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4
Land degradation
Coordinating Lead Authors:
Lennart Olsson (Sweden), Humberto Barbosa (Brazil)
Lead Authors:
Suruchi Bhadwal (India), Annette Cowie (Australia), Kenel Delusca (Haiti), Dulce Flores-Renteria
(Mexico), Kathleen Hermans (Germany), Esteban Jobbagy (Argentina), Werner Kurz (Canada),
Diqiang Li (China), Denis Jean Sonwa (Cameroon), Lindsay Stringer (United Kingdom)
Contributing Authors:
Timothy Crews (The United States of America), Martin Dallimer (United Kingdom), Joris Eekhout
(The Netherlands), Karlheinz Erb (Italy), Eamon Haughey (Ireland), Richard Houghton
(The United States of America), Muhammad Mohsin Iqbal (Pakistan), Francis X. Johnson
(The United States of America), Woo-Kyun Lee (The Republic of Korea), John Morton
(United Kingdom), Felipe Garcia Oliva (Mexico), Jan Petzold (Germany), Mohammad Rahimi
(Iran), Florence Renou-Wilson (Ireland), Anna Tengberg (Sweden), Louis Verchot (Colombia/
The United States of America), Katharine Vincent (South Africa)
Review Editors:
José Manuel Moreno (Spain), Carolina Vera (Argentina)
Chapter Scientist:
Aliyu Salisu Barau (Nigeria)
This chapter should be cited as:
Olsson, L., H. Barbosa, S. Bhadwal, A. Cowie, K. Delusca, D. Flores-Renteria, K. Hermans, E. Jobbagy, W. Kurz, D. Li,
D.J. Sonwa, L. Stringer, 2019: Land Degradation. In:
Climate Change and Land: an IPCC special report on climate
change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in
terrestrial ecosystems
[P.R. Shukla, J. Skea, E. Calvo Buendia, V. Masson-Delmotte, H.-O. Pörtner, D. C. Roberts, P. Zhai,
R. Slade, S. Connors, R. van Diemen, M. Ferrat, E. Haughey, S. Luz, S. Neogi, M. Pathak, J. Petzold, J. Portugal Pereira,
P. Vyas, E. Huntley, K. Kissick, M. Belkacemi, J. Malley, (eds.)]. In press.
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4
Executive summary
Land degradation affects people and ecosystems throughout
the planet and is both affected by climate change and
contributes to it.
In this report, land degradation is defined as
a
negative trend
in land condition, caused by direct or indirect
human-induced processes including anthropogenic climate change,
expressed as long-term reduction or loss of at least one of the
following: biological productivity, ecological integrity, or value to
humans.
Forest degradation is land degradation that occurs in forest
land. Deforestation is the conversion of forest to non-forest land and
can result in land degradation. {4.1.3}
Land degradation adversely affects people’s livelihoods (
very
high confidence
)
and occurs over a quarter of the Earth’s
ice-free land area (
medium confidence
)
.
The majority of
the 1.3 to 3.2 billion affected people (
low confidence
) are
living in poverty in developing countries (
medium confidence
).
Land-use changes and unsustainable land management are direct
human causes of land degradation (
very high confidence
), with
agriculture being a dominant sector driving degradation (
very high
confidence
). Soil loss from conventionally tilled land exceeds the rate
of soil formation by >2 orders of magnitude (
medium confidence
).
Land degradation affects humans in multiple ways, interacting
with social, political, cultural and economic aspects, including
markets, technology, inequality and demographic change (
very
high
confidence
). Land degradation impacts extend beyond the land
surface itself, affecting marine and freshwater systems, as well as
people and ecosystems far away from the local sites of degradation
(
very high confidence
). {4.1.6, 4.2.1, 4.2.3, 4.3, 4.6.1, 4.7, Table 4.1}
Climate change exacerbates the rate and magnitude of
several ongoing land degradation processes and
introduces
new degradation patterns (
high confidence
).
Human-induced
global warming has already caused observed changes in two drivers
of land degradation: increased frequency, intensity and/or amount
of heavy precipitation (
medium confidence
); and increased heat
stress (
high confidence
). In some areas sea level rise has exacerbated
coastal erosion (
medium confidence
). Global warming beyond
present day will further exacerbate ongoing land degradation
processes through increasing floods (
medium confidence
), drought
frequency and severity (
medium confidence
), intensified cyclones
(
medium confidence
), and sea level rise (
very high confidence
),
with outcomes being modulated by land management (
very high
confidence
). Permafrost thawing due to warming (
high confidence
),
and coastal erosion due to sea level rise and impacts of changing
storm paths (
low confidence
), are examples of land degradation
affecting places where it has not typically been a problem. Erosion of
coastal areas because of sea level rise will increase worldwide (
high
confidence
). In cyclone prone areas, the combination of sea level rise
and more intense cyclones will cause land degradation with serious
consequences for people and livelihoods (
very high confidence
).
{4.2.1, 4.2.2, 4.2.3, 4.4.1, 4.4.2, 4.9.6, Table 4.1}
Land degradation and climate change, both individually
and in combination, have profound implications for natural
resource-based livelihood systems and societal groups
(
high
confidence
).
The number of people whose livelihood depends on
degraded lands has been estimated to be about 1.5 billion worldwide
(
very low confidence
). People in degraded areas who directly depend
on natural resources for subsistence, food security and income,
including women and youth with limited adaptation options, are
especially vulnerable to land degradation and climate change
(
high confidence
). Land degradation reduces land productivity and
increases the workload of managing the land, affecting women
disproportionally in some regions. Land degradation and climate
change act as threat multipliers for already precarious livelihoods
(
very high confidence
), leaving them highly sensitive to extreme
climatic events, with consequences such as poverty and food
insecurity (
high confidence
) and, in some cases, migration, conflict
and loss of cultural heritage (
low confidence
). Changes in vegetation
cover and distribution due to climate change increase the risk of land
degradation in some areas (
medium confidence
). Climate change will
have detrimental effects on livelihoods, habitats and infrastructure
through increased rates of land degradation (
high confidence
) and
from new degradation patterns (
low evidence
,
high agreement
).
{4.1.6, 4.2.1, 4.7}
Land degradation is a driver of climate change through
emission of greenhouse gases (GHGs) and reduced rates of
carbon uptake (
very high confidence
).
Since 1990, globally the
forest area has decreased by 3% (
low confidence
) with net decreases
in the tropics and net increases outside the tropics (
high confidence
).
Lower carbon density in re-growing forests, compared to carbon stocks
before deforestation, results in net emissions from land-use change
(
very high confidence
). Forest management that reduces carbon
stocks of forest land also leads to emissions, but global estimates
of these emissions are uncertain. Cropland soils have lost 20–60%
of their organic carbon content prior to cultivation, and soils under
conventional agriculture continue to be a source of GHGs (
medium
confidence
). Of the land degradation processes, deforestation,
increasing wildfires, degradation of peat soils, and permafrost
thawing contribute most to climate change through the release of
GHGs and the reduction in land carbon sinks following deforestation
(
high confidence
). Agricultural practices also emit non-CO
2
GHGs
from soils and these emissions are exacerbated by climate change
(
medium confidence
). Conversion of primary to managed forests,
illegal logging and unsustainable forest management result in
GHG emissions (
very high confidence
) and can have additional
physical effects on the regional climate including those arising from
albedo shifts (
medium confidence
). These interactions call for more
integrative climate impact assessments. {4.2.2, 4.3, 4.5.4, 4.6}
Large-scale implementation of dedicated biomass production
for bioenergy increases competition for land with potentially
serious consequences for food security and land degradation
(
high confidence
)
.
Increasing the extent and intensity of biomass
production, for example, through fertiliser additions, irrigation or
monoculture energy plantations, can result in local land degradation.
Poorly implemented intensification of land management contributes
to land degradation (e.g., salinisation from irrigation) and disrupted
livelihoods (
high confidence
). In areas where afforestation and
reforestation occur on previously degraded lands, opportunities
exist to restore and rehabilitate lands with potentially significant
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co-benefits (
high confidence
) that depend on whether restoration
involves natural or plantation forests. The total area of degraded
lands has been estimated at 10–60 Mkm
2
(
very low confidence
).
The extent of degraded and marginal lands suitable for dedicated
biomass production is highly uncertain and cannot be established
without due consideration of current land use and land tenure.
Increasing the area of dedicated energy crops can lead to land
degradation elsewhere through indirect land-use change (
medium
confidence
). Impacts of energy crops can be reduced through
strategic integration with agricultural and forestry systems
(
high confidence
) but the total quantity of biomass that can be
produced through synergistic production systems is unknown.
{4.1.6, 4.4.2, 4.5, 4.7.1, 4.8.1, 4.8.3, 4.8.4, 4.9.3}
Reducing unsustainable use of traditional biomass reduces
land degradation and emissions of CO
2
while providing social
and economic co-benefits (
very high confidence
).
Traditional
biomass in the form of fuelwood, charcoal and agricultural residues
remains a primary source of energy for more than one-third
of
the global population, leading to unsustainable use of biomass
resources and forest degradation and contributing around 2% of
global GHG emissions (
low confidence
). Enhanced forest protection,
improved forest and agricultural management, fuel-switching and
adoption of efficient cooking and heating appliances can promote
more sustainable biomass use and reduce land degradation, with
co-benefits of reduced GHG emissions, improved human health,
and reduced workload especially for women and youth (
very high
confidence
). {4.1.6, 4.5.4}
Land degradation can be avoided, reduced or reversed by
implementing sustainable land management, restoration
and rehabilitation practices that simultaneously provide
many co-benefits, including adaptation to and mitigation of
climate change (
high confidence
).
Sustainable land management
involves a comprehensive array of technologies and enabling
conditions, which have proven to address land degradation at
multiple landscape scales, from local farms (
very high confidence
)
to entire watersheds (
medium confidence
). Sustainable forest
management can prevent deforestation, maintain and enhance
carbon sinks and can contribute towards GHG emissions-reduction
goals. Sustainable forest management generates socio-economic
benefits, and provides fibre, timber and biomass to meet society’s
growing needs. While sustainable forest management sustains high
carbon sinks, the conversion from primary forests to sustainably
managed forests can result in carbon emission during the transition
and loss of biodiversity (
high confidence
). Conversely, in areas of
degraded forests, sustainable forest management can increase
carbon stocks and biodiversity (
medium confidence
). Carbon storage
in long-lived wood products and reductions of emissions from use of
wood products to substitute for emissions-intensive materials also
contribute to mitigation objectives. {4.8, 4.9, Table 4.2}
Lack of action to address land degradation will increase
emissions and reduce carbon sinks and is inconsistent with
the emissions reductions required to limit global warming
to 1.5°C or 2°C. (
high confidence
).
Better management of soils
can offset 5–20% of current global anthropogenic GHG emissions
(
medium confidence
). Measures to avoid, reduce and reverse land
degradation are available but economic, political, institutional, legal
and socio-cultural barriers, including lack of access to resources
and knowledge, restrict their uptake (
very high confidence
). Proven
measures that facilitate implementation of practices that avoid,
reduce, or reverse land degradation include tenure reform, tax
incentives, payments for ecosystem services, participatory integrated
land-use planning, farmer networks and rural advisory services.
Delayed action increases the costs of addressing land degradation,
and can lead to irreversible biophysical and human outcomes
(
high confidence
). Early actions can generate both site-specific and
immediate benefits to communities affected by land degradation,
and contribute to long-term global benefits through climate change
mitigation (
high confidence
). {4.1.5, 4.1.6, 4.7.1, 4.8, Table 4.2}
Even with adequate implementation of measures to avoid,
reduce and reverse land degradation, there will be residual
degradation in some situations (
high confidence
)
.
Limits to
adaptation are dynamic, site specific and determined through the
interaction of biophysical changes with social and institutional
conditions. Exceeding the limits of adaptation will trigger escalating
losses or result in undesirable changes, such as forced migration,
conflicts, or poverty. Examples of potential limits to adaptation due
to climate-change-induced land degradation are coastal erosion
(where land disappears, collapsing infrastructure and livelihoods
due to thawing of permafrost), and extreme forms of soil erosion.
{4.7, 4.8.5, 4.8.6, 4.9.6, 4.9.7, 4.9.8}
Land degradation is a serious and widespread problem, yet
key uncertainties remain concerning its extent, severity, and
linkages to climate change (
very high confidence
)
.
Despite
the difficulties of objectively measuring the extent and severity of
land degradation, given its complex and value-based characteristics,
land degradation represents – along with climate change – one
of the biggest and most urgent challenges for humanity (
very
high confidence
). The current global extent, severity and rates
of land degradation are not well quantified. There is no single
method by which land degradation can be measured objectively
and consistently over large areas because it is such a complex
and value-laden concept (
very high confidence
). However, many
existing scientific and locally-based approaches, including the use
of indigenous and local knowledge, can assess different aspects of
land degradation or provide proxies. Remote sensing, corroborated
by other data, can generate geographically explicit and globally
consistent data that can be used as proxies over relevant time
scales (several decades). Few studies have specifically addressed
the impacts of proposed land-based negative emission technologies
on land degradation. Much research has tried to understand how
livelihoods and ecosystems are affected by a particular stressor – for
example, drought, heat stress, or waterlogging. Important knowledge
gaps remain in understanding how plants, habitats and ecosystems
are affected by the cumulative and interacting impacts of several
stressors, including potential new stressors resulting from large-scale
implementation of negative emission technologies. {4.10}
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4.1
Introduction
4.1.1
Scope of the chapter
This chapter examines the scientific understanding of how climate
change impacts land degradation, and vice versa, with a focus on
non-drylands. Land degradation of drylands is covered in Chapter 3.
After providing definitions and the context (Section 4.1) we proceed
with a theoretical explanation of the different processes of land
degradation and how they are related to climate and to climate
change, where possible (Section 4.2). Two sections are devoted to
a systematic assessment of the scientific literature on status and
trend of land degradation (Section 4.3) and projections of land
degradation (Section 4.4). Then follows a section where we assess
the impacts of climate change mitigation options, bioenergy and
land-based technologies for carbon dioxide removal (CDR), on land
degradation (Section 4.5). The ways in which land degradation can
impact on climate and climate change are assessed in Section 4.6.
The impacts of climate-related land degradation on human and
natural systems are assessed in Section 4.7. The remainder of the
chapter assesses land degradation mitigation options based on
the concept of sustainable land management: avoid, reduce and
reverse land degradation (Section 4.8), followed by a presentation
of eight illustrative case studies of land degradation and remedies
(Section 4.9). The chapter ends with a discussion of the most critical
knowledge gaps and areas for further research (Section 4.10).
4.1.2
Perspectives of land degradation
Land degradation has accompanied humanity at least since the
widespread adoption of agriculture during Neolithic time, some 10,000
to 7,500 years ago (Dotterweich 2013; Butzer 2005; Dotterweich
2008) and the associated population increase (Bocquet-Appel 2011).
There are indications that the levels of greenhouse gases (GHGs) –
particularly carbon dioxide (CO
2
) and methane (CH
4
) – in the
atmosphere already started to increase more than 3,000 years ago as
a result of expanding agriculture, clearing of forests, and domestication
of wild animals (Fuller et al. 2011; Kaplan et al. 2011; Vavrus
et al. 2018; Ellis et al. 2013). While the development of agriculture
(cropping and animal husbandry) underpinned the development of
civilisations, political institutions and prosperity, farming practices led
to conversion of forests and grasslands to farmland, and the heavy
reliance on domesticated annual grasses for our food production
meant that soils started to deteriorate through seasonal mechanical
disturbances (Turner et al. 1990; Steffen et al. 2005; Ojima et al. 1994;
Ellis et al. 2013). More recently, urbanisation has significantly altered
ecosystems (Cross-Chapter Box 4 in Chapter 2). Since around 1850,
about 35% of human-caused CO
2
emissions to the atmosphere
has come from land as a combined effect of land degradation and
land-use change (Foley et al. 2005) and about 38% of the Earth’s
land area has been converted to agriculture (Foley et al. 2011). See
Chapter 2 for more details.
Not all human impacts on land result in degradation according to the
definition of land degradation used in this report (Section 4.1.3). There
are many examples of long-term sustainably managed land around
the world (such as terraced agricultural systems and sustainably
managed forests) although degradation and its management are the
focus of this chapter. We also acknowledge that human use of land
and ecosystems provides essential goods and services for society
(Foley et al. 2005; Millennium Ecosystem Assessment 2005).
Land degradation was long subject to a polarised scientific debate
between disciplines and perspectives in which social scientists often
proposed that natural scientists exaggerated land degradation as
a global problem (Blaikie and Brookfield 1987; Forsyth 1996; Lukas
2014; Zimmerer 1993). The elusiveness of the concept in combination
with the difficulties of measuring and monitoring land degradation
at global and regional scales by extrapolation and aggregation of
empirical studies at local scales, such as the Global Assessment of
Soil Degradation database (GLASOD) (Sonneveld and Dent 2009)
contributed to conflicting views. The conflicting views were not
confined to science only, but also caused tension between the scientific
understanding of land degradation and policy (Andersson et al. 2011;
Behnke and Mortimore 2016; Grainger 2009; Toulmin and Brock 2016).
Another weakness of many land degradation studies is the exclusion
of the views and experiences of the land users, whether farmers or
forest-dependent communities (Blaikie and Brookfield 1987; Fairhead
and Scoones 2005; Warren 2002; Andersson et al. 2011). More
recently, the polarised views described above have been reconciled
under the umbrella of Land Change Science, which has emerged as
an interdisciplinary field aimed at examining the dynamics of land
cover and land-use as a coupled human-environment system (Turner
et al. 2007). A comprehensive discussion about concepts and different
perspectives of land degradation was presented in Chapter 2 of the
recent report from the Intergovernmental Science-Policy Platform
on Biodiversity and Ecosystem Services (IPBES) on land degradation
(Montanarella et al. 2018).
In summary, agriculture and clearing of land for food and wood
products have been the main drivers of land degradation for millennia
(
high confidence
). This does not mean, however, that agriculture and
forestry always cause land degradation (
high confidence
); sustainable
management is possible but not always practised (
high confidence
).
Reasons for this are primarily economic, political and social.
4.1.3
Definition of land degradation
To clarify the scope of this chapter, it is important to start by
defining land itself. The Special Report on Climate Change and Land
(SRCCL) defines land as ‘the terrestrial portion of the biosphere that
comprises the natural resources (soil, near surface air, vegetation
and other biota, and water), the ecological processes, topography,
and human settlements and infrastructure that operate within that
system’ (Henry et al. 2018, adapted from FAO 2007; UNCCD 1994).
Land degradation is defined in many different ways within the
literature, with differing emphases on biodiversity, ecosystem
functions and ecosystem services (e.g., Montanarella et al. 2018). In
this report,
land degradation is defined as a
negative trend
in land
condition, caused by direct or indirect human-induced processes
including anthropogenic climate change, expressed as long-term
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reduction or loss of at least one of the following: biological
productivity, ecological integrity or value to humans
. This definition
applies to forest and non-forest land: forest degradation is land
degradation that occurs in forest land. Soil degradation refers to
a subset of land degradation processes that directly affect soil.
The SRCCL definition
is derived from the IPCC AR5 definition of
desertification, which is in turn taken from the United Nations
Convention to Combat Desertification (UNCCD): ’Land degradation
in arid, semi-arid, and dry sub-humid areas resulting from various
factors, including climatic variations and human activities. Land
degradation in arid, semi-arid, and dry sub-humid areas is a reduction
or loss of the biological or economic productivity and integrity of
rainfed cropland, irrigated cropland, or range, pasture, forest, and
woodlands resulting from land uses or from a process or combination
of processes, including processes arising from human activities and
habitation patterns, such as (i) soil erosion caused by wind and/
or water; (ii) deterioration of the physical, chemical, biological,
or economic properties of soil; and (iii) long-term loss of natural
vegetation’ (UNCCD 1994, Article 1).
For this report, the SRCCL definition is intended to complement the
more detailed UNCCD definition above, expanding the scope to all
regions, not just drylands, providing an operational definition that
emphasises the relationship between land degradation and climate.
Through its attention to the three aspects – biological productivity,
ecological integrity and value to humans – the SRCCL definition
is consistent with the Land Degradation Neutrality (LDN) concept,
which aims to maintain or enhance the land-based natural capital,
and the ecosystem services that flow from it (Cowie et al. 2018).
In the SRCCL definition of land degradation, changes in land condition
resulting solely from natural processes (such as volcanic eruptions
and tsunamis) are not considered land degradation, as these are
not direct or indirect human-induced processes. Climate variability
exacerbated by human-induced climate change can contribute to
land degradation. Value to humans can be expressed in terms of
ecosystem services or Nature’s Contributions to People.
The definition recognises the reality presented in the literature that
land-use and land management decisions often result in trade-offs
between time, space, ecosystem services, and stakeholder groups
(e.g., Dallimer and Stringer 2018). The interpretation of a negative
trend in land condition is somewhat subjective, especially where
there is a trade-off between ecological integrity and value to humans.
The definition also does not consider the magnitude of the negative
trend or the possibility that a negative trend in one criterion may
be an acceptable trade-off for a positive trend in another criterion.
For example, reducing timber yields to safeguard biodiversity by
leaving on site more wood that can provide habitat, or vice versa,
is a trade-off that needs to be evaluated based on context (i.e. the
broader landscape) and society’s priorities. Reduction of biological
productivity
or
ecological integrity
or
value to humans
can
constitute
degradation, but any one of these changes need not necessarily
be considered degradation. Thus, a land-use change that reduces
ecological integrity and enhances sustainable food production
at a specific location is not necessarily degradation. Different
stakeholder groups with different world views value ecosystem
services differently. As Warren (2002) explained: land degradation is
contextual. Further, a decline in biomass carbon stock does not always
signify degradation, such as when caused by periodic forest harvest.
Even a decline in productivity may not equate to land degradation,
such as when a high-intensity agricultural system is converted to
a lower-input, more sustainable production system.
In the SRCCL definition, degradation is indicated by a negative trend
in land condition during the period of interest, thus the baseline is
the land condition at the start of this period. The concept of baseline
is theoretically important but often practically difficult to implement
for conceptual and methodological reasons (Herrick et al. 2019;
Prince et al. 2018; also Sections 4.3.1 and 4.4.1). Especially in biomes
characterised by seasonal and interannual variability, the baseline
values of the indicators to be assessed should be determined by
averaging data over a number of years prior to the commencement
of the assessment period (Orr et al. 2017) (Section 4.2.4).
Forest degradation is land degradation in forest remaining forest.
In contrast, deforestation refers to the conversion of forest to
non-forest that involves a loss of tree cover and a change in land use.
Internationally accepted definitions of forest (FAO 2015; UNFCCC
2006) include lands where tree cover has been lost temporarily, due
to disturbance or harvest, with an expectation of forest regrowth.
Such temporary loss of forest cover, therefore, is not deforestation.
4.1.4
Land degradation in previous IPCC reports
Several previous IPCC assessment reports include brief discussions
of land degradation. In AR5 WGIII land degradation is one factor
contributing to uncertainties of the mitigation potential of land-based
ecosystems, particularly in terms of fluxes of soil carbon (Smith et al.
2014, p. 817). In AR5 WGI, soil carbon was discussed comprehensively
but not in the context of land degradation, except forest degradation
(Ciais et al. 2013) and permafrost degradation (Vaughan et al. 2013).
Climate change impacts were discussed comprehensively in AR5 WGII,
but land degradation was not prominent. Land-use and land-cover
changes were treated comprehensively in terms of effects on the
terrestrial carbon stocks and flows (Settele et al. 2015) but links to
land degradation were, to a large extent, missing. Land degradation
was discussed in relation to human security as one factor which, in
combination with extreme weather events, has been proposed to
contribute to human migration (Adger et al. 2014), an issue discussed
more comprehensively in this chapter (Section 4.7.3). Drivers and
processes of degradation by which land-based carbon is released to
the atmosphere and/or the long-term reduction in the capacity of the
land to remove atmospheric carbon and to store this in biomass and
soil carbon, have been discussed in the methodological reports of
IPCC (IPCC 2006, 2014a) but less so in the assessment reports.
The Special Report on Land Use, Land-Use Change and Forestry
(SR-LULUCF) (Watson et al. 2000) focused on the role of the biosphere
in the global cycles of GHG. Land degradation was not addressed in
a comprehensive way. Soil erosion was discussed as a process by which
soil carbon is lost and the productivity of the land is reduced. Deposition
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of eroded soil carbon in marine sediments was also mentioned as
a possible mechanism for permanent sequestration of terrestrial
carbon (Watson et al. 2000, p. 194). The possible impacts of climate
change on land productivity and degradation were not discussed
comprehensively. Much of the report was about how to account for
sources and sinks of terrestrial carbon under the Kyoto Protocol.
The IPCC Special Report on Managing the Risks of Extreme Events
and Disasters to Advance Climate Change Adaptation (SREX) (IPCC
2012) did not provide a definition of land degradation. Nevertheless,
it addressed different aspects related to some types of land
degradation in the context of weather and climate extreme events.
From this perspective, it provided key information on both observed
and projected changes in weather and climate (extremes) events that
are relevant to extreme impacts on socio-economic systems and on
the physical components of the environment, notably on permafrost
in mountainous areas and coastal zones for different geographic
regions, but few explicit links to land degradation. The report also
presented the concept of sustainable land management as an
effective risk-reduction tool.
Land degradation has been treated in several previous IPCC
reports, but mainly as an aggregated concept associated with GHG
emissions, or as an issue that can be addressed through adaptation
and mitigation.
4.1.5
Sustainable land management (SLM)
and sustainable forest management (SFM)
Sustainable land management (SLM) is defined as ‘the stewardship
and use of land resources, including soils, water, animals and plants,
to meet changing human needs, while simultaneously ensuring the
long-term productive potential of these resources and the maintenance
of their environmental functions’ – adapted from World Overview of
Conservation Approaches and Technologies (WOCAT n.d.). Achieving
the objective of ensuring that productive potential is maintained in
the long term will require implementation of adaptive management
and ‘triple loop learning’, that seeks to monitor outcomes, learn from
experience and emerging new knowledge, modifying management
accordingly (Rist et al. 2013).
Sustainable Forest Management (SFM) is defined as ‘the stewardship
and use of forests and forest lands in a way, and at a rate, that maintains
their biodiversity, productivity, regeneration capacity, vitality and
their potential to fulfill, now and in the future, relevant ecological,
economic and social functions, at local, national, and global levels,
and that does not cause damage to other ecosystems’ (Forest Europe
1993; Mackey et al. 2015). This SFM definition was developed by the
Ministerial Conference on the Protection of Forests in Europe and
has since been adopted by the Food and Agriculture Organization.
Forest management that fails to meet these sustainability criteria can
contribute to land degradation.
Land degradation can be reversed through restoration and
rehabilitation. These terms are defined in the Glossary, along with
other terms that are used but not explicitly defined in this section of
the report. While the definitions of SLM and SFM are very similar and
could be merged, both are included to maintain the subtle differences
in the existing definitions. SFM can be considered a subset of SLM –
that is, SLM applied to forest land.
Climate change impacts interact with land management to determine
sustainable or degraded outcome (Figure 4.1). Climate change can
exacerbate many degradation processes (Table 4.1) and introduce
novel ones (e.g., permafrost thawing or biome shifts). To avoid, reduce
or reverse degradation, land management activities can be selected
to mitigate the impact of, and adapt to, climate change. In some
cases, climate change impacts may result in increased productivity
and carbon stocks, at least in the short term. For example, longer
growing seasons due to climate warming can lead to higher forest
productivity (Henttonen et al. 2017; Kauppi et al. 2014; Dragoni et al.
2011), but warming alone may not increase productivity where other
factors such a water supply are limiting (Hember et al. 2017).
The types and intensity of human land-use and climate change
impacts on lands affect their carbon stocks and their ability to
operate as carbon sinks. In managed agricultural lands, degradation
can result in reductions of soil organic carbon stocks, which also
adversely affects land productivity and carbon sinks (Figure 4.1).
The transition from natural to managed forest landscapes usually
results in an initial reduction of landscape-level carbon stocks. The
magnitude of this reduction is a function of the differential in frequency
of stand-replacing natural disturbances (e.g., wildfires) and harvest
disturbances, as well as the age-dependence of these disturbances
(Harmon et al. 1990; Kurz et al. 1998; Trofymow et al. 2008).
SFM applied at the landscape scale to existing unmanaged forests can
first reduce average forest carbon stocks and subsequently increase
the rate at which CO
2
is removed from the atmosphere, because net
ecosystem production of forest stands is highest in intermediate
stand ages (Kurz et al. 2013; Volkova et al. 2018; Tang et al. 2014).
The net impact on the atmosphere depends on the magnitude of the
reduction in carbon stocks, the fate of the harvested biomass (i.e. use
in short – or long-lived products and for bioenergy, and therefore
displacement of emissions associated with GHG-intensive building
materials and fossil fuels), and the rate of regrowth. Thus, the impacts
of SFM on one indicator (e.g., past reduction in carbon stocks in
the forested landscape) can be negative, while those on another
indicator (e.g., current forest productivity and rate of CO
2
removal
from the atmosphere, avoided fossil fuel emissions) can be positive.
Sustainably managed forest landscapes can have a lower biomass
carbon density than unmanaged forest, but the younger forests can
have a higher growth rate, and therefore contribute stronger carbon
sinks than older forests (Trofymow et al. 2008; Volkova et al. 2018;
Poorter et al. 2016).
Selective logging and thinning can maintain and enhance forest
productivity and achieve co-benefits when conducted with due
care for the residual stand and at intensity and frequency that
does not exceed the rate of regrowth (Romero and Putz 2018). In
contrast, unsustainable logging practices can lead to stand-level
degradation. For example, degradation occurs when selective logging
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4
(high-grading) removes valuable large-diameter trees, leaving behind
damaged, diseased, non-commercial or otherwise less productive
trees, reducing carbon stocks and also adversely affecting subsequent
forest recovery (Belair and Ducey 2018; Nyland 1992).
SFM is defined using several criteria (see above) and its
implementation will typically involve trade-offs among these criteria.
The conversion of primary forests to sustainably managed forest
ecosystems increases relevant economic, social and other functions
but often with adverse impacts on biodiversity (Barlow et al. 2007). In
regions with infrequent or no stand-replacing natural disturbances,
the timber yield per hectare harvested in managed secondary forests
is typically lower than the yield per hectare from the first harvest in
the primary forest (Romero and Putz 2018).
The sustainability of timber yield has been achieved in temperate
and boreal forests where intensification of management has resulted
in increased growing stocks and increased harvest rates in countries
where forests had previously been overexploited (Henttonen et al.
2017; Kauppi et al. 2018). However, intensification of management
to increase forest productivity can be associated with reductions in
biodiversity. For example, when increased productivity is achieved
by periodic thinning and removal of trees that would otherwise die
due to competition, thinning reduces the amount of dead organic
matter of snags and coarse woody debris that can provide habitat,
and this loss reduces biodiversity (Spence 2001; Ehnström 2001)
and forest carbon stocks (Russell et al. 2015; Kurz et al. 2013).
Recognition of adverse biodiversity impacts of high-yield forestry
is leading to modified management aimed at increasing habitat
availability through, for example, variable retention logging and
continuous cover management (Roberts et al. 2016) and through
the re-introduction of fire disturbances in landscapes where fires
have been suppressed (Allen et al. 2002). Biodiversity losses are also
observed during the transition from primary to managed forests in
tropical regions (Barlow et al. 2007) where tree species diversity can
be very high – for example, in the Amazon region, about 16,000 tree
species are estimated to exist (ter Steege et al. 2013).
Forest certification schemes have been used to document SFM
outcomes (Rametsteiner and Simula 2003) by assessing a set of
Land management options
Unsustainable land management
Sustainable land management
Sustainably managed land
Degraded land
Net carbon uptake
Source
Sink
Forest
Agriculture
Forest
Agriculture
Restoration and rehabilitation
Degradation
Climate change
Carbon stock
+
–
More degraded
Less degraded
Figure 4.1 | Conceptual figure illustrating that climate change impacts interact with land management to determine sustainable or degraded outcome.
Climate change can exacerbate many degradation processes (Table 4.1) and introduce novel ones (e.g., permafrost thawing or biome shifts), hence management needs to
respond to climate impacts in order to avoid, reduce or reverse degradation. The types and intensity of human land-use and climate change impacts on lands affect their carbon
stocks and their ability to operate as carbon sinks. In managed agricultural lands, degradation typically results in reductions of soil organic carbon stocks, which also adversely
affects land productivity and carbon sinks. In forest land, reduction in biomass carbon stocks alone is not necessarily an indication of a reduction in carbon sinks. Sustainably
managed forest landscapes can have a lower biomass carbon density but the younger forests can have a higher growth rate, and therefore contribute stronger carbon sinks,
than older forests. Ranges of carbon sinks in forest and agricultural lands are overlapping. In some cases, climate change impacts may result in increased productivity and carbon
stocks, at least in the short term.
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criteria and indicators (e.g., Lindenmayer et al. 2000). While many
of the certified forests are found in temperate and boreal countries
(Rametsteiner and Simula 2003; MacDicken et al. 2015), examples
from the tropics also show that SFM can improve outcomes. For
example, selective logging emits 6% of the tropical GHG annually
and improved logging practices can reduce emissions by 44% while
maintaining timber production (Ellis et al. 2019). In the Congo Basin,
implementing reduced impact logging (RIL-C) practices can cut
emissions in half without reducing the timber yield (Umunay et al.
2019). SFM adoption depends on the socio-economic and political
context, and its improvement depends mainly on better reporting
and verification (Siry et al. 2005).
The successful implementation of SFM requires well-established and
functional governance, monitoring, and enforcement mechanisms to
eliminate deforestation, illegal logging, arson, and other activities
that are inconsistent with SFM principles (Nasi et al. 2011). Moreover,
following human and natural disturbances, forest regrowth must be
ensured through reforestation, site rehabilitation activities or natural
regeneration. Failure of forests to regrow following disturbances will
lead to unsustainable outcomes and long-term reductions in forest
area, forest cover, carbon density, forest productivity and land-based
carbon sinks (Nasi et al. 2011).
Achieving all of the criteria of the definitions of SLM and SFM is an
aspirational goal that will be made more challenging where climate
change impacts, such as biome shifts and increased disturbances,
are predicted to adversely affect future biodiversity and contribute
to forest degradation (Warren et al. 2018). Land management to
enhance land sinks will involve trade-offs that need to be assessed
within their spatial, temporal and societal context.
4.1.6
The human dimension of land degradation and
forest degradation
Studies of land and forest degradation are often biased towards
biophysical aspects, both in terms of its processes, such as erosion
or nutrient depletion, and its observed physical manifestations, such
as gullying or low primary productivity. Land users’ own perceptions
and knowledge about land conditions and degradation have often
been neglected or ignored by both policymakers and scientists (Reed
et al. 2007; Forsyth 1996; Andersson et al. 2011). A growing body
of work is nevertheless beginning to focus on land degradation
through the lens of local land users (Kessler and Stroosnijder 2006;
Fairhead and Scoones 2005; Zimmerer 1993; Stocking et al. 2001)
and the importance of local and indigenous knowledge within land
management is starting to be appreciated (Montanarella et al. 2018).
Climate change impacts directly and indirectly on the social reality,
the land users, and the ecosystem, and vice versa. Land degradation
can also have an impact on climate change (Section 4.6).
The use and management of land is highly gendered and is expected
to remain so for the foreseeable future (Kristjanson et al. 2017).
Women often have less formal access to land than men and less
influence over decisions about land, even if they carry out many of
the land management tasks (Jerneck 2018a; Elmhirst 2011; Toulmin
2009; Peters 2004; Agarwal 1997; Jerneck 2018b). Many oft-cited
general statements about women’s subordination in agriculture are
difficult to substantiate, yet it is clear that gender inequality persists
(Doss et al. 2015). Even if women’s access to land is changing formally
(Kumar and Quisumbing 2015), the practical outcome is often limited
due to several other factors related to both formal and informal
institutional arrangements and values (Lavers 2017; Kristjanson
et al. 2017; Djurfeldt et al. 2018). Women are also affected differently
than men when it comes to climate change, having lower adaptive
capacities due to factors such as prevailing land tenure frameworks,
less access to other capital assets and dominant cultural practices
(Vincent et al. 2014; Antwi-Agyei et al. 2015; Gabrielsson et al. 2013).
This affects the options available to women to respond to both land
degradation and climate change. Indeed, access to land and other
assets (e.g., education and training) is key in shaping land-use and
land management strategies (Liu et al. 2018b; Lambin et al. 2001).
Young people are also often disadvantaged in terms of access to
resources and decision-making power, even though they carry out
much of the day-to-day work (Wilson et al. 2017; Kosec et al. 2018;
Naamwintome and Bagson 2013).
Land rights differ between places and are dependent on the
political-economic and legal context (Montanarella et al. 2018).
This means that there is no universally applicable best arrangement.
Agriculture in highly erosion-prone regions requires site-specific
and long-lasting soil and water conservation measures, such as
terraces (Section 4.8.1), which may benefit from secure private
land rights (Tarfasa et al. 2018; Soule et al. 2000). Pastoral modes
of production and community-based forest management systems
are often dominated by, and benefit from, communal land tenure
arrangements, which may conflict with agricultural/forestry
modernisation policies implying private property rights (Antwi-Agyei
et al. 2015; Benjaminsen and Lund 2003; Itkonen 2016; Owour et al.
2011; Gebara 2018).
Cultural ecosystem services, defined as the non-material benefits
people obtain from ecosystems through spiritual enrichment,
cognitive development, reflection, recreation and aesthetic
experiences (Millennium Ecosystem Assessment 2005) are closely
linked to land and ecosystems, although often under-represented
in the literature on ecosystem services (Tengberg et al. 2012;
Hernández-Morcillo et al. 2013). Climate change interacting with
land conditions can impact on cultural aspects, such as sense of place
and sense of belonging (Olsson et al. 2014).
4.2
Land degradation in the context
of climate change
Land degradation results from a complex chain of causes making the
clear distinction between direct and indirect drivers difficult. In the
context of climate change, an additional complex aspect is brought
by the reciprocal effects that both processes have on each other
(i.e. climate change influencing land degradation and vice versa).
In this chapter, we use the terms ‘processes’ and ‘drivers’ with the
following meanings:
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Processes of land degradation
are those direct mechanisms
by which land is degraded and are similar to the notion of ‘direct
drivers’ in the Millennium Ecosystem Assessment framework
(Millennium Ecosystem Assessment, 2005). A comprehensive list of
land degradation processes is presented in Table 4.1.
Drivers of land degradation
are those indirect conditions which
may drive processes of land degradation and are similar to the
notion of ‘indirect drivers’ in the Millennium Ecosystem Assessment
framework. Examples of indirect drivers of land degradation are
changes in land tenure or cash crop prices, which can trigger land-use
or management shifts that affect land degradation.
An exact demarcation between processes and drivers is not possible.
Drought and fires are described as drivers of land degradation
in the next section but they can also be a process: for example, if
repeated fires deplete seed sources, they can affect regeneration and
succession of forest ecosystems. The responses to land degradation
follow the logic of the LDN concept: avoiding, reducing and reversing
land degradation (Orr et al. 2017; Cowie et al. 2018).
In research on land degradation, climate and climate variability are
often intrinsic factors. The role of climate change, however, is less
articulated. Depending on what conceptual framework is used,
climate change is understood either as a process or a driver of land
degradation, and sometimes both.
4.2.1
Processes of land degradation
A large array of interactive physical, chemical, biological and human
processes lead to what we define in this report as land degradation
(Johnson and Lewis 2007). The biological productivity, ecological
integrity (which encompasses both functional and structural attributes
of ecosystems) or the human value (which includes any benefit that
people get from the land) of a given territory can deteriorate as
the result of processes triggered at scales that range from a single
furrow (e.g., water erosion under cultivation) to the landscape level
(e.g., salinisation through raising groundwater levels under irrigation).
While pressures leading to land degradation are often exerted on
specific components of the land systems (i.e., soils, water, biota), once
degradation processes start, other components become affected
through cascading and interactive effects. For example, different
pressures and degradation processes can have convergent effects,
as can be the case of overgrazing leading to wind erosion, landscape
drainage resulting in wetland drying, and warming causing more
frequent burning; all of which can independently lead to reductions
of the soil organic matter (SOM) pools as a second-order process.
Still, the reduction of organic matter pools is also a first-order process
triggered directly by the effects of rising temperatures (Crowther
et al. 2016) as well as other climate changes such as precipitation
shifts (Viscarra Rossel et al. 2014). Beyond this complexity, a practical
assessment of the major land degradation processes helps to reveal
and categorise the multiple pathways in which climate change exerts
a degradation pressure (Table 4.1).
Conversion of freshwater wetlands to agricultural land has historically
been a common way of increasing the area of arable land. Despite
the small areal extent – about 1% of the earth’s surface (Hu et al.
2017; Dixon et al. 2016) – freshwater wetlands provide a very large
number of ecosystem services, such as groundwater replenishment,
flood protection and nutrient retention, and are biodiversity hotspots
(Reis et al. 2017; Darrah et al. 2019; Montanarella et al. 2018). The loss
of wetlands since 1900 has been estimated at about 55% globally
(Davidson 2014) (
low confidence
) and 35% since 1970 (Darrah et al.
2019) (
medium confidence
) which in many situations pose a problem
for adaptation to climate change. Drainage causes loss of wetlands,
which can be exacerbated by climate change, further reducing the
capacity to adapt to climate change (Barnett et al. 2015; Colloff et al.
2016; Finlayson et al. 2017) (
high confidence
).
4.2.1.1 Types of land degradation processes
Land degradation processes can affect the soil, water or biotic
components of the land as well as the reactions between them
(Table 4.1). Across land degradation processes, those affecting the
soil have received more attention. The most widespread and studied
land degradation processes affecting soils are water and wind
erosion, which have accompanied agriculture since its onset and are
still dominant (Table 4.1). Degradation through erosion processes is
not restricted to soil loss in detachment areas but includes impacts
on transport and deposition areas as well (less commonly, deposition
areas can have their soils improved by these inputs). Larger-scale
degradation processes related to the whole continuum of soil
erosion, transport and deposition include dune field expansion/
displacement, development of gully networks and the accumulation
of sediments in natural and artificial water-bodies (siltation) (Poesen
and Hooke 1997; Ravi et al. 2010). Long-distance sediment transport
during erosion events can have remote effects on land systems, as
documented for the fertilisation effect of African dust on the Amazon
(Yu et al. 2015).
Coastal erosion represents a special case among erosional
processes, with reports linking it to climate change. While human
interventions in coastal areas (e.g., expansion of shrimp farms) and
rivers (e.g., upstream dams cutting coastal sediment supply), and
economic activities causing land subsidence (Keogh and Törnqvist
2019; Allison et al. 2016) are dominant human drivers, storms and
sea-level rise have already left a significant global imprint on coastal
erosion (Mentaschi et al. 2018). Recent projections that take into
account geomorphological and socioecological feedbacks suggest
that coastal wetlands may not be reduced by sea level rise if their
inland growth is accommodated with proper management actions
(Schuerch et al. 2018).
Other physical degradation processes in which no material
detachment and transport are involved include soil compaction,
hardening, sealing and any other mechanism leading to the loss
of porous space crucial for holding and exchanging air and water
(Hamza and Anderson 2005). A very extreme case of degradation
through pore volume loss, manifested at landscape or larger scales, is
ground subsidence. Typically caused by the lowering of groundwater
or oil levels, subsidence involves a sustained collapse of the ground
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surface, which can lead to other degradation processes such as
salinisation and permanent flooding. Chemical soil degradation
processes include relatively simple changes, like nutrient depletion
resulting from the imbalance of nutrient extraction on harvested
products and fertilisation, and more complex ones, such as
acidification and increasing metal toxicity. Acidification in croplands
is increasingly driven by excessive nitrogen fertilisation and, to
a lower extent, by the depletion of cation like calcium, potassium
or magnesium through exports in harvested biomass (Guo et al.
2010). One of the most relevant chemical degradation processes of
soils in the context of climate change is the depletion of its organic
matter pool. Reduced in agricultural soils through the increase of
respiration rates by tillage and the decline of below-ground plant
biomass inputs, SOM pools have been diminished also by the direct
effects of warming, not only in cultivated land, but also under natural
vegetation (Bond-Lamberty et al. 2018). Debate persists, however, on
whether in more humid and carbon-rich ecosystems the simultaneous
stimulation of decomposition and productivity may result in the lack
of effects on soil carbon (Crowther et al. 2016; van Gestel et al. 2018).
In the case of forests, harvesting – particularly if it is exhaustive, as
in the case of the use of residues for energy generation – can also
lead to organic matter declines (Achat et al. 2015). Many other
degradation processes (e.g., wildfire increase, salinisation) have
negative effects on other pathways of soil degradation (e.g., reduced
nutrient availability, metal toxicity). SOM can be considered a ‘hub’
of degradation processes and a critical link with the climate system
(Minasny et al. 2017).
Land degradation processes can also start from alterations in the
hydrological system that are particularly important in the context
of climate change. Salinisation, although perceived and reported in
soils, is typically triggered by water table-level rises, driving salts to
the surface under dry to sub-humid climates (Schofield and Kirkby
2003). While salty soils occur naturally under these climates (primary
salinity), human interventions have expanded their distribution,
secondary salinity with irrigation without proper drainage being
the predominant cause of salinisation (Rengasamy 2006). Yet, it has
also taken place under non-irrigated conditions where vegetation
changes (particularly dry forest clearing and cultivation) have
reduced the magnitude and depth of soil water uptake, triggering
water table rises towards the surface. Changes in evapotranspiration
and rainfall regimes can exacerbate this process (Schofield and
Kirkby 2003). Salinisation can also result from the intrusion of sea
water into coastal areas, both as a result of sea level rise and ground
subsidence (Colombani et al. 2016).
Recurring flood and waterlogging episodes (Bradshaw et al. 2007;
Poff 2002), and the more chronic expansion of wetlands over dryland
ecosystems, are mediated by the hydrological system, on occasions
aided by geomorphological shifts as well (Kirwan et al. 2011). This
is also the case for the drying of continental water bodies and
wetlands, including the salinisation and drying of lakes and inland
seas (Anderson et al. 2003; Micklin 2010; Herbert et al. 2015). In the
context of climate change, the degradation of peatland ecosystems
is particularly relevant given their very high carbon storage and their
sensitivity to changes in soils, hydrology and/or vegetation (Leifeld
and Menichetti 2018). Drainage for land-use conversion together
with peat mining are major drivers of peatland degradation, yet other
factors such as the extractive use of their natural vegetation and the
interactive effects of water table levels and fires (both sensitive to
climate change) are important (Hergoualc’h et al. 2017a; Lilleskov
et al. 2019).
The biotic components of the land can also be the focus of
degradation processes. Vegetation clearing processes associated with
land-use changes are not limited to deforestation but include other
natural and seminatural ecosystems such as grasslands (the most
cultivated biome on Earth), as well as dry steppes and shrublands,
which give place to croplands, pastures, urbanisation or just barren
land. This clearing process is associated with net carbon losses from
the vegetation and soil pool. Not all biotic degradation processes
involve biomass losses. Woody encroachment of open savannahs
involves the expansion of woody plant cover and/or density over
herbaceous areas and often limits the secondary productivity of
rangelands (Asner et al. 2004; Anadon et al. 2014). These processes
have accelerated since the mid-1800s over most continents (Van
Auken 2009). Change in plant composition of natural or semi-natural
ecosystems without any significant vegetation structural changes is
another pathway of degradation affecting rangelands and forests.
In rangelands, selective grazing and its interaction with climate
variability and/or fire can push ecosystems to new compositions with
lower forage value and a higher proportion of invasive species (Illius
and O´Connor 1999; Sasaki et al. 2007), in some cases with higher
carbon sequestration potential, yet with very complex interactions
between vegetation and soil carbon shifts (Piñeiro et al. 2010). In
forests, extractive logging can be a pervasive cause of degradation,
leading to long-term impoverishment and, in extreme cases, a full
loss of the forest cover through its interaction with other agents such
as fires (Foley et al. 2007) or progressive intensification of land use.
Invasive alien species are another source of biological degradation.
Their arrival into cultivated systems is constantly reshaping crop
production strategies, making agriculture unviable on occasions. In
natural and seminatural systems such as rangelands, invasive plant
species not only threaten livestock production through diminished
forage quality, poisoning and other deleterious effects, but have
cascading effects on other processes such as altered fire regimes and
water cycling (Brooks et al. 2004). In forests, invasions affect primary
productivity and nutrient availability, change fire regimes, and alter
species composition, resulting in long-term impacts on carbon pools
and fluxes (Peltzer et al. 2010).
Other biotic components of ecosystems have been shown as a focus of
degradation processes. Invertebrate invasions in continental waters
can exacerbate other degradation processes such as eutrophication,
which is the over-enrichment of nutrients, leading to excessive algal
growth (Walsh et al. 2016a). Shifts in soil microbial and mesofaunal
composition – which can be caused by pollution with pesticides or
nitrogen deposition and by vegetation or disturbance regime shifts –
alter many soil functions, including respiration rates and carbon
release to the atmosphere (Hussain et al. 2009; Crowther et al. 2015).
The role of the soil biota in modulating the effects of climate change
on soil carbon has been recently demonstrated (Ratcliffe et al. 2017),
highlighting the importance of this lesser-known component of the
biota as a focal point of land degradation. Of special relevance as both
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indicators and agents of land degradation recovery are mycorrhiza,
which are root-associated fungal organisms (Asmelash et al. 2016;
Vasconcellos et al. 2016). In natural dry ecosystems, biological soil
crusts composed of a broad range of organisms, including mosses,
are a particularly sensitive focus for degradation (Field et al. 2010)
with evidenced sensitivity to climate change (Reed et al. 2012).
4.2.1.2 Land degradation processes and climate change
While the subdivision of individual processes is challenged by their
strong interconnectedness, it provides a useful setting to identify the
most important ‘focal points’ of climate change pressures on land
degradation. Among land degradation processes, those responding
more directly to climate change pressures include all types of
erosion and SOM declines (soil focus), salinisation, sodification
and permafrost thawing (soil/water focus), waterlogging of dry
ecosystems and drying of wet ecosystems (water focus), and a broad
group of biologically-mediated processes like woody encroachment,
biological invasions, pest outbreaks (biotic focus), together with
biological soil crust destruction and increased burning (soil/biota
focus) (Table 4.1). Processes like ground subsidence can be affected
by climate change indirectly through sea level rise (Keogh and
Törnqvist 2019).
Even when climate change exerts a direct pressure on degradation
processes, it can be a secondary driver subordinated to other
overwhelming human pressures. Important exceptions are three
processes in which climate change is a dominant global or regional
pressure and the main driver of their current acceleration. These are:
coastal erosion as affected by sea level rise and increased storm
frequency/intensity (
high agreement
,
medium evidence
) (Johnson
et al. 2015; Alongi 2015; Harley et al. 2017; Nicholls et al. 2016);
permafrost thawing responding to warming (
high agreement
,
robust evidence
) (Liljedahl et al. 2016; Peng et al. 2016; Batir et al.
2017); and increased burning responding to warming and altered
precipitation regimes (
high
agreement
,
robust evidence
) (Jolly
et al. 2015; Abatzoglou and Williams 2016; Taufik et al. 2017; Knorr
et al. 2016). The previous assessment highlights the fact that climate
change not only exacerbates many of the well-acknowledged
ongoing land degradation processes of managed ecosystems
(i.e., croplands and pastures), but becomes a dominant pressure that
introduces novel degradation pathways in natural and seminatural
ecosystems. Climate change has influenced species invasions and the
degradation that they cause by enhancing the transport, colonisation,
establishment, and ecological impact of the invasive species, and also
by impairing their control practices (
medium agreement
,
medium
evidence
) (Hellmann et al. 2008).
Table 4.1 | Major land degradation processes and their connections with climate change.
For each process a ‘focal point’ (soil, water, biota) on which degradation
occurs in the first place is indicated, acknowledging that most processes propagate to other land components and cascade into or interact with some of the
other processes listed below. The impact of climate change on each process is categorised based on the proximity (very direct = high, very indirect = low) and
dominance (dominant = high, subordinate to other pressures = low) of effects. The major effects of climate change on each process are highlighted together with
the predominant pressures from other drivers. Feedbacks of land degradation processes on climate change are categorised according to the intensity (very intense
= high, subtle = low) of the chemical (GHG emissions or capture) or physical (energy and momentum exchange, aerosol emissions) effects. Warming effects are
indicated in red and cooling effects in blue. Specific feedbacks on climate change are highlighted.
Processes
Focal point
Impacts of climate change
Feedbacks on climate change
Pr
oximity
Dominance
Climate change pressures
Other pressures
Intensity of
chemical effects
Intensity of
physical effects
Global extent
Specific impacts
Wind erosion
Soil
high
medium
Altered wind/drought patterns
(
high confidence
on effect,
medium-low confidence
on
trend) (1). Indirect effect through
vegetation type and biomass
production shifts
Tillage, leaving low cover,
overgrazing, deforestation/
vegetation clearing, large plot sizes,
vegetation and fire regime shifts
low
medium
high
Radiative cooling by dust release
(
medium
confidence
). Ocean and land
fertilisation and carbon burial (
medium
confidence
). Albedo increase. Dust effect
as condensation nuclei (19)
Water erosion
Soil
high
medium
Increasing rainfall intensity
(
high confidence
on effect and trend)
(2). Indirect effects on fire frequency/
intensity, permafrost thawing,
biomass production
Tillage, cultivation leaving low
cover, overgrazing, deforestation/
vegetation clearing, vegetation
burning, poorly designed roads
and paths
medium
medium
high
Net carbon release. Net release is
probably less than site-specific loss due
to deposition and burial (
high
confidence
). Albedo increase (20)
Coastal
erosion
Soil
/w
ater
high
high
Sea level rise, increasing intensity/
frequency of storm surges (
high
confidence
on effects and trends) (3)
Retention of sediments by
upstream dams, coastal aquiculture,
elimination of mangrove forests,
subsidence
high
low
low
Release of old buried carbon pools
(
medium confidence
) (21)
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Processes
Focal point
Impacts of climate change
Feedbacks on climate change
Pr
oximity
Dominance
Climate change pressures
Other pressures
Intensity of
chemical effects
Intensity of
physical effects
Global extent
Specific impacts
Subsidence
Soil
/w
ater
low
low
Indirect through increasing drought
leading to higher ground water
use. Indirect through enhanced
decomposition (e.g., through
drainage) in organic soils
Groundwater depletion/
overpumping, peatland drainage
low
/ high
low
low
Unimportant in the case of groundwater
depletion. Very high net carbon release in
the case of drained peatlands
Compaction/
hardening
Soil
low
low
Indirect through reduced organic
matter content
Land-use conversion,
machinery overuse, intensive
grazing, poor tillage/grazing
management (e.g., under wet
or waterlogged conditions)
low
low
medium
Contradictory effects of reduced aeration
on N
2
O emissions
Nutrient
depletion
Soil
low
low
Indirect (e.g., shifts in cropland
distribution, BECCS)
Insufficient replenishment
of harvested nutrients
low
low
medium
Net carbon release due shrinking SOC
pools. Larger reliance on soil liming with
associated CO
2
releases
Acidification/
overfertilisa-
tion
Soil
low
low
Indirect (e.g., shifts in cropland
distribution, BECCS). Sulfidic wetland
drying due to increased drought as
special direct effect
High nitrogen fertilisation, high
cation depletion, acid rain/deposition
medium
low
medium
N
2
O release from overfertilised soils,
increased by acidification. Inorganic
carbon release from acidifying soils
(
medium to high confidence
) (22)
Pollution
Soil
/biota
low
low
Indirect (e.g., increased pest
and weed incidence)
Intensifying chemical control
of weed and pests
low
low
medium
Unknown, probably unimportant
Organic
matter
decline
Soil
high
medium
Warming accelerates soil respiration
rates (
medium confidence
on effects
and trends) (4). Indirect effects
through changing quality of plant
litter or fire/waterlogging regimes
Tillage. reduced plant input to
soil. Drainage of waterlogged soils.
Influenced by most of the other
soil degradation processes.
high
low
high
Net carbon release (
high confidence
) (23)
Metal toxicity
Soil
low
low
Indirect
High cation depletion, fertilisation,
mining activities
low
low
low
Unknown, probably unimportant
Salinisation
Soil
/ w
ater
high
low
Sea level rise (
high confidence
on effects and trends) (5). Water
balance shifts (
medium confidence
on effects and trends) (6). Indirect
effects through irrigation expansion
Irrigation without good drainage
infrastructure. Deforestation and
water table-level rises under
dryland agriculture
low
medium
medium
Reduced methane emissions with
high sulfate load. Albedo increase
Sodification
(increased
sodium and
associated
physical
degradation
in soils)
Soil
/ w
ater
high
low
Water balance shifts (
medium
confidence
on effects and trends)
(7). Indirect effects through
irrigation expansion
Poor water management
low
medium
low
Net carbon release due to soil
structure and organic matter dispersion.
Albedo increase
Permafrost
thawing
Soil
/ w
ater
high
high
Warming (
very high confidence
on
effects and trends) (8), seasonality
shifts and accelerated snow melt
leading to higher erosivity.
high
low
high
Net carbon release. CH
4
release
(
high confidence
) (24)
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4
Processes
Focal point
Impacts of climate change
Feedbacks on climate change
Pr
oximity
Dominance
Climate change pressures
Other pressures
Intensity of
chemical effects
Intensity of
physical effects
Global extent
Specific impacts
Waterlogging
of dry systems
W
ater
high
medium
Water balance shifts (
medium
confidence
on effects and trends)
(9). Indirect effects through
vegetation shifts
Deforestation. Irrigation without
good drainage infrastructure
medium
medium
low
CH
4
release. Albedo decrease
Drying of
continental
waters/
wetland/
lowlands
W
ater
high
medium
Increasing extent and duration
of drought (
high confidence
on effects,
medium confidence
on trends) (10). Indirect effects
through vegetation shifts
Upstream surface and groundwater
consumption. Intentional drainage.
Trampling/overgrazing
medium
medium
medium
Net carbon release. N
2
O release.
Albedo increase
Flooding
W
ater
high
medium
Sea level rise, increasing intensity/
frequency of storm surges, increasing
rainfall intensity causing flash floods
(
high confidence
on effects and
trends) (11)
Land clearing. Increasing impervious
surface. Transport infrastructure
medium
medium
low
CH
4
and N
2
O release. Albedo decrease
Eutrophica-
tion of
continental
waters
W
ater
/ biota
low
low
Indirect through warming effects
on nitrogen losses from the land or
climate change effects on erosion
rates. Interactive effects of warming
and nutrient loads on algal blooms
Excess fertilisation. Erosion.
Poor management of livestock/
human sewage
medium
low
low
CH
4
and N
2
0 release
Woody
encroachment
Biota
high
medium
Rainfall shifts (
medium confidence
on effects and trends), CO
2
rise
(
medium confidence
on effects,
very high confidence
on trends) (12)
Overgrazing. Altered fire regimes, fire
suppression. Invasive alien species
high
high
high
Net carbon storage. Albedo decrease
Species loss,
compositional
shifts
Biota
high
medium
Habitat loss as a result of climate
shifts (
medium confidence
on effects
and trends) (13)
Selective grazing and logging
causing plant species loss, Pesticides
causing soil microbial and soil faunal
losses, large animal extinctions,
interruption of disturbance regimes
low
low
medium
Unknown
Soil microbial
and
mesofaunal
shifts
Biota
high
low
Habitat loss as a result of climate
shifts (
medium confidence
on effects
and trends) (14)
Altered fire regimes, nitrogen
deposition, pesticide pollution,
vegetation shifts, disturbance
regime shifts
low
low
medium
Unknown
Biological
soil crust
destruction
Biota
/ soil
high
medium
Warming. Changing rainfall regimes.
(
medium confidence
on effects, high
confidence and trends). Indirect
through fire regime shifts and/or
invasions (15)
Overgrazing and trampling.
Land-use conversion
low
high
high
Radiative cooling through albedo rise
and dust release (
high confidence
) (25)
Invasions
Biota
high
medium
Habitat gain as a result of climate
shifts (
medium confidence
on effects
and trends) (16)
Intentional and unintentional
species introductions
low
low
medium
Unknown
Pest
outbreaks
Biota
high
medium
Habitat gain and accelerated
reproduction as a result of climate
shifts (
medium confidence
on effects
and trends) (17)
Large-scale monocultures. Poor pest
management practices
medium
low
medium
Net carbon release
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4
Processes
Focal point
Impacts of climate change
Feedbacks on climate change
Pr
oximity
Dominance
Climate change pressures
Other pressures
Intensity of
chemical effects
Intensity of
physical effects
Global extent
Specific impacts
Increased
burning
Soil
/biota
high
high
Warming, drought, shifting
precipitation regimes, also wet spells
rising fuel load (
high confidence
on
effects and trends) (18)
Fire suppression policies increasing
wildfire intensity. Increasing use
of fire for rangeland management.
Agriculture introducing fires in
humid climates without previous
fire history. Invasions
high
medium
medium
Net carbon release. CO, CH
4
, N
2
O release.
Albedo increase (
high confidence
).
Long-term decline of NPP in non-adapted
ecosystems (26)
References in Table 4.1:
(1) Bärring et al. 2003; Munson et al. 2011; Sheffield et al. 2012, (2) Nearing et al. 2004; Shakesby 2011; Panthou et al. 2014, (3) Johnson et al. 2015; Alongi 2015; Harley
et al. 2017, (4) Bond-Lamberty et al. 2018; Crowther et al. 2016; van Gestel et al. 2018, (5) Colombani et al. 2016, (6) Schofield and Kirkby 2003; Aragüés et al. 2015; Benini et al.
2016, (7) Jobbágy et al. 2017, (8) Liljedahl et al. 2016; Peng et al. 2016; Batir et al. 2017, (9) Piovano et al. 2004; Osland et al. 2016, (10) Burkett and Kusler 2000; Nielsen
and Brock 2009; Johnson et al. 2015; Green et al. 2017, (11) Panthou et al. 2014; Arnell and Gosling 2016; Vitousek et al. 2017, (12) Van Auken 2009; Wigley et al. 2010,
(13) Vincent et al. 2014; Gonzalez et al. 2010; Scheffers et al. 2016, (14) Pritchard 2011; Ratcliffe et al. 2017, (15) Reed et al. 2012; Maestre et al. 2013, (16) Hellmann et al.
2008; Hulme 2017, (17) Pureswaran et al. 2015; Cilas et al. 2016; Macfadyen et al. 2018, (18) Jolly et al. 2015; Abatzoglou and Williams 2016; Taufik et al. 2017; Knorr
et al. 2016, (19) Davin et al. 2010; Pinty et al. 2011, (20) Wang et al. 2017b; Chappell et al. 2016, (21) Pendleton et al. 2012, (22) Oertel et al. 2016, (23) Houghton et al. 2012;
Eglin et al. 2010, (24) Schuur et al. 2015; Christensen et al. 2004; Walter Anthony et al. 2016; Abbott et al. 2016, (25) Belnap, Walker, Munson & Gill, 2014; Rutherford et al.
2017, (26) Page et al. 2002; Pellegrini et al. 2018.
4.2.2
Drivers of land degradation
Drivers of land degradation and land improvement are many and they
interact in multiple ways. Figure 4.2 illustrates how some of the most
important drivers interact with the land users. It is important to keep
in mind that natural and human factors can drive both degradation
and improvement (Kiage 2013; Bisaro et al. 2014).
Land degradation is driven by the entire spectrum of factors, from
very short and intensive events, such as individual rain storms
of 10 minutes removing topsoil or initiating a gully or a landslide
(Coppus and Imeson 2002; Morgan 2005b) to century-scale slow
depletion of nutrients or loss of soil particles (Johnson and Lewis
2007, pp. 5–6). But, instead of focusing on absolute temporal
variations, the drivers of land degradation can be assessed in relation
to the rates of possible recovery. Unfortunately, this is impractical
to do in a spatially explicit way because rates of soil formation are
difficult to measure due to the slow rate, usually <5mm/century
(Delgado and Gómez 2016). Studies suggest that erosion rates of
conventionally tilled agricultural fields exceed the rate at which soil is
generated by one to two orders of magnitude (Montgomery 2007a).
The landscape effects of gully erosion from one short intensive
rainstorm can persist for decades and centuries (Showers 2005).
Intensive agriculture under the Roman Empire in occupied territories
in France is still leaving its marks and can be considered an example
of irreversible land degradation (Dupouey et al. 2002).
The climate-change-related drivers of land degradation are gradual
changes of temperature, precipitation and wind, as well as changes
of the distribution and intensity of extreme events (Lin et al. 2017).
Importantly, these drivers can act in two directions: land improvement
and land degradation. Increasing CO
2
level in the atmosphere is
a driver of land improvement, even if the net effect is modulated by
other factors, such as the availability of nitrogen (Terrer et al. 2016)
and water (Gerten et al. 2014; Settele et al. 2015; Girardin et al. 2016).
The gradual and planetary changes that can cause land degradation/
improvement have been studied by global integrated models and
Earth observation technologies. Studies of global land suitability
for agriculture suggest that climate change will increase the area
suitable for agriculture by 2100 in the Northern high latitudes by
16% (Ramankutty et al. 2002) or 5.6 million km
2
(Zabel et al. 2014),
while tropical regions will experience a loss (Ramankutty et al. 2002;
Zabel et al. 2014).
Temporal and spatial patterns of tree mortality can be used as an
indicator of climate change impacts on terrestrial ecosystems. Episodic
mortality of trees occurs naturally even without climate change,
but more widespread spatio-temporal anomalies can be a sign of
climate-induced degradation (Allen et al. 2010). In the absence of
systematic data on tree mortality, a comprehensive meta-analysis of
150 published articles suggests that increasing tree mortality around
the world can be attributed to increasing drought and heat stress in
forests worldwide (Allen et al. 2010).
Other and more indirect drivers can be a wide range of factors such as
demographic changes, technological change, changes of consumption
patterns and dietary preferences, political and economic changes, and
social changes (Mirzabaev et al. 2016). It is important to stress that
there are no simple or direct relationships between underlying drivers
and land degradation, such as poverty or high population density,
that are necessarily causing land degradation (Lambin et al. 2001).
However, drivers of land degradation need to be studied in the context
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4
of spatial, temporal, economic, environmental and cultural aspects
(Warren 2002). Some analyses suggest an overall negative correlation
between population density and land degradation (Bai et al. 2008)
but we find many local examples of both positive and negative
relationships (Brandt et al. 2018a, 2017). Even if there are correlations
in one or the other direction, causality is not always the same.
Land degradation is inextricably linked to several climate variables,
such as temperature, precipitation, wind, and seasonality. This
means that there are many ways in which climate change and land
degradation are linked. The linkages are better described as a web of
causality rather than a set of cause–effect relationships.
4.2.3
Attribution in the case of land degradation
The question here is whether or not climate change can be attributed
to land degradation and vice versa. Land degradation is a complex
phenomenon often affected by multiple factors such as climatic
(rainfall, temperature, and wind), abiotic ecological factors (e.g., soil
characteristics and topography), type of land use (e.g., farming of
various kinds, forestry, or protected area), and land management
practices (e.g., tilling, crop rotation, and logging/thinning). Therefore,
attribution of land degradation to climate change is extremely
challenging. Because land degradation is highly dependent on land
management, it is even possible that climate impacts would trigger
land management changes reducing or reversing land degradation,
sometimes called transformational adaptation (Kates et al. 2012).
There is not much research on attributing land degradation explicitly
to climate change, but there is more on climate change as a threat
multiplier for land degradation. However, in some cases, it is
possible to infer climate change impacts on land degradation, both
theoretically and empirically. Section 4.2.3.1 outlines the potential
direct linkages of climate change on land degradation based on
current theoretical understanding of land degradation processes
and drivers. Section 4.2.3.2 investigates possible indirect impacts on
land degradation.
4.2.3.1 Direct linkages with climate change
The most important direct impacts of climate change on land
degradation are the results of increasing temperatures, changing
rainfall patterns, and intensification of rainfall. These changes will,
in various combinations, cause changes in erosion rates and the
processes driving both increases and decreases of soil erosion. From
an attribution point of view, it is important to note that projections
of precipitation are, in general, more uncertain than projections of
temperature changes (Murphy et al. 2004; Fischer and Knutti 2015;
IPCC 2013a). Precipitation involves local processes of larger complexity
than temperature, and projections are usually less robust than those
for temperature (Giorgi and Lionello 2008; Pendergrass 2018).
Theoretically the intensification of the hydrological cycle as a result
of human-induced climate change is well established (Guerreiro
External drivers and shock
(e.g. market fluctuation, new policy
or technology, and climate change)
Effects of decision-making
(especially
agricultural and forestry practice)
Local environmental
knowledge about
environment sub-system
capabilities and
responses
Ecosystem services
(provisioning,
supporting, regulating, cultural)
and
disservices
(e.g. land degradation)
Evolving human sub-system
(changing
technology, institutions and human capital)
Evolving environment sub-system
(changing
agriculture and forestry systems)
External drivers and shocks
(e.g. extreme weather events
and climate change)
HUMAN
H H
H E
ENVIRONMENT
E E
E H
Figure 4.2 | Schematic representation of the interactions between the human (H) and environmental (E) components of the land system showing
decision-making and ecosystem services as the key linkages between the components (moderated by an effective system of local and scientific
knowledge), and indicating how the rates of change and the way these linkages operate must be kept broadly in balance for functional coevolution of
the components.
Modified with permission from Stafford Smith et al. (2007).
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Land degradation
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4
et al. 2018; Trenberth 1999; Pendergrass et al. 2017; Pendergrass
and Knutti 2018) and also empirically observed (Blenkinsop
et al. 2018; Burt et al. 2016a; Liu et al. 2009; Bindoff et al. 2013).
AR5 WGI concluded that heavy precipitation events have increased
in frequency, intensity, and/or amount since 1950 (
likely
) and that
further changes in this direction are
likely
to
very likely
during the 21st
century (IPCC 2013). The IPCC Special Report on 1.5°C concluded that
human-induced global warming has already caused an increase in
the frequency, intensity and/or amount of heavy precipitation events
at the global scale (Hoegh-Guldberg et al. 2018). As an example,
in central India, there has been a threefold increase in widespread
extreme rain events during 1950–2015 which has influenced several
land degradation processes, not least soil erosion (Burt et al. 2016b).
In Europe and North America, where observation networks are dense
and extend over a long time, it is
likely
that the frequency or intensity
of heavy rainfall have increased (IPCC 2013b). It is also expected that
seasonal shifts and cycles such as monsoons and El Niño–Southern
Oscillation (ENSO) will further increase the intensity of rainfall events
(IPCC 2013).
When rainfall regimes change, it is expected to drive changes in
vegetation cover and composition, which may be a cause of land
degradation in and of itself, as well as impacting on other aspects
of land degradation. Vegetation cover, for example, is a key factor
in determining soil loss through water (Nearing et al. 2005) and
wind erosion (Shao 2008). Changing rainfall regimes also affect
below-ground biological processes, such as fungi and bacteria
(Meisner et al. 2018; Shuab et al. 2017; Asmelash et al. 2016).
Changing snow accumulation and snow melt alter volume and timing
of hydrological flows in and from mountain areas (Brahney et al.
2017; Lutz et al. 2014), with potentially large impacts on downstream
areas. Soil processes are also affected by changing snow conditions
with partitioning between evaporation and streamflow and between
subsurface flow and surface runoff (Barnhart et al. 2016). Rainfall
intensity is a key climatic driver of soil erosion. Early modelling
studies and theory suggest that light rainfall events will decrease
while heavy rainfall events increase at about 7% per degree of
warming (Liu et al. 2009; Trenberth 2011). Such changes result in
increased intensity of rainfall, which increases the erosive power
of rainfall (erosivity) and hence enhances the likelihood of water
erosion. Increases in rainfall intensity can even exceed the rate of
increase of atmospheric moisture content (Liu et al. 2009; Trenberth
2011). Erosivity is highly correlated to the product of total rainstorm
energy and the maximum 30-minute rainfall intensity of the storm
(Nearing et al. 2004) and increased erosivity will exacerbate water
erosion substantially (Nearing et al. 2004). However, the effects will
not be uniform, but highly variable across regions (Almagro et al.
2017; Mondal et al. 2016). Several empirical studies around the world
have shown the increasing intensity of rainfall (IPCC 2013b; Ma et al.
2015, 2017) and also suggest that this will be accentuated with
future increased global warming (Cheng and AghaKouchak 2015;
Burt et al. 2016b; O’Gorman 2015).
The very comprehensive database of direct measurements of water
erosion presented by García-Ruiz et al. (2015) contains 4377 entries
(North America: 2776, Europe: 847, Asia: 259, Latin America: 237,
Africa: 189, Australia and Pacific: 67), even though not all entries are
complete (Figure 4.3).
An important finding from that database is that almost any erosion
rate is possible under almost any climatic condition (García-Ruiz
et al. 2015). Even if the results show few clear relationships
between erosion and land conditions, the authors highlighted four
observations (i) the highest erosion rates were found in relation to
agricultural activities – even though moderate erosion rates were
also found in agricultural settings, (ii) high erosion rates after forest
fires were not observed (although the cases were few), (iii) land
covered by shrubs showed generally low erosion rates, (iv) pasture
land showed generally medium rates of erosion. Some important
Figure 4.3. | Map of observed soil erosion rates in database of 4,377 entries by García-Ruiz et al. (2015).
The map was published by Li and Fang (2016).
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4
findings for the link between soil erosion and climate change can be
noted from erosion measurements: erosion rates tend to increase with
increasing mean annual rainfall, with a peak in the interval of 1000 to
1400 mm annual rainfall (García-Ruiz et al. 2015) (
low confidence
).
However, such relationships are overshadowed by the fact that most
rainfall events do not cause any erosion, instead erosion is caused
by a few high-intensity rainfall events (Fischer et al. 2016; Zhu et al.
2019). Hence, mean annual rainfall is not a good predictor of erosion
(Gonzalez-Hidalgo et al. 2012, 2009). In the context of climate
change, it means that the tendency for rainfall patterns to change
towards more intensive precipitation events is serious. Such patterns
have already been observed widely, even in cases where the total
rainfall is decreasing (Trenberth 2011). The findings generally confirm
the strong consensus about the importance of vegetation cover
as a protection against soil erosion, emphasising how extremely
important land management is for controlling erosion.
In the Mediterranean region, the observed and expected decrease in
annual rainfall due to climate change is accompanied by an increase
of rainfall intensity, and hence erosivity (Capolongo et al. 2008). In
tropical and sub-tropical regions, the on-site impacts of soil erosion
dominate, and are manifested in very high rates of soil loss, in some
cases exceeding 100 t ha
–1
yr
–1
(Tadesse 2001; García-Ruiz et al.
2015). In temperate regions, the off-site costs of soil erosion are
often a greater concern, for example, siltation of dams and ponds,
downslope damage to property, roads and other infrastructure
(Boardman 2010). In cases where water erosion occurs, the
downstream effects, such as siltation of dams, are often significant
and severe in terms of environmental and economic damages (Kidane
and Alemu 2015; Reinwarth et al. 2019; Quiñonero-Rubio et al. 2016;
Adeogun et al. 2018; Ben Slimane et al. 2016).
The distribution of wet and dry spells also affects land degradation,
although uncertainties remain depending on resolution of climate
models used for prediction (Kendon et al. 2014). Changes in timing
of rainfall events may have significant impacts on processes of
soil erosion through changes in wetting and drying of soils (Lado
et al. 2004).
Soil moisture content is affected by changes in evapotranspiration
and evaporation, which may influence the partitioning of water
into surface and subsurface runoff (Li and Fang 2016; Nearing et al.
2004). This portioning of rainfall can have a decisive effect on erosion
(Stocking et al. 2001).
Wind erosion is a serious problem in agricultural regions, not only in
drylands (Wagner 2013). Near-surface wind speeds over land areas
have decreased in recent decades (McVicar and Roderick 2010),
partly as a result of changing surface roughness (Vautard et al.
2010). Theoretically (Bakun 1990; Bakun et al. 2015) and empirically
(Sydeman et al. 2014; England et al. 2014) average winds along
coastal regions worldwide have increased with climate change
(
medium evidence
,
high agreement
). Other studies of wind and
wind erosion have not detected any long-term trend, suggesting
that climate change has altered wind patterns outside drylands in
a way that can significantly affect the risk of wind erosion (Pryor
and Barthelmie 2010; Bärring et al. 2003). Therefore, the findings
regarding wind erosion and climate change are inconclusive, partly
due to inadequate measurements.
Global mean temperatures are rising worldwide, but particularly in
the Arctic region (
high confidence
) (IPCC 2018a). Heat stress from
extreme temperatures and heatwaves (multiple days of hot weather
in a row) have increased markedly in some locations in the last three
decades (
high confidence
), and are
virtually certain
to continue
during the 21st century (Olsson et al. 2014a). The IPCC Special Report
on Global Warming of 1.5°C concluded that human-induced global
warming has already caused more frequent heatwaves in most of land
regions, and that climate models project robust differences between
present-day and global warming up to 1.5°C and between 1.5°C and
2°C (Hoegh-Guldberg et al. 2018). Direct temperature effects on soils
are of two kinds. Firstly, permafrost thawing leads to soil degradation
in boreal and high-altitude regions (Yang et al. 2010; Jorgenson
and Osterkamp 2005). Secondly, warming alters the cycling of
nitrogen and carbon in soils, partly due to impacts on soil microbiota
(Solly et al. 2017). There are many studies with particularly strong
experimental evidence, but a full understanding of cause and effect
is contextual and elusive (Conant et al. 2011a,b; Wu et al. 2011). This
is discussed comprehensively in Chapter 2.
Climate change, including increasing atmospheric CO
2
levels, affects
vegetation structure and function and hence conditions for land
degradation. Exactly how vegetation responds to changes remains
a research task. In a comparison of seven global vegetation models
under four representative concentration pathways, Friend et al.
(2014) found that all models predicted increasing vegetation carbon
storage, however, with substantial variation between models. An
important insight compared with previous understanding is that
structural dynamics of vegetation seems to play a more important
role for carbon storage than vegetation production (Friend et al.
2014). The magnitude of CO
2
fertilisation of vegetation growth, and
hence conditions for land degradation, is still uncertain (Holtum and
Winter 2010), particularly in tropical rainforests (Yang et al. 2016).
For more discussion on this topic, see Chapter 2 in this report.
In summary, rainfall changes attributed to human-induced climate
change have already intensified drivers of land degradation (
robust
evidence
,
high agreement
) but attributing land degradation to
climate change is challenging because of the importance of land
management (
medium evidence
,
high agreement
). Changes in
climate variability modes, such as in monsoons and El Niño–Southern
Oscillation (ENSO) events, can also affect land degradation (
low
evidence
,
low agreement
).
4.2.3.2 Indirect and complex linkages with climate change
Many important indirect linkages between land degradation and
climate change occur via agriculture, particularly through changing
outbreaks of pests (Rosenzweig et al. 2001; Porter et al. 1991;
Thomson et al. 2010; Dhanush et al. 2015; Lamichhane et al. 2015),
which is covered comprehensively in Chapter 5. More negative
impacts have been observed than positive ones (IPCC 2014b). After
2050, the risk of yield loss increases as a result of climate change in
combination with other drivers (
medium confidence
) and such risks
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4
will increase dramatically if global mean temperatures increase by
about 4°C (
high confidence
) (Porter et al. 2014). The reduction (or
plateauing) in yields in major production areas (Brisson et al. 2010;
Lin and Huybers 2012; Grassini et al. 2013) may trigger cropland
expansion elsewhere, either into natural ecosystems, marginal arable
lands or intensification on already cultivated lands, with possible
consequences for increasing land degradation.
Precipitation and temperature changes will trigger changes in land
and crop management, such as changes in planting and harvest
dates, type of crops, and type of cultivars, which may alter the
conditions for soil erosion (Li and Fang 2016).
Much research has tried to understand how plants are affected by
a particular stressor, for example, drought, heat, or waterlogging,
including effects on below-ground processes. But less research has
tried to understand how plants are affected by several simultaneous
stressors – which of course is more realistic in the context of climate
change (Mittler 2006; Kerns et al. 2016) and from a hazards point
of view (Section 7.2.1). From an attribution point of view, such
a complex web of causality is problematic if attribution is only done
through statistically-significant correlation. It requires a combination
of statistical links and theoretically informed causation, preferably
integrated into a model. Some modelling studies have combined
several stressors with geomorphologically explicit mechanisms –
using the Water Erosion Prediction Project (WEPP) model – and
realistic land-use scenarios, and found severe risks of increasing
erosion from climate change (Mullan et al. 2012; Mullan 2013). Other
studies have included various management options, such as changing
planting and harvest dates (Zhang and Nearing 2005; Parajuli
et al. 2016; Routschek et al. 2014; Nunes and Nearing 2011), type of
cultivars (Garbrecht and Zhang 2015), and price of crops (Garbrecht
et al. 2007; O’Neal et al. 2005) to investigate the complexity of how
new climate regimes may alter soil erosion rates.
In summary, climate change increases the risk of land degradation,
both in terms of likelihood and consequence, but the exact attribution
to climate change is challenging due to several confounding factors.
But since climate change exacerbates most degradation processes, it
is clear that, unless land management is improved, climate change
will result in increasing land degradation (
very high confidence
).
4.2.4
Approaches to assessing land degradation
In a review of different approaches and attempts to map global
land degradation, Gibbs and Salmon (2015) identified four main
approaches to map the global extent of degraded lands: expert
opinions (Oldeman and van Lynden 1998; Dregne 1998; Reed 2005;
Bot et al. 2000); satellite observation of vegetation greenness –
for example, remote sensing of Normalized Difference Vegetation
Index (NDVI), Enhanced Vegetation Index (EVI), Plant Phenology
Index (PPI) – (Yengoh et al. 2015; Bai et al. 2008c; Shi et al. 2017;
Abdi et al. 2019; JRC 2018); biophysical models (biogeographical/
topological) (Cai et al. 2011b; Hickler et al. 2005; Steinkamp and
Hickler 2015; Stoorvogel et al. 2017); and inventories of land use/
condition. Together they provide a relatively complete evaluation,
but none on its own assesses the complexity of the process (Vogt
et al. 2011; Gibbs and Salmon 2015). There is, however, a robust
consensus that remote sensing and field-based methods are critical
to assess and monitor land degradation, particularly over large areas
(such as global, continental and sub-continental) although there are
still knowledge gaps to be filled (Wessels et al. 2007, 2004; Prince
2016; Ghazoul and Chazdon 2017) as well as the problem of baseline
values (Section 4.1.3).
Remote sensing can provide meaningful proxies of land degradation
in terms of severity, temporal development, and areal extent. These
proxies of land degradation include several indexes that have been
used to assess land conditions, and monitoring changes of land
conditions – for example, extent of gullies, severe forms of rill and
sheet erosion, and deflation. The presence of open-access, quality
controlled and continuously updated global databases of remote
sensing data is invaluable, and is the only method for consistent
monitoring of large areas over several decades (Sedano et al. 2016;
Brandt et al. 2018b; Turner 2014).The NDVI, as a proxy for Net Primary
Production (NPP) (see Glossary), is one of the most commonly used
methods to assess land degradation, since it indicates land cover, an
important factor for soil protection. Although NDVI is not a direct
measure of vegetation biomass, there is a close coupling between
NDVI integrated over a season and in situ NPP (
high agreement
,
robust evidence
) (see Higginbottom et al. 2014; Andela et al. 2013;
Wessels et al. 2012).
Distinction between land degradation/improvement and the effects of
climate variation is an important and contentious issue (Murthy and
Bagchi 2018; Ferner et al. 2018). There is no simple and straightforward
way to disentangle these two effects. The interaction of different
determinants of primary production is not well understood. A key
barrier to this is a lack of understanding of the inherent interannual
variability of vegetation (Huxman et al. 2004; Knapp and Smith
2001; Ruppert et al. 2012; Bai et al. 2008a; Jobbágy and Sala 2000).
One possibility is to compare potential land productivity modelled
by vegetation models and actual productivity measured by remote
sensing (Seaquist et al. 2009; Hickler et al. 2005; van der Esch et al.
2017), but the difference in spatial resolution, typically 0.5 degrees
for vegetation models compared to 0.25–0.5 km for remote sensing
data, is hampering the approach. The Moderate Resolution Imaging
Spectroradiometer (MODIS) provides higher spatial resolution (up
to 0.25 km), delivers data for the EVI, which is calculated in the same
way as NDVI, and has showed a robust approach to estimate spatial
patterns of global annual primary productivity (Shi et al. 2017; Testa
et al. 2018).
Another approach to disentangle the effects of climate and land use/
management is to use the Rain Use Efficiency (RUE), defined as the
biomass production per unit of rainfall, as an indicator (Le Houerou
1984; Prince et al. 1998; Fensholt et al. 2015). A variant of the RUE
approach is the residual trend (RESTREND) of a NDVI time series,
defined as the fraction of the difference between the observed NDVI
and the NDVI predicted from climate data (Yengoh et al. 2015; John
et al. 2016). These two metrics aim to estimate the NPP, rainfall
and the time dimensions. They are simple transformations of the
same three variables: RUE shows the NPP relationship with rainfall
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for individual years, while RESTREND is the interannual change
of RUE; also, both consider that rainfall is the only variable that
affects biomass production. They are legitimate metrics when used
appropriately, but in many cases they involve oversimplifications and
yield misleading results (Fensholt et al. 2015; Prince et al. 1998).
Furthermore, increases in NPP do not always indicate improvement
in land condition/reversal of land degradation, since this does not
account for changes in vegetation composition. It could, for example,
result from conversion of native forest to plantation, or due to bush
encroachment, which many consider to be a form of land degradation
(Ward 2005). Also, NPP may be increased by irrigation, which can
enhance productivity in the short to medium term while increasing
risk of soil salinisation in the long term (Niedertscheider et al. 2016).
Recent progress and expanding time series of canopy characterisations
based on passive microwave satellite sensors have offered rapid
progress in regional and global descriptions of forest degradation and
recovery trends (Tian et al. 2017). The most common proxy is vertical
optical depth (VOD) and has already been used to describe global
forest/savannah carbon stock shifts over two decades, highlighting
strong continental contrasts (Liu et al. 2015a) and demonstrating
the value of this approach to monitor forest degradation at large
scales. Contrasting with NDVI, which is only sensitive to vegetation
‘greenness’, from which primary production can be modelled, VOD is
also sensitive to water in woody parts of the vegetation and hence
provides a view of vegetation dynamics that can be complementary
to NDVI. As well as the NDVI, VOD also needs to be corrected to take
into account the rainfall variation (Andela et al. 2013).
Even though remote sensing offers much potential, its application
to land degradation and recovery remains challenging as structural
changes often occur at scales below the detection capabilities
of most remote-sensing technologies. Additionally, if the remote
sensing is based on vegetation index data, other forms of land
degradation, such as nutrient depletion, changes of soil physical
or biological properties, loss of values for humans, among others,
cannot be inferred directly by remote sensing. The combination of
remotely sensed images and field-based approach can give improved
estimates of carbon stocks and tree biodiversity (Imai et al. 2012;
Fujiki et al. 2016).
Additionally, the majority of trend techniques employed would be
capable of detecting only the most severe of degradation processes,
and would therefore not be useful as a degradation early-warning
system (Higginbottom et al. 2014; Wessels et al. 2012). However,
additional analyses using higher-resolution imagery, such as the
Landsat and SPOT satellites, would be well suited to providing
further localised information on trends observed (Higginbottom et al.
2014). New approaches to assess land degradation using high spatial
resolution are developing, but the need for time series makes progress
slow. The use of synthetic aperture radar (SAR) data has been shown
to be advantageous for the estimation of soil surface characteristics,
in particular, surface roughness and soil moisture (Gao et al. 2017;
Bousbih et al. 2017), and detecting and quantifying selective logging
(Lei et al. 2018). Continued research effort is required to enable full
assessment of land degradation using remote sensing.
Computer simulation models can be used alone or combined with the
remote sensing observations to assess land degradation. The Revised
Universal Soil Loss Equation (RUSLE) can be used, to some extent,
to predict the long-term average annual soil loss by water erosion.
RUSLE has been constantly revisited to estimate soil loss based on
the product of rainfall–runoff erosivity, soil erodibility, slope length
and steepness factor, conservation factor, and support practice
parameter (Nampak et al. 2018). Inherent limitations of RUSLE
include data-sparse regions, inability to account for soil loss from
gully erosion or mass wasting events, and that it does not predict
sediment pathways from hillslopes to water bodies (Benavidez et al.
2018). Since RUSLE models only provide gross erosion, the integration
of a further module in the RUSLE scheme to estimate the sediment
yield from the modelled hillslopes is needed. The spatially distributed
sediment delivery model, WaTEM/SEDEM, has been widely tested in
Europe (Borrelli et al. 2018). Wind erosion is another factor that needs
to be taken into account in the modelling of soil erosion (Webb et al.
2017a, 2016). Additional models need to be developed to include the
limitations of the RUSLE models.
Regarding the field-based approach to assess land degradation, there
are multiple indicators that reflect functional ecosystem processes
linked to ecosystem services and thus to the value for humans. These
indicators are a composite set of measurable attributes from different
factors, such as climate, soil, vegetation, biomass, management,
among others, that can be used together or separately to develop
indexes to better assess land degradation (Allen et al. 2011; Kosmas
et al. 2014).
Declines in vegetation cover, changes in vegetation structure, decline
in mean species abundances, decline in habitat diversity, changes in
abundance of specific indicator species, reduced vegetation health
and productivity, and vegetation management intensity and use, are
the most common indicators in the vegetation condition of forest and
woodlands (Stocking et al. 2001; Wiesmair et al. 2017; Ghazoul and
Chazdon 2017; Alkemade et al. 2009).
Several indicators of the soil quality (SOM, depth, structure,
compaction, texture, pH, C:N ratio, aggregate size distribution and
stability, microbial respiration, soil organic carbon, salinisation, among
others) have been proposed (Schoenholtz et al. 2000) (Section 2.2).
Among these, SOM directly and indirectly drives the majority of soil
functions. Decreases in SOM can lead to a decrease in fertility and
biodiversity, as well as a loss of soil structure, causing reductions
in water-holding capacity, increased risk of erosion (both wind and
water) and increased bulk density and hence soil compaction (Allen
et al. 2011; Certini 2005; Conant et al. 2011a). Thus, indicators related
with the quantity and quality of the SOM are necessary to identify
land degradation (Pulido et al. 2017; Dumanski and Pieri 2000). The
composition of the microbial community is
very likely
to be positive
impacted by both climate change and land degradation processes
(Evans and Wallenstein 2014; Wu et al. 2015; Classen et al. 2015), thus
changes in microbial community composition can be very useful to
rapidly reflect land degradation (e.g., forest degradation increased the
bacterial alpha-diversity indexes) (Flores-Rentería et al. 2016; Zhou
et al. 2018). These indicators might be used as a set of site-dependent
indicators, and in a plant-soil system (Ehrenfeld et al. 2005).
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Useful indicators of degradation and improvement include changes
in ecological processes and disturbance regimes that regulate the
flow of energy and materials and that control ecosystem dynamics
under a climate change scenario. Proxies of dynamics include spatial
and temporal turnover of species and habitats within ecosystems
(Ghazoul et al. 2015; Bahamondez and Thompson 2016). Indicators
in agricultural lands include crop yield decreases and difficulty in
maintaining yields (Stocking et al. 2001). Indicators of landscape
degradation/improvement in fragmented forest landscapes include
the extent, size and distribution of remaining forest fragments, an
increase in edge habitat, and loss of connectivity and ecological
memory (Zahawi et al. 2015; Pardini et al. 2010).
In summary, as land degradation is such a complex and global
process, there is no single method by which land degradation can
be estimated objectively and consistently over large areas (
very high
confidence
). However, many approaches exist that can be used to
assess different aspects of land degradation or provide proxies of
land degradation. Remote sensing, complemented by other kinds
of data (i.e., field observations, inventories, expert opinions), is the
only method that can generate geographically explicit and globally
consistent data over time scales relevant for land degradation
(several decades).
4.3
Status and current trends
of land degradation
The scientific literature on land degradation often excludes forest
degradation, yet here we attempt to assess both issues. Because of
the different bodies of scientific literature, we assess land degradation
and forest degradation under different sub-headings and, where
possible, draw integrated conclusions.
4.3.1
Land degradation
There are no reliable global maps of the extent and severity of land
degradation (Gibbs and Salmon 2015; Prince et al. 2018; van der Esch
et al. 2017), despite the fact that land degradation is a severe problem
(Turner et al. 2016). The reasons are both conceptual – that is, how
land degradation is defined, using what baseline (Herrick et al. 2019)
or over what time period – and methodological – that is, how it can
be measured (Prince et al. 2018). Although there is a strong consensus
that land degradation is a reduction in productivity of the land or
soil, there are diverging views regarding the spatial and temporal
scales at which land degradation occurs (Warren 2002), and how
this can be quantified and mapped. Proceeding from the definition
in this report, there are also diverging views concerning ecological
integrity and the value to humans. A comprehensive treatment of
the conceptual discussion about land degradation is provided by
the recent report on land degradation from the Intergovernmental
Science-Policy Platform on Biodiversity and Ecosystem Services
(IPBES) (Montanarella et al. 2018).
A review of different attempts to map global land degradation, based
on expert opinion, satellite observations, biophysical models and
a database of abandoned agricultural lands, suggested that between
<10 Mkm
2
to 60 Mkm
2
(corresponding to 8–45% of the ice-free land
area) have been degraded globally (Gibbs and Salmon, 2015) (
very
low confidence
).
One often-used global assessment of land degradation uses trends in
NDVI as a proxy for land degradation and improvement during the
period 1983 to 2006 (Bai et al. 2008b,c) with an update to 2011 (Bai
et al. 2015). These studies, based on very coarse resolution satellite
data (NOAA AVHRR data with a resolution of 8 km), indicated that,
between 22% and 24% of the global ice-free land area was subject
to a downward trend, while about 16% showed an increasing trend.
The study also suggested, contrary to earlier assessments (Middleton
and Thomas 1997), that drylands were not among the most affected
regions. Another study using a similar approach for the period
1981–2006 suggested that about 29% of the global land area is
subject to ‘land degradation hotspots’, that is, areas with acute land
degradation in need of particular attention. These hotspot areas were
distributed over all agro-ecological regions and land cover types. Two
different studies have tried to link land degradation, identified by
NDVI as a proxy, and number of people affected: Le et al. (2016)
estimated that at least 3.2 billion people were affected, while Barbier
and Hochard (2016, 2018) estimated that 1.33 billion people were
affected, of which 95% were living in developing countries.
Yet another study, using a similar approach and type of
remote-sensing data, compared NDVI trends with biomass trends
calculated by a global vegetation model over the period 1982–2010
and found that 17–36% of the land areas showed a negative NDVI
trend, while a positive or neutral trend was predicted in modelled
vegetation (Schut et al. 2015). The World Atlas of Desertification (3rd
edition) includes a global map of land productivity change over the
period 1999 to 2013, which is one useful proxy for land degradation
(Cherlet et al. 2018). Over that period, about 20% of the global
ice-free land area shows signs of declining or unstable productivity,
whereas about 20% shows increasing productivity. The same report
also summarised the productivity trends by land categories and
found that most forest land showed increasing trends in productivity,
while rangelands had more declining trends than increasing trends
(Figure 4.4). These productivity assessments, however, do not
distinguish between trends due to climate change and trends due
to other factors. A recent analysis of ‘greening’ of the world using
MODIS time series of NDVI 2000–2017, shows a striking increase in
the greening over China and India. In China the greening is seen over
forested areas, 42%, and cropland areas, in which 32% is increasing
(Section 4.9.3). In India, the greening is almost entirely associated
with cropland (82%) (Chen et al. 2019).
All these studies of vegetation trends show that there are regionally
differentiated trends of either decreasing or increasing vegetation.
When comparing vegetation trends with trends in climatic variables,
Schut et al. (2015) found very few areas (1–2%) where an increase
in vegetation trend was independent of the climate drivers, and that
study suggested that positive vegetation trends are primarily caused
by climatic factors.
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In an attempt to go beyond the mapping of global vegetation trends
for assessing land degradation, Borelli et al. (2017) used a soil erosion
model (RUSLE) and suggested that soil erosion is mainly caused in
areas of cropland expansion, particularly in Sub-Saharan Africa, South
America and Southeast Asia. The method is controversial for conceptual
reasons (i.e., the ability of the model to capture the most important
erosion processes) and data limitations (i.e., the availability of relevant
data at regional to global scales), and its validity for assessing erosion
over large areas has been questioned by several studies (Baveye 2017;
Evans and Boardman 2016a,b; Labrière et al. 2015).
An alternative to using remote sensing for assessing the state of land
degradation is to compile field-based data from around the globe
(Turner et al. 2016). In addition to the problems of definitions and
baselines, this approach is also hampered by the lack of standardised
methods used in the field. An assessment of the global severity of
soil erosion in agriculture, based on 1673 measurements around the
world (compiled from 201 peer-reviewed articles), indicated that the
global net median rate of soil formation (i.e., formation minus erosion)
is about 0.004 mm yr
–1
(about 0.05 t ha
–1
yr
–1
) compared with the
median net rate of soil loss in agricultural fields, 1.52 mm yr
–1
(about
18 t ha
–1
yr
–1
) in tilled fields and 0.065 mm yr
–1
(about 0.8 t ha
–1
yr
–1
)
in no-till fields (Montgomery 2007a). This means that the rate of
soil erosion from agricultural fields is between 380 (conventional
tilling) and 16 times (no-till) the natural rate of soil formation
(
medium agreement, limited evidence
). These approximate figures
are supported by another large meta-study including over 4000 sites
around the world (see Figure 4.4) where the average soil loss from
agricultural plots was about 21 t ha
–1
yr
–1
(García-Ruiz et al. 2015).
Climate change, mainly through the intensification of rainfall, will
further increase these rates unless land management is improved
(
high agreement
,
medium evidence
).
Soils contain about 1500 Gt of organic carbon (median across
28 different estimates presented by Scharlemann et al. (2014)),
which is about 1.8 times more carbon than in the atmosphere
(Ciais et al. 2013) and 2.3–3.3 times more than what is held in the
terrestrial vegetation of the world (Ciais et al. 2013). Hence, land
degradation, including land conversion leading to soil carbon losses,
has the potential to impact on the atmospheric concentration of CO
2
substantially. When natural ecosystems are cultivated they lose soil
carbon that accumulated over long time periods. The loss rate depends
on the type of natural vegetation and how the soil is managed.
Estimates of the magnitude of loss vary but figures between 20%
and 59% have been reported in several meta studies (Poeplau and
Don 2015; Wei et al. 2015; Li et al. 2012; Murty et al. 2002; Guo and
Gifford 2002). The amount of soil carbon lost explicitly due to land
degradation after conversion is hard to assess due to large variation
in local conditions and management, see also Chapter 2.
From a climate change perspective, land degradation plays an
important role in the dynamics of nitrous oxide (N
2
O) and methane
(CH
4
). N
2
O is produced by microbial activity in the soil and the
dynamics are related to both management practices and weather
Figure 4.4 | Proportional global land productivity trends by land-cover/land-use class.
(Cropland includes arable land, permanent crops and mixed classes with
over 50% crops; grassland includes natural grassland and managed pasture land; rangelands include shrubland, herbaceous and sparsely vegetated areas; forest land includes
all forest categories and mixed classes with tree cover greater than 40%.) Data source: Copernicus Global Land SPOT VGT, 1999–2013, adapted from (Cherlet et al. 2018).
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conditions, while CH
4
dynamics are primarily determined by the
amount of soil carbon and to what extent the soil is subject to
waterlogging (Palm et al. 2014), see also Chapter 2.
Several attempts have been made to map the human footprint
on the planet (Čuček et al. 2012; Venter et al. 2016) but, in some
cases, they confuse human impact on the planet with degradation.
From our definition it is clear that human impact (or pressure) is
not synonymous with degradation, but information on the human
footprint provides a useful mapping of potential non-climatic drivers
of degradation.
In summary, there are no uncontested maps of the location, extent
and severity of land degradation. Proxy estimates based on remote
sensing of vegetation dynamics provide one important information
source, but attribution of the observed changes in productivity to
climate change, human activities, or other drivers is hard. Nevertheless,
the different attempts to map the extent of global land degradation
using remotely sensed proxies show some convergence and suggest
that about a quarter of the ice-free land area is subject to some form
of land degradation (
limited evidence
,
medium agreement
) affecting
about 3.2 billion people (
low confidence
). Attempts to estimate
the severity of land degradation through soil erosion estimates
suggest that soil erosion is a serious form of land degradation in
croplands closely associated with unsustainable land management in
combination with climatic parameters, some of which are subject to
climate change (
limited evidence
,
high agreement
). Climate change
is one among several causal factors in the status and current trends
of land degradation (
limited evidence
,
high agreement
).
4.3.2
Forest degradation
Quantifying degradation in forests has also proven difficult. Remote
sensing based inventory methods can measure reductions in canopy
cover or carbon stocks more easiliy than reductions in biological
productivity, losses of ecological integrity or value to humans.
However, the causes of reductions in canopy cover or carbon stocks
can be many (Curtis et al. 2018), including natural disturbances
(e.g., fires, insects and other forest pests), direct human activities
(e.g., harvest, forest management) and indirect human impacts (such
as climate change) and these may not reduce long-term biological
productivity. In many boreal, some temperate and other forest
types natural disturbances are common, and consequently these
disturbance-adapted forest types are comprised of a mosaic of stands
of different ages and stages of stand recovery following natural
disturbances. In those managed forests where natural disturbances
are uncommon or suppressed, harvesting is the primary determinant
of forest age-class distributions.
Quantifying forest degradation as a reduction in productivity,
carbon stocks or canopy cover also requires that an initial condition
(or baseline) is established, against which this reduction is assessed
(Section 4.1.4). In forest types with rare stand-replacing disturbances,
the concept of ‘intact’ or ‘primary’ forest has been used to define the
initial condition (Potapov et al. 2008) but applying a single metric
can be problematic (Bernier et al. 2017). Moreover, forest types with
frequent stand-replacing disturbances, such as wildfires, or with
natural disturbances that reduce carbon stocks, such as some insect
outbreaks, experience over time a natural variability of carbon stocks
or canopy density, making it more difficult to define the appropriate
baseline carbon density or canopy cover against which to assess
degradation. In these systems, forest degradation cannot be assessed
at the stand level, but requires a landscape-level assessment that takes
into consideration the stand age-class distribution of the landscape,
which reflects natural and human disturbance regimes over past
decades to centuries and also considers post-disturbance regrowth
(van Wagner 1978; Volkova et al. 2018; Lorimer and White 2003).
The lack of a consistent definition of forest degradation also affects
the ability to establish estimates of the rates or impacts of forest
degradation because the drivers of degradation are not clearly
defined (Sasaki and Putz 2009). Moreover, the literature at times
confounds estimates of forest degradation and deforestation
(i.e., the conversion of forest to non-forest land uses). Deforestation
is a change in land use, while forest degradation is not, although
severe forest degradation can ultimately lead to deforestation.
Based on empirical data provided by 46 countries, the drivers
for deforestation (due to commercial agriculture) and forest
degradation (due to timber extraction and logging) are similar
in Africa, Asia and Latin America (Hosonuma et al. 2012). More
recently, global forest disturbance over the period 2001–2015 was
attributed to commodity-driven deforestation (27 ± 5%), forestry
(26 ± 4%), shifting agriculture (24 ± 3%) and wildfire (23 ± 4%).
The remaining 0.6 ± 0.3% was attributed to the expansion of urban
centres (Curtis et al. 2018).
The trends of productivity shown by several remote-sensing studies
(see previous section) are largely consistent with mapping of forest
cover and change using a 34-year time series of coarse resolution
satellite data (NOAA AVHRR) (Song et al. 2018). This study, based
on a thematic classification of satellite data, suggests that (i) global
tree canopy cover increased by 2.24 million km² between 1982 and
2016 (corresponding to +7.1%) but with regional differences that
contribute a net loss in the tropics and a net gain at higher latitudes,
and (ii) the fraction of bare ground decreased by 1.16 million km²
(corresponding to –3.1%), mainly in agricultural regions of Asia (Song
et al. 2018), see Figure 4.5. Other tree or land cover datasets show
opposite global net trends (Li et al. 2018b), but high agreement in
terms of net losses in the tropics and large net gains in the temperate
and boreal zones (Li et al. 2018b; Song et al. 2018; Hansen et al.
2013). Differences across global estimates are further discussed in
Chapter 1 (Section 1.1.2.3) and Chapter 2.
The changes detected from 1982 to 2016 were primarily linked to
direct human action, such as land-use changes (about 60% of the
observed changes), but also to indirect effects, such as human-induced
climate change (about 40% of the observed changes) (Song et al.
2018), a finding also supported by a more recent study (Chen et al.
2019). The climate-induced effects were clearly discernible in some
regions, such as forest decline in the US Northwest due to increasing
pest infestation and increasing fire frequency (Lesk et al. 2017;
Abatzoglou and Williams 2016; Seidl et al. 2017), warming-induced
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vegetation increase in the Arctic region, general greening in the Sahel
probably as a result of increasing rainfall and atmospheric CO
2
, and
advancing treelines in mountain regions (Song et al. 2018).
Keenan et al. (2015) and Sloan and Sayer (2015) studied the 2015
Forest Resources Assessment (FRA) of the Food and Agriculture
Organization of the United Nations (FAO) (FAO 2016) and found
that the total forest area from 1990 to 2015 declined by 3%, an
estimate that is supported by a global remote-sensing assessment of
forest area change that found a 2.8% decline between 1990–2010
(D’Annunzio et al. 2017; Lindquist and D’Annunzio 2016). The
trend in deforestation is, however, contradicted between these two
global assessments, with FAO (2016) suggesting that deforestation
is slowing down, while the remote sensing assessments finds it to
be accelerating (D’Annunzio et al. 2017). Recent estimates (Song
et al. 2018) owing to semantic and methodological differences
(see Chapter 1, Section 1.1.2.3) suggest that global tree cover has
increased over the period 1982–2016, which contradicts the forest
area dynamics assessed by FAO (2016) and Lindquist and D’Annunzio
(2016). The loss rate in tropical forest areas from 2010 to 2015 is
55,000 km
2
yr
–1
. According to the FRA, the global natural forest area
also declined from 39.61 Mkm
2
to 37.21 Mkm
2
during the period
1990 to 2015 (Keenan et al. 2015).
Since 1850, deforestation globally contributed 77% of the emissions
from land-use and land-cover change while degradation contributed
10% (with the remainder originating from non-forest land uses)
(Houghton and Nassikas 2018). That study also showed large
temporal and regional differences with northern mid-latitude forests
currently contributing to carbon sinks due to increasing forest
area and forest management. However, the contribution to carbon
emissions of degradation as percentage of total forest emissions
(degradation and deforestation) are uncertain, with estimates varying
from 25% (Pearson et al. 2017) to nearly 70% of carbon losses
(Baccini et al. 2017). The 25% estimate refers to an analysis of 74
developing countries within tropical and subtropical regions covering
22 million km
2
for the period 2005–2010, while the 70% estimate
refers to an analysis of the tropics for the period 2003–2014, but, by
and large, the scope of these studies is the same. Pearson et al. (2017)
estimated annual gross emissions of 2.1 GtCO
2
, of which 53% were
derived from timber harvest, 30% from woodfuel harvest and 17%
from forest fire. Estimating gross emissions only, creates a distorted
representation of human impacts on the land sector carbon cycle.
While forest harvest for timber and fuelwood and land-use change
(deforestation) contribute to gross emissions, to quantify impacts on
the atmosphere, it is necessary to estimate net emissions, that is, the
balance of gross emissions and gross removals of carbon from the
atmosphere through forest regrowth (Chazdon et al. 2016a; Poorter
et al. 2016; Sanquetta et al. 2018).
Current efforts to reduce atmospheric CO
2
concentrations can be
supported by reductions in forest-related carbon emissions and
increases in sinks, which requires that the net impact of forest
management on the atmosphere be evaluated (Griscom et al. 2017).
Forest management and the use of wood products in GHG mitigation
strategies result in changes in forest ecosystem carbon stocks,
changes in harvested wood product carbon stocks, and potential
changes in emissions resulting from the use of wood products and
forest biomass that substitute for other emissions-intensive materials
such as concrete, steel and fossil fuels (Kurz et al. 2016; Lemprière
et al. 2013; Nabuurs et al. 2007). The net impact of these changes on
GHG emissions and removals, relative to a scenario without forest
mitigation actions, needs to be quantified, (e.g., Werner et al. 2010;
Smyth et al. 2014; Xu et al. 2018). Therefore, reductions in forest
ecosystem carbon stocks alone are an incomplete estimator of the
impacts of forest management on the atmosphere (Nabuurs et al.
2007; Lemprière et al. 2013; Kurz et al. 2016; Chen et al. 2018b). The
impacts of forest management and the carbon storage in long-lived
products and landfills vary greatly by region, however, because of
the typically much shorter lifespan of wood products produced from
tropical regions compared to temperate and boreal regions (Earles
et al. 2012; Lewis et al. 2019; Iordan et al. 2018) (Section 4.8.4).
Assessments of forest degradation based on remote sensing of
changes in canopy density or land cover, (e.g., Hansen et al. 2013;
Pearson et al. 2017) quantify changes in above-ground biomass
carbon stocks and require additional assumptions or model-based
analyses to also quantify the impacts on other ecosystem carbon pools
including below-ground biomass, litter, woody debris and soil carbon.
Depending on the type of disturbance, changes in above-ground
biomass may lead to decreases or increases in other carbon pools,
for example, windthrow and insect-induced tree mortality may
result in losses in above-ground biomass that are (initially) offset
by corresponding increases in dead organic matter carbon pools
(Yamanoi et al. 2015; Kurz et al. 2008), while deforestation will
reduce the total ecosystem carbon pool (Houghton et al. 2012).
A global study of current vegetation carbon stocks (450 Gt C), relative
to a hypothetical condition without land use (916 Gt C), attributed
42–47% of carbon stock reductions to land management effects
without land-use change, while the remaining 53–58% of carbon
stock reductions were attributed to deforestation and other land-use
changes (Erb et al. 2018). While carbon stocks in European forests are
lower than hypothetical values in the complete absence of human
land use, forest area and carbon stocks have been increasing over
recent decades (McGrath et al. 2015; Kauppi et al. 2018). Studies by
Gingrich et al. (2015) on the long-term trends in land use over nine
European countries (Albania, Austria, Denmark, Germany, Italy, the
Netherlands, Romania, Sweden and the United Kingdom) also show
an increase in forest land and reduction in cropland and grazing land
from the 19th century to the early 20th century. However, the extent
to which human activities have affected the productive capacity of
forest lands is poorly understood. Biomass Production Efficiency
(BPE), i.e. the fraction of photosynthetic production used for biomass
production, was significantly higher in managed forests (0.53)
compared to natural forests (0.41) (and it was also higher in managed
(0.63) compared to natural (0.44) grasslands) (Campioli et al. 2015).
Managing lands for production may involve trade-offs. For example,
a larger proportion of NPP in managed forests is allocated to biomass
carbon storage, but lower allocation to fine roots is hypothesised to
reduce soil carbon stocks in the long term (Noormets et al. 2015).
Annual volume increment in Finnish forests has more than doubled
over the last century, due to increased growing stock, improved forest
management and environmental changes (Henttonen et al. 2017).
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As economies evolve, the patterns of land-use and carbon stock
changes associated with human expansion into forested areas often
include a period of rapid decline of forest area and carbon stocks,
recognition of the need for forest conservation and rehabilitation,
and a transition to more sustainable land management that is
often associated with increasing carbon stocks, (e.g., Birdsey et al.
2006). Developed and developing countries around the world are
in various stages of forest transition (Kauppi et al. 2018; Meyfroidt
and Lambin 2011). Thus, opportunities exist for SFM to contribute to
atmospheric carbon targets through reduction of deforestation and
degradation, forest conservation, forest restoration, intensification
of management, and enhancements of carbon stocks in forests and
harvested wood products (Griscom et al. 2017) (
medium evidence
,
medium agreement
).
4.4
Projections of land degradation
in a changing climate
Land degradation will be affected by climate change in both direct
and indirect ways, and land degradation will, to some extent, also feed
back into the climate system. The direct impacts are those in which
climate and land interact directly in time and space. Examples of
direct impacts are when increasing rainfall intensity exacerbates soil
erosion, or when prolonged droughts reduce the vegetation cover of
the soil, making it more prone to erosion and nutrient depletion. The
indirect impacts are those where climate change impacts and land
degradation are separated in time and/or space. Examples of such
impacts are when declining agricultural productivity due to climate
change drives an intensification of agriculture elsewhere, which may
cause land degradation. Land degradation, if sufficiently widespread,
may also feed back into the climate system by reinforcing ongoing
climate change.
Although climate change is exacerbating many land degradation
processes (
high to very high confidence
), prediction of future land
degradation is challenging because land management practices
determine, to a very large extent, the state of the land. Scenarios of
climate change in combination with land degradation models can
provide useful knowledge on what kind and extent of land management
will be necessary to avoid, reduce and reverse land degradation.
4.4.1
Direct impacts on land degradation
There are two main levels of uncertainty in assessing the risks of
future climate-change-induced land degradation. The first level,
where uncertainties are comparatively low, involves changes of the
degrading agent, such as erosive power of precipitation, heat stress
from increasing temperature extremes (Hüve et al. 2011), water
stress from droughts, and high surface wind speed. The second level
of uncertainties, and where the uncertainties are much larger, relates
to the above – and below-ground ecological changes as a result of
changes in climate, such as rainfall, temperature, and increasing level
of CO
2
. Vegetation cover is crucial to protect against erosion (Mullan
et al. 2012; García-Ruiz et al. 2015).
Changes in rainfall patterns, such as distribution in time and space,
and intensification of rainfall events will increase the risk of land
degradation, both in terms of likelihood and consequences (
high
agreement
,
medium evidence
). Climate-induced vegetation changes
will increase the risk of land degradation in some areas (where
90º N
60º N
30º N
0º
30º S
60º S
90º S
Latitud
e
Δ
TC area (10
3
km
2
)
Δ
SV area (10
3
km
2
)
Δ
BG area (10
3
km
2
)
–100 –50
0
50
100 –100 –50
0
50
100 –100 –50
0
50
100
Loss
Gain
Net
a
b
c
Figure 4.5 | Diagrams showing latitudinal profiles of land cover change over the period 1982 to 2016 based on analysis of time-series of NOAA AVHRR
imagery:
a)
tree canopy cover change (ΔTC);
b)
short vegetation cover change (ΔSV);
c)
bare ground cover change (ΔBG). Area statistics were calculated for every 1° of
latitude (Song et al. 2018). Source of data: NOAA AVHRR.
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4
vegetation cover will decline) (
medium confidence
). Landslides are
a form of land degradation, induced by extreme rainfall events. There
is a strong theoretical reason for increasing landslide activity due
to intensification of rainfall, but so far, the empirical evidence that
climate change has contributed to landslides is lacking (Crozier 2010;
Huggel et al. 2012; Gariano and Guzzetti 2016). Human disturbance
may be a more important future trigger than climate change (Froude
and Petley 2018).
Erosion of coastal areas as a result of sea level rise will increase
worldwide (
very high confidence
). In cyclone-prone areas (such
as the Caribbean, Southeast Asia, and the Bay of Bengal) the
combination of sea level rise and more intense cyclones (Walsh
et al. 2016b) and, in some areas, land subsidence (Yang et al. 2019;
Shirzaei and Bürgmann 2018; Wang et al. 2018; Fuangswasdi et al.
2019; Keogh and Törnqvist 2019), will pose a serious risk to people
and livelihoods (
very high confidence
), in some cases even exceeding
limits to adaption (Sections 4.8.4.1, 4.9.6 and 4.9.8).
4.4.1.1 Changes in water erosion risk
due to precipitation changes
The hydrological cycle is intensifying with increasing warming of
the atmosphere. The intensification means that the number of heavy
rainfall events is increasing, while the total number of rainfall events
tends to decrease (Trenberth 2011; Li and Fang 2016; Kendon et al.
2014; Guerreiro et al. 2018; Burt et al. 2016a; Westra et al. 2014;
Pendergrass and Knutti 2018) (
robust evidence
,
high agreement
).
Modelling of the changes in land degradation that are a result of
climate change alone is hard because of the importance of local
contextual factors. As shown above, actual erosion rate is extremely
dependent on local conditions, primarily vegetation cover and
topography (García-Ruiz et al. 2015). Nevertheless, modelling of soil
erosion risks has advanced substantially in recent decades, and such
studies are indicative of future changes in the risk of soil erosion,
while actual erosion rates will still primarily be determined by land
management. In a review article, Li and Fang (2016) summarised 205
representative modelling studies around the world where erosion
models were used in combination with downscaled climate models to
assess future (between 2030 to 2100) erosion rates. The meta-study
by Li and Fang, where possible, considered climate change in terms of
temperature increase and changing rainfall regimes and their impacts
on vegetation and soils. Almost all of the sites had current soil loss
rates above 1 t ha
–1
(assumed to be the upper limit for acceptable
soil erosion in Europe) and 136 out of 205 studies predicted increased
soil erosion rates. The percentage increase in erosion rates varied
between 1.2% to as much as over 1600%, whereas 49 out of 205
studies projected more than 50% increase. Projected soil erosion
rates varied substantially between studies because the important of
local factors, hence climate change impacts on soil erosion, should
preferably be assessed at the local to regional scale, rather than the
global (Li and Fang 2016).
Mesoscale convective systems (MCS), typically thunder storms, have
increased markedly in the last three to four decades in the USA and
Australia and they are projected to increase substantially (Prein et al.
2017). Using a climate model with the ability to represent MCS, Prein
and colleagues were able to predict future increases in frequency,
intensity and size of such weather systems. Findings include the 30%
decrease in number of MCS of <40 mm h
–1
, but a sharp increase of
380% in the number of extreme precipitation events of >90 mm h
–1
over the North American continent. The combined effect of increasing
precipitation intensity and increasing size of the weather systems
implies that the total amount of precipitation from these weather
systems is expected to increase by up to 80% (Prein et al. 2017),
which will substantially increase the risk of land degradation in terms
of landslides, extreme erosion events, flashfloods, and so on.
The potential impacts of climate change on soil erosion can be
assessed by modelling the projected changes in particular variables of
climate change known to cause erosion, such as erosivity of rainfall.
A study of the conterminous United States based on three climate
models and three scenarios (A2, A1B, and B1) found that rainfall
erosivity will increase in all scenarios, even if there are large spatial
differences – a strong increase in the north-east and north-west, and
either weak or inconsistent trends in the south-west and mid-west
(Segura et al. 2014).
In a study of how climate change will impact on future soil erosion
processes in the Himalayas, Gupta and Kumar (2017) estimated that
soil erosion will increase by about 27% in the near term (2020s)
and 22% in the medium term (2080s), with little difference between
scenarios. A study from Northern Thailand estimated that erosivity
will increase by 5% in the near term (2020s) and 14% in the medium
term (2080s), which would result in a similar increase of soil erosion,
all other factors being constant (Plangoen and Babel 2014). Observed
rainfall erosivity has increased significantly in the lower Niger Basin
(Nigeria) and is predicted to increase further based on statistical
downscaling of four General Circulation Models (GCM) scenarios,
with an estimated increase of 14%, 19% and 24% for the 2030s,
2050s, and 2070s respectively (Amanambu et al. 2019).
Many studies from around the world where statistical downscaling
of GCM results have been used in combination with process-based
erosion models show a consistent trend of increasing soil erosion.
Using a comparative approach, Serpa et al. (2015) studied two
Mediterranean catchments (one dry and one humid) using a spatially
explicit hydrological model – soil and water assessment tool
(SWAT) – in combination with land-use and climate scenarios for
2071–2100. Climate change projections showed, on the one hand,
decreased rainfall and streamflow for both catchments, whereas
sediment export decreased only for the humid catchment; projected
land-use change, from traditional to more profitable, on the other
hand, resulted in increase in streamflow. The combined effect of
climate and land-use change resulted in reduced sediment export for
the humid catchment (–29% for A1B; –22% for B1) and increased
sediment export for the dry catchment (+222% for A1B; +5% for B1).
Similar methods have been used elsewhere, also showing the
dominant effect of land-use/land cover for runoff and soil erosion
(Neupane and Kumar 2015).
A study of future erosion rates in Northern Ireland, using a spatially
explicit erosion model in combination with downscaled climate
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projections (with and without sub-daily rainfall intensity changes),
showed that erosion rates without land management changes would
decrease by the 2020s, 2050s and 2100s, irrespective of changes in
intensity, mainly as a result of a general decline in rainfall (Mullan
et al. 2012). When land management scenarios were added to the
modelling, the erosion rates started to vary dramatically for all three
time periods, ranging from a decrease of 100% for no-till land use,
to an increase of 3621% for row crops under annual tillage and
sub-days intensity changes (Mullan et al. 2012). Again, it shows
how crucial land management is for addressing soil erosion, and the
important role of rainfall intensity changes.
There is a large body of literature based on modelling future land
degradation due to soil erosion concluding that, in spite of the
increasing trend of erosive power of rainfall, (
medium evidence
,
high agreement
) land degradation is primarily determined by land
management (
very high confidence
).
4.4.1.2 Climate-induced vegetation changes,
implications for land degradation
The spatial mosaic of vegetation is determined by three factors: the
ability of species to reach a particular location, how species tolerate
the environmental conditions at that location (e.g., temperature,
precipitation, wind, the topographic and soil conditions), and
the interaction between species (including above/below ground
species (Settele et al. 2015). Climate change is projected to alter the
conditions and hence impact on the spatial mosaic of vegetation,
which can be considered a form of land degradation. Warren
et al. (2018) estimated that only about 33% of globally important
biodiversity conservation areas will remain intact if global mean
temperature increases to 4.5°C, while twice that area (67%) will
remain intact if warming is restricted to 2°C. According to AR5, the
clearest link between climate change and ecosystem change is when
temperature is the primary driver, with changes of Arctic tundra as
a response to significant warming as the best example (Settele et al.
2015). Even though distinguishing climate-induced changes from
land-use changes is challenging, Boit et al. (2016) suggest that 5–6%
of biomes in South America will undergo biome shifts until 2100,
regardless of scenario, attributed to climate change. The projected
biome shifts are primarily forests shifting to shrubland and dry forests
becoming fragmented and isolated from more humid forests (Boit
et al. 2016). Boreal forests are subject to unprecedented warming in
terms of speed and amplitude (IPCC 2013b), with significant impacts
on their regional distribution (Juday et al. 2015). Globally, tree lines
are generally expanding northward and to higher elevations, or
remaining stable, while a reduction in tree lines was rarely observed,
and only where disturbances occurred (Harsch et al. 2009). There is
limited evidence
of a slow northward migration of the boreal forest in
eastern North America (Gamache and Payette 2005). The thawing of
permafrost may increase drought-induced tree mortality throughout
the circumboreal zone (Gauthier et al. 2015).
Forests are a prime regulator of hydrological cycling, both fluxes of
atmospheric moisture and precipitation, hence climate and forests
are inextricably linked (Ellison et al. 2017; Keys et al. 2017). Forest
management influences the storage and flow of water in forested
watersheds. In particular, harvesting, forest thinning and the
construction of roads increase the likelihood of floods as an outcome
of extreme climate events (Eisenbies et al. 2007). Water balance of
at least partly forested landscapes is, to a large extent, controlled by
forest ecosystems (Sheil and Murdiyarso 2009; Pokam et al. 2014).
This includes surface runoff, as determined by evaporation and
transpiration and soil conditions, and water flow routing (Eisenbies
et al. 2007). Water-use efficiency (i.e., the ratio of water loss to
biomass gain) is increasing with increased CO
2
levels (Keenan et al.
2013), hence transpiration is predicted to decrease which, in turn, will
increase surface runoff (Schlesinger and Jasechko 2014). However,
the interaction of several processes makes predictions challenging
(Frank et al. 2015; Trahan and Schubert 2016). Surface runoff is an
important agent in soil erosion.
Generally, removal of trees through harvesting or forest death
(Anderegg et al. 2012) will reduce transpiration and hence increase
the runoff during the growing season. Management-induced soil
disturbance (such as skid trails and roads) will affect water flow
routing to rivers and streams (Zhang et al. 2017; Luo et al. 2018;
Eisenbies et al. 2007).
Climate change affects forests in both positive and negative ways
(Trumbore et al. 2015; Price et al. 2013) and there will be regional and
temporal differences in vegetation responses (Hember et al. 2017;
Midgley and Bond 2015). Several climate-change-related drivers
interact in complex ways, such as warming, changes in precipitation
and water balance, CO
2
fertilisation, and nutrient cycling, which
makes projections of future net impacts challenging (Kurz et al. 2013;
Price et al. 2013) (Section 2.3.1.2). In high latitudes, a warmer climate
will extend the growing seasons. However, this could be constrained
by summer drought (Holmberg et al. 2019), while increasing levels
of atmospheric CO
2
will increase water-use efficiency but not
necessarily tree growth (Giguère-Croteau et al. 2019). Improving one
growth-limiting factor will only enhance tree growth if other factors
are not limiting (Norby et al. 2010; Trahan and Schubert 2016; Xie
et al. 2016; Frank et al. 2015). Increasing forest productivity has been
observed in most of Fennoscandia (Kauppi et al. 2014; Henttonen
et al. 2017), Siberia and the northern reaches of North America as
a response to a warming trend (Gauthier et al. 2015) but increased
warming may also decrease forest productivity and increase risk of
tree mortality and natural disturbances (Price et al. 2013; Girardin
et al. 2016; Beck et al. 2011; Hember et al. 2016; Allen et al. 2011).
The climatic conditions in high latitudes are changing at a magnitude
faster than the ability of forests to adapt with detrimental, yet
unpredictable, consequences (Gauthier et al. 2015).
Negative impacts dominate, however, and have already been
documented (Lewis et al. 2004; Bonan et al. 2008; Beck et al. 2011)
and are predicted to increase (Miles et al. 2004; Allen et al. 2010;
Gauthier et al. 2015; Girardin et al. 2016; Trumbore et al. 2015).
Several authors have emphasised a concern that tree mortality
(forest dieback) will increase due to climate-induced physiological
stress as well as interactions between physiological stress and other
stressors, such as insect pests, diseases, and wildfires (Anderegg et al.
2012; Sturrock et al. 2011; Bentz et al. 2010; McDowell et al. 2011).
Extreme events such as extreme heat and drought, storms, and floods
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also pose increased threats to forests in both high – and low-latitude
forests (Lindner et al. 2010; Mokria et al. 2015). However, comparing
observed forest dieback with modelled climate-induced damages did
not show a general link between climate change and forest dieback
(Steinkamp and Hickler 2015). Forests are subject to increasing
frequency and intensity of wildfires which is projected to increase
substantially with continued climate change (Price et al. 2013)
(Cross-Chapter Box 3 in Chapter 2, and Chapter 2). In the tropics,
interaction between climate change, CO
2
and fire could lead to
abrupt shifts between woodland – and grassland-dominated states
in the future (Shanahan et al. 2016).
Within the tropics, much research has been devoted to understanding
how climate change may alter regional suitability of various crops.
For example, coffee is expected to be highly sensitive to both
temperature and precipitation changes, both in terms of growth and
yield, and in terms of increasing problems of pests (Ovalle-Rivera
et al. 2015). Some studies conclude that the global area of coffee
production will decrease by 50% (Bunn et al. 2015). Due to increased
heat stress, the suitability of Arabica coffee is expected to deteriorate
in Mesoamerica, while it can improve in high-altitude areas in
South America. The general pattern is that the climatic suitability for
Arabica coffee will deteriorate at low altitudes of the tropics as well
as at the higher latitudes (Ovalle-Rivera et al. 2015). This means that
climate change in and of itself can render unsustainable previously
sustainable land-use and land management practices, and vice versa
(Laderach et al. 2011).
Rangelands are projected to change in complex ways due to climate
change. Increasing levels of atmospheric CO
2
directly stimulate plant
growth and can potentially compensate for negative effects from
drying by increasing rain-use efficiency. But the positive effect of
increasing CO
2
will be mediated by other environmental conditions,
primarily water availability, but also nutrient cycling, fire regimes
and invasive species. Studies over the North American rangelands
suggest, for example, that warmer and dryer climatic conditions will
reduce NPP in the southern Great Plains, the Southwest, and northern
Mexico, but warmer and wetter conditions will increase NPP in the
northern Plains and southern Canada (Polley et al. 2013).
4.4.1.3 Coastal erosion
Coastal erosion is expected to increase dramatically by sea level
rise and, in some areas, in combination with increasing intensity of
cyclones (highlighted in Section 4.9.6) and cyclone-induced coastal
erosion. Coastal regions are also characterised by high population
density, particularly in Asia (Bangladesh, China, India, Indonesia,
Vietnam), whereas the highest population increase in coastal regions
is projected in Africa (East Africa, Egypt, and West Africa) (Neumann
et al. 2015). For coastal regions worldwide, and particularly in
developing countries with high population density in low-lying
coastal areas, limiting the warming to 1.5°C to 2.0°C will have major
socio-economic benefits compared with higher temperature scenarios
(IPCC 2018a; Nicholls et al. 2018). For more in-depth discussions on
coastal process, please refer to Chapter 4 of the IPCC Special Report
on the Ocean and Cryosphere in a Changing Climate (IPCC SROCC).
Despite the uncertainty related to the responses of the large ice
sheets of Greenland and west Antarctica, climate-change-induced
sea level rise is largely accepted and represents one of the biggest
threats faced by coastal communities and ecosystems (Nicholls et al.
2011; Cazenave and Cozannet 2014; DeConto and Pollard 2016;
Mengel et al. 2016). With significant socio-economic effects, the
physical impacts of projected sea level rise, notably coastal erosion,
have received considerable scientific attention (Nicholls et al. 2011;
Rahmstorf 2010; Hauer et al. 2016).
Rates of coastal erosion or recession will increase due to rising sea
levels and, in some regions, also in combination with increasing
oceans waves (Day and Hodges 2018; Thomson and Rogers 2014;
McInnes et al. 2011; Mori et al. 2010), lack or absence of sea-ice
(Savard et al. 2009; Thomson and Rogers 2014) thawing of
permafrost (Hoegh-Guldberg et al. 2018), and changing cyclone
paths (Tamarin-Brodsky and Kaspi 2017; Lin and Emanuel 2016a).
The respective role of the different climate factors in the coastal
erosion process will vary spatially. Some studies have shown that
the role of sea level rise on the coastal erosion process can be less
important than other climate factors, like wave heights, changes in the
frequency of the storms, and the cryogenic processes (Ruggiero 2013;
Savard et al. 2009). Therefore, in order to have a complete picture
of the potential effects of sea level rise on rates of coastal erosion,
it is crucial to consider the combined effects of the aforementioned
climate controls and the geomorphology of the coast under study.
Coastal wetlands around the world are sensitive to sea level rise.
Projections of the impacts on global coastlines are inconclusive, with
some projections suggesting that 20% to 90% (depending on sea
level rise scenario) of present day wetlands will disappear during
the 21st century (Spencer et al. 2016). Another study, which included
natural feedback processes and management responses, suggested
that coastal wetlands may actually increase (Schuerch et al. 2018).
Low-lying coastal areas in the tropics are particularly subject to the
combined effect of sea level rise and increasing intensity of tropical
cyclones, conditions that, in many cases, pose limits to adaptation
(Section 4.8.5.1).
Many large coastal deltas are subject to the additional stress of
shrinking deltas as a consequence of the combined effect of reduced
sediment loads from rivers due to damming and water use, and land
subsidence resulting from extraction of ground water or natural gas,
and aquaculture (Higgins et al. 2013; Tessler et al. 2016; Minderhoud
et al. 2017; Tessler et al. 2015; Brown and Nicholls 2015; Szabo et al.
2016; Yang et al. 2019; Shirzaei and Bürgmann 2018; Wang et al.
2018; Fuangswasdi et al. 2019). In some cases the rate of subsidence
can outpace the rate of sea level rise by one order of magnitude
(Minderhoud et al. 2017) or even two (Higgins et al. 2013). Recent
findings from the Mississippi Delta raise the risk of a systematic
underestimation of the rate of land subsidence in coastal deltas
(Keogh and Törnqvist 2019).
In sum, from a land degradation point of view, low-lying coastal
areas are particularly exposed to the nexus of climate change
and increasing concentration of people (Elliott et al. 2014) (
robust
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evidence
,
high agreement
) and the situation will become particularly
acute in delta areas shrinking from both reduced sediment loads and
land subsidence (
robust evidence
,
high agreement
).
4.4.2
Indirect impacts on land degradation
Indirect impacts of climate change on land degradation are difficult to
quantify because of the many conflating factors. The causes of land-use
change are complex, combining physical, biological and socio-economic
drivers (Lambin et al. 2001; Lambin and Meyfroidt 2011). One such
driver of land-use change is the degradation of agricultural land,
which can result in a negative cycle of natural land being converted
to agricultural land to sustain production levels. The intensive
management of agricultural land can lead to a loss of soil function,
negatively impacting on the many ecosystem services provided by soils,
including maintenance of water quality and soil carbon sequestration
(Smith et al. 2016a). The degradation of soil quality due to cropping
is of particular concern in tropical regions, where it results in a loss
of productive potential of the land, affecting regional food security
and driving conversion of non-agricultural land, such as forestry, to
agriculture (Lambin et al. 2003; Drescher et al. 2016; Van der Laan et al.
2017). Climate change will exacerbate these negative cycles unless
sustainable land management practices are implemented.
Climate change impacts on agricultural productivity (see Chapter 5)
will have implications for the intensity of land use and hence
exacerbate the risk of increasing land degradation. There will be both
localised effects (i.e., climate change impacts on productivity affecting
land use in the same region) and teleconnections (i.e., climate change
impacts and land-use changes that are spatially and temporally
separate) (Wicke et al. 2012; Pielke et al. 2007). If global temperature
increases beyond 3°C it will have negative yield impacts on all crops
(Porter et al. 2014) which, in combination with a doubling of demands
by 2050 (Tilman et al. 2011), and increasing competition for land
from the expansion of negative emissions technologies (IPCC 2018a;
Schleussner et al. 2016), will exert strong pressure on agricultural
lands and food security.
In sum, reduced productivity of most agricultural crops will drive
land-use changes worldwide (
robust evidence
,
medium agreement
),
but predicting how this will impact on land degradation is
challenging because of several conflating factors. Social change,
such as widespread changes in dietary preferences, will have a huge
impact on agriculture and hence land degradation (
medium evidence
,
high agreement
)
.
4.5
Impacts of bioenergy and
technologies for CO
2
removal (CDR)
on land degradation
4.5.1
Potential scale of bioenergy and land-based CDR
In addition to the traditional land-use drivers (e.g., population
growth, agricultural expansion, forest management), a new driver
will interact to increase competition for land throughout this century:
the potential large-scale implementation of land-based technologies
for CO
2
removal (CDR). Land-based CDR includes afforestation and
reforestation, bioenergy with carbon capture and storage (BECCS),
soil carbon management, biochar and enhanced weathering (Smith
et al. 2015; Smith 2016).
Most scenarios, including two of the four pathways in the IPCC
Special Report on 1.5°C (IPCC 2018a), compatible with stabilisation
at 2°C involve substantial areas devoted to land-based CDR,
specifically afforestation/reforestation and BECCS (Schleussner
et al. 2016; Smith et al. 2016b; Mander et al. 2017). Even larger
land areas are required in most scenarios aimed at keeping average
global temperature increases to below 1.5°C, and scenarios that
avoid BECCS also require large areas of energy crops in many cases
(IPCC 2018b), although some options with strict demand-side
management avoid this need (Grubler et al. 2018). Consequently, the
addition of carbon capture and storage (CCS) systems to bioenergy
facilities enhances mitigation benefits because it increases the
carbon retention time and reduces emissions relative to bioenergy
facilities without CCS. The IPCC SR15 states that, ‘When considering
pathways limiting warming to 1.5°C with no or limited overshoot, the
full set of scenarios shows a conversion of 0.5–11 Mkm
2
of pasture
into 0–6 Mkm
2
for energy crops, a 2 Mkm
2
reduction to 9.5 Mkm
2
increase [in] forest, and a 4 Mkm
2
decrease to a 2.5 Mkm
2
increase
in non-pasture agricultural land for food and feed crops by 2050
relative to 2010.’ (Rogelj et al. 2018, p. 145). For comparison, the
global cropland area in 2010 was 15.9 Mkm
2
(Table 1.1), and Woods
et al. (2015) estimate that the area of abandoned and degraded land
potentially available for energy crops (or afforestation/reforestation)
exceeds 5 Mkm
2
. However, the area of available land has long been
debated, as much marginal land is subject to customary land tenure
and used informally, often by impoverished communities (Baka 2013,
2014; Haberl et al. 2013; Young 1999). Thus, as noted in SR15, ‘The
implementation of land-based mitigation options would require
overcoming socio-economic, institutional, technological, financing
and environmental barriers that differ across regions.’ (IPCC,
2018a, p. 18).
The wide range of estimates reflects the large differences among the
pathways, availability of land in various productivity classes, types of
negative emission technology implemented, uncertainties in computer
models, and social and economic barriers to implementation (Fuss et
al. 2018; Nemet et al. 2018; Minx et al. 2018).
4.5.2
Risks of land degradation from expansion
of bioenergy and land-based CDR
The large-scale implementation of high-intensity dedicated energy
crops, and harvest of crop and forest residues for bioenergy, could
contribute to increases in the area of degraded lands: intensive land
management can result in nutrient depletion, over-fertilisation and
soil acidification, salinisation (from irrigation without adequate
drainage), wet ecosystems drying (from increased evapotranspiration),
as well as novel erosion and compaction processes (from high-impact
biomass harvesting disturbances) and other land degradation
processes described in Section 4.2.1.
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Global integrated assessment models used in the analysis of
mitigation pathways vary in their approaches to modelling CDR
(Bauer et al. 2018) and the outputs have large uncertainties due to
their limited capability to consider site-specific details (Krause et al.
2018). Spatial resolutions vary from 11 world regions to 0.25 degrees
gridcells (Bauer et al. 2018). While model projections identify potential
areas for CDR implementation (Heck et al. 2018), the interaction
with climate-change-induced biome shifts, available land and its
vulnerability to degradation are unknown. The crop/forest types and
management practices that will be implemented are also unknown,
and will be influenced by local incentives and regulations. While
it is therefore currently not possible to project the area at risk of
degradation from the implementation of land-based CDR, there is
a clear risk that expansion of energy crops at the scale anticipated could
put significant strain on land systems, biosphere integrity, freshwater
supply and biogeochemical flows (Heck et al. 2018). Similarly,
extraction of biomass for energy from existing forests, particularly
where stumps are utilised, can impact on soil health (de Jong et al.
2017). Reforestation and afforestation present a lower risk of land
degradation and may in fact reverse degradation (Section 4.5.3)
although potential adverse hydrological and biodiversity impacts will
need to be managed (Caldwell et al. 2018; Brinkman et al. 2017). Soil
carbon management can deliver negative emissions while reducing
or reversing land degradation. Chapter 6 discusses the significance of
context and management in determining environmental impacts of
implementation of land-based options.
4.5.3
Potential contributions of land-based CDR
to reducing and reversing land degradation
Although large-scale implementation of land-based CDR has
significant potential risks, the need for negative emissions and
the anticipated investments to implement such technologies can
also create significant opportunities. Investments into land-based
CDR can contribute to halting and reversing land degradation, to
the restoration or rehabilitation of degraded and marginal lands
(Chazdon and Uriarte 2016; Fritsche et al. 2017) and can contribute
to the goals of LDN (Orr et al. 2017).
Estimates of the global area of degraded land range from less than
10 to 60 Mkm
2
(Gibbs and Salmon 2015) (Section 4.3.1). Additionally,
large areas are classified as marginal lands and may be suitable for
the implementation of bioenergy and land-based CDR (Woods et al.
2015). The yield per hectare of marginal and degraded lands is lower
than on fertile lands, and if CDR will be implemented on marginal
and degraded lands, this will increase the area demand and costs per
unit area of achieving negative emissions (Fritsche et al. 2017). The
selection of lands suitable for CDR must be considered carefully to
reduce conflicts with existing users, to assess the possible trade-offs
in biodiversity contributions of the original and the CDR land uses, to
quantify the impacts on water budgets, and to ensure sustainability
of the CDR land use.
Land use and land condition prior to the implementation of CDR
affect climate change benefits (Harper et al. 2018). Afforestation/
reforestation on degraded lands can increase carbon stocks in
vegetation and soil, increase carbon sinks (Amichev et al. 2012),
and deliver co-benefits for biodiversity and ecosystem services,
particularly if a diversity of local species are used. Afforestation and
reforestation on native grasslands can reduce soil carbon stocks,
although the loss is typically more than compensated by increases in
biomass and dead organic matter carbon stocks (Bárcena et al. 2014;
Li et al. 2012; Ovalle-Rivera et al. 2015; Shi et al. 2013), and may
impact on biodiversity (Li et al. 2012).
Strategic incorporation of energy crops into agricultural production
systems, applying an integrated landscape management approach,
can provide co-benefits for management of land degradation and
other environmental objectives. For example, buffers of Miscanthus
and other grasses can enhance soil carbon and reduce water
pollution (Cacho et al. 2018; Odgaard et al. 2019), and strip-planting
of short-rotation tree crops can reduce the water table where crops
are affected by dryland salinity (Robinson et al. 2006). Shifting to
perennial grain crops has the potential to combine food production
with carbon sequestration at a higher rate than annual grain crops
and avoid the trade-off between food production and climate change
mitigation (Crews et al. 2018; de Olivera et al. 2018; Ryan et al. 2018)
(Section 4.9.2).
Changes in land cover can affect surface reflectance, water balances
and emissions of volatile organic compounds and thus the non-GHG
impacts on the climate system from afforestation/reforestation or
planting energy crops (Anderson et al. 2011; Bala et al. 2007; Betts
2000; Betts et al. 2007) (see Section 4.6 for further details). Some of
these impacts reinforce the GHG mitigation benefits, while others offset
the benefits, with strong local (slope, aspect) and regional (boreal vs.
tropical biomes) differences in the outcomes (Li et al. 2015). Adverse
effects on albedo from afforestation with evergreen conifers in boreal
zones can be reduced through planting of broadleaf deciduous species
(Astrup et al. 2018; Cai et al. 2011a; Anderson et al. 2011).
Combining CDR technologies may prove synergistic. Two soil
management techniques with an explicit focus on increasing the
soil carbon content rather than promoting soil conservation more
broadly have been suggested: addition of biochar to agricultural
soils (Section 4.9.5) and addition of ground silicate minerals to soils
in order to take up atmospheric CO
2
through chemical weathering
(Taylor et al. 2017; Haque et al. 2019; Beerling 2017; Strefler et al.
2018). The addition of biochar is comparatively well understood and
also field tested at large scale, see Section 4.9.5 for a comprehensive
discussion. The addition of silicate minerals to soils is still highly
uncertain in terms of its potential (from 95 GtCO
2
yr
–1
(Strefler et al.
2018) to only 2–4 GtCO
2
yr
–1
(Fuss et al. 2018)) and costs (Schlesinger
and Amundson 2018).
Effectively addressing land degradation through implementation
of bioenergy and land-based CDR will require site-specific local
knowledge, matching of species with the local land, water balance,
nutrient and climatic conditions, ongoing monitoring and, where
necessary, adaptation of land management to ensure sustainability
under global change (Fritsche et al. 2017). Effective land governance
mechanisms including integrated land-use planning, along with
strong sustainability standards could support deployment of
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4
energy crops and afforestation/reforestation at appropriate scales
and geographical contexts (Fritsche et al. 2017). Capacity-building
and technology transfer through the international cooperation
mechanisms of the Paris Agreement could support such efforts.
Modelling to inform policy development is most useful when
undertaken with close interaction between model developers and
other stakeholders including policymakers to ensure that models
account for real world constraints (Dooley and Kartha 2018).
International initiatives to restore lands, such as the Bonn Challenge
(Verdone and Seidl 2017) and the New York Declaration on Forests
(Chazdon et al. 2017), and interventions undertaken for LDN and
implementation of NDCs (see Glossary) can contribute to NET
objectives. Such synergies may increase the financial resources
available to meet multiple objectives (Section 4.8.4).
4.5.4
Traditional biomass provision
and land degradation
Traditional biomass (fuelwood, charcoal, agricultural residues, animal
dung) used for cooking and heating by some 2.8 billion people
(38% of global population) in non-OECD countries accounts for
more than half of all bioenergy used worldwide (IEA 2017; REN21
2018) (Cross-Chapter Box 7 in Chapter 6). Cooking with traditional
biomass has multiple negative impacts on human health, particularly
for women, children and youth (Machisa et al. 2013; Sinha and Ray
2015; Price 2017; Mendum and Njenga 2018; Adefuye et al. 2007)
and on household productivity, including high workloads for women
and youth (Mendum and Njenga 2018; Brunner et al. 2018; Hou et al.
2018; Njenga et al. 2019). Traditional biomass is land-intensive due
to reliance on open fires, inefficient stoves and overharvesting of
woodfuel, contributing to land degradation, losses in biodiversity and
reduced ecosystem services (IEA 2017; Bailis et al. 2015; Masera et al.
2015; Specht et al. 2015; Fritsche et al. 2017; Fuso Nerini et al. 2017).
Traditional woodfuels account for 1.9–2.3% of global GHG emissions,
particularly in ‘hotspots’ of land degradation and fuelwood depletion
in eastern Africa and South Asia, such that one-third of traditional
woodfuels globally are harvested unsustainably (Bailis et al. 2015).
Scenarios to significantly reduce reliance on traditional biomass in
developing countries present multiple co-benefits (
high evidence
,
high agreement
), including reduced emissions of black carbon,
a short-lived climate forcer that also causes respiratory disease
(Shindell et al. 2012).
A shift from traditional to modern bioenergy, especially in the African
context, contributes to improved livelihoods and can reduce land
degradation and impacts on ecosystem services (Smeets et al. 2012;
Gasparatos et al. 2018; Mudombi et al. 2018). In Sub-Saharan Africa,
most countries mention woodfuel in their Nationally Determined
Contribution (NDC) but fail to identify transformational processes
to make fuelwood a sustainable energy source compatible with
improved forest management (Amugune et al. 2017). In some regions,
especially in South and Southeast Asia, a scarcity of woody biomass
may lead to excessive removal and use of agricultural wastes and
residues, which contributes to poor soil quality and land degradation
(Blanco-Canqui and Lal 2009; Mateos et al. 2017).
In Sub-Saharan Africa, forest degradation is widely associated with
charcoal production, although in some tropical areas rapid re-growth
can offset forest losses (Hoffmann et al. 2017; McNicol et al. 2018).
Overharvesting of wood for charcoal contributes to the high rate of
deforestation in Sub-Saharan Africa, which is five times the world
average, due in part to corruption and weak governance systems
(Sulaiman et al. 2017). Charcoal may also be a by-product of forest
clearing for agriculture, with charcoal sale providing immediate
income when the land is cleared for food crops (Kiruki et al. 2017;
Ndegwa et al. 2016). Besides loss of forest carbon stock, a further
concern for climate change is methane and black carbon emissions
from fuelwood burning and traditional charcoal-making processes
(Bond et al. 2013; Patange et al. 2015; Sparrevik et al. 2015).
A fundamental difficulty in reducing environmental impacts
associated with charcoal lies in the small-scale nature of much
charcoal production in Sub-Saharan Africa, leading to challenges in
regulating its production and trade, which is often informal, and in
some cases illegal, but nevertheless widespread since charcoal is the
most important urban cooking fuel (Zulu 2010; Zulu and Richardson
2013; Smith et al. 2015; World Bank 2009). Urbanisation combined
with population growth has led to continuously increasing charcoal
production. Low efficiency of traditional charcoal production results
in a four-fold increase in raw woody biomass required and thus much
greater biomass harvest (Hojas-Gascon et al. 2016; Smeets et al.
2012). With continuing urbanisation anticipated, increased charcoal
production and use will probably contribute to increasing land
pressures and increased land degradation, especially in Sub-Saharan
Africa (
medium evidence
,
high agreement
).
Although it could be possible to source this biomass more
sustainably, the ecosystem and health impacts of this increased
demand for cooking fuel would be reduced through use of other
renewable fuels or, in some cases, non-renewable fuels (LPG), as
well as through improved efficiency in end-use and through better
resource and supply chain management (Santos et al. 2017; Smeets
et al. 2012; Hoffmann et al. 2017). Integrated response options such
as agro-forestry (Chapter 6) and good governance mechanisms for
forest and agricultural management (Chapter 7) can support the
transition to sustainable energy for households and reduce the
environmental impacts of traditional biomass.
4.6
Impacts of land degradation on climate
While Chapter 2 has its focus on land cover changes and their
impacts on the climate system, this chapter focuses on the influences
of individual land degradation processes on climate (see Table 4.1)
which may or may not take place in association with land cover
changes. The effects of land degradation on CO
2
and other GHGs as
well as those on surface albedo and other physical controls of the
global radiative balance are discussed.
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4.6.1
Impact on greenhouse gases (GHGs)
Land degradation processes with direct impact on soil and
terrestrial biota have great relevance in terms of CO
2
exchange
with the atmosphere, given the magnitude and activity of these
reservoirs in the global carbon cycle. As the most widespread form
of soil degradation, erosion detaches the surface soil material,
which typically hosts the highest organic carbon stocks, favouring
the mineralisation and release as CO
2
. Yet complementary processes
such as carbon burial may compensate for this effect, making soil
erosion a long-term carbon sink (
low agreement, limited evidence
),
(Wang et al. (2017b), but see also Chappell et al. (2016)). Precise
estimation of the CO
2
released from eroded lands is challenged by
the fact that only a fraction of the detached carbon is eventually lost
to the atmosphere. It is important to acknowledge that a substantial
fraction of the eroded material may preserve its organic carbon load
in field conditions. Moreover, carbon sequestration may be favoured
through the burial of both the deposited material and the surface
of its hosting soil at the deposition location (Quinton et al. 2010).
The cascading effects of erosion on other environmental processes
at the affected sites can often cause net CO
2
emissions through
their indirect influence on soil fertility, and the balance of organic
carbon inputs and outputs, interacting with other non-erosive soil
degradation processes (such as nutrient depletion, compaction
and salinisation), which can lead to the same net carbon effects
(see Table 4.1) (van de Koppel et al. 1997).
As natural and human-induced erosion can result in net carbon
storage in very stable buried pools at the deposition locations,
degradation in those locations has a high C-release potential. Coastal
ecosystems such as mangrove forests, marshes and seagrasses are at
typical deposition locations, and their degradation or replacement
with other vegetation is resulting in a substantial carbon release
(0.15 to 1.02 GtC yr
–1
) (Pendleton et al. 2012), which highlights
the need for a spatially integrated assessment of land degradation
impacts on climate that considers in-situ but also ex-situ emissions.
Cultivation and agricultural management of cultivated land are
relevant in terms of global CO
2
land–atmosphere exchange
(Section 4.8.1). Besides the initial pulse of CO
2
emissions associated
with the onset of cultivation and associated vegetation clearing
(Chapter 2), agricultural management practices can increase or
reduce carbon losses to the atmosphere. Although global croplands
are considered to be at a relatively neutral stage in the current
decade (Houghton et al. 2012), this results from a highly uncertain
balance between coexisting net losses and gains. Degradation losses
of soil and biomass carbon appear to be compensated by gains
from soil protection and restoration practices such as cover crops,
conservation tillage and nutrient replenishment favouring organic
matter build-up. Cover crops, increasingly used to improve soils,
have the potential to sequester 0.12 GtC yr
–1
on global croplands
with a saturation time of more than 150 years (Poeplau and Don
2015). No-till practices (i.e., tillage elimination favouring crop residue
retention in the soil surface) which were implemented to protect
soils from erosion and reduce land preparation times, were also
seen with optimism as a carbon sequestration option, which today
is considered more modest globally and, in some systems, even less
certain (VandenBygaart 2016; Cheesman et al. 2016; Powlson et al.
2014). Among soil fertility restoration practices, lime application for
acidity correction, increasingly important in tropical regions, can
generate a significant net CO
2
source in some soils (Bernoux et al.
2003; Desalegn et al. 2017).
Land degradation processes in seminatural ecosystems driven by
unsustainable uses of their vegetation through logging or grazing
lead to reduced plant cover and biomass stocks, causing net carbon
releases from soils and plant stocks. Degradation by logging activities
is particularly prevalent in developing tropical and subtropical
regions, involving carbon releases that exceed by far the biomass
of harvested products, including additional vegetation and soil
sources that are estimated to reach 0.6 GtC yr
–1
(Pearson et al. 2014,
2017). Excessive grazing pressures pose a more complex picture with
variable magnitudes and even signs of carbon exchanges. A general
trend of higher carbon losses in humid overgrazed rangelands
suggests a high potential for carbon sequestration following the
rehabilitation of those systems (Conant and Paustian 2002) with
a global potential sequestration of 0.045 GtC yr
–1
. A special case
of degradation in rangelands is the process leading to the woody
encroachment of grass-dominated systems, which can be responsible
for declining animal production but high carbon sequestration rates
(Asner et al. 2003; Maestre et al. 2009).
Fire regime shifts in wild and seminatural ecosystems can become
a degradation process in itself, with high impact on net carbon
emission and with underlying interactive human and natural drivers
such as burning policies (Van Wilgen et al. 2004), biological invasions
(Brooks et al. 2009), and plant pest/disease spread (Kulakowski et al.
2003). Some of these interactive processes affecting unmanaged
forests have resulted in massive carbon release, highlighting how
degradation feedbacks on climate are not restricted to intensively
used land but can affect wild ecosystems as well (Kurz et al. 2008).
Agricultural land and wetlands represent the dominant source of
non-CO
2
greenhouse gases (GHGs) (Chen et al. 2018d). In agricultural
land, the expansion of rice cultivation (increasing CH
4
sources),
ruminant stocks and manure disposal (increasing CH
4
, N
2
O and NH
3
fluxes) and nitrogen over-fertilisation combined with soil acidification
(increasing N
2
O fluxes) are introducing the major impacts (
medium
agreement
,
medium evidence
) and their associated emissions appear
to be exacerbated by global warming (
medium agreement
,
medium
evidence
) (Oertel et al. 2016).
As the major sources of global N
2
O emissions, over-fertilisation and
manure disposal are not only increasing in-situ sources but also
stimulating those along the pathway of dissolved inorganic nitrogen
transport all the way from draining waters to the ocean (
high
agreement
,
medium evidence
). Current budgets of anthropogenically
fixed nitrogen on the Earth System (Tian et al. 2015; Schaefer et al.
2016; Wang et al. 2017a) suggest that N
2
O release from terrestrial
soils and wetlands accounts for 10–15% of the emissions, yet
many further release fluxes along the hydrological pathway remain
uncertain, with emissions from oceanic ‘dead-zones’ being a major
aspect of concern (Schlesinger 2009; Rabalais et al. 2014).
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Environmental degradation processes focused on the hydrological
system, which are typically manifested at the landscape scale,
include both drying (as in drained wetlands or lowlands) and wetting
trends (as in waterlogged and flooded plains). Drying of wetlands
reduces CH
4
emissions (Turetsky et al. 2014) but favours pulses of
organic matter mineralisation linked to high N
2
O release (Morse and
Bernhardt 2013; Norton et al. 2011). The net warming balance of
these two effects is not resolved and may be strongly variable across
different types of wetlands. In the case of flooding of non-wetland
soils, a suppression of CO
2
release is typically overcompensated in
terms of net greenhouse impact by enhanced CH
4
fluxes that stem
from the lack of aeration but are aided by the direct effect of extreme
wetting on the solubilisation and transport of organic substrates
(McNicol and Silver 2014). Both wetlands rewetting/restoration and
artificial wetland creation can increase CH
4
release (Altor and Mitsch
2006; Fenner et al. 2011). Permafrost thawing is another major
source of CH
4
release, with substantial long-term contributions to the
atmosphere that are starting to be globally quantified (Christensen
et al. 2004; Schuur et al. 2015; Walter Anthony et al. 2016).
4.6.2
Physical impacts
Among the physical effects of land degradation, surface albedo
changes are those with the most evident impact on the net global
radiative balance and net climate warming/cooling. Degradation
processes affecting wild and semi-natural ecosystems, such as fire
regime changes, woody encroachment, logging and overgrazing, can
trigger strong albedo changes before significant biogeochemical shifts
take place. In most cases these two types of effects have opposite
signs in terms of net radiative forcing, making their joint assessment
critical for understanding climate feedbacks (Bright et al. 2015).
In the case of forest degradation or deforestation, the albedo impacts
are highly dependent on the latitudinal/climatic belt to which they
belong. In boreal forests, the removal or degradation of the tree
cover increases albedo (net cooling effect) (
medium evidence
,
high
agreement
) as the reflective snow cover becomes exposed, which
can exceed the net radiative effect of the associated carbon release
to the atmosphere (Davin et al. 2010; Pinty et al. 2011). On the other
hand, progressive greening of boreal and temperate forests has
contributed to net albedo declines (
medium agreement
,
medium
evidence
) (Planque et al. 2017; Li et al. 2018a). In the northern
treeless vegetation belt (tundra), shrub encroachment leads to the
opposite effect as the emergence of plant structures above the snow
cover level reduce winter-time albedo (Sturm 2005).
The extent to which albedo shifts can compensate for carbon storage
shifts at the global level has not been estimated. A significant but
partial compensation takes place in temperate and subtropical dry
ecosystems in which radiation levels are higher and carbon stocks
smaller compared to their more humid counterparts (
medium
agreement
,
medium evidence
). In cleared dry woodlands, half of the
net global warming effect of net carbon release has been compensated
by albedo increase (Houspanossian et al. 2013), whereas in afforested
dry rangelands, albedo declines cancelled one-fifth of the net carbon
sequestration (Rotenberg and Yakir 2010). Other important cases
in which albedo effects impose a partial compensation of carbon
exchanges are the vegetation shifts associated with wildfires, as
shown for the savannahs, shrublands and grasslands of Sub-Saharan
Africa (Dintwe et al. 2017). Besides the net global effects discussed
above, albedo shifts can play a significant role in local climate (
high
agreement
,
medium evidence
), as exemplified by the effect of no-till
agriculture reducing local heat extremes in European landscapes
(Davin et al. 2014) and the effects of woody encroachment causing
precipitation rises in the North American Great Plains (Ge and Zou
2013). Modelling efforts that integrate ground data from deforested
areas worldwide accounting for both physical and biogeochemical
effects, indicate that massive global deforestation would have
a net warming impact (Lawrence and Vandecar 2015) at both local
and global levels with highlight non-linear effects of forest loss on
climate variables.
Beyond the albedo effects presented above, other physical impacts
of land degradation on the atmosphere can contribute to global
and regional climate change. Of particular relevance, globally and
continentally, are the net cooling effects of dust emissions (
low
agreement
,
medium evidence
) (Lau and Kim (2007), but see also
Huang et al. (2014)). Anthropogenic emission of mineral particles
from degrading land appear to have a similar radiative impact than
all other anthropogenic aerosols (Sokolik and Toon 1996). Dust
emissions may explain regional climate anomalies through reinforcing
feedbacks, as suggested for the amplification of the intensity, extent
and duration of the low precipitation anomaly of the North American
Dust Bowl in the 1930s (Cook et al. 2009). Another source of physical
effects on climate are surface roughness changes which, by affecting
atmospheric drag, can alter cloud formation and precipitation
(
low agreement
,
low evidence
), as suggested by modelling studies
showing how the massive deployment of solar panels in the Sahara
could increase rainfall in the Sahel (Li et al. 2018c), or how woody
encroachment in the Arctic tundra could reduce cloudiness and
raise temperature (Cho et al. 2018). The complex physical effects of
deforestation, as explored through modelling, converge into general
net regional precipitation declines, tropical temperature increases
and boreal temperature declines, while net global effects are less
certain (Perugini et al. 2017). Integrating all the physical effects of
land degradation and its recovery or reversal is still a challenge,
yet modelling attempts suggest that, over the last three decades,
the slow but persistent net global greening caused by the average
increase of leaf area in the land has caused a net cooling of the
Earth, mainly through the rise in evapotranspiration (Zeng et al.
2017) (
low confidence
).
4.7
Impacts of climate-related land
degradation on poverty and livelihoods
Unravelling the impacts of climate-related land degradation on
poverty and livelihoods is highly challenging. This complexity is due
to the interplay of multiple social, political, cultural and economic
factors, such as markets, technology, inequality, population growth,
(Barbier and Hochard 2018) each of which interact and shape the
ways in which social-ecological systems respond (Morton 2007).
We find
limited evidence
attributing the impacts of climate-related
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4
land degradation to poverty and livelihoods, with climate often not
distinguished from any other driver of land degradation. Climate
is nevertheless frequently noted as a risk multiplier for both land
degradation and poverty (
high agreement
,
robust evidence
) and is
one of many stressors people live with, respond to and adapt to in
their daily lives (Reid and Vogel 2006). Climate change is considered to
exacerbate land degradation and potentially accelerate it due to heat
stress, drought, changes to evapotranspiration rates and biodiversity,
as well as a result of changes to environmental conditions that
allow new pests and diseases to thrive (Reed and Stringer 2016). In
general terms, the climate (and climate change) can increase human
and ecological communities’ sensitivity to land degradation. Land
degradation then leaves livelihoods more sensitive to the impacts
of climate change and extreme climatic events (
high agreement
,
robust evidence
). If human and ecological communities exposed to
climate change and land degradation are sensitive and cannot adapt,
they can be considered vulnerable to it; if they are sensitive and can
adapt, they can be considered resilient (Reed and Stringer 2016). The
impacts of land degradation will vary under a changing climate, both
spatially and temporally, leading some communities and ecosystems
to be more vulnerable or more resilient than others under different
scenarios. Even within communities, groups such as women and
youth are often more vulnerable than others.
4.7.1
Relationships between land degradation,
climate change and poverty
This section sets out the relationships between land degradation and
poverty, and climate change and poverty, leading to inferences about
the three-way links between them. Poverty is multidimensional and
includes a lack of access to the whole range of capital assets that can
be used to pursue a livelihood. Livelihoods constitute the capabilities,
assets and activities that are necessary to make a living (Chambers
and Conway 1992; Olsson et al. 2014b).
The literature shows
high agreement
in terms of speculation that
there are
potential
links between land degradation and poverty.
However, studies have not provided robust quantitative assessments
of the extent and incidence of poverty within populations affected by
land degradation (Barbier and Hochard 2016). Some researchers, for
example, Nachtergaele et al. (2011) estimate that 1.5 billion people
were dependent upon degraded land to support their livelihoods in
2007, while >42% of the world’s poor population inhabit degraded
areas. However, there is overall
low confidence
in the evidence base,
a lack of studies that look beyond the past and present, and the
literature calls for more in-depth research to be undertaken on these
issues (Gerber et al. 2014). Recent work by Barbier and Hochard
(2018) points to biophysical constraints such as poor soils and
limited rainfall, which interact to limit land productivity, suggesting
that those farming in climatically less-favourable agricultural areas
are challenged by poverty. Studies such as those by Coomes et al.
(2011), focusing on an area in the Amazon, highlight the importance
of the initial conditions of land holding in the dominant (shifting)
cultivation system in terms of long-term effects on household
poverty and future forest cover, showing that initial land tenure and
socio-economic aspects can make some areas less favourable too.
Much of the qualitative literature is focused on understanding the
livelihood and poverty impacts of degradation through a focus on
subsistence agriculture, where farms are small, under traditional or
informal tenure and where exposure to environmental (including
climate) risks is high (Morton 2007). In these situations, poorer people
lack access to assets (financial, social, human, natural and physical)
and in the absence of appropriate institutional supports and social
protection, this leaves them sensitive and unable to adapt, so a vicious
cycle of poverty and degradation can ensue. To further illustrate the
complexity, livelihood assessments often focus on a single snapshot
in time. Livelihoods are dynamic and people alter their livelihood
activities and strategies depending on the internal and external
stressors to which they are responding (O’Brien et al. 2004). When
certain livelihood activities and strategies are no longer tenable as
a result of land degradation (and may push people into poverty), land
degradation can have further effects on issues such as migration (Lee
2009), as people adapt by moving (Section 4.7.3); and may result in
conflict (Section 4.7.3), as different groups within society compete
for scarce resources, sometimes through non-peaceful actions. Both
migration and conflict can lead to land-use changes elsewhere that
further fuel climate change through increased emissions.
Similar challenges as for understanding land degradation–poverty
linkages are experienced in unravelling the relationship between
climate change and poverty. A particular issue in examining climate
change–poverty links relates to the common use of aggregate
economic statistics like GDP, as the assets and income of the
poor constitute a minor proportion of national wealth (Hallegatte
et al. 2018). Aggregate quantitative measures also fail to capture the
distributions of costs and benefits from climate change. Furthermore,
people fall into and out of poverty, with climate change being one
of many factors affecting these dynamics, through its impacts on
livelihoods. Much of the literature on climate change and poverty
tends to look backward rather than forward (Skoufias et al. 2011),
providing a snapshot of current or past relationships (for example,
Dell et al. (2009) who examine the relationship between temperature
and income (GDP) using cross-sectional data from countries in the
Americas). Yet, simulations of future climate change impacts on
income or poverty are largely lacking.
Noting the
limited evidence
that exists that explicitly focuses on the
relationship between land degradation, climate change and poverty,
Barbier and Hochard (2018b) suggest that those people living in
less-favoured agricultural areas face a poverty–environment trap
that can result in increased land degradation under climate change
conditions. The emergent relationships between land degradation,
climate change and poverty are shown in Figure 4.6 (see also
Figure 6.1).
The poor have access to few productive assets – so land, and the
natural resource base more widely, plays a key role in supporting the
livelihoods of the poor. It is, however, hard to make generalisations
about how important income derived from the natural resource
base is for rural livelihoods in the developing world (Angelsen
et al. 2014). Studies focusing on forest resources have shown that
approximately one quarter of the total rural household income in
developing countries stems from forests, with forest-based income
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4
shares being tentatively higher for low-income households (Vedeld
et al. 2007; Angelsen et al. 2014). Different groups use land in different
ways within their overall livelihood portfolios and are, therefore, at
different levels of exposure and sensitivity to climate shocks and
stresses. The literature nevertheless displays
high evidence
and
high agreement
that those populations whose livelihoods are more
sensitive to climate change and land degradation are often more
dependent on environmental assets, and these people are often the
poorest members of society. There is further
high evidence
and
high
agreement
that both climate change and land degradation can affect
livelihoods and poverty through their threat multiplier effect. Research
in Bellona, in the Solomon Islands in the South Pacific (Reenberg
et al. 2008) examined event-driven impacts on livelihoods, taking into
account weather events as one of many drivers of land degradation
and links to broader land use and land cover changes that have taken
place. Geographical locations experiencing land degradation are often
the same locations that are directly affected by poverty, and also by
extreme events linked to climate change and variability.
Much of the assessment presented above has considered
place-based analyses examining the relationships between poverty,
land degradation and climate change in the locations in which these
outcomes have occurred. Altieri and Nicholls (2017) note that, due
to the globalised nature of markets and consumption systems, the
impacts of changes in crop yields linked to climate-related land
degradation (manifest as lower yields) will be far reaching, beyond
the sites and livelihoods experiencing degradation. Despite these
teleconnections, farmers living in poverty in developing countries will
be especially vulnerable due to their exposure, dependence on the
environment for income and limited options to engage in other ways
to make a living (Rosenzweig and Hillel 1998). In identifying ways in
which these interlinkages can be addressed, Scherr (2000) observes
that key actions that can jointly address poverty and environmental
improvement often seek to increase access to natural resources,
enhance the productivity of the natural resource assets of the poor,
and engage stakeholders in addressing public natural resource
management issues. In this regard, it is increasingly recognised that
those suffering from, and being vulnerable to, land degradation and
poverty need to have a voice and play a role in the development
of solutions, especially where the natural resources and livelihood
activities they depend on are further threatened by climate change.
4.7.2
Impacts of climate-related land degradation
on food security
How and where we grow food, compared to where and when we need
to consume it, is at the crux of issues surrounding land degradation,
climate change and food security, especially because more than
75% of the global land surface (excluding Antarctica) faces rain-fed
crop production constraints (Fischer et al. 2009), see also Chapter 5.
Taken separately, knowledge on land degradation processes and
human-induced climate change has attained a great level of maturity.
However, their combined effects on food security, notably food supply,
remain underappreciated (Webb et al. 2017b), and quantitative
information is lacking. Just a few studies have shown how the
interactive effects of the aforementioned challenging, interrelated
phenomena can impact on crop productivity and hence food security
and quality (Karami et al. 2009; Allen et al. 2001; Högy and Fangmeier
2008) (
low evidence
). Along with socio-economic drivers, climate
change accelerates land degradation due to its influence on land-use
systems (Millennium Ecosystem Assessment 2005; UNCCD 2017),
potentially leading to a decline in agri-food system productivity,
particularly on the supply side. Increases in temperature and changes
in precipitation patterns are expected to have impacts on soil quality,
including nutrient availability and assimilation (St.Clair and Lynch
2010). Those climate-related changes are expected to have net
negative impacts on agricultural productivity, particularly in tropical
regions, though the magnitude of impacts depends on the models
used. Anticipated supply-side issues linked to land and climate relate
to biocapacity factors (including e.g., whether there is enough water
to support agriculture); production factors (e.g., chemical pollution
of soil and water resources or lack of soil nutrients) and distribution
issues (e.g., decreased availability of and/or accessibility to the
necessary diversity of quality food where and when it is needed)
(Stringer et al. 2011). Climate-sensitive transport infrastructure is
Climate change
Land and resource
degradation
Increased sensitivity
to climatic stress
Increased use of
natural resources and land
Declining agricultural
productivity and income
Figure 4.6 | Schematic representation of links between climate change, land management and socio-economic conditions.
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4
also problematic for food security (Islam et al. 2017), and can lead to
increased food waste, while poor siting of roads and transport links
can lead to soil erosion and forest loss (Xiao et al. 2017), further
feeding back into climate change.
Over the past decades, crop models have been useful tools for
assessing and understanding climate change impacts on crop
productivity and food security (White et al. 2011; Rosenzweig et al.
2014). Yet, the interactive effects of soil parameters and climate
change on crop yields and food security remain limited, with
low
evidence
of how they play out in different economic and climate
settings (e.g., Sundström et al. 2014). Similarly, there have been few
meta-analyses focusing on the adaptive capacity of land-use practices
such as conservation agriculture in light of climate stress (see e.g.,
Steward et al. 2018), as well as
low evidence
quantifying the role
of wild foods and forests (and, by extension, forest degradation) in
both the global food basket and in supporting household-scale food
security (Bharucha and Pretty 2010; Hickey et al. 2016).
To be sustainable, any initiative aimed at addressing food
security – encompassing supply, diversity and quality – must take
into consideration the interactive effects between climate and
land degradation in a context of other socio-economic stressors.
Such socio-economic factors are especially important if we look at
demand-side issues too, which include lack of purchasing power,
large-scale speculation on global food markets, leading to exponential
price rises (Tadesse et al. 2014), competition in appropriation of
supplies and changes to per capita food consumption (Stringer
et al. 2011) (Chapter 5). Lack of food security, combined with
lack of livelihood options, is often an important manifestation of
vulnerability, and can act as a key trigger for people to migrate. In
this way, migration becomes an adaptation strategy.
4.7.3
Impacts of climate-related land degradation
on migration and conflict
Land degradation may trigger competition for scarce natural resources,
potentially leading to migration and/or conflict, though, even with
medium evidence,
there is
low agreement
in the literature. Linkages
between land degradation and migration occur within a larger context
of multi-scale interaction of environmental and non-environmental
drivers and processes, including resettlement projects, searches
for education and/or income, land shortages, political turmoil, and
family-related reasons (McLeman 2017; Hermans and Ide 2019).
The complex contribution of climate to migration and conflict
hampers retrieving any level of confidence on climate-migration and
climate-conflict linkages, therefore constituting a major knowledge
gap (Cramer et al. 2014; Hoegh-Guldberg et al. 2018).
There is
low evidence
on the causal linkages between climate change,
land degradation processes (other than desertification) and migration.
Existing studies on land degradation and migration – particularly
in drylands – largely focus on the effect of rainfall variability and
drought, and show how migration serves as adaptation strategy
(Piguet et al. 2018; McLeman 2017; Chapter 3). For example, in the
Ethiopian highlands, severe topsoil erosion and forest degradation
is a major environmental stressor which is amplified by recurring
droughts, with migration being an important household adaptation
strategy (Morrissey 2013). In the humid tropics, land degradation,
mainly as a consequence of deforestation, has been a reported
reason for people leaving their homes during the Amazonian
colonisation (Hecht 1983) but was also observed more recently, for
example in Guatemala, where soil degradation was one of the most
frequently cited migration pushes (López-Carr 2012) and Kenya,
where households respond to low soil quality by sending temporary
migrants for additional income generation (Gray 2011). In contrast,
in the Andean highlands and the Pacific coastal plain, migration
increased with land quality, probably because revenues from
additional agricultural production was invested in costly forms of
migration (Gray and Bilsborrow 2013). These mixed results illustrate
the complex, non-linear relationship of land degradation–migration
linkages and suggest that explaining land degradationand migration
linkages requires considering a broad range of socio-ecological
conditions (McLeman 2017).
In addition to people moving away from an area due to ‘lost’
livelihood activities, climate-related land degradation can also reduce
the availability of livelihood safety nets – environmental assets that
people use during times of shocks or stress. For example, Barbier (2000)
notes that wetlands in north-east Nigeria around Hadejia–Jama’are
floodplain provide dry season pastures for seminomadic herders,
agricultural surpluses for Kano and Borno states, groundwater
recharge of the Chad formation aquifer and ‘insurance’ resources in
times of drought. The floodplain also supports many migratory bird
species. As climate change and land degradation combine, delivery of
these multiple services can be undermined, particularly as droughts
become more widespread, reducing the utility of this wetland
environment as a safety net for people and wildlife alike.
Early studies conducted in Africa hint at a significant causal link
between land degradation and violent conflict (Homer-Dixon et al.
1993). For example, Percival and Homer-Dixon (1995) identified land
degradation as one of the drivers of the crisis in Rwanda in the early
1990s, which allowed radical forces to stoke ethnic rivalries. With
respect to the Darfur conflict, some scholars and United Nations
Environment Programme (UNEP) concluded that land degradation,
together with other environmental stressors, constitute a major
security threat for the Sudanese people (Byers and Dragojlovic 2004;
Sachs 2007; UNEP 2007). Recent studies show
low agreement
,
suggesting that climate change can increase the likelihood of civil
violence if certain economic, political and social factors, including
low development and weak governance mechanisms, are present
(Scheffran et al. 2012; Benjaminsen et al. 2012). In contrast, Raleigh
and Urdal (2007) found in a global study that land degradation is
a weak predictor for armed conflict. As such, studies addressing
possible linkages between climate change – a key driver of land
degradation – and the risks of conflict have yielded contradictory
results, and it remains largely unclear whether land degradation
resulting from climate change leads to conflict or cooperation
(Salehyan 2008; Solomon et al. 2018).
Land degradation–conflict linkages can be bi-directional. Research
suggests that households experiencing natural resource degradation
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4
often engage in migration for securing livelihoods (Kreamer 2012),
which potentially triggers land degradation at the destination,
leading to conflict there (Kassa et al. 2017). While this indeed holds
true for some cases, it may not for others, given the complexity of
processes, contexts and drivers. Where conflict and violence do
ensue, it is often as a result of a lack of appreciation for the cultural
practices of others.
4.8
Addressing land degradation
in the context of climate change
Land degradation in the form of soil carbon loss is estimated to have
been ongoing for at least 12,000 years, but increased exponentially
in the last 200 years (Sanderman et al. 2017). Before the advent of
modern sources of nutrients, it was imperative for farmers to maintain
and improve soil fertility through the prevention of runoff and
erosion, and management of nutrients through vegetation residues
and manure. Many ancient farming systems were sustainable for
hundreds and even thousands of years, such as raised-field agriculture
in Mexico (Crews and Gliessman 1991), tropical forest gardens in
Southeast Asia and Central America (Ross 2011; Torquebiau 1992;
Turner and Sabloff 2012), terraced agriculture in East Africa, Central
America, Southeast Asia and the Mediterranean basin (Turner and
Sabloff 2012; Preti and Romano 2014; Widgren and Sutton 2004;
Håkansson and Widgren 2007; Davies and Moore 2016; Davies
2015), and integrated rice–fish cultivation in East Asia (Frei and
Becker 2005).
Such long-term sustainable farming systems evolved in very different
times and geographical contexts, but they share many common
features, such as: the combination of species and structural diversity
in time and space (horizontally and vertically) in order to optimise
the use of available land; recycling of nutrients through biodiversity
of plants, animals and microbes; harnessing the full range of
site-specific micro-environments (e.g., wet and dry soils); biological
interdependencies which help suppression of pests; reliance on
mainly local resources; reliance on local varieties of crops, and
sometimes incorporation of wild plants and animals; the systems are
often labour and knowledge intensive (Rudel et al. 2016; Beets 1990;
Netting 1993; Altieri and Koohafkan 2008). Such farming systems
have stood the test of time and can provide important knowledge
for adapting farming systems to climate change (Koohafkann and
Altieri 2011).
In modern agriculture, the importance of maintaining the biological
productivity and ecological integrity of farmland has not been
a necessity in the same way as in pre-modern agriculture because
nutrients and water have been supplied externally. The extreme
land degradation in the US Midwest during the Dust Bowl period
in the 1930s became an important wake-up call for agriculture and
agricultural research and development, from which we can still
learn much in order to adapt to ongoing and future climate change
(McLeman et al. 2014; Baveye et al. 2011; McLeman and Smit 2006).
SLM is a unifying framework for addressing land degradation and can
be defined as the stewardship and use of land resources, including
soils, water, animals and plants, to meet changing human needs,
while simultaneously ensuring the long-term productive potential
of these resources and the maintenance of their environmental
functions. It is a comprehensive approach comprising technologies
combined with social, economic and political enabling conditions
(Nkonya et al. 2011). It is important to stress that farming systems
are informed by both scientific and local/traditional knowledge. The
power of SLM in small-scale diverse farming was demonstrated
effectively in Nicaragua after the severe cyclone Mitch in 1998
(Holt-Giménez 2002). Pairwise analysis of 880 fields with and
without implementation of SLM practices showed that the SLM fields
systematically fared better than the fields without SLM in terms of
more topsoil remaining, higher field moisture, more vegetation, less
erosion and lower economic losses after the cyclone. Furthermore,
the difference between fields with and without SLM increased with
increasing levels of storm intensity, slope gradient, and age of SLM
(Holt-Giménez 2002).
When addressing land degradation through SLM and other approaches,
it is important to consider feedbacks that impact on climate change.
Table 4.2 shows some of the most important land degradation
issues, their potential solutions, and their impacts on climate change.
This table provides a link between the comprehensive lists of land
degradation processes (Table 4.1) and land management solutions.
4.8.1
Actions on the ground to address
land degradation
Concrete actions on the ground to address land degradation are
primarily focused on soil and water conservation. In the context of
adaptation to climate change, actions relevant for addressing land
degradation are sometimes framed as ecosystem-based adaptation
(Scarano 2017) or Nature-Based Solutions (Nesshöver et al. 2017),
and in an agricultural context, agroecology (see Glossary) provides
an important frame. The site-specific biophysical and social
conditions, including local and indigenous knowledge, are important
for successful implementation of concrete actions.
Responses to land degradation generally take the form of agronomic
measures (methods related to managing the vegetation cover),
soil management (methods related to tillage, nutrient supply), and
mechanical methods (methods resulting in durable changes to the
landscape) (Morgan 2005a). Measures may be combined to reinforce
benefits to land quality, as well as improving carbon sequestration
that supports climate change mitigation. Some measures offer
adaptation options and other co-benefits, such as agroforestry,
involving planting fruit trees that can support food security in the face
of climate change impacts (Reed and Stringer 2016), or application
of compost or biochar that enhances soil water-holding capacity, so
increases resilience to drought.
There are important differences in terms of labour and capital
requirements for different technologies, and also implications for land
tenure arrangements. Agronomic measures and soil management
require generally little extra capital input and comprise activities
repeated annually, so have no particular implication for land tenure
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Land degradation
4
arrangements. Mechanical methods require substantial upfront
investments in terms of capital and labour, resulting in long-lasting
structural change, requiring more secure land tenure arrangements
(Mekuriaw et al. 2018). Agroforestry is a particularly important
strategy for SLM in the context of climate change because of the
large potential to sequester carbon in plants and soil and enhance
resilience of agricultural systems (Zomer et al. 2016).
Implementation of SLM practices has been shown to increase
the productivity of land (Branca et al. 2013) and to provide good
economic returns on investment in many different settings around the
world (Mirzabaev et al. 2015). Giger et al. (2018) showed, in a meta
study of 363 SLM projects over the period 1990 to 2012, that 73%
of the projects were perceived to have a positive or at least neutral
cost-benefit ratio in the short term, and 97% were perceived to have
a positive or very positive cost-benefit ratio in the long term (
robust
evidence
,
high agreement
). Despite the positive effects, uptake is far
from universal. Local factors, both biophysical conditions (e.g., soils,
drainage, and topography) and socio-economic conditions (e.g., land
tenure, economic status, and land fragmentation) play decisive roles in
the interest in, capacity to undertake, and successful implementation of
SLM practices (Teshome et al. 2016; Vogl et al. 2017; Tesfaye et al. 2016;
Cerdà et al. 2018; Adimassu et al. 2016). From a landscape perspective,
SLM can generate benefits, including adaptation to and mitigation of
climate change, for entire watersheds, but challenges remain regarding
coordinated and consistent implementation (
medium evidence
,
medium agreement
) (Kerr et al. 2016; Wang et al. 2016a).
4.8.1.1 Agronomic and soil management measures
Rebuilding soil carbon is an important goal of SLM, particularly in the
context of climate change (Rumpel et al. 2018). The two most important
reasons why agricultural soils have lost 20–60% of the soil carbon
they contained under natural ecosystem conditions are the frequent
disturbance through tillage and harvesting, and the change from deep-
rooted perennial plants to shallow-rooted annual plants (Crews and
Table 4.2 |
Interaction of human and climate drivers can exacerbate desertification and land degradation.
Climate change exacerbates the rate and magnitude
of several ongoing land degradation and desertification processes. Human drivers of land degradation and desertification include expanding agriculture, agricultural
practices and forest management. In turn, land degradation and desertification are also drivers of climate change through GHG emissions, reduced rates of carbon
uptake, and reduced capacity of ecosystems to act as carbon sinks into the future. Impacts on climate change are either warming (in red) or cooling (in blue).
Issue/
syndrome
Impact on cli-
mate change
Human
driver
Climate
driver
Land management
options
References
Erosion of
agricultural soils
Emission: CO
2
, N
2
O
Increase soil organic
matter, no-till, perennial
crops, erosion control,
agroforestry, dietary change
3.1.4, 3.4.1,
3.5.2, 3.7.1,
4.8.1, 4.8.5,
4.9.2, 4.9.5
Deforestation
Emission of CO
2
Forest protection, sustain-
able forest management
and dietary change
4.1.5, 4.5, 4.8.3,
4.8.4, 4.9.3
Forest degradation
Emission of CO
2
Reduced carbon sink
Forest protection,
sustainable forest
management
4.1.5, 4.5, 4.8.3,
4.8.4, 4.9.3
Overgrazing
Emission: CO
2
, CH
4
Increasing albedo
Controlled grazing,
rangeland management
3.1.4.2, 3.4.1,
3.6.1, 3.7.1,
4.8.1.4
Firewood and
charcoal production
Emission: CO
2
, CH
4
Increasing albedo
Clean cooking (health
co-benefits, particularly
for women and children)
3.6.3, 4.5.4,
4.8.3, 4.8.4
Increasing fire
frequency and
intensity
Emission: CO
2
, CH
4
,
N
2
O
Emission: aerosols,
increasing albedo
Fuel management,
fire management
3.1.4, 3.6.1,
4.1.5, 4.8.3,
Cross-Chapter
Box 3 in Chp 2
Degradation of
tropical peat soils
Emission: CO
2
, CH
4
Peatland restoration,
erosion control, regulating
the use of peat soils
4.9.4
Thawing of
permafrost
Emission: CO
2
, CH
4
Relocation of settlement
and infrastructure
4.8.5.1
Coastal erosion
Emission: CO
2
, CH
4
Wetland and coastal
restoration, mangrove
conservation, long-term
land-use planning
4 .9.6, 4.9. 7,
4.9.8
Sand and dust
storms, wind
erosion
Emission: aerosols
Vegetation management,
afforestation, windbreaks
3.3.1, 3.4.1,
3.6.1, 3.7.1, 3.7.2
Bush encroachment
Capturing: CO
2
,
Decreasing albedo
Grazing land management,
fire management
3.6.1.3, 3.7.3.2
Human driver
Climate driver
Grazing
pressure
Warming
trend
Agriculture
practice
Extreme
temperature
Expansion of
agriculture
Drying
trend
Forest
clearing
Extreme
rainfall
Wood
fuel
Shifting
rains
Intensifying
cyclones
Sea level
rise
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Chapter 4
4
Rumsey 2017). Practices that build soil carbon are those that increase
organic matter input to soil, or reduce decomposition of SOM.
Agronomic practices can alter the carbon balance significantly, by
increasing organic inputs from litter and roots into the soil. Practices
include retention of residues, use of locally adapted varieties,
inter-cropping, crop rotations, and green manure crops that replace
the bare field fallow during winter and are eventually ploughed before
sowing the next main crop (Henry et al. 2018). Cover crops (green
manure crops and catch crops that are grown between the main
cropping seasons) can increase soil carbon stock by between 0.22
and 0.4 t C ha
–1
yr
–1
(Poeplau and Don 2015; Kaye and Quemada 2017).
Reduced tillage (or no-tillage) is an important strategy for reducing
soil erosion and nutrient loss by wind and water (Van Pelt et al. 2017;
Panagos et al. 2015; Borrelli et al. 2016). But the evidence that no-till
agriculture also sequesters carbon is not compelling (VandenBygaart
2016). Soil sampling of only the upper 30 cm can give biased results,
suggesting that soils under no-till practices have higher carbon
content than soils under conventional tillage (Baker et al. 2007; Ogle
et al. 2012; Fargione et al. 2018; VandenBygaart 2016).
Changing from annual to perennial crops can increase soil carbon
content (Culman et al. 2013; Sainju et al. 2017). A perennial grain
crop (intermediate wheatgrass) was, on average, over four years
a net carbon sink of about 13.5 tCO
2
ha
–1
yr
–1
(de Oliveira et al. 2018).
Sprunger et al. (2018) compared an annual winter wheat crop with
a perennial grain crop (intermediate wheatgrass) and found that the
perennial grain root biomass was 15 times larger than winter wheat,
however, there was no significant difference in soil carbon pools after
the four-year experiment. Exactly how much, and over what time
period, carbon can be sequestered through changing from annual to
perennial crops depends on the degree of soil carbon depletion and
other local biophysical factors (Section 4.9.2).
Integrated soil fertility management is a sustainable approach to
nutrient management that uses a combination of chemical and
organic amendments (manure, compost, biosolids, biochar), rhizobial
nitrogen fixation, and liming materials to address soil chemical
constraints (Henry et al. 2018). In pasture systems, management
of grazing pressure, fertilisation, diverse species including legumes
and perennial grasses can reduce erosion and enhance soil carbon
(Conant et al. 2017).
4.8.1.2 Mechanical soil and water conservation
In hilly and mountainous terrain, terracing is an ancient but still
practised soil conservation method worldwide (Preti and Romano
2014) in climatic zones from arid to humid tropics (Balbo 2017).
By reducing the slope gradient of hillsides, terraces provide flat
surfaces. Deep, loose soils that increase infiltration, reduce erosion
and thus sediment transport. They also decrease the hydrological
connectivity and thus reduce hillside runoff (Preti et al. 2018; Wei
et al. 2016; Arnáez et al. 2015; Chen et al. 2017). In terms of climate
change, terraces are a form of adaptation that helps in cases where
rainfall is increasing or intensifying (by reducing slope gradient and
the hydrological connectivity), and where rainfall is decreasing (by
increasing infiltration and reducing runoff) (
robust evidence
,
high
agreement
). There are several challenges, however, to continued
maintenance and construction of new terraces, such as the high costs
in terms of labour and/or capital (Arnáez et al. 2015) and disappearing
local knowledge for maintaining and constructing new terraces (Chen
et al. 2017). The propensity of farmers to invest in mechanical soil
conservation methods varies with land tenure; farmers with secure
tenure arrangements are more willing to invest in durable practices
such as terraces (Lovo 2016; Sklenicka et al. 2015; Haregeweyn et al.
2015). Where the slope is less severe, erosion can be controlled by
contour banks, and the keyline approach (Duncan 2016; Stevens et al.
2015) to soil and water conservation.
4.8.1.3 Agroforestry
Agroforestry is defined as a collective name for land-use systems in
which woody perennials (trees, shrubs, etc.) are grown in association
with herbaceous plants (crops, pastures) and/or livestock in a spatial
arrangement, a rotation, or both, and in which there are both
ecological and economic interactions between the tree and non-tree
components of the system (Young, 1995, p. 11). At least since the 1980s,
agroforestry has been widely touted as an ideal land management
practice in areas vulnerable to climate variations and subject to soil
erosion. Agroforestry holds the promise of improving soil and climatic
conditions, while generating income from wood energy, timber and
non-timber products – sometimes presented as a synergy of adaptation
and mitigation of climate change (Mbow et al. 2014).
There is strong scientific consensus that a combination of forestry with
agricultural crops and/or livestock, agroforestry systems can provide
additional ecosystem services when compared with monoculture crop
systems (Waldron et al. 2017; Sonwa et al. 2011, 2014, 2017; Charles
et al. 2013). Agroforestry can enable sustainable intensification by
allowing continuous production on the same unit of land with higher
productivity without the need to use shifting agriculture systems to
maintain crop yields (Nath et al. 2016). This is especially relevant
where there is a regional requirement to find a balance between the
demand for increased agricultural production and the protection of
adjacent natural ecosystems such as primary and secondary forests
(Mbow et al. 2014). For example, the use of agroforestry for perennial
crops such as coffee and cocoa is increasingly promoted as offering
a route to sustainable farming, with important climate change
adaptation and mitigation co-benefits (Sonwa et al. 2001; Kroeger
et al. 2017). Reported co-benefits of agroforestry in cocoa production
include increased carbon sequestration in soils and biomass, improved
water and nutrient use efficiency and the creation of a favourable
micro-climate for crop production (Sonwa et al. 2017; Chia et al.
2016). Importantly, the maintenance of soil fertility using agroforestry
has the potential to reduce the practice of shifting agriculture
(of cocoa) which results in deforestation (Gockowski and Sonwa
2011). However, positive interactions within these systems can be
ecosystem and/or species specific, but co-benefits such as increased
resilience to extreme climate events, or improved soil fertility are
not always observed (Blaser et al. 2017; Abdulai et al. 2018). These
contrasting outcomes indicate the importance of field-scale research
programmes to inform agroforestry system design, species selection
and management practices (Sonwa et al. 2014).
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Despite the many proven benefits, adoption of agroforestry has been
low and slow (Toth et al. 2017; Pattanayak et al. 2003; Jerneck and
Olsson 2014). There are several reasons for the slow uptake, but the
perception of risks and the time lag between adoption and realisation
of benefits are often important (Pattanayak et al. 2003; Mercer 2004;
Jerneck and Olsson 2013).
An important question for agroforestry is whether it supports poverty
alleviation, or if it favours comparatively affluent households.
Experiences from India suggest that the overall adoption is low,
with a differential between rich and poor households. Brockington
el al. (2016), studied agroforestry adoption over many years in South
India and found that, overall, only 18% of the households adopted
agroforestry. However, among the relatively rich households who
adopted agroforestry, 97% were still practising it after six to eight
years, and some had expanded their operations. Similar results were
obtained in Western Kenya, where food-secure households were much
more willing to adopt agroforestry than food-insecure households
(Jerneck and Olsson 2013, 2014). Other experiences from Sub-Saharan
Africa illustrate the difficulties (such as local institutional support)
of having a continued engagement of communities in agroforestry
(Noordin et al. 2001; Matata et al. 2013; Meijer et al. 2015).
4.8.1.4 Crop–livestock interaction as an approach
to managing land degradation
The integration of crop and livestock production into ‘mixed farming’
for smallholders in developing countries became an influential
model, particularly for Africa, in the early 1990s (Pritchard et al. 1992;
McIntire et al. 1992). Crop–livestock integration under this model
was seen as founded on three pillars: improved use of manure for
crop fertility management; expanded use of animal traction (draught
animals); and promotion of cultivated fodder crops. For Asia, emphasis
was placed on draught power for land preparation, manure for soil
fertility enhancement, and fodder production as an entry point for
cultivation of legumes (Devendra and Thomas 2002). Mixed farming
was seen as an evolutionary process to expand food production in the
face of population increase, promote improvements in income and
welfare, and protect the environment. The process could be further
facilitated and steered by research, agricultural advisory services and
policy (Pritchard et al. 1992; McIntire et al. 1992; Devendra 2002).
Scoones and Wolmer (2002) place this model in historical context,
including concern about population pressure on resources and the
view that mobile pastoralism was environmentally damaging. The
latter view had already been critiqued by developing understandings
of pastoralism, mobility and communal tenure of grazing lands
(e.g., Behnke 1994; Ellis 1994). They set out a much more
differentiated picture of crop–livestock interactions, which can take
place either within a single-farm household, or between crop and
livestock producers, in which case they will be mediated by formal
and informal institutions governing the allocation of land, labour and
capital, with the interactions evolving through multiple place-specific
pathways (Ramisch et al. 2002; Scoones and Wolmer 2002).
Promoting a diversity of approaches to crop–livestock interactions
does not imply that the integrated model necessarily leads to land
degradation, but increases the space for institutional support to local
innovation (Scoones and Wolmer 2002).
However, specific managerial and technological practices that link
crop and livestock production will remain an important part of
the repertoire of on-farm adaptation and mitigation. Howden and
coauthors (Howden et al. 2007) note the importance of innovation
within existing integrated systems, including use of adapted forage
crops. Rivera-Ferre et al. (2016) list as adaptation strategies with high
potential for grazing systems, mixed crop–livestock systems or both:
crop–livestock integration in general; soil management, including
composting; enclosure and corralling of animals; improved storage
of feed. Most of these are seen as having significant co-benefits
for mitigation, and improved management of manure is seen as
a mitigation measure with adaptation co-benefits.
4.8.2
Local and indigenous knowledge for addressing
land degradation
In practice, responses are anchored in scientific research, as well
as local, indigenous and traditional knowledge and know-how.
For example, studies in the Philippines by Camacho et al. (2016)
examine how traditional integrated watershed management by
indigenous people sustain regulating services vital to agricultural
productivity, while delivering co-benefits in the form of biodiversity
and ecosystem resilience at a landscape scale. Although responses
can be site specific and sustainable at a local scale, the multi-scale
interplay of drivers and pressures can nevertheless cause practices
that have been sustainable for centuries to become less so. Siahaya
et al. (2016) explore the traditional knowledge that has informed rice
cultivation in the uplands of East Borneo, grounded in sophisticated
shifting cultivation methods (
gilir balik
) which have been passed on
for generations (more than 200 years) in order to maintain local food
production. Gilir balik involves temporary cultivation of plots, after
which, abandonment takes place as the land user moves to another
plot, leaving the natural (forest) vegetation to return. This approach
is considered sustainable if it has the support of other subsistence
strategies, adapts to and integrates with the local context, and if
the carrying capacity of the system is not surpassed (Siahaya et al.
2016). Often gilir balik cultivation involves intercropping of rice with
bananas, cassava and other food crops. Once the abandoned plot has
been left to recover such that soil fertility is restored, clearance takes
place again and the plot is reused for cultivation. Rice cultivation in
this way plays an important role in forest management, with several
different types of succession forest being found in the study by
Siahaya et al. (2016). Nevertheless, interplay of these practices with
other pressures (large-scale land acquisitions for oil palm plantation,
logging and mining), risk their future sustainability. Use of fire is
critical in processes of land clearance, so there are also trade-offs for
climate change mitigation, which have been sparsely assessed.
Interest appears to be growing in understanding how indigenous
and local knowledge inform land users’ responses to degradation,
as scientists engage farmers as experts in processes of knowledge
co-production and co-innovation (Oliver et al. 2012; Bitzer and
Bijman 2015). This can help to introduce, implement, adapt and
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promote the use of locally appropriate responses (Schwilch et al.
2011). Indeed, studies strongly agree on the importance of engaging
local populations in both sustainable land and forest management.
Meta-analyses in tropical regions that examined both forests in
protected areas and community-managed forests suggest that
deforestation rates are lower, with less variation in deforestation rates
presenting in community-managed forests compared to protected
forests (Porter-Bolland et al. 2012). This suggests that consideration
of the social and economic needs of local human populations is vital
in preventing forest degradation (Ward et al. 2018). However, while
disciplines such as ethnopedology seek to record and understand
how local people perceive, classify and use soil, and draw on that
information to inform its management (Barrera-Bassols and Zinck
2003), links with climate change and its impacts (perceived and
actual) are not generally considered.
4.8.3
Reducing deforestation and forest degradation
and increasing afforestation
Improved stewardship of forests through reduction or avoidance
of deforestation and forest degradation, and enhancement of
forest carbon stocks can all contribute to land-based natural
climate solutions (Angelsen et al. 2018; Sonwa et al. 2011; Griscom
et al. 2017). While estimates of annual emissions from tropical
deforestation and forest degradation range widely from 0.5
to 3.5 GtC yr
–1
(Baccini et al. 2017; Houghton et al. 2012; Mitchard
2018; see also Chapter 2), they all indicate the large potential to
reduce annual emissions from deforestation and forest degradation.
Recent estimates of forest extent for Africa in 1900 may result in
downward adjustments of historic deforestation and degradation
emission estimates (Aleman et al. 2018). Emissions from forest
degradation in non-Annex I countries have declined marginally
from 1.1 GtCO
2
yr
–1
in 2001–2010 to 1 GtCO
2
yr
–1
in 2011–2015, but
the relative emissions from degradation compared to deforestation
have increased from a quarter to a third (Federici et al. 2015). Forest
sector activities in developing countries were estimated to represent
a technical mitigation potential in 2030 of 9 GtCO
2
(Miles et al. 2015).
This was partitioned into reduction of deforestation (3.5 GtCO
2
),
reduction in degradation and forest management (1.7 GtCO
2
) and
afforestation and reforestation (3.8 GtCO
2
). The economic mitigation
potential will be lower than the technical potential (Miles et al. 2015).
Natural regeneration of second-growth forests enhances carbon
sinks in the global carbon budget (Chazdon and Uriarte 2016).
In Latin America, Chazdon et al. (2016) estimated that, in 2008,
second-growth forests (up to 60 years old) covered 2.4 Mkm
2
of
land (28.1% of the total study area). Over 40 years, these lands can
potentially accumulate 8.5 GtC in above-ground biomass via low-cost
natural regeneration or assisted regeneration, corresponding to
a total CO
2
sequestration of 31.1 GtCO
2
(Chazdon et al. 2016b). While
above-ground biomass carbon stocks are estimated to be declining
in the tropics, they are increasing globally due to increasing stocks in
temperate and boreal forests (Liu et al. 2015b), consistent with the
observations of a global land sector carbon sink (Le Quéré et al. 2013;
Keenan et al. 2017; Pan et al. 2011).
Moving from technical mitigation potentials (Miles et al. 2015) to real
reduction of emissions from deforestation and forest degradation
required transformational changes (Korhonen-Kurki et al. 2018).
This transformation can be facilitated by two enabling conditions:
the presence of already initiated policy change; or the scarcity of
forest resources combined with an absence of any effective forestry
framework and policies. These authors and others (Angelsen
et al. 2018) found that the presence of powerful transformational
coalitions of domestic pro-REDD+ (the United Nations Collaborative
Programme on Reducing Emissions from Deforestation and Forest
Degradation in Developing Countries) political actors combined with
strong ownership and leadership, regulations and law enforcement,
and performance-based funding, can provide a strong incentive for
achieving REDD+ goals.
Implementing schemes such as REDD+ and various projects related
to the voluntary carbon market is often regarded as a no-regrets
investment (Seymour and Angelsen 2012) but the social and
ecological implications (including those identified in the Cancun
Safeguards) must be carefully considered for REDD+ projects to be
socially and ecologically sustainable (Jagger et al. 2015). In 2018,
34 countries have submitted a REDD+ forest reference level and/
or forest reference emission level to the United Nations Framework
Convention on Climate Change (UNFCCC). Of these REDD+ reference
levels, 95% included the activity ‘reducing deforestation’ while 34%
included the activity ‘reducing forest degradation’ (FAO 2018). Five
countries submitted REDD+ results in the technical annex to their
Biennial Update Report totalling an emission reduction of 6.3 GtCO
2
between 2006 and 2015 (FAO 2018).
Afforestation is another mitigation activity that increases carbon
sequestration (Cross-Chapter Box 2 in Chapter 1). Yet, it requires
careful consideration about where to plant trees to achieve potential
climatic benefits, given an altering of local albedo and turbulent
energy fluxes and increasing night-time land surface temperatures
(Peng et al. 2014). A recent hydro-climatic modelling effort has
shown that forest cover can account for about 40% of the observed
decrease in annual runoff (Buendia et al. 2016). A meta-analysis of
afforestation in Northern Europe (Bárcena et al. 2014) concluded
that significant soil organic carbon sequestration in Northern Europe
occurs after afforestation of croplands but not grasslands. Additional
sequestration occurs in forest floors and biomass carbon stocks.
Successful programmes of large-scale afforestation activities in
South Korea and China are discussed in-depth in a special case study
(Section 4.9.3).
The potential outcome of efforts to reduce emissions from
deforestation and degradation in Indonesia through a 2011
moratorium on concessions to convert primary forests to either
timber or palm oil uses was evaluated against rates of emissions
over the period 2000 to 2010. The study concluded that less than 7%
of emissions would have been avoided had the moratorium been
implemented in 2000 because it only curtailed emissions due
to a subset of drivers of deforestation and degradation (Busch
et al. 2015).
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In terms of ecological integrity of tropical forests, the policy focus
on carbon storage and tree cover can be problematic if it leaves
out other aspects of forests ecosystems, such as biodiversity –
and particularly fauna (Panfil and Harvey 2016; Peres et al. 2016;
Hinsley et al. 2015). Other concerns of forest-based projects under
the voluntary carbon market are potential negative socio-economic
side effects (Edstedt and Carton 2018; Carton and Andersson 2017;
Osborne 2011; Scheidel and Work 2018; Richards and Lyons 2016;
Borras and Franco 2018; Paladino and Fiske 2017) and leakage
(particularly at the subnational scale), that is, when interventions to
reduce deforestation or degradation at one site displace pressures
and increase emissions elsewhere (Atmadja and Verchot 2012;
Phelps et al. 2010; Lund et al. 2017; Balooni and Lund 2014).
Maintaining and increasing forest area, in particular native forests
rather than monoculture and short-rotation plantations, contributes
to the maintenance of global forest carbon stocks (Lewis et al. 2019)
(
robust evidence
,
high agreement
).
4.8.4
Sustainable forest management (SFM)
and CO
2
removal (CDR) technologies
While reducing deforestation and forest degradation may directly
help to meet mitigation goals, SFM aimed at providing timber, fibre,
biomass and non-timber resources can provide long-term livelihood
for communities, reduce the risk of forest conversion to non-forest
uses (settlement, crops, etc.), and maintain land productivity, thus
reducing the risks of land degradation (Putz et al. 2012; Gideon Neba
et al. 2014; Sufo Kankeu et al. 2016; Nitcheu Tchiadje et al. 2016;
Rossi et al. 2017).
Developing SFM strategies aimed at contributing towards negative
emissions throughout this century requires an understanding of
forest management impacts on ecosystem carbon stocks (including
soils), carbon sinks, carbon fluxes in harvested wood, carbon storage
in harvested wood products, including landfills and the emission
reductions achieved through the use of wood products and bioenergy
(Nabuurs et al. 2007; Lemprière et al. 2013; Kurz et al. 2016; Law
et al. 2018; Nabuurs et al. 2017). Transitions from natural to managed
forest landscapes can involve a reduction in forest carbon stocks, the
magnitude of which depends on the initial landscape conditions,
the harvest rotation length relative to the frequency and intensity
of natural disturbances, and on the age-dependence of managed
and natural disturbances (Harmon et al. 1990; Kurz et al. 1998).
Initial landscape conditions, in particular the age-class distribution
and therefore carbon stocks of the landscape, strongly affect the
mitigation potential of forest management options (Ter-Mikaelian
et al. 2013; Kilpeläinen et al. 2017). Landscapes with predominantly
mature forests may experience larger reductions in carbon stocks
during the transition to managed landscapes (Harmon et al. 1990;
Kurz et al. 1998; Lewis et al. 2019). In landscapes with predominantly
young or recently disturbed forests, SFM can enhance carbon stocks
(Henttonen et al. 2017).
Forest growth rates, net primary productivity, and net ecosystem
productivity are age-dependent, with maximum rates of CO
2
removal
(CDR) from the atmosphere occurring in young to medium-aged
forests and declining thereafter (Tang et al. 2014). In boreal forest
ecosystem, estimation of carbon stocks and carbon fluxes indicate
that old growth stands are typically small carbon sinks or carbon
sources (Gao et al. 2018; Taylor et al. 2014; Hadden and Grelle 2016).
In tropical forests, carbon uptake rates in the first 20 years of forest
recovery were 11 times higher than uptake rates in old-growth
forests (Poorter et al. 2016). Age-dependent increases in forest carbon
stocks and declines in forest carbon sinks mean that landscapes with
older forests have accumulated more carbon but their sink strength
is diminishing, while landscapes with younger forests contain less
carbon but they are removing CO
2
from the atmosphere at a much
higher rate (Volkova et al. 2017; Poorter et al. 2016). The rates of
CDR are not just age-related but also controlled by many biophysical
factors and human activities (Bernal et al. 2018). In ecosystems with
uneven-aged, multispecies forests, the relationships between carbon
stocks and sinks are more difficult and expensive to quantify.
Whether or not forest harvest and use of biomass is contributing
to net reductions of atmospheric carbon depends on carbon losses
during and following harvest, rates of forest regrowth, and the use
of harvested wood and carbon retention in long-lived or short-lived
products, as well as the emission reductions achieved through the
substitution of emissions-intensive products with wood products
(Lemprière et al. 2013; Lundmark et al. 2014; Xu et al. 2018b; Olguin
et al. 2018; Dugan et al. 2018; Chen et al. 2018b; Pingoud et al. 2018;
Seidl et al. 2007). Studies that ignore changes in forest carbon stocks
(such as some lifecycle analyses that assume no impacts of harvest on
forest carbon stocks), ignore changes in wood product pools (Mackey
et al. 2013) or assume long-term steady state (Pingoud et al. 2018), or
ignore changes in emissions from substitution benefits (Mackey et al.
2013; Lewis et al. 2019) will arrive at diverging conclusions about the
benefits of SFM. Moreover, assessments of climate benefits of any
mitigation action must also consider the time dynamics of atmospheric
impacts, as some actions will have immediate benefits (e.g., avoided
deforestation), while others may not achieve net atmospheric benefits
for decades or centuries. For example, the climate benefits of woody
biomass use for bioenergy depend on several factors, such as the
source and alternate fate of the biomass, the energy type it substitutes,
and the rates of regrowth of the harvested forest (Laganière et al.
2017; Ter-Mikaelian et al. 2014; Smyth et al. 2017). Conversion of
primary forests in regions of very low stand-replacing disturbances
to short-rotation plantations where the harvested wood is used for
short-lived products with low displacement factors will increase
emissions. In general, greater mitigation benefits are achieved if
harvested wood products are used for products with long carbon
retention time and high displacement factors.
With increasing forest age, carbon sinks in forests will diminish until
harvest or natural disturbances, such as wildfire, remove biomass
carbon or release it to the atmosphere (Seidl et al. 2017). While
individual trees can accumulate carbon for centuries (Köhl et al. 2017),
stand-level carbon accumulation rates depend on both tree growth
and tree mortality rates (Hember et al. 2016; Lewis et al. 2004). SFM,
including harvest and forest regeneration, can help maintain active
carbon sinks by maintaining a forest age-class distribution that
includes a share of young, actively growing stands (Volkova et al.
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2018; Nabuurs et al. 2017). The use of the harvested carbon in either
long-lived wood products (e.g., for construction), short-lived wood
products (e.g., pulp and paper), or biofuels affects the net carbon
balance of the forest sector (Lemprière et al. 2013; Matthews et al.
2018). The use of these wood products can further contribute to GHG
emission-reduction goals by avoiding the emissions from the products
with higher embodied emissions that have been displaced (Nabuurs
et al. 2007; Lemprière et al. 2013). In 2007 the IPCC concluded that ‘[i]n
the long term, a sustainable forest management strategy aimed at
maintaining or increasing forest carbon stocks, while producing an
annual sustained yield of timber, fibre or energy from the forest, will
generate the largest sustained mitigation benefit’ (Nabuurs et al.
2007). The apparent trade-offs between maximising forest carbon
stocks and maximising ecosystem carbon sinks are at the origin of
ongoing debates about optimum management strategies to achieve
negative emissions (Keith et al. 2014; Kurz et al. 2016; Lundmark
et al. 2014). SFM, including the intensification of carbon-focused
management strategies, can make long-term contributions towards
negative emissions if the sustainability of management is assured
through appropriate governance, monitoring and enforcement. As
specified in the definition of SFM, other criteria such as biodiversity
must also be considered when assessing mitigation outcomes
(Lecina-Diaz et al. 2018). Moreover, the impacts of changes in
management on albedo and other non-GHG factors also need to be
considered (Luyssaert et al. 2018) (Chapter 2). The contribution of
SFM for negative emissions is strongly affected by the use of the wood
products derived from forest harvest and the time horizon over which
the carbon balance is assessed. SFM needs to anticipate the impacts
of climate change on future tree growth, mortality and disturbances
when designing climate change mitigation and adaptation strategies
(Valade et al. 2017; Seidl et al. 2017).
4.8.5
Policy responses to land degradation
The 1992 United Nations Conference on Environment and Development
(UNCED), also known as the Rio de Janeiro Earth Summit, recognised
land degradation as a major challenge to sustainable development, and
led to the establishment of the UNCCD, which specifically addressed
land degradation in the drylands. The UNCCD emphasises sustainable
land use to link poverty reduction on one hand and environmental
protection on the other. The two other ‘Rio Conventions’ emerging from
the UNCED – the UNFCCC and the Convention on Biological Diversity
(CBD) – focus on climate change and biodiversity, respectively. The
land has been recognised as an aspect of common interest to the three
conventions, and SLM is proposed as a unifying theme for current
global efforts on combating land degradation, climate change and loss
of biodiversity, as well as facilitating land-based adaptation to climate
change and sustainable development.
The Global Environmental Facility (GEF) funds developing countries
to undertake activities that meet the goals of the conventions and
deliver global environmental benefits. Since 2002, the GEF has
invested in projects that support SLM through its Land Degradation
Focal Area Strategy, to address land degradation within and beyond
the drylands.
Under the UNFCCC, parties have devised National Adaptation Plans
(NAPs) that identify medium- and long-term adaptation needs.
Parties have also developed their climate change mitigation plans,
presented as NDCs. These programmes have the potential of assisting
the promotion of SLM. It is understood that the root causes of land
degradation and successful adaptation will not be realised until
holistic solutions to land management are explored. SLM can help
address root causes of low productivity, land degradation, loss of
income-generating capacity, as well as contribute to the amelioration
of the adverse effects of climate change.
The ‘4 per 1000’ (4p1000) initiative (Soussana et al. 2019) launched
by France during the UNFCCC COP21 in 2015 aims at capturing CO
2
from the atmosphere through changes to agricultural and forestry
practices at a rate that would increase the carbon content of soils
by 0.4% per year (Rumpel et al. 2018). If global soil carbon content
increases at this rate in the top 30–40 cm, the annual increase in
atmospheric CO
2
would be stopped (Dignac et al. 2017). This is an
illustration of how extremely important soils are for addressing
climate change. The initiative is based on eight steps: stop carbon
loss (priority #1 is peat soils); promote carbon uptake; monitor, report
and verify impacts; deploy technology for tracking soil carbon; test
strategies for implementation and upscaling; involve communities;
coordinate policies; and provide support (Rumpel et al. 2018).
Questions remain, however, about the extent that the 4p1000 is
achievable as a universal goal (van Groenigen et al. 2017; Poulton
et al. 2018; Schlesinger and Amundson 2018).
LDN was introduced by the UNCCD at Rio +20, and adopted at
UNCCD COP12 (UNCCD 2016a). LDN is defined as ‘a state whereby
the amount and quality of land resources necessary to support
ecosystem functions and services and enhance food security remain
stable or increase within specified temporal and spatial scales and
ecosystems’(Cowie et al. 2018). Pursuit of LDN requires effort to
avoid further net loss of the land-based natural capital relative to
a reference state, or baseline. LDN encourages a dual-pronged effort
involving SLM to reduce the risk of land degradation, combined with
efforts in land restoration and rehabilitation, to maintain or enhance
land-based natural capital, and its associated ecosystem services (Orr
et al. 2017; Cowie et al. 2018). Planning for LDN involves projecting
the expected cumulative impacts of land-use and land management
decisions, then counterbalancing anticipated losses with measures to
achieve equivalent gains, within individual land types (where land type
is defined by land potential). Under the LDN framework developed by
UNCCD, three primary indicators are used to assess whether LDN is
achieved by 2030: land cover change; net primary productivity; and
soil organic carbon (Cowie et al. 2018; Sims et al. 2019). Achieving
LDN therefore requires integrated landscape management that seeks
to optimise land use to meet multiple objectives (ecosystem health,
food security, human well-being) (Cohen-Shacham et al. 2016). The
response hierarchy of Avoid > Reduce > Reverse land degradation
articulates the priorities in planning LDN interventions. LDN provides
the impetus for widespread adoption of SLM and efforts to restore
or rehabilitate land. Through its focus, LDN ultimately provides
tremendous potential for mitigation of, and adaptation to, climate
change by halting and reversing land degradation and transforming
land from a carbon source to a sink. There are strong synergies
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between the concept of LDN and the NDCs of many countries, with
linkages to national climate plans. LDN is also closely related to many
Sustainable Development Goals (SDG) in the areas of poverty, food
security, environmental protection and sustainable use of natural
resources (UNCCD 2016b). The GEF is supporting countries to set LDN
targets and implement their LDN plans through its land degradation
focal area, which encourages application of integrated landscape
approaches to managing land degradation (GEF 2018).
The 2030 Agenda for Sustainable Development, adopted by the United
Nations in 2015, comprises 17 SDGs. Goal 15 is of direct relevance to
land degradation, with the objective to protect, restore and promote
sustainable use of terrestrial ecosystems, sustainably manage forests,
combat desertification and halt and reverse land degradation and
halt biodiversity loss. Target 15.3 specifically addresses LDN. Other
goals that are relevant for land degradation include Goal 2 (Zero
hunger), Goal 3 (Good health and well-being), Goal 7 (Affordable
and clean energy), Goal 11 (Sustainable cities and communities),
and Goal 12 (Responsible production and consumption). Sustainable
management of land resources underpins the SDGs related to hunger,
climate change and environment. Further goals of a cross-cutting
nature include 1 (No poverty), 6 (Clean water and sanitation) and 13
(Climate action). It remains to be seen how these interconnections
are dealt with in practice.
With a focus on biodiversity, IPBES published a comprehensive
assessment of land degradation in 2018 (Montanarella et al. 2018).
The IPBES report, together with this report focusing on climate
change, may contribute to creating a synergy between the two
main global challenges for addressing land degradation in order to
help achieve the targets of SDG 15 (protect, restore and promote
sustainable use of terrestrial ecosystems, sustainably manage forests,
combat desertification, and halt and reverse land degradation and
halt biodiversity loss).
Market-based mechanisms like the Clean Development Mechanism
(CDM) under the UNFCCC and the voluntary carbon market provide
incentives to enhance carbon sinks on the land through afforestation
and reforestation. Implications for local land use and food security
have been raised as a concern and need to be assessed (Edstedt
and Carton 2018; Olsson et al. 2014b). Many projects aimed at
reducing emissions from deforestation and forest degradations (not
to be confused with the national REDD+ programmes in accordance
with the UNFCCC Warsaw Framework) are being planned and
implemented to primarily target countries with high forest cover and
high deforestation rates. Some parameters of incentivising emissions
reduction, quality of forest governance, conservation priorities, local
rights and tenure frameworks, and sub-national project potential are
being looked into, with often very mixed results (Newton et al. 2016;
Gebara and Agrawal 2017).
Besides international public initiatives, some actors in the private
sector are increasingly aware of the negative environmental impacts
of some global value chains producing food, fibre, and energy
products (Lambin et al. 2018; van der Ven and Cashore 2018; van der
Ven et al. 2018; Lyons-White and Knight 2018). While improvements
are underway in many supply chains, measures implemented so
far are often insufficient to be effective in reducing or stopping
deforestation and forest degradation (Lambin et al. 2018). The GEF
is investing in actions to reduce deforestation in commodity supply
chains through its Food Systems, Land Use, and Restoration Impact
Program (GEF 2018).
4.8.5.1 Limits to adaptation
SLM can be deployed as a powerful adaptation strategy in most
instances of climate change impacts on natural and social systems,
yet there are limits to adaptation (Klein et al. 2014; Dow, Berhout and
Preston 2013). Such limits are dynamic and interact with social and
institutional conditions (Barnett et al. 2015; Filho and Nalau 2018).
Exceeding adaptation limits will trigger escalating losses or require
undesirable transformational change, such as forced migration. The
rate of change in relation to the rate of possible adaptation is crucial
(Dow et al. 2013). How limits to adaptation are defined, and how
they can be measured, is contextual and contested. Limits must be
assessed in relation to the ultimate goals of adaptation, which is
subject to diverse and differential values (Dow et al. 2013; Adger et al.
2009). A particularly sensitive issue is whether migration is accepted
as adaptation or not (Black et al. 2011; Tacoli 2009; Bardsley and
Hugo 2010). If migration were understood and accepted as a form
of successful adaptation, it would change the limits to adaptation
by reducing, or even avoiding, future humanitarian crises caused by
climate extremes (Adger et al. 2009; Upadhyay et al. 2017; Nalau
et al. 2018).
In the context of land degradation, potential limits to adaptation
exist if land degradation becomes so severe and irreversible that
livelihoods cannot be maintained, and if migration is either not
acceptable or not possible. Examples are coastal erosion where land
disappears (Gharbaoui and Blocher 2016; Luetz 2018), collapsing
livelihoods due to thawing of permafrost (Landauer and Juhola
2019), and extreme forms of soil erosion, (e.g., landslides (Van der
Geest and Schindler 2016) and gully erosion leading to badlands
(Poesen et al. 2003)).
4.8.6
Resilience and thresholds
Resilience refers to the capacity of interconnected social, economic
and ecological systems, such as farming systems, to absorb
disturbance (e.g., drought, conflict, market collapse), and respond or
reorganise, to maintain their essential function, identity and structure.
Resilience can be described as ‘coping capacity’. The disturbance may
be a shock – sudden events such as a flood or disease epidemic – or
it may be a trend that develops slowly, like a drought or market shift.
The shocks and trends anticipated to occur due to climate change are
expected to exacerbate risk of land degradation. Therefore, assessing
and enhancing resilience to climate change is a critical component of
designing SLM strategies.
Resilience as an analytical lens is particularly strong in ecology and
related research on natural resource management (Folke et al. 2010;
Quinlan et al. 2016) while, in the social sciences, the relevance of
resilience for studying social and ecological interactions is contested
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(Cote and Nightingale 2012; Olsson et al. 2015; Cretney 2014;
Béné et al. 2012; Joseph 2013). In the case of adaptation to climate
change (and particularly regarding limits to adaptation), a crucial
ambiguity of resilience is the question of whether resilience is
a normative concept (i.e., resilience is good or bad) or a descriptive
characteristic of a system (i.e., neither good nor bad). Previous IPCC
reports have defined resilience as a normative (positive) attribute
(see AR5 Glossary), while the wider scientific literature is divided on
this (Weichselgartner and Kelman 2015; Strunz 2012; Brown 2014;
Grimm and Calabrese 2011; Thorén and Olsson 2018). For example,
is outmigration from a disaster-prone area considered a successful
adaptation (high resilience) or a collapse of the livelihood system
(lack of resilience) (Thorén and Olsson 2018)? In this report, resilience
is considered a positive attribute when it maintains capacity for
adaptation, learning and/or transformation.
Furthermore, ‘resilience’ and the related terms ‘adaptation’ and
‘transformation’ are defined and used differently by different
communities (Quinlan et al. 2016). The relationship and hierarchy
of resilience with respect to vulnerability and adaptive capacity
are also debated, with different perspectives between disaster
management and global change communities, (e.g., Cutter et al.
2008). Nevertheless, these differences in usage need not inhibit the
application of ‘resilience thinking’ in managing land degradation;
researchers using these terms, despite variation in definitions,
apply the same fundamental concepts to inform management of
human-environment systems, to maintain or improve the resource
base, and sustain livelihoods.
Applying resilience concepts involves viewing the land as
a component of an interlinked social-ecological system; identifying
key relationships that determine system function and vulnerabilities
of the system; identifying thresholds or tipping points beyond
which the system transitions to an undesirable state; and devising
management strategies to steer away from thresholds of potential
concern, thus facilitating healthy systems and sustainable production
(Walker et al. 2009).
A threshold is a non-linearity between a controlling variable and
system function, such that a small change in the variable causes
the system to shift to an alternative state. Bestelmeyer et al. (2015)
and Prince et al. (2018) illustrate this concept in the context of
land degradation. Studies have identified various biophysical and
socio-economic thresholds in different land-use systems. For example,
50% ground cover (living and dead plant material and biological
crusts) is a recognised threshold for dryland grazing systems
(e.g., Tighe et al. 2012); below this threshold, the infiltration rate
declines, risk of erosion causing loss of topsoil increases,
a switch from
perennial to annual grass species occurs and there is a consequential
sharp decline in productivity. This shift to a lower-productivity state
cannot be reversed without significant human intervention. Similarly,
the combined pressure of water limitations and frequent fire can lead
to transition from closed forest to savannah or grassland: if fire is
too frequent, trees do not reach reproductive maturity and post-fire
regeneration will fail; likewise, reduced rainfall/increased drought
prevents successful forest regeneration (Reyer et al. 2015; Thompson
et al. 2009) (Cross-Chapter Box 3 in Chapter 2).
In managing land degradation, it is important to assess the resilience
of the existing system, and the proposed management interventions.
If the existing system is in an undesirable state or considered
unviable under expected climate trends, it may be desirable to
promote adaptation or even transformation to a different system
that is more resilient to future changes. For example, in an irrigation
district where water shortages are predicted, measures could be
implemented to improve water use efficiency, for example, by
establishing drip irrigation systems for water delivery, although
transformation to pastoralism or mixed dryland cropping/livestock
production may be more sustainable in the longer term, at least
for part of the area. Application of SLM practices, especially those
focused on ecological functions (e.g., agroecology, ecosystem-based
approaches, regenerative agriculture, organic farming), can be
effective in building resilience of agro-ecosystems (Henry et al. 2018).
Similarly, the resilience of managed forests can be enhanced by SFM
that protects or enhances biodiversity, including assisted migration
of tree species within their current range limit (Winder et al. 2011;
Pedlar et al. 2012) or increasing species diversity in plantation
forests (Felton et al. 2010; Liu et al. 2018a). The essential features
of a resilience approach to management of land degradation under
climate change are described by O’Connell et al. (2016) and Simonsen
et al. (2014).
Consideration of resilience can enhance effectiveness of interventions
to reduce or reverse land degradation (
medium agreement, limited
evidence
). This approach will increase the likelihood that SLM/SFM
and land restoration/rehabilitation interventions achieve long-term
environmental and social benefits. Thus, consideration of resilience
concepts can enhance the capacity of land systems to cope with
climate change and resist land degradation, and assist land-use
systems to adapt to climate change.
4.8.7
Barriers to implementation of sustainable land
management (SLM)
There is a growing recognition that addressing barriers and designing
solutions to complex environmental problems, such as land degradation,
requires awareness of the larger system into which the problems and
solutions are embedded (Laniak et al. 2013). An ecosystem approach
to sustainable land management (SLM) based on an understanding
of land degradation processes has been recommended to separate
multiple drivers, pressures and impacts (Kassam et al. 2013), but large
uncertainty in model projections of future climate, and associated
ecosystem processes (IPCC 2013a) pose additional challenges to the
implementation of SLM. As discussed earlier in this chapter, many SLM
practices, including technologies and approaches, are available that
can increase yields and contribute to closing the yield gap between
actual and potential crop or pasture yield, while also enhancing
resilience to climate change (Yengoh and Ardö 2014; WOCAT n.d.).
However, there are often systemic barriers to adoption and scaling up
of SLM practices, especially in developing countries.
Uitto (2016) identified areas that the GEF, the financial mechanism of
the UNCCD, UNFCCC and other multilateral environmental agreements,
can address to solve global environmental problems. These include:
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removal of barriers related to knowledge and information; strategies
for implementation of technologies and approaches; and institutional
capacity. Strengthening these areas would drive transformational
change, leading to behavioural change and broader adoption of
sustainable environmental practices. Detailed analysis of barriers as
well as strategies, methods and approaches to scale up SLM have
been undertaken for GEF programmes in Africa, China and globally
(Tengberg and Valencia 2018; Liniger et al. 2011; Tengberg et al. 2016).
A number of interconnected barriers and bottlenecks to the scaling up
of SLM have been identified in this context and are related to:
•
limited access to knowledge and information, including new SLM
technologies and problem-solving capacities
•
weak enabling environment, including the policy, institutional
and legal framework for SLM, and land tenure and property rights
•
inadequate learning and adaptive knowledge management in
the project cycle, including monitoring and evaluation of impacts
• limited access to finance for scaling up, including public and
private funding, innovative business models for SLM technologies
and financial mechanisms and incentives, such as payment for
ecosystem services (PES), insurance and micro-credit schemes
(see also Shames et al. 2014).
Adoption of innovations and new technologies are increasingly
analysed using the transition theory framework (Geels 2002), the
starting point being the recognition that many global environmental
problems cannot be solved by technological change alone, but
require more far-reaching change of social-ecological systems. Using
transition theory makes it possible to analyse how adoption and
implementation follow the four stages of sociotechnical transitions,
from predevelopment of technologies and approaches at the niche
level, take-off and acceleration, to regime shift and stabilisation at
the landscape level. According to a recent review of sustainability
transitions in developing countries (Wieczorek 2018), three internal
niche processes are important, including the formation of networks
that support and nurture innovation, the learning process, and the
articulation of expectations to guide the learning process. While
technologies are important, institutional and political aspects form
the major barriers to transition and upscaling. In developing and
transition economies, informal institutions play a pivotal role, and
transnational linkages are also important, such as global value chains.
In these countries, it is therefore more difficult to establish fully
coherent regimes or groups of individuals who share expectations,
beliefs or behaviour, as there is a high level of uncertainty about rules
and social networks or dominance of informal institutions, which
creates barriers to change. This uncertainty is further exacerbated by
climate change. Landscape forces comprise a set of slow-changing
factors, such as broad cultural and normative values, long-term
economic effects such as urbanisation, and shocks such as war and
crises that can lead to change.
A study on SLM in the Kenyan highlands using transition theory
concluded that barriers to adoption of SLM included high poverty
levels, a low-input/low-output farming system with limited potential
to generate income, diminishing land sizes, and low involvement of
the youth in farming activities. Coupled with a poor coordination
of government policies for agriculture and forestry, these barriers
created negative feedbacks in the SLM transition process. Other
factors to consider include gender issues and lack of secure land
tenure. Scaling up of SLM technologies would require collaboration of
Strategic partnerships
in support of SLM
SLM multistakeholder
knowledge platforms
Policy integration
in support of SLM
SLM technologies
and practices
SLM technologies
and approaches
Innovation systems for SLM –
knowledge co-production, institutional
innovations, social capital
Financial mechanisms (e.g. LDN)
and incentives (e.g. value chains, PES)
in place
Behavioural change
and scaling up of SLM
Markets, user practices
Technological
niches
Socio-technical
regimes
Landscape
development
Time
Figure 4.7 | The transition from SLM niche adoption to regime shift and landscape development.
Figure draws inspiration from Geels (2002), adapted from
Tengberg and Valencia (2018).
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diverse stakeholders across multiple scales, a more supportive policy
environment and substantial resource mobilisation (Mutoko et al. 2014).
Tengberg and Valencia (2018) analysed the findings from a review
of the GEF’s integrated natural resources management portfolio of
projects using the transition theory framework (Figure 4.7). They
concluded that to remove barriers to SLM, an agricultural innovations
systems approach that supports co-production of knowledge with
multiple stakeholders, institutional innovations, a focus on value
chains and strengthening of social capital to facilitate shared learning
and collaboration could accelerate the scaling up of sustainable
technologies and practices from the niche to the landscape level. Policy
integration and establishment of financial mechanisms and incentives
could contribute to overcoming barriers to a regime shift. The new SLM
regime could, in turn, be stabilised and sustained at the landscape level
by multi-stakeholder knowledge platforms and strategic partnerships.
However, transitions to more sustainable regimes and practices are
often challenged by lock-in mechanisms in the current system (Lawhon
and Murphy 2012) such as economies of scale, investments already
made in equipment, infrastructure and competencies, lobbying, shared
beliefs, and practices, that could hamper wider adoption of SLM.
Adaptive, multi-level and participatory governance of social-ecological
systems is considered important for regime shifts and transitions to
take place (Wieczorek 2018) and essential to secure the capacity of
environmental assets to support societal development over longer
time periods (Folke et al. 2005). There is also recognition that effective
environmental policies and programmes need to be informed by
a comprehensive understanding of the biophysical, social, and economic
components and processes of a system, their complex interactions, and
how they respond to different changes (Kelly (Letcher) et al. 2013).
But blueprint policies will not work, due to the wide diversity of rules
and informal institutions used across sectors and regions of the world,
especially in traditional societies (Ostrom 2009).
The most effective way of removing barriers to funding of SLM has
been mainstreaming of SLM objectives and priorities into relevant
policy and development frameworks, and combining SLM best
practices with economic incentives for land users. As the short-term
costs for establishing and maintaining SLM measures are generally
high and constitute a barrier to adoption, land users may need to
be compensated for generation of longer-term public goods, such as
ecosystem services. Cost-benefit analyses can be conducted on SLM
interventions to facilitate such compensations (Liniger et al. 2011;
Nkonya et al. 2016; Tengberg et al. 2016). The landscape approach
is a means to reconcile competing demands on the land and remove
barriers to implementation of SLM (e.g., Sayer et al. 2013; Bürgi et al.
2017). It involves an increased focus on participatory governance,
development of new SLM business models, and innovative funding
schemes, including insurance (Shames et al. 2014). The LDN Fund
takes a landscape approach and raises private finance for SLM and
promotes market-based instruments, such as PES, certification and
carbon trading, that can support scaling up of SLM to improve local
livelihoods, sequester carbon and enhance the resilience to climate
change (Baumber et al. 2019).
4.9
Case studies
Climate change impacts on land degradation can be avoided, reduced
or even reversed, but need to be addressed in a context-sensitive
manner. Many of the responses described in this section can also
provide synergies of adaptation and mitigation. In this section we
provide more in-depth analysis of a number of salient aspects of how
land degradation and climate change interact. Table 4.3 is a synthesis
of how of these case studies relate to climate change and other
broader issues in terms of co-benefits.
4.9.1
Urban green infrastructure
Over half of the world’s population now lives in towns and cities,
a proportion that is predicted to increase to about 70% by the
middle of the century (United Nations 2015). Rapid urbanisation
is a severe threat to land and the provision of ecosystem services
(Seto et al. 2012). However, as cities expand, the avoidance of
land degradation, or the maintenance/enhancement of ecosystem
services is rarely considered in planning processes. Instead, economic
development and the need for space for construction is prioritised,
which can result in substantial pollution of air and water sources,
the degradation of existing agricultural areas and indigenous, natural
or semi-natural ecosystems both within and outside of urban areas.
For instance, urban areas are characterised by extensive impervious
surfaces. Degraded, sealed soils beneath these surfaces do not
provide the same quality of water retention as intact soils. Urban
landscapes comprising 50–90% impervious surfaces can therefore
result in 40–83% of rainfall becoming surface water runoff (Pataki
et al. 2011). With rainfall intensity predicted to increase in many parts
of the world under climate change (Royal Society 2016), increased
water runoff is going to get worse. Urbanisation, land degradation
and climate change are therefore strongly interlinked, suggesting the
need for common solutions (Reed and Stringer 2016).
There is now a large body of research and application demonstrating
the importance of retaining urban green infrastructure (UGI) for
the delivery of multiple ecosystem services (DG Environment News
Alert Service, 2012; Wentworth, 2017) as an important tool to
mitigate and adapt to climate change. UGI can be defined as all
green elements within a city, including, but not limited to, retained
indigenous ecosystems, parks, public greenspaces, green corridors,
street trees, urban forests, urban agriculture, green roofs/walls and
private domestic gardens (Tzoulas et al. 2007). The definition is
usually extended to include ‘blue’ infrastructure, such as rivers, lakes,
bioswales and other water drainage features. The related concept
of Nature-Based Solutions (defined as:
living solutions inspired by,
continuously supported by and using nature, which are designed
to address various societal challenges in a resource-efficient and
adaptable manner and to provide simultaneously economic, social,
and environmental benefits
) has gained considerable traction within
the European Commission as one approach to mainstreaming the
importance of UGI (Maes and Jacobs 2017; European Union 2015).
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Through retaining existing vegetation and ecosystems, revegetating
previous developed land or integrating vegetation into buildings
in the form of green walls and roofs, UGI can play a direct role in
mitigating climate change through carbon sequestration. However,
compared to overall carbon emissions from cities, effects will be small.
Given that UGI necessarily involves the retention and management
of non-sealed surfaces, co-benefits for land degradation (e.g., soil
compaction avoidance, reduced water runoff, carbon storage and
vegetation productivity (Davies et al. 2011; Edmondson et al. 2011,
2014; Yao et al. 2015) will also be apparent. Although not currently
a priority, its role in mitigating land degradation could be substantial.
For instance, appropriately managed innovative urban agricultural
production systems, such as vertical farms, could have the potential
to meet some of the food needs of cities and reduce the production
(and therefore degradation) pressure on agricultural land in rural
areas, although thus far this is unproven (for a recent review, see
Wilhelm and Smith 2018).
The importance of UGI as part of a climate change adaptation
approach has received greater attention and application (Gill et al.
2007; Fryd et al. 2011; Demuzere et al. 2014; Sussams et al. 2015).
The EU’s Adapting to Climate Change white paper emphasises
the ‘crucial role in adaptation in providing essential resources for
social and economic purposes under extreme climate conditions’
(CEC, 2009, p. 9). Increasing vegetation cover, planting street trees
and maintaining/expanding public parks reduces temperatures
(Cavan et al. 2014; Di Leo et al. 2016; Feyisa et al. 2014; Tonosaki
and Kawai 2014; Zölch et al. 2016). Further, the appropriate design
and spatial distribution of greenspaces within cities can help to alter
urban climates to improve human health and comfort (e.g., Brown
and Nicholls 2015; Klemm et al. 2015). The use of green walls and
roofs can also reduce energy use in buildings (e.g., Coma et al.
2017). Similarly, natural flood management and ecosystem-based
approaches of providing space for water, renaturalising rivers and
reducing surface runoff through the presence of permeable surfaces
and vegetated features (including walls and roofs) can manage
flood risks, impacts and vulnerability (e.g., Gill et al. 2007; Munang
et al. 2013). Access to UGI in times of environmental stresses and
shock can provide safety nets for people, and so can be an important
adaptation mechanism, both to climate change (Potschin et al. 2016)
and land degradation.
Most examples of UGI implementation as a climate change
adaptation strategy have centred on its role in water management
for flood risk reduction. The importance for land degradation is either
not stated, or not prioritised. In Beira, Mozambique, the government
is using UGI to mitigate increased flood risks predicted to occur under
climate change and urbanisation, which will be done by improving
the natural water capacity of the Chiveve River. As part of the UGI
approach, mangrove habitats have been restored, and future phases
include developing new multi-functional urban green spaces along
the river (World Bank 2016). The retention of green spaces within
the city will have the added benefit of halting further degradation in
those areas. Elsewhere, planning mechanisms promote the retention
and expansion of green areas within cities to ensure ecosystem
service delivery, which directly halts land degradation, but are largely
Table 4.3 | Synthesis of how the case studies interact with climate change and a broader set of co-benefits.
Legend
carbon sink
reduced emission
reduced flood risk
reduced heat stress
drought resistance
storm protection
protection against
sea level rise
Case studies (4.9)
Mitigation
benefits and
potential
Adaptation
benefits
Co-benefits
Urban green infrastructure (4.9.1)
An increasing majority of the world population live in cities and land
degradation is an urgent matter for urban areas
human health, recreation
Perennial grains (4.9.2)
After 40 years of breeding, perennial grains now seem to
have the potential of reducing climate impacts of agriculture while
increasing its overall sustainability
reduced use of herbicides, reduced
soil erosion and nutrient leakage
Reforestation (4.9.3)
Two cases of successful reforestation serve as illustrations
of the potential of sustained efforts into reforestation
economic return from sustainable
forestry, reduced flood risk
downstream
Management of peat soils (4.9.4)
Degradation of peat soils in tropical and Arctic regions is a major source
of greenhouse gases, hence an urgent mitigation option
improved air quality in tropical
regions
Biochar (4.9.5)
Biochar is a land-management technique of high potential,
but controversial
improved soil fertility
Protection against hurricane damages (4.9.6)
More severe tropical cyclones increase the risk of land degradation
in some areas, hence the need for increased adaptation
reduced losses (human lives,
livelihoods, and assets)
Responses to saltwater intrusion (4.9.7)
The combined effect of climate-induced sea level rise and land-use
change in coastal regions increases the risk of saltwater intrusion in
many coastal regions
improved food and water security
Avoiding coastal maladaptation (4.9.8)
Low-lying coastal areas are in urgent need of adaptation, but examples
have resulted in maladaptation
reduced losses (human lives,
livelihoods, and assets)
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viewed and justified in the context of climate change adaptation
and mitigation. For instance, the Berlin Landscape Programme
includes five plans, one of which covers adapting to climate change
through the recognition of the role of UGI (Green Surge 2016). Major
climate-related challenges facing Durban, South Africa, include sea
level rise, urban heat island, water runoff and conservation (Roberts
and O’Donoghue 2013). Now considered a global leader in climate
adaptation planning (Roberts 2010), Durban’s Climate Change
Adaptation plan includes the retention and maintenance of natural
ecosystems, in particular those that are important for mitigating
flooding, coastal erosion, water pollution, wetland siltation and
climate change (eThekwini Municipal Council 2014).
4.9.2
Perennial grains and soil organic carbon (SOC)
The severe ecological perturbation that is inherent in the conversion of
native perennial vegetation to annual crops, and the subsequent high
frequency of perturbation required to maintain annual crops, results
in at least four forms of soil degradation that will be exacerbated by
the effects of climate change (Crews et al. 2016). First, soil erosion is
a very serious consequence of annual cropping, with median losses
exceeding rates of formation by one to two orders of magnitude in
conventionally plowed agroecosystems, and while erosion is reduced
with conservation tillage, median losses still exceed formation
by several fold (Montgomery 2007). More severe storm intensity
associated with climate change is expected to cause even greater
losses to wind and water erosion (Nearing et al. 2004). Second, the
periods of time in which live roots are reduced or altogether absent
from soils in annual cropping systems allow for substantial losses
of nitrogen from fertilised croplands, averaging 50% globally (Ladha
et al. 2005). This low retention of nitrogen is also expected to worsen
with more intense weather events (Bowles et al. 2018). A third
impact of annual cropping is the degradation of soil structure caused
by tillage, which can reduce infiltration of precipitation, and increase
surface runoff. It is predicted that the percentage of precipitation
that infiltrates into agricultural soils will decrease further under
climate-change scenarios (Basche and DeLonge 2017; Wuest et al.
2006). The fourth form of soil degradation that results from annual
cropping is the reduction of soil organic matter (SOM), a topic of
particular relevance to climate change mitigation and adaptation.
Undegraded cropland soils can theoretically hold far more SOM
(which is about 58% carbon) than they currently do (Soussana
et al. 2006). We know this deficiency because, with few exceptions,
comparisons between cropland soils and those of proximate
mature native ecosystems commonly show a 40–75% decline in
soil carbon attributable to agricultural practices. What happens
when native ecosystems are converted to agriculture that induces
such significant losses of SOM? Wind and water erosion commonly
results in preferential removal of light organic matter fractions that
can accumulate on or near the soil surface (Lal 2003). In addition to
the effects of erosion, the fundamental practices of growing annual
food and fibre crops alters both inputs and outputs of organic matter
from most agroecosystems, resulting in net reductions in soil carbon
equilibria (Soussana et al. 2006; McLauchlan 2006; Crews et al. 2016).
Native vegetation of almost all terrestrial ecosystems is dominated
by perennial plants, and the below-ground carbon allocation of
these perennials is a key variable in determining formation rates of
stable soil organic carbon (SOC) (Jastrow et al. 2007; Schmidt et al.
2011). When perennial vegetation is replaced by annual crops, inputs
of root-associated carbon (roots, exudates, mycorrhizae) decline
substantially. For example, perennial grassland species allocate
around 67% of productivity to roots, whereas annual crops allocate
between 13–30% (Saugier 2001; Johnson et al. 2006).
At the same time, inputs of SOC are reduced in annual cropping
systems, and losses are increased because of tillage, compared to
native perennial vegetation. Tillage breaks apart soil aggregates
which, among other functions, are thought to inhibit soil bacteria,
fungi and other microbes from consuming and decomposing SOM
(Grandy and Neff 2008). Aggregates reduce microbial access to
organic matter by restricting physical access to mineral-stabilised
organic compounds as well as reducing oxygen availability (Cotrufo
et al. 2015; Lehmann and Kleber 2015). When soil aggregates are
broken open with tillage in the conversion of native ecosystems
to agriculture, microbial consumption of SOC and subsequent
respiration of CO
2
increase dramatically, reducing soil carbon stocks
(Grandy and Robertson 2006; Grandy and Neff 2008).
Many management approaches are being evaluated to reduce soil
degradation in general, especially by increasing mineral-protected
forms of SOC in the world’s croplands (Paustian et al. 2016). The
menu of approaches being investigated focuses either on increasing
below-ground carbon inputs, usually through increases in total crop
productivity, or by decreasing microbial activity, usually through
reduced soil disturbance (Crews and Rumsey 2017). However,
the basic biogeochemistry of terrestrial ecosystems managed for
production of annual crops presents serious challenges to achieving
the standing stocks of SOC accumulated by native ecosystems that
preceded agriculture. A novel new approach that is just starting to
receive significant attention is the development of perennial cereal,
legume and oilseed crops (Glover et al. 2010; Baker 2017).
There are two basic strategies that plant breeders and geneticists are
using to develop new perennial grain crop species. The first involves
making wide hybrid crosses between existing elite lines of annual
crops, such as wheat, sorghum and rice, with related wild perennial
species in order to introgress perennialism into the genome of the
annual (Cox et al. 2018; Huang et al. 2018; Hayes et al. 2018). The
other approach is
de novo
domestication of wild perennial species
that have crop-like traits of interest (DeHaan et al. 2016; DeHaan
and Van Tassel 2014). New perennial crop species undergoing
de
novo
domestication include intermediate wheatgrass, a relative of
wheat that produces grain known as Kernza (DeHaan et al. 2018;
Cattani and Asselin 2018) and
Silphium integrifolium
, an oilseed crop
in the sunflower family (Van Tassel et al. 2017). Other grain crops
receiving attention for perennialisation include pigeon pea, barley,
buckwheat and maize (Batello et al. 2014; Chen et al. 2018c) and
a number of legume species (Schlautman et al. 2018). In most cases,
the seed yields of perennial grain crops under development are
well below those of elite modern grain varieties. During the period
that it will take for intensive breeding efforts to close the yield and
other trait gaps between annual and perennial grains, perennial
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proto-crops may be used for purposes other than grain, including
forage production (Ryan et al. 2018). Perennial rice stands out as
a high-yielding exception, as its yields matched those of elite local
varieties in the Yunnan Province for six growing seasons over three
years (Huang et al. 2018).
In a perennial agroecosystem, the biogeochemical controls on
SOC accumulation shift dramatically, and begin to resemble the
controls that govern native ecosystems (Crews et al. 2016). When
erosion is reduced or halted, and crop allocation to roots increases
by 100–200%, and when soil aggregates are not disturbed thus
reducing microbial respiration, SOC levels are expected to increase
(Crews and Rumsey 2017). Deep roots growing year round are
also effective at increasing nitrogen retention (Culman et al.
2013; Jungers et al. 2019). Substantial increases in SOC have been
measured where croplands that had historically been planted to
annual grains were converted to perennial grasses, such as in the US
Conservation Reserve Program or in plantings of second-generation
perennial biofuel crops. Two studies have assessed carbon
accumulation in soils when croplands were converted to the
perennial grain Kernza. In one, researchers found no differences
in soil labile (permanganate-oxidisable) carbon after four years
of cropping to perennial Kernza versus annual wheat in a sandy
textured soil. Given that coarse textured soils do not offer the same
physicochemical protection against microbial attack as many finer
textured soils, these results are not surprising, but these results do
underscore how variable the rates of carbon accumulation can be
(Jastrow et al. 2007). In the second study, researchers assessed the
carbon balance of a Kernza field in Kansas, USA over 4.5 years using
eddy covariance observations (de Oliveira et al. 2018). They found
that the net carbon accumulation rate of about 1500 gC m
–2
yr
–1
in the first year of the study corresponding to the biomass of
Kernza, increasing to about 300 gC m
–2
yr
–1
in the final year, where
CO
2
respiration losses from the decomposition of roots and SOM
approached new carbon inputs from photosynthesis. Based on
measurements of soil carbon accumulation in restored grasslands
in this part of the USA, the net carbon accumulation in stable
organic matter under a perennial grain crop might be expected to
sequester 30–50 gC m
–2
yr
–1
(Post and Kwon 2000) until a new
equilibrium is reached. Sugar cane, a highly productive perennial,
has been shown to accumulate a mean of 187 gC m
–2
yr
–1
in Brazil
(La Scala Júnior et al. 2012).
Reduced soil erosion, increased nitrogen retention, greater water
uptake efficiency and enhanced carbon sequestration represent
improved ecosystem functions, made possible in part by deep and
extensive root systems of perennial crops (Figure 4.8).
When compared to annual grains like wheat, single species stands
of deep-rooted perennial grains such as Kernza are expected to
reduce soil erosion, increase nitrogen retention, achieve greater
water uptake efficiency and enhance carbon sequestration (Crews
et al. 2018) (Figure 4.8). An even higher degree of ecosystem
services can, at least theoretically, be achieved by strategically
combining different functional groups of crops such as a cereal and
a nitrogen-fixing legume (Soussana and Lemaire 2014). Not only is
there evidence from plant-diversity experiments that communities
with higher species richness sustain higher concentrations of SOC
(Hungate et al. 2017; Sprunger and Robertson 2018; Chen, S. 2018;
Yang et al. 2019), but other valuable ecosystem services such as pest
suppression, lower GHG emissions, and greater nutrient retention
may be enhanced (Schnitzer et al. 2011; Culman et al. 2013).
Similar to perennial forage crops such as alfalfa, perennial grain
crops are expected to have a definite productive lifespan, probably
in the range of three to 10 years. A key area of research on perennial
grains cropping systems is to minimise losses of SOC during
conversion of one stand of perennial grains to another. Recent
work demonstrates that no-till conversion of a mature perennial
grassland to another perennial crop will experience several years of
high net CO
2
emissions as decomposition of copious crop residues
exceed ecosystem uptake of carbon by the new crop (Abraha et al.
2018). Most, if not all, of this lost carbon will be recaptured in
the replacement crop. It is not known whether mineral-stabilised
carbon that is protected in soil aggregates is vulnerable to loss in
perennial crop succession.
Figure 4.8 | Comparison of root systems between the newly domesticated intermediate wheatgrass (left) and annual wheat (right).
Photo:
Copyright © Jim Richardson.
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Perennial grains hold promises of agricultural practices, which
can significantly reduce soil erosion and nutrient leakage while
sequestering carbon. When cultivated in mixes with N-fixing species
(legumes) such polycultures also reduce the need for external inputs
of nitrogen – a large source of GHG from conventional agriculture.
4.9.3
Reversing land degradation through
reforestation
4.9.3.1 South Korea case study on reforestation success
In the first half of the 20th century, forests in the Republic of Korea
(South Korea) were severely degraded and deforested during foreign
occupations and the Korean War. Unsustainable harvest for timber
and fuelwood resulted in severely degraded landscapes, heavy soil
erosion and large areas denuded of vegetation cover. Recognising
that South Korea’s economic health would depend on a healthy
environment, South Korea established a national forest service
(1967) and embarked on the first phase of a 10-year reforestation
programme in 1973 (Forest Development Program), which was
followed by subsequent reforestation programmes that ended in
1987, after 2.4 Mha of forests were restored (Figure 4.9).
As a consequence of reforestation, forest volume increased from
11.3 m
3
ha
–1
in 1973 to 125.6 m
3
ha
–1
in 2010 and 150.2 m
3
ha
–1
in 2016 (Korea Forest Service 2017). Increases in forest volume had
significant co-benefits such as increasing water yield by 43% and
reducing soil losses by 87% from 1971 to 2010 (Kim et al. 2017).
The forest carbon density in South Korea has increased
from 5–7 MgC ha
–1
in the period 1955–1973 to more than 30 MgC ha
–1
in the late 1990s (Choi et al. 2002). Estimates of carbon uptake rates
in the late 1990s were 12 TgC yr
–1
(Choi et al. 2002). For the period
1954 to 2012, carbon uptake was 8.3 TgC yr
–1
(Lee et al. 2014), lower
than other estimates because reforestation programmes did not
start until 1973. Net ecosystem production in South Korea was 10.55
± 1.09 TgC yr
−1
in the 1980s, 10.47 ± 7.28 Tg C yr
−1
in the 1990s,
and 6.32 ± 5.02 Tg C yr
−1
in the 2000s, showing a gradual decline
as average forest age increased (Cui et al. 2014). The estimated past
and projected future increase in the carbon content of South Korea’s
forest area during 1992–2034 was 11.8 TgC yr
–1
(Kim et al. 2016).
During the period of forest restoration, South Korea also promoted
inter-agency cooperation and coordination, especially between
the energy and forest sectors, to replace firewood with fossil fuels,
and to reduce demand for firewood to help forest recovery (Bae
et al. 2012). As experience with forest restoration programmes has
increased, emphasis has shifted from fuelwood plantations, often
Figure 4.9 | Example of severely degraded hills in South Korea and stages of forest restoration.
The top two photos are taken in the early 1970s, before and after
restoration, the third photo about five years after restoration, and the bottom photo was taken about 20 years after restoration. Many examples of such restoration success exist
throughout South Korea. (Photos: Copyright © Korea Forest Service)
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with exotic species and hybrid varieties to planting more native
species and encouraging natural regeneration (Kim and Zsuffa 1994;
Lee et al. 2015). Avoiding monocultures in reforestation programmes
can reduce susceptibility to pests (Kim and Zsuffa 1994). Other
important factors in the success of the reforestation programme
were that private landowners were heavily involved in initial efforts
(both corporate entities and smallholders) and that the reforestation
programme was made part of the national economic development
programme (Lamb 2014).
The net present value and the cost-benefit ratio of the reforestation
programme were 54.3 billion and 5.84 billion USD in 2010,
respectively. The breakeven point of the reforestation investment
appeared within two decades. Substantial benefits of the
reforestation programme included disaster risk reduction and carbon
sequestration (Lee et al. 2018a).
In summary, the reforestation programme was a comprehensive
technical and social initiative that restored forest ecosystems,
enhanced the economic performance of rural regions, contributed
to disaster risk reduction, and enhanced carbon sequestration (Kim
et al. 2017; Lee et al. 2018a; UNDP 2017).
The success of the reforestation programme in South Korea and
the associated significant carbon sink indicate a high mitigation
potential that might be contributed by a potential future reforestation
programme in the Democratic People’s Republic of Korea (North
Korea) (Lee et al. 2018b).
4.9.3.2 China case study on reforestation success
The dramatic decline in the quantity and quality of natural forests
in China resulted in land degradation, such as soil erosion, floods,
droughts, carbon emission, and damage to wildlife habitat (Liu and
Diamond 2008). In response to failures of previous forestry and
land policies, the severe droughts in 1997, and the massive floods
in 1998, the central government decided to implement a series of
land degradation control policies, including the National Forest
Protection Program (NFPP), Grain for Green or the Conversion of
Cropland to Forests and Grassland Program (GFGP) (Liu et al. 2008;
Yin 2009; Tengberg et al. 2016; Zhang et al. 2000). The NFPP aimed
to completely ban logging of natural forests in the upper reaches of
the Yangtze and Yellow rivers as well as in Hainan Province by 2000
and to substantially reduce logging in other places (Xu et al. 2006).
In 2011, NFPP was renewed for the 10-year second phase, which also
added another 11 counties around Danjiangkou Reservoir in Hubei
and Henan Provinces, the water source for the middle route of the
South-to-North Water Diversion Project (Liu et al. 2013). Furthermore,
the NFPP afforested 31 Mha by 2010 through aerial seeding, artificial
planting, and mountain closure (i.e., prohibition of human activities
such as fuelwood collection and lifestock grazing) (Xu et al. 2006).
China banned commercial logging in all natural forests by the end
of 2016, which imposed logging bans and harvesting reductions
in 68.2 Mha of forest land – including 56.4 Mha of natural forest
(approximately 53% of China’s total natural forests).
GFGP became the most ambitious of China’s ecological restoration
efforts, with more than 45 billion USD devoted to its implementation
since 1990 (Kolinjivadi and Sunderland 2012) The programme involves
the conversion of farmland on slopes of 15–25° or greater to forest
or grassland (Bennett 2008). The pilot programme started in three
provinces – Sichuan, Shaanxi and Gansu – in 1999 (Liu and Diamond
2008). After its initial success, it was extended to 17 provinces by
2000 and finally to all provinces by 2002, including the headwaters
of the Yangtze and Yellow rivers (Liu et al. 2008).
NFPP and GFGP have dramatically improved China’s land conditions
and ecosystem services, and thus have mitigated the unprecedented
land degradation in China (Liu et al. 2013; Liu et al. 2002; Long
et al. 2006; Xu et al. 2006). NFPP protected 107 Mha forest area
and increased forest area by 10 Mha between 2000 and 2010. For
the second phase (2011–2020), the NFPP plans to increase forest
cover by a further 5.2 Mha, capture 416 million tons of carbon,
provide 648,500 forestry jobs, further reduce land degradation, and
enhance biodiversity (Liu et al. 2013). During 2000–2007, sediment
concentration in the Yellow River had declined by 38%. In the Yellow
River basin, it was estimated that surface runoff would be reduced
by 450 million m
3
from 2000 to 2020, which is equivalent to 0.76%
of the total surface water resources (Jia et al. 2006). GFGP had
cumulatively increased vegetative cover by 25 Mha, with 8.8 Mha
of cropland being converted to forest and grassland, 14.3 Mha
barren land being afforested, and 2.0 Mha of forest regeneration
from mountain closure. Forest cover within the GFGP region has
increased 2% during the first eight years (Liu et al. 2008). In Guizhou
Province, GFGP plots had 35–53% less loss of phosphorus than
non-GFGP plots (Liu et al. 2002). In Wuqi County of Shaanxi Province,
the Chaigou Watershed had 48% and 55% higher soil moisture
and moisture-holding capacity in GFGP plots than in non-GFGP
plots, respectively (Liu et al. 2002). According to reports on China’s
first national ecosystem assessment (2000–2010), for carbon
sequestration and soil retention, coefficients for the GFGP targeting
forest restoration and NFPP are positive and statistically significant.
For sand fixation, GFGP targeting grassland restoration is positive
and statistically significant. Remote sensing observations confirm
that vegetation cover increased and bare soil declined in China over
the period 2001 to 2015 (Qiu et al. 2017). But, where afforestation
is sustained by drip irrigation from groundwater, questions about
plantation sustainability arise (Chen et al. 2018a). Moreover, greater
gains in biodiversity could be achieved by promoting mixed forests
over monocultures (Hua et al. 2016).
NFPP-related activities received a total commitment of 93.7 billion
yuan (about 14 billion USD at 2018 exchange rate) between 1998
and 2009. Most of the money was used to offset economic losses of
forest enterprises caused by the transformation from logging to tree
plantations and forest management (Liu et al. 2008). By 2009, the
cumulative total investment through the NFPP and GFGP exceeded
50 billion USD2009 and directly involved more than 120 million
farmers in 32 million households in the GFGP alone (Liu et al. 2013).
All programmes reduce or reverse land degradation and improve
human well-being. Thus, a coupled human and natural systems
perspective (Liu et al. 2008) would be helpful to understand the
complexity of policies and their impacts, and to establish long-term
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management mechanisms to improve the livelihood of participants
in these programmes and other land management policies in China
and many other parts of the world.
4.9.4
Degradation and management of peat soils
Globally, peatlands cover 3–4% of the Earth’s land area (about
430 Mha) (Xu et al. 2018a) and store 26–44% of estimated global
SOC (Moore 2002). They are most abundant in high northern
latitudes, covering large areas in North America, Russia and Europe.
At lower latitudes, the largest areas of tropical peatlands are located
in Indonesia, the Congo Basin and the Amazon Basin in the form of
peat swamp forests (Gumbricht et al. 2017; Xu et al. 2018a). It is
estimated that, while 80–85% of the global peatland areas is still
largely in a natural state, they are such carbon-dense ecosystems that
degraded peatlands (0.3% of the terrestrial land) are responsible for
a disproportional 5% of global anthropogenic CO
2
emissions – that
is, an annual addition of 0.9–3 GtCO
2
to the atmosphere (Dommain
et al. 2012; IPCC 2014c).
Peatland degradation is not well quantified globally, but regionally
peatland degradation can involve a large percentage of the areas.
Land-use change and degradation in tropical peatlands have
primarily been quantified in Southeast Asia, where drainage and
conversion to plantation crops is the dominant transition (Miettinen
et al. 2016). Degradation of peat swamps in Peru is also a growing
concern and one pilot survey showed that more than 70% of the
peat swamps were degraded in one region surveyed (Hergoualc’h
et al. 2017a). Around 65,000 km
2
or 10% of the European peatland
area has been lost and 44% of the remaining European peatlands
are degraded (Joosten, H., Tanneberger 2017). Large areas of fens
have been entirely ‘lost’ or greatly reduced in thickness due to peat
wastage (Lamers et al. 2015).
The main drivers of the acceleration of peatland degradation in the
20th century were associated with drainage for agriculture, peat
extraction and afforestation related activities (burning, over-grazing,
fertilisation) with a variable scale and severity of impact depending
on existing resources in the various countries (O’Driscoll et al. 2018;
Cobb, A.R. et al. Dommain et al. 2018; Lamers et al. 2015). New drivers
include urban development, wind farm construction (Smith et al.
2012), hydroelectric development, tar sands mining and recreational
uses (Joosten and Tanneberger 2017). Anthropogenic pressures are
now affecting peatlands in previously geographically isolated areas
with consequences for global environmental concerns and impacts
on local livelihoods (Dargie et al. 2017; Lawson et al. 2015; Butler
et al. 2009).
Drained and managed peatlands are GHG-emission hotspots (Swails
et al. 2018; Hergoualc’h et al. 2017a, 2017b; Roman-Cuesta et al.
2016). In most cases, lowering of the water table leads to direct
and indirect CO
2
and N
2
O emissions to the atmosphere, with rates
dependent on a range of factors, including the groundwater level and
the water content of surface peat layers, nutrient content, temperature,
and vegetation communities. The exception is nutrient-limited boreal
peatlands (Minkkinen et al. 2018; Ojanen et al. 2014). Drainage also
increases erosion and dissolved organic carbon loss, removing stored
carbon into streams as dissolved and particulate organic carbon,
which ultimately returns to the atmosphere (Moore et al. 2013; Evans
et al. 2016).
In tropical peatlands, oil palm is the most widespread plantation crop
and, on average, it emits around 40 tCO
2
ha
–1
yr
–1
; Acacia plantations
for pulpwood are the second most widespread plantation crop and
emit around 73 tCO
2
ha
–1
yr
–1
(Drösler et al. 2013). Other land uses
typically emit less than 37 tCO
2
ha
–1
yr
–1
. Total emissions from
peatland drainage in the region are estimated to be between 0.07
and 1.1 GtCO
2
yr
–1
(Houghton and Nassikas 2017; Frolking et al. 2011).
Land-use change also affects the fluxes of N
2
O and CH
4
. Undisturbed
tropical peatlands emit about 0.8 MtCH
4
yr
–1
and 0.002 MtN
2
O yr
–1
,
while disturbed peatlands emit 0.1 MtCH
4
yr
–1
and 0.2 MtN
2
O–N yr
–1
(Frolking et al. 2011). These N
2
O emissions are probably low, as new
findings show that emissions from fertilised oil palm can exceed
20 kgN
2
O–N ha
–1
yr
–1
(Oktarita et al. 2017).
In the temperate and boreal zones, peatland drainage often leads to
emissions in the order of 0.9 to 9.5 tCO
2
ha
–1
y
–1
in forestry plantations
and 21 to 29 tCO
2
ha
–1
y
–1
in grasslands and croplands. Nutrient-poor
sites often continue to be CO
2
sinks for long periods (e.g., 50 years)
following drainage and, in some cases, sinks for atmospheric CH
4
,
even when drainage ditch emissions are considered (Minkkinen
et al. 2018; Ojanen et al. 2014). Undisturbed boreal and temperate
peatlands emit about 30 MtCH
4
yr
–1
and 0.02 MtN
2
O–N yr
–1
, while
disturbed peatlands emit 0.1 MtCH
4
yr
–1
and 0.2 MtN
2
O–N yr
–1
(Frolking et al. 2011).
Fire emissions from tropical peatlands are only a serious issue in
Southeast Asia, where they are responsible for 634 (66–4070)
MtCO
2
yr
–1
(van der Werf et al. 2017). Much of the variability is
linked with the El Niño–Southern Oscillation (ENSO), which produces
drought conditions in this region. Anomalously active fire seasons
have also been observed in non-drought years and this has been
attributed to the increasing effect of high temperatures that dry
vegetation out during short dry spells in otherwise normal rainfall
years (Fernandes et al. 2017; Gaveau et al. 2014). Fires have
significant societal impacts; for example, the 2015 fires caused more
than 100,000 additional deaths across Indonesia, Malaysia and
Singapore, and this event was more than twice as deadly as the 2006
El Niño event (Koplitz et al. 2016).
Peatland degradation in other parts of the world differs from Asia. In
Africa, for large peat deposits like those found in the Cuvette Centrale
in the Congo Basin or in the Okavango inland delta, the principle threat
is changing rainfall regimes due to climate variability and change
(Weinzierl et al. 2016; Dargie et al. 2017). Expansion of agriculture is not
yet a major factor in these regions. In the Western Amazon, extraction
of non-timber forest products like the fruits of
Mauritia flexuosa
(moriche palm) and Suri worms are major sources of degradation that
lead to losses of carbon stocks (Hergoualc’h et al. 2017a).
The effects of peatland degradation on livelihoods have not been
systematically characterised. In places where plantation crops are
driving the conversion of peat swamps, the financial benefits can
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be considerable. One study in Indonesia found that the
net present
value of an oil palm plantation is between 3,835 and 9,630 USD
per ha to land owners (Butler et al. 2009). High financial returns are
creating incentives for the expansion of smallholder production in
peatlands. Smallholder plantations extend over 22% of the peatlands
in insular Southeast Asia compared to 27% for industrial plantations
(Miettinen et al. 2016). In places where income is generated from
extraction of marketable products, ecosystem degradation probably
has a negative effect on livelihoods. For example, the sale of fruits
of
M. flexuosa
in some parts of the western Amazon constitutes as
much as 80% of the winter income of many rural households, but
information on trade values and value chains of
M. flexuosa
is still
sparse (Sousa et al. 2018; Virapongse et al. 2017).
There is little experience with peatland restoration in the tropics.
Experience from northern latitudes suggests that extensive damage
and changes in hydrological conditions mean that restoration in
many cases is unachievable (Andersen et al. 2017). In the case of
Southeast Asia, where peatlands form as raised bogs, drainage leads
to collapse of the dome, and this collapse cannot be reversed by
rewetting. Nevertheless, efforts are underway to develop solutions,
or at least partial solutions in Southeast Asia, for example, by the
Indonesian Peatland Restoration Agency. The first step is to restore
the hydrological regime in drained peatlands, but so far experiences
with canal blocking and reflooding of the peat have been only
partially successful (Ritzema et al. 2014). Market incentives with
certification through the Roundtable on Sustainable Palm Oil have
also not been particularly successful as many concessions seek
certification only after significant environmental degradation has
occurred (Carlson et al. 2017). Certification had no discernible effect
on forest loss or fire detection in peatlands in Indonesia. To date there
is no documentation of restoration methods or successes in many
other parts of the tropics. However, in situations where degradation
does not involve drainage, ecological restoration may be possible. In
South America, for example, there is growing interest in restoration
of palm swamps, and as experiences are gained it will be important
to document success factors to inform successive efforts (Virapongse
et al. 2017).
In higher latitudes where degraded peatlands have been drained,
the most effective option to reduce losses from these large organic
carbon stocks is to change hydrological conditions and increase
soil moisture and surface wetness (Regina et al. 2015). Long-term
GHG monitoring in boreal sites has demonstrated that rewetting
and restoration noticeably reduce emissions compared to degraded
drained sites and can restore the carbon sink function when
vegetation is re-established (Wilson et al. 2016; IPCC 2014a; Nugent
et al. 2018) although, restored ecosystems may not yet be as resilient
as their undisturbed counterparts (Wilson et al. 2016). Several
studies have demonstrated the co-benefits of rewetting specific
degraded peatlands for biodiversity, carbon sequestration, (Parry
et al. 2014; Ramchunder et al. 2012; Renou-Wilson et al. 2018) and
other ecosystem services, such as improvement of water storage and
quality (Martin-Ortega et al. 2014) with beneficial consequences for
human well-being (Bonn et al. 2016; Parry et al. 2014).
4.9.5
Biochar
Biochar is organic matter that is carbonised by heating in an
oxygen-limited environment, and used as a soil amendment. The
properties of biochar vary widely, dependent on the feedstock and
the conditions of production. Biochar could make a significant
contribution to mitigating both land degradation and climate
change, simultaneously.
4.9.5.1 Role of biochar in climate change mitigation
Biochar is relatively resistant to decomposition compared with fresh
organic matter or compost, so represents a long-term carbon store
(
very high confidence
). Biochars produced at higher temperature
(>450°C) and from woody material have greater stability than those
produced at lower temperature (300–450°C), and from manures
(
very high confidence
) (Singh et al. 2012; Wang et al. 2016b). Biochar
stability is influenced by soil properties: biochar carbon can be further
stabilised by interaction with clay minerals and native SOM (
medium
evidence
) (Fang et al. 2015). Biochar stability is estimated to range
from decades to thousands of years, for different biochars in different
applications (Singh et al. 2015; Wang et al. 2016). Biochar stability
decreases as ambient temperature increases (
limited evidence
) (Fang
et al. 2017).
Biochar can enhance soil carbon stocks through ‘negative priming’, in
which rhizodeposits are stabilised through sorption of labile carbon
on biochar, and formation of biochar-organo-mineral complexes
(Weng et al. 2015, 2017, 2018; Wang et al. 2016b). Conversely, some
studies show increased turnover of native soil carbon (‘positive
priming’) due to enhanced soil microbial activity induced by biochar.
In clayey soils, positive priming is minor and short-lived compared
to negative priming effects, which dominate in the medium to long
term (Singh and Cowie 2014; Wang et al. 2016b). Negative priming
has been observed particularly in loamy grassland soil (Ventura et al.
2015) and clay-dominated soils, whereas positive priming is reported
in sandy soils (Wang et al. 2016b) and those with low carbon content
(Ding et al. 2018).
Biochar can provide additional climate-change mitigation by
decreasing nitrous oxide (N
2
O) emissions from soil, due in part to
decreased substrate availability for denitrifying organisms, related to
the molar H/C ratio of the biochar (Cayuela et al. 2015). However,
this impact varies widely: meta-analyses found an average decrease
in N
2
O emissions from soil of 30–54%, (Cayuela et al. 2015; Borchard
et al. 2019; Moore 2002), although another study found no significant
reduction in field conditions when weighted by the inverse of the
number of observations per site (Verhoeven et al. 2017). Biochar has
been observed to reduce methane emissions from flooded soils, such
as rice paddies, though, as for N
2
O, results vary between studies and
increases have also been observed (He et al. 2017; Kammann et al.
2017). Biochar has also been found to reduce methane uptake by
dryland soils, though the effect is small in absolute terms (Jeffery
et al. 2016).
Additional climate benefits of biochar can arise through: reduced
nitrogen fertiliser requirements, due to reduced losses of nitrogen
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through leaching and/or volatilisation (Singh et al. 2010) and enhanced
biological nitrogen fixation (Van Zwieten et al. 2015); increased yields
of crop, forage, vegetable and tree species (Biederman and Harpole
2013), particularly in sandy soils and acidic tropical soils (Simon et al.
2017); avoided GHG emissions from manure that would otherwise be
stockpiled, crop residues that would be burned or processing residues
that would be landfilled; and reduced GHG emissions from compost
when biochar is added (Agyarko-Mintah et al. 2017; Wu et al. 2017a).
Climate benefits of biochar could be substantially reduced through
reduction in albedo if biochar is surface-applied at high rates to
light-coloured soils (Genesio et al. 2012; Bozzi et al. 2015; Woolf
et al. 2010), or if black carbon dust is released (Genesio et al. 2016).
Pelletising or granulating biochar, and applying below the soil surface
or incorporating into the soil, minimises the release of black carbon
dust and reduces the effect on albedo (Woolf et al. 2010).
Biochar is a potential ‘negative emissions’ technology: the
thermochemical conversion of biomass to biochar slows mineralisation
of the biomass, delivering long-term carbon storage; gases released
during pyrolysis can be combusted for heat or power, displacing fossil
energy sources, and could be captured and sequestered if linked with
infrastructure for CCS (Smith 2016). Studies of the lifecycle climate
change impacts of biochar systems generally show emissions reduction
in the range 0.4 –1.2 tCO
2
e t
–1
(dry) feedstock (Cowie et al. 2015). Use of
biomass for biochar can deliver greater benefits than use for bioenergy,
if applied in a context where it delivers agronomic benefits and/or
reduces non-CO
2
GHG emissions (Ji et al. 2018; Woolf et al. 2010, 2018;
Xu et al. 2019). A global analysis of technical potential, in which biomass
supply constraints were applied to protect against food insecurity,
loss of habitat and land degradation, estimated technical potential
abatement of 3.7–6.6 GtCO
2
e yr
–1
(including 2.6–4.6 GtCO
2
e yr
–1
carbon stabilisation), with theoretical potential to reduce total
emissions over the course of a century by 240–475 GtCO
2
e (Woolf et al.
2010). Fuss et al. (2018) propose a range of 0.5–2 GtCO
2
e per year
as the sustainable potential for negative emissions through biochar.
Mitigation potential of biochar is reviewed in Chapter 2.
4.9.5.2 Role of biochar in management of land degradation
Biochars generally have high porosity, high surface area and
surface-active properties that lead to high absorptive and adsorptive
capacity, especially after interaction in soil (Joseph et al. 2010). As
a result of these properties, biochar could contribute to avoiding,
reducing and reversing land degradation through the following
documented benefits:
•
Improved nutrient use efficiency due to reduced leaching of
nitrate and ammonium (e.g., Haider et al. 2017) and increased
availability of phosphorus in soils with high phosphorus fixation
capacity (Liu et al. 2018c), potentially reducing nitrogen and
phosphorus fertiliser requirements.
•
Management of heavy metals and organic pollutants: through
reduced bioavailability of toxic elements (O’Connor et al. 2018;
Peng et al. 2018), by reducing availability, through immobilisation
due to increased pH and redox effects (Rizwan et al. 2016) and
adsorption on biochar surfaces (Zhang et al. 2013) thus providing
a means of remediating contaminated soils, and enabling their
utilisation for food production.
• Stimulation of beneficial soil organisms, including earthworms
and mycorrhizal fungi (Thies et al. 2015).
•
Improved porosity and water-holding capacity (Quin et al. 2014),
particularly in sandy soils (Omondi et al. 2016), enhancing
microbial function during drought (Paetsch et al. 2018).
• Amelioration of soil acidification, through application of biochars
with high pH and acid-neutralising capacity (Chan et al. 2008;
Van Zwieten et al. 2010).
Biochar systems can deliver a range of other co-benefits, including
destruction of pathogens and weed propagules, avoidance of landfill,
improved handling and transport of wastes such as sewage sludge,
management of biomass residues such as environmental weeds and
urban greenwaste, reduction of odours and management of nutrients
from intensive livestock facilities, reduction in environmental nitrogen
pollution and protection of waterways. As a compost additive, biochar
has been found to reduce leaching and volatilisation of nutrients,
increasing nutrient retention through absorption and adsorption
processes (Joseph et al. 2018).
While many studies report positive responses, some studies have
found negative or zero impacts on soil properties or plant response
(e.g., Kuppusamy et al. 2016). The risk that biochar may enhance
polycyclic aromatic hydrocarbon (PAH) in soil or sediments has been
raised (Quilliam et al. 2013; Ojeda et al. 2016), but bioavailability
of PAH in biochar has been shown to be very low (Hilber
et al. 2017) Pyrolysis of biomass leads to losses of volatile nutrients,
especially nitrogen. While availability of nitrogen and phosphorus in
biochar is lower than in fresh biomass, (Xu et al. 2016) the impact of
biochar on plant uptake is determined by the interactions between
biochar, soil minerals and activity of microorganisms (e.g., Vanek and
Lehmann 2015; Nguyen et al. 2017). To avoid negative responses, it
is important to select biochar formulations to address known soil
constraints, and to apply biochar prior to planting (Nguyen et al.
2017). Nutrient enrichment improves the performance of biochar
from low nutrient feedstocks (Joseph et al. 2013). While there are
many reports of biochar reducing disease or pest incidence, there are
also reports of nil or negative effects (Bonanomi et al. 2015). Biochar
may induce systemic disease resistance (e.g., Elad et al. 2011),
though Viger et al. (2015) reported down-regulation of plant defence
genes, suggesting increased susceptibility to insect and pathogen
attack. Disease suppression where biochar is applied is associated
with increased microbial diversity and metabolic potential of the
rhizosphere microbiome (Kolton et al. 2017). Differences in properties
related to feedstock (Bonanomi et al. 2018) and differential response
to biochar dose, with lower rates more effective (Frenkel et al. 2017),
contribute to variable disease responses.
The constraints on biochar adoption include: the high cost and
limited availability due to limited large-scale production; limited
amount of unutilised biomass; and competition for land for growing
biomass. While early biochar research tended to use high rates of
application (10 t ha
–1
or more) subsequent studies have shown that
biochar can be effective at lower rates, especially when combined
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4
with chemical or organic fertilisers (Joseph et al. 2013). Biochar can
be produced at many scales and levels of engineering sophistication,
from simple cone kilns and cookstoves to large industrial-scale units
processing several tonnes of biomass per hour (Lehmann and Joseph
2015). Substantial technological development has occurred recently,
though large-scale deployment is limited to date.
Governance of biochar is required to manage climate, human health
and contamination risks associated with biochar production in poorly
designed or operated facilities that release methane or particulates
(Downie et al. 2012; Buss et al. 2015), to ensure quality control of
biochar products, and to ensure that biomass is sourced sustainably
and is uncontaminated. Measures could include labelling standards,
sustainability certification schemes and regulation of biochar
production and use. Governance mechanisms should be tailored to
context, commensurate with risks of adverse outcomes.
In summary, application of biochar to soil can improve soil chemical,
physical and biological attributes, enhancing productivity and
resilience to climate change, while also delivering climate-change
mitigation through carbon sequestration and reduction in GHG
emissions (
medium agreement
,
robust evidence
). However, responses
to biochar depend on the biochar’s properties, which are in turn
dependent on feedstock and biochar production conditions, and the
soil and crop to which it is applied. Negative or nil results have been
recorded. Agronomic and methane-reduction benefits appear greatest
in tropical regions, where acidic soils predominate and suboptimal
rates of lime and fertiliser are common, while carbon stabilisation is
greater in temperate regions. Biochar is most effective when applied
in low volumes to the most responsive soils and when properties are
matched to the specific soil constraints and plant needs. Biochar is
thus a practice that has potential to address land degradation and
climate change simultaneously, while also supporting sustainable
development. The potential of biochar is limited by the availability
of biomass for its production. Biochar production and use requires
regulation and standardisation to manage risks (
strong agreement
).
4.9.6
Management of land degradation induced
by tropical cyclones
Tropical cyclones are normal disturbances that natural ecosystems
have been affected by and recovered from for millennia. Climate
models mostly predict decreasing frequency of tropical cyclones, but
dramatically increasing intensity of the strongest storms, as well as
increasing rainfall rates (Bacmeister et al. 2018; Walsh et al. 2016b).
Large amplitude fluctuations in the frequency and intensity complicate
both the detection and attribution of tropical cyclones to climate
change (Lin and Emanuel 2016b). Yet, the force of high-intensity
cyclones has increased and is expected to escalate further due
to global climate change (
medium agreement
,
robust evidence
)
(Knutson et al. 2010; Bender et al. 2010; Vecchi et al. 2008; Bhatia
et al. 2018; Tu et al. 2018; Sobel et al. 2016). Tropical cyclone paths
are also shifting towards the poles, increasing the area subject to
tropical cyclones (Sharmila and Walsh 2018; Lin and Emanuel 2016b).
Climate change alone will affect the hydrology of individual wetland
ecosystems, mostly through changes in precipitation and temperature
regimes with great global variability (Erwin 2009). Over the last seven
decades, the speed at which tropical cyclones move has decreased
significantly, as expected from theory, exacerbating the damage on
local communities from increasing rainfall amounts and high wind
speed (Kossin 2018). Tropical cyclones will accelerate changes in
coastal forest structure and composition. The heterogeneity of land
degradation at coasts that are affected by tropical cyclones can be
further enhanced by the interaction of its components (for example,
rainfall, wind speed, and direction) with topographic and biological
factors (for example, species susceptibility) (Luke et al. 2016).
Small Island Developing States (SIDS) are particularly affected by
land degradation induced by tropical cyclones; recent examples are
Matthew (2016) in the Caribbean, and Pam (2015) and Winston
(2016) in the Pacific (Klöck and Nunn 2019; Handmer and Nalau 2019).
Even if the Pacific Ocean has experienced cyclones of unprecedented
intensity in recent years, their geomorphological effects may not be
unprecedented (Terry and Lau 2018).
Cyclone impacts on coastal areas is not restricted to SIDS, but
a problem for all low-lying coastal areas (Petzold and Magnan 2019).
The Sundarbans, one of the world’s largest coastal wetlands, covers
about one million hectares between Bangladesh and India. Large
areas of the Sundarbans mangroves have been converted into paddy
fields over the past two centuries and, more recently, into shrimp
farms (Ghosh et al. 2015). In 2009, cyclone Aila caused incremental
stresses on the socio-economic conditions of the Sundarbans coastal
communities through rendering huge areas of land unproductive for
a long time (Abdullah et al. 2016). The impact of Aila was widespread
throughout the Sundarbans mangroves, showing changes between the
pre- and post-cyclonic period of 20–50% in the enhanced vegetation
index (Dutta et al. 2015), although the magnitude of the effects of the
Sundarbans mangroves derived from climate change is not yet defined
(Payo et al. 2016; Loucks et al. 2010; Gopal and Chauhan 2006; Ghosh
et al. 2015; Chaudhuri et al. 2015). There is
high agreement
that
the joint effect of climate change and land degradation will be very
negative for the area, strongly affecting the environmental services
provided by these forests, including the extinction of large mammal
species (Loucks et al. 2010). The changes in vegetation are mainly due
to inundation and erosion (Payo et al. 2016).
Tropical cyclone Nargis unexpectedly hit the Ayeyarwady River delta
(Myanmar) in 2008 with unprecedented and catastrophic damages
to livelihoods, destruction of forests and erosion of fields (Fritz et al.
2009) as well as eroding the shoreline 148 m compared with the
long-term average (1974–2015) of 0.62 m yr
–1
. This is an example of
the disastrous effects that changing cyclone paths can have on areas
previously not affected by cyclones (Fritz et al. 2010).
4.9.6.1 Management of coastal wetlands
Tropical cyclones mainly, but not exclusively, affect coastal regions,
threatening maintenance of the associated ecosystems, mangroves,
wetlands, seagrasses, and so on. These areas not only provide food,
water and shelter for fish, birds and other wildlife, but also provide
important ecosystem services such as water-quality improvement,
flood abatement and carbon sequestration (Meng et al. 2017).
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Despite their importance, coastal wetlands are listed amongst the
most heavily damaged of natural ecosystems worldwide. Starting in
the 1990s, wetland restoration and re-creation became a ‘hotspot’
in the ecological research fields (Zedler 2000). Coastal wetland
restoration and preservation is an extremely cost-effective strategy
for society, for example, the preservation of coastal wetlands in the
USA provides storm protection services, with a cost of 23.2 billion
USD yr
–1
(Costanza et al. 2008).
There is a
high agreement
with
medium evidence
that the success
of wetland restoration depends mainly on the flow of the water
through the system, the degree to which re-flooding occurs,
disturbance regimes, and the control of invasive species (Burlakova
et al. 2009; López-Rosas et al. 2013). The implementation of the
Ecological Mangrove Rehabilitation protocol (López-Portillo et al.
2017) that includes monitoring and reporting tasks, has been proven
to deliver successful rehabilitation of wetland ecosystem services.
4.9.7
Saltwater intrusion
Current environmental changes, including climate change, have
caused sea levels to rise worldwide, particularly in tropical and
subtropical regions (Fasullo and Nerem 2018). Combined with
scarcity of water in river channels, such rises have been instrumental
in the intrusion of highly saline seawater inland, posing a threat
to coastal areas and an emerging challenge to land managers and
policymakers. Assessing the extent of salinisation due to sea water
intrusion at a global scale nevertheless remains challenging. Wicke
et al. (2011) suggest that across the world, approximately 1.1 Gha
of land is affected by salt, with 14% of this categorised as forest,
wetland or some other form of protected area. Seawater intrusion is
generally caused by (i) increased tidal activity, storm surges, cyclones
and sea storms due to changing climate, (ii) heavy groundwater
extraction or land-use changes as a result of changes in precipitation,
and droughts/floods, (iii) coastal erosion as a result of destruction of
mangrove forests and wetlands, (iv) construction of vast irrigation
canals and drainage networks leading to low river discharge in the
deltaic region; and (v) sea level rise contaminating nearby freshwater
aquifers as a result of subsurface intrusion (Uddameri et al. 2014).
The Indus Delta, located in the south-eastern coast of Pakistan near
Karachi in the North Arabian Sea, is one of the six largest estuaries in
the world, spanning an area of 600,000 ha. The Indus delta is a clear
example of seawater intrusion and land degradation due to local
as well as up-country climatic and environmental conditions (Rasul
et al. 2012). Salinisation and waterlogging in the up-country areas
including provinces of Punjab and Sindh is, however, caused by the
irrigation network and over-irrigation (Qureshi 2011).
Such degradation takes the form of high soil salinity, inundation and
waterlogging, erosion and freshwater contamination. The interannual
DEGRADED
MANGROVE SITE
RESTORED
MANGROVE SITE
Successful natural regeneration
Positive assessment outcome
Step 1
Understand both
autoecology and
community ecology
of mangrove species
Failed natural
regeneration
Step 2
Understand appropriate
hydrologic regimes
Step 4
Survey subsistence users,
traditions, perceptions,
need and alternatives
(sustainable utilization)
Step 6
Restore hydrology and
remove any barrier to
natural regeneration
Step 7
Select appropriate species, popula-
tions and individuals for planting
Step 8
Mangrove planting
Step 9: Assessment of success and functionality
Monitor and
assess
potential for
sustainable
utilization
Monitor and
assess
vegetation
development
and floristic
succession
Monitor and
assess
faunistic
recruitment
Monitor and
assess
environmental
factors and
processes
Step 10
Give recommendation for
improved management
Failed aided
natural regeneration
Successful aided
natural regeneration
Negative
assessment
outcome
Step 5
Select appropriate
restoration sites based
on steps 1–4
Step 3
Assess factors
hampering succession
Figure 4.10 | Decision tree showing recommended steps and tasks to restore a mangrove wetland based on original site conditions.
(Modified from
Bosire et al. 2008.)
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variability of precipitation with flooding conditions in some years
and drought conditions in others has caused variable river flows and
sediment runoff below Kotri Barrage (about 200 km upstream of the
Indus delta). This has affected hydrological processes in the lower
reaches of the river and the delta, contributing to the degradation
(Rasul et al. 2012).
Over 480,000 ha of fertile land is now affected by sea water intrusion,
wherein eight coastal subdivisions of the districts of Badin and Thatta
are mostly affected (Chandio et al. 2011). A very high intrusion rate
of 0.179 ± 0.0315 km yr
–1
, based on the analysis of satellite data,
was observed in the Indus delta during the 10 years between 2004
and 2015 (Kalhoro et al. 2016). The area of agricultural crops under
cultivation has been declining, with economic losses of millions of
USD (IUCN 2003). Crop yields have reduced due to soil salinity, in
some places failing entirely. Soil salinity varies seasonally, depending
largely on the river discharge: during the wet season (August 2014),
salinity (0.18 mg L
–1
) reached 24 km upstream, while during the dry
season (May 2013), it reached 84 km upstream (Kalhoro et al. 2016).
The freshwater aquifers have also been contaminated with sea water,
rendering them unfit for drinking or irrigation purposes. Lack of clean
drinking water and sanitation causes widespread diseases, of which
diarrhoea is most common (IUCN 2003).
Lake Urmia in northwest Iran, the second-largest saltwater lake in
the world and the habitat for endemic Iranian brine shrimp,
Artemia
urmiana
, has also been affected by salty water intrusion. During a 17-
year period between 1998 and 2014, human disruption, including
agriculture and years of dam building affected the natural flow of
freshwater as well as salty sea water in the surrounding area of Lake
Urmia. Water quality has also been adversely affected, with salinity
fluctuating over time, but in recent years reaching a maximum of
340 g L
–1
(similar to levels in the Dead Sea). This has rendered the
underground water unfit for drinking and agricultural purposes and
risky to human health and livelihoods. Adverse impacts of global
climate change as well as direct human impacts have caused changes
in land use, overuse of underground water resources and construction
of dams over rivers, which resulted in the drying-up of the lake in
large part. This condition created sand, dust and salt storms in the
region which affected many sectors including agriculture, water
resources, rangelands, forests and health, and generally presented
desertification conditions around the lake (Karbassi et al. 2010;
Marjani and Jamali 2014; Shadkam et al. 2016).
Rapid irrigation expansion in the basin has, however, indirectly
contributed to inflow reduction. Annual inflow to Lake Urmia has
dropped by 48% in recent years. About three-fifths of this change
was caused by climate change and two-fifths by water resource
development and agriculture (Karbassi et al. 2010; Marjani and
Jamali 2014; Shadkam et al. 2016).
In the drylands of Mexico, intensive production of irrigated wheat
and cotton using groundwater (Halvorson et al. 2003) resulted
in sea water intrusion into the aquifers of La Costa de Hermosillo,
a coastal agricultural valley at the centre of Sonora Desert in
Northwestern Mexico. Production of these crops in 1954 was on
64,000 ha of cultivated area, increasing to 132,516 ha in 1970,
but decreasing to 66,044 ha in 2009 as a result of saline intrusion
from the Gulf of California (Romo-Leon et al. 2014). In 2003, only
15% of the cultivated area was under production, with around
80,000 ha abandoned due to soil salinisation whereas in 2009,
around 40,000 ha was abandoned (Halvorson et al. 2003; Romo-Leon
et al. 2014). Salinisation of agricultural soils could be exacerbated by
climate change, as Northwestern Mexico is projected to be warmer
and drier under climate change scenarios (IPCC 2013a).
In other countries, intrusion of seawater is exacerbated by
destruction of mangrove forests. Mangroves are important coastal
ecosystems that provide spawning bed for fish, timber for building,
and livelihoods to dependent communities. They also act as barriers
against coastal erosion, storm surges, tropical cyclones and tsunamis
(Kalhoro et al. 2017)
and are among the most carbon-rich stocks
on Earth (Atwood et al. 2017). They nevertheless face a variety of
threats: climatic (storm surges, tidal activities, high temperatures) and
human (coastal developments, pollution, deforestation, conversion
to aquaculture, rice culture, oil palm plantation), leading to declines
in their areas. In Pakistan, using remote sensing, the mangrove
forest cover in the Indus delta decreased from 260,000 ha in 1980s
to 160,000 ha in 1990 (Chandio et al. 2011). Based on remotely
sensed data, a sharp decline in the mangrove area was also found
in the arid coastal region of Hormozgan province in southern Iran
during 1972, 1987 and 1997 (Etemadi et al. 2016). Myanmar has the
highest rate (about 1% yr
–1
) of mangrove deforestation in the world
(Atwood et al. 2017). Regarding global loss of carbon stored in the
mangrove due to deforestation, four countries exhibited high levels
of loss: Indonesia (3410 GgCO
2
yr
–1
), Malaysia (1288 GgCO
2
yr
–1
), US
(206 GgCO
2
yr
–1
) and Brazil (186 GgCO
2
yr
–1
). Only in Bangladesh
and Guinea Bissau was there no decline in the mangrove area from
2000 to 2012 (Atwood et al. 2017).
Frequency and intensity of average tropical cyclones will continue to
increase (Knutson et al. 2015) and global sea level will continue to
rise. The IPCC (2013) projected with
medium confidence
that the sea
level in the Asia Pacific region will rise from 0.4 to 0.6 m, depending
on the emission pathway, by the end of this century. Adaptation
measures are urgently required to protect the world’s coastal areas
from further degradation due to saline intrusion. A viable policy
framework is needed to ensure that the environmental flows to
deltas in order to repulse the intruding seawater.
4.9.8
Avoiding coastal maladaptation
Coastal degradation – for example, beach erosion, coastal squeeze,
and coastal biodiversity loss – as a result of rising sea levels is a major
concern for low lying coasts and small islands (
high confidence
). The
contribution of climate change to increased coastal degradation has
been well documented in AR5 (Nurse et al. 2014; Wong et al. 2014)
and is further discussed in Section 4.4.1.3 as well as in the IPCC
Special Report on the Ocean and Cryosphere in a Changing Climate
(SROCC). However, coastal degradation can also be indirectly induced
by climate change as the result of adaptation measures that involve
changes to the coastal environment, for example, coastal protection
measures against increased flooding and erosion due to sea level
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rise, and storm surges transforming the natural coast to a ‘stabilised’
coastline (Cooper and Pile 2014; French 2001). Every kind of adaptation
response option is context-dependent, and, in fact, sea walls play an
important role for adaptation in many places. Nonetheless, there are
observed cases where the construction of sea walls can be considered
‘maladaptation’ (Barnett and O’Neill 2010; Magnan et al. 2016) by
leading to increased coastal degradation, such as in the case of small
islands where, due to limitations of space, coastal retreat is less of an
option than in continental coastal zones. There is emerging literature
on the implementation of alternative coastal protection measures and
mechanisms on small islands to avoid coastal degradation induced by
sea walls (e.g., Mycoo and Chadwick 2012; Sovacool 2012).
In many cases, increased rates of coastal erosion due to the
construction of sea walls are the result of the negligence of local
coastal morphological dynamics and natural variability as well as
the interplay of environmental and anthropogenic drivers of coastal
change (
medium evidence
,
high agreement
). Sea walls in response
to coastal erosion may be ill-suited for extreme wave heights under
cyclone impacts and can lead to coastal degradation by keeping
overflowing sea water from flowing back into the sea, and therefore
affect the coastal vegetation through saltwater intrusion, as observed
in Tuvalu (Government of Tuvalu 2006; Wairiu 2017). Similarly, in
Kiribati, poor construction of sea walls has resulted in increased
erosion and inundation of reclaimed land (Donner 2012; Donner
and Webber 2014). In the Comoros and Tuvalu, sea walls have been
constructed from climate change adaptation funds and ‘often by
international development organisations seeking to leave tangible
evidence of their investments’ (Marino and Lazrus 2015, p. 344). In
these cases, they have even increased coastal erosion, due to poor
planning and the negligence of other causes of coastal degradation,
such as sand mining (Marino and Lazrus 2015; Betzold and Mohamed
2017; Ratter et al. 2016). On the Bahamas, the installation of sea walls
as a response to coastal erosion in areas with high wave action has
led to the contrary effect and has even increased sand loss in those
areas (Sealey 2006). The reduction of natural buffer zones – such as
beaches and dunes – due to vertical structures, such as sea walls,
increased the impacts of tropical cyclones on Reunion Island (Duvat
et al. 2016). Such a process of ‘coastal squeeze’ (Pontee 2013) also
results in the reduction of intertidal habitat zones, such as wetlands
and marshes (Zhu et al. 2010). Coastal degradation resulting from the
construction of sea walls, however, is not only observed in SIDS, as
described above, but also on islands in the Global North, for example,
the North Atlantic (Muir et al. 2014; Young et al. 2014; Cooper and
Pile 2014; Bush 2004).
The adverse effects of coastal protection measures may be avoided
by the consideration of local social-ecological dynamics, including
critical study of the diverse drivers of ongoing shoreline changes,
and the appropriate implementation of locally adequate coastal
protection options (French 2001; Duvat 2013). Critical elements for
avoiding maladaptation include profound knowledge of local tidal
regimes, availability of relative sea level rise scenarios and projections
for extreme water levels. Moreover, the downdrift effects of sea walls
need to be considered, since undefended coasts may be exposed to
increased erosion (Zhu et al. 2010). In some cases, it may be possible
to keep intact and restore natural buffer zones as an alternative to the
construction of hard engineering solutions. Otherwise, changes in land
use, building codes, or even coastal realignment can be an option in
order to protect and avoid the loss of the buffer function of beaches
(Duvat et al. 2016; Cooper and Pile 2014). Examples in Barbados show
that combinations of hard and soft coastal protection approaches can
be sustainable and reduce the risk of coastal ecosystem degradation
while keeping the desired level of protection for coastal users (Mycoo
and Chadwick 2012). Nature-based solutions and approaches
such as ‘building with nature’ (Slobbe et al. 2013) may allow for
more sustainable coastal protection mechanisms and avoid coastal
degradation. Examples from the Maldives, several Pacific islands and
the North Atlantic show the importance of the involvement of local
communities in coastal adaptation projects, considering local skills,
capacities, as well as demographic and socio-political dynamics, in
order to ensure the proper monitoring and maintenance of coastal
adaptation measures (Sovacool 2012; Muir et al. 2014; Young et al.
2014; Buggy and McNamara 2016; Petzold 2016).
4.10
Knowledge gaps and key uncertainties
The co-benefits of improved land management, such as mitigation
of climate change, increased climate resilience of agriculture, and
impacts on rural areas/societies are well known in theory, but there
is a lack of a coherent and systematic global inventory of such
integrated efforts. Both successes and failures are important to
document systematically.
Efforts to reduce climate change through land-demanding mitigation
actions aimed at removing atmospheric carbon, such as afforestation,
reforestation, bioenergy crops, intensification of land management
and plantation forestry can adversely affect land conditions and lead
to degradation. However, they may also lead to avoidance, reduction
and reversal of degradation. Regionally differentiated, socially
and ecologically appropriate SLM strategies need to be identified,
implemented, monitored and the results communicated widely to
ensure climate effective outcomes.
Impacts of new technologies on land degradation and their social
and economic ramifications need more research.
Improved quantification of the global extent, severity and rates of
land degradation by combining remote sensing with a systematic
use of ancillary data is a priority. The current attempts need better
scientific underpinning and appropriate funding.
Land degradation is defined using multiple criteria but the definition
does not provide thresholds or the magnitude of acceptable change.
In practice, human interactions with land will result in a variety
of changes; some may contribute positively to one criterion while
adversely affecting another. Research is required on the magnitude
of impacts and the resulting trade-offs. Given the urgent need to
remove carbon from the atmosphere and to reduce climate change
impacts, it is important to reach agreement on what level of reduction
in one criterion (biological productivity, ecological integrity) may
be acceptable for a given increase in another criterion (ecological
integrity, biological productivity).
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Attribution of land degradation to the underlying drivers is
a challenge because it is a complex web of causality rather than
a simple cause–effect relationship. Also, diverging views on land
degradation in relation to other challenges is hampering such efforts.
A more systematic treatment of the views and experiences of land
users would be useful in land degradation studies.
Much research has tried to understand how social and ecological
systems are affected by a particular stressor, for example, drought,
heat, or waterlogging. But less research has tried to understand how
such systems are affected by several simultaneous stressors – which
is more realistic in the context of climate change (Mittler 2006).
More realistic modelling of carbon dynamics, including better
appreciation of below-ground biota, would help us to better quantify
the role of soils and soil management for soil carbon sequestration.
Frequently Asked Questions
FAQ 4.1 |
How do climate change and land degradation interact with land use?
Climate change, land degradation and land use are linked in a complex web of causality. One important impact of climate change
on land degradation is that increasing global temperatures intensify the hydrological cycle, resulting in more intense rainfall, which
is an important driver of soil erosion. This means that sustainable land management (SLM) becomes even more important with
climate change. Land-use change in the form of clearing of forest for rangeland and cropland (e.g., for provision of bio-fuels), and
cultivation of peat soils, is a major source of greenhouse gas (GHG) emission from both biomass and soils. Many SLM practices
(e.g., agroforestry, perennial crops, organic amendments, etc.) increase carbon content of soil and vegetation cover and hence
provide both local and immediate adaptation benefits, combined with global mitigation benefits in the long term, while providing
many social and economic co-benefits. Avoiding, reducing and reversing land degradation has a large potential to mitigate climate
change and help communities to adapt to climate change.
FAQ 4.2
|
How does climate change affect land-related ecosystem
services and biodiversity?
Climate change will affect land-related ecosystem services (e.g., pollination, resilience to extreme climate events, water yield, soil
conservation, carbon storage, etc.) and biodiversity, both directly and indirectly. The direct impacts range from subtle reductions or
enhancements of specific services, such as biological productivity, resulting from changes in temperature, temperature variability
or rainfall, to complete disruption and elimination of services. Disruptions of ecosystem services can occur where climate change
causes transitions from one biome to another, for example, forest to grassland as a result of changes in water balance or natural
disturbance regimes. Climate change will result in range shifts and, in some cases, extinction of species. Climate change can
also alter the mix of land-related ecosystem services, such as groundwater recharge, purification of water, and flood protection.
While the net impacts are specific to time as well as ecosystem types and services, there is an asymmetry of risk such that overall
impacts of climate change are expected to reduce ecosystem services. Indirect impacts of climate change on land-related ecosystem
services include those that result from changes in human behaviour, including potential large-scale human migrations or the
implementation of afforestation, reforestation or other changes in land management, which can have positive or negative outcomes
on ecosystem services.
Page 61
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