm (or sandstorm in some contexts) is a meteorological phenomenon common
in arid and semi-arid regions. Such a storm is usually the result of convection currents
created by intense heating of the ground. These currents then carry sand over large
distances. Wind erosion may lead to loss of fertile topsoil and/or to sedimentation of
wind-blown material on agricultural areas.
Floods may be caused by rivers or by the sea. A flood from sea may be caused by a
heavy storm (storm surge), a high tide, a tsunami, or a combination thereof. As many
urban communities are located near the coast this is a major threat around the world.
Floods from rivers are a natural and common phenomenon and are not necessarily
contributing to land degradation (for many flood plains the contrary is true because of
the often fertile fresh sediments which are deposited). Flash floods which usually result
from intense rainfall over a relatively small area, are often devastating.
Volcanic eruptions produce volcanic ash consisting of very fine rock and mineral
particles less than 2 mm in diameter that are ejected from a volcanic vent (ash is created
when solid rock shatters and magma separates into minute particles during explosive
volcanic activity) and/or lava which is molten rock expelled by a volcano during an
eruption (lava solidifies to form igneous rock; the term "lava flow" refers to the hardened
formation).
A tsunami is a series of waves when a body of water, such as an ocean is rapidly
displaced on a massive scale. Earthquakes, mass movements above or below water,
volcanic eruptions and other underwater explosions, landslides and large meteorite
impacts all have the potential to generate a tsunami. The effects of a tsunami can range
from unnoticeable to devastating.
Remote sensing (D. Lantieri, 2003: Meteosat CCD, HR or VHR imagery, supported by
field surveys)
The indicator provides a rough estimate of the extent of the area affected
Rainfall variability, aridity index, soil moisture, soil loss,
National, aggregation to continental and global levels
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Time series of aerial photographs and/or VHR satellite imagery
Space agencies
Data bases of national and international institutions
See Lantieri, 2003 (http://lada.virtualcentre.org/eims/download.asp?pub_id=92920)
Columbia Center for Hazards and Risk Research (www.ldeo.columbia.edu/chrr)
Centre for Research on the Epidemiology of Disasters (www.em-dat.net/disasters), The
International Disaster Database. Data provided: number of events, casualties, damages
etc for drought, disasters, tsunamis, landslides, volcanic eruptions.
6. Institutions that have participated in developing the indicator
Main institutions
FAO
40
responsible
Other contributing
organizations
-
7. Practical applications of the indicator (references to case studies)
-
8. Additional information
-
4. SOIL NUTRIENT BALANCE (PRESSURE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Soil nutrient balance
The difference between nutrient (N, P and K) inputs and nutrient outputs (N, P and K)
Kg N, kg P and kg K per ha (excess or deficit) per year
A land use system (LUS) is considered a homogeneous entity and forms the basis for
calculating the nutrient balance. The information at LUS level can be aggregated at
district and at national levels.
Annual
2. Position within the logical framework DPSIR
Type of indicator
Pressure
3. Target and political pertinence
Objective
Importance with respect to
land degradation
International conventions
and agreements
To demonstrate trends towards nutrient depletion or excessive use of fertilizers.
To guide towards measures that restore the soil nutrient balance.
In natural ecosystems, loss of nutrients (outputs) is generally compensated by nutrient
gains (inputs). Even in traditional bush-fallow systems with some nutrient input by
manure and household waste, the soil fertility level can be stable. However, as soon as
land is transferred to agricultural use on a more perennial basis, soil fertility tends to
decline (nutrient depletion) at a rate that is largely governed by the type of land use
systems introduced and their management (E. Smaling, 1993).
Excess use of N and P containing fertilizer or manure causes eutrophication and related
impacts on aquatic life and water quality. Excess concentrations of such nutrients in
water bodies lead to overnourishment, proliferation of aquatic plants (blue-green algae),
reduced light penetration, depletion of dissolved oxygen in surface water, disappearance
of benthic invertebrates, production of toxins which are potentially poisonous to fish,
cattle and humans, and changes in biological structure. These effects are generally most
apparent in lakes, reservoirs and coastal areas, and also in large slow-flowing rivers.
(http://esl.jrc.it/envind/meth_sht)
UNCED (1992), Agenda 21: Chapter 18: Protection of the quality and supply of
freshwater resources: application of integrated approaches to the development,
management and use of water resources.
4. Methodological description and basic definition
Definitions and basic
concepts
Continuous cropping without adequate restorative practices leads to nutrient depletion
and may endanger the sustainability of agriculture. Nutrient depletion is a major form of
soil degradation. A quantitative knowledge on the depletion of plant nutrients from soils
helps to understand the state of soil degradation and may be helpful in devising nutrient
management strategies. The impact of a negative nutrient balance cannot be seen
independently from actual soil fertility, i.e. the nutrient stocks. A negative nutrient
balance on a rich soil will not affect yield in the short term, while on a poor soil, crop
yield may decline each year as a result of nutrient depletion. At some stage in marginal
areas, a negative nutrient balance may no longer affect production as yields reach a
bottom-line level where natural inputs such as atmospheric deposition make up for losses
(FAO, 2003).
Nitrogen and phosphorus are both essential to plant growth but fertilizers are often
applied in excess of the quantities plants really need and surplus fertilizer tends to leach
41
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
into surface or groundwater, causing eutrophication.
Nutrient depletion: for a given land use system a persistent net loss of nutrients is
unsustainable.
Eutrophication: The European Directive on Nitrates (91/676/EEC) restricts the use of
nitrogen in artificial fertilizers or manure to 170 kg/ha in nitrate-sensitive areas.
Directive 80/778/EEC on Drinking water indicates a recommended nitrogen level of
25mg/L and maximum concentration 50 mg/L. Compliance with Directive 91/271/EEC
of 21 May 1991 on Urban Waste Water Treatment Action Programme for the Rhine:
50% reduction of nitrogen and phosphorus discharges (http://esl.jrc.it/envind/meth_sht).
The soil nutrient balance is based on 5 input factors (mineral fertilizer, manure,
deposition, biological fixation and sedimentation) and 5 output factors (harvested
product, crop residues, leaching, gaseous losses and erosion).
A simplified (or proxy) indicator could be one which takes into account only the most
prominent of the above-mentioned factors. They would be sufficient to allow statements
about the trend (within the LUS) (H. Wattenbach and K.H. Friedrich in FAO, 1997).
The indicator is based on approximation and aggregation at country level – which may
mask the bright spots – where agriculture is sustainable – and the hot spots – where
urgent nutrient replenishment is needed. Assessment of fertility decline at microwatershed or community scale would be more appropriate as a basis for action, but it
would be costly and time-consuming (FAO, 2004).
The indicator does not take into account the nutrient loss due to forest fires or the
nutrient losses of degrading rangeland.
Soil fertility, soil loss, soil health, rainfall variability
The largest unit for which soil nutrient balances can be quantified is the field. Larger
spatial scales can only be dealt with through generalization and aggregations. Land use
systems in a region are generalized into a typology with a known or unknown variation.
Aggregations then describe how the generalized, larger ‘uniform’ units are added
together to yield one overall soil nutrient balance for the region. Aggregation is a delicate
issue as the balance itself is made up of several parameters that are in some cases
outcomes of regression analysis on more basic parameters. Model validation becomes
difficult because of the lack of independent data sets that meet all the input requirements.
At macrolevel, the nutrient-balance model raises awareness of soil fertility problems,
indicates areas with nutrient depletion or accumulation, and gives a quantified picture of
the nutrient flows. A macrolevel assessment can provide a basis for selecting areas for
soil fertility improvement. A mesolevel study can then identify specific constraints, and
it should reveal the best options.
The introduction of mesolevel studies adds value to existing national- and farm-level
approaches. Provided that sufficient data are available, mesolevel nutrient balances can
be compiled properly. Mesolevel results provide information that cannot be deduced
from macrolevel and or microlevel studies. The mesolevel offers a suitable entry point
for policy-makers and private sector intervention, where macrolevel and microlevel are
not appropriate for policy-making at the subnational level. Further methodological
refinements are feasible through making them more spatially explicit (accounting for
spatial variation in soils and climate) and through improving procedures for calculating
nutrient flows and quantifying soil nutrient stocks.
(FAO, 2003:)
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Primary data at district/LUS level on applied mineral fertilizers and manure, crop yields,
nutrient contents, residue removal and erosion.
District agricultural offices, national statistics, FAOSTAT
Usually available at district and national level but data not always reliable
6. Institutions that have participated in developing the indicator
42
Main institutions
responsible
Other contributing
organizations
FAO, 2003
E. Smaling, 1993
7. Practical applications of the indicator (references to case studies)
See FAO, 2003 www.fao.org/docrep/006/y5066e/y5066e00.htm for a description of soil nutrient balance studies at
macrolevel (e.g. Subsaharan Africa), at mesolevel (e.g. Kisii District, Kenya, Southern Mali, India) and microlevel.
8. Additional information
Much of the soil nutrient debate ignores the role that farmers play in shaping the processes of environmental change.
However, despite broadly similar access to resources and opportunities, marked differences often exist within a single
setting in which soil fertility is handled by different farmers. Among different farmers and between areas, the relative
value of land, labour and capital endowments over time may have important implications for the form and efficiency of
any farm-level nutrient cycle. Statements on soil fertility decline must refer to the relevant context. The orientation of
studies towards a targeted approach to soil fertility intervention that distinguishes between farm component, agroecological zone (AEZ) and socio-economic groups is an appropriate approach. Non-consideration of socio-economic
aspects in nutrient budget and balance studies may lead to the exclusion of many relevant factors (FAO, 2003).
See also: Scaling soil nutrient balances. Enabling mesolevel applications for African realities. Fertilizer and Plant
Nutrition Bulletin 15, FAO, Rome, 2004.
5. SOIL SEALING (PRESSURE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Soil sealing
Increase in the amount of agriculture, forest and other semi-natural and natural land
taken by urban and other artificial land development. It includes areas sealed by
construction and urban infrastructure as well as urban green areas and sport and leisure
facilities. The main drivers of soil sealing are grouped in processes resulting in the
extension of: housing, services and industrial and commercial sites, transport networks &
infrastructures mines, quarries and waste dumpsites.
(http://themes.eea.europa.eu/IMS/CSI →Terrestrial →Land take CSI014).
Area (ha)/year; results are presented as average annual change, % of total area of the
country and % of the various land cover types taken by urban development
(http://themes.eea.europa.eu/IMS/CSI).
Local to national
Once every year to once every five years
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objective(s)
Importance with respect to
land degradation
To monitor and assess the degree of loss of (productive) land by urbanization
Millions of hectares of good farmland are being lost each year to nonfarm purposes; they
are being flooded for reservoirs or paved over for highways, airports, and parking lots.
The result of all this mismanagement will be less productive agricultural land at a time
when world population is growing and expectations are rising among people everywhere
for a better life (www.fao.org/docrep/T0389E/To389E02.htm)
When land is sealed, the area for soil to carry out its functions including the absorption
of rainwater for infiltration and filtering is reduced. The sealing of land leads to the
direct run-off of precipitation into rivers which, in turn, enhances the risk of flooding at
the regional level. In addition sealed areas may have a great impact on surrounding soils
by changing water flow patterns and by increasing the fragmentation of biodiversity. Soil
sealing is almost irreversible (Montanarella, 2002).
Areas that are threatened by sealing are often fertile peri-urban areas (e.g. Hortic and
Terric Anthrosols). By covering such soils by buildings and roads, productive land is lost
and the pressure on the remaining land increases.
43
International conventions
and agreements
World Soil Charter , adopted at the 21st Session of the FAO Conference, in November
1981. The Charter establishes a set of principles for the optimum use of the world's land
resources, for the improvement of their productivity, and for their conservation for future
generations.
Many of the global conventions organized by the United Nations are responses to global
degradation:
1992: the United Nations Conference on Environment and Development (also known as
UNCED or the Rio Earth Summit); Agenda 21, Chapter 12
1994: the United Nations Convention to Combat Desertification (also known as
UNCCD); Article 4.2.
Millennium Development Goal (MDG) nr 7: ensure environmental sustainability
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
Soil sealing is included in the land cover “Artificial surfaces and associated areas” (FAO,
2000). Two main classes are distinguished: built-up areas and non-built-up areas. Builtup areas comprise linear (roads, railways, communication lines/pipelines) and non-linear
industrial and urban) areas. Non built-up areas comprise waste dump deposits and
extraction sites.
Not known
Aerial photography and VHR satellite imagery/civilian optical data
(www.ball.com/aerospace/quickbird.html); D. Lantieri, 2003.
Soil loss, soil health, vegetation activity, land cover change
Soil sealing can be measured at local scale (village, town, industrial area, roads etc) with
the help of aerial photography or VHR satellite imagery; the information on soil sealing
can then be compiled at national level and represented on digital maps
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Aerial photography and/or VHR satellite imagery (optical data); preferably time series to
assess increase of soil sealing
NASA, SPOT, ESA
D. Lantieri, 2003; FAO/SDRN
Information may be available at national level on areas that have been surveyed/assessed.
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
- Environmental indicator (UNCSD)
- Urbanization and urban environment possible core and supplementary indicator
(www.epa.nsw.gov.au/soe/issues/7_5.htm)
- Economic indicator (urban sprawl), Desertification indicators for Mediterranean Europe
www.kcl.ac.uk/projects/desertlinks/downloads.htm
- Similar to European Environmental Agency indicator “land take” denoting land
conversion to urban and related structures http://themes.eea.eu.int/IMS/CSI
-
7. Practical applications of the indicator (references to case studies)
-
8. Additional information
Land use by urban and related infrastructures has the highest impacts on the environment due to sealing of soil as well
as disturbances resulting from transport, noise, resource use, waste dumping and pollution. Transport networks, which
connect cities, add to the fragmentation and degradation of the natural landscape. The intensity and patterns of urban
sprawl are the result of three main factors: economic development, demand for housing and extension of transport
networks. (http://themes.eea.europa.eu/IMS/CSI →Terrestrial →Land take CSI014).
Economic development within the Asia Pacific region has led to expansion of urban and industrial land. This has been
44
particularly rapid over the last few years in many of the newly emergent countries of Asia. Much of the present urban
and industrial development has taken place on what was formerly good agricultural land. The expansion of such cities
as Bangkok, Jakarta and Metro Manila has resulted in the loss of considerable areas of good quality paddy rice land.
Given the present shortage of such land, in Thailand, Java and the Philippines respectively, these losses increase the
pressure on the remaining areas. Being so mountainous, with only 10% being arable land, and a population in excess of
30 million Fujian province has possibly the most acute land shortage problem in China. The uncontrolled urban and
industrial development in the coastal zone (following the new policy of economic liberalisation) has seen the loss of
much prime agricultural land to new roads and buildings.14
The expansion of urban settlements is a particularly acute problem in the small Pacific countries with little arable land.
Population pressure is most acute in the capitals of the atoll nations (e.g. Tarawa in Kiribati) but is of concern even in
those countries with larger land areas (e.g. Port Moresby in Papua New Guinea). Throughout the Pacific, people are
gravitating from the mountains to the coastal cities, from the outer islands to the provincial or national seats of
government, and from scattered rural hamlets to larger villages and towns. Under customary ownership laws, land is
not readily available for housing estates. Hence squatter towns develop with health problems from overcrowding,
unsanitary conditions and water pollution; valuable agricultural resources are lost; and forests, lagoons and reefs are
degraded. Farm households affected by urban expansion may be forced to use their remaining plots more intensively or
to seek land elsewhere, which in a land scarcity situation usually means moving into marginal upland areas. Hence
urban and industrial expansion may be a contributory factor to soil degradation elsewhere (Poverty alleviation and food
security in Asia. FAO Regional Office for Asia and the Pacific, July 1999: Losses to urban/industrial development).
6. STOCKING RATE (PRESSURE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Stocking rate
Stocking rate is defined as the amount of grazing land actually available to a Tropical
Livestock Unit (TLU). The correct stocking rate should always be less than the carrying
or grazing capacity.
Number of ha per TLU
Local
Annual
2. Position within the logical framework DPSIR
Type of indicator
Pressure
3. Target and political pertinence
Objective(s)
Importance with respect to
desertification
To monitor the number of tropical livestock units per unit of surface in relation to the
carrying or grazing capacity of the grassland.
To detect and map areas of increasing pressure and areas where the grazing capacity of
rangelands is being or has been exceeded as hot spots.
To recommend a number of TLUs for a given area that would not lead to overgrazing
and land degradation.
Increasing livestock populations in Africa, the Middle East and Asia are placing
mounting pressure on deteriorating rangelands. Africa, the Middle East, Central Asia, the
northern part of the Indian subcontinent, Mongolia, and most of northern China have lost
billions of dollars in livestock production capacity as a result of overstocking land with
cattle, sheep and goats. As a result of growth in human population and increased demand
for meat in developing nations, the world's population of cattle has increased from 720
million in 1950 to about 1.5 billion in 2001 (statistics compiled by FAO).The number of
sheep and goats expanded from 1.04 billion to 1.75 billion during the same time period.
This increase in livestock production since 1950 has led to severe overgrazing
worldwide. Since mid-century, 20 percent - some 680 million hectares - of global
rangeland has been degraded by overgrazing. Overstocking rangeland reduces soil
fertility and ultimately the size of the herd that can be sustained. Once severely damaged
by overgrazing, grasslands are hard to restore. Overgrazing of rangelands initially
45
International conventions
and agreements
reduces their productivity but eventually it destroys them, leaving desert.
Herds in nearly all developing countries have come to need more fodder than local
rangelands and forage resources can sustainably yield. The number of livestock in Africa
often exceeds grasslands capacity by half or more, he says. Some 230 million cattle, 246
million sheep and 175 million goats on the continent are supported almost entirely by
grazing. In India, with the world's largest cattle herd, the demand for livestock feed in
2000 was an estimated 700 million tons, while the sustainable supply amounted to just
540 million tons. http://ipsnews.net/fao_magazine/environment.shtml (FAO, Earth
Policy Institute and International Food Policy Research Institute, 2002)
Many of the global conventions organized by the United Nations are responses to global
degradation:
1992: the United Nations Conference on Environment and Development (also known as
UNCED or the Rio Earth Summit); Agenda 21, Chapter 12
1994: the United Nations Convention to Combat Desertification (also known as
UNCCD); Article 4.2.
Millennium Development Goal (MDG) nr 7: ensure environmental sustainability
4. Methodological description and basic definition
Definitions and basic
concepts
Carrying capacity is commonly expressed as number of animal units per hectare when
speaking of intensive ranching or dairy operations, but when referring to extensive
nomadic grazing carrying capacity is usually hectares per animal unit. The carrying or
grazing capacity of a rangeland is defined as the amount of grazing land which should be
made available to a Tropical Livestock Unit so that it can be maintained without
deterioration of the natural resources of the area over the long term. Other definitions
include the necessity of efficient production (milk or meat) but it is believed that under
arid and semi-arid area conditions, the main purpose of grazing over a large part of the
year is the maintenance of the livestock.
The standard used for one Tropical Livestock Unit is one cattle with a body weight of
250 kg. If the feed eaten is reasonably the same for both species being compared, the
ratio of metabolic weights provides the best means of comparison. This relationship
expresses that the fact that smaller animals produce more heat and consume more food
per unit of body size than do larger animals. Basal metabolic rate (energy expenditure
per unit body weight per unit time; i.e., kcal heat/weight/day) varies as a function of a
fractional power of body weight, usually determined to be body weight raised to the 0.75
power. Loss of protein from the body also varies by a similar fractional power of body
weight and is presumed to be related by the same exponential power of body weight.
Under resource driven grazing conditions the average voluntary feed intake amongst
species is remarkable similar, about 1.25 times maintenance requirements (1 for
maintenance, 0.25 for production = growth, reproduction, milk etc.). Metabolic weight is
therefore considered as the best unit for aggregation of animals from different species,
whether this is for the total amount of feed consumed, manure produced, or product
produced. Exchange ratios for animals with different body weights in Tropical Livestock
Units are based on metabolic weight. For instance 5 sheep or goats of 30 kg will
consume as much as 1 cow of 250 kg. Similarly, two buffalo of about 425 kg will
consume as much as 3 cattle of 250 kg. However, strictly speaking, they can only be
compared in this way when the different species consume the same feed, something that
is often not the case. (http://lead.virtualcentre.org/en/dec/toolbox/Index.htm and
http://www.fao.org/docrep/R7488E/r7488e04.htm#3.4.2%20stocking%20rate%20and%2
0carrying%20capacity).
Overgrazing is the continuous act of an animal biting the same plant over and over again,
causing the plant root system and crown reserves to be depleted. The plant loses its
ability to be dominant or competitive with less desirable species that are seldom grazed.
To stop overgrazing, plants have to rest after being grazed. Even when plants are only
50% grazed these plants need rest; a plant tries to regrow shortly after being bitten and
it's that regrowth that animals relish. By eating fresh regrowth, further stress is put on the
plant; therefore, regrowth is diminished
(http://www.noble.org/ag/Forage/IndicatorPlants/index.html).
46
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
Benchmark values depend on the carrying or grazing capacity of the grazing land. There
is often a fair relationship between annual rainfall and carrying capacity but for each
value of rainfall the carrying capacity may vary from a maximum corresponding to bad
quality soil and low nutrient content to a minimum corresponding to ideal soil
conditions. In assessing the carrying capacity of rangelands from data on dry matter
production, several factors are taken into account including the slow deterioration of
forage quality over the dry season (decrease of protein content) and the increasing risk of
forage destruction as time goes on. Attention should also be drawn to the variability of
rainfall over a given area from year to year. The simple knowledge of the average annual
rainfall is not sufficient to predict the range of variations of the carrying capacity of an
area. For the Sahel region the following approximate relationship between mean annual
rainfall and grazing capacity has been established:
Mean annual rainfall of 1000 mm: Grazing capacity of 5 ha/TSU; Mar of 750 mm: Gc of
10 ha/TSU; Mar of 500 mm: Gc of 15 ha/TSU; Mar of 250 mm: Gc of 30 ha/TSU.
(http://www.fao.org/docrep/R7488E/r7488e04.htm#3.4.2%20stocking%20rate%20and%
20carrying%20capacity)
The actual stocking rate can be calculated on the basis of the number of TLUs present in
an area measuring a certain number of ha or km2. As mentioned above a sustainable
stocking rate depends on the carrying capacity of grazing capacity of the land. The
grazing capacity can be assessed by several methods (FAO, 1991): 1. experiments
(expensive), 2. calculations and models and 3. inferences made from recorded or
estimated stocking rates and related rangeland conditions.
In the calculation of grazing capacity for a specific area, the main variable to be
measured or estimated is the standing crop available for forage. The latter may be
estimated by the “difference method” (the forage weight in a grazed area (A) is
measured; simultaneously, grazing exclosures are constructed and after a set period of
days or weeks the herbage weight inside the exclosures (C) is determined; forage
production is C-A). An alternative method would be the use of remote sensing
techniques, in particular the NDVI. A pragmatic approach when using NDVI is to
stratify the grazing land ino a small number of classes of herbage that are relatively
homogenous. Reflectance measurements are then taken of each of them, NDVI values
are produced and these are related to the standing crop production.
The assessment of the grazing capacity depends on many factors and remains an
approximation at best. The more accurate the estimate has to be the more expensive the
method will be (reliance on field experiments and models that need to be validated on the
ground).
Aridity index, rainfall variability, soil health, soil fertility, soil loss, vegetation density,
soil moisture, soil salinity
Local, national and global
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
The number of different types of livestock within a given area; exchange ratios for
livestock in Tropical Livestock Units based on Metabolic Body Weight; weight of dry
matter (DM) production per unit area during the grazing season (obtained by experiment
or modelling) plus one of the following three coefficients: g = grazing efficiency, l =
forage loss factor or p = proper use multiplier (FAO, 1991. Guidelines: land evaluation
for extensive grazing).
National statistics and records; international organizations and research institutes (e.g.
FAO, ILRI, CIAT, IFPRI, ICARDA)
Data on livestock types and numbers are mostly available; reliable data for estimating the
grazing capacity are probably not always available.
6. Institutions that have participated in developing the indicator
47
Main institutions
responsible
Other contributing
organizations
FAO, ILRI
-
7. Practical applications of the indicator (references to case studies)
-
8. Additional information
A number of points need to be noted when using TLUs:
In respect of grazing pressure there are differences amongst species in grazing / browsing behaviour and in feeding
abilities that will alter the exchange ratios. The optimum number of each species in a pasture depends upon the amount
of grass and browse available, not upon the total number of TLUs and total biomass.
For example, there is little competition for feed between grazers and browsers and therefore little basis for exchange
exists. As a result, a TLU per ha of 0.4 composed of cattle only can so result in overgrazing while a TLU of 0.5 per ha
composed of cattle sheep and goats can be sustainable.
Species differ in grazing behaviour and abilities - resulting in different opportunities to utilise vegetation (see also
species). For example, when feed density is low (onset of rainy season, poor fallow vegetation, early re-growth of
perennial grasses etc.) sheep can find enough feed to grow. For cattle the equivalent density will still be too low even
for maintenance. Camels can reach browse at higher levels than goats, while goats can reach browse between rocks and
on hill slopes, feed that camels are unable to reach.
The impact of grazing and browsing on the composition of the vegetation is different. Repeated grazing will result in
more browse and repeated browsing in more pasture. Early grazing by sheep can reduce grass production - notably
when the growing season is short. This can reduce feed availability for cattle more than the equivalent of the feed
consumed by sheep.
In situations of communal pastures, farmers in general adjust herd composition and periods of grazing according to the
available grazing and browse resources. Therefore, at equal levels of TLUs per ha but with large difference in
composition of the vegetation, herds may be composed of quite different combinations of livestock species
(http://lead.virtualcentre.org/en/dec/toolbox/Index.htm).
7. WATER CONSUMPTION (PRESSURE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Water consumption
The total annual demand for drinking water, process water, irrigation water and cooling
water by all economic sectors.
Cubic metres per year per capita
National
Annual
2. Position within the logical framework DPSIR
Type of indicator
Pressure
3. Target and political pertinence
Objective(s)
Importance with respect to
land degradation
International conventions
and agreements
The indicator presents the overall pressure on the fresh water resources of a certain
quality level. Because all economic sectors are involved the total demand is pictured.
The quantitative demand puts the highest pressure on the surface water and ground water
resources (http://esl.jrc.it/envind/met_sht/ms_we056.htm).
Water consumption depicts the use of fresh surface water and fresh ground water for the
production of drinking water, process water and cooling water for industry and the use of
irrigation water for agriculture. In dryland areas agriculture (i.e. irrigation) consumes the
largest percentage (probably more than 80%) of abstracted water. Over-use of the water
resource would lead to water shortage and severe damage to the environment and the
economy.
Article 130 of the Treaty on the European Union (Maastricht, 1992) calls for prudent and
rational utilization of natural resources. UN GEMS/Water introduces a Global
Environment Monitoring System for water. UN EMINWA calls for an
environmentally-sound management of inland waters. Action Plan Mar del Plata (1977)
48
calls for sustainable use of water resources (UN Conference). Declaration of New Delhi
calls for provision of drinking water for all. IAP/WASAD gives the FAO International
Action Plan for water use in relation with Sustainable Agricultural Development.
UNCED (1992): Agenda 21 in chapter 18 calls for the protection of the quality and
supply of water resources and for policies and actions in various areas, that take into
account the vulnerability and availability of natural resources, and for an increased
efficiency in the use of these resources.
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
The quantity of fresh (surface and ground) water used per capita is directly related to
individual and industrial water consumption patters. It also directly reflects any
precautionary measure taken, such as promotion of water saving behaviour. The demand
can be quantified on the basis of figures on the supply to the users. If required abstraction
figures can be used. Water abstraction from surface water to be used as cooling water is
returned and does not contribute to the depletion. This fraction is not included in the
indicator.
Reference levels for this indicator can be derived from existing policy targets, but it is
also possible to estimate a sustainable level of water consumption on the basis of the
existing resources and the natural recharge.
The indicator is measured in units per capita to enable direct comparisons between
countries. Water use is very simple to aggregate. The use by the different economical
sectors for the given purposes can be added up directly. Finally the total use will be
divided by the number of inhabitants.
The indicator does not allow for the differentiation of water resources such as surface
water and ground water
Water availability, water contamination, water salinity
National
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Annual consumption of fresh water per sector.
Water distribution companies
National statistics; WRI (http://earthtrends.wri.org/text/)
FAO AQUASTAT:
http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.stm
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
EEC, FAO
UNESCO
7. Practical applications of the indicator (references to case studies)
-
8. Additional information
In many countries the most important pressure on fresh ground water resources are withdrawals for industry and
agriculture. Very often the quality of the ground water is far better than required for agricultural or industrial use.
Nevertheless ground water is used because it is cheaper. This leads to a situation that, because of huge withdrawals by
agriculture and industry, shortages of good quality ground water can occur for drinking water production.
49
ANNEX 5 STATE INDICATORS
1. ARIDITY INDEX (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal scale
Aridity Index
The ratio of mean annual precipitation to potential evapotranspiration : AI = P/PET
P and PET must be expressed in the same units, e.g., in mm; numerical rating (ratio)
Local, National, Global
Onetime measurement; may be repeated every 5 or 10 years
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation
International conventions
and agreements
To identify and map areas prone to drought, vulnerable areas and drought-affected zones.
To measure and map the degree of aridity of drylands.
To determine average amount of available water for livestock and agricultural utilization
in the drylands zones.
To provide soil and crop management practices that can reduce the impact of water
scarcity.
Aridity is a natural environmental condition that describes climate extremities
particularly referring to water scarcity. It is a determinant of ecosystem characteristics
affecting soil health capacity.
Soil moisture influences the distribution and growth pattern of vegetation, soil microbial
activity, soil erosion, wind erosion, nutrient movements and other soil properties.
A part of land degradation assessment procedure that will provide understanding on
developing strategies and approaches to mitigate/combat land degradation in drylands.
Millennium Development Goals (MDG) Poverty alleviation and environmental
sustainability
Agenda 21 Chapter 12 Combating desertification and drought
UNCCD Article 4.2 (a) commits Parties to “adopt an integrated approach addressing the
physical, biological and socio-economic aspects of the processes of desertification and
drought”
UNCCD Article 4.2 (c); to “integrate strategies for poverty eradication into efforts to
combat desertification and mitigate the effects of drought”
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
An aridity index (AI) is a numerical indicator of the degree of dryness of the climate at a
given location and serves to identify, locate or delimit regions that suffer from a deficit
of available water, a condition that can severely affect the effective use of the land for
such activities as agriculture or stock-farming.
(Hyperarid: AI < 0.05%)
Arid: 0.05 < AI < 0.20%
Semi-arid: 0.20 < AI < 0.50%
Dry subhumid: 0.50 < AI < 0.65%
Analysis climatic stations (L0CLIM)
Estimation is based on long- term means of local climate data (at least 20 years), on more
or less permanent climatic condition.
Climate extreme events, vegetation cover, frequency of forest fires, under-management
resource, changes in land cover, incidence of poverty, livestock pressure
Measured at a given station; if data for a sufficient number of stations are available, a
digital map can be compiled showing various aridity classes at national or subnational
levels; regionalization can be aided by constructing Thiessen polygons (corrected for
topographic features) for each climatic (reference) station
5. Evaluation of data needs and availability
50
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Rainfall and evapotranspiration data, in time series of 30 years (minimum) or more per
climatic (reference) station
National and international meteorological data records
Rainfall and potential evapotranspiration data are generally available at national offices
(provincial, regional, national scale) and at various international research institutions
(national scale); potential evapotranspiration data are usually only available from
reference meteorological stations (see: 8. Addition information)
FAO AQUASTAT – climate info tool
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
Budyko, M. I., 1974
World desertification map, FAO-Unesco, 1977
UNEP adopted the index of aridity in 1992
UNCOD, UNCCD
7. Practical applications of the indicator (references to case studies)
The UNCCD distinguishes six aridity zones on the basis of the ratio of mean annual precipitation to mean annual
potential evapotranspiration and provides a framework for sustainable development of drylands, including susceptible
drylands characterized as arid, semi-arid, and dry sub-humid. Climate statistics for these three dryland zones translate
to an aridity index of 0.05 – 0.065. Biannual country reports to the UNCCD include a country profile with biophysical
indicators relating to desertification and drought, one of which is the aridity index
8. Additional information
The MEDALUS project (www.kcl.ac.uk/projects/desertlink/downloads.htm) uses the concept of
Bagnouls-Gaussen bioclimatic aridity index which can be successfully used for determining the degree of aridity from
easily available meteorological data.
n
The Bagnouls-Gaussen aridity index (BGI) is defined as following: BGI    2t i  Pi   k i
i=1
where: ti is the mean air temperature for month i in oC, Pi is the total precipitation for month i in mm; and ki represents
the proportion of the month during which 2ti - Pi >0.
Palmer drought index (US): The Palmer Index was developed by Wayne Palmer in the 1960s and uses temperature and
rainfall information in a formula to determine dryness. It has become the semi-official drought index.
The Palmer Index is most effective in determining long term drought—a matter of several months—and is not as good
with short-term forecasts (a matter of weeks). It uses a 0 as normal, and drought is shown in terms of minus numbers;
for example, minus 2 is moderate drought, minus 3 is severe drought, and minus 4 is extreme drought. The Palmer
Index can also reflect excess rain using a corresponding level reflected by plus figures; i.e., 0 is normal, plus 2 is
moderate rainfall, etc. The advantage of the Palmer Index is that it is standardized to local climate, so it can be applied
to any part of the country to demonstrate relative drought or rainfall conditions. The negative is that it is not as good for
short term forecasts, and is not particularly useful in calculating supplies of water locked up in snow, so it works best
east of the Continental Divide (US). www.drought.noaa.gov/palmer.html
2. GROUND WATER LEVEL (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Ground water level
The level of water in (shallow and deep) aquifers
Depth of ground water level below land surface in cm or dm
Local, national
Annual (monthly or weekly in benchmark areas if necessary)
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
To determine the status of ground water, its hydrological balance and quality of water
from the aquifer.
To generate regional or national maps on ground water level trends.
51
Importance with respect to
land degradation
International conventions
and agreements
To indicate sustainable use of ground water and whether abstraction and recharge of
groundwater quantity are in balance or not.
The annual groundwater use for the world as a whole can be placed at 750–800 km3,
which value appears modest compared to overall water availability. But an
overwhelming majority of the world’s cities and towns depend on groundwater for
municipal water supplies. Half of the US population draws its domestic water supply
from groundwater (Morris 1997). Groundwater is also critical in supplying the industrial
water demand in most countries. In some of the most populous and poverty-stricken
regions of the world—particularly in South Asia—groundwater has emerged at the
center-stage of the food-agricultural economy. In India, for example, some 60 percent of
the irrigated food grain production now depends on irrigation from groundwater wells.
Between India, China, the US and Pakistan, some 325 km3 of groundwater is used every
year; the 14 countries included in figure 2 use some 520 km3 (FAO’s AQUASTAT);
over 35 countries of the world use more than 1 km3 of groundwater annually (Llamas,
Back, and Margat 1992). (The Global Groundwater Situation, IWMI, 2000)
Significant and persistent lowering of the ground water table indicates over-exploitation
(unsustainable) of the resource and may forecast depletion and hence severe shortage of
drinking and irrigation water (serious economic damage) if measures are not taken in
time. Over-exploitation of coastal aquifers may lead to intrusion of sea water and hence
salinization of the groundwater resource.
See: FAO Legislative Study 86: Groundwater in international law. Compilation of
treaties and other legal instruments. S. Burchi and K. Mechlem.
FAO/Unesco, Rome 2005 (www.fao.org/docrep/008/y5739e/y5739e00.HTM)
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
The depth below land surface of the static ground water level (or aquifer level) is
measured; the static level is the level that is not affected by pumping stress
(measurement should wait for a period of time after pumping stops).
Essentially measurements of static ground water levels but at national and regional level
the following additional attributes may be taken into account:
- change of base flow
- change of ground water quality characteristics
- land subsidence (subsidence, in the order of fractions of centimetres, measured
by extensometer, and DEM information derived from ERS data; these can be
transformed into volume change)
Ground water level in wells (< 20 m deep) are usually measured as follows:
a) well location (GPS)
b) altitude of land surface (topographic map/GPS)
c) depth of ground water level measured by e.g. steel tape (lower end is marked by
carpenter’s chalk; the wetted chalk mark will identify the section of the tape that
was submerged)
The evolution of the groundwater resource may also be monitored by using information
on head levels of selected piezometers per country.
Monitoring of shallow groundwater/aquifer levels requires organization (e.g. collection
of shallow aquifer level data from private sources); monitoring the level of deep aquifers
requires more sophisticated means.
Water availability, Rainfall variability, water salinity
Subnational, national; aggregation to global
5. Evaluation of data needs and availability
Data required to calculate
Depth of groundwater/aquifer level below land surface; land subsidence measurements
52
the indicator
Data sources
Availability of data from
national and international
sources
(extensometer) from satellite data.
International Water Management Institute:
www.iwmi.cgiar.org/pubs/wwvisn/GrWater.htm
FAO AQUASTAT:
www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/dbase/index.stm
World Resources Institute: http://earthtrends.wri.org/searchable_db/ → Water Resources
and Freshwater Ecosystems
OECD: www.oecd.org →Statistics →Environment
Data availability: information on the intensity of the use of water resources is available
for most OECD countries (Source: OECD).
FAO AQUASTAT
World Resources Institute
International Water Management Institute
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
-
7. Practical applications of the indicator (references to case studies)
US: Shrinking aquifer looms as big problem for farms. Nancy Cole, 24.09.06
(www.nwanews.com/adg/Business/167660). Drought conditions since December 2004 and increased pumping for
irrigation have caused water levels in wells across 26 counties in eastern Arkansas to drop by more than twice their
normal amount, according to data collected by the Arkansas Natural Resources Commission and the U. S. Geological
Survey. Between spring 2005 and spring 2006, water levels in 665 wells drilled in the relatively shallow Mississippi
River Valley alluvial aquifer fell, on average, by 2. 14 feet, compared with a normal annual decline of about 1 foot.
Other parts of the nation are experiencing the problem, as well. In some parts of the Ogallala Aquifer that stretches
across eight states in the Great Plains, aquifer depletion has caused increased pumping costs and decreased land values,
forcing some farmers into bankruptcy. In 2000, Arkansas farmers used the equivalent of 7. 917 billion gallons of water
per day to irrigate their crops, according to an estimate by the U. S. Geological Survey. With 82 percent of that water
coming from wells, Arkansas ranked as the fourth-largest user of groundwater in the United States, exceeded by only
California, Texas and Nebraska. In Arkansas the aquifer generally measures 50 feet to 150 feet thick, with the top of
the formation lying, on average, about 50 feet underground. The U. S. Army Corps of Engineers has estimated that
parts of Arkansas’ alluvial aquifer will become commercially useless by 2015 unless changes are made in the state’s
water budget.
China: In North China’s Henan province, China’s largest, where some 2 million hectares—52 percent of irrigated
lands—are served by tube wells, water table monitoring data on 358 observation wells encompassing 75,000 km2
showed water table declines of 0.75–3.68 meters during 1975–87. In the Changzhou prefecture of Hebei province—
where 76,800 wells irrigate 0.29 million hectares—37 percent of the irrigated area—the area covered by saline water
increased by 9.1 percent during 1980–90 (Lunzhang 1994). In the Fuyang river basin of North China where IWMI has
been studying basin institutions, surface water supplies to agriculture have been drastically curtailed over a 20-year
period for meeting industrial needs; farmers have responded by resorting to groundwater irrigation; the number of tube
wells in the basin has increased to some 91,000, mostly during the 1970s and the water table has fallen from 8 to 50
meters during 1967–2000. Aquifers in the Fuyang basin are under double assault: farmers are depleting the lower
aquifers and industries are polluting the upper ones. (The Global Groundwater Situation, IWMI, 2000)
Jordan: Unplanned groundwater exploitation can wreak havoc on fragile ecologies such as wetlands. A good example
of how groundwater overexploitation can ruin ecologies is offered by the Azraq Oasis in the heart of the Jordanian
Badia. The Azraq, a Ramsar wetland of over 7,500 hectares, has provided a natural habitat for numerous, unique,
indigenous, aquatic and terrestrial species; and the oasis was acclaimed internationally as a major station for migratory
birds until it dried up completely as a result of groundwater overexploitation upstream through mechanical pumps for
irrigation and for feeding the city of Amman. The two springs that supplied the reserve with water were fed by an
underground aquifer which has been heavily over-exploited. Extraction from the aquifer has exceeded the natural
recharge rate every year since 1983, and in 1996 was over twice as high. About 50% is pumped to supply Amman and
Zarqa, while the remainder is used locally for irrigation. Discharge from the springs that fed the reserve fell rapidly
during the 1980s, until June 1992 when both ceased to flow and the wetland dried out completely. The peaty soil
became dry subsequently and slow-burning, underground fires began to spread through the site. The result was the
53
collapse of the whole ecosystem, increase in the salinity of groundwater from 1,200 to 3,000 ppm, and the decline of
the tourist economy around the oasis. Sources: (www.ramsar.org/forum/forum_jordan_azraq.htm) and
(www.iwmi.cgiar.org - The Global Groundwater Situation, IWMI, 2000).
8. Additional information
Aquifer depletion, a worldwide problem, can lead to faltering wells, reduced surface-water flows, ground subsidence,
aquifer compaction and water-quality deterioration. Aquifers play a key role in the circulation of water between the
earth and its atmosphere, a cycle that includes precipitation, evaporation, runoff, transpiration from plants and
infiltration into the ground. Aquifers are a bit like water savings accounts. And, just like bank accounts, they cannot
sustain indefinitely a “water-budget imbalance” where the rate of water withdrawal exceeds the rate of water recharge.
Aquifer pollution—from both point and nonpoint sources—is becoming extensive worldwide. In the Gediz basin of
Anatolia, Turkey, nonpoint pollutants—mostly agrochemicals—have polluted the groundwater and the river
downstream so badly that cities like Izmir, and strawberry orchard owners in Menemen, would rather pump
groundwater than use the river water. In North Arcot district in the Indian State of Tamilnadu, coconut water contained
0.2 percent of residual chromium derived from chrome-tanning process-based tanneries that contaminated the
groundwater (K. Sarabhai in foreword to Mudrakartha 1999). In the west Indian State of Gujarat, groundwater pollution
by textile processing and the rapidly growing chemical industry earned such notoriety that, in 1998, acting suo moto,
the State’s High Court ordered an entire industrial estate—housing over 1,200 manufacturing units, 70 percent of them
chemical—closed, pending the establishment of a wastewater treatment and disposal system.
(The Global Groundwater Situation, IWMI, 2000)
Saltwater intrusion: Groundwater over-exploitation occurs when groundwater abstraction exceeds recharge and leads to
lowering of the groundwater table. The rapid expansion of groundwater abstraction over the past 30 to 40 years has
supported new agricultural and socioeconomic development in regions where alternative surface water resources are
insufficient, uncertain or too costly. Over-abstraction leads to groundwater depletion, loss of habitats and deteriorating
water quality. It is a significant problem in many European countries. One of its impacts is the intrusion of saltwater
into aquifers. Large areas of the Mediterranean coastline in Italy, Spain and Turkey have been reported to be affected
by saltwater intrusion. The main cause is groundwater over-abstraction for public water supply. Irrigation is the main
cause of groundwater over-exploitation in agricultural areas. Some examples are the Greek Argolid plain of the eastern
Peloponnesus, where it is common to find boreholes 400 m deep contaminated by saltwater intrusion. Once saltwater
intrudes into a groundwater body its recovery is nearly impossible even in the longer term. Hence freshwater demand
has to be met by abstractions from other groundwater or surface water bodies, often over long distances, transferring
and increasing the water stress to distant areas. Alternatively in many regions the freshwater demand is met by a
desalination of saltwater. Common to both solutions is that they are very costly.
3. RAINFALL VARIABILITY (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal scale
Rainfall variability
Rainfall coefficient of variation
%
Local, National, Global
Annual
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation
International conventions
and agreements
To identify and map areas affected by different degrees of variability.
To guide towards soil and crop management practices that can reduce the impact of
rainfall variability
The rainfall coefficient of variation is a measure for the occurrence of extremes (more
dry and very wet years alternating) and allows one to compare the variability of rainfall
at any location, regardless of mean precipitation
Millennium Development Goals (MDG) Poverty alleviation and environmental
sustainability
Agenda 21 Chapter 12 Combating desertification and drought
UNCCD Article 4.2 (a) commits Parties to “adopt an integrated approach addressing the
54
physical, biological and socio-economic aspects of the processes of desertification and
drought”
UNCCD Article 4.2 (c); to “integrate strategies for poverty eradication into efforts to
combat desertification and mitigate the effects of drought”
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
The coefficient of variation of annual rainfall is calculated by dividing the standard
deviation of annual rainfall by the average annual rainfall; it expresses the standard
deviation of annual totals as a percentage of average annual rainfall; the higher the
coefficient, the more variable the rainfall is from year to year.
Critical values of the CV may be expressed in percentage classes (e.g. <10%, 10-20%,
20-30%, … >100% etc)
Simple calculation on the basis of available data
The indicator does not reveal a change but rather contributes to the characterization of
areas for identifying land degradation hot spots and/or bright spots
Aridity index, soil moisture, vegetation activity
Rainfall is measured at a given station; if data for a sufficient number of stations are
available, a digital map can be compiled showing the variability classes for annual
rainfall at national or subnational levels
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Time series of annual rainfall of at least 10 years for ordinary rainfall stations and long
term (> 30 years) average rainfall data for reference meteorological stations
National and international meteorological data records
Rainfall data are generally available at national offices (provincial, regional, national
scale) and at various international research institutions (national scale
FAO AQUASTAT – climate info tool
6. Institutions that have participated in developing the indicator or are applying it
Main institutions
responsible
Other contributing
organizations
Biannual country reports to the UNCCD comprise a country profile with biophysical
indicators relating to desertification and drought, including normal rainfall and standard
deviation; the coefficient of variability can be calculated on the basis of these two
parameters
n.a.
7. Practical applications of the indicator (references to case studies)
The coefficient of variation of annual rainfall is used by the Ministry of Environment and Tourism of Namibia (Atlas of
Namibia, 2002) to illustrate the variation in annual rainfall
8. Additional information
Example to illustrate the coefficient of variation: Take an area with an average rainfall of 400 mm per year and a
coefficient of variation of 40%. A value of 40% means that the standard deviation is 160 mm (400 mm x40/100 = 160
mm). As 68 % of values fall within one standard deviation of the mean (average), that area would see annual rainfall
totals of between 240 and 560 mm in two-thirds of all years. In the remaining third of all years, annual rainfall totals
would be below 240 or above 560 mm.
http://tornado.sfsu.edu/geosciences/classes/m356/RainfallVariability/TempVar.htm
4. SOIL FERTILITY (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Soil fertility
The state of fertility of the soil
Semi-quantitative score
Local (farm, village)
Once every year to once every five years
55
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation
International conventions
and agreements
To characterize the soil in terms of relative productivity
To contribute to the identification of land degradation hot spots and bright spots.
To produce digital maps presenting the relative soil fertility/productivity from local level
to national level
Soil fertility/productivity is a good indicator of the land conditions, since it directly
reflects changes in the qualities and limitations of the land. Assessment of the
productivity of specific target areas and comparison with similar neighbouring areas that
are already used under adequate practices of crop management allows identification of
the needs for applying particular soil improvement practices.
World Soil Charter , adopted at the 21st Session of the FAO Conference, in November
1981. The Charter establishes a set of principles for the optimum use of the world's land
resources, for the improvement of their productivity, and for their conservation for future
generations.
Many of the global conventions organized by the United Nations are responses to global
degradation:
1992: the United Nations Conference on Environment and Development (also known as
UNCED or the Rio Earth Summit); Agenda 21, Chapter 12
1994: the United Nations Convention to Combat Desertification (also known as
UNCCD); Article 4.2.
Millennium Development Goal (MDG) nr 7: ensure environmental sustainability
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
The indicator is based on the assessment, in close cooperation with the farmer(s) of the
expected yield, fertilization practices, crop growth characteristics and nutrient
deficiencies; see Section 8 below: Additional information
The soil fertility indicator ranges from 0 to 3:
0: very low fertility
1: low fertility
2: moderate fertility
3: high fertility
Combination of visual assessment and measurements (e.g. height of plants, number of
tillers)
The assessment of the various indications of soil fertility needs to be done in a systematic
and careful way and requires training; even the assessment by trained/experienced
farmers or technical staff remains partly subjective; information on yield, especially past
yields or neighbourhood fields, depends on farmer’s memory; comparison between areas
assessed by different teams may therefore be difficult.
Soil loss, soil health, vegetation density, land cover/land use change, nutrient balance,
water contamination, soil salinity, water salinity
The various parameters are measured and /or estimated at field level (farm, village) and
summarized into one single soil fertility indicator.
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Field observations, estimations and measurements of soil fertility parameters such as
expected yield, fertilization practices, crop growth characteristics and nutrient
deficiencies
Field evidence, farmers’ knowledge
Information may be available at national level on areas that have been surveyed.
6. Institutions that have participated in developing the indicator
56
Main institutions
responsible
Other contributing
organizations
M.A. Stocking and N. Murnaghan, 2001: Field assessment of land degradation
M. Douglas, 1997: Guidelines for the monitoring and evaluation of better land husbandry
7. Practical applications of the indicator (references to case studies)
-
8. Additional information
Productivity is a good indicator of the land conditions, since it directly reflects changes in the qualities and limitations
of the land. Assessment of the productivity of specific target areas and comparison with similar neighbouring areas that
are already used under adequate practices of crop management allows identification of the needs for applying particular
soil improvement practices.
A proxy indicator for the nutrient balance for the major crops may be obtained by estimating the nutrient flows with the
help of the farmer. The nutrient flows are divided into inflow (manure, fertilizer, mulch, by-products left or added to
the field, mineralization, N-fixation and atmospheric addition of nutrients) and outflow (harvest, by-product removal,
erosion, N-evaporation and nutrient leaching). It is expected that assessing only the most prominent of these parameters
would be sufficient to allow statements about the trend on that plot and would thereby qualify as a proxy indicator.
The guidelines provided by Stocking & Murnaghan (2001) for identifying indicators of production constraints(crop
yield, crop growth characteristics and nutrient deficiencies) may be followed to arrive at a semi-quantitative soil
fertility indicator as follows:
SOIL FERTILITY ASSESSMENT (Sources of information: farmer(s), cooperative, district statistics)
Ranking
Crop yield level
Comments
Crop yield less
Poor yield not necessarily caused by season’s rainfall and distribution; farmer used
0
than half the local
to apply some fertilizer but has abandoned the practice on the field in question;
average based on
within field differences are clearly visible but there are hardly patches with close to
farmer’s
normal crop; height of crop much too low and number of tillers too low; nutrient
assessment of
deficiencies clearly visible on more than 50% of the plants
expected bags
Crop yield between Relatively low yield not necessarily caused by season’s rainfall and distribution;
1
50 and 90 % of
fertilizer has been applied but farmer is planning to give up on fertilizer on the field
local average based in question; within field differences are clearly visible; height of crop too low and
on farmer’s
number of tillers insufficient; nutrient deficiencies clearly visible on 25 – 50% of
assessment of
the plants
expected bags
Crop yield around
Satisfactory yield compared to past years; mineral fertilizer has been applied,
2
local average based manure is sometimes applied, and farmer is planning to continue the practice; yield
on farmer’s
is rather uniformly distributed over field with few within field differences; height of
assessment of
crop and number of tiller are normal; less than 25% of plants with nutrient
expected bags
deficiency symptoms.
Crop yield above
Good yield expected from this field; farmer applies fertilizer and manure; yield
3
local average based uniformly distributed over field, no clear within field differences; height of crop
on farmer’s
and number of tillers exceed average conditions; plants with nutrient deficiency
assessment of
symptoms are exceptional
expected bags
5. SOIL HEALTH (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Soil health
The biophysical condition of soil
Semi-quantitative score
Local (farm, village)
Once every year to once every five years
2. Position within the logical framework DPSIR
57
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation
International conventions
and agreements
To summarize the biophysical condition of soil taking into account soil depth, structure,
tillage pan/compaction, texture, coarse fragments, rooting condition, organic
matter/colour, biological activity, surface crusts and sodicity.
To contribute to the identification of land degradation hot spots and bright spots.
To produce digital maps presenting the biophysical condition of soil plus main limiting
parameters from local level to national level
Soil health is an indicator for the capacity of the land to perform soil-related ecosystem
functions and services. Water being by far the most limiting production factor in
drylands, the water holding capacity of the soil and the accessibility of that water to the
rooting systems of crops and natural vegetation are crucial aspects which are totally
conditioned by soil health.
World Soil Charter , adopted at the 21st Session of the FAO Conference, in November
1981. The Charter establishes a set of principles for the optimum use of the world's land
resources, for the improvement of their productivity, and for their conservation for future
generations.
Many of the global conventions organized by the United Nations are responses to global
degradation:
1992: the United Nations Conference on Environment and Development (also known as
UNCED or the Rio Earth Summit); Agenda 21, Chapter 12
1994: the United Nations Convention to Combat Desertification (also known as
UNCCD); Article 4.2.
Millennium Development Goal (MDG) nr 7: ensure environmental sustainability
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
The indicator is composed of sub-indicators for soil depth, structure, tillage
pan/compaction, texture, coarse fragments, rooting condition, organic matter/colour,
biological activity, surface crusts and sodicity; the ten parameters are assessed separately
in the field and are given a score ranging from 0 (severe limitation) to 3 (no limitation),
see Section 8 below: Additional information
The aggregated soil loss indicator ranges from 0 to 42:
Score of >35: very good (no severe or moderate constraints)
Score of 29 – 35: good (no severe constraints)
Score of 21 – 28: medium
Score of 11 – 20: poor
Score of ≤ 10: very poor
Combination of visual assessment and measurements (e.g. soil colour with colour chart)
The assessment of the various indications of soil health needs to be done in a systematic
and careful way and requires training; even the assessment by trained/experienced
farmers or technical staff remains partly subjective; comparison between areas assessed
by different teams may therefore be difficult.
Soil fertility, soil loss, vegetation density, land cover/land use change, rainfall variability
The sub-indicators for soil depth, structure, tillage pan/compaction, texture, coarse
fragments, rooting condition, organic matter/colour, biological activity, surface crusts
and sodicity are measured and /or estimated at field level (farm, village) and aggregated
into one single soil health indicator which may be reflected in digital maps at national
level.
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
Field observations and measurements of soil depth, structure, tillage pan/compaction,
texture, coarse fragments, rooting condition, organic matter/colour, biological activity,
surface crusts and sodicity according to the guidelines.
Field evidence, farmers’ knowledge
Information may be available at national level on areas that have been surveyed.
58
sources
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
1. Des McGarry, 2005, Natural Resources Sciences, Queensland Government, Australia
A methodology of a Visual Soil – Field Assessment Tool
2. M.A. Stocking and N. Murnaghan, 2001. Handbook for the field assessment of land
degradation. Earthscan Publications Ltd, London, Great Britain
3. D. Tongway, 1994. Rangeland soil condition assessment manual. CSIRO, Division of
wildlife and ecology, Canberra, Australia
4. FAO, 2006, Guidelines for soil description. Rome, Italy
5. World reference base for soil resources 2006. FAO World soil resources report 103,
FAO, 2006, Rome, Italy
6. USDA Soil Test Kit (http://soils.usda.gov/sqi/assessment/test_kit.html)
M. Douglas, 1997: Guidelines for the monitoring and evaluation of better land husbandry
7. Practical applications of the indicator (references to case studies)
-
8. Additional information: assessment of sub-indicators
1. SOIL DEPTH
Class
1. Very shallow
2. Shallow
3. Moderately deep
4. Deep
2. SOIL STRUCTURE
Class
1. None
2. Weak
3. Moderate
4. Strong
3. TILLAGE PAN/COMPACTION
Class
1. None
2. Slight
2. Moderate
3. Severe
4. SOIL TEXTURE
Class
Sand, loamy sand
Sandy loam, silt loam, heavy clay
Medium clay, sandy clayloam, silty
clay, sandy clay, silty clayloam
Loam, clayloam
Range
< 25 cm
25 – 50 cm
50 – 100 cm
> 100 cm
Rating/Score
0
1
2
3
Range
Soil is single grain or massive
Poorly formed aggregates
Well formed aggregates
Very well formed aggregates
Rating/Score
0
1
2
3
Range
No tillage pan, friable consistence (moist) and abundant
pores/voids throughout
Slightly developed tillage pan, friable to firm consistence
(moist) and many fine pores throughout but with few large
pores
Moderately developed tillage pan, firm consistence (moist) and
moderate amount of pores but very few large pores
Strongly developed tillage pan, with massive structure, very
firm to extremely firm consistence (moist) and very few or no
pores
Rating/Score
3
Range
Low water and nutrient holding capacity, good workability,
high to very high infiltration rate
Sandy loam and silt loam: Low to medium water and nutrient
holding capacity; good workability, moderate to high
infiltration rate. Heavy clay: medium to high available water
holding capacity, very high nutrient holding capacity; poor
workability; very slow infiltration rate.
Medium to high available water holding capacity; high nutrient
holding capacity; medium to poor workability, moderate to
slow infiltration rate
Very high water holding capacity, high nutrient holding
Rating/Score
0
2
1
0
1
2
3
59
capacity, medium workability, moderate infiltration rate
5. COARSE FRAGMENTS
Class
1. None to common
2. Common to many
3. Many to abundant
4. Dominant
6. ROOTING CONDITIONS
Class
Good condition
Range
0 – 15 %
15 – 40 %
40 – 80 %
> 80 %
Rating/Score
3
2
1
0
Range
Rating/Score
Unrestricted root development, many (<2mm, > 50/dm2; >
3
2mm, > 5/dm2)
Moderate condition
Horizontal and vertical root development somewhat limited;
2
more roots between coarse structural elements than inside;
common roots (<2mm, 50-200/dm2; > 2mm, > 5 - 20/dm2)
Poor condition
Horizontal and vertical root development clearly limited; most
1
roots concentrated in cracks between structural units, almost no
roots inside units; few roots (<2mm, 20 - 50/dm2; > 2mm, > 2
- 5/dm2)
Very poor condition
Severe restriction of horizontal and vertical root development;
0
presence of L-shaped roots, over-thickening of roots or roots
squashed between coarse structural units or concentrated above
dense layer, no roots inside units; none to very few roots
(<2mm, 0 - 20/dm2; > 2mm, 0 - 2/dm2)
7. ORGANIC MATTER/TOPSOIL COLOUR
Organic matter class
Colour/value/percentage range
Rating/Score
1. Very low
White; value 8
0
1. Low
Grey; value 5-7
1
2. Medium
Dark grey to black grey; value 3-4.5
2
3. High
Black; value 2-2.5
3
8. BIOLOGICAL ACTIVITY
Biological activity class
Description
Rating/Score
1. None
No biological features, no earthworms
0
1. Low
Few biological features or soil biota; 1- 4 earthworms counted
1
in spadeful
2. Medium
Common biological features or biota; 4 – 8 earthworms
2
counted in spadeful
3. High
Many biological features or biota; > 8 earthworms counted in
3
spadeful
9a. PHYSICAL CRUST (Soil crusting and sealing in West Africa and possible approaches to improved management.
FAO Soils Bulletin 69, 1993: Soil tillage in Africa, needs and challenges)
Class
Description
Rating/Score
1. None
No crust present
3
2. Slight
Thin to medium crust (1 – 5 mm) on up to 20 % of the surface
2
3. Moderate
Thin to medium crust (1 – 5 mm) present on 20 - 50 % of the
1
surface, thick crust (> 5 mm) present in few patches
4. Severe
Thin, medium and thick crust present on more than 50 % of the 0
surface with common patches of thick crust
9b. BIOLOGICAL CRUST/ CRYPTOGAM COVER (An introduction to biological crusts. www.soilcrust.org;
Cryptobiotic soil crusts. http://eduscapes.com/nature/cryptsoil/index1.htm)
Class
Description
Rating/Score
1. None
No cryptogam cover present (completely destroyed)
0
1. Slight
Cryptogam cover present on less than 20 % of the surface
1
2. Moderate
Cryptogam cover present on 20 - 50 % of the surface
2
3. Extensive
Cryptogam cover present on more than 50 % of the surface
3
60
10. SODICITY
Class
Description of sodicity signs
Rating/
Score
3
1. None
No signs of sodicity, also not in nearby areas, see below; depth of
groundwater > 2m; pH (field) of subsoil in shallow pit < 7.5
1. Slight
Sodicity: in shallow pit soil structure is weak; in close-by areas
2
some puddles of surface water are coloured black by dispersed
organic colloids (slick spots); upon drying, black crusts are formed;
pH (field) in shallow pit between 7.5 and 8.0
2. Moderate
Sodicity: waterlogging is a common surface feature; some puddles
1
of surface water are coloured black by dispersed organic colloids
(slick spots); upon drying, black crusts are formed; hardsetting
surface, but when worked soil becomes easily dusty when dry;
corrosion of road furniture such as steel posts, road signs, guard rail
is an increasing problem; tunnel/pipe erosion is visible in some
degraded areas. Crops sensitive to high sodium saturation (e.g.
beans) perform poorly; tolerant crops (e.g. cotton) do not appear to
be affected. pH (field) of subsoil in shallow pit between 8.0 and 8.5
0
3. Severe
Sodicity: in shallow pit the top of the B-horizon is visible in the
form of well defined vertical columns or prisms, having a rounded
top with lighter colour and smooth, shiny and well defined sides;
soil structure in topsoil is poorly developed.; waterlogging is a
common surface feature; puddles of surface water are frequently
coloured black by dispersed organic colloids (slick spots); upon
drying, black crusts are formed; hardsetting surface, but when
worked soil becomes very dusty when dry; corrosion of road
furniture such as steel posts, road signs, guard rail is a severe
problem; tunnel/pipe erosion is visible in some degraded areas. pH
(field) of subsoil in shallow pit > 8.5
11. AGGREGATION of the 10 sub-indicators into one single indicator
Suggested
Soil property
Rating of constraint
weighting factor
1. Soil depth
2. Structure
3. Tillage pan/compaction
4. Texture
5. Coarse fragments
6. Rooting condition
7. Org. matter/colour
8. Biological activity
9a. Physical or
9b. Biological
10. Sodicity
Maximum score
0
0
0
0
0
0
0
0
0
0
0
0
2
1
1.5
2
1.5
1
1.5
1
1.5
1
1.5
14 (13.5)
4
2
3
4
3
2
3
2
3
2
3
28 (27)
6
3
4.5
6
4.5
3
4.5
3
4.5
3
4.5
42 (40.5)
2
1
1.5
2
1.5
1
1.5
1
1.5
1
1.5
6. SOIL LOSS (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Soil loss
Soil erosion by water (sheet, rill and gully) and wind
Semi-quantitative score
Local (farm, village)
Once every year to once every five years
2. Position within the logical framework DPSIR
61
Type of indicator
State
3. Target and political pertinence
Objective
Importance with respect to
land degradation
International conventions
and agreements
To summarize the degree of soil loss due to sheet, rill, gully and wind erosion.
To contribute to the identification of land degradation hot spots and bright spots.
To produce digital maps presenting the degree of soil loss plus main cause(s) from local
level to national level
Soil erosion and water scarcity are the main concerns in semiarid areas, where
population pressure on land is very intense. The processes of soil erosion in various
regions of the world have their origin in social, economic and cultural factors that
translate into the over-exploitation of the natural resources and the application of
inadequate practices for the management of soils and water. The consequences of this are
damage to much of the agricultural land, with detrimental effects on food production for
the growing population in the continents. Soil erosion, or soil resources depletion, is
therefore a wide-spread, direct threat to the sustainability of agricultural production
(Land and Water Bulletin 8, FAO, Rome, 2000).
World Soil Charter , adopted at the 21st Session of the FAO Conference, in November
1981. The Charter establishes a set of principles for the optimum use of the world's land
resources, for the improvement of their productivity, and for their conservation for future
generations.
Many of the global conventions organized by the United Nations are responses to global
degradation:
1992: the United Nations Conference on Environment and Development (also known as
UNCED or the Rio Earth Summit); Agenda 21, Chapter 12
1994: the United Nations Convention to Combat Desertification (also known as
UNCCD); Article 4.2.
Millennium Development Goal (MDG) nr 7: ensure environmental sustainability
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
The indicator is composed of sub-indicators for sheet, rill, gully and wind erosion; the
four types of erosion are assessed separately in the field or by remote sensing and are
given a score ranging from 0 (no erosion) to 3 (severe erosion), see Section 8 below.
The aggregated soil loss indicator ranges from 0 to 12:
Score of 10-12: none or almost none (No severe or moderate constraints)
Score of 7-9: slight (No severe constraints)
Score of 4-6: moderate
Score of 2-3: severe
Score of 0-1: extreme
Combination of visual assessment and measurements (e.g. depth and width of rills).
Gully erosion to be assessed at subnational and national levels with the help of satellite
imagery and/or aerial photographs; field checks (e.g. for depth of gullies) are desirable.
The assessment of the various indications of soil erosion needs to be done in a systematic
and careful way and requires training; even the assessment by trained/experienced
farmers or technical staff remains partly subjective; comparison between areas assessed
by different teams may therefore be difficult.
Soil fertility, soil health, vegetation density, vegetation quality, land cover/land use
change
The sub-indicators for sheet, rill, gully and wind erosion are measured and /or estimated
at field level (farm, village) and with remote sensing techniques and aggregated into one
single soil loss indicator.
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Field observations and measurements of signs of sheet and/or rill and/or wind erosion
according to the guidelines.
VHR satellite imagery and/or aerial photographs (preferably time series) for assessing
moderate and severe gully erosion and/or moderate and severe wind erosion.
Satellite imagery, aerial photographs, maps, field evidence, farmers’ knowledge
62
Data availability (national
and international sources)
Information may be available at national level on areas that have been surveyed.
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
M.A. Stocking and N. Murnaghan, 2001: Field assessment of land degradation
M. Douglas, 1997: Guidelines for the monitoring and evaluation of better land husbandry
7. Practical applications of the indicator (references to case studies)
-
8. Additional information
1. SHEET EROSION ASSESSMENT
Degree
Sheet erosion description
No sheet erosion
No obvious signs of sheet erosion, but evidence of minor sheet erosion may have
been masked, for instance by tillage
Slight
Some visual evidence of the movement of topsoil particles downslope through
surface wash; thin armour layer (1 mm) may be developing in some places (less
than 10 % of the surface) on stony/gravely soils; some accumulation of soil against
tree trunks but maximum depth of accumulation no more than 10 cm in most cases;
some pedestal development but individual pedestals no more than 5 mm; only a
few superficial roots exposed
Moderate
Clear signs of transportation and deposition of topsoil particles downslope through
surface wash: moderate armour layer (1.5 -2 mm) present on stony/gravelly soils on
10 – 50 % of the surface; frequent accumulation of soil against tree trunks but
maximum depth of accumulation no more than 20 - 30 cm in most cases; some
pedestalling but individual pedestals no more than 50 mm high; some exposure (1 –
5 cm) of rocks (solution notches) and below ground portions of fence posts;
presence of tree mounds (difference in soil level less than 5 cm); seeds or seedlings
are washed away in some places and redeposited lower down; some tree and crop
roots exposed within the topsoil; evidence of topsoil removal but no subsoil
horizons exposed; differences in topsoil colour visible
Severe
Clear evidence of considerable transportation and deposition of topsoil particles
downslope through surface wash: well developed armour layer (> 2 mm) present on
stony/gravelly soils on > 50 % of the surface; frequent accumulation of soil against
tree trunks with maximum depth of accumulation exceeding 30 cm in most cases;
important build-up (> 30 cm) against barriers (fence, hedge); frequent pedestalling
with individual pedestals over 50 mm high; frequent exposure (> 5 cm) of rocks
(solution notches) and below ground portions of fence posts; presence of tree
mounds (difference in soil level > 5 cm); seeds or seedlings are washed away in
many places and partly redeposited lower down, partly washed into drains;
extensive exposure of tree and crop roots; subsoil horizons exposed at or close to
the surface
2. RILL EROSION ASSESSMENT
Degree
Rill erosion description
No rill erosion
No rills present within the field, but evidence of minor rills may have been masked,
for instance by tillage
Slight
A few shallow (< 10 cm depth) rills affecting no more than 5 % of the surface area;
re-deposition of most eroded materials at lower end of field
Moderate
Presence of shallow to moderately deep rills (< 20 cm depth) and/or rills affecting
up to 25 % of the surface area; some re-deposition of eroded materials in the same
field but mostly in drains and lower down the slope; limited damage to SWC
structures
Severe
Presence of deep rills (up to 30 cm depth) and/or rills affecting more than 25 % of
the surface area; no re-deposition of eroded materials in the same field but in drains
or much lower down the slope; considerable damage to SWC structures
3. GULLY EROSION ASSESSMENT (satellite imagery interpretation supported by field checks)
63
Ranking
3
2
1
0
Ranking
3
2
1
0
Degree
None
Slight
Moderate
Gully erosion description
Ranking
No gully erosion
3
Few shallow (30-50 cm) gullies, affecting no more than 5 % of the surface area
2
Presence of shallow and moderately deep (50 cm to 1.5 m) gullies, affecting
1
between 5 and 25 % of the surface area
Severe
Presence of moderately deep and deep (>1.5 m) gullies affecting more than 25 % of 0
the area.
4. WIND EROSION ASSESSMENT (field assessment and satellite imagery interpretation)
Degree
Wind erosion and deposition description
Ranking
No wind erosion
No signs of wind erosion or deposition (see below); litter common.
3
Slight
Litter sparse and mainly under canopy; deposition (sand accumulation) less than 5
2
cm thick mainly leeward of permanent obstacles (e.g. fence posts)
Moderate
Common ripples more than 5 cm deep; topsoil either (partly) removed or covered;
1
formation of hummocks around clumps of vegetation (the soil in the hummocks is
unconsolidated and if sectioned with a paint-scraper reveals layers of accumulated
soil); common dunes up to 1 m high; no litter; small blowouts possible; limited
desert pavement formation in stony areas;
limited sand encroachment on arable and/or irrigated land
Severe
Moving sand dunes more than 1 m high; almost no vegetative cover; large
0
blowouts possible; extensive desert pavement possible in stony areas; serious sand
encroachment on arable and/or irrigated land
4. AGGREGATION of the 4 sub-indicators into one single indicator
Type of erosion
Rating of constraint
Severe
Moderate
Slight
None
1. Sheet erosion
0
1
2
3
2. Rill erosion
0
1
2
3
3. Gully erosion
0
1
2
3
4. Wind erosion
0
1
2
3
Soil loss indicator
0
4
8
12
Score of 10-12: none or almost none (No severe or moderate constraints)
Score of 7-9: slight (No severe constraints)
Score of 4-6: moderate
Score of 2-3: severe
Score of 0-1: extreme
Sources:
1. Guidelines for agricultural land use planning in Botswana. Fourth draft by LUPSAD Project working group.
BOT/91/001(FAO/UNDP)-Field document 10, October 1995
2. D. Tongway, 1994. Rangeland soil condition assessment manual. CSIRO, Division of wildlife and ecology,
Canberra, Australia
3. A.N. Strahler, Physical Geography, 4th edition. Wiley International Edition. USA. 1975
4. M.A. Stocking and N. Murnaghan, 2001: Field assessment of land degradation
5. O. Vigiak et al. Water erosion assessment using farmers’ indicators in the West Usambara Mountains, Tanzania.
Catena 64 (2005) 307-320
6. E. Bergsma et al., 1996. Terminology for soil erosion and conservation. ISSS, ITC, ISRIC.
64
Wind erosion:
The assessment is not meant to cover real desert areas but to assess and monitor expressions of land degradation in
semi-arid (steppe) areas in the form of wind erosion and depositon, triggered by pressures such as overgrazing (e.g.
when “fixed” dunes lose their protective grass and shrubs cover through overgrazing and turn into “active” or “shifting”
dunes).
Wind performs two kinds of erosional work (Strahler, 1975):
1. Deflation: loose particles lying upon the ground surface may be lifted into the air or rolled along the ground.
2. Abrasion: the wind drives sand and dust particles against an exposed rock or soil surface, causing it to be worn away
by the impact of the particles.
The principal land form produced by deflation is a hollow depression termed a blowout or deflation hollow. This
depression may be from a few metres to a km or more in diameter, but is usually only less than a metre deep. Blowouts
form in plains regions in dry climates. Any small depression in the surface of a plain, particularly where the the grass
cover is broken through, may develop into a blowout.
Where deflation has been active on a ground surface littered with loose fragments of a wide range of sizes, the pebbles
that remain behind tend to accumulate, until they cover the entire surface. By rolling or jostling about as the fine
particles are blown away, the pebbles may become closely fitted together, forming a desert pavement.
Hummocking is confined to deeper, sandy-textured soils and is the result of wind-resorting of sand which accumulates
around shrubs and trees, often to depths of many centimetres. This form of erosion indicates that fine-grained materials
and litter have been widely dispersed during the resorting events and have probably been lost to the system. The soil in
the hummocks is unconsolidated and if sectioned with a paint-scraper reveals layers of accumulated soil.
Wind erosion in the marginal drylands of the Mediterranean region is a serious problem, through its direct influence on
particularly vulnerable land. Drylands are extremely susceptible to wind erosion because soils tend to be dry, poorly
structured and sparsely covered by vegetation. Evidence of wind erosion is widespread throughout the dry areas of
West Asia and North Africa. A major limitation in these areas is the lack of adequate and reliable rainfall to support a
sustainable, protective land cover against the erosive forces of the wind. The replacement of natural, drought-tolerant
species with crop cultivation and over-grazing of rangelands by small ruminants, due to growing population pressure
and food demand, further exacerbate the problem.
7. SOIL MOISTURE (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Soil moisture
Lack of soil moisture, or soil moisture stress, for an extended period of time that causes
vegetation stress, is called drought. A soil moisture index is a measure of the amount of
moisture in the soil and hence indicative of potential drought or warning of possible
flooding, and can help predict amounts of run-off, evaporation rates and soil erosion.
Several soil moisture indexes have been proposed using a wide range of completely
different methods and sensors but further research is still needed to assess which of these
SMIs can be most useful for LADA (Lantieri, 2003). The example of the Soil Moisture
Index, developed by the University of Technology in Vienna (applying to conditions in
Austria) is given below (other examples of soil moisture indexes are briefly described
under “Additional Information”).
Dimensionless
Subnational, national, global
3 to 4 days
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
To measure the amount and degree of variability of soil moisture at a given period of the
year in drylands.
To study the trend of soil moisture change and recommend approaches to mitigate/
reverse the effect of soil moisture change.
65
Importance with respect to
land degradation
International conventions
and agreements
To map drylands ecosystems prone to soil moisture losses and vulnerable to drought.
To guide towards soil and crop management practices that can reduce the impact of soil
moisture fluctuations.
Soil moisture is the key state variable in hydrology: it is the switch that controls the
proportion of rainfall that percolates, runs off, or evaporates from the land. It is the lifegiving substance for vegetation
(http://www.ars.usda.gov/Research/docs.htm?docid=8974).
Soil moisture change/fluctuations from the normal available water requirement or
prolonged water stress can cause irreversible damage to crop yields. Soil moisture
influences the distribution and growth pattern of vegetation, soil microbial activity, soil
erosion, wind erosion, nutrient movements and other soil properties.
Soil moisture stress or drought can alter the balance of fresh and salt water in estuaries
and cause damage to marine ecosystems.
Millennium Development Goals (MDG) Poverty alleviation and environmental
sustainability
Agenda 21 Chapter 12 Combating desertification and drought
UNCCD Article 4.2 (a) commits Parties to “adopt an integrated approach addressing the
physical, biological and socio-economic aspects of the processes of desertification and
drought”
UNCCD Article 4.2 (c); to “integrate strategies for poverty eradication into efforts to
combat desertification and mitigate the effects of drought”
4. Methodological description and basic definition
Definitions and basic
concepts
Soil Water Index SWI):
SWI(t ) 
 ms (t i )e
i
e


t  ti
T
t  ti
T
i
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
where ms is the retrieved surface soil moisture value at time ti , ranging between 0 and 1.
The main parameter in the approach is the time constant of the filter or pseudo diffusivity
T. In general, the pseudo diffusivity depends on soil properties including soil depth and
the moisture state. As soil properties are not known quantitatively on a regional scale
(Austria), this parameter was set to an a priori value of T =20 days (Parajka etal, 2006,
www.hydrol-earth-syst-sci.net/10/353/2006). This value is consistent with hydrological
reasoning (The field capacity of Austrian soils as obtained by calibrating hydrological
models, is typically on the order of 150 mm. Assuming a porosity of 0.3, a pseudodiffusivity of 20 days would then translate into a wetting front celerity of 25 mm per day.
This is a typical value for Austrian soils (Parajka et al.,2006)).
The Soil Water Index is a non-dimensional index between 0 (completely dry), and 1
(saturated)
To retrieve soil moisture information, use is made of scatterometers (active microwave
sensors, coarse spatial, high temporal resolution) on board the European Remote Sensing
satellites (ERS-1, 1991-2000; ERS-2, 1995-present). The sensor achieves global
coverage within 3 to 4 days where each beam provides measurements of radar
backscatter from the sea and land surface for overlapping 50 km resolution cells with a
25 km grid spacing at approximately 10:30 am and 10:30 pm for ascending and
descending tracks, respectively.
Radar measurements of bare soil surfaces are very sensitive to the water content in the
soil surface layer due to the pronounced increase in the soil dielectric constant with
increasing water content (Parajka et al, 2006). The ERS scatterometer operates at 5.3
GHz (C-band) vertical polarization, collecting backscatter measurements over an
incidence angle range from 18º to 57º using three sideways looking antennae.
1. Coarse spatial resolution.
2. The main limitation of the use of spaceborne sensors for soil moisture retrieval is
66
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
probably the limited penetration depth. Microwaves only sense the top few centimetres
of the soil or less while, for hydrologic predictions, the soil moisture in deeper soil layers
is just as important.
To address this limitation the root zone soil moisture is estimated from surface values.
The main idea is that surface soil moisture tends to fluctuate much more rapidly than root
zone soil moisture. This is because the soil dampens the high frequencies as soil moisture
infiltrates into the soil. Rather than solving the flow equations we represent the
dampening effect by a linear, exponential filter in the time domain. In other words, a
Taylor hypothesis (G.I. Taylor, Proc. Roy. Soc. London A164, 476, 1938) is made to
trade space for time. The filtered values are termed the Soil Water Index (see
“Definition”).
Rainfall variability, aridity index, land cover/land use change, soil health, vegetation
activity, soil loss
Soil moisture information from scatterometer measurements allow SWI calculations for
each 25x25 km pixel (625 km2); if information is available for all the pixels covering a
country, a coarse soil moisture map at national scale can be presented.
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Backscatter measurements, estimate of pseudo-diffusivity based on soil characteristics,
such as porosity and wetting front celerity
European Space Agency (ESA)
Available from ESA
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
University of Technology, Vienna, Austria
n.a.
7. Practical applications of the indicator (references to case studies)
Assimilation of scatterometer soil moisture data into hydrologic models at the regional scale (Austria): Parajka et al,
2006)
8. Additional information
1. The University of Montana (US) SMI is based on regression of NDVI and surface temperature (from channels 4 and
5 of NOAA’s Advanced Very High Resolution Radiometer (AVHRR)); the slope of the regression is then correlated to
soil moisture stress.
2. EARS Ltd (Environmental Analysis & Remote Sensing laboratory, The Netherlands) estimates a SMI as the end
product of assessment of the terms of the water balance, which are derived at a 5 km resolution using Meteosat data; the
SMI is expressed as yearly actual evapotranspiration/yearly potential evapotranspiration.
3. NASA, Earth Observing System: soil moisture is routinely mapped by the Advanced Microwave Scanning
Radiometer on board the Aqua satellite (passive microwave radiometer system).
4. FAO, 2003: The ability of radar to penetrate clouds and obtain data under most weather conditions and to obtain
daily and nightly images, makes the use of such an active sensor system especially interesting for routine monitoring.
Two types of radar sensors may be used: 1. synthetic aperture radar (SAR) – which typically provides high spatial
(e.g. for the ERS-1 and -2 SAR at 25 m for a 100x100 km area) but poor temporal resolution (e.g. for the ERS-1
and -2 SAR a 35 day repeat cycle) – and 2. scatterometer (see the above). The full article by Smith et al. is available
under Theme 2 at ftp://ftp.fao.org/agl/agll/ladadocs/activesensor.doc
8. SOIL SALINITY (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Soil salinity
Saline soils are (non-sodic) soils containing sufficient soluble salts to interfere with the
growth of most crops. Saline soils are identified by the salt content
67
Unit of Measure
Spatial Scale
Temporal Scale
Semi-quantitative score (visual) or dS/m (conductivity; dS =deciSiemens)
Local (farm, village)
Once every year to once every five years
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation
International conventions
and agreements
To determine the severity and extent of salinity in drylands
To contribute to the identification of land degradation hot spots and bright spots.
To produce digital maps presenting the degree of salinization of soils from local level
(e.g. irrigation area) to national level.
To develop/design approaches and strategies managing saline and saline-affected dryland
ecosystems.
Saline soils develop in arid regions where the precipitation is less than 500 mm annually
and in semi-arid regions with poor drainage. Under such conditions evaporation and
evapotranspiration are not compensated by precipitation and irrigation. Salts weathered
from rocks and minerals, deposited by rainfall and wind, groundwater and irrigation, are
insufficiently washed from the upper layers. With evaporation these accumulated salts
precipitate from upward-moving water on or near the surface (FAO, 2004). Excessive
accumulation of salts aggravates drought stress and upsets the balance of ions in the soil
solution (nutrients are proportionally less available. Strongly salt-affected soils have little
agricultural value (FAO, 2001). Soils, especially soils of coastal plains, may also become
(more) saline due to flooding with sea water; such flooding may be caused by a tsunami
for instance (see Natural calamities indicator).
World Soil Charter , adopted at the 21st Session of the FAO Conference, in November
1981. The Charter establishes a set of principles for the optimum use of the world's land
resources, for the improvement of their productivity, and for their conservation for future
generations.
Many of the global conventions organized by the United Nations are responses to global
degradation:
1992: the United Nations Conference on Environment and Development (also known as
UNCED or the Rio Earth Summit); Agenda 21, Chapter 12
1994: the United Nations Convention to Combat Desertification (also known as
UNCCD); Article 4.2.
Millennium Development Goal (MDG) nr 7: ensure environmental sustainability
4. Methodological description and basic definition
Definitions and basic
concepts
Soil salinity can arise due to saline parent material, seawater flooding (e.g. tsunami),
wind-borne salts or irrigation with saline water. However, the majority of saline soils are
formed through capillary rise and evaporation of water which accumulates salt over time.
Most salt-affected soils can be identified in the field. The indicator is based on the
assessment, in close cooperation with the farmer(s), of visual signs of salinization and, if
available, measurements of electrical conductivity of the saturation extract (ECe); see
Section 8 below: Additional information.
The processes of salinization can happen when poorly drained land is irrigated in hot
climates. The sun evaporates the surface water, leaving behind the salts. At the same
time, inadequate drainage causes the water table to rise, bringing saline groundwater into
contact with plant roots. It is typically a human-induced process brought about by
incorrect planning and management of irrigation schemes.
Saline intrusion is a localised form of salinization which occurs as the result of the
incursion of sea water into coastal soils arising from over-abstraction of groundwater.
This is of particular concern to several Pacific nations in the context of rising sea levels.
One very serious effect of such a sea level rise is its impact on freshwater lenses
underlying atolls. The risk of saltwater intrusion will rise as the sea level rises; lateral
leakage will increase, the lenses will become thinner and salt water will move within
reach of pump intakes. As the sea level rises salt water will reach the roots of pit grown
taro, coconut palms and other tree crops (Poverty alleviation and food security in Asia.
68
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
FAO Regional Office for Asia and the Pacific. July 1999).
The soil salinity indicator ranges from 0 to 3 (0: severe; 1: moderate; 2: slight; 3: none)
Indicative soil salinity classes and implications for crop performance (FAO, 2001)
ECe at 25 C (dS/m) Salt concentration (cmol/l)
Effect on crops
< 2.0
<2
Mostly negligible
2.0 – 4.0
2–4
Some damage to sensitive crops
4.0 – 8.0
4–8
Serious damage to most crops
8.0 – 15.0
8 – 15
Only tolerant crops succeed
> 15.0
> 15
Few crops survive
1. This indicator: combination of visual assessment and measurements (e.g. electrical
conductivity)
2. Some salinization patterns can be seen directly on HR, or even better on VHR
imagery, or using mapping techniques of “surface state”. However, most of this kind of
soil deterioration cannot be seen with RS data and still relies on ground measurements. It
should also be noted that the sampling design of full survey can be largely assisted by
the use of RS derived products (D. Lantieri, 2003).
The assessment of the various indications of soil salinity needs to be done in a systematic
and careful way and requires training; even the assessment by trained/experienced
farmers or technical staff remains partly subjective; comparison between areas assessed
by different teams may therefore be difficult.
Soil fertility, soil health, aridity index, vegetation density, land cover/land use change,
rainfall variability, natural calamities, water salinity.
The indicator is measured and /or estimated at field level (farm, village) and aggregated
into one single soil salinity indicator which may be reflected in digital maps at
subnational and national levels.
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Field observations and measurements of electrical conductivity.
Field evidence, farmers’ knowledge, existing surveys
Information may be available at national level on areas that have been surveyed.
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Mainly based on:
1. FAO, 2004: Guiding principles for the quantitative assessment of soil degradation.
AGL/MISC/36/2004
2. FAO, 2006, Guidelines for soil description. Rome, Italy
3. FAO, 2001, Lecture notes on the major soils of the world. WSRR 94
4. World reference base for soil resources 2006. FAO World soil resources report 103, FAO, 2006,
Rome, Italy
Other contributing
organizations
-
7. Practical applications of the indicator (references to case studies)
-
8. Additional information: assessment of sub-indicators:
Soil salinity can arise due to saline parent material, seawater flooding, wind-borne salts or irrigation with saline water.
However, the majority of saline soils are formed through capillary rise and evaporation of water which accumulates salt
over time (Manual on integrated soil management and conservation practices. Land and Water Bulletin 8, FAO, Rome,
2000). Most salt-affected soils can be identified in the field (Van Lynden, FAO, 2004)
Class
Description
Rating/
Salinity (low lying soils, basins/plains)
Score
1. None
No signs of salinity, also not in nearby areas, see below; depth of groundwater > 2m;
3
ECe < 2 dS/m
1. Slight
In close-by lower lying areas salinity is apparent in the form of few wet spots that turn 2
69
2. Moderate
3. Severe
whitish when dry; farmer reports that crop yields are lower than expected; ECe
between 2 and 8 dS/m
Numerous wet spots in the morning that turn whitish later in the day upon drying
(small salt crystals visible); salt tolerant crops such as barley are few to common;
halophytic vegetation species are common (e.g. Tamarix, Salsola, Artemisia,
Halosarcia, Atriplex etc); in shallow pit soil structure is well developed; groundwater
table is within 1 m depth and in easy reach of capillary rise; salt crystals are often
visible on surface of structural elements; ECe between 8 and 15 dS/m.
Formation of salt crust on the surface which gives a puffy feeling when stepping on it;
absence of salt sensitive species and dominance of salt tolerant species such as
Salsola, Halosarcia, Atriplex etc. Very few salt tolerant crops may still be observed
(barley, date palms etc). Corrosion of road furniture such as steel posts, road signs,
guard rail, concrete structures is a severe problem; ECe > 15 dS/m
1
0
9. VEGETATION ACTIVITY (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Vegetation activity
Vegetation indexes are designed to assess the condition of vegetation. One such index is
the Normalized Difference Vegetation Index (NDVI)
Dimensionless
AVHRR: 1 pixel represents 1 km2 of land surface; MODIS: 1 pixel = 250x250m
Almost daily global cover possible; vegetation indexes aim at regular and frequent
monitoring, from 10 days to monthly, seasonal or yearly (D. Lantieri, 2003)
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation
International conventions
and agreements
To monitor vegetation conditions over the years and detect trends.
To map areas with different NDVI values (e.g. forest/bush fires).
To analyse spatial and temporal changes in biomass.
Vegetation (soil cover) protects the surface from the force of the rain droplets and
reduces the separation of the particles of soil aggregates, which is the first step in the
process of erosion by water. There is evidence that a 40 percent soil cover reduces the
soil losses due to splash erosion to values of less than 10 percent of what would be
expected from same soil when bare (Figure 1). When soil erosion is caused by a
combination of erosive processes, such as splash erosion and rill erosion, then it is likely
that a cover of more than 40 percent is needed to reduce losses to only 10 percent of
those incurred by the same soil in a bare condition.
RS derived vegetation indexes have a high potential for assessing the vegetation activity,
as a result of both rainfall availability and land conditions. A continuously decreasing
vegetation index along the years is a sign of desertification (Lantieri, 2003).
HR or HVR satellite imagery may be used to detect and map burned areas by means of
images of the NDVI vegetation index. Time series of images can be used to monitor through NDVI values- the regeneration process followed by plant communities after
forest fires. Regeneration ratios of different plant communities are compared and the
effect of other environmental factors on such a process is also studied. Finally, spatial
patterns of fire sizes have been also analyzed
(http://www.creaf.uab.es/miramon/publicat/papers/lisboa98/for_fire.htm)..
Millennium Development Goals (MDG) Poverty alleviation and environmental
sustainability
Agenda 21 Chapter 12 Combating desertification and drought
UNCCD Article 4.2 (a) commits Parties to “adopt an integrated approach addressing the
physical, biological and socio-economic aspects of the processes of desertification and
70
drought”
UNCCD Article 4.2 (c); to “integrate strategies for poverty eradication into efforts to
combat desertification and mitigate the effects of drought”
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
The spectral characteristics of green leaves are such that they are highly absorptive of
energy in visible wavelengths (0.55-0.68 μm) of the electromagnetic spectrum, but
highly reflective in the near infrared wavelengths (0.725-1.10 μm). The relative
differences between visible (VIS) and near infrared (NIR) spectral characteristics form
the basis of several vegetation indexes. A commonly used vegetation index is the
Normalized Difference Vegetation Index (NDVI) which is expressed as:
NDVI = (NIR – VIS)/(NIR + VIS).
Index values can range from -1.0 and +1.0, but vegetation values typically range between
0.1 and 0.7. A zero means no vegetation and close to +1.0 (0.8 – 0.9) indicates the
highest possible density of green leaves.
The various reflected wavelengths used to be recorded by the NOAA’s Advanced Very
High Resolution Radiometer (AVHRR); in December 1999, NASA launched the Terra
spacecraft (Earth Observing System program) with a Moderate Resolution Imaging
Spectroradiometer (MODIS) which provides much higher spatial resolution (and similar
almost daily cover) than AVHRR.
The NDVI value is sensitive to a number of perturbing factors including atmospheric,
clouds, soil, anisotropic and spectral effects and should be used with caution.
Rainfall variability, drought index, land cover change, soil loss, soil health.
Users of NDVI have tended to estimate a number of vegetation properties from the
NDVI value. Typical examples include the Leaf Area Index (the variation of NDVI as a
function of LAI can be expressed by a modified Beer’s law (Baret and Guyot, 1991*),
biomass, chlorophyll concentration in leaves, plant productivity, fractional vegetation
cover, accumulated rainfall etc
The spatial resolution (250x250 m) provided by MODIS would allow the compilation of
maps at subnational scale (watershed) which can be aggregated to national scale.
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Radiance of bandwidths 0.55-0.68 and 0.725-1.10 μm
Landsat MSS, Landsat TM, Landsat ETM, NOAA-AVHRR, Terra MODIS, SPOT
Throughout the world, up to now, the most used LMR data are the NOAA/AVHRR
and Spot4/Vegetation ones, due among other things to the very low cost per surface unit.
The most widely used HR data for land studies are: Landsat 4-5 / TM,
Landsat 7 / ETM+, Spot 1-3 / HRV, Spot 4 / HRVIR ones, and Spot 5/ HRG; for large
geographical covers, Landsat TM has in general a strong comparative advantage
regarding its cost per km2. LADA should even get, free of charge, a global cover of
Landsat TM data for the years 1990 and 2000 (1990 is acquired, 2000 is under
negotiation) (Lantieri, 2003).
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
In 1973 Rouse et al.** proposed the NDVI as a simple algorithm to process ERTS data
and locate the distribution of vegetation in the Great Plains (US)
n.a.
7. Practical applications of the indicator (references to case studies)
Since 1988, by the ARTEMIS system (Africa Real Time Environmental Monitoring Information Systems) of the FAO
Environment and Natural Resources Service (SDRN) routinely processes and disseminates data from a number of
satellites for operational use in the field of early warning for food security. ARTEMIS provides information on
environmental conditions using data from low-resolution earth observation satellites. ARTEMIS processes
METEOSAT TIR and NOAA-AVHRR GAC data to monitor rainfall and vegetation development using the
Normalized Difference Vegetation Index (NDVI) derived from the AVHRR instrument. All products are available at
71
a common resolution of 7.6 km. ARTEMIS maintains an archive of (a) Cold Cloud Duration (CCD) images dating back
to 1988 and (b) 10-day, 8-kilometer resolution vegetation since from AVHRR sensor on NOAA polar orbiting satellites
since 1981. In addition, 10-day real-time vegetation images from the VEGETATION instrument on-board the SPOT-4
satellite have been integrated into the system. This data is particularly useful for sub-national monitoring of vegetation
since (a) it has a global coverage, (b) the spatial resolution (1 Km) is much higher than NOAA GAC data (7.6 Km)
AND (c) the spectral characteristics of the sensor are specifically designed for vegetation monitoring.
In combination with data from other sources, ARTEMIS enables specialists to make assessments of crop growing
conditions, detect droughts at an early stage, and locate potential breeding grounds for desert locusts. ARTEMIS is also
an important tool for the Global Information and Early Warning System (GIEWS).
8. Additional information
A. In the case of LADA, vegetation indexes could be used as follows (D. Lantieri, 2003):
i) comparison of the NDVI image of the present with an image from the archive of the reference year and for which
drought conditions are well known. In this case, the comparison would allow a qualitative assessment of land
conditions in relation to the availability of water as a result of both rainfall and land conditions;
ii) comparison of the NDVI series with the rainfall series (see above section). In this case, one can expect to
differentiate on the NDVI data, the component due to rainfall and the one due to land conditions or desertification. The
interest of undertaking such a comparison between NDVI and rainfall has been mentioned and even investigated
several times (e.g. University of Arizona http://ag.arizona.edu/OALS/oals/), but in practice no real operational work has
really been done on this subject. It is recommended that LADA consider this possibility. In addition to comparing the
present NDVI with the one of a reference year and/or with rainfall data, it is also suggested to superimpose these NDVI
images with:
- land cover maps or databases as it allows to assess and understand the types of vegetation (e.g. woodland, grassland,
agricultural lands) which are concerned by the NDVI data;
- biomass maps or databases, as it can also give an indication of the level of biomass affected by the decrease of the
vegetation activity.
B. The Rain Use Efficiency (RUE) is an index that links NDVI to rainfall (NPP=Net Primary Production per mm of
rain). Until recently studies assumed that for a given site with no land degradation a linear relationship exists between
NPP and rainfall. Hein and De Ridder (2006) studied RUE in six field sites and found that in the absence of land
degradation the relationship between NPP and rainfall was non-linear (followed a quadratic curve). When they looked
at expected RUE values based on their quadratic estimates they found that the RUE from satellite estimates were lower
than the expected ones, and thus land degradation may have occurred. They conclude:
If anthropogenic degradation of the Sahel is demonstrated, this would have repercussions for the debate on the causes
of climate change in the Sahel. Currently, a weakness in the argumentations that anthropogenic land cover changes
have contributed to the occurrence of the extreme Sahelian droughts of the last decades of the 20th century is a lack of
evidence of degradation from remote sensing data. Hence, if new remote sensing analyses confirm anthropogenic
degradation, this would support the hypothesis that degradation of the vegetation layer, in particular through sustained
high grazing pressures, has contributed to the occurrence of the 20th century droughts in the Sahel. Furthermore, if
degradation of the Sahelian vegetation cover is confirmed, this would indicate that Sahelian pastoralists may be more
vulnerable for future droughts than currently assumed. Because degradation of the Sahel in the 1980s and 1990s has
been masked by an upward trend in annual rainfall, the sequences of a future drought for the local population could be
unexpectedly severe (L. Hein and N. de Ridder, Desertification in the Sahel: a reinterpretation. Global Change Biology,
Vol. 12, May 2006).
C. The NDVI may also be used as an indicator of agricultural drought: the difference between the average NDVI for a
particular month of a given year and the average NDVI for the same month over the last 20 years is called NDVI
anomaly. In most climates, vegetation growth is limited by water so the relative density of vegetation is a good
indicator of agricultural drought (http://earthobservatory.nasa.gov/Library/MeasuringVegetation).
* Baret, F. and G. Guyot. 1991. Potentials and limits of vegetation indices for LAI and APAR assessment.
Remote Sensing of Environment 35: 161-173
**Rouse, J.W., R.H. Haas, J.A. Scherll, and D.W. Deering (1973) ‘Monitoring vegetation systems in the
Great Plains with ERTS’, Third ERTS Symposium, NASA SP-351 I, 309-317
10. WATER AVAILABILITY (STATE INDICATOR)
1. Definition
Indicator Name
Water availability
72
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
The annualized total actual renewable (fresh)water resource (TARWR) per capita is the
theoretical maximum annual volume of freshwater resources available per capita in a
country.
Per capita measure: m3/capita/year
Scale of application: Data available at country level.
Geographical coverage: Global
Yearly
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation
International conventions
and agreements
To obtain knowledge of the amount of potentially available water resources per country
which is important for planning in all sectors.
This indicator provides an estimate of the maximum theoretical amount of water
resources in a country. The available water resources will be less according various
factors but it is an overall measure of the country’s resources which is also normalized to
provide an average annual per capita volume available to individuals within the country.
Within the indicator are five important dependencies which relate to each nation’s
TARWR as to how much of that water resource volume is:
• flowing from outside the country (a security issue)
• generated surface water runoff ( a precipitation issue)
• generated groundwater recharge (a sustainability issue)
• shared in both the groundwater and surface water regimes
• committed to downstream nations.
Example for Africa (source: FAO’s AQUASTAT):
1. The Nile Basin Initiative (NBI), created in 1999;
2. The Lake Chad Basin Commission (LCBC) was created in May 1964;
3. The Niger Basin Authority (NBA), created in 1980;
4. The Zambezi Watercourse Commission (ZAMCOM) was created in 2004 by the eight
countries of the Zambezi Basin: Angola, Botswana, Malawi, Mozambique, Namibia,
United Republic of Tanzania, Zambia and Zimbabwe;
5. The International Commission for the Orange Senqu River (ORASECOM) was
created in 2000 by the four states that share the basin: Botswana, Lesotho, Namibia and
South Africa;
6. The Organization for the Development of the Senegal River (OMVS), created in 1972,
comprises Mali, Mauritania and Senegal. Although Guinea shares the basin’s waters, it is
not a member of the OMVS;
7. In 2002, the four countries located in the Limpopo River Basin (Botswana,
Mozambique, South Africa and Zimbabwe) set up the Limpopo Basin Permanent
Technical Committee (LBPTC), which replaced the Permanent Technical Committee of
the Limpopo Basin.
8. An agency does not yet exist for the Volta River Basin, but its creation seems
imminent. The Volta Basin Technical Committee (VBTC) brings together experts from
ministries in charge of water for the six countries that share the Volta River Basin. It held
its first session in March 2005, enabling the adoption of internal regulations and the
election of the VBTC officials whose mission is to work on the establishment of a Volta
River Basin agency.
4. Methodological description and basic definition
Definitions and basic
concepts
The maximum theoretical amount of water actually available for the country is calculated
from:
(a) Sources of water within a country itself;
(b) water flowing into a country
(c) water flowing out of a country (treaty commitments).
Availability, defined as the surface and ground water resource volume renewed each year
in each country, is how much water is theoretically available for use on a sustainable
basis. Exploitability is a different matter. While availability undoubtedly exceeds
73
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
exploitability, there is unlikely adequate data to define the degree of exploitability at this
stage. In more specific terms TARWR is the sum of:
• External water resources entering the country
• Surface water runoff (SWAR) volumes generated in the country
• Ground water recharge (GAR) taking place in the country
Less:
• The volume in the country of the total resource effectively shared as it interacts and
flows in both the groundwater and surface water systems. Not to subtract this volume
would result in its being counted twice. FAO refers to it as “Overlap” (5) and,
• The volume that flows to downstream countries based on formal or informal
agreements or treaties.
The Falkenmark threshold indicates a water stress index based on approximate minimum
level of water required per capita to maintain an adequate quality of life in a moderately
developed country in the arid zone:
1,500 m3/cap/yr: chronic threshold
1,000 m3/cap/yr: stressed threshold
Source: www.mpu.agric.za (→State of the environment, →Water, →Total Surface
Water Resources available per capita)
Computation TARWR (in km3/yr) = (External inflows + Surface water runoff +
Groundwater Recharge) - (Overlap + Treaty obligations)
TARWR PC = (TARWR / population) 109 m3/km3
Limitations on the indicator:
1. See extensive notes from FAO in publication and at web site.
2. Does not yet apply at the level of basins or hydrographic units although some work in
this regard has been started by FAO. (Africa, Asia partial)
3. Does not include non-renewable groundwater.
4. Size of large countries can mask high range in variability.
5. Quality of data is variable by country as qualified in FAO database and country
datasheets.
6. In the determinant “External renewable water resources”, groundwater outflows
through transboundary aquifers can be substantial in some countries even if they in
general are small compared with surface water flows. Transboundary groundwater flows
are difficult to quantify.
Precipitation (FAO data is calculated from IPCC data unless considered not
representative. Approximately 80% of FAO’s precipitation data originates from IPCC).
Water use (WU) by different sectors (is included as part of the AQUASTAT Data base)
National, and aggregation to global
5. Evaluation of data needs and availability
Data required to calculate
the indicator
The specific determinants required are:
(1) Actual/natural: indicates whether it corresponds to a natural situation, i.e. a measure
of the water balance without human influence, or an actual situation, i.e. the conditions at
a given time taking into account human influence either through uptake abstraction of
water or through agreements or treaties. Natural conditions are considered stable over
time while actual situations may vary with time and refer to a given period. (FAO, 2003)
(2) Renewable water resources: Total resources that are offered by the average annual
natural inflow and runoff that feed each hydrosystem (catchment area or aquifer). (FAO,
2003)
(3) Internal renewable water resources (km3/year): Average annual flow of rivers and
recharge of aquifers generated from (endogenous) precipitation that originates within the
countries borders. (FAO, 2003)
(4) External renewable water resources (km3/year): That part of the country’s renewable
water resources which is not generated in the country which includes inflows from
74
Data sources
Availability of data from
national and international
sources
upstream countries (groundwater and surface water), and part of the water of border
lakes or rivers. (FAO, 2003)
(5) Overlap between surface water and groundwater resources (km3/year): Overlap
defines the part of the country’s water resources that is common to surface waters and to
aquifers. Surface water flows can contribute to groundwater as recharge from, for
example, river beds or lakes or reservoirs or wetlands. Aquifers can discharge into rivers,
lakes and wetlands and can be manifest as base flow, the sole source of river flow during
dry periods, or can be recharged by lakes or rivers during wet periods. Therefore, the
respective flows of both systems are neither additive nor deductible.
Source: FAO, computed on the basis of available country water resources data sheets and
country water balance computational spreadsheets.
TARWR-FAO, 2003. Review Of World Water Resources By Country
Quality: FAO refers to the data as the "Best Estimate" and updates the data when further
information is provided.
Water resource data by country compiled by other institutions working in the sector are
provided by the World Resources Institute, the Pacific Institute, the St Petersburg State
Hydrological Institute (I. Shiklomanov) , the University of New Hampshire and several
regional institutions.
FAO AQUASTAT, see
http://www.fao.org/waicent/faoinfo/agricult/agl/aglw/aquastat/water_res/index.stm
Sources of further information: information on the intensity of the use of water resources
is available for most OECD countries (Source: OECD).
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
FAO
World Resources Institute (WRI has this information on web site with link and credit for
the data to FAO)
7. Practical applications of the indicator (references to case studies)
A detailed description of the methodology is given in FAO Water Report 23: "Review of world water resources by
country"
8. Additional information
See also: Crops and drops. Making the best use of water for agriculture, FAO, Rome, 2002.
(www.fao.org/DOCREP/005/Y3918E/Y3918E00.HTM)
Alternative methods and definitions
FAO collected statistics on water resources for most of the developing countries within the AQUASTAT programme.
Data on water resources were ob from national sources and reviewed by FAO to ensure consistency in definitions. A
methodology was developed and rules were established to compute the different elements of national water balances.
From those data, FAO has compiled a comparative analysis of available country water resources data.
The Falkenmark indicator is one of the most widespread used indicators for assessing the stress on water. It relates the
total freshwater resources with the total population in a country and indicates the pressure that population puts on water
resources, including the needs for natural ecosystems. Falkenmark developed this indicator on the grounds of a
minimum need of 100 l/day/cap for household use and from 5 to 20 times as much as for agricultural and
industrial uses. The threshold for this indicator is that water stress begins at less than 1 700 m3/cap/year. When the
indicator drops below 1 000 m3/cap/yr, the country can face water scarcity. Shiklomanov made a classification of what
he called “the specific water availability” (in m3/cap/year) of countries, based on this indicator: < 1 000 is
catastrophically low, between 1 000 and 2 000 is very low, between 2 000 and 5 000 is low, between 5 000 and 10 000
is average, between 10 000 and 20 000 is high and above 20 000 is very high
(http://themes.eea.europa.eu/Specific_media/water/indicators → Water exploitation index).
Source:
Adapted from “The UN World Water Development Report 2 – Water, a Shared Responsibility, 2006”
11. WATER SALINITY (STATE INDICATOR)
75
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Water salinity
Water salinity is the dissolved salt content of a body of water
Concentration of Total Dissolved Solids (TDS) in ppm
Electrical conductivity in dS/m
Chloride content (ppm)
Local (farm, village) to national
Once every year to once every five years
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation
International conventions
and agreements
To monitor the salinity of water bodies (surface and ground water) for domestic and
agricultural use.
To produce digital maps presenting the degree of salinization of surface and ground
water soils from local level (e.g. irrigation area) to national level.
About one-third of the world's population live in countries with moderate to high water
stress i.e. in areas where the withdrawal of fresh water exceeds 10% of the renewable
storage (UNEP, 1999). Constantly increasing populations aggravate the problem of
access to fresh water. The worst situation is found in Africa and western Asia. In these
regions more than 80% of the fresh water is used in agriculture (UNEP, 1999). Water
scarcity is becoming one of the major limiting factors to economic development and
welfare in large parts of the semi-arid regions of the world (e.g. large parts of subSaharan Africa). Unfavourable agricultural premises such as erratic rainfall, high
evaporative demand and inherently low-fertility soils, make future population support a
matter of strong concern (Falkenmark & Rockström, 1993).
http://www.lwr.kth.se/Forskningsprojekt/Salinity_Growth/Index.htm
Because of the increasing scarcity of good quality water it is of the utmost importance to
closely monitor the salinity of water bodies, increased salinity in the water causing
extreme damage to the soil, reduces crop yields and renders water unsuitable for
domestic use.
Many of the global conventions organized by the United Nations are responses to global
degradation:
1992: the United Nations Conference on Environment and Development (also known as
UNCED or the Rio Earth Summit); Agenda 21, Chapter 12
1994: the United Nations Convention to Combat Desertification (also known as
UNCCD); Article 4.2.
Millennium Development Goal (MDG) nr 7: ensure environmental sustainability
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Salinity is the dissolved salt content of a body of water; salinization is the progressive
increase in the concentration of salts in water. It can be measured by means of total
dissolved solids (TDS) levels. It occurs in both surface and ground water.
Water salinity classes (Land cover Classification System, FAO, 2000)
TDS (ppm)
Classification
< 1,000
Fresh
1,000 – 3,000
Slightly saline
3,000 – 10,000
Moderately saline
10,000 – 35,000
Very saline
> 35,000
Brine
South African Water Quality Guidelines: upper limit of TDS concentration not to exceed
200 mg/l for domestic use (http://www.nwpg.gov.za/soer/FullReport/water.html).
EEC (80/778/EEC): the drinking water guideline value is 25 mg Cl/l (25 ppm Chloride/l)
(http://themes.eea.europa.eu/Specific_media/water/indicators/WQ03b,2003.1001)
Laboratory: The two principal methods of measuring total dissolved solids are
gravimetry and electrical conductivity. Gravimetric methods are the most accurate and
76
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
involve evaporating the liquid solvent to leave a residue which can subsequently be
weighed with a precision analytical balance (normally capable of .0001 gram accuracy).
Electrical conductivity of water is directly related to the concentration of dissolved
ionized solids in the water. When correlated with laboratory TDS measurements,
electrical conductivity provides an approximate value for the TDS concentration, usually
to within ten percent accuracy.
Field kit: Take a water sample, insert the EC pocket meter into the sample and measure
the electrical conductivity in dS/m.
(http://soils.usda.gov/sqi/assessment/files/Chpt12.pdf)
Changes of the water salinity level must be based on sufficient water samples which can
make the monitoring process costly.
Soil fertility, soil health, aridity index, vegetation density, land cover/land use change,
rainfall variability, ground water level
Water samples are taken for a specific water body (surface or ground water); the
aggregation of data at national level will illustrate the hot spots and bright spots
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Total dissolved solids concentrations (ppm) or electrical conductivity data (dS/m)
Field surveys
Information may be available at national level on areas that have been surveyed.
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
FAO, USDA
-
7. Practical applications of the indicator (references to case studies)
-
8. Additional information
Currently, about 3600 km3 of freshwater are withdrawn for human use - the equivalent of 580 m3 per capita per year.
The bar chart on the right shows that, in all regions except Europe and North America, agriculture is by far the biggest
user of water, accounting worldwide for about 69 percent of all withdrawals, with domestic (municipal) use amounting
to about 10 percent and industry using some 21 percent. Of the 3600 km3 of water withdrawn annually, roughly half of
it is consumed as a result of evaporation and transpiration from plants. Water that is abstracted but not consumed, by
contrast, flows back over the surface to rivers or infiltrates the ground and is stored in aquifers. However this water is
generally of a lower quality than the water that was withdrawn. Irrigation consumes much of the water it withdraws
(often half or more) as a result of evaporation, incorporation into crops and transpiration from crops. The other half
recharges groundwater or surface flows or is lost in unproductive evaporation. Figures for water withdrawal in
agriculture do not include the direct use that is made of rainwater in rainfed agriculture. In fact, more food is produced
from the direct use of rainwater than from the use of irrigation water - and even irrigated agriculture uses considerable
rainwater.
http://www.fao.org/DOCREP/005/Y3918E/y3918e03.htm#P0_0
When irrigation water is allowed to infiltrate in the soil, the ground water table starts to rise and may, in time, bring the
zone of saturation close to the surface - a phenomenon called water logging.
The other side effect of irrigation is so called secondary salinization, which is caused by an accumulation of salts in the
root zone. In areas where the climate is hot and dry e.g. the Sahel region, irrigated lands are subject to substantial water
losses through evapotranspiration. Salts contained in precipitation and irrigation water remain in the soil and increase in
concentration when the water evaporates from the soil or when the plants take up water for transpiration. If the salt is
not leached from the soil, the salt concentration increases constantly and subsequently causing reductions in crop yield.
If the salinization process is allowed to continue, the land eventually has to be abandoned. To avoid salinization, excess
irrigation water has to be applied to the field in order to leach the salt from the root zone.
http://www.lwr.kth.se/Forskningsprojekt/Salinity_Growth/Index.htm
77
It is estimated that poor drainage and irrigation practices have led to waterlogging and salinization of about 10 percent
of the world’s irrigated lands, thereby reducing productivity. In particular, mobilization of resident salts is a widelyoccurring phenomenon in irrigated river basins in arid regions. Waterlogging and salinization in large-scale irrigation
projects are often the result of unavailable drainage infrastructure that was not included in the engineering design to
make projects look economically more attractive. These problems are generally associated with large-scale irrigation
development under arid and semi-arid conditions, as in the Indus, the Tigris-Euphrates and the Nile river basins. The
solutions to these problems are known but their implementation is costly.
(ftp://ftp.fao.org/agl/aglw/docs/agricfoodwater.pdf)
Over-pumping of ground water may yield extremely serious results: salinization by salt water intrusion, which
eliminates at least 10 million cubic meters per year annually (Israel), thus reducing the availability of fresh water
sources. As an example, nearly 20% of the coastal aquifer cannot be utilized due to salinity.
http://www.biu.ac.il/soc/besa/water/zaslavsky.html
78
ANNEX 6 CONTAMINATION INDICATORS
1. EMISSION OF CONTAMINATING SUBSTANCES (PRESSURE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Emission of contaminating substances
Measure for the release of pollutants or wastes into the environment; the indicator may
be composed of two or more sub-indicators which reflect the pollution categories such as
eutrophication, acidification, heavy metals, radioactive substances, persistent organic
pollutants etc.
Contamination of water and soils in dryland areas probably occur mainly because of the
emission into the environment of eutrophying (N and P compounds) and toxic substances
(heavy metals and POPs). The eutrophication of the environment by N and P compounds
is taken into account by the (pressure) indicator: ‘Nutrient balance’. The emission of
heavy metals and POPs may be expressed by sub-indicators and aggregated into one
single emission indicator.
1. Measured in tonnes/year and translated into toxics dispersion equivalents;
2. Aggregation into one single emission indicator (or index) requires the translation of
the two separate indicators into e.g. units of environmental pressure equivalents (WRI,
1995); the releases are weighted according to their toxicity and longevity in the
environment.
National
Annually
2. Position within the logical framework DPSIR
Type of indicator
Pressure
3. Target and political pertinence
Objective(s)
Importance with respect to
land degradation
International conventions
and agreements
To represent the total emissions of contaminating substances by economic activity as
better knowledge of emitted amounts per sector helps activities aiming at decreasing
emissions.
Heavy metals: while some heavy metals are known to be toxic, the environmental impact
of many others remains to be elucidated. Taken together, these contaminants
nevertheless represent a threat both to aquatic ecosystems and to human health, since
they tend to accumulate in all living organisms. The threat is increasing because of the
enormous number of new compounds constantly being produced and released into the
environment. Heavy metals in surface water can damage plants and affect humans
through the food chain or exposure to contaminated drinking water.
Persistent organic pollutants (POPs): the POPs are handled by different economic
activities and subsequent emissions of these pollutants take place during production and
use , and as waste. Increasing POP emissions, or keeping POP emissions at today’s level
is clearly not sustainable. (http://esl.jrc.it/envind/meth_sht)
HELCOM (Helsinki, 1994) adopted a Recommendation in May 2001 for the cessation of
hazardous substance discharges/emissions by 2020, with the ultimate aim of achieving
concentrations in the environment near to background values for naturally occurring
substances and close to zero for man-made synthetic substances.
OSPARCOM (Oslo, 1972 and Paris, 1974 conventions on the preventions of marine
pollution; conference on the protection of the North-East Atlantic (Paris, 1992).
North Sea Conference on co-operation in dealing with pollution of the North Sea by oil
and other harmful substances (Bonn, 1983); North Sea Ministerial Conference (Esbjerg
Declaration, 1995).
International Commission for the Protection of the Rhine (Bern, 1963).
Basel Convention 1989 (on the control of transboundary movements of hazardous waste
79
and their disposal);
UNEP Chemicals/International register of Potentially Toxic Chemicals (IRPTC);
FAO Specifications for Plant Protection Products
Stockholm Convention (2001) on Persistent Organic Pollutants (POPs); International
POPs Elimination Network (IPEN)
UNCED (1992): Globally Harmonized System for classification and labelling of
chemicals. Agenda 21: Chapter 18: Protection of the quality and supply of freshwater
resources: application of integrated approaches to the development, management and use
of water resources. Chapter 19: Environmentally-sound management of toxic chemicals,
including prevention of illegal international traffic in toxic and hazardous products.
Clean Water Act (US, 1977); Clean Air Act (US, amended 1990)
United Nations Economic Commission for Europe (UNECE): Convention on Longrange Transboundary Air Pollution (Protocol on heavy metals, 1998, effective 29.12.03).
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
The indicator measures the total discharge to the environment of heavy metals (the
environmental focus is on mercury, lead, cadmium, chromium, copper, arsenic, nickel
and zinc), persistent organic pollutants (POPs) from all sources (industrial, agricultural,
domestic). Human activity leads to large quantities of chemical compounds being
discharged into the environment in various ways. Pollution by heavy metals, POPs arises
from point and diffuse sources (industry, agriculture, mining activities, waste
incineration, burning of fossil fuels and road traffic) and also from accidental releases.
Heavy metals: European standards for heavy metal discharges are set out in the subdirectives of Directive 76/464/EEC on pollution caused by certain dangerous substances
discharged into the aquatic environment. The 5EAP calls for at least 70% reduction from
all pathways of Cd, Hg and Pb emissions in 1995.
POPs: The 5th Environmental Action Plan (5EAP, 1992-1998) only sets European targets
for more environment-friendly agricultural/industrial practices (e.g. significant reduction
of pesticide use per unit of land, chemicals: list of 50 priority chemicals, etc); the 6th
EAP (2002-2012) did not propose new quantifiable targets.
In the US the Environmental protection Agency (EPA) sets targets for emission of
pollutants (see: www.epa.gov).
Heavy metals: metal emissions from gas-solid bed combustion can be monitored by a
mobile laboratory, for instance designed around the Spectro Ciros- CCD ICP-OES, and
equipped with a 40 m heated sampling line (the concentrations of over 30 elements,
including As, Ca, Cd, Hg, K, Na, Pb, Sn, V, Zn, with detection limits as low as
0.0004mg m-3, and a time resolution of one minute or less, can be monitored)
(http://www.suwic.group.shef.ac.uk/proj-metal.htm).
POPs: information will be generated by emission inventories. A large part of the
emissions from products containing POPs will take place not only during production, but
also during use and waste treatment. Emitted amounts are estimated by the use of
emission factors, describing emission rates of substances from various matrices in
different media (http://esl.jrc.it/envind/meth_sht).
Appropriate data at national level are often not available because the measurement of
contaminating substances is often not included in monitoring programmes.
Heavy metals and POPs are a heterogeneous group. Their inherent characteristics, such
as reactivity, persistence and toxic potential (accumulation in organisms), differ. The
relationships may vary from one location to another and from time to time. The emission
sources are often diffuse and the emission factors are in some cases unreliable or nonexisting.
Water consumption, water contamination, water availability, soil contamination, soil
health, soil fertility.
Measurements of emissions need to be made on a routine basis (e.g. every year) in periurban industrial areas or more frequently (e.g. every year) in sensitive areas.
Georeferenced emission data are represented on digital maps (national), highlighting
areas where critical thresholds are exceeded (hotspots).
80
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Information from the various sectors causing emissions and direct measurements at point
sources.
National statistics, industry.
The availability of information is the major limiting factor due to the number of
industrial sectors and substances to be taken into account, and the difficulty in obtaining
data from industries.
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
EEC, EPA (US)
UNEP
7. Practical applications of the indicator (references to case studies)
-
8. Additional information
Heavy metals are generally found in the environment at low concentrations, but are extracted and used in many human
activities, including electric and electronic devices, and the metal and chemical industries, all of which result in various
domestic products and wastes. Synthetic substances are produced in great quantities, mainly by the chemical industry
for domestic products, but also from the burning of fuels and domestic waste disposal. This in turn leads to discharges
to waste waters directly or by rainwater run-off, where these substances may be stored in sediments, drained to seas or
assimilated by various living beings (bio-accumulation).
2. SOIL CONTAMINATION (STATE INDICATOR)
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Soil contamination
Soil contamination occurs mainly in the form of acidification, eutrophication and
accumulation of toxic substances. Contamination can be the result of solid waste
disposal, emissions of sulphur, phosphates and nitrogen compounds, persistent organic
Pollutants (POPs), oil spills and dispersion of heavy metals by the energy sector,
industry, mining, traffic, agriculture etc,. The various types of contamination may be
expressed by sub-indicators or, when aggregated, by a composite contamination
indicator. Radioactive fallout is not included here but should be kept in mind (see section
8: Additional information)
Concentration of N and P compounds and concentration of toxic substances in ppm.
Depending on the density of soil sampling the indicator may be determined on a national
scale or for selected areas only.
The indicator is determined on a yearly basis
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation in
dryland areas
To monitor the state of accumulation of toxic substances in soils.
To guide decision makers to allow clean-up operations in areas where threshold values
are exceeded.
To identify contamination hot spots that need subsequently to be mapped in detail for
cleanup.
The main soil contamination issue in agricultural dryland areas is probably the
application of pesticides and herbicides and, to a lesser extent, the over-application of
mineral fertilizer in irrigated areas, while other soil contamination issues such as the
accumulation of heavy metals, solid waste disposal, oil spills and the disposal of obsolete
and unwanted pesticide stocks is more typical of urban, peri-urban/industrial and mining
areas. The concern over soil contamination is mainly because of health risks, both
through direct contact and secondary contamination of water resources. Nevertheless the
over-application of herbicides, pesticides and mineral fertilizers in agricultural areas (e.g.
81
International conventions
and agreements
irrigation schemes) is likely to affect the local vegetation cover and local ecosystems and
may cause a decline of biodiversity.
Basel Convention 1989 (on the control of transboundary movements of hazardous waste
and their disposal);
UNEP Chemicals/International register of Potentially Toxic Chemicals (IRPTC);
FAO Specifications for Plant Protection Products
Stockholm Convention (2001) on Persistent Organic Pollutants (POPs); International
POPs Elimination Network (IPEN)
UNCED (1992): Globally Harmonized System for classification and labelling of
chemicals.
Clean Air Act (US, amended 1990)
United Nations Economic Commission for Europe (UNECE): Convention on Longrange Transboundary Air Pollution (Protocol on heavy metals, 1998, effective 29.12.03).
4. Methodological description and basic definition
Definitions and basic
concepts
Benchmarks indication of
the values/ranges of values
Methods of measurement
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
Soil contamination is the issue. Emissions of contaminating substances act as the
pressure while concentrations in the soil reflect the state of the soil. The main soil
contaminating substances released by human activities in drylands are probably heavy
metals, POPs and eutrophying substances (Phosphates and N compounds). Although the
excessive dispersion of N and P in soils may affect plant species that thrive on lownutrient environments, they eventually affect the quality of groundwater and surface
water; therefore they will not be considered under soil contamination but under water
contamination. Soil contamination will thus be represented by heavy metals (esp. lead,
cadmium and mercury) and POP concentration. This is done as follows (based on WRI,
1995):
e) The dispersions of heavy metals and POPs are measured in soil samples;
f) the concentrations are weighted according to their persistence and their
longevity in the environment (soil) and expressed in units of dispersion
equivalents;
g) the weighted summation of the dispersion equivalents, forms the indicator for
soil contamination.
The critical concentration marks the threshold from where, when exceeded the soil pass
from a sink into a potential source of ‘risk compounds’. This critical concentration is
based on experimental and field experience on eco-toxicological effects over defined
pathways (German Soil Protection Ordinance, BBodSchV,1999).
Table 1: Soil guide respectively threshold values (ppm) for soils
Heavy
Cd
Cr
Cu
Hg
Ni
Pb
Zn
metal
Sand
0.4
30
20
0.1
15
40
60
Loam/silt 1.0
60
40
0.5
50
70
150
Clay
1.5
100
60
1.0
70
100
200
In Germany, soils containing PCBs at levels exceeding 0,1 ppm are prohibited for use as
re-cultivation layers in landfills
Total content of heavy metals per kg of soil is determined as follows: the fine earth or the
separated clay fraction is dried and ignited and then fused with lithium tetraborate; the
formed bead can be analyzed by X-ray fluorescence spectroscopy (ISRIC/FAO, 1995).
To conduct analysis of POPs capillary gas chromatography (GC) equipment with either
electron capture or low resolution mass spectrometry (MS) detection to separate and
quantify Organochlorine pesticides (OCPs)/Polychlorinated Biphenyls (PCBs) is
essential (stapgef.unep.org/activities/technicalworkshops/document.2005-1228.3314580945).
Laboratory analysis can be expensive.
Past studies mainly focused on human health and less on land degradation
Water contamination, Soil health
Soil samples need to be taken and analyzed on a routine basis (e.g. every 10 years) in
82
measured and how to
aggregate/disaggregate to
other levels
peri-urban areas and more frequently (e.g. every 2 years) in sensitive areas.
Georeferenced soil contamination data are represented on digital maps, highlighting
areas where critical thresholds are exceeded (hotspots).
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Concentration, toxicity level and longevity
National and private soil and water laboratories
-
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
WRI (1995)
Dutch Government
-
7. Practical applications of the indicator (references to case studies)
The indicator may guide decision makers to allow clean-up operations in areas where threshold values are exceeded.
8. Additional information
Human health effects: The major concern is that there are many sensitive land uses where people are in direct contact
with soils such as residences, parks, schools and playgrounds. Other contact mechanisms include contamination of
drinking water or inhalation of soil contaminants which have vaporized. There is a very large set of health
consequences from exposure to soil contamination depending on pollutant type, pathway of attack and vulnerability of
the exposed population. Chromium and many of the pesticide and herbicide formulations are carcinogenic to all
populations. Lead is especially hazardous to young children, in which group there is a high risk of developmental
damage to the brain and nervous system, while to all populations kidney damage is a risk.
Chronic exposure to benzene at sufficient concentrations is known to be associated with higher incidence of leukemia.
Mercury and cyclodienes (aldrin, dieldrin, chlordane and endrin) are known to induce higher incidences of kidney
damage, some irreversible. PCBs and cyclodienes are linked to liver toxicity. Organophosphates and carbamates can
induce a chain of responses leading to neuromuscular blockage. Many chlorinated solvents induce liver changes,
kidney changes and depression of the central nervous system. There is an entire spectrum of further health effects such
as headache, nausea, fatigue, eye irritation and skin rash for the above cited and other chemicals. Clearly at sufficient
dosages a large number of soil contaminants cause death.
Ecosystem effects: not unexpectedly, soil contaminants can have significant deleterious consequences for ecosystems.
There are radical soil chemistry changes which can arise from the presence of many hazardous chemicals even at low
concentration of the contaminant species. These changes can manifest in the alteration of metabolism of endemic
microorganisms and arthropods resident in a given soil environment. The result can be virtual eradication of some of
the primary food chain, which in turn have major consequences for predator or consumer species. Even if the chemical
effect on lower life forms is small, the lower pyramid levels of the food chain may ingest alien chemicals, which
normally become more concentrated for each consuming rung of the food chain. Many of these effects are now well
known, such as the concentration of persistent DDT materials for avian consumers, leading to weakening of egg shells,
increased chick mortality and potentially species extinction.
Effects occur to agricultural lands which have certain types of soil contamination. Contaminants typically alter plant
metabolism, most commonly to reduce crop yields. This has a secondary effect upon soil conservation, since the
languishing crops cannot shield the soil from erosion phenomena. Some of these chemical contaminants have long halflives and in other cases derivative chemicals are formed from decay of primary soil contaminants
(http://en.wikipedia.org/wiki/Soil_contamination).
Radioactive fallout in soils, crops and food
The Chernobyl nuclear power plant accident in the USSR in April 1986 demonstrated how the release of radioactive
substances into the environment can rapidly achieve international and even global significance. In addition to its tragic
human consequences, the problems were especially significant in relation to agriculture, contingent food supplies and
to their dependent communities. FAO Soils Bulletin 61, FAO, Rome, 1989
3. WATER CONTAMINATION (STATE INDICATOR)
83
1. Definition
Indicator Name
Definition/Description
Unit of Measure
Spatial Scale
Temporal Scale
Water contamination
Water contamination in dryland areas occurs mainly in the form of eutrophication and
accumulation of toxic substances. Contamination can be the result of solid waste
disposal, emissions of sulphur, phosphates and nitrogen compounds, persistent organic
pollutants (POPs), oil spills and dispersion of heavy metals by the energy sector,
industry, mining, traffic, agriculture etc,. The various types of contamination may be
expressed by sub-indicators or, when aggregated, by a composite contamination
indicator.
Concentration of N and P compounds and concentration of toxic substances in ppm.
Depending on the density of water sampling the indicator may be determined on a
national scale or for selected areas only.
The indicator is determined on a yearly basis or more frequently in sensitive areas
2. Position within the logical framework DPSIR
Type of indicator
State
3. Target and political pertinence
Objectives
Importance with respect to
land degradation in
dryland areas
International conventions
and agreements
To monitor the state of accumulation of toxic and eutrophying substances in groundwater
and surface water.
To identify contamination hot spots.
To guide decision makers to allow clean-up or mitigating operations in areas where
threshold values are exceeded.
The main water contamination issue in agricultural dryland areas is probably the
application of pesticides and herbicides and, to a lesser extent, the application of excess
mineral and organic fertilizer in irrigated areas, while other water contamination issues
such as the accumulation of heavy metals, solid waste disposal, oil spills and the disposal
of obsolete and unwanted pesticide stocks is more typical of urban, peri-urban/industrial
and mining areas. The concern over water contamination is mainly because of health
risks. Nevertheless the application of herbicides, pesticides and mineral fertilizers in
agricultural areas (e.g. irrigation schemes) is likely to affect the water ecosystems and
may cause a decline of biodiversity. Nitrogen and phosphorus are the main plant
nutrients causing eutrophication and related impacts on aquatic life and water quality.
Excess concentrations of such nutrients in water bodies lead to over-nourishment,
proliferation of aquatic plants (blue-green algae), reduced light penetration, depletion of
dissolved oxygen in surface water, disappearance of benthic invertebrates, production of
toxins which are potentially poisonous to fish, cattle and humans, and changes in
biological structure. These effects are generally most apparent in lakes, reservoirs and
coastal areas, and also in large, slow-flowing rivers.
Basel Convention 1989 (on the control of transboundary movements of hazardous waste
and their disposal);
UNEP Chemicals/International register of Potentially Toxic Chemicals (IRPTC);
FAO Specifications for Plant Protection Products
Stockholm Convention (2001) on Persistent Organic Pollutants (POPs); International
POPs Elimination Network (IPEN)
UNCED (1992): Globally Harmonized System for classification and labelling of
chemicals.
Clean Water Act (US, 1977); Clean Air Act (US, amended 1990)
United Nations Economic Commission for Europe (UNECE): Convention on Longrange Transboundary Air Pollution (Protocol on heavy metals, 1998, effective 29.12.03).
4. Methodological description and basic definition
Definitions and basic
concepts
Water contamination is the issue. Emissions of contaminating substances act as the
pressure while concentrations in water reflect the state of the water resources. The main
water contaminating substances released by human activities in drylands are probably
heavy metals, Persistent Organic Pollutants (POPs) and eutrophying substances
(Phosphates and N compounds). The excessive dispersion of N and P in soils may affect
eventually affect the quality of groundwater and surface water. Water contamination will
84
Benchmarks indication of
the values/ranges of values
Methods of measurement
thus be represented by eutrophying substances (N and P), heavy metals (esp. cadmium,
lead, chromium VI and 0, mercury; arsenic) and POPs. This is done as follows (based on
WRI, 1995):
1. Eutrophication:
The concentration of nitrates as nitrogen (N) and phosphates as phosphorus (P) are
measured in water samples. They are weighted on their potential eutrophication effect. In
The Netherlands for instance the ratio 1: 10 has been chosen based on the natural
proportion of P and N in Dutch groundwaters, surface waters, soils and organisms. A
unit of N is considered to have a ten times smaller effect than a unit of P. It is an average
value. The respective concentrations are then expressed in units of eutrophication
equivalents and added
2. Dispersion of toxic substances:
The heavy metals and POPs are measured in water samples; the concentrations are
weighted according to their persistence and their longevity in the environment and
expressed in units of dispersion equivalents; the weighted summation of the dispersion
equivalents, forms the indicator for water contamination by toxic substances.
Nitrogen and phosphorus: In Europe maximum admissible nitrate concentration limits
have been set for drinking water. Directive 98/83/EC on the quality of drinking water
specifies a limit of 50 mg/L, matching the WHO guideline value. The directive also sets
a guide level of 25 mg/L. Phosphorus is the main cause of eutrophication and of water
quality deterioration. Even a minimal phosphorus content (some tens of µg/l) can
constitute a dangerous pollutant. Thus, according to the UN ECE classification of surface
water, water is considered fairly eutrophic at 25 µg/L.
In the US the maximum allowable concentrations for total phosphorus and total nitrogen
in stagnant fresh surface water are 0.15 mg/L total phosphorus and 2.2 mg/L total
nitrogen. To make possible any real ecological recovery of lakes and ponds, nutrient
concentrations must be reduced to the desired quality standards (0.05 mg/L total
phosphorus and 1 mg/L total nitrogen).
(http://arch.rivm.nl/environmentaldata/E_Environmenta_quality/E2_Surface_water_quality/)
In the US lake water concentrations of P above 20 µg/L are considered to generally
accelerate eutrophication (Agricultural Phosphorus and Eutrophication, 2nd edition,
USDA, ARS-149, September 2003).
Heavy metals: The EU environmental quality standards for cadmium and mercury are 5
μg/L and 1 μg/L respectively
(www.grid.unep.ch/product/publication/freshwater_europe/quality.php);
US/EPA standards: Cadmium maximum level drinking water: 0.01 mg/L (goal: 5 μg/L)
Lead WHO drinking water guideline 0.05 mg/L maximum (goal is 0)
Chromium: US/EPA Regulation maximum contaminant level: 0.05 mg/L
(www.lehigh.cdu/kaf3/public/www-data/background/hvymt/2.html).
POPs: The EU Directive on the quality of drinking water has set the maximum
admissible concentrations of each substance at 0.1 µg/L, and the total concentration of all
pesticides at 0.5 µ g/L. This Directive sets threshold values at 0.03 µg/L for the most
toxic substances. The WHO threshold values for concentrations of pesticides in drinking
water, based on toxicological considerations, are less strict than the maximum
concentrations allowed by EU.
Total N: a) HUP method: heated UV/Persulfate Method, involves ultraviolet radiation,
heated (70 oC) digestion, and the oxidation of nitrite, nitrate, ammonia, and organic
nitrogen into nitrate. The nitrate concentration is determined by measuring the
absorbance at 220 nm; or b) CC method: total nitrogen analyzer using a catalytic
combustion procedure at 720 (or 850) °C followed by chemiluminescence detection
(www.ssi.shimadzu.com/products/pdfs/toc/tnpc5.pdf).
Total P: Samples are digested in an autoclave for 30 minutes at 121EC with ammonium
persulfate and sulfuric acid to convert all phosphorus to orthophosphate. The
orthophosphate is then analyzed using the ascorbic acid procedure and colorimeter
equipped with 50 mm flow cells and 880 nm interference filters
(www.epa.gov/glnpo/lmmb/methods/methd310.2.pdf).
85
Limitations of the Indicator
Linkages with other Land
Degradation Indicators
Scale at which it can be
measured and how to
aggregate/disaggregate to
other levels
Total content of heavy metals per L of water is determined by X-ray fluorescence
spectroscopy (ISRIC/FAO, Technical paper 9, Procedures for soil analysis, 6th edition,
2002).
To conduct analysis of POPs capillary gas chromatography (GC) equipment with either
electron capture or low resolution mass spectrometry (MS) detection to separate and
quantify OrganoChlorine Pesticides (OCPs)/Polychlorinated Biphenyls (PCBs) is
essential (stapgef.unep.org/activities/technicalworkshops/document.2005-1228.3314580945).
Laboratory analysis can be expensive.
Past studies mainly focused on human health and less on land degradation
Soil contamination
Water samples need to be taken and analyzed on a routine basis (e.g. every 2 to 5 years)
in peri-urban areas and more frequently (e.g. every year) in sensitive areas.
Georeferenced water contamination data are represented on digital maps, highlighting
areas where critical thresholds are exceeded (hotspots).
5. Evaluation of data needs and availability
Data required to calculate
the indicator
Data sources
Availability of data from
national and international
sources
Concentration, toxicity level and longevity
National and private soil and water laboratories
Data on dissolved nitrogen are available from the UNEP GEMS/Water Programme. The
indicator is based on actual NO3 and NO2 measurements received regularly from
monitoring stations of participating countries. Data is site specific and is statistically
evaluated to be indicative of watershed and regional conditions. Data quality is assessed
by UNEP GEMS/Water (www.gemswater.org).
6. Institutions that have participated in developing the indicator
Main institutions
responsible
Other contributing
organizations
WRI (1995)
Dutch Government
-
7. Practical applications of the indicator (references to case studies)
-
8. Additional information
Nitrogen and phosphorus stem from various point and non-point sources such as domestic and industrial wastewater discharges,
runoff from forestry and agriculture, and atmospheric deposit s. However, much of the excess phosphorus load in inland waters
originates from point sources , especially municipal sewage and industrial effluent, although inputs from agricultural land can also be
significant. Nitrogen loading is primarily due to agricultural activity, and especially to the use of nitrogen fertilisers and manure.
Nitrogen emissions from the domestic and industrial sectors are much lower than agricultural emissions.
86