Special Report: Special Report on Climate Change and Land
Ch 01

Framing and Context

Coordinating Lead Authors

  • Almut Arneth (Germany)
  • Fatima Denton (The Gambia)

Lead Authors

  • Fahmuddin AGUS (Indonesia)
  • Aziz Elbehri (Morocco)
  • Karlheinz ERB (Italy)
  • Balgis OSMAN-ELASHA (Sudan)
  • Mohammad RAHIMI (Iran)
  • Mark Rounsevell (United Kingdom, Germany)
  • Adrian SPENCE (Jamaica)
  • Riccardo VALENTINI (Italy)

Review Editors

  • Edvin ALDRIAN (Indonesia)
  • Bruce MCCARL (United States)
  • María José SÁNZ SÁNCHEZ (Spain)

Chapter Scientists

  • Yuping Bai (China)
  • Baldur Janz (Germany)

Contributing Authors

  • Peter Alexander (United Kingdom)
  • Yuping Bai (China)
  • Ana Bastos (Germany, Portugal)
  • Niels Debonne (Netherlands)
  • Jan Fuglestvedt (Norway)
  • Rafaela Hillerbrand (Germany)
  • Baldur Janz (Germany)
  • Thomas Kastner (Austria)
  • Ylva Longva (United Kingdom)
  • Patrick Meyfroidt (Belgium)
  • Michael O’Sullivan (United Kingdom)

FAQ 1.1 | What are the approaches to study the interactions between land and climate?

Climate change shapes the way land is able to support supply of food and water for humans. At the same time the land surface interacts with the overlying atmosphere, thus human modifications of land use, land cover and urbanisation affect global, regional and local climate. The complexity of the land–climate interactions requires multiple study approaches embracing different spatial and temporal scales. Observations of land atmospheric exchanges, such as of carbon, water, nutrients and energy can be carried out at leaf level and soil with gas exchange systems, or at canopy scale by means of micrometeorological techniques (i.e. eddy covariance). At regional scale, atmospheric measurements by tall towers, aircraft and satellites can be combined with atmospheric transport models to obtain spatial explicit maps of relevant greenhouse gases fluxes. At longer temporal scale (>10 years) other approaches are more effective, such as tree-ring chronologies, satellite records, population and vegetation dynamics and isotopic studies. Models are important to bring information from measurement together and to extend the knowledge in space and time, including the exploration of scenarios of future climate–land interactions.

FAQ 1.2 | How region-specific are the impacts of different land-based adaptation and mitigation options?

Land-based adaptation and mitigation options are closely related to region-specific features for several reasons. Climate change has a definite regional pattern with some regions already suffering from enhanced climate extremes and others being impacted little, or even benefiting. From this point of view increasing confidence in regional climate change scenarios is becoming a critical step forward towards the implementation of adaptation and mitigation options. Biophysical and socio-economic impacts of climate change depend on the exposures of natural ecosystems and economic sectors, which are again specific to a region, reflecting regional sensitivities due to governance. The overall responses in terms of adaptation or mitigation capacities to avoid and reduce vulnerabilities and enhance adaptive capacity, depend on institutional arrangements, socio-economic conditions, and implementation of policies, many of them having definite regional features. However global drivers, such as agricultural demand, food prices, changing dietary habits associated with rapid social transformations (i.e. urban vs rural, meat-eating vs vegetarian) may interfere with region-specific policies for mitigation and adaptation options and need to be addressed at the global level.

FAQ 1.3 | What is the difference between desertification and land degradation? And where are they happening?

The difference between land degradation and desertification is geographic. Land degradation is a general term used to describe a negative trend in land condition caused by direct or indirect human-induced processes (including anthropogenic climate change). Degradation can be identified by the long-term reduction or loss in biological productivity, ecological integrity or value to humans. Desertification is land degradation when it occurs in arid, semi-arid, and dry sub-humid areas, which are also called drylands. Contrary to some perceptions, desertification is not the same as the expansion of deserts. Desertification is also not limited to irreversible forms of land degradation.

Figure 1.1
View details
Figure 1.2
View details
Figure 1.3
View details
Figure 1.4
View details
Figure 1.5
View details
View details

Executive Summary

Land, including its water bodies, provides the basis for human livelihoods and well-being through primary productivity, the supply of food, freshwater, and multiple other ecosystem services (high confidence). Neither our individual or societal identities, nor the world’s economy would exist without the multiple resources, services and livelihood systems provided by land ecosystems and biodiversity. The annual value of the world’s total terrestrial ecosystem services has been estimated at 75 trillion USD in 2011, approximately equivalent to the annual global Gross Domestic Product (based on USD2007 values) (medium confidence). Land and its biodiversity also represent essential, intangible benefits to humans, such as cognitive and spiritual enrichment, sense of belonging and aesthetic and recreational values. Valuing ecosystem services with monetary methods often overlooks these intangible services that shape societies, cultures and quality of life and the intrinsic value of biodiversity. The Earth’s land area is finite. Using land resources sustainably is fundamental for human well-being (high confidence). {1.1.1}

The current geographic spread of the use of land, the large appropriation of multiple ecosystem services and the loss of biodiversity are unprecedented in human history (high confidence). By 2015, about three-quarters of the global ice-free land surface was affected by human use. Humans appropriate one-quarter to one-third of global terrestrial potential net primary production (high confidence). Croplands cover 12–14% of the global ice-free surface. Since 1961, the supply of global per capita food calories increased by about one-third, with the consumption of vegetable oils and meat more than doubling. At the same time, the use of inorganic nitrogen fertiliser increased by nearly ninefold, and the use of irrigation water roughly doubled (high confidence). Human use, at varying intensities, affects about 60–85% of forests and 70–90% of other natural ecosystems (e.g., savannahs, natural grasslands) (high confidence). Land use caused global biodiversity to decrease by around 11–14% (medium confidence). {1.1.2}

Warming over land has occurred at a faster rate than the global mean and this has had observable impacts on the land system (high confidence). The average temperature over land for the period 2006–2015 was 1.53°C higher than for the period 1850–1900, and 0.66°C larger than the equivalent global mean temperature change. These warmer temperatures (with changing precipitation patterns) have altered the start and end of growing seasons, contributed to regional crop yield reductions, reduced freshwater availability, and put biodiversity under further stress and increased tree mortality (high confidence). Increasing levels of atmospheric CO2, have contributed to observed increases in plant growth as well as to increases in woody plant cover in grasslands and savannahs (medium confidence). {1.1.2}

Urgent action to stop and reverse the over-exploitation of land resources would buffer the negative impacts of multiple pressures, including climate change, on ecosystems and society (high confidence). Socio-economic drivers of land-use change such as technological development, population growth and increasing per capita demand for multiple ecosystem services are projected to continue into the future (high confidence). These and other drivers can amplify existing environmental and societal challenges, such as the conversion of natural ecosystems into managed land, rapid urbanisation, pollution from the intensification of land management and equitable access to land resources (high confidence). Climate change will add to these challenges through direct, negative impacts on ecosystems and the services they provide (high confidence). Acting immediately and simultaneously on these multiple drivers would enhance food, fibre and water security, alleviate desertification, and reverse land degradation, without compromising the non-material or regulating benefits from land (high confidence). {1.1.2, 1.2.1, 1.3.2–1.3.6, Cross-Chapter Box 1 in Chapter 1}

Rapid reductions in anthropogenic greenhouse gas (GHG) emissions that restrict warming to “well-below” 2°C would greatly reduce the negative impacts of climate change on land ecosystems (high confidence). In the absence of rapid emissions reductions, reliance on large-scale, land-based, climate change mitigation is projected to increase, which would aggravate existing pressures on land (high confidence). Climate change mitigation efforts that require large land areas (e.g., bioenergy and afforestation/reforestation) are projected to compete with existing uses of land (high confidence). The competition for land could increase food prices and lead to further intensification (e.g., fertiliser and water use) with implications for water and air pollution, and the further loss of biodiversity (medium confidence). Such consequences would jeopardise societies’ capacity to achieve many Sustainable Development Goals (SDGs) that depend on land (high confidence). {1.3.1, Cross-Chapter Box 2 in Chapter 1}

Nonetheless, there are many land-related climate change mitigation options that do not increase the competition for land (high confidence). Many of these options have co-benefits for climate change adaptation (medium confidence). Land use contributes about one-quarter of global greenhouse gas emissions, notably CO2 emissions from deforestation, CH4 emissions from rice and ruminant livestock and N2O emissions from fertiliser use (high confidence). Land ecosystems also take up large amounts of carbon (high confidence). Many land management options exist to both reduce the magnitude of emissions and enhance carbon uptake. These options enhance crop productivity, soil nutrient status, microclimate or biodiversity, and thus, support adaptation to climate change (high confidence). In addition, changes in consumer behaviour, such as reducing the over-consumption of food and energy would benefit the reduction of GHG emissions from land (high confidence). The barriers to the implementation of mitigation and adaptation options include skills deficit, financial and institutional barriers, absence of incentives, access to relevant technologies, consumer awareness and the limited spatial scale at which the success of these practices and methods have been demonstrated. {1.2.1, 1.3.2, 1.3.3, 1.3.4, 1.3.5, 1.3.6}

Sustainable food supply and food consumption, based on nutritionally balanced and diverse diets, would enhance food security under climate and socio-economic changes (high confidence). Improving food access, utilisation, quality and safety to enhance nutrition, and promoting globally equitable diets compatible with lower emissions have demonstrable positive impacts on land use and food security (high confidence). Food security is also negatively affected by food loss and waste (estimated as 25–30% of total food produced) (medium confidence). Barriers to improved food security include economic drivers (prices, availability and stability of supply) and traditional, social and cultural norms around food eating practices. Climate change is expected to increase variability in food production and prices globally (high confidence), but the trade in food commodities can buffer these effects. Trade can provide embodied flows of water, land and nutrients (medium confidence). Food trade can also have negative environmental impacts by displacing the effects of overconsumption (medium confidence). Future food systems and trade patterns will be shaped as much by policies as by economics (medium confidence). {1.2.1, 1.3.3}

A gender-inclusive approach offers opportunities to enhance the sustainable management of land (medium confidence). Women play a significant role in agriculture and rural economies globally. In many world regions, laws, cultural restrictions, patriarchy and social structures such as discriminatory customary laws and norms reduce women’s capacity in supporting the sustainable use of land resources (medium confidence). Therefore, acknowledging women’s land rights and bringing women’s land management knowledge into land-related decision-making would support the alleviation of land degradation, and facilitate the take-up of integrated adaptation and mitigation measures (medium confidence). {1.4.1, 1.4.2}

Regional and country specific contexts affect the capacity to respond to climate change and its impacts, through adaptation and mitigation (high confidence). There is large variability in the availability and use of land resources between regions, countries and land management systems. In addition, differences in socio-economic conditions, such as wealth, degree of industrialisation, institutions and governance, affect the capacity to respond to climate change, food insecurity, land degradation and desertification. The capacity to respond is also strongly affected by local land ownership. Hence, climate change will affect regions and communities differently (high confidence). {1.3, 1.4}

Cross-scale, cross-sectoral and inclusive governance can enable coordinated policy that supports effective adaptation and mitigation (high confidence). There is a lack of coordination across governance levels, for example, local, national, transboundary and international, in addressing climate change and sustainable land management challenges. Policy design and formulation is often strongly sectoral, which poses further barriers when integrating international decisions into relevant (sub)national policies. A portfolio of policy instruments that are inclusive of the diversity of governance actors would enable responses to complex land and climate challenges (high confidence). Inclusive governance that considers women’s and indigenous people’s rights to access and use land enhances the equitable sharing of land resources, fosters food security and increases the existing knowledge about land use, which can increase opportunities for adaptation and mitigation (medium confidence). {1.3.5, 1.4.1, 1.4.2, 1.4.3}

Scenarios and models are important tools to explore the trade-offs and co-benefits of land management decisions under uncertain futures (high confidence). Participatory, co-creation processes with stakeholders can facilitate the use of scenarios in designing future sustainable development strategies (medium confidence). In addition to qualitative approaches, models are critical in quantifying scenarios, but uncertainties in models arise from, for example, differences in baseline datasets, land cover classes and modelling paradigms (medium confidence). Current scenario approaches are limited in quantifying time-dependent policy and management decisions that can lead from today to desirable futures or visions. Advances in scenario analysis and modelling are needed to better account for full environmental costs and non-monetary values as part of human decision-making processes. {1.2.2, Cross-Chapter Box 1 in Chapter 1}


Introduction and scope of the report


Objectives and scope of the assessment

Land, including its water bodies, provides the basis for our livelihoods through basic processes such as net primary production that fundamentally sustain the supply of food, bioenergy and freshwater, and the delivery of multiple other ecosystem services and biodiversity (Hoekstra and Wiedmann 2014; Mace et al. 2012; Newbold et al. 2015; Runting et al. 2017; Isbell et al. 2017) (Cross-Chapter Box 8 in Chapter 6). The annual value of the world’s total terrestrial ecosystem services has been estimated to be about 75 trillion USD in 2011, approximately equivalent to the annual global Gross Domestic Product (based on USD2007 values) (Costanza et al. 2014; IMF 2018). Land also supports non-material ecosystem services such as cognitive and spiritual enrichment and aesthetic values (Hernández-Morcillo et al. 2013; Fish et al. 2016), intangible services that shape societies, cultures and human well-being. Exposure of people living in cities to (semi-)natural environments has been found to decrease mortality, cardiovascular disease and depression (Rook 2013; Terraube et al. 2017). Non-material and regulating ecosystem services have been found to decline globally and rapidly, often at the expense of increasing material services (Fischer et al. 2018; IPBES 2018a). Climate change will exacerbate diminishing land and freshwater resources, increase biodiversity loss, and will intensify societal vulnerabilities, especially in regions where economies are highly dependent on natural resources. Enhancing food security and reducing malnutrition, whilst also halting and reversing desertification and land degradation, are fundamental societal challenges that are increasingly aggravated by the need to both adapt to and mitigate climate change impacts without compromising the non-material benefits of land (Kongsager et al. 2016; FAO et al. 2018).

Annual emissions of GHGs and other climate forcers continue to increase unabatedly. Confidence is very high that the window of opportunity, the period when significant change can be made, for limiting climate change within tolerable boundaries is rapidly narrowing (Schaeffer et al. 2015; Bertram et al. 2015; Riahi et al. 2015; Millar et al. 2017; Rogelj et al. 2018a). The Paris Agreement formulates the goal of limiting global warming this century to well below 2°C above pre-industrial levels, for which rapid actions are required across the energy, transport, infrastructure and agricultural sectors, while factoring in the need for these sectors to accommodate a growing human population (Wynes and Nicholas 2017; Le Quere et al. 2018). Conversion of natural land, and land management, are significant net contributors to GHG emissions and climate change, but land ecosystems are also a GHG sink (Smith et al. 2014; Tubiello et al. 2015; Le Quere et al. 2018; Ciais et al. 2013a). It is not surprising, therefore, that land plays a prominent role in many of the Nationally Determined Contributions (NDCs) of the parties to the Paris Agreement (Rogelj et al. 2018a,b; Grassi et al. 2017; Forsell et al. 2016), and land-measures will be part of the NDC review by 2023.

A range of different climate change mitigation and adaptation options on land exist, which differ in terms of their environmental and societal implications (Meyfroidt 2018; Bonsch et al. 2016; Crist et al. 2017; Humpenoder et al. 2014; Harvey and Pilgrim 2011; Mouratiadou et al. 2016; Zhang et al. 2015; Sanz-Sanchez et al. 2017; Pereira et al. 2010; Griscom et al. 2017; Rogelj et al. 2018a) (Chapters 4–6). The Special Report on climate change, desertification, land degradation, sustainable land management, food security, and GHG fluxes in terrestrial ecosystems (SRCCL) synthesises the current state of scientific knowledge on the issues specified in the report’s title (Figure 1.1 and Figure 1.2). This knowledge is assessed in the context of the Paris Agreement, but many of the SRCCL issues concern other international conventions such as the United Nations Convention on Biodiversity (UNCBD), the UN Convention to Combat Desertification (UNCCD), the UN Sendai Framework for Disaster Risk Reduction (UNISDR) and the UN Agenda 2030 and its Sustainable Development Goals (SDGs). The SRCCL is the first report in which land is the central focus since the IPCC Special Report on land use, land-use change and forestry (Watson et al. 2000) (Box 1.1). The main objectives of the SRCCL are to:

  1. Assess the current state of the scientific knowledge on the impacts of socio-economic drivers and their interactions with climate change on land, including degradation, desertification and food security;
  2. Evaluate the feasibility of different land-based response options to GHG mitigation, and assess the potential synergies and trade-offs with ecosystem services and sustainable development;
  3. Examine adaptation options under a changing climate to tackle land degradation and desertification and to build resilient food systems, as well as evaluating the synergies and trade-offs between mitigation and adaptation;
  4. Delineate the policy, governance and other enabling conditions to support climate mitigation, land ecosystem resilience and food security in the context of risks, uncertainties and remaining knowledge gaps.
Figure 1.1

A representation of the principal land challenges and land-climate system processes covered in this assessment report. A. The warming curves are averages of four datasets (Section 2.1, Figure 2.2 and Table 2.1). B. N2O and CH4 from agriculture are from FAOSTAT; Net land-use change emissions of CO2 from forestry and other land use (including emissions […]

A representation of the principal land challenges and land-climate system processes covered in this assessment report.
A. The warming curves are averages of four datasets (Section 2.1, Figure 2.2 and Table 2.1). B. N2O and CH4 from agriculture are from FAOSTAT; Net land-use change emissions of CO2 from forestry and other land use (including emissions from peatland fires since 1997) are from the annual Global Carbon Budget, using the mean of two bookkeeping models. All values expressed in units of CO2-eq are based on AR5 100-year Global Warming Potential values without climate-carbon feedbacks (N2O = 265; CH4 = 28) (Table SPM.1 and Section 2.3). C. Depicts shares of different uses of the global, ice-free land area for approximately the year 2015, ordered along a gradient of decreasing land-use intensity from left to right. Each bar represents a broad land cover category; the numbers on top are the total percentage of the ice-free area covered, with uncertainty ranges in brackets. Intensive pasture is defined as having a livestock density greater than 100 animals/km². The area of ‘forest managed for timber and other uses’ was calculated as total forest area minus ‘primary/intact’ forest area. (Section 1.2, Table 1.1, Figure 1.3). D. Note that fertiliser use is shown on a split axis (source: International Fertiliser Industry Association, www.ifastat.org/databases). The large percentage change in fertiliser use reflects the low level of use in 1961 and relates to both increasing fertiliser input per area as well as the expansion of fertilised cropland and grassland to increase food production (1.1, Figure 1.3). E. Overweight population is defined as having a body mass index (BMI) >25 kg m–2 (source: Abarca-Gómez et al. 2017); underweight is defined as BMI <18.5 kg m–2. (Population density, source: United Nations, Department of Economic and Social Affairs 2017) (Sections 5.1 and 5.2). F. Dryland areas were estimated using TerraClimate precipitation and potential evapotranspiration (1980–2015) (Abatzoglou et al. 2018) to identify areas where the Aridity Index is below 0.65. Areas experiencing human caused desertification, after accounting for precipitation variability and CO2 fertilisation, are identified in Le et al. 2016. Population data for these areas were extracted from the gridded historical population database HYDE3.2 (Goldewijk et al. 2017). Areas in drought are based on the 12-month accumulation Global Precipitation Climatology Centre Drought Index (Ziese et al. 2014). The area in drought was calculated for each month (Drought Index below –1), and the mean over the year was used to calculate the percentage of drylands in drought that year. The inland wetland extent (including peatlands) is based on aggregated data from more than 2000 time series that report changes in local wetland area over time (Dixon et al. 2016; Darrah et al. 2019) (Sections 3.1, 4.2 and 4.6).

The SRCCL identifies and assesses land-related challenges and response options in an integrative way, aiming to be policy relevant across sectors. Chapter 1 provides a synopsis of the main issues addressed in this report, which are explored in more detail in Chapters 2–7. Chapter 1 also introduces important concepts and definitions and highlights discrepancies with previous reports that arise from different objectives (a full set of definitions is provided in the Glossary). Chapter 2 focuses on the natural system dynamics, assessing recent progress towards understanding the impacts of climate change on land, and the feedbacks arising from altered biogeochemical and biophysical exchange fluxes (Figure 1.2).

Figure 1.2

Overview over the SRCCL.

Overview over the SRCCL.


Status and dynamics of the (global) land system Land ecosystems and climate change

Land ecosystems play a key role in the climate system, due to their large carbon pools and carbon exchange fluxes with the atmosphere (Ciais et al. 2013b). Land use, the total of arrangements, activities and inputs applied to a parcel of land (such as agriculture, grazing, timber extraction, conservation or city dwelling; see Glossary), and land management (sum of land-use practices that take place within broader land-use categories; see Glossary) considerably alter terrestrial ecosystems and play a key role in the global climate system. An estimated one-quarter of total anthropogenic GHG emissions arise mainly from deforestation, ruminant livestock and fertiliser application (Smith et al. 2014; Tubiello et al. 2015; Le Quere et al. 2018; Ciais et al. 2013a), and especially methane (CH4) and nitrous oxide (N2O) emissions from agriculture have been rapidly increasing over the last decades (Hoesly et al. 2018; Tian et al. 2019) (Figure 1.1 and Sections 2.3.2–2.3.3).

Globally, land also serves as a large CO2 sink, which was estimated for the period 2008–2017 to be nearly 30% of total anthropogenic emissions (Le Quere et al. 2015; Canadell and Schulze 2014; Ciais et al. 2013a; Zhu et al. 2016) (Section 2.3.1). This sink has been attributed to increasing atmospheric CO2 concentration, a prolonged growing season in cool environments, or forest regrowth (Le Quéré et al. 2013; Pugh et al. 2019; Le Quéré et al. 2018; Ciais et al. 2013a; Zhu et al. 2016). Whether or not this sink will persist into the future is one of the largest uncertainties in carbon cycle and climate modelling (Ciais et al. 2013a; Bloom et al. 2016; Friend et al. 2014; Le Quere et al. 2018). In addition, changes in vegetation cover caused by land use (such as conversion of forest to cropland or grassland, and vice versa) can result in regional cooling or warming through altered energy and momentum transfer between ecosystems and the atmosphere. Regional impacts can be substantial, but whether the effect leads to warming or cooling depends on the local context (Lee et al. 2011; Zhang et al. 2014; Alkama and Cescatti 2016) (Section 2.6). Due to the current magnitude of GHG emissions and CO2 carbon dioxide removal in land ecosystems, there is high confidence that GHG reduction measures in agriculture, livestock management and forestry would have substantial climate change mitigation potential, with co-benefits for biodiversity and ecosystem services (Smith and Gregory 2013; Smith et al. 2014; Griscom et al. 2017) (Sections 2.6 and 6.3).

The mean temperature over land for the period 2006–2015 was 1.53°C higher than for the period 1850–1900, and 0.66°C larger than the equivalent global mean temperature change (Section 2.2). Climate change affects land ecosystems in various ways (Section 7.2). Growing seasons and natural biome boundaries shift in response to warming or changes in precipitation (Gonzalez et al. 2010; Wärlind et al. 2014; Davies-Barnard et al. 2015; Nakamura et al. 2017). Atmospheric CO2 increases have been attributed to underlie, at least partially, observed woody plant cover increase in grasslands and savannahs (Donohue et al. 2013). Climate change-induced shifts in habitats, together with warmer temperatures, cause pressure on plants and animals (Pimm et al. 2014; Urban et al. 2016). National cereal crop losses of nearly 10% have been estimated for the period 1964–2007 as a consequence of heat and drought weather extremes (Deryng et al. 2014; Lesk et al. 2016). Climate change is expected to reduce yields in areas that are already under heat and water stress (Schlenker and Lobell 2010; Lobell et al. 2011, 2012; Challinor et al. 2014) (Section 5.2.2). At the same time, warmer temperatures can increase productivity in cooler regions (Moore and Lobell 2015) and might open opportunities for crop area expansion, but any overall benefits might be counterbalanced by reduced suitability in warmer regions (Pugh et al. 2016; Di Paola et al. 2018). Increasing atmospheric CO2 is expected to increase productivity and water use efficiency in crops and in forests (Muller et al. 2015; Nakamura et al. 2017; Kimball 2016). The increasing number of extreme weather events linked to climate change is also expected to result in forest losses; heat waves and droughts foster wildfires (Seidl et al. 2017; Fasullo et al. 2018) (Cross-Chapter Box 3 in Chapter 2). Episodes of observed enhanced tree mortality across many world regions have been attributed to heat and drought stress (Allen et al. 2010; Anderegg et al. 2012), whilst weather extremes also impact local infrastructure and hence transportation and trade in land-related goods (Schweikert et al. 2014; Chappin and van der Lei 2014). Thus, adaptation is a key challenge to reduce adverse impacts on land systems (Section 1.3.6).

Current patterns of land use and land cover

Around three-quarters of the global ice-free land, and most of the highly productive land area, are by now under some form of land use (Erb et al. 2016a; Luyssaert et al. 2014; Venter et al. 2016) (Table 1.1). One-third of used land is associated with changed land cover. Grazing land is the single largest land-use category, followed by used forestland and cropland. The total land area used to raise livestock is notable: it includes all grazing land and an estimated additional one-fifth of cropland for feed production (Foley et al. 2011). Globally, 60–85% of the total forested area is used, at different levels of intensity, but information on management practices globally is scarce (Erb et al. 2016a). Large areas of unused (primary) forests remain only in the tropics and northern boreal zones (Luyssaert et al. 2014; Birdsey and Pan 2015; Morales-Hidalgo et al. 2015; Potapov et al. 2017; Erb et al. 2017), while 73–89% of other, non-forested natural ecosystems (natural grasslands, savannahs, etc.) are used. Large uncertainties relate to the extent of forest (32.0–42.5 million km2) and grazing land (39–62 million km2), due to discrepancies in definitions and observation methods (Luyssaert et al. 2014; Erb et al. 2017; Putz and Redford 2010; Schepaschenko et al. 2015; Birdsey and Pan 2015; FAO 2015a; Chazdon et al. 2016a; FAO 2018a). Infrastructure areas (including settlements, transportation and mining), while being almost negligible in terms of extent, represent particularly pervasive land-use activities, with far-reaching ecological, social and economic implications (Cherlet et al. 2018; Laurance et al. 2014).

The large imprint of humans on the land surface has led to the definition of anthromes, i.e. large-scale ecological patterns created by the sustained interactions between social and ecological drivers. The dynamics of these ‘anthropogenic biomes’ are key for land-use impacts as well as for the design of integrated response options (Ellis and Ramankutty 2008; Ellis et al. 2010; Cherlet et al. 2018; Ellis et al. 2010) (Chapter 6).

The intensity of land use varies hugely within and among different land-use types and regions. Averaged globally, around 10% of the ice-free land surface was estimated to be intensively managed (such as tree plantations, high livestock density grazing, large agricultural inputs), two-thirds moderately and the remainder at low intensities (Erb et al. 2016a). Practically all cropland is fertilised, with large regional variations. Irrigation is responsible for 70% of ground- or surface-water withdrawals by humans (Wisser et al. 2008; Chaturvedi et al. 2015; Siebert et al. 2015; FAOSTAT 2018). Humans appropriate one-quarter to one-third of the total potential net primary production (NPP), i.e. the NPP that would prevail in the absence of land use (estimated at about 60 GtC yr–1; Bajželj et al. 2014; Haberl et al. 2014), about equally through biomass harvest and changes in NPP due to land management. The current total of agricultural (cropland and grazing) biomass harvest is estimated at about 6 GtC yr–1, around 50–60% of this is consumed by livestock. Forestry harvest for timber and wood fuel amounts to about 1 GtC yr–1 (Alexander et al. 2017; Bodirsky and Müller 2014; Lassaletta et al. 2014, 2016; Mottet et al. 2017; Haberl et al. 2014; Smith et al. 2014; Bais et al. 2015; Bajželj et al. 2014) (Cross-Chapter Box 7 in Chapter 6).

Table 1.1

Extent of global land use and management around the year 2015.


Key challenges related to land use change


Land system change, land degradation, desertification and food security

Land degradation

As discussed in Chapter 4, the concept of land degradation, including its definition, has been used in different ways in different communities and in previous assessments (such as the IPBES Land Degradation and Restoration Assessment). In the SRCCL, 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. This definition applies to forest and non-forest land (Chapter 4 and Glossary).

Land degradation is a critical issue for ecosystems around the world due to the loss of actual or potential productivity or utility (Ravi et al. 2010; Mirzabaev et al. 2015; FAO and ITPS 2015; Cerretelli et al. 2018). Land degradation is driven to a large degree by unsustainable agriculture and forestry, socio-economic pressures, such as rapid urbanisation and population growth, and unsustainable production practices in combination with climatic factors (Field et al. 2014b; Lal 2009; Beinroth et al. 1994; Abu Hammad and Tumeizi 2012; Ferreira et al. 2018; Franco and Giannini 2005; Abahussain et al. 2002).

Global estimates of the total degraded area vary from less than 10 million km2 to over 60 million km2, with additionally large disagreement regarding the spatial distribution (Gibbs and Salmon 2015) (Section 4.3). The annual increase in the degraded land area has been estimated as 50,000–100,000 million km2 yr–1 (Stavi and Lal 2015), and the loss of total ecosystem services equivalent to about 10% of the world’s GDP in the year 2010 (Sutton et al. 2016). Although land degradation is a common risk across the globe, poor countries remain most vulnerable to its impacts. Soil degradation is of particular concern, due to the long period necessary to restore soils (Lal 2009; Stockmann et al. 2013; Lal 2015), as well as the rapid degradation of primary forests through fragmentation (Haddad et al. 2015). Among the most vulnerable ecosystems to degradation are high-carbon- stock wetlands (including peatlands). Drainage of natural wetlands for use in agriculture leads to high CO2 emissions and degradation (high confidence) (Strack 2008; Limpens et al. 2008; Aich et al. 2014; Murdiyarso et al. 2015; Kauffman et al. 2016; Dohong et al. 2017; Arifanti et al. 2018; Evans et al. 2019). Land degradation is an important factor contributing to uncertainties in the mitigation potential of land-based ecosystems (Smith et al. 2014). Furthermore, degradation that reduces forest (and agricultural) biomass and soil organic carbon leads to higher rates of runoff (high confidence) (Molina et al. 2007; Valentin et al. 2008; Mateos et al. 2017; Noordwijk et al. 2017) and hence to increasing flood risk (low confidence) (Bradshaw et al. 2007; Laurance 2007; van Dijk et al. 2009).


The SRCCL adopts the definition of the UNCCD of desertification, being land degradation in arid, semi-arid and dry sub-humid areas (drylands) (Glossary and Section 3.1.1). Desertification results from various factors, including climate variations and human activities, and is not limited to irreversible forms of land degradation (Tal 2010; Bai et al. 2008). A critical challenge in the assessment of desertification is to identify a ‘non-desertified’ reference state (Bestelmeyer et al. 2015). While climatic trends and variability can change the intensity of desertification processes, some authors exclude climate effects, arguing that desertification is a purely human-induced process of land degradation with different levels of severity and consequences (Sivakumar 2007).

As a consequence of varying definitions and different methodologies, the area of desertification varies widely (D’Odorico et al. 2013; Bestelmeyer et al. 2015; and references therein). Arid regions of the world cover up to about 46% of the total terrestrial surface (about 60 million km2) (Pravalie 2016; Koutroulis 2019). Around 3 billion people reside in dryland regions (D’Odorico et al. 2013; Maestre et al. 2016) (Section 3.1.1). In 2015, about 500 (360–620) million people lived within areas which experienced desertification between 1980s and 2000s (Figure 1.1and Section 3.1.1). The combination of low rainfall with frequently infertile soils renders these regions, and the people who rely on them, vulnerable to both climate change, and unsustainable land management (high confidence). In spite of the national, regional and international efforts to combat desertification, it remains one of the major environmental problems (Abahussain et al. 2002; Cherlet et al. 2018).

Food security, food systems and linkages to land-based ecosystems

The High Level Panel of Experts of the Committee on Food Security define the food system as to “gather all the elements (environment, people, inputs, processes, infrastructures, institutions, etc.) and activities that relate to the production, processing, distribution, preparation and consumption of food, and the output of these activities, including socio-economic and environmental outcomes” (HLPE 2017). Likewise, food security has been defined as “a situation that exists when all people, at all times, have physical, social and economic access to sufficient, safe and nutritious food that meets their dietary needs and food preferences for an active and healthy life” (FAO 2017). By this definition, food security is characterised by food availability, economic and physical access to food, food utilisation and food stability over time. Food and nutrition security is one of the key outcomes of the food system (FAO 2018b; Figure 1.4).

After a prolonged decline, world hunger appears to be on the rise again, with the number of undernourished people having increased to an estimated 821 million in 2017, up from 804 million in 2016 and 784 million in 2015, although still below the 900 million reported in 2000 (FAO et al. 2018) (Section 5.1.2). Of the total undernourished in 2018, for example, 256.5 million lived in Africa, and 515.1 million in Asia (excluding Japan). The same FAO report also states that child undernourishment continues to decline, but levels of overweight populations and obesity are increasing. The total number of overweight children in 2017 was 38–40 million worldwide, and globally up to around two billion adults are by now overweight (Section 5.1.2). FAO also estimated that close to 2000 million people suffer from micronutrient malnutrition (FAO 2018b).

Food insecurity most notably occurs in situations of conflict, and conflict combined with droughts or floods (Cafiero et al. 2018; Smith et al. 2017). The close parallel between food insecurity prevalence and poverty means that tackling development priorities would enhance sustainable land use options for climate mitigation.

Climate change affects the food system as changes in trends and variability in rainfall and temperature variability impact crop and livestock productivity and total production (Osborne and Wheeler 2013; Tigchelaar et al. 2018; Iizumi and Ramankutty 2015), the nutritional quality of food (Loladze 2014; Myers et al. 2014; Ziska et al. 2016; Medek et al. 2017), water supply (Nkhonjera 2017), and incidence of pests and diseases (Curtis et al. 2018). These factors also impact on human health, increasing morbidity and affecting human ability to process ingested food (Franchini and Mannucci 2015; Wu et al. 2016; Raiten and Aimone 2017). At the same time, the food system generates negative externalities (the environmental effects of production and consumption) in the form of GHG emissions

(Sections 1.1.2 and 2.3), pollution (van Noordwijk and Brussaard 2014; Thyberg and Tonjes 2016; Borsato et al. 2018; Kibler et al. 2018), water quality (Malone et al. 2014; Norse and Ju 2015), and ecosystem services loss (Schipper et al. 2014; Eeraerts et al. 2017) with direct and indirect impacts on climate change and reduced resilience to climate variability. As food systems are assessed in relation to their contribution to global warming and/or to land degradation (e.g., livestock systems) it is critical to evaluate their contribution to food security and livelihoods and to consider alternatives, especially for developing countries where food insecurity is prevalent (Röös et al. 2017; Salmon et al. 2018).

Figure 1.4

Food system (and its relations to land and climate):The food system is conceptualised through supply (production, processing, marketing and retailing) and demand (consumption and diets) that are shaped by physical, economic, social and cultural determinants influencing choices, access, utilisation, quality, safety and waste. Food system drivers (ecosystem services, economics and technology, social and cultural norms […]

Food system (and its relations to land and climate):The food system is conceptualised through supply (production, processing, marketing and retailing) and demand (consumption and diets) that are shaped by physical, economic, social and cultural determinants influencing choices, access, utilisation, quality, safety and waste. Food system drivers (ecosystem services, economics and technology, social and cultural norms and traditions, and demographics) combine with the enabling conditions (policies, institutions and governance) to affect food system outcomes including food security, nutrition and health, livelihoods, economic and cultural benefits as well as environmental outcomes or side-effects (nutrient and soil loss, water use and quality, GHG emissions and other pollutants). Climate and climate change have direct impacts on the food system (productivity, variability, nutritional quality) while the latter contributes to local climate (albedo, evapotranspiration) and global warming (GHGs). The land system (function, structures, and processes) affects the food system directly (food production) and indirectly (ecosystem services) while food demand and supply processes affect land (land-use change) and land-related processes (e.g., land degradation, desertification) (Chapter 5).

Challenges arising from land governance

Land-use change has both positive and negative effects: it can lead to economic growth, but it can become a source of tension and social unrest leading to elite capture, and competition (Haberl 2015). Competition for land plays out continuously among different use types (cropland, pastureland, forests, urban spaces, and conservation and protected lands) and between different users within the same land-use category (subsistence vs commercial farmers) (Dell’Angelo et al. 2017b). Competition is mediated through economic and market forces (expressed through land rental and purchases, as well as trade and investments). In the context of such transactions, power relations often disfavour disadvantaged groups such as small-scale farmers, indigenous communities or women (Doss et al. 2015; Ravnborg et al. 2016). These drivers are influenced to a large degree by policies, institutions and governance structures. Land governance determines not only who can access the land, but also the role of land ownership (legal, formal, customary or collective) which influences land use, land-use change and the resulting land competition (Moroni 2018).

Globally, there is competition for land because it is a finite resource and because most of the highly productive land is already exploited by humans (Lambin and Meyfroidt 2011; Lambin 2012; Venter et al. 2016). Driven by growing population, urbanisation, demand for food and energy, as well as land degradation, competition for land is expected to accentuate land scarcity in the future (Tilman et al. 2011; Foley et al. 2011; Lambin 2012; Popp et al. 2016) (robust evidence, high agreement). Climate change influences land use both directly and indirectly, as climate policies can also a play a role in increasing land competition via forest conservation policies, afforestation, or energy

crop production (Section 1.3.1), with the potential for implications for food security (Hussein et al. 2013) and local land-ownership.

An example of large-scale change in land ownership is the much-debated large-scale land acquisition (LSLA) by investors which peaked in 2008 during the food price crisis, the financial crisis, and has also been linked to the search for biofuel investments (Dell’Angelo et al. 2017a). Since 2000, almost 50 million hectares of land have been acquired, and there are no signs of stagnation in the foreseeable future (Land Matrix2018).TheLSLAphenomenon,whichlargelytargetsagriculture, is widespread, including Sub-Saharan Africa, Southeast Asia, Eastern Europe and Latin America (Rulli et al. 2012; Nolte et al. 2016; Constantin et al. 2017). LSLAs are promoted by investors and host governments on economic grounds (infrastructure, employment, market development) (Deininger et al. 2011), but their social and environmental impacts can be negative and significant (Dell’Angelo et al. 2017a).

Much of the criticism of LSLA focuses on its social impacts, especially the threat to local communities’ land rights (especially indigenous people and women) (Anseeuw et al. 2011) and displaced communities creating secondary land expansion (Messerli et al. 2014; Davis et al. 2015). The promises that LSLAs would develop efficient agriculture on non-forested, unused land (Deininger et al. 2011) has so far not been fulfilled. However, LSLA is not the only outcome of weak land governance structures (Wang et al. 2016): other forms of inequitable or irregular land acquisition can also be home-grown, pitting one community against a more vulnerable group (Xu 2018) or land capture by urban elites (McDonnell 2017). As demands on land are increasing, building governance capacity and securing land tenure becomes essential to attain sustainable land use, which has the potential to mitigate climate change, promote food security, and potentially reduce risks of climate-induced migration and associated risks of conflicts (Section 7.6).



Progress in dealing with uncertainties in assessing land processes in the climate system

Uncertainties in decision-making

Decision-makers develop and implement policy in the face of many uncertainties (Rosenzweig and Neofotis 2013; Anav et al. 2013; Ciais et al. 2013a; Stocker et al. 2013b) (Section 7.5). In context of climate change, the term ‘deep uncertainty’ is frequently used to denote situations in which either the analysis of a situation is inconclusive, or parties to a decision cannot agree on a number of criteria that would help to rank model results in terms of likelihood (e.g., Hallegatte and Mach 2016; Maier et al. 2016) (Sections 7.1 and 7.5, and Table SM.1.2 in Supplementary Material). However, existing uncertainty does not support societal and political inaction.

The many ways of dealing with uncertainty in decision-making can be summarised by two decision approaches: (economic) cost-benefit analysis, and the precautionary approach. A typical variant of cost-benefit analysis is the minimisation of negative consequences. This approach needs reliable probability estimates (Gleckler et al. 2016; Parker 2013) and tends to focus on the short term. The precautionary approach does not take account of probability estimates (cf. Raffensperger and Tickner 1999), but instead focuses on avoiding the worst outcome (Gardiner 2006).

Between these two extremes, various decision approaches seek to address uncertainties in a more reflective manner that avoids the limitations of cost-benefit analysis and the precautionary approach. Climate-informed decision analysis combines various approaches to explore options and the vulnerabilities and sensitivities of certain decisions. Such an approach includes stakeholder involvement (e.g., elicitation methods), and can be combined with, for example, analysis of climate or land-use change modelling (Hallegatte and Rentschler 2015; Luedeling and Shepherd 2016).

Flexibility is facilitated by political decisions that are not set in stone and can change over time (Walker et al. 2013; Hallegatte and Rentschler 2015). Generally, within the research community that investigates deep uncertainty, a paradigm is emerging that requires the development of a strategic vision of the long – or mid-term future, while committing to short-term actions and establishing a framework to guide future actions, including revisions and flexible adjustment of decisions (Haasnoot 2013) (Section 7.5).


Response options to the key challenges

A number of response options underpin solutions to the challenges arising from GHG emissions from land, and the loss of productivity arising from degradation and desertification. These options are discussed in Sections 2.5 and 6.2 and rely on (i) land management, (ii) value chain management, and (iii) risk management (Table 1.2). None of these response options are mutually exclusive, and it is their combination in a regionally, context-specific manner that is most likely to achieve co-benefits between climate change mitigation, adaptation and other environmental challenges in a cost-effective way (Griscom et al. 2017; Kok et al. 2018). Sustainable solutions affecting both demand and supply are expected to yield most co-benefits if these rely not only on the carbon footprint, but are extended to other vital ecosystems such as water, nutrients and biodiversity footprints (van Noordwijk and Brussaard 2014; Cremasch 2016). As an entry point to the discussion in Chapter 6, we introduce here a selected number of examples that cut across climate change mitigation, food security, desertification, and degradation issues, including potential trade-offs and co-benefits.

Table 1.2

Broad categorisation of response options into three main classes and eight sub-classes.

For illustration, the table includes examples of individual response options. A complete list and description is provided in Chapter 6.

Response options based on land management

in agriculture

Improved management of: cropland, grazing land, livestock; agro-forestry; avoidance of conversion of grassland to cropland; integrated water management

in forests

Improved management of forests and forest restoration; reduced deforestation and degradation; afforestation

of soils

Increased soil organic carbon content; reduced soil erosion; reduced soil salinisation

across all/other ecosystems

Reduced landslides and natural hazards; reduced pollution including acidification; biodiversity conservation; restoration and reduced conversion of peatlands

specifically for CO2 removal

Enhanced weathering of minerals; bioenergy and BECCS

Response options based on value chain management

through demand management

Dietary change; reduced post-harvest losses; reduced food waste

through supply management

Sustainable sourcing; improved energy use in food systems; improved food processing and retailing

Response options based on risk management

Risk management

Risk-sharing instruments; use of local seeds; disaster risk management


Targeted decarbonisation relying on large land-area need

Most global future scenarios that aim to achieve global warming of 2°C or well below rely on bioenergy (BE; BECCS, with carbon capture and storage; Cross-Chapter Box 7 in Chapter 6) or afforestation and reforestation (de Coninck et al. 2018; Rogelj et al. 2018b,a; Anderson and Peters 2016; Popp et al. 2016; Smith et al. 2016) (Cross-Chapter Box 2 in Chapter 1). In addition to the very large area requirements projected for 2050 or 2100, several other aspects of these scenarios have also been criticised. For instance, they simulate very rapid technological and societal uptake rates for the land-related mitigation measures, when compared with historical observations (Turner et al. 2018; Brown et al. 2019; Vaughan and Gough 2016). Furthermore, confidence in the projected bioenergy or BECCS net carbon uptake potential is low, because of many diverging assumptions. This includes assumptions about bioenergy crop yields, the possibly large energy demand for CCS, which diminishes the net-GHG-saving of bioenergy systems, or the incomplete accounting for ecosystem processes and of the cumulative carbon-loss arising from natural vegetation clearance for bioenergy crops or bioenergy forests and subsequent harvest regimes (Anderson and Peters 2016; Bentsen 2017; Searchinger et al. 2017; Bayer et al. 2017; Fuchs et al. 2017; Pingoud et al. 2018; Schlesinger 2018). Bioenergy provision under politically unstable conditions may also be a problem (Erb et al. 2012; Searle and Malins 2015).

Large-scale bioenergy plantations and forests may compete for the same land area (Harper et al. 2018). Both potentially have adverse side effects on biodiversity and ecosystem services, as well as socio-economic trade-offs such as higher food prices due to land-area competition (Shi et al. 2013; Bárcena et al. 2014; Fernandez-Martinez et al. 2014; Searchinger et al. 2015; Bonsch et al. 2016; Creutzig et al. 2015; Kreidenweis et al. 2016; Santangeli et al. 2016; Williamson 2016; Graham et al. 2017; Krause et al. 2017; Hasegawa et al. 2018; Humpenoeder et al. 2018). Although forest-based mitigation could have co-benefits for biodiversity and many ecosystem services, this depends on the type of forest planted and the vegetation cover it replaces (Popp et al. 2014; Searchinger et al. 2015) (Cross-Chapter Box 2 in Chapter 1).

There is high confidence that scenarios with large land requirements for climate change mitigation may not achieve SDGs, such as no poverty, zero hunger and life on land, if competition for land and the need for agricultural intensification are greatly enhanced (Creutzig et al. 2016; Dooley and Kartha 2018; Hasegawa et al. 2015; Hof et al. 2018; Roy et al. 2018; Santangeli et al. 2016; Boysen et al. 2017; Henry et al. 2018; Kreidenweis et al. 2016; UN 2015). This does not mean that smaller-scale land-based climate mitigation could not have positive outcomes for then achieving these goals (e.g., Sections 6.2, and 4.5, Cross-Chapter Box 7 in Chapter 6).


Land management

Agricultural, forest and soil management

Sustainable land management (SLM) describes “the stewardship and use of land resources, including soils, water, animals and plants, to meet changing human needs while simultaneously assuring the long-term productive potential of these resources and the maintenance of their environmental functions” (Alemu 2016; Altieri and Nicholls 2017) (e.g., Section 4.1.5), and includes ecological, technological and governance aspects.

The choice of SLM strategy is a function of regional context and land-use types, with high agreement on (a combination of) choices such as agroecology (including agroforestry), conservation agriculture and forestry practices, crop and forest species diversity, appropriate crop and forest rotations, organic farming, integrated pest management, the preservation and protection of pollination services, rainwater harvesting, range and pasture management, and precision agriculture systems (Stockmann et al. 2013; Ebert, 2014; Schulte et al. 2014; Zhang et al. 2015; Sunil and Pandravada 2015; Poeplau and Don 2015; Agus et al. 2015; Keenan 2015; MacDicken et al. 2015; Abberton et al. 2016). Conservation agriculture and forestry uses management practices with minimal soil disturbance such as no tillage or minimum tillage, permanent soil cover with mulch, combined with rotations to ensure a permanent soil surface, or rapid regeneration of forest following harvest (Hobbs et al. 2008; Friedrich et al. 2012). Vegetation and soils in forests and woodland ecosystems play a crucial role in regulating critical ecosystem processes, therefore reduced deforestation together with sustainable forest management are integral to SLM (FAO 2015b) (Section 4.8). In some circumstances, increased demand for forest products can also lead to increased management of carbon storage in forests (Favero and Mendelsohn 2014). Precision agriculture is characterised by a “management system that is information and technology based, is site specific and uses one or more of the following sources of data: soils, crops, nutrients, pests, moisture, or yield, for optimum profitability, sustainability, and protection of the environment” (USDA 2007) (Cross-Chapter Box 6 in Chapter 5). The management of protected areas that reduce deforestation also plays an important role in climate change mitigation and adaptation while delivering numerous ecosystem services and sustainable development benefits (Bebber and Butt 2017). Similarly, when managed in an integrated and sustainable way, peatlands are also known to provide numerous ecosystem services, as well as socio-economic and mitigation and adaptation benefits (Ziadat et al. 2018).

Biochar is an organic compound used as soil amendment and is believed to be potentially an important global resource for mitigation. Enhancing the carbon content of soil and/or use of biochar (Chapter 4) have become increasingly important as a climate change mitigation option with possibly large co-benefits for other ecosystem services. Enhancing soil carbon storage and the addition of biochar can be practiced with limited competition for land, provided no productivity/ yield loss and abundant unused biomass, but evidence is limited and impacts of large scale application of biochar on the full GHG balance of soils, or human health are yet to be explored (Gurwick et al. 2013; Lorenz and Lal 2014; Smith 2016).


Value chain management

Supply management

Food losses from harvest to retailer. Approximately one-third of losses and waste in the food system occurs between crop production and food consumption, increasing substantially if losses in livestock production and overeating are included (Gustavsson et al. 2011; Alexander et al. 2017). This includes on-farm losses, farm to retailer losses, as well retailer and consumer losses (Section

Post-harvest food loss – on farm and from farm to retailer – is a widespread problem, especially in developing countries (Xue et al. 2017), but are challenging to quantify. For instance, averaged for eastern and southern Africa an estimated 10–17% of annual grain production is lost (Zorya et al. 2011). Across 84 countries and different time periods, annual median losses in the supply chain before retailing were estimated at about 28 kg per capita for cereals or about 12 kg per capita for eggs and dairy products (Xue et al. 2017). For the year 2013, losses prior to the reaching retailers were estimated at 20% (dry weight) of the production amount (22% wet weight) (Gustavsson et al. 2011; Alexander et al. 2017). While losses of food cannot be realistically reduced to zero, advancing harvesting technologies (Bradford et al. 2018; Affognon et al. 2015), storage capacity (Chegere 2018) and efficient transportation could all contribute to reducing these losses with co-benefits for food availability, the land area needed for food production and related GHG emissions.

Stability of food supply, transport and distribution. Increased climate variability enhances fluctuations in world food supply and price variability (Warren 2014; Challinor et al. 2015; Elbehri et al. 2017). ‘Food price shocks’ need to be understood regarding their transmission across sectors and borders and impacts on poor and food insecure populations, including urban poor subject to food deserts and inadequate food accessibility (Widener et al. 2017; Lehmann et al. 2013; Le 2016; FAO 2015b). Trade can play an important stabilising role in food supply, especially for regions with agro-ecological limits to production, including water scarce regions, as well as regions that experience short-term production variability due to climate, conflicts or other economic shocks (Gilmont 2015; Marchand et al. 2016). Food trade can either increase or reduce the overall environmental impacts of agriculture (Kastner et al. 2014). Embedded in trade are virtual transfers of water, land area, productivity, ecosystem services, biodiversity, or nutrients (Marques et al. 2019; Wiedmann and Lenzen 2018; Chaudhary and Kastner 2016) with either positive or negative implications (Chen et al. 2018; Yu et al. 2013). Detrimental consequences in countries in which trade dependency may accentuate the risk of food shortages from foreign production shocks could be reduced by increasing domestic reserves or importing food from a diversity of suppliers (Gilmont 2015; Marchand et al. 2016).

Climate mitigation policies could create new trade opportunities (e.g., biomass) (Favero and Massetti 2014) or alter existing trade patterns. The transportation GHG footprints of supply chains may be causing a differentiation between short and long supply chains (Schmidt et al. 2017) that may be influenced by both economics and policy measures (Section 5.4). In the absence of sustainable practices and when the ecological footprint is not valued through the market system, trade can also exacerbate resource exploitation and environmental leakages, thus weakening trade mitigation contributions (Dalin and Rodríguez-Iturbe 2016; Mosnier et al. 2014; Elbehri et al. 2017). Ensuring stable food supply while pursuing climate mitigation and adaptation will benefit from evolving trade rules and policies that allow internalisation of the cost of carbon (and costs of other vital resources such as water, nutrients). Likewise, future climate change mitigation policies would gain from measures designed to internalise the environmental costs of resources and the benefits of ecosystem services (Elbehri et al. 2017; Brown et al. 2007).

Demand management

Dietary change. Demand-side solutions to climate mitigation are an essential complement to supply-side, technology and productivity driven solutions (high confidence) (Creutzig et al. 2016; Bajželj et al. 2014; Erb et al. 2016b; Creutzig et al. 2018) (Sections 5.5.1 and 5.5.2). The environmental impacts of the animal-rich ‘western diets’ are being examined critically in the scientific literature (Hallström et al. 2015; Alexander et al. 2016b; Alexander et al. 2015; Tilman and Clark 2014; Aleksandrowicz et al. 2016; Poore and Nemecek 2018)

(Section 5.4.6). For example, if the average diet of each country were consumed globally, the agricultural land area needed to supply these diets would vary 14-fold, due to country differences in ruminant protein and calorific intake (–55% to +178% compared to existing cropland areas). Given the important role enteric fermentation plays in methane (CH4) emissions, a number of studies have examined the implications of lower animal-protein diets (Swain et al. 2018; Röös et al. 2017; Rao et al. 2018). Reduction of animal protein intake has been estimated to reduce global green water (from precipitation) use by 11% and blue water (from rivers, lakes, groundwater) use by 6% (Jalava et al. 2014). By avoiding meat from producers with above-median GHG emissions and halving animal-product intake, consumption change could free-up 21 million km2 of agricultural land and reduce GHG emissions by nearly 5 GtCO2-eq yr–1 or up to 10.4 GtCO2-eq yr–1 when vegetation carbon uptake is considered on the previously agricultural land (Poore and Nemecek 2018, 2019).

Diets can be location and community specific, are rooted in culture and traditions while responding to changing lifestyles driven for instance by urbanisation and changing income. Changing dietary and consumption habits would require a combination of non-price (government procurement, regulations, education and awareness raising) and price incentives (Juhl and Jensen 2014) to induce consumer behavioural change with potential synergies between climate, health and equity (addressing growing global nutrition imbalances that emerge as undernutrition, malnutrition, and obesity) (FAO 2018b).

Reduced waste and losses in the food demand system. Global averaged per capita food waste and loss (FWL) have increased by 44% between 1961 and 2011 (Porter et al. 2016) and are now around 25–30% of global food produced (Kummu et al. 2012; Alexander et al. 2017). Food waste occurs at all stages of the food supply chain from the household to the marketplace (Parfitt et al. 2010) and is found to be larger at household than at supply chain levels. A meta-analysis of 55 studies showed that the highest share of food waste was at the consumer stage (43.9% of total) with waste increasing with per capita GDP for high-income countries until a plateaux at about 100 kg cap–1 yr–1 (around 16% of food consumption) above about 70,000 USD cap–1 (van der Werf and Gilliland 2017; Xue et al. 2017). Food loss from supply chains tends to be more prevalent in less developed countries where inadequate technologies, limited infrastructure, and imperfect markets combine to raise the share of the food production lost before use.

There are several causes behind food waste including economics (cheap food), food policies (subsidies) as well as individual behaviour (Schanes et al. 2018). Household level food waste arises from overeating or overbuying (Thyberg and Tonjes 2016). Globally, overconsumption was found to waste 9–10% of food bought (Alexander et al. 2017).

Solutions to FWL thus need to address technical and economic aspects. Such solutions would benefit from more accurate data on the loss-source, loss-magnitude and causes along the food supply chain. In the long run, internalising the cost of food waste into the product price would more likely induce a shift in consumer behaviour towards less waste and more nutritious, or alternative, food intake (FAO 2018b). Reducing FWL would bring a range of benefits for health, reducing pressures on land, water and nutrients, lowering emissions and safeguarding food security. Reducing food waste by 50% would generate net emissions reductions in the range of 20 to 30% of total food-sourced GHGs (Bajželj et al. 2014). SDG 12 (“Ensure sustainable consumption and production patterns”) calls for per capita global food waste to be reduced by one-half at the retail and consumer level, and reducing food losses along production and supply chains by 2030.


Risk management

Risk management refers to plans, actions, strategies or policies to reduce the likelihood and/or magnitude of adverse potential consequences, based on assessed or perceived risks. Insurance and early warning systems are examples of risk management, but risk can also be reduced (or resilience enhanced) through a broad set of options ranging from seed sovereignty, livelihood diversification, to reducing land loss through urban sprawl. Early warning systems support farmer decision-making on management strategies (Section 1.2) and are a good example of an adaptation measure with mitigation co-benefits such as reducing carbon losses (Section 1.3.6). Primarily designed to avoid yield losses, early warning systems also support fire management strategies in forest ecosystems, which prevents financial as well as carbon losses (de Groot et al. 2015). Given that over recent decades on average around 10% of cereal production was lost through extreme weather events (Lesk et al. 2016), where available and affordable, insurance can buffer farmers and foresters against the financial losses incurred through such weather and other (fire, pests) extremes (Falco et al. 2014) (Sections 7.2 and 7.4). Decisions to take up insurance are influenced by a range of factors such as the removal of subsidies or targeted education (Falco et al. 2014). Enhancing access and affordability of insurance in low-income countries is a specific objective of the UNFCCC (Linnerooth-Bayer and Mechler 2006). A global mitigation co-benefit of insurance schemes may also include incentives for future risk reduction (Surminski and Oramas-Dorta 2014).


Economics of land-based mitigation pathways: Costs versus benefits of early action under uncertainty

The overarching societal costs associated with GHG emissions and the potential implications of mitigation activities can be measured by various metrics (cost-benefit analysis, cost effectiveness analysis) at different scales (project, technology, sector or the economy) (IPCC 2018) (Section 1.4). The social cost of carbon (SCC) measures the total net damages of an extra metric tonne of CO2 emissions due to the associated climate change (Nordhaus 2014; Pizer et al. 2014). Both negative and positive impacts are monetised and discounted to arrive at the net value of consumption loss. As the SCC depends on discount rate assumptions and value judgements (e.g., relative weight given to current vs future generations), it is not a straightforward policy tool to compare alternative options. At the sectoral level, marginal abatement cost curves (MACCs) are widely used for the assessment  of costs related to GHG emissions reduction. MACCs measure the cost of reducing one more GHG unit and are either expert-based or model-derived and offer a range of approaches and assumptions on discount rates or available abatement technologies (Kesicki 2013). In land-based sectors, Gillingham and Stock (2018) reported short-term static abatement costs for afforestation of between 1 and 10 USD2017 per tCO2, soil management at 57 and livestock management at 71 USD2017 per tCO2. MACCs are more reliable when used to rank alternative options compared to a baseline (or business as usual) rather than offering absolute numerical measures (Huang et al. 2016). The economics of land-based mitigation options encompass also the “costs of inaction” that arise either from the economic damages due to continued accumulation of GHGs in the atmosphere and from the diminution in value of ecosystem services or the cost of their restoration where feasible (Rodriguez-Labajos 2013; Ricke et al. 2018). Overall, it remains challenging to estimate the costs of alternative mitigation options owing to the context – and scale-specific interplay between multiple drivers (technological, economic, and socio-cultural) and enabling policies and institutions (IPCC 2018) (Section 1.4).

The costs associated with mitigation (both project-linked such as capital costs or land rental rates, or sometimes social costs) generally increase with stringent mitigation targets and over time. Sources of uncertainty include the future availability, cost and performance of technologies (Rosen and Guenther 2015; Chen et al. 2016) or lags in decision-making, which have been demonstrated by the uptake of land use and land utilisation policies (Alexander et al. 2013; Hull et al. 2015; Brown et al. 2018b). There is growing evidence of significant mitigation gains through conservation, restoration and improved land management practices (Griscom et al. 2017; Kindermann et al. 2008; Golub et al. 2013; Favero et al. 2017) (Chapters 4 and 6), but the mitigation cost efficiency can vary according to region and specific ecosystem (Albanito et al. 2016). Recent model developments that treat process-based, human–environment interactions have recognised feedbacks that reinforce or dampen the original stimulus for land-use change (Robinson et al. 2017; Walters and Scholes 2017). For instance, land mitigation interventions that rely on large-scale, land-use change (e.g., afforestation) would need to account for the rebound effect (which dampens initial impacts due to feedbacks) in which raising land prices also raises the cost of land-based mitigation (Vivanco et al. 2016). Although there are few direct estimates, indirect assessments strongly point to much higher costs if action is delayed or limited in scope (medium confidence). Quicker response options are also needed to avoid loss of high-carbon ecosystems and other vital ecosystem services that provide multiple services that are difficult to replace (peatlands, wetlands, mangroves, forests) (Yirdaw et al. 2017; Pedrozo-Acuña et al. 2015). Delayed action would raise relative costs in the future or could make response options less feasible (medium confidence) (Goldstein et al. 2019; Butler et al. 2014).


Adaptation measures and scope for co-benefits with mitigation

Adaptation and mitigation have generally been treated as two separate discourses, both in policy and practice, with mitigation addressing cause and adaptation dealing with the consequences of climate change (Hennessey et al. 2017). While adaptation (e.g., reducing flood risks) and mitigation (e.g., reducing non-CO2 emissions from agriculture) may have different objectives and operate at different scales, they can also generate joint outcomes (Locatelli et al. 2015b) with adaptation generating mitigation co-benefits. Seeking to integrate strategies for achieving adaptation and mitigation goals is attractive in order to reduce competition for limited resources and trade-offs (Lobell et al. 2013; Berry et al. 2015; Kongsager and Corbera 2015). Moreover, determinants that can foster adaptation and mitigation practices are similar. These tend to include available technology and resources, and credible information for policymakers to act on (Yohe 2001).

Four sets of mitigation–adaptation interrelationships can be distinguished: (i) mitigation actions that can result in adaptation benefits; (ii) adaptation actions that have mitigation benefits; (iii) processes that have implications for both adaptation and mitigation; and (iv) strategies and policy processes that seek to promote an integrated set of responses for both adaptation and mitigation (Klein et al. 2007). A high level of adaptive capacity is a key ingredient to developing successful mitigation policy. Implementing mitigation action can result in increasing resilience especially if it is able to reduce risks. Yet, mitigation and adaptation objectives, scale of implementation, sector and even metrics to identify impacts tend to differ (Ayers and Huq 2009), and institutional setting, often does not enable an environment where synergies are sought (Kongsager et al. 2016). Trade-offs between adaptation and mitigation exist as well and need to be understood (and avoided) to establish win-win situations (Porter et al. 2014; Kongsager et al. 2016).

Forestry and agriculture offer a wide range of lessons for the integration of adaptation and mitigation actions given the vulnerability of forest ecosystems or cropland to climate variability and change (Keenan 2015; Gaba et al. 2015) (Sections 5.6 and 4.8). Increasing adaptive capacity in forested areas has the potential to prevent deforestation and forest degradation (Locatelli et al. 2011). Reforestation projects, if well managed, can increase community economic opportunities that encourage conservation (Nelson and de Jong 2003), build capacity through training of farmers and installation of multifunctional plantations with income generation (Reyer et al. 2009), strengthen local institutions (Locatelli et al. 2015a) and increase cash-flow to local forest stakeholders from foreign donors (West 2016). A forest plantation that sequesters carbon for mitigation can also reduce water availability to downstream populations and heighten their vulnerability to drought. Inversely, not recognising mitigation in adaptation projects may yield adaptation measures that increase greenhouse gas emissions, a prime example of ‘maladaptation’. Analogously, ‘mal-mitigation’ would result in reducing GHG emissions, but increasing vulnerability (Barnett and O’Neill 2010; Porter et al. 2014). For instance, the cost of pursuing large-scale adaptation and mitigation projects has been associated with higher failure risks, onerous transactions costs and the complexity of managing big projects (Swart and Raes 2007).

Adaptation encompasses both biophysical and socio-economic vulnerability and underlying causes (informational, capacity, financial, institutional, and technological; Huq et al. 2014) and it is increasingly linked to resilience and to broader development goals (Huq et al. 2014). Adaptation measures can increase performance of mitigation projects under climate change and legitimise mitigation measures through the more immediately felt effects of adaptation (Locatelli et al. 2011; Campbell et al. 2014; Locatelli et al. 2015b). Effective climate policy integration in the land sector is expected to gain from (i) internal policy coherence between adaptation and mitigation objectives, (ii) external climate coherence between climate change and development objectives, (iii) policy integration that favours vertical governance structures to foster effective mainstreaming of climate change into sectoral policies, and (iv) horizontal policy integration through overarching governance structures to enable cross-sectoral coordination (Sections 1.4 and 7.4).



Enabling the response

Climate change and sustainable development are challenges to society that require action at local, national, transboundary and global scales. Different time-perspectives are also important in decision-making, ranging from immediate actions to long-term planning and investment. Acknowledging the systemic link between food production and consumption, and land-resources more broadly is expected to enhance the success of actions (Bazilian et al. 2011; Hussey and Pittock 2012). Because of the complexity of challenges and the diversity of actors involved in addressing these challenges, decision-making would benefit from a portfolio of policy instruments. Decision-making would also be facilitated by overcoming barriers such as inadequate education and funding mechanisms, as well as integrating international decisions into all relevant (sub)national sectoral policies (Section 7.4).

‘Nexus thinking’ emerged as an alternative to the sector-specific governance of natural resource use to achieve global securities of water (D’Odorico et al. 2018), food and energy (Hoff 2011; Allan et al. 2015), and also to address biodiversity concerns (Fischer et al. 2017). Yet, there is no agreed definition of “nexus” nor a uniform framework to approach the concept, which may be land-focused (Howells et al. 2013), water-focused (Hoff 2011) or food-centred (Ringler and Lawford 2013; Biggs et al. 2015). Significant barriers remain to establish nexus approaches as part of a wider repertoire of responses to global environmental change, including challenges to cross-disciplinary collaboration, complexity, political economy and the incompatibility of current institutional structures (Hayley et al. 2015; Wichelns 2017) (Sections 7.5.6 and 7.6.2).



Governance to enable the response

Governance includes the processes, structures, rules and traditions applied by formal and informal actors including governments, markets, organisations, and their interactions with people. Land governance actors include those affecting policies and markets, and those directly changing land use (Hersperger et al. 2010). The former includes governments and administrative entities, large companies investing in land, non-governmental institutions and international institutions. It also includes UN agencies that are working at the interface between climate change and land management, such as the FAO and the World Food Programme that have inter alia worked on advancing knowledge to support food security through the improvement of techniques and strategies for more resilient farm systems. Farmers and foresters directly act on land (actors in proximate causes) (Hersperger et al. 2010) (Chapter 7).

Policy design and formulation has often been strongly sectoral. For example, agricultural policy might be concerned with food security, but have little concern for environmental protection or human health. As food, energy and water security and the conservation of biodiversity rank highly on the Agenda 2030 for Sustainable Development, the promotion of synergies between and across sectoral policies is important (IPBES 2018a). This can also reduce the risks of anthropogenic climate forcing through mitigation, and bring greater collaboration between scientists, policymakers, the private sector and land managers in adapting to climate change (FAO 2015a). Polycentric governance (Section 7.6) has emerged as an appropriate way of handling resource management problems, in which the decision-making centres take account of one another in competitive and cooperative relationships and have recourse to conflict resolution mechanisms (Carlisle and Gruby 2017). Polycentric governance is also multi-scale and allows the interaction between actors at different levels (local, regional, national and global) in managing common pool resources such as forests or aquifers.

Implementation of systemic, nexus approaches has been achieved through socio-ecological systems (SES) frameworks that emerged from studies of how institutions affect human incentives, actions and outcomes (Ostrom and Cox 2010). Recognition of the importance of SES laid the basis for alternative formulations to tackle the sustainable management of land resources focusing specifically on institutional and governance outcomes (Lebel et al. 2006; Bodin 2017). The SES approach also addresses the multiple scales in which the social and ecological dimensions interact (Veldkamp et al. 2011; Myers et al. 2016; Azizi et al. 2017) (Section 6.1).

Adaptation or resilience pathways within the SES frameworks require several attributes, including indigenous and local knowledge (ILK) and trust building for deliberative decision-making and effective collective action, polycentric and multi-layered institutions and responsible authorities that pursue just distributions of benefits to enhance the adaptive capacity of vulnerable groups and communities (Lebel et al. 2006). The nature, source and mode of knowledge generation are critical to ensure that sustainable solutions are community-owned and fully integrated within the local context (Mistry and Berardi 2016; Schneider and Buser 2018). Integrating ILK with scientific information is a prerequisite for such community-owned solutions (Cross-Chapter Box 13 in Chapter 7). ILK is context-specific, transmitted orally or through imitation and demonstration, adaptive to changing environments, and collectivised through a shared social memory (Mistry and Berardi 2016). ILK is also holistic since indigenous people do not seek solutions aimed at adapting to climate change alone, but instead look for solutions to increase their resilience to a wide range of shocks and stresses (Mistry and Berardi 2016). ILK can be deployed in the practice of climate governance, especially at the local level where actions are informed by the principles of decentralisation and autonomy (Chanza and de Wit 2016). ILK need not be viewed as needing confirmation or disapproval by formal science, but rather it can complement scientific knowledge (Klein et al. 2014).

The capacity to apply individual policy instruments and policy mixes is influenced by governance modes. These modes include hierarchical governance that is centralised and imposes policy through top-down measures, decentralised governance in which public policy is devolved to regional or local government, public-private partnerships that aim for mutual benefits for the public and private sectors and self or private governance that involves decisions beyond the realms of the public sector (IPBES 2018a). These governance modes provide both constraints and opportunities for key actors that impact the effectiveness, efficiency and equity of policy implementation.


Gender agency as a critical factor in climate and land sustainability outcomes

Environmental resource management is not gender neutral. Gender is an essential variable in shaping ecological processes and change, building better prospects for livelihoods and sustainable development (Resurrección 2013) (Cross-Chapter Box 11 in Chapter 7). Entrenched legal and social structures and power relations constitute additional stressors that render women’s experience of natural resources disproportionately negative when compared to men. Socio-economic drivers and entrenched gender inequalities affect land-based management (Agarwal 2010). The intersections between climate change, gender and climate adaptation takes place at multiple scales: household, national and international, and adaptive capacities are shaped through power and knowledge.

Germaine to the gender inequities is the unequal access to land-based resources. Women play a significant role in agriculture (Boserup 1989; Darity 1980) and rural economies globally (FAO 2011), but are well below their share of labour in agriculture globally (FAO 2011). In 59% of 161 surveyed countries, customary, traditional and religious practices hinder women’s land rights (OECD 2014). Moreover, women typically shoulder disproportionate responsibility for unpaid domestic work including care-giving activities (Beuchelt and Badstue 2013) and the provision of water and firewood (UNEP 2016). Exposure to violencerestricts,inlargeregions,theirmobilityforcapacity-building activities and productive work outside the home (Day et al. 2005; UNEP 2016). Large-scale development projects can erode rights, and lead to over-exploitation of natural resources. Hence, there are cases where reforms related to land-based management, instead of enhancing food security, have tended to increase the vulnerability of both women and men and reduce their ability to adapt to climate change (Pham et al. 2016). Access to, and control over, land and land-based resources is essential in taking concrete action on land-based mitigation, and inadequate access can affect women’s rights and participation in land governance and management of productive assets.

Timely information, such as from early warning systems, is critical in managing risks, disasters, and land degradation, and in enabling land-based adaptation. Gender, household resources and social status, are all determinants that influence the adoption of land-based strategies (Theriault et al. 2017). Climate change is not a lone driver in the marginalisation of women; their ability to respond swiftly to its impacts will depend on other socio-economic drivers that may help or hinder action towards adaptive governance. Empowering women and removing gender-based inequities constitutes a mechanism for greater participation in the adoption of sustainable practices of land management (Mello and Schmink 2017). Improving women’s access to land (Arora-Jonsson 2014) and other resources (water) and means of economic livelihoods (such as credit and finance) are the prerequisites to enable women to participate in governance and decision-making structures (Namubiru-Mwaura 2014). Still, women are not a homogenous group, and distinctions through elements of ethnicity, class, age and social status, require a more nuanced approach and not a uniform treatment through vulnerability lenses only. An intersectional approach that accounts for various social identifiers under different situations of power (Rao 2017) is considered suitable to integrate gender into climate change research and helps to recognise overlapping and interdependent systems of power (Djoudi et al. 2016; Kaijser and Kronsell 2014; Moosa and Tuana 2014; Thompson-Hall et al. 2016).


Policy instruments

Policy instruments enable governance actors to respond to environmental and societal challenges through policy action. Examples of the range of policy instruments available to public policymakers are discussed below based on four categories of instruments: (i) legal and regulatory instruments, (ii) rights-based instruments and customary norms, (iii) economic and financial instruments, and (iv) social and cultural instruments.

Economic and financial instruments

Economic (such as taxes, subsidies) and financial (weather-index insurance) instruments deal with the many ways in which public policy organisations can intervene in markets. A number of instruments are available to support climate mitigation actions including public provision, environmental regulations, creating property rights and markets (Sterner 2003). Market-based policies such as carbon taxes, fuel taxes, cap and trade systems or green payments have been promoted (mostly in industrial economies) to encourage markets and businesses to contribute to climate mitigation, but their effectiveness to date has not always matched expectations (Grolleau et al. 2016) (Section 7.4.4). Market-based instruments in ecosystem services generate both positive (incentives for conservation), but also negative environmental impacts, and also push food prices up or increase price instability (Gómez-Baggethun and Muradian 2015; Farley and Voinov 2016). Footprint labels can be an effective means of shifting consumer behaviour. However, private labels focusing on a single metric (e.g., carbon) may give misleading signals if they target a portion of the life cycle (e.g., transport) (Appleton 2009) or ignore other ecological indicators (water, nutrients, biodiversity) (van Noordwijk and Brussaard 2014).

Effective and durable, market-led responses for climate mitigation depend on business models that internalise the cost of emissions into economic calculations. Such ‘business transformation’ would itself require integrated policies and strategies that aim to account for emissions in economic activities (Biagini and Miller 2013; Weitzman 2014; Eidelwein et al. 2018). International initiatives such as REDD+ and agricultural commodity roundtables (beef, soybeans, palm oil, sugar) are expanding the scope of private sector participation in climate mitigation (Nepstad et al. 2013), but their impacts have not always been effective (Denis et al. 2014). Payments for environmental services (PES) defined as “voluntary transactions between service users and service providers that are conditional on agreed rules of natural resource management for generating offsite services” (Wunder 2015) have not been widely adopted and have not yet been demonstrated to deliver as effectively as originally hoped (Börner et al. 2017) (Sections 7.4 and 7.5). PES in forestry were shown to be effective only when coupled with appropriate regulatory measures (Alix-Garcia and Wolff 2014). Better designed and expanded PES schemes would encourage integrated soil–water–nutrient management packages (Stavi et al. 2016), services for pollinator protection (Nicole 2015), water use governance under scarcity, and engage both public and private actors (Loch et al. 2013). Effective PES also requires better economic metrics to account for human- directed losses in terrestrial ecosystems and to food potential, and to address market failures or externalities unaccounted for in market valuation of ecosystem services.

Resilient strategies for climate adaptation can rely on the construction of markets through social networks as in the case of livestock systems (Denis et al. 2014) or when market signals encourage adaptation through land markets or supply chain incentives for sustainable land management practices (Anderson et al. 2018). Adequate policy (through regulations, investments in research and development or support to social capabilities) can support private initiatives for effective solutions to restore degraded lands (Reed and Stringer 2015), or mitigate against risk and to avoid shifting risks to the public (Biagini and Miller 2013). Governments, private business, and community groups could also partner to develop sustainable production codes (Chartres and Noble 2015), and in co-managing land-based resources (Baker and Chapin 2018), while public-private partnerships can be effective mechanisms in deploying infrastructure to cope with climatic events (floods) and for climate-indexed insurance (Kunreuther 2015). Private initiatives that depend on trade for climate adaptation and mitigation require reliable trading systems that do not impede climate mitigation objectives (Elbehri et al. 2015; Mathews 2017).

Rights-based instruments and customary norms

Rights-based instruments and customary norms deal with the equitable and fair management of land resources for all people (IPBES 2018a). These instruments emphasise the rights in particular of indigenous peoples and local communities, including for example, recognition of the rights embedded in the access to, and use of, common land. Common land includes situations without legal ownership (e.g., hunter-gathering communities in South America or Africa, and bushmeat), where the legal ownership is distinct from usage rights (Mediterranean transhumance grazing systems), or mixed ownership-common grazing systems (e.g., crofting in Scotland). A lack of formal (legal) ownership has often led to the loss of access rights to land, where these rights were also not formally enshrined in law, which especially effects indigenous communities, for example, deforestation in the Amazon basin. Overcoming the constraints associated with common-pool resources (forestry, fisheries, water) are often of economic and institutional nature (Hinkel et al. 2014) and require tackling the absence or poor functioning of institutions and the structural constraints that they engender through access and control levers using policies and markets and other mechanisms (Schut et al. 2016). Other examples of rights-based instruments include the protection of heritage sites, sacred sites and peace parks (IPBES 2018a). Rights-based instruments and customary norms are consistent with the aims of international and national human rights, and the critical issue of liability in the climate change problem.

Social and cultural norms

Social and cultural instruments are concerned with the communication of knowledge about conscious consumption patterns and resource-effective ways of life through awareness raising, education and communication of the quality and the provenance of land-based products. Examples of the latter include consumption choices aided by ecolabelling (Section and certification. Cultural indicators (such as social capital, cooperation, gender equity, women’s knowledge, socio-ecological mobility) contribute to the resilience of social-ecological systems (Sterling et al. 2017). Indigenous communities (such as the Inuit and Tsleil Waututh Nation in Canada) that continue to maintain traditional foods exhibit greater dietary quality and adequacy (Sheehy et al. 2015). Social and cultural instruments also include approaches to self-regulation and voluntary agreements, especially with respect to environmental management and land resource use. This is becoming especially irrelevant for the increasingly important domain of corporate social responsibility (Halkos and Skouloudis 2016).


The interdisciplinary nature of the SRCCL

Assessing the land system in view of the multiple challenges that are covered by the SRCCL requires a broad, inter-disciplinary perspective. Methods, core concepts and definitions are used differently in different sectors, geographic regions, and across academic communities addressing land systems, and these concepts and approaches to research are also undergoing a change in their interpretation through time. These differences reflect varying perspectives, in nuances or emphasis, on land as components of the climate and socio-economic systems. Because of its inter-disciplinary nature, the SRCCL can take advantage of these varying perspectives and the diverse methods that accompany them. That way, the report aims to support decision- makers across sectors and world regions in the interpretation of its main findings and support the implementation of solutions.


  1. Different communities have a different understanding of the concept of pathways (IPCC 2018). Here, we refer to pathways as a description of the time-dependent actions required to move from today’s world to a set of future visions (IPCC 2018). However, the term pathways is commonly used in the climate change literature as a synonym for projections or trajectories (e.g., shared socio-economic pathways).
  2. Uncertainty here is defined as the coefficient of variation CV. In the case of micrometeorological fluxes they refer to random errors and CV of daily average.
  3. >100 for fluxes less than 5 gN2O-N ha–1 d–1.


  1. Abahussain, A.A., A.S. Abdu, W.K. Al-Zubari, N.A. El-Deen and M. Abdul-Raheem, 2002: Desertification in the Arab region: Analysis of current status and trends. J. Arid Environ., 51, 521–545, doi:10.1006/ jare.2002.0975.
  2. Abarca-Gómez, L. et al., 2017: Worldwide trends in body-mass index, underweight, overweight and obesity from 1975 to 2016: A pooled analysis of 2416 population-based measurement studies in 128.9 million children, adolescents and adults. Lancet, 390, 2627–2642, doi:10.1016/ S0140-6736(17)32129-3.
  3. Abatzoglou, J.T., S.Z. Dobrowski, S.A. Parks and K.C. Hegewisch, 2018: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data, 5, 170191, doi:10.1038/ sdata.2017.191.
  4. Abberton, M., 2016: Global agricultural intensification during climate change: a role for genomics. Plant Biotechnol. J., 14, 1095–1098, doi:10.1111/ pbi.12467.
  5. Abreu, R.C.R. et al., 2017: The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv., 3, e1701284, doi:10.1126/sciadv.1701284.
  6. Abu Hammad, A. and A. Tumeizi, 2012: Land degradation: Socioeconomic and environmental causes and consequences in the eastern Mediterranean. L. Degrad. Dev., 23, 216–226, doi:10.1002/ldr.1069.
  7. Achard, F. et al., 2014: Determination of tropical deforestation rates and related carbon losses from 1990 to 2010. Glob. Chang. Biol., 20, 2540– 2554, doi:10.1111/GCB.12605.
  8. Affognon, H. et al., 2015: Unpacking postharvest losses in Sub-Saharan Africa: A meta-analysis. World Dev., 66, 49–68, doi:10.1016/J. WORLDDEV.2014.08.002.
  9. Agarwal, B., 2010: Gender and Green Governance. Oxford University Press, Oxford, UK.
  10. Agus, F., H. Husnain and R.D. Yustika, 2015: Improving agricultural resilience to climate change through soil management. J. Penelit. dan Pengemb. Pertan., 34, 147–158. doi:10.21082/jp3.v34n4.2015. pp. 147–158.
  11. Ahlstrom, A., G. Schurgers, A. Arneth and B. Smith, 2012: Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections. Environ. Res. Lett., 7, doi:04400810.1088/1748- 9326/7/4/044008.
  12. Ahlstrom, A., B. Smith, J. Lindstrom, M. Rummukainen and C.B. Uvo, 2013: GCM characteristics explain the majority of uncertainty in projected 21st century terrestrial ecosystem carbon balance. Biogeosciences, 10, 1517– 1528, doi:10.5194/bg-10-1517-2013.
  13. Ahrends, A. et al., 2017: China’s fight to halt tree cover loss. Proc. R. Soc. B Biol. Sci., 284, 1–10, doi:10.1098/rspb.2016.2559.
  14. Aich, S., S.M.L. Ewe, B. Gu, T.W. Dreschel, 2014: An evaluation of peat loss from an Everglades tree island, Florida, USA. Mires Peat, 14, 1–15.
  15. Albanito, F. et al., 2016: Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: A global assessment. GCB Bioenergy, 8, 81–95, doi:10.1111/gcbb.12242.
  16. Aleksandrowicz, L., R. Green, E.J.M. Joy, P. Smith, and A. Haines, 2016: The impacts of dietary change on greenhouse gas emissions, land use, water use and health: A systematic review. PLoS One, 11, e0165797, doi:10.1371/ journal.pone.0165797.
  17. Alemu, M.M., 2016: Sustainable land management. J. Environ. Prot., 7, 502– 506, doi:10.4236/jep.2016.74045.
  18. Alexander, P., D. Moran, M.D.A. Rounsevell, and P. Smith, 2013: Modelling the perennial energy crop market: The role of spatial diffusion. J.R. Soc. Interface, 10, in press, doi:10.1098/rsif.2013.0656.
  19. Alexander, P. et al., 2015: Drivers for global agricultural land use change: The nexus of diet, population, yield and bioenergy. Glob. Environ. Chang., doi:10.1016/j.gloenvcha.2015.08.011.
  20. Alexander, P. et al., 2016a: Assessing uncertainties in land cover projections. Glob. Chang. Biol., doi:10.1111/gcb.13447.
  21. Alexander, P., C. Brown, A. Arneth, J. Finnigan, and M.D.A. Rounsevell, 2016b: Human appropriation of land for food: The role of diet. Glob. Environ. Chang. Policy Dimens., 41, 88–98, doi:10.1016/j.gloenvcha.2016.09.005.
  22. Alexander, P. et al., 2017: Losses, inefficiencies and waste in the global food syste. Agric. Syst., 153, 190–200, doi:10.1016/j.agsy.2017.01.014.
  23. Alexander, P. et al., 2018: Adaptation of global land use and management intensity to changes in climate and atmospheric carbon dioxide. Glob. Chang. Biol., doi:10.1111/gcb.14110.
  24. Alix-Garcia, J. and H. Wolff, 2014: Payment for ecosystem services from forests. Annu. Rev. Resour. Econ., 6, 361–380, doi:10.1146/annurev-resource-100 913-012524.
  25. Alkama, R. and A. Cescatti, 2016: Biophysical climate impacts of recent changes in global forest cover. Science, 351, 600–604, doi:10.1126/ science.aac8083.
  26. Allan, T., M. Keulertz, and E. Woertz, 2015: The water–food–energy nexus: An introduction to nexus concepts and some conceptual and operational problems (vol 31, pg 301, 2015). Int. J. Water Resour. Dev., 31, 800, doi:10 .1080/07900627.2015.1060725.
  27. Allen, C.D. et al., 2010: A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manage., 259, 660–684, doi:10.1016/j.foreco.2009.09.001.
  28. Altieri, M.A., and C.I. Nicholls, 2017: The adaptation and mitigation potential of traditional agriculture in a changing climate. Clim. Change, 140, 33–45, doi:10.1007/s10584-013-0909-y.
  29. Anav, A. et al., 2013: Evaluating the land and ocean components of the global carbon cycle in the CMIP5 Earth system models. J. Clim., 26, 6801–6843, doi:10.1175/jcli-d-12-00417.1.
  30. Anderegg, W.R.L., J.M. Kane, and L.D.L. Anderegg, 2012: Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Chang., 3, 30.
  31. Anderegg, W.R.L. et al., 2015: Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol., 208, 674–683, doi:10.1111/nph.13477.
  32. Anderson-Teixeira, K.J., 2018: Prioritizing biodiversity and carbon. Nat. Clim. Chang., 8, 667–668, doi:10.1038/s41558-018-0242-6.
  33. Anderson, K. and G.P. Peters, 2016: The trouble with negative emissions. Science, 354, 182–183, doi:10.1126/science.aah4567.
  34. Anderson, R.G. et al., 2011: Biophysical considerations in forestry for climate protection. Front. Ecol. Environ., 9, 174–182, doi:10.1890/090179.
  35. Anderson, S.E. et al., 2018: The Critical Role of Markets in Climate Change Adaptation. National Bureau of Economic Research.
  36. Anseeuw, W., L.A. Wily, L. Cotula, and M. Taylor, 2011: Land Rights and the Rush for Land: Findings of the Global Commercial Pressures on Land Research Project. International Land Coalition, Rome, Italy, 72 pp.
  37. Appleton, A.E., 2009: Private climate change standards and labelling schemes under the WTO agreement on technical barriers to trade. In: International trade regulation and the mitigation of climate change: World Trade Forum. Cambridge University Press, Cambridge, United Kingdom, pp. 131–152.
  38. Ardö, J., T. Tagesson, S. Jamali, and A. Khatir, 2018: MODIS EVI-based net primary production in the Sahel 2000–2014. Int. J. Appl. Earth Obs. Geoinf., 65, 35–45, doi:10.1016/j.jag.2017.10.002.
  39. Arifanti, V.B., J.B. Kauffman, D. Hadriyanto, D. Murdiyarso, and R. Diana, 2018: Carbon dynamics and land use carbon footprints in mangrove-converted aquaculture: The case of the Mahakam Delta, Indonesia. For. Ecol. Manage., 432, 17–29, doi:10.1016/j.foreco.2018.08.047.
  40. Arneth, A., C. Brown, and M.D.A. Rounsevell, 2014: Global models of human decision-making for land-based mitigation and adaptation assessment. Nat. Clim. Chang., 4, 550–557, doi:10.1038/nclimate2250.
  41. Arneth, A. et al., 2017: Historical carbon dioxide emissions caused by land-use changes are possibly larger than assumed. Nat. Geosci., 10, 79, doi:10.1038/ngeo2882.
  42. Aronson, M.F.J. et al., 2014: A global analysis of the impacts of urbanization on bird and plant diversity reveals key anthropogenic drivers. Proc. R. Soc. B Biol. Sci., 281, 20133330–20133330, doi:10.1098/rspb.2013.3330.
  43. Arora-Jonsson, S., 2014: Forty years of gender research and environmental policy: Where do we stand? Womens. Stud. Int. Forum, 47, 295–308, doi:10.1016/J.WSIF.2014.02.009.
  44. Ashworth, K., O. Wild, and C.N. Hewitt, 2013: Impacts of biofuel cultivation on mortality and crop yields. Nat. Clim. Chang., 3, 492–496, doi:10.1038/ nclimate1788.
  45. Ayers, J.M. and S. Huq, 2009: The value of linking mitigation and adaptation: A case study of Bangladesh. Environ. Manage., 43, 753–764, doi:10.1007/ s00267-008-9223-2.
  46. Azizi, A., A. Ghorbani, B. Malekmohammadi, and H.R. Jafari, 2017: Government management and overexploitation of groundwater resources: absence of local community initiatives in Ardabil plain-Iran. J. Environ. Plan. Manag., 60, 1785–1808, doi:10.1080/09640568.2016.1257975.
  47. Bai, Z.G., D.L. Dent, L. Olsson, and M.E. Schaepman, 2008: Proxy global assessment of land degradation. Soil Use Manag., 24, 223–234, doi:10.1111/j.1475-2743.2008.00169.x.
  48. Bais, A.L.S., C. Lauk, T. Kastner, and K. Erb, 2015: Global patterns and trends of wood harvest and use between 1990 and 2010. Ecol. Econ., 119, 326–337, doi:10.1016/j.ecolecon.2015.09.011.
  49. Bajželj, B. et al., 2014: Importance of food-demand management for climate mitigation. Nat. Clim. Chang., 4, 924, doi:10.1038/nclimate2353.
  50. Baker, S. and F.S. Chapin III, 2018: Going beyond “it depends:” the role of context in shaping participation in natural resource management. Ecol. Soc., 23, doi:10.5751/ES-09868-230120.
  51. Baldos, U.L.C. and T.W. Hertel, 2015: The role of international trade in managing food security risks from climate change. Food Secur., 7, 275– 290, doi:10.1007/s12571-015-0435-z.
  52. Baral, H., M.R. Guariguata, and R.J. Keenan, 2016: A proposed framework for assessing ecosystem goods and services from planted forests. Ecosyst. Serv., 22, 260–268, doi:10.1016/j.ecoser.2016.10.002.
  53. Bárcena, T.G. et al., 2014: Soil carbon stock change following afforestation in Northern Europe: a meta-analysis. Glob. Chang. Biol., 20, 2393–2405, doi:10.1111/gcb.12576.
  54. Barlow, J. et al., 2007: Quantifying the biodiversity value of tropical primary, secondary, and plantation forests. Proc. Natl. Acad. Sci. U.S.A., 104, 18555– 18560, doi:10.1073/pnas.0703333104.
  55. Barnett, J., and S. O’Neill, 2010: Maladaptation. Glob. Environ. Chang., 2, 211–213, doi:10.1016/j.gloenvcha.2009.11.004.
  56. Bastin, J.-F. et al., 2017: The extent of forest in dryland biomes. Science, 356, 635–638, doi:10.1126/science.aam6527.
  57. Bayer, A.D. et al., 2017: Uncertainties in the land-use flux resulting from land-use change reconstructions and gross land transitions. Earth Syst. Dyn., 8, 91–111, doi:10.5194/esd-8-91-2017.
  58. Bazilian, M. et al., 2011: Considering the energy, water and food nexus: Towards an integrated modelling approach. Energy Policy, 39, 7896–7906, doi:10.1016/J.ENPOL.2011.09.039.
  59. Bebber, D.P. and N. Butt, 2017: Tropical protected areas reduced deforestation carbon emissions by one third from 2000–2012. Sci. Rep., 7, doi:1400510.1038/s41598-017-14467-w.
  60. Beinroth, F.H., H. Eswaran, P.F. Reich and E. Van Den Berg, 1994: Land related stresses. In: Stressed Ecosystems and Sustainable Agriculture [Virmani, S.M., J.C. Katyal, H. Eswaran and I.P. Abrol, (eds.)]. Oxford and IBH, New Delhi, India.
  61. Bentsen, N.S., 2017: Carbon debt and payback time – Lost in the forest? Renew. Sustain. Energy Rev., 73, 1211–1217, doi:10.1016/j.rser.2017.02.004.
  62. Bernal, B., L.T. Murray, and T.R.H. Pearson, 2018: Global carbon dioxide removal rates from forest landscape restoration activities. Carbon Balance Manag., 13, doi:10.1186/s13021-018-0110-8.
  63. Berry, P.M. et al., 2015: Cross-sectoral interactions of adaptation and mitigation measures. Clim. Change, 128, 381–393, doi:10.1007/s10584- 014-1214-0.
  64. Bertram, C. et al., 2015: Carbon lock-in through capital stock inertia associated with weak near-term climate policies. Technol. Forecast. Soc. Change, 90, 1 62–72, doi:10.1016/j.techfore.2013.10.001.
  65. Bestelmeyer, B.T. et al., 2015: Desertification, land use and the transformation of global drylands. Front. Ecol. Environ., 13, 28–36, doi:10.1890/140162. Beuchelt, T.D. and L. Badstue, 2013: Gender, nutrition – and climate-smart food production: Opportunities and trade-offs. Food Secur., 5, 709–721, doi:10.1007/s12571-013-0290-8.
  66. Bhojvaid, P.P., M.P. Singh, S.R. Reddy, and J. Ashraf, 2016: Forest transition curve of India and related policies, acts and other major factors. Trop. Ecol., 57, 133–141.
  67. Biagini, B. and A. Miller, 2013: Engaging the private sector in adaptation to climate change in developing countries: importance, status and challenges. Clim. Dev., 5, 242–252, doi:10.1080/17565529.2013.821053.
  68. Biggs, E.M. et al., 2015: Sustainable development and the water–energy– food nexus: A perspective on livelihoods. Environ. Sci. Policy, 54, 389–397, doi:10.1016/J.ENVSCI.2015.08.002.
  69. Billen, G., L. Lassaletta, and J. Garnier, 2015: A vast range of opportunities for feeding the world in 2050: Trade-off between diet, N contamination and international trade. Environ. Res. Lett., 10, doi:10.1088/1748- 9326/10/2/025001.
  70. Birdsey, R. and Y. Pan, 2015: Trends in management of the world’s forests and impacts on carbon stocks. For. Ecol. Manage., 355, 83–90, doi:10.1016/j. foreco.2015.04.031.
  71. Bloom, A.A., J.-F. Exbrayat, I.R. van der Velde, L. Feng, and M. Williams, 2016: The decadal state of the terrestrial carbon cycle: Global retrievals of terrestrial carbon allocation, pools, and residence times. Proc. Natl. Acad. Sci., 113, 1285–1290, doi:10.1073/pnas.1515160113.
  72. Bodin, Ö., 2017: Collaborative environmental governance: Achieving collective action in social-ecological systems. Science, 357, eaan1114, doi:10.1126/science.aan1114.
  73. Bodirsky, B.L. and C. Müller, 2014: Robust relationship between yields and nitrogen inputs indicates three ways to reduce nitrogen pollution. Environ. Res. Lett., doi:10.1088/1748-9326/9/11/111005.
  74. Bonsch, M. et al., 2016: Trade-offs between land and water requirements for large-scale bioenergy production. GCB Bioenergy, 8, 11–24, doi:10.1111/ gcbb.12226.
  75. Börner, J. et al., 2017: The effectiveness of payments for environmental services. World Dev., 96, 359–374, doi:10.1016/J.WORLDDEV.2017.03.020. Borsato, E., P. Tarolli, and F. Marinello, 2018: Sustainable patterns of main agricultural products combining different footprint parameters. J. Clean. Prod., 179, 357–367, doi:10.1016/J.JCLEPRO.2018.01.044.
  76. Boserup, E., 1989: Population, the status of women, and rural development. Popul. Dev. Rev., 15, 45–60, doi:10.2307/2807921.
  77. Boysen, L.R., W. Lucht, and D. Gerten, 2017: Trade-offs for food production, nature conservation and climate limit the terrestrial carbon dioxide removal potential. Glob. Chang. Biol., 23, 4303–4317, doi:10.1111/gcb.13745.
  78. Bradford, K.J. et al., 2018: The dry chain: Reducing postharvest losses and improving food safety in humid climates. Trends Food Sci. Technol., 71, 84–93, doi:10.1016/J.TIFS.2017.11.002.
  79. Bradshaw, C.J.A., N.S. Sodhi, K.S.-H. Peh, and B.W. Brook, 2007: Global evidence that deforestation amplifies flood risk and severity in the developing world. Glob. Chang. Biol., 13, 2379–2395, doi:10.1111/j.1365- 2486.2007.01446.x.
  80. Bren d’Amour, C. et al., 2016: Future urban land expansion and implications for global croplands. Proc. Natl. Acad. Sci., 114, 201606036, doi:10.1073/ pnas.1606036114.
  81. Brockerhoff, E.G., H. Jactel, J.A. Parrotta, and S.F.B. Ferraz, 2013: Role of eucalypt and other planted forests in biodiversity conservation and the provision of biodiversity-related ecosystem services. For. Ecol. Manage., 301, 43–50, doi:10.1016/j.foreco.2012.09.018.
  82. Brown, C. et al., 2014: Analysing uncertainties in climate change impact assessment across sectors and scenarios. Clim. Change, 128, 293–306, doi:10.1007/s10584-014-1133-0.
  83. Brown, C., P. Alexander, S. Holzhauer, and M.D.A. Rounsevell, 2017: Behavioral models of climate change adaptation and mitigation in land-based sectors. Wiley Interdiscip. Rev. Clim. Chang., 8, e448, doi:10.1002/wcc.448.
  84. Brown, C., P. Alexander, A. Arneth, I. Holman, and M. Rounsevell, 2019: Achievement of Paris climate goals unlikely due to time lags in the land system. Nat. Clim. Chang., 9, 203–208, doi:10.1038/s41558-019-0400-5.
  85. Brown, C., P. Alexander, and M. Rounsevell, 2018: Empirical evidence for the diffusion of knowledge in land use change. J. Land Use Sci., 13(3), 269– 283, doi:10.1080/1747423X.2018.1515995.
  86. Brümmer, C. et al., 2017: Gas chromatography vs. quantum cascade laser-based N2O flux measurements using a novel chamber design. Biogeosciences, 14, 1365–1381, doi:10.5194/bg-14-1365-2017.
  87. Bryan, B.A. and N.D. Crossman, 2013: Impact of multiple interacting financial incentives on land use change and the supply of ecosystem services. Ecosyst. Serv., 4, 60–72, doi:10.1016/j.ecoser.2013.03.004.
  88. Buisson, L., W. Thuiller, N. Casajus, S. Lek, and G. Grenouillet, 2009: Uncertainty in ensemble forecasting of species distribution. Glob. Chang. Biol., 16, 1145–1157, doi:10.1111/j.1365-2486.2009.02000.x.
  89. Bull, G.Q. et al., 2006: Industrial forest plantation subsidies: Impacts and implications. For. Policy Econ., 9, 13–31, doi:10.1016/j.forpol.2005.01.004. Butler, M.P., P.M. Reed, K. Fisher-Vanden, K. Keller, and T. Wagener, 2014: Inaction and climate stabilization uncertainties lead to severe economic risks. Clim. Change, 127, 463–474, doi:10.1007/s10584-014-1283-0.
  90. Caffi, T., S.E. Legler, V. Rossi, and R. Bugiani, 2012: Evaluation of a warning system for early-season control of grapevine powdery mildew. Plant Dis., 96, 104–110, doi:10.1094/PDIS-06-11-0484.
  91. Cafiero, C., S. Viviani, and M. Nord, 2018: Food security measurement in a global context: The food insecurity experience scale. Measurement, 116, 146–152,doi:10.1016/J.MEASUREMENT.2017.10.065.
  92. Calvin, K. and B. Bond-Lamberty, 2018: Integrated human-earth system modeling – State of the science and future directions. Environ. Res. Lett., 13, doi:10.1088/1748-9326/aac642.
  93. Campbell, B.M., P. Thornton, R. Zougmoré, P. van Asten, and L. Lipper, 2014: Sustainable intensification: What is its role in climate smart agriculture?
  94. Curr. Opin. Environ. Sustain., 8, 39–43, doi:10.1016/j.cosust.2014.07.002. Canadell, J.G., and E.D. Schulze, 2014: Global potential of biospheric carbon management for climate mitigation. Nat. Commun., 5, doi:528210.1038/ ncomms6282.
  95. Cao, S., J. Zhang, L. Chen, and T. Zhao, 2016: Ecosystem water imbalances created during ecological restoration by afforestation in China, and lessons for other developing countries. J. Environ. Manage., 183, 843–849, doi:10.1016/j.jenvman.2016.07.096.
  96. Carlisle, K., and R.L. Gruby, 2017: Polycentric systems of governance: A theoretical model for the commons. Policy Stud. J., doi:10.1111/ psj.12212.
  97. Ceballos, G. et al., 2015: Accelerated modern human-induced species losses: Entering the sixth mass extinction. Sci. Adv., doi:10.1126/sciadv.1400253.
  98. Cerretelli, S. et al., 2018: Spatial assessment of land degradation through key ecosystem services: The role of globally available data. Sci. Total Environ., 628–629, 539–555, doi:10.1016/J.SCITOTENV.2018.02.085.
  99. Challinor, A.J. et al., 2014: A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Chang., 4, 287–291, doi:10.1038/nclimate2153.
  100. Challinor, A.J., B. Parkes, and J. Ramirez-Villegas, 2015: Crop yield response to climate change varies with cropping intensity. Glob. Chang. Biol., 21,1679–1688, doi:10.1111/gcb.12808.
  101. Challinor, A.J. et al., 2018: Transmission of climate risks across sectors and borders Subject Areas.
  102. Chanza, N. and A. de Wit, 2016: Enhancing climate governance through indigenous knowledge: Case in sustainability science. S. Afr. J. Sci., 112, 1–7, doi:10.17159/sajs.2016/20140286.
  103. Chappin, E.J.L. and T. van der Lei, 2014: Adaptation of interconnected infrastructures to climate change: A socio-technical systems perspective. Util. Policy, 31, 10–17, doi: 10.1016/j.jup.2014.07.003.
  104. Chartres, C.J. and A. Noble, 2015: Sustainable intensification: Overcoming land and water constraints on food production. Food Secur., 7, 235–245, doi:10.1007/s12571-015-0425-1.
  105. Chaturvedi, V. et al., 2015: Climate mitigation policy implications for global irrigation water demand. Mitig. Adapt. Strateg. Glob. Chang., 20, 389–407, doi:10.1007/s11027-013-9497-4.
  106. Chaudhary, A. and T. Kastner, 2016: Land use biodiversity impacts embodied in international food trade. Glob. Environ. Chang., 38, 195–204, doi:10.1016/J.GLOENVCHA.2016.03.013.
  107. Chazdon, R.L. et al., 2016a: When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration. Ambio, doi:10.1007/s13280-016-0772-y.
  108. Chazdon, R.L. et al., 2016b: Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv., 2, e1501639– e1501639, doi:10.1126/sciadv.1501639.
  109. Chegere, M.J., 2018: Post-harvest losses reduction by small-scale maize farmers: The role of handling practices. Food Policy, 77, 103–115, doi:10.1016/J.FOODPOL.2018.05.001.
  110. Chen, B. et al., 2018: Global land-water nexus: Agricultural land and freshwater use embodied in worldwide supply chains. Sci. Total Environ., 613–614, 931–943, doi:10.1016/J.SCITOTENV.2017.09.138.
  111. Chen, C. et al., 2019: China and India lead in greening of the world through land-use management. Nat. Sustain., 2, 122–129, doi:10.1038/s41893- 019-0220-7.
  112. Chen, J. et al., 2014: Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens., 103, 7–27, doi:10.1016/j.isprsjprs.2014.09.002.
  113. Chen,Y.-H.,M.Babiker,S.Paltsev,andJ.Reilly,2016:CostsofClimateMitigation Policies. MIT Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA, 22 pp.
  114. Cherlet, M. et al., (eds.), 2018: World Atlas of Desertification: Rethinking Land Degradation and Sustainable Land Management (3rd edition). Publication Office of the European Union, Luxembourg, 247 pp.
  115. Chipanshi, A. et al., 2015: Evaluation of the Integrated Canadian Crop Yield Forecaster (ICCYF) model for in-season prediction of crop yield across the Canadian agricultural landscape. Agric. For. Meteorol., 206, 137–150, doi:10.1016/J.AGRFORMET.2015.03.007.
  116. Clark, D.A. et al., 2017: Reviews and syntheses: Field data to benchmark the carbon cycle models for tropical forests. Biogeosciences, 14, 4663–4690, doi:10.5194/bg-14-4663-2017.
  117. de Coninck, H., A. Revi, M. Babiker, P. Bertoldi, M. Buckeridge, A. Cartwright, W. Dong, J. Ford, S. Fuss, J.-C. Hourcade, D. Ley, R. Mechler, P. Newman, A. Revokatova, S. Schultz, L. Steg, and T. Sugiyama, 2018: Strengthening and implementing the global response. In: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change [V. Masson-Delmotte, P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press.
  118. Constantin, C., C. Luminița, and A.J. Vasile, 2017: Land grabbing: A review of extent and possible consequences in Romania. Land use policy, 62, 143–150, doi:10.1016/j.landusepol.2017.01.001
  119. Costanza, R. et al., 2014: Changes in the global value of ecosystem services. Glob. Environ. Chang., 26, 152–158, doi:10.1016/j.gloenvcha.2014.04.002. Cotula, L. et al., 2014: Testing claims about large land deals in Africa: Findings from a multi-country study. J. Dev. Stud., 50, 903–925, doi:10.1080/0022 0388.2014.901501.
  120. Coyle, D.R. et al., 2017: Soil fauna responses to natural disturbances, invasive species and global climate change: Current state of the science and a call to action. Soil Biol. Biochem., 110, 116–133, doi:10.1016/J. SOILBIO.2017.03.008.
  121. Cremasch, G.D., 2016: Sustainability Metrics for Agri-food Supply Chains. PhD Thesis, Wageningen School of Social Sciences (WASS), Wageningen, Netherlands, doi:10.18174/380247.
  122. Creutzig, F. et al., 2015: Bioenergy and climate change mitigation: an assessment. Glob. Chang. Biol. Bioenergy, 7, 916–944, doi:10.1111/ gcbb.12205.
  123. Creutzig, F. et al., 2016: Beyond technology: Demand-side solutions for climate change mitigation. Annu. Rev. Environ. Resour., 41, 173–198, doi:10.1146/ annurev-environ-110615-085428.
  124. Creutzig, F. et al., 2018: Towards demand-side solutions for mitigating climate change. Nat. Clim. Chang., 8, 260–263, doi:10.1038/s41558-018-0121-1.
  125. Crist, E., C. Mora, and R. Engelman, 2017: The interaction of human population, food production, and biodiversity protection. Science, 356, 260–264, doi:10.1126/science.aal2011.
  126. Crouzeilles, R. et al., 2016: A global meta-analysis on the ecological drivers of forest restoration success. Nat. Commun., 7, 1–8, doi:1166610.1038/ ncomms11666.
  127. Cunningham, S.C. et al., 2015a: Reforestation with native mixed-species plantings in a temperate continental climate effectively sequesters and stabilizes carbon within decades. Glob. Chang. Biol., 21, 1552–1566, doi:10.1111/gcb.12746.
  128. Cunningham, S.C. et al., 2015b: Balancing the environmental benefits of reforestation in agricultural regions. Perspect. Plant Ecol. Evol. Syst., 17, 301–317, doi:10.1016/J.PPEES.2015.06.001.
  129. Curtis, P.G., C.M. Slay, N.L. Harris, A. Tyukavina, and M.C. Hansen, 2018: Classifying drivers of global forest loss. Science, 361, 1108–1111, doi:10.1126/science.aau3445.
  130. D’Odorico, P., A. Bhattachan, K.F. Davis, S. Ravi, and C.W. Runyan, 2013: Global desertification: Drivers and feedbacks. Adv. Water Resour., 51, 326–344, doi:10.1016/j.advwatres.2012.01.013.
  131. D’Odorico, P. et al., 2018: The Global Food-Energy-Water Nexus. Rev. Geophys., 56, 456–531, doi:10.1029/2017RG000591.
  132. Daliakopoulos, I.N. et al., 2016: The threat of soil salinity: A European scale review. Sci. Total Environ., 573, 727–739, doi:10.1016/J. SCITOTENV.2016.08.177.
  133. Dalin, C. and I. Rodríguez-Iturbe, 2016: Environmental impacts of food trade via resource use and greenhouse gas emissions. Environ. Res. Lett., 11, 035012, doi:10.1088/1748-9326/11/3/035012.
  134. Darity, W.A., 1980: The Boserup theory of agricultural growth: A model for anthropological economics. J. Dev. Econ., 7, 137–157, doi:10.1016/0304- 3878(80)90001-2.
  135. Darrah, S.E. et al., 2019: Improvements to the Wetland Extent Trends (WET) index as a tool for monitoring natural and human-made wetlands. Ecol. Indic., 99, 294–298, doi:10.1016/j.ecolind.2018.12.032.
  136. Davidson, N.C., 2014: How much wetland has the world lost? Long-term and recent trends in global wetland area. Mar. Freshw. Res., 65, 934–941, doi:10.1071/MF14173.
  137. Davies-Barnard, T., P.J. Valdes, J.S. Singarayer, A.J. Wiltshire and C.D. Jones, 2015: Quantifying the relative importance of land cover change from climate and land-use in the representative concentration pathway. Global Biogeochem. Cycles, 842–853, doi:10.1002/2014GB004949.
  138. Davis, K.F., K. Yu, M.C. Rulli, L. Pichdara and P. D’Odorico, 2015: Accelerated deforestation driven by large-scale land acquisitions in Cambodia. Nat. Geosci., 8, 772–775, doi:10.1038/ngeo2540.
  139. Day, T., K. McKenna, and A. Bowlus, 2005: The Economic Costs of Violence Against Women: An Evaluation of the Literature. Expert brief compiled in preparation for the Secretary-General’s in-depth study on all forms of violence against women. NY: United Nations. Barzman, New York City, pp. 1–66.
  140. Deininger, K. et al., 2011: Rising Global Interest in Farmland: Can it Yield Sustainable and Equitable Benefits? 1st ed. The World Bank, Washington D.C., 164 pp. doi:10.1596/978-0-8213-8591-3.
  141. Dell’Angelo, J., P. D’Odorico, and M.C. Rulli, 2017a: Threats to sustainable development posed by land and water grabbing. Curr. Opin. Environ. Sustain., 26–27, 120–128, doi:10.1016/j.cosust.2017.07.007.
  142. Dell’Angelo, J., P. D’Odorico, M.C. Rulli, and P. Marchand, 2017b: The tragedy of the grabbed commons: Coercion and dispossession in the global land rush. World Dev., 92, 1–12, doi:10.1016/J.WORLDDEV.2016.11.005.
  143. Dendy, J., S. Cordell, C.P. Giardina, B. Hwang, E. Polloi and K. Rengulbai, 2015: The role of remnant forest patches for habitat restoration in degraded areas of Palau. Restor. Ecol., 23, 872–881, doi:10.1111/rec.12268.
  144. Deng, L., Z. Shangguan, and S. Sweeney, 2015: “Grain for Green” driven land use change and carbon sequestration on the Loess Plateau, China. Sci. Rep., 4, 7039, doi:10.1038/srep07039.
  145. Denis, G. et al., 2014: Global changes, livestock and vulnerability: The social construction of markets as an adaptive strategy. Geogr. J., 182, 153–164, doi:10.1111/geoj.12115.
  146. Dietrich, J.P. et al., 2018: MAgPIE 4 – A modular open source framework for modeling global land-systems. Geosci. Model Dev. 12(4) 1299–1317, doi:10.5194/gmd-2018-295.
  147. van Dijk, A.I.J.M. et al., 2009: Forest – flood relation still tenuous – comment on ‘Global evidence that deforestation amplifies flood risk and severity in the developing world’ by C.J.A. Bradshaw, N.S. Sodi, K.S.-H. Peh and B.W. Brook. Glob. Chang. Biol., 15, 110–115, doi:10.1111/j.1365- 2486.2008.01708.x.
  148. Dinerstein, E. et al., 2015: Guiding agricultural expansion to spare tropical forests. Conserv. Lett., 8, 262–271, doi:10.1111/conl.12149.
  149. Dixon, M.J.R. et al., 2016: Tracking global change in ecosystem area: The Wetland Extent Trends index. Biol. Conserv., doi:10.1016/j. biocon.2015.10.023.
  150. Djoudi, H. et al., 2016: Beyond dichotomies: Gender and intersecting inequalities in climate change studies. Ambio, 45, 248–262, doi:10.1007/ s13280-016-0825-2.
  151. Doelman, J.C. et al., 2018: Exploring SSP land-use dynamics using the IMAGE model: Regional and gridded scenarios of land-use change and land-based climate change mitigation. Glob. Environ. Chang., 48, 119– 135, doi:10.1016/j.gloenvcha.2017.11.014.
  152. Dohong, A., A.A. Aziz, and P. Dargusch, 2017: A review of the drivers of tropical peatland degradation in South-East Asia. Land use policy, 69, 349–360, doi:10.1016/j.landusepol.2017.09.035.
  153. IPCC, 2014: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp.
  154. Donohue, R.J., M.L. Roderick, T.R. McVicar, and G.D. Farquhar, 2013: Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett., 40, 3031–3035, doi:10.1002/grl.50563.
  155. Dooley, K. and S. Kartha, 2018: Land-based negative emissions: risks for climate mitigation and impacts on sustainable development. Int. Environ. Agreements Polit. Law Econ., 18, 79–98, doi:10.1007/s10784-017-9382-9.
  156. Doss, C., C. Kovarik, A. Peterman, A. Quisumbing, and M. van den Bold, 2015: Gender inequalities in ownership and control of land in Africa: Myth and reality. Agric. Econ., 46, 403–434, doi:10.1111/agec.12171.
  157. Dunford, R., P.A. Harrison, J. Jäger, M.D.A. Rounsevell, and R. Tinch, 2014: Exploring climate change vulnerability across sectors and scenarios using indicators of impacts and coping capacity. Clim. Change, 128, 339–354, doi:10.1007/s10584-014-1162-8.
  158. Duveiller, G., J. Hooker, and A. Cescatti, 2018: The mark of vegetation change on Earth’s surface energy balance. Nat. Commun., 9, 679, doi:10.1038/ s41467-017-02810-8.
  159. Ebert, A.W., 2014: Potential of underutilized traditional vegetables and legume crops to contribute to food and nutritional security, income and more sustainable production systems. Sustainability, 6, 319–335, doi:10.3390/su6010319.
  160. Eeraerts, M., I. Meeus, S. Van Den Berge, and G. Smagghe, 2017: Landscapes with high intensive fruit cultivation reduce wild pollinator services to sweet cherry. Agric. Ecosyst. Environ., 239, 342–348, doi:10.1016/J. AGEE.2017.01.031.
  161. Egginton, P., F. Beall, and J. Buttle, 2014: Reforestation – Climate change and water resource implications. For. Chron., 90, 516–524, doi:10.5558/ tfc2014-102.
  162. Eidelwein, F., D.C. Collatto, L.H. Rodrigues, D.P. Lacerda, and F.S. Piran, 2018: Internalization of environmental externalities: Development of a method for elaborating the statement of economic and environmental results. J. Clean. Prod., 170, 1316–1327, doi:10.1016/J.JCLEPRO.2017.09.208.
  163. Eitelberg, D.A., J. van Vliet, J.C. Doelman, E. Stehfest, and P.H. Verburg, 2016: Demand for biodiversity protection and carbon storage as drivers of global land change scenarios. Glob. Environ. Chang., 40, 101–111, doi:10.1016/j. gloenvcha.2016.06.014.
  164. Elbehri, A., J. Elliott, and T. Wheeler, 2015: Climate change, food security and trade: An overview of global assessments and policy insights. In: Climate Change and Food Systems: Global assessments and implications for food security and trade [Elbehri, A. (ed.)]. FAO, Rome, Italy, pp. 1–27.
  165. Elbehri, A. et al., 2017: FAO-IPCC Expert Meeting on Climate Change, Land Use and Food Security: Final Meeting Report; January 23–25, 2017, FAO and IPCC, Food and Agriculture Organization of the United Nations, Rome, Italy, pp. 1–27.
  166. Ellis, E.C. and N. Ramankutty, 2008: Putting people in the map: Anthropogenic biomes of the world. Front. Ecol. Environ., 6, 439–447, doi:10.1890/070062. Ellis, E.C., K.K. Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty, 2010: Anthropogenic transformation of the biomes, 1700 to 2000. Glob. Ecol. Biogeogr., doi:10.1111/j.1466-8238.2010.00540.x.
  167. Ellison, D. et al., 2017: Trees, forests and water: Cool insights for a hot world.
  168. Glob. Environ. Chang., 43, 51–61, doi:10.1016/j.gloenvcha.2017.01.002. Engstrom, K. et al., 2016: Assessing uncertainties in global cropland futures using a conditional probabilistic modelling framework. Earth Syst. Dyn., 7, 893–915, doi:10.5194/esd-7-893-2016.
  169. Erb, K.-H. et al., 2007: A comprehensive global 5 min resolution land-use data set for the year 2000 consistent with national census data. J. Land Use Sci., 2, 191–224, doi:10.1080/17474230701622981.
  170. Erb, K.-H. et al., 2016a: Land management: Data availability and process understanding for global change studies. Glob. Chang. Biol., 23, 512–533, doi:10.1111/gcb.13443.
  171. Erb, K.-H. et al., 2016b: Exploring the biophysical option space for feeding the world without deforestation. Nat. Commun., 7.
  172. Erb, K.-H., H. Haberl, and C. Plutzar, 2012: Dependency of global primary bioenergy crop potentials in 2050 on food systems, yields, biodiversity conservation and political stability. Energy Policy, 47, 260–269, doi:10.1016/j.enpol.2012.04.066.
  173. Erb, K.-H. et al., 2016c: Biomass turnover time in terrestrial ecosystems halved by land use. Nat. Geosci., 9, 674–678, doi:10.1038/ngeo2782.
  174. Erb, K.-H. et al., 2017: Unexpectedly large impact of forest management and grazing on global vegetation biomass. Nature, 553, 73–76, doi:10.1038/ nature25138.
  175. Evans, C.D. et al., 2019: Rates and spatial variability of peat subsidence in Acacia plantation and forest landscapes in Sumatra, Indonesia. Geoderma, 338, 410–421, doi:10.1016/j.geoderma.2018.12.028.
  176. Falco, S. Di, F. Adinolfi, M. Bozzola, and F. Capitanio, 2014: Crop Insurance as a Strategy for Adapting to Climate Change. J. Agric. Econ., 65, 485–504, doi:10.1111/1477-9552.12053.
  177. FAO, 1963: World Forest Inventory 1963. Food and Agriculture Organization of the United Nations, Rome, 113 pp.
  178. FAO, 2011: The State of Food and Agriculture: Women in agriculture – Closing the gender gap for development. Food and Agriculture Organization of the United Nations, Rome, Italy.
  179. FAO, 2015a: Global Forest Resources Assessments 2015. Food and Agriculture Organization of the United Nations, Rome.
  180. FAO, 2015b: Learning Tool on Nationally Appropriate Mitigation Actions (NAMAs) in the agriculture, forestry and other land use (AFOLU) sector. Food and Agriculture Organization of the United Nations, Rome, Italy, 162 pp.
  181. FAO, 2016: State of the World’s Forests 2016. Forests and agriculture: Land-use challenges and opportunities. Food and Agriculture Organization of the United Nations, Rome, Italy.
  182. FAO, 2017: The Future of Food and Agriculture: Trends and Challenges. Food and Agriculture Organization of the United Nations, Rome, Italy.
  183. FAO, 2018a: The State of the World’s Forests 2018 – Forest Pathways to Sustainable Development. Food and Agriculture Organization of the United Nations, Rome, Italy, 139 pp.
  184. FAO, 2018b: The Future of Food and Agriculture: Alternative Pathways to 2050. Food and Agricultural Organization of the United Nations, Rome, Italy, 228 pp.
  185. FAO, IFAD, UNICEF, WFP and WHO, 2018: The State of Food Security and Nutrition in the World 2018. Building climate resilience for food security and nutrition. Food and Agriculture Organization of the United Nations, Rome, Italy.
  186. FAO and ITPS, 2015: Status of the World’s Soil Resources (SWSR) – Main Report. Food and Agriculture Organization of the United Nations, Rome, Italy.
  187. FAOSTAT, 2018: Statistical Databases. http://faostat.fao.org. Farley, J. and A. Voinov, 2016: Economics, socio-ecological resilience and ecosystem services. J. Environ. Manage., 183, 389–398, doi:10.1016/J. JENVMAN.2016.07.065.
  188. Fasullo, J.T., B.L. Otto-Bliesner and S. Stevenson, 2018: ENSO’s changing influence on temperature, precipitation, and wildfire in a warming climate. Geophys. Res. Lett., 0, doi:10.1029/2018GL079022.
  189. Favero, A. and E. Massetti, 2014: Trade of woody biomass for electricity generation under climate mitigation policy. Resour. Energy Econ., 36, 166–190, doi:10.1016/J.RESENEECO.2013.11.00.
  190. Favero, A. and R. Mendelsohn, 2014: Using markets for woody biomass energy to sequester carbon in forests. J. Assoc. Environ. Resour. Econ., 1, 75–95, doi:10.1086/676033.
  191. Favero, A., R. Mendelsohn, and B. Sohngen, 2017: Using forests for climate mitigation: Sequester carbon or produce woody biomass? Clim. Change, 144, 195–206, doi:10.1007/s10584-017-2034-9.
  192. Feng, X. et al., 2016: Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang., 6, 1019–1022, doi:10.1038/nclimate3092.
  193. Fernandez-Martinez, M. et al., 2014: Nutrient availability as the key regulator of global forest carbon balance. Nat. Clim. Chang., 4, 471–476, doi:10.1038/nclimate2177.
  194. Ferreira, C.S.S., R.P.D. Walsh and A.J.D. Ferreira, 2018: Degradation in urban areas. Curr. Opin. Environ. Sci. Heal., 5, 19–25, doi:10.1016/j. coesh.2018.04.001.
  195. IPCC, 2014: Summary for policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1–32 pp.
  196. Field, C.B., V.R. Barros, K.J. Mach, M.D. Mastrandrea, M. van Aalst,W.N. Adger, D.J. Arent, J. Barnett, R. Betts, T.E. Bilir, J. Birkmann, J. Carmin, D.D. Chadee, A.J. Challinor, M. Chatterjee,W. Cramer, D.J. Davidson, Y.O. Estrada, J.-P. Gattuso, Y. Hijioka, O. Hoegh-Guldberg, H.Q. Huang, G.E. Insarov, R.N. Jones, R.S. Kovats, P. Romero-Lankao, J.N. Larsen, I.J. Losada, J.A. Marengo, R.F. McLean, L.O. Mearns, R. Mechler, J.F. Morton, I. Niang, T. Oki, J.M. Olwoch, M. Opondo, E.S. Poloczanska, H.-O. Pörtner, M.H. Redsteer, A. Reisinger, A. Revi, D.N. Schmidt, M.R. Shaw, W. Solecki, D.A.Stone,J.M.R.Stone,K.M.Strzepek,A.G.Suarez,P.Tschakert,R.Valentini, S. Vicuña, A. Villamizar, K.E. Vincent, R. Warren, L.L. White, T.J. Wilbanks, P.P. Wong and G.W. Yohe., 2014b: Technical Summary. In: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 35–94 pp.
  197. Filoso, S., M.O. Bezerra, K.C.B. Weiss and M.A. Palmer, 2017: Impacts of forest restoration on water yield: A systematic review. PLoS One, 12, e0183210, doi:10.1371/journal.pone.0183210.
  198. Fischer, J. et al., 2017: Reframing the food–biodiversity challenge. Trends Ecol. Evol., 32, 335–345, doi:10.1016/j.tree.2017.02.009.
  199. Fischer, M. et al., 2018: IPBES: Summary for Policymakers of the Regional Assessment Report on Biodiversity and Ecosystem Services for Europe and Central Asia of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. Bonn, Germany, 48 pp.
  200. Fish, R., A. Church, and M. Winter, 2016: Conceptualising cultural ecosystem services: A novel framework for research and critical engagement. Ecosyst. Serv., 21, 208–217, doi:10.1016/j.ecoser.2016.09.002.
  201. Foley, J.A. et al., 2011: Solutions for a cultivated planet. Nature, 478, 337– 342, doi:10.1038/nature10452.
  202. Font Vivanco, D., R. Kemp, and E. van der Voet, 2016: How to deal with the rebound effect? A policy-oriented approach. Energy Policy, 94, 114–125, doi:10.1016/J.ENPOL.2016.03.054.
  203. Forsell, N., O. Turkovska, M. Gusti, M. Obersteiner, M. Elzen and P. Havlík, 2016: Assessing the INDCs’ land use, land use change and forest emission projections. Carbon Balance Manage., 11, 1–17, doi:10.1186/s13021-016- 0068-3.
  204. Franchini, M. and P.M. Mannucci, 2015: Impact on human health of climate changes. Eur. J. Intern. Med., 26, 1–5, doi:10.1016/j.ejim.2014.12.008.
  205. Franco, A. and N. Giannini, 2005: Perspectives for the use of biomass as fuel in combined cycle power plants. Int. J. Therm. Sci., 44, 163–177, doi:10.1016/J. IJTHERMALSCI.2004.07.005.
  206. Friedrich, T., R. Derpsch, and A. Kassam, 2012: Overview of the global spread of conservation agriculture. F. Actions Sci. Reports, 1–7, doi:10.1201/9781315365800-4.
  207. Friend, A.D. et al., 2014: Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO2. Proc. Natl. Acad. Sci., 111, 3280–3285, doi:10.1073/pnas.1222477110.
  208. Friis, C. and J.Ø. Nielsen, 2017: Land-use change in a telecoupled world: The relevance and applicability of the telecoupling framework in the case of banana plantation expansion in Laos. Ecol. Soc., doi:10.5751/ES-09480- 220430.
  209. Friis, C. et al., 2016: From teleconnection to telecoupling: Taking stock of an emerging framework in land system science. J. Land Use Sci., doi:10.1080/ 1747423X.2015.1096423.
  210. Fuchs, R., M. Herold, P.H. Verburg, J.G.P.W. Clevers, and J. Eberle, 2015: Gross changes in reconstructions of historic land cover/use for Europe between 1900 and 2010. Glob. Chang. Biol., 21, 299–313, doi:10.1111/gcb.12714.
  211. Fuchs, R., R. Prestele, and P.H. Verburg, 2017: A global assessment of gross and net land change dynamics for current conditions and future scenarios.Earth Syst. Dyn. Discuss., 1–29, doi:10.5194/esd-2017-121.
  212. Fuss, S. et al., 2018: Negative emissions—Part 2: Costs, potentials and side effects. Environ. Res. Lett., 13, 063002, doi:10.1088/1748-9326/aabf9f. Gaba, S. et al., 2015: Multiple cropping systems as drivers for providing multiple ecosystem services: From concepts to design. Agron. Sustain. Dev., 35, 607–623, doi:10.1007/s13593-014-0272-z.
  213. Gardiner, S.M., 2006: A Core Precautionary Principle. J. Polit. Philos., 14, 33–60, doi:10.1111/j.1467-9760.2006.00237.x.
  214. Gerber, J.F., 2011: Conflicts over industrial tree plantations in the South: Who, how and why? Glob. Environ.
  215. Chang., 21, 165–176, doi:10.1016/j. gloenvcha.2010.09.005.
  216. Gibbs, H.K. and J.M. Salmon, 2015: Mapping the world’s degraded lands. Appl. Geogr., 57, 12–21, doi:10.1016/j.apgeog.2014.11.024.
  217. Gilbert, M. et al., 2018: Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Scientific Data, 5, doi: 10.1038/sdata.2018.227.
  218. Gilbert-Norton, L., R.Wilson, J.R. Stevens and K.H. Beard, 2010:A meta-analytic review of corridor effectiveness. Conserv. Biol., 24, 660–668, doi:10.1111/  j.1523-1739.2010.01450.x.
  219. Gillett, N.P. et al., 2016: The detection and attribution model intercomparison project (DAMIP v1.0) contribution to CMIP6. Geosci. Model Dev., 9, 3685– 3697, doi:10.5194/gmd-9-3685-2016.
  220. Gillingham, K. and J.H. Stock, 2018: The cost of reducing greenhouse gas emissions. J. Econ. Perspect., 32, 53–72, doi:10.1257/jep.32.4.53. Gilmont, M., 2015: Water resource decoupling in the MENA through food trade as a mechanism for circumventing national water scarcity. Food Secur., 7, 1113–1131, doi:10.1007/s12571-015-0513-2.
  221. Gleckler P.J., et al., 2016: A more powerful reality test for climate models. Eos (Washington. DC)., 97, doi:10.1029/2016EO051663.
  222. Goldewijk, K.K., A. Beusen, J. Doelman, and E. Stehfest, 2017: Anthropogenic land use estimates for the Holocene – HYDE 3.2. Earth Syst. Sci. Data, 9, 927–953, doi:10.5194/essd-9-927-2017.
  223. Goldstein, A., W.R. Turner, J. Gladstone, and D.G. Hole, 2019: The private sector’s climate change risk and adaptation blind spots. Nat. Clim. Chang.,9, 18–25, doi:10.1038/s41558-018-0340-5.
  224. Golub, A.A., et al., 2013: Global climate policy impacts on livestock, land use, livelihoods and food security. Proc. Natl. Acad. Sci. U.S.A., 110, 20894–20899, doi:10.1073/pnas.1108772109.
  225. Gómez-Baggethun, E. and R. Muradian, 2015: In markets we trust? Setting the boundaries of market-based instruments in ecosystem services governance. Ecol. Econ., 117, 217–224, doi:10.1016/J.ECOLECON.2015.03.016.
  226. Gonzalez, P., R.P. Neilson, J.M. Lenihan, and R.J. Drapek, 2010: Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Glob. Ecol. Biogeogr., 19, 755–768, doi:10.1111/j.1466-8238.2010.00558.x.
  227. Gossner, M.M. et al., 2016: Land-use intensification causes multitrophic homogenization of grassland communities. Nature, 540, 266–269, doi:10.1038/nature20575.
  228. Graham, C.T., et al., 2017: Implications of afforestation for bird communities: the importance of preceding land-use type. Biodivers. Conserv., 26, 3051– 3071, doi:10.1007/s10531-015-0987-4.
  229. Grassi, G., et al., 2017: The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Chang., 7, 220–226, doi:10.1038/nclimate3227.
  230. Griffith, D.M. et al., 2017: Comment on “The extent of forest in dryland biomes”. Science, 358, eaao1309, doi:10.1126/science.aao1309.
  231. Griscom, B.W. et al., 2017: Natural climate solutions. Proc. Natl. Acad. Sci. USA, 114, 11645–11650, doi:10.1073/pnas.1710465114.
  232. Hasegawa, T. et al., 2018: Risk of increased food insecurity under stringent global climate change mitigation policy. Nat. Clim. Chang., 8, 699–703, doi:10.1038/s41558-018-0230-x.
  233. Hayley, L., C. Declan, B. Michael, and R. Judith, 2015:Tracing the water–energy– food nexus: Description, theory and practice. Geogr. Compass, 9, 445–460, doi:10.1111/gec3.12222.
  234. He, T. et al., 2018: Evaluating land surface albedo estimation from Landsat MSS, TM, ETM + and OLI data based on the unified direct estimation approach. Remote Sens. Environ., 204, 181–196, doi:10.1016/j.rse.2017.10.031.
  235. Heck, V., D. Gerten, W. Lucht and A. Popp, 2018: Biomass-based negative emissions difficult to reconcile with planetary boundaries. Nat. Clim. Chang., 8, 151–155, doi:10.1038/s41558-017-0064-y.
  236. Heilmayr, R., C. Echeverría, R. Fuentes, and E.F. Lambin, 2016: A plantation-dominated forest transition in Chile. Appl. Geogr., 75, 71–82, doi:10.1016/j.apgeog.2016.07.014.
  237. Hennessey, R., J. Pittman, A. Morand, and A. Douglas, 2017: Co-benefits of integrating climate change adaptation and mitigation in the Canadian energy sector. Energy Policy, 111, 214–221, doi:10.1016/J. ENPOL.2017.09.025.
  238. Henry, R.C. et al., 2018: Food supply and bioenergy production within the global cropland planetary boundary. PLoS One, 13, e0194695–e0194695, doi:10.1371/journal.pone.0194695.
  239. Hernández-Morcillo, M., T. Plieninger and C. Bieling, 2013: An empirical review of cultural ecosystem service indicators. Ecol. Indic., 29, 434–444, doi:10.1016/j.ecolind.2013.01.013.
  240. Hersperger, A.M., M.-P. Gennaio, P.H. Verburg and M. Bürgi, 2010: Linking land change with driving forces and actors: Four conceptual models. Ecol. Soc., 15, doi:10.5751/ES-03562-150401.
  241. Hinkel, J., P.W.G. Bots and M. Schlüter, 2014: Enhancing the Ostrom social-ecological system framework through formalization. Ecol. Soc., 19(3), doi:10.5751/ES-06475-190351.
  242. HLPE, 2017: Nutrition and Food Systems. A report by the High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security. High Level Panel of Experts on Food Security and Nutrition of the Committee on World Food Security, Rome, Italy, 151 pp.
  243. Hobbs, P.R., K. Sayre, and R. Gupta, 2008: The role of conservation agriculture in sustainable agriculture. Philos. Trans. R. Soc. B Biol. Sci., 363, 543–555, doi:10.1098/rstb.2007.2169.
  244. Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K.L. Ebi, F. Engelbrecht, J. Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S.I. Seneviratne, A. Thomas, R. Warren, and G. Zhou, 2018: Impacts of 1.5°C Global Warming on Natural and Human Systems. In: Global warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change [Masson-Delmotte, V.P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)]. In press.
  245. Hoekstra, A.Y. and T.O. Wiedmann, 2014: Humanity’s unsustainable environmental footprint. Science, 344, 1114–1117, doi:10.1126/ science.1248365.
  246. Hoesly, R.M. et al., 2018: Historical (1750–2014) anthropogenic emissions of reactive gases and aerosols from the Community Emissions Data System (CEDS). Geosci. Model Dev., 11, 369–408, doi:10.5194/gmd-11-369-2018.
  247. Hof, C., et al., 2018: Bioenergy cropland expansion may offset positive effects of climate change mitigation for global vertebrate diversity. Proc. Natl. Acad. Sci., 115, 13294–13299, doi:10.1073/pnas.1807745115.
  248. Hoff, H., 2011: Bonn 2011 Conference: The Water, Energy and Food Security Nexus – Solutions for the Green Economy. Stockholm, 1–52 pp.
  249. Houghton, R.A., 2013: The emissions of carbon from deforestation and degradation in the tropics: Past trends and future potential. Carbon Manag., 4, 539–546, doi:10.4155/cmt.13.41.
  250. Grolleau, G., L. Ibanez, N. Mzoughi, and M. Teisl, 2016: Helping eco-labels to fulfil their promises. Clim. Policy, 16, 792–802, doi:10.1080/14693062.20 15.1033675.
  251. 1 de Groot, W.J., B.M. Wotton, and M.D. Flannigan, 2015: Chapter 11 – Wildland Fire Danger Rating and Early Warning Systems. In:Wildfire Hazards, Risks and Disasters [Shroder, J.F. and D. Paton (eds.)], Elsevier, Oxford, pp. 207– 228, doi:10.1016/B978-0-12-410434-1.00011-7.
  252. Güneralp, B., K.C. Seto, B. Gueneralp, and K.C. Seto, 2013: Futures of global urban expansion: Uncertainties and implications for biodiversity conservation. Environ. Res. Lett., 8, doi:10.1088/1748-9326/8/1/014025.
  253. Gurwick, N.P., L.A. Moore, C. Kelly, and P. Elias, 2013: A systematic review of biochar research, with a focus on its stability in situ and its promise as a climate mitigation strategy. PLoS One, 8, doi:10.1371/journal. pone.0075932.
  254. Gustavsson, J., C. Cederberg, U. Sonesson, R. van Otterdijk and A. Meybeck, 2011: Global Food Losses and Food Waste – Extent, Causes and Prevention. Study conducted for the International Congress, Swedish Institute for Food and Biotechnology (SIK), Gothenburg, Sweden.
  255. Haasnoot, M., 2013: Dynamic adaptive policy pathways: A method for crafting robust decisions for a deeply uncertain world. Glob. Environ. Chang., 23, 485–498, doi:10.1016/j.gloenvcha.2012.12.006.
  256. Haberl, H., 2015: Competition for land: A sociometabolic perspective. Elsevier, 119, 424–431, doi:10.1016/j.ecolecon.2014.10.002.
  257. Haberl, H., K.-H. Erb and F. Krausmann, 2014: Human appropriation of net primary production: Patterns, trends and planetary boundaries. Annu. Rev. Environ. Resour., 39, 363–391, doi:10.1146/annurev-environ-121912-094620.
  258. Haddad, N.M. et al., 2015: Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv., 1, doi:10.1126/sciadv.1500052.
  259. Halkos, G. and A. Skouloudis, 2016: Cultural dimensions and corporate social responsibility: A cross-country analysis. MPRA Paper 6922, University Library of Munich, Germany.
  260. Hallegatte, S. and J. Rentschler, 2015: Risk management for development-assessing obstacles and prioritizing action. Risk Anal., 35, 193–210, doi:10.1111/risa.12269.
  261. Hallegatte, S. and K.J. Mach, 2016: Make climate-change assessments more relevant. Nature, 534, 613–615, doi:10.1038/534613a.
  262. Hallström, E., A. Carlsson-Kanyama and P. Börjesson, 2015: Environmental impact of dietary change: A systematic review. J. Clean. Prod., 91, 1–11, doi:10.1016/J.JCLEPRO.2014.12.008.
  263. Hansen, M.C. et al., 2013: High-resolution global maps of 21st-century forest cover change. Science, 342, 850–853, doi:10.1126/science.1244693.
  264. Harper, A.B. et al., 2018: Land-use emissions play a critical role in land-based mitigation for Paris climate targets. Nat. Commun., 9, doi:10.1038/s41467- 018-05340-z.
  265. Harrison, P.A., R. Dunford, C. Savin, M.D.A. Rounsevell, I.P. Holman, A.S.KebedeandB.Stuch,2014:Cross-sectoralimpactsofclimatechange and socio-economic change for multiple, European land – and water-based sectors. Clim. Change, 128, 279–292, doi:10.1007/s10584-014-1239-4.
  266. Harrison, P.A., R.W. Dunford, I.P. Holman, and M.D.A. Rounsevell, 2016: Climate change impact modelling needs to include cross-sectoral interactions. Nat. Clim. Chang., 6, 885–890, doi:10.1038/nclimate3039.
  267. Harrison, S.P. et al., 2013: Volatile isoprenoid emissions from plastid to planet. New Phytol., 197, 49–57, doi:10.1111/nph.12021.
  268. Harvey, M. and S. Pilgrim, 2011: The new competition for land: Food, energy and climate change. Food Policy, 36, S40–S51, doi:10.1016/J. FOODPOL.2010.11.009.
  269. Hasegawa, T. et al., 2015: Consequence of climate mitigation on the risk of hunger.Environ.Sci.Technol.,49,7245–7253,doi:10.1021/es5051748.
  270. Houghton, R.A. and A.A. Nassikas, 2017: Global and regional fluxes of carbon from land use and land cover change 1850–2015. Global Biogeochem. Cycles, 31, 456–472, doi:10.1002/2016GB005546.
  271. Houghton, R.A., B. Byers, and A.A. Nassikas, 2015: A role for tropical forests in stabilizing atmospheric CO2. Nat. Clim. Chang., 5, 1022–1023, doi:10.1038/nclimate2869.
  272. Howells, M. et al., 2013: Integrated analysis of climate change, land-use, energy and water strategies. Nat. Clim. Chang., 3, 621–626. doi:10.1038/ nclimate1789.
  273. Hua, F. et al., 2016: Opportunities for biodiversity gains under the world’s largest reforestation programme. Nat. Commun., 7, 1–11, doi:10.1038/ ncomms12717.
  274. Hua, F. et al., 2018: Tree plantations displacing native forests: The nature and drivers of apparent forest recovery on former croplands in Southwestern China from 2000 to 2015. Biol. Conserv., 222, 113–124, doi:10.1016/j. biocon.2018.03.034.
  275. Huang, S.K., L. Kuo, and K.-L. Chou, 2016: The applicability of marginal abatement cost approach: A comprehensive review. J. Clean. Prod., 127, 59–71, doi:10.1016/J.JCLEPRO.2016.04.013.
  276. Huang, Y. et al., 2018: Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science, 362, 80–83, doi:10.1126/science.aat6405.
  277. Hull, V., M.-N. Tuanniu and J. Liu, 2015: Synthesis of human-nature feedbacks. Ecol. Soc., 20(3), doi.org/10.5751/ES-07404-200317.
  278. Humpenoder, F. et al., 2014: Investigating afforestation and bioenergy CCS as climate change mitigation strategies. Environ. Res. Lett., 9, 064029, doi:10.1088/1748-9326/9/6/064029.
  279. Humpenoeder, F. et al., 2018: Large-scale bioenergy production: How to resolve sustainability trade-offs? Environ. Res. Lett., 13, doi:10.1088/1748- 9326/aa9e3b.
  280. Noble, I.R., S. Huq, Y.A. Anokhin, J. Carmin, D. Goudou, F.P. Lansigan, B. Osman-Elasha and A. Villamizar, 2014: Adaptation needs and options. In: Climate Change 2014: Impacts, Adaptation and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 833–868.
  281. Hussein, Z., T. Hertel and A. Golub, 2013: Climate change mitigation policies and poverty in developing countries. Environ. Res. Lett., 8, 035009, [PAGE CITATION?] doi:10.1088/1748-9326/8/3/035009.
  282. Hussey, K. and J. Pittock, 2012: The energy–water nexus: Managing the links between energy and water for a sustainable future. Ecol. Soc., 17, doi:10.5751/ES-04641-170131.
  283. IFASTAT, 2018: Statistical Databases. http://www.ifastat.org/.
  284. Iizumi, T. and N. Ramankutty, 2015: How do weather and climate influence cropping area and intensity? Glob. Food Sec., 4, 46–50, doi:10.1016/j. gfs.2014.11.003. IMF, 2018: World Economic Outlook. World Economic Outlook Database, International Monetary Fund, Washington D.C., USA.
  285. IPBES, 2016: The Methodological Assessment Report on Scenarios and Models of Biodiversity and Ecosystem Services [S. Ferrier et al., (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 348 pp.
  286. IPBES, 2018a: The Regional Assessment Report on Biodiversity and Ecosystem services from Europe and Central Asia Biodiversity [Rounsevell, M., Fischer, M., Torre-Marin Rando, A. and Mader, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 892 pp.
  287. IPBES, 2018b: The IPBES Assessment Report on Land Degradation and Restoration [Montanarella, L., Scholes, R. and Brainich, A. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 744 pp.
  288. IPBES, 2018c: The Regional Assessment Report on Biodiversity and Ecosystem Services for Africa [Archer, E. Dziba, L., Mulongoy, K.J., Maoela, M.A. and Walters, M. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 492 pp.
  289. IPBES, 2018d: The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for the Americas [Rice, J., Seixas, C.S., Zaccagnini, 1 M.E., Bedoya-Gaitán, M. and Valderrama N. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 656 pp.
  290. IPBES, 2018e: The IPBES Regional Assessment Report on Biodiversity and Ecosystem Services for Asia and the Pacific [Karki, M., Senaratna Sellamuttu, S., Okayasu, S. and Suzuki, W. (eds.)]. Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany, 612 pp.
  291. IPCC, 2000: Special Report on Emissions Scenarios. Nature Publishing Group [Nakićenović, N. and R. Swart (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 612 pp.
  292. IPCC, 2000: Land Use, Land-Use Change and Forestry: A special report of the Intergovernmental Panel on Climate Change [Watson, R.T., I.R. Noble, B. Bolin, N.H. Ravindranath, D.J. Verardo and D.J. Dokken (eds.).]. Cambridge University Press Cambridge, United Kingdom, pp 375.
  293. IPCC, 2013: Summary for policymakers. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)], Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1–29.
  294. IPCC, 2014: Summary for policymakers. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1–32 pp.
  295. IPCC, 2018: Global Warming of 1.5°C: An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor and T. Waterfield (eds.)]. In press, 1552 pp.
  296. Isbell, F. et al., 2017: Linking the influence and dependence of people on biodiversity across scales. Nature, 546, 65–72, doi:10.1038/nature22899.
  297. Iwata, Y., T. Miyamoto, K. Kameyama and M. Nishiya, 2017: Effect of sensor installation on the accurate measurement of soil water content. Eur. J. Soil Sci., 68, 817–828, doi:10.1111/ejss.12493.
  298. Jadin, I., P. Meyfroidt and E.F. Lambin, 2016: International trade and land use intensification and spatial reorganization explain Costa Rica’s forest transition. Environ. Res. Lett., 11, 035005, doi:10.1088/1748- 9326/11/3/035005.
  299. Jalava, M., M. Kummu, M. Porkka, S. Siebert, and O. Varis, 2014: Diet change—a solution to reduce water use? Environ. Res. Lett., 9, 74016.
  300. EL Jarroudi, M. et al., 2015: Economics of a decision-support system for managing the main fungal diseases of winter wheat in the Grand-Duchy of Luxembourg. F. Crop. Res., 172, 32–41, doi:10.1016/J.FCR.2014.11.012. Jiang, L. and B.C. O’Neill, 2017: Global urbanization projections for the shared socioeconomic pathways. Glob. Environ. Chang., 42, 193–199, doi:10.1016/J.GLOENVCHA.2015.03.008.
  301. Jiang, L. and B.C. O’Neill, 2017: Global urbanization projections for the shared socioeconomic pathways. Glob. Environ. Chang., 42, 193–199, doi:10.1016/J.GLOENVCHA.2015.03.008.
  302. de Jong, R., M.E. Schaepman, R. Furrer, S. de Bruin, and P.H. Verburg, 2013: Spatial relationship between climatologies and changes in global vegetation activity. Glob. Chang. Biol., 19, 1953–1964, doi:10.1111/ gcb.12193.
  303. Joshi, A.K., P. Pant, P. Kumar, A. Giriraj, and P.K. Joshi, 2011: National forest policy in India: Critique of targets and implementation. Small-scale For., 10, 83–96, doi:10.1007/s11842-010-9133-z.
  304. Juhl, H.J. and M.B. Jensen, 2014: Relative price changes as a tool to stimulate more healthy food choices – A Danish household panel study. Food Policy, 46, 178–182, doi:10.1016/J.FOODPOL.2014.03.008.
  305. Kaijser, A. and A. Kronsell, 2014: Climate change through the lens of intersectionality. Env. Polit., 23, 417–433, doi:10.1080/09644016.2013.8 35203.
  306. Kanter, D.R. et al., 2016: Evaluating agricultural trade-offs in the age of sustainable development. AGSY, 163, 73–88, doi:10.1016/j. agsy.2016.09.010.
  307. Kastner, T., M.J.I. Rivas, W. Koch, and S. Nonhebel, 2012: Global changes in diets and the consequences for land requirements for food. Proc. Natl. Acad. Sci., doi:10.1073/pnas.1117054109.
  308. Kastner, T., K.H. Erb, and H. Haberl, 2014: Rapid growth in agricultural trade: Effects on global area efficiency and the role of management. Environ. Res. Lett., 9, doi:10.1088/1748-9326/9/3/034015.
  309. Kauffman, J.B., H. Hernandez Trejo, M. del Carmen Jesus Garcia, C. Heider, and W.M. Contreras, 2016: Carbon stocks of mangroves and losses arising from their conversion to cattle pastures in the Pantanos de Centla, Mexico. Wetl. Ecol. Manag., 24, 203–216, doi:10.1007/s11273-015-9453-z.
  310. De Kauwe, M.G., T.F. Keenan, B.E. Medlyn, I.C. Prentice, and C. Terrer, 2016: Satellite-based estimates underestimate the effect of CO2 fertilization on net primary productivity. Nat. Clim. Chang., 6, pages 892–893, doi:10.1038/nclimate3105.
  311. Keenan, R.J., 2015: Climate change impacts and adaptation in forest management: A review. Ann. For. Sci., 72, 145–167, doi:10.1007/s13595- 014-0446-5.
  312. Keenan, R.J. et al., 2015: Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manage., 352, 9–20, doi:10.1016/j.foreco.2015.06.014.
  313. Kelley, D.I. et al., 2013: A comprehensive benchmarking system for evaluating global vegetation models. Biogeosciences, 10, 3313–3340, doi:10.5194/ bg-10-3313-2013.
  314. Kesicki, F., 2013: What are the key drivers of MAC curves? A partial-equilibrium modelling approach for the UK. Energy Policy, 58, 142–151, doi:10.1016/J. ENPOL.2013.02.043.
  315. Kibler, K.M., D. Reinhart, C. Hawkins, A.M. Motlagh, and J. Wright, 2018: Food waste and the food-energy-water nexus: A review of food waste management alternatives. Waste Manag., 74, 52–62, doi:10.1016/J. WASMAN.2018.01.014.
  316. Kimball, B.A., 2016: Crop responses to elevated CO2 and interactions with H2O, N, and temperature. Curr. Opin. Plant Biol., 31, 36–43, doi:10.1016/j. pbi.2016.03.006.
  317. Kindermann, G., I. McCallum, S. Fritz, and M. Obersteiner, 2008: A global forest growing stock, biomass and carbon map based on FAO statistics. Silva Fenn., 42, doi:10.14214/sf.244.
  318. Klein, J.A. et al., 2014: Unexpected climate impacts on the Tibetan Plateau: Local and scientific knowledge in findings of delayed summer. Glob. Environ. Chang., 28, 141–152, doi:10.1016/J.GLOENVCHA.2014.03.007.
  319. Kok, K. et al., 2014: European participatory scenario development: Strengthening the link between stories and models. Clim. Change, 128, 187–200, doi:10.1007/s10584-014-1143-y.
  320. Kok, M.T.J. et al., 2018: Pathways for agriculture and forestry to contribute to terrestrial biodiversity conservation: A global scenario-study. Biol. Conserv., 221, 137–150, doi:10.1016/j.biocon.2018.03.003.
  321. Kolby Smith, W. et al., 2015: Large divergence of satellite and earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Chang., 6, 306–310, doi:10.1038/nclimate2879.
  322. Konar, M., J.J. Reimer, Z. Hussein, and N. Hanasaki, 2016: The water footprint of staple crop trade under climate and policy scenarios. Environ. Res. Lett., 11, 035006, doi:10.1088/1748-9326/11/3/035006.
  323. Kongsager, R. and E. Corbera, 2015: Linking mitigation and adaptation in carbon forestry projects: Evidence from Belize. World Dev., 76, 132–146, doi:10.1016/J.WORLDDEV.2015.07.003.
  324. Kongsager, R., B. Locatelli, and F. Chazarin, 2016: Addressing climate change mitigation and adaptation together: A global assessment of agriculture and forestry projects. Environ. Manage., 57, 271–282, doi:10.1007/ s00267-015-0605-y.
  325. Kostyanovsky, K.I., D.R. Huggins, C.O. Stockle, S. Waldo, and B. Lamb, 2018: Developing a flow through chamber system for automated measurements of soil N2O and CO2 emissions. Meas. J. Int. Meas. Confed., 113, 172–180, doi:10.1016/j.measurement.2017.05.040.
  326. Koutroulis, A.G., 2019: Dryland changes under different levels of global warming. Sci. Total Environ., 655, 482–511, doi:10.1016/J. SCITOTENV.2018.11.215.
  327. Krause, A. et al., 2017: Global consequences of afforestation and bioenergy cultivation on ecosystem service indicators. Biogeosciences, 4829–4850, doi:10.5194/bg-2017-160.
  328. Krausmann, F. and E. Langthaler, 2019: Food regimes and their trade links: A socio-ecological perspective. Ecol. Econ., 160, 87–95, doi:10.1016/J. ECOLECON.2019.02.011.
  329. Krausmann, F. et al., 2013: Global human appropriation of net primary production doubled in the 20th century. Proc. Natl. Acad. Sci. U.S.A., 110, 10324–10329, doi:10.1073/pnas.1211349110.
  330. Kraxner, F. et al., 2013: Global bioenergy scenarios – Future forest development, land-use implications and trade-offs. Biomass and Bioenergy, 57, 86–96, doi:10.1016/j.biombioe.2013.02.003.
  331. Kreidenweis, U. et al., 2016: Afforestation to mitigate climate change: impacts on food prices under consideration of albedo effects. Environ. Res. Lett., 11, 1–12, doi:10.1088/1748-9326/11/8/085001.
  332. Kreidenweis, U. et al., 2018: Pasture intensification is insufficient to relieve pressure on conservation priority areas in open agricultural markets. Glob. Chang. Biol., 24, 3199–3213, doi:10.1111/gcb.14272.
  333. Kummu, M. et al., 2012: Lost food, wasted resources: Global food supply chain losses and their impacts on freshwater, cropland and fertiliser use. Sci. Total Environ., 438, 477–489, doi:10.1016/J.SCITOTENV.2012.08.092.
  334. Kunreuther, H., 2015: The role of insurance in reducing losses from extreme events: The need for public–private partnerships. Geneva Pap. Risk Insur. Issues Pract., 40, 741–762, doi:10.1057/gpp.2015.14.
  335. Lacaze, R. et al., 2015: Operational 333m biophysical products of the copernicus global land service for agriculture monitoring. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. – ISPRS Arch., 40, 53–56, doi:10.5194/isprsarchives-XL-7-W3-53-2015.
  336. Laestadius, L. et al., 2011: Mapping opportunities for forest landscape restoration. Unasylva, 62, 47–48.
  337. Lal, R., 2009: Soils and world food security. Soil and Tillage Research, 102, 1–4, doi:10.1016/j.still.2008.08.001.
  338. Lal, R., 2015: Restoring soil quality to mitigate soil degradation. Sustainability, 7, 5875, doi:10.3390/su7055875.
  339. Lamb, D., 2018: Undertaking large-scale forest restoration to generate ecosystem services. Restor. Ecol., 26, 657–666, doi:10.1111/rec.12706. Lambin, E.F., 2012: Global land availability: Malthus versus Ricardo.Global Food Security, 1, 83–87, doi:10.1016/j.gfs.2012.11.002.
  340. Lambin, E.F. and P. Meyfroidt, 2011: Global land use change, economic globalization and the looming land scarcity. Proc Natl Acad Sci U S A, 108, 3465–3472, doi:10.1073/pnas.1100480108.
  341. Lambin, E.F. and P Patrick Meyfroidt, 2014: Trends in gobal land-use competition. In Rethinking Global Land Use in an Urban Era, Vol. 14, [Seto, K.C. and A. Reenberg (eds.)]. The MIT Press, Cambridge,Massachusetts, pp. 11–22.
  342. Land Matrix, 2018: Land Matrix Global Observatory. http://www.landmatrix.org. Lapola, D.M. et al., 2010: Indirect land-use changes can overcome carbon savings from biofuels in Brazil. Proc. Natl. Acad. Sci. U.S.A., 107, 3388–3393, doi:10.1073/pnas.0907318107. Lassaletta, L., G. Billen, B. Grizzetti, J. Anglade, and J. Garnier, 2014: 50 year trends in nitrogen use efficiency of world cropping systems: The relationship between yield and nitrogen input to cropland. Environ. Res. Lett., doi:10.1088/1748-9326/9/10/105011.
  343. Lassaletta, L. et al., 2016: Nitrogen use in the global food system: Past trends and future trajectories of agronomic performance, pollution, trade and dietary demand. Environ. Res. Lett., 11, 095007, doi:10.1088/1748- 9326/11/9/095007.
  344. Laurance, W.F., 2007: Forests and floods. Nature, 449, 409–410, doi: 10.1038/449409a.
  345. Laurance, W.F., J. Sayer and K.G. Cassman, 2014: Agricultural expansion and its impacts on tropical nature. Trends Ecol. Evol., 29, 107–116, doi:10.1016/J. TREE.2013.12.001.
  346. Le, H.D., C. Smith, J. Herbohn and S. Harrison, 2012: More than just trees: Assessing reforestation success in tropical developing countries. J. Rural Stud., 28, 5–19, doi:10.1016/j.jrurstud.2011.07.006.
  347. Le, Q.B., E. Nkonya and A. Mirzabaev, 2016: Biomass productivity-based mapping of global land degradation hotspots. In: Economics of Land Degradation and Improvement – A Global Assessment for Sustainable Development [Nkonya, E., A. Mirzabaev and J. Von Braun, (eds.)]. Springer International Publishing, Cham, Switzerland, pp. 55–84, doi:10.1007/978- 3-319-19168-3_4.
  348. Le, T.T.H. Trang, 2016: Effects of climate change on rice yield and rice market in Vietnam. J. Agric. Appl. Econ., 48, 366–382, doi:10.1017/aae.2016.21. Lean, J.L., 2018: Observation-based detection and attribution of 21st century climate change. Wiley Interdiscip. Rev. Chang., 9, doi:0.1002/wcc.511.
  349. Lebel, L. et al., 2006: Governance and the capacity to manage resilience in regional social-ecological systems. Ecol. Soc., 11, 19, doi:10.5751/ES- 01606-110119.
  350. Lee, J. et al., 2018: Economic viability of the national-scale forestation program: The case of success in the Republic of Korea. Ecosyst. Serv., 29, 40–46, doi:10.1016/j.ecoser.2017.11.001.
  351. Lee, X. et al., 2011: Observed increase in local cooling effect of deforestation at higher latitudes. Nature, 479, 384–387, doi:10.1038/nature10588.
  352. Lees, K.J., T. Quaife, R.R.E. Artz, M. Khomik, and J.M. Clark, 2018: Potential for using remote sensing to estimate carbon fluxes across northern peatlands – A review. Sci. Total Environ., 615, 857–874, doi:10.1016/j. scitotenv.2017.09.103.
  353. Lehmann, C.E.R. and C.L. Parr, 2016: Tropical grassy biomes: Linking ecology, human use and conservation. Philos. Trans. R. Soc. B-Biological Sci., 371, 20160329, doi:20160329 10.1098/rstb.2016.0329.
  354. Lehmann, N., S. Briner, and R. Finger, 2013: The impact of climate and price  risks on agricultural land use and crop management decisions. Land use policy, 35, 119–130, doi:10.1016/J.LANDUSEPOL.2013.05.008.
  355. Lempert,R.,N.Nakicenovic,D.Sarewitz,and M.Schlesinger,2004:Characterizing climate-change uncertainties for decision-makers: An editorial essay. Clim. Change, 65, 1–9, doi:10.1023/B:CLIM.0000037561.75281.b3.
  356. Lenton, T.M., 2014: The global potential for carbon dioxide removal. In: Geoengineering of the Climate System [Hester, R.E. and R.M. Harrison, (eds.)]. Royal Society of Chemistry, pp. 52–79.
  357. Lesk, C., P. Rowhani, and N. Ramankutty, 2016: Influence of extreme weather disasters on global crop production. Nature, 529, 84, doi:10.1038/ nature16467.
  358. Li, D., S. Niu, and Y. Luo, 2012: Global patterns of the dynamics of soil carbon and nitrogen stocks following afforestation: A meta-analysis. New Phytol., 195,172–181,doi:10.1111/j.1469-8137.2012.04150.x.
  359. Li, S., M. Xu and B. Sun, 2014: Long-term hydrological response to reforestation in a large watershed in southeastern China. Hydrol. Process., 28, 5573– 5582, doi:10.1002/hyp.10018.
  360. Li, W. et al., 2016: Major forest changes and land cover transitions based on plant functional types derived from the ESA CCI Land Cover product. Int. J. Appl. Earth Obs. Geoinf., 47, 30–39, doi:10.1016/J.JAG.2015.12.006.
  361. Li, W. et al., 2017: Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations. Biogeosciences, 1 145194, 5053–5067, doi:10.5194/bg-14-5053-2017.
  362. Limpens, J. et al., 2008: Peatlands and the carbon cycle: from local processes to global implications – a synthesis. Biogeosciences, 5, 1475–1491, doi:10.5194/bg-5-1475-2008.
  363. Lin, M. and P. Huybers, 2012: Reckoning wheat yield trends. Environ. Res. Lett., 7, 24016, doi:10.1088/1748-9326/7/2/024016.
  364. Lindenmayer, D.B. and R.J. Hobbs, 2004: Fauna conservation in Australian plantation forests – a review. Biol. Conserv., 119, 151–168, doi:10.1016/J. BIOCON.2003.10.028.
  365. Linnerooth-Bayer, J. and R. Mechler, 2006: Insurance for assisting adaptation to climate change in developing countries: A proposed strategy. Clim. Policy, 6, 621–636, doi:10.1080/14693062.2006.9685628.
  366. Lipper, L. et al., 2014: Climate-smart agriculture for food security. Nat. Clim. Chang., 4, 1068–1072, doi:10.1038/nclimate2437.
  367. Liu, J. et al., 2013: Framing Sustainability in a Telecoupled World. Ecol. Soc., 2, doi:10.5751/ES-05873-180226.
  368. Lobell, D.B., W. Schlenker, and J. Costa-Roberts, 2011: Climate trends and global crop production since 1980. Science, 333, 616–620, doi:10.1126/ science.1204531.
  369. Lobell, D.B., A. Sibley, and J. Ivan Ortiz-Monasterio, 2012: Extreme heat effects on wheat senescence in India. Nat. Clim. Chang., 2, 186–189, doi:10.1038/ nclimate1356.
  370. Lobell, D.B., C.B. Uris Lantz, and T.W. Hertel, 2013: Climate adaptation as mitigation: The case of agricultural investments. Environ. Res. Lett., 8, 15012, doi:10.1088/1748-9326/8/1/015012.
  371. Locatelli, B., V. Evans, A. Wardell, A. Andrade, and R. Vignola, 2011: Forests and climate change in Latin America: Linking adaptation and mitigation. Forests, 2, doi:10.3390/f2010431.
  372. Locatelli, B. et al., 2015a: Tropical reforestation and climate change: Beyond carbon. Restor. Ecol., 23, 337–343, doi:10.1111/rec.12209.
  373. Locatelli, B., C. Pavageau, E. Pramova, and M. Di Gregorio, 2015b: Integrating climate change mitigation and adaptation in agriculture and forestry: Opportunities and trade-offs. Wiley Interdiscip. Rev. Clim. Chang., 6, 585- 598, doi:10.1002/wcc.357.
  374. Loch, A. et al., 2013: The Role of Water Markets in Climate Change Adaptation. National Climate Change Adaptation Research Facility, Gold Coast, Australia, 125 pp.
  375. Loladze, I., 2014: Hidden shift of the ionome of plants exposed to elevated CO2 depletes minerals at the base of human nutrition. Elife, 3, e02245, doi:10.7554/eLife.02245.
  376. Lorenz, K. and R. Lal, 2014: Biochar application to soil for climate change mitigation by soil organic carbon sequestration. J. Plant Nutr. Soil Sci., 177, 651–670, doi:10.1002/jpln.201400058.
  377. Luedeling, E. and E. Shepherd, 2016: Decision-Focused Agricultural Research. Solutions, 7, 46–54.
  378. Luo, Y.Q. et al., 2012: A framework of benchmarking land models. Biogeosciences, 10, 3857–3874, doi:10.5194/bgd-9-1899-2012.
  379. Luyssaert, S. et al., 2014: Land management and land-cover change have impacts of similar magnitude on surface temperature. Nat. Clim. Chang., 4, 389–393, doi:10.1038/nclimate2196.
  380. MacDicken, K.G., 2015: Global forest resources assessment 2015: What, why and how? For. Ecol. Manage., 352, 3–8, doi:10.1016/j.foreco.2015.02.006. MacDicken, K.G. et al., 2015: Global progress toward sustainable forest management. For. Ecol. Manage., 352, 47–56, doi:10.1016/j. foreco.2015.02.005.
  381. Mace, G.M., K. Norris, and A.H. Fitter, 2012: Biodiversity and ecosystem services: A multilayered relationship. Trends Ecol. Evol., 27, 19–25, doi:10.1016/j.tree.2011.08.006.
  382. Maestre, F.T. et al., 2012: Plant species richness and ecosystem multifunctionality in global drylands. Science, 335, 214–218, doi:10.1126/ science.1215442.
  383. Maestre F.T. et al., 2016: Structure and functioning of dryland ecosystems in a changing world. Annual Review of Ecology, Evolution and Systematics, 47, 215–237, doi:10.1146/annurev-ecolsys-121415-032311.
  384. Maier, H.R. et al., 2016: An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together? Environ. Model. Softw., 81, 154–164, doi:10.1016/j.envsoft.2016.03.014.
  385. Malkamäki, A. et al., 2018: A systematic review of the socio-economic impacts of large-scale tree plantations, worldwide. Glob. Environ. Chang., 53, 90–103, doi:10.1016/j.gloenvcha.2018.09.001.
  386. Malone, R.W. et al., 2014: Cover crops in the upper midwestern United States: Simulated effect on nitrate leaching with artificial drainage. J. Soil Water Conserv., 69, 292–305, doi:10.2489/jswc.69.4.292.
  387. Marchand, P. et al., 2016: Reserves and trade jointly determine exposure to food supply shocks. Environ. Res. Lett., 11, 095009, doi:10.1088/1748- 9326/11/9/095009.
  388. Marques, A. et al., 2019: Increasing impacts of land use on biodiversity and carbon sequestration driven by population and economic growth. Nat. Ecol. Evol., 3, 628–637, doi:10.1038/s41559-019-0824-3.
  389. Martellozzo, F. et al., 2015: Urbanization and the loss of prime farmland: A case study in the Calgary–Edmonton corridor of Alberta. Reg. Environ. Chang., 15, 881–893, doi:10.1007/s10113-014-0658-0.
  390. Martin-Guay, M.O., A. Paquette, J. Dupras, and D. Rivest, 2018: The new Green Revolution: Sustainable intensification of agriculture by intercropping. Sci. Total Environ., 615, 767–772, doi:10.1016/j.scitotenv.2017.10.024.
  391. Mastrandrea, M.D. et al., 2011: The IPCC AR5 guidance note on consistent treatment of uncertainties: A common approach across the working groups. Clim. Change, 108, 675, doi:10.1007/s10584-011-0178-6.
  392. Mateos, E., J.M. Edeso, and L. Ormaetxea, 2017: Soil erosion and forests biomass as energy resource in the basin of the Oka River in Biscay. Forests, 8, 1–20, doi:10.3390/f8070258.
  393. Mathews, J.A., 2017: Global trade and promotion of cleantech industry: A post-Paris agenda. Clim. Policy, 17, 102–110, doi:10.1080/14693062. 2016.1215286.
  394. Maxwell, S.L., R.A. Fuller, T.M. Brooks, and J.E.M. Watson, 2016: Biodiversity: The ravages of guns, nets and bulldozers. Nature, 536, 143–145, doi:10.1038/536143a.
  395. McDonnell, S., 2017: Urban land grabbing by political elites: Exploring the political economy of land and the challenges of regulation. In: Kastom, property and ideology: Land transformations in Melanesia [McDonnell, S., M.G. Allen, C. Filer (Eds.)]. Australian National University Press, Canberra, Australia, pp. 283–304.
  396. Medek, Danielle E., Joel Schwartz, S.S.M., 2017: Estimated effects of future atmospheric CO2 concentrations on protein intake and the risk of protein deficiency by country and region. Env. Heal. Perspect, 125, 087002. doi:10.1289/EHP41.
  397. Mello, D. and M. Schmink, 2017: Amazon entrepreneurs: Women’s economic empowerment and the potential for more sustainable land use practices. Womens. Stud. Int. Forum, 65, 28–36, doi:10.1016/J.WSIF.2016.11.008.
  398. Messerli, P., M. Giger, M.B. Dwyer, T. Breu and S. Eckert, 2014: The geography of large-scale land acquisitions: Analysing socio-ecological patterns of target contexts in the global South. Appl. Geogr., 53, 449–459, doi:10.1016/j. apgeog.2014.07.005.
  399. Meyfroidt, P., 2018: Trade-offs between environment and livelihoods: Bridging the global land use and food security discussions. Glob. Food Sec., 16, 9–16, doi:10.1016/J.GFS.2017.08.001.
  400. Millar, R.J. et al., 2017: Emission budgets and pathways consistent with limiting warming to 1.5°C. Nat. Geosci., 10, 741–747, doi:10.1038/ NGEO3031.
  401. Mirzabaev, A., E. Nkonya and J. von Braun, 2015: Economics of sustainable land management. Elsevier, 15, 9–19, doi:10.1016/j.cosust.2015.07.004.
  402. Mistry, J. and A. Berardi, 2016: Bridging indigenous and scientific knowledge. Science, 352, 1274–1275, doi:10.1126/science.aaf1160.
  403. Miyamoto, A., M. Sano, H. Tanaka and K. Niiyama, 2011: Changes in forest resource utilization and forest landscapes in the southern Abukuma Mountains, Japan during the twentieth century. J. For. Res., 16, 87–97, doi:10.1007/s10310-010-0213-x.
  404. Molina, A., G. Govers, V. Vanacker, and J. Poesen, 2007: Runoff generation in a degraded Andean ecosystem: Interaction of vegetation cover and land use. Catena, 71, 357–370, doi:10.1016/j.catena.2007.04.002.
  405. Moore, F.C. and D.B. Lobell, 2015: The fingerprint of climate trends on European crop yields. Proc. Natl. Acad. Sci., 112, 2670–2675, doi:10.1073/ pnas.1409606112.
  406. Moosa, C.S. and N. Tuana, 2014: Mapping a research agenda concerning gender and climate change: A review of the literature. Hypatia, 29, 677– 694, doi:10.1111/hypa.12085.
  407. Morales-Hidalgo, D., S.N. Oswalt, and E. Somanathan, 2015: Status and trends in global primary forest, protected areas, and areas designated for conservation of biodiversity from the Global Forest Resources Assessment 2015. For. Ecol. Manage., 352, 68–77, doi:10.1016/j.foreco.2015.06.011.
  408. Moroni, S., 2018: Property as a human right and property as a special title. Rediscussing private ownership of land. Land use policy, 70, 273–280, doi:10.1016/J.LANDUSEPOL.2017.10.037.
  409. Mosnier, A. et al., 2014: Global food markets, trade and the cost of climate change adaptation. Food Secur., 6, 29–44, doi:10.1007/s12571-013-0319-z. Mottet, A. et al., 2017: Livestock: On our plates or eating at our table? A new analysis of the feed/food debate. Glob. Food Sec., 14, 1–8, doi:10.1016/J. GFS.2017.01.001.
  410. Mouratiadou, I. et al., 2016: The impact of climate change mitigation on water
  411. demand for energy and food: An integrated analysis based on the Shared Socioeconomic Pathways. Environ. Sci. Policy, 64, 48–58, doi:10.1016/J. ENVSCI.2016.06.007.
  412. Muller, A. et al., 2017: Strategies for feeding the world more sustainably with organic agriculture. Nat. Commun., 8, doi:10.1038/s41467-017-01410-w.
  413. Muller, C. et al., 2015: Implications of climate mitigation for future agricultural production. Environ. Res. Lett., 10, doi:12500410.1088/1748- 9326/10/12/125004.
  414. Murdiyarso, D. et al., 2015: The potential of Indonesian mangrove forests for global climate change mitigation. Nat. Clim. Chang., 5, 1089–1092, doi:10.1038/NCLIMATE2734.
  415. Myers, S.S., Zanobetti, A., Kloog, I., Huybers, P., Leakey, A.D., Bloom, A.J., 2014: Increasing CO2 threatens human nutrition. Nature, 510, 139, doi:10.1038/ nature13179.
  416. Myers, S.S. et al., 2017: Climate change and global food systems: Potential impacts on food security and undernutrition. Annu. Rev. Public Health, 38, 259–277, doi:10.1146/annurev-publhealth-031816-044356.
  417. Nachtergaele, F., 2008: Mapping Land Use Systems at Global and Regional Scales for Land Degradation Assessment Analysis Version 1.0, Food and Agriculture Organization of the United Nations, Rome, Italy, 77 pp.
  418. Nakamura, A. et al., 2017: Forests and their canopies: Achievements and horizons in canopy science, Trends Ecol. Evol., 32, 438–451, doi:10.1016/j. tree.2017.02.020.
  419. Namubiru-Mwaura, E., 2014: Land tenure and gender: Approaches and challenges for strengthening rural women’s land rights. Women’s Voice, Agency, & Participation Research Series No. 06, World Bank, Washington, DC, USA, 32 pp.
  420. Nelson, K.C. and B.H.J. de Jong, 2003: Making global initiatives local realities: Carbon mitigation projects in Chiapas, Mexico. Glob. Environ. Chang., 13, 19–30, doi: 10.1016/S0959-3780(02)00088-2.
  421. Nepstad, D.C., W. Boyd, C.M. Stickler, T. Bezerra, and A.A. Azevedo, 2013: Responding to climate change and the global land crisis: REDD+, market transformation and low-emissions rural development. Philos. Trans. R. Soc. Lond. B. Biol. Sci., 368, 20120167, doi:10.1098/rstb.2012.0167.
  422. Newbold, T. et al., 2015: Global effects of land use on local terrestrial biodiversity. Nature, 520, 45–50, doi:10.1038/nature14324.
  423. Newbold, T., D.P. Tittensor, M.B.J. Harfoot, J.P.W. Scharlemann, and D.W. Purves, 2018: Non-linear changes in modelled terrestrial ecosystems subjected to perturbations. bioRxiv, doi:10.1101/439059.
  424. Nicole, W., 2015: Pollinator power: Nutrition security benefits of an ecosystem service. Environ. Health Perspect., 123, A210–A215, doi:10.1289/ ehp.123-A210.
  425. Nkhonjera, G.K., 2017: Understanding the impact of climate change on the dwindling water resources of South Africa, focusing mainly on Olifants River basin: A review. Environ. Sci. Policy, 71, 19–29, doi:10.1016/J. ENVSCI.2017.02.004.
  426. Nolte, K., W. Chamberlain and M. Giger, 2016: International Land Deals for Agriculture: Fresh insights from the Land Matrix: Analytical Report II. Centre for Development and Environment, University of Bern; Centre de coopération internationale en recherche agronomique pour le développement; German Institute of Global and Area Studies; University of Pretoria; Bern Open Publishing, Bern, Montpellier, Hamburg, Pretoria, 1–56 pp.
  427. van Noordwijk, M. and L. Brussaard, 2014: Minimizing the ecological footprint of food: Closing yield and efficiency gaps simultaneously? Curr. Opin. Environ. Sustain., 8, 62–70, doi:10.1016/J.COSUST.2014.08.008.
  428. Noordwijk, M. Van, L. Tanika and B. Lusiana, 2017: Flood risk reduction and flow buffering as ecosystem services – Part 2: Land use and rainfall intensity effects in Southeast Asia. Hydrol. Earth Syst. Sci., 2341–2360, doi:10.5194/hess-21-2341-2017.
  429. Nordhaus, W., 2014: Estimates of the social cost of carbon: Concepts and results from the DICE-2013R model and alternative approaches. J. Assoc. Environ. Resour. Econ., 1, 273–312, doi:10.1086/676035.
  430. Norse, D. and X. Ju, 2015: Environmental costs of China’s food security. Agric. Ecosyst. Environ., 209, 5–14, doi:10.1016/J.AGEE.2015.02.014.
  431. Nowosad, J., T.F. Stepinski and P. Netzel, 2018: Global assessment and mapping of changes in mesoscale landscapes: 1992–2015. Int. J. Appl. Earth Obs. Geoinf., 78, 332–40, doi:10.1016/j.jag.2018.09.013.
  432. O’Neill, B.C. et al., 2014: A new scenario framework for climate change research: The concept of shared socioeconomic pathways. Clim. Change, 122, 387–400, doi:10.1007/s10584-013-0905-2.
  433. OECD, 2014: Social Institutions and Gender Index (SIGI). OECD Development Centre’sSocialCohesionUnit,Paris,France,www.oecd.org/dev/development- gender/BrochureSIGI2015-web.pdf.
  434. Ogle, S.M. et al., 2018: Delineating managed land for reporting national greenhouse gas emissions and removals to the United Nations framework convention on climate change. Carbon Balance Manag., 13, doi:10.1186/ s13021-018-0095-3.
  435. O’Neill, B. C, 2004: Conditional Probabilistic Population Projections: An Application to Climate Change. International Statistical Institute, 72(2), 167–184.
  436. Ordway, E.M., G.P. Asner and E.F. Lambin, 2017: Deforestation risk due to commodity crop expansion in sub-Saharan Africa. Environ. Res. Lett., 12, 044015, doi:10.1088/1748-9326/aa6509.
  437. Osborne, T.M. and T.R. Wheeler, 2013: Evidence for a climate signal in trends of global crop yield variability over the past 50 years. Environ. Res. Lett., 8, 024001, doi:10.1088/1748-9326/8/2/024001.
  438. Ostrom, E. and M. Cox, 2010: Moving beyond panaceas: A multi-tiered diagnostic approach for social-ecological analysis. Environ. Conserv., 37, 451–463, doi:10.1017/S0376892910000834.
  439. Padmanaba, M. and R.T. Corlett, 2014: Minimizing risks of invasive alien plant species in tropical production forest management. Forests, 5, 1982–1998, doi:10.3390/f5081982.
  440. Paillet, Y. et al., 2010: Biodiversity differences between managed and unmanaged forests: Meta-analysis of species richness in Europe, Conserv. Biol., 24(1), 101–112, doi:10.1111/j.1523-1739.2009.01399.x.
  441. Di Paola, A. et al., 2018: The expansion of wheat thermal suitability of Russia in response to climate change. Land use policy, 78, 70–77, doi:10.1016/J. LANDUSEPOL.2018.06.035.
  442. Parfitt, J., M. Barthel, and S. Macnaughton, 2010: Food waste within food supply chains: quantification and potential for change to 2050. Philos. Trans. R. Soc. Lond. B. Biol. Sci., 365, 3065–3081, doi:10.1098/rstb.2010.0126.
  443. Parker, W.S., 2013: Ensemble modeling, uncertainty and robust predictions. Wiley Interdiscip. Rev. Chang., 4, 213–223, doi:10.1002/wcc.220.
  444. Parr, C.L., C.E.R.R. Lehmann, W.J. Bond, W.A. Hoffmann A.N. Andersen, 2014: Tropical grassy biomes: misunderstood, neglected and under threat. Trends Ecol. Evol., 29, 205–213, doi:10.1016/j.tree.2014.02.004.
  445. Pawson, S.M. et al., 2013: Plantation forests, climate change and biodiversity. Biodivers. Conserv., 22, 1203–1227, doi:10.1007/s10531-013-0458-8.
  446. Payn, T. et al., 2015: Changes in planted forests and future global implications. For. Ecol. Manage., 352, 57–67, doi:10.1016/J.FORECO.2015.06.021.
  447. Pedrozo-Acuña, A., R. Damania, M.A. Laverde-Barajas, and D. Mira-Salama, 2015: Assessing the consequences of sea-level rise in the coastal zone of Quintana Roo, México: The costs of inaction. J. Coast. Conserv., 19, 227– 240, doi:10.1007/s11852-015-0383-y.
  448. Pereira, H.M. et al., 2010: Scenarios for global biodiversity in the 21st century. Science, 330, 1496–1501, doi:10.1126/science.1196624.
  449. Perugini, L. et al., 2017: Biophysical effects on temperature and precipitation due to land cover change. Environ. Res. Lett., 12, 1–21, doi:10.1088/1748- 9326/aa6b3f.
  450. Peterson, E.E., S.A. Cunningham, M. Thomas, S. Collings, G.D. Bonnett and B. Harch, 2017: An assessment framework for measuring agroecosystem health. Ecol. Indic., 79, 265–275, doi:10.1016/j.ecolind.2017.04.002.
  451. Pham, P., P. Doneys, and D.L. Doane, 2016: Changing livelihoods, gender roles and gender hierarchies: The impact of climate, regulatory and socio-economic changes on women and men in a Co Tu community in Vietnam. Women’s Stud. Int. Forum, 54, 48–56, doi:10.1016/J. WSIF.2015.10.001.
  452. Pimm, S.L. et al., 2014: The biodiversity of species and their rates of extinction, distribution, and protection. Science, 344, 1246752–1246752, doi:10.1126/science.1246752.
  453. Pingoud, K., T. Ekholm, R. Sievänen, S. Huuskonen, and J. Hynynen, 2018: Trade-offs between forest carbon stocks and harvests in a steady state – A multi-criteria analysis. J. Environ. Manage., 210, 96–103, doi:10.1016/J. JENVMAN.2017.12.076.
  454. Pizer, W. et al., 2014: Using and improving the social cost of carbon. Science, 346, 1189–1190, doi:10.1126/science.1259774.
  455. Poeplau, C. and A. Don, 2015: Carbon sequestration in agricultural soils via cultivation of cover crops – A meta-analysis. Agric. Ecosyst. Environ., 200, 33–41, doi:10.1016/J.AGEE.2014.10.024.
  456. Poeplau, C. et al., 2011: Temporal dynamics of soil organic carbon after land-use change in the temperate zone – carbon response functions as a model approach. Glob. Chang. Biol., 17, 2415–2427, doi:10.1111/j.1365- 2486.2011.02408.x.
  457. Poore, J. and T. Nemecek, 2018: Reducing food’s environmental impacts through producers and consumers. Science, 360, 987–992, doi:10.1126/ science.aaq0216.
  458. Popp, A. et al., 2014: Land-use protection for climate change mitigation. Nat. Clim. Chang., 4, 1095–1098, doi:10.1038/nclimate2444.
  459. Popp, A. et al., 2016: Land-use futures in the shared socio-economic pathways. Glob. Environ. Chang., 42, doi:10.1016/j.gloenvcha.2016.10.002.
  460. Porter, J.R., L. Xie, A.J. Challinor, K. Cochrane, S.M. Howden, M.M. Iqbal, D.B. Lobell, and M.I. Travasso, 2014: Food security and food production systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom, pp. 485–533, doi:10.1017/CBO9781107415379.
  461. Porter, S.D., D.S. Reay, P. Higgins and E. Bomberg, 2016: A half-century of production-phase greenhouse gas emissions from food loss and waste in the global food supply chain. Sci. Total Environ., 571, 721–729, doi:10.1016/J.SCITOTENV.2016.07.041.
  462. Potapov, P. et al., 2017: The last frontiers of wilderness: Tracking loss of intact forest landscapes from 2000 to 2013. Sci. Adv., 3, e1600821, doi:10.1126/ sciadv.1600821.
  463. Pradhan, P., M.K.B. Lüdeke, D.E. Reusser, and J.P. Kropp, 2013: Embodied crop calories in animal products. Environ. Res. Lett., 8, doi:10.1088/1748- 9326/8/4/044044.
  464. Pradhan, P., M.K.B. Lüdeke, D.E. Reusser, and J.P. Kropp, 2014: Food Self-Sufficiency across Scales: How Local Can We Go? 15, 9779, doi:10.1021/es5005939.
  465. Pravalie, R., 2016: Drylands extent and environmental issues.A global approach. Earth-Science Rev., 161, 259–278, doi:10.1016/j.earscirev.2016.08.003. Prestele, R. et al., 2016: Hotspots of uncertainty in land-use and land-cover change projections: A global-scale model comparison. Glob. Chang. Biol., 22, 3967–3983, doi:10.1111/gcb.13337. Pugh, T.A.M. et al., 2016: Climate analogues suggest limited potential for intensification of production on current croplands under climate change. Nat. Commun., 7, doi:1260810.1038/ncomms12608. Pugh,T.A.M. et al., 2019: Role of forest regrowth in global carbon sink dynamics.
  466. Proc. Natl. Acad. Sci., 201810512, doi:10.1073/pnas.1810512116. Putz, F.E. and K.H. Redford, 2010: The importance of defining “Forest”: Tropical forest degradation, deforestation, long-term phase shifts and further transitions. Biotropica, doi:10.1111/j.1744-7429.2009.00567.x. Le Quéré, C. et al., 2015: Global Carbon Budget 2015. Earth Syst. Sci. Data, 7, 349–396, doi:10.5194/essd-7-349-2015. Le Quéré, C. et al., 2018: Global Carbon Budget 2017. Earth Syst. Sci. Data, 10, 405–448, doi:10.5194/essd-10-405-2018. Le Quéré, C. et al., 2013: The global carbon budget 1959–2011. Earth Syst. Sci. Data, 5, 165–185, doi:10.5194/essd-5-165-2013. Le Quéré, C. et al., 2018: Global Carbon Budget 2018. Earth Syst. Sci. Data Discuss., 1–3, doi:10.5194/essd-2018-120. Raffensperger, C. and J.A. Tickner, 1999: Introduction: To Foresee and Forestall. In: Protecting Public Health & The Environment: Implementing The Precautionary Principle, [Raffensperger, C. and J.A. Tickner (eds.)]. Island Press, Washington, DC, USA, pp. 1–11.
  467. Raiten, D.J. and A.M. Aimone, 2017: The intersection of climate/environment, food, nutrition and health: Crisis and opportunity. Curr. Opin. Biotechnol., 44, 52–62, doi:10.1016/J.COPBIO.2016.10.006.
  468. Ramankutty, N.,A.T.Evan,C.MonfredaandJ.A.Foley,2008:Farmingtheplanet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochem. Cycles, 22, GB1003, doi:10.1029/2007GB002952.
  469. Ramankutty, N. et al., 2018: Trends in global agricultural land use: Implications for environmental health and food security. Annu. Rev. Plant Biol., 69, 789–815, doi:10.1146/annurev-arplant-042817-040256.
  470. Randerson, J.T. et al., 2009: Systematic assessment of terrestrial biogeochemistry in coupled climate-carbon models. Glob. Chang. Biol., 15, 2462–2484, doi:10.1111/j.1365-2486.2009.01912.x.
  471. Rao, N., 2017: Assets, agency and legitimacy: Towards a relational understanding of gender equality policy and practice. World Dev., 95, 43–54, doi:10.1016/J.WORLDDEV.2017.02.018.
  472. Rao, Y. et al., 2018: Integrating ecosystem services value for sustainable land-use management in semi-arid region. J. Clean. Prod., 186, 662–672, doi:10.1016/J.JCLEPRO.2018.03.119.
  473. Ravi, S., D.D. Breshears, T.E. Huxman, and P. D’Odorico, 2010: Land degradation in drylands: Interactions among hydrologic–aeolian erosion and vegetation dynamics. Geomorphology, 116, 236–245, doi:10.1016/j. geomorph.2009.11.023.
  474. Ravnborg, H.M., R. Spichiger, R.B. Broegaard, and R.H. Pedersen, 2016: Land governance, gender equality and development: Past achievements and remaining challenges. J. Int. Dev., 28, 412–427, doi:10.1002/jid.3215.
  475. Ray, D.K., N. Ramankutty, N.D. Mueller, P.C. West and J.A. Foley, 2012: Recent patterns of crop yield growth and stagnation. Nat. Commun., 3, doi:10.1038/ncomms2296.
  476. Reed, M. and L.C. Stringer, 2015: Climate change and desertification: Anticipating, assessing & adapting to future change in drylands. Impulse Report for the 3rd UNCCD Scientific Conference, Agropolis International, Montpellier, France, 1–140 pp.
  477. Resurrección, B.P., 2013: Persistent women and environment linkages in climate change and sustainable development agendas. Womens. Stud. Int. Forum, 40, 33–43, doi:10.1016/J.WSIF.2013.03.011.
  478. Reyer, C., M. Guericke, and P.L. Ibisch, 2009: Climate change mitigation via afforestation, reforestation and deforestation avoidance: and what about adaptation to environmental change? New For., 38, 15–34, doi:10.1007/ s11056-008-9129-0.
  479. Riahi, K. et al., 2015: Locked into Copenhagen pledges – implications of short-term emission targets for the cost and feasibility of long-term climate goals. Technol. Forecast. Soc. Change, 90, 8–23, doi:10.1016/j. techfore.2013.09.016.
  480. Riahi, K. et al., 2017: The shared socioeconomic pathways and their energy, land use and greenhouse gas emissions implications: An overview. Glob. Environ. Chang., 42, 153–168, doi:10.1016/j.gloenvcha.2016.05.009.
  481. Richards, D.R. and D.A. Friess, 2016: Rates and drivers of mangrove deforestation in Southeast Asia, 2000-2012. Proc. Natl. Acad. Sci. U.S.A., 113, 344–349, doi:10.1073/pnas, 1510272113.
  482. Ricke, K., L. Drouet, K. Caldeira, and M. Tavoni, 2018: Country-level social cost of carbon. Nat. Clim. Chang., 8, 895–900, doi:10.1038/s41558-018- 0282-y.
  483. Ringler, C. and R. Lawford, 2013: The nexus across water, energy, land and food (WELF): Potential for improved resource use efficiency? Curr. Opin. Environ. Sustain., 5, 617–624, doi:10.1016/J.COSUST.2013.11.002.
  484. Robinson, D.A. et al., 2017: Modelling feedbacks between human and natural processes in the land system. Earth Syst. Dyn. Discuss., doi:10.5194/esd- 2017-68. Myers, R., A.J.P. Sanders, A.M. Larson, R.D. Prasti, A. Ravikumar, 2016: Analyzing multilevel governance in Indonesia: Lessons for REDD+ from the study of landuse change in Central and West Kalimantan, CIFOR Working Paper no. 202, Center for International Forestry Research (CIFOR), Bogor, Indonesia, 69 pp.
  485. Rodriguez-Labajos, B., 2013: Climate change, ecosystem services and costs of action and inaction: Scoping the interface. Wiley Interdiscip. Rev. Chang., 4, 555–573, doi:10.1002/wcc.247.
  486. Rogelj, J.D. Shindell, K. Jiang, S. Fifita, P. Forster, V. Ginzburg, C. Handa, H. Kheshgi, S. Kobayashi, E. Kriegler, L. Mundaca, R. Séférian and M.V.Vilariño, 2018a: Mitigation Pathways Compatible with 1.5°C in the Context of Sustainable Development. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, pp. 93–174.
  487. Rogelj, J. et al., 2018b: Scenarios towards limiting global mean temperature increase below 1.5 degrees C. Nat. Clim. Chang., 8, 325–332, doi:10.1038/ s41558-018-0091-3.
  488. Rook, G.A., 2013: Regulation of the immune system by biodiversity from the natural environment: An ecosystem service essential to health. Proc. Natl. Acad. Sci., 110, 18360–18367, doi:10.1073/pnas.1313731110.
  489. Röös, E. et al., 2017: Greedy or needy? Land use and climate impacts of food in 2050 under different livestock futures. Glob. Environ. Chang., 47, 1–12, doi:10.1016/J.GLOENVCHA.2017.09.001.
  490. Rosa, I.M.D.I.M.D. et al., 2017: Multiscale scenarios for nature futures. Nat. Ecol. Evol., 1, 1416–1419, doi:10.1038/s41559-017-0273-9.
  491. Rose, S.K., 2014: Integrated assessment modeling of climate change adaptation in forestry and pasture land use: A review. Energy Econ., 46, 548–554, doi:10.1016/J.ENECO.2014.09.018.
  492. Rosen, R.A. and E. Guenther, 2015: The economics of mitigating climate change: What can we know? Technol. Forecast. Soc. Change, 91, 93–106, doi:10.1016/J.TECHFORE.2014.01.013.
  493. Rosenzweig, C. and P. Neofotis, 2013: Detection and attribution of anthropogenic climate change impacts. Wiley Interdiscip. Rev. Chang., 4, 121–150, doi:10.1002/wcc.209.
  494. Rosenzweig, C. et al., 2014: Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl. Acad. Sci. U.S.A., 111, 3268–3273, doi:10.1073/pnas.1222463110.
  495. Rounsevell, M.D.A. and M.J. Metzger, 2010: Developing qualitative scenario storylines for environmental change assessment. Wiley Interdiscip. Rev. Clim. Chang., 1, 606–619, doi:10.1002/wcc.63.
  496. Rounsevell, M.D.A. et al., 2006: A coherent set of future land use change scenarios for Europe. Agric. Ecosyst. Environ., 114, 57–68, doi:10.1016/j. agee.2005.11.027.
  497. Rounsevell, M.D.A. et al., 2014: Towards decision-based global land use models for improved understanding of the Earth system. Earth Syst. Dyn., 5, 117–137, doi:10.5194/esd-5-117-2014.
  498. Roy, J., P. Tschakert, H. Waisman, S. Abdul Halim, P. Antwi-Agyei, P. Dasgupta, B. Hayward, M. Kanninen, D. Liverman, C. Okereke, P.F. Pinho, K. Riahi, and A.G. Suarez Rodriguez, 2018: Sustainable Development, Poverty Eradication and Reducing Inequalities. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. In press, pp. 445–538.
  499. Rulli, M.C., A. Saviori, and P. D’Odorico, 2012: Global land and water grabbing. Pnas, 110, 892–897, doi:10.1073/pnas.1213163110/-/DCSupplemental. Runting, R.K. et al., 2017: Incorporating climate change into ecosystem service assessments and decisions: a review. Glob. Chang. Biol., 23, 28–41, doi:10.1111/gcb.13457. Ryan, C.M. et al., 2016: Ecosystem services from southern African woodlands and their future under global change. Philos. Trans. R. Soc. B-Biological Sci., 371, doi:2015031210.1098/rstb.2015.0312. Salmon, G. et al., 2018: Trade-offs in livestock development at farm level: Different actors with different objectives. Glob. Food Sec., doi:10.1016/J. GFS.2018.04.002. Salvati, L. and M. Carlucci, 2014: Zero net land degradation in Italy: The role of socioeconomic and agro-forest factors. J. Environ. Manage., 145, 299–306, doi:10.1016/J.JENVMAN.2014.07.006. Salvati, L., A. Sabbi, D. Smiraglia, and M. Zitti, 2014: Does forest expansion mitigate the risk of desertification? Exploring soil degradation and land-use changes in a Mediterranean country. Int. For. Rev., 16, 485–496, doi:10.1505/146554814813484149.
  500. Santangeli, A. et al., 2016: Global change synergies and trade-offs between renewable energy and biodiversity. Glob. Chang. Biol. Bioenergy, 8, doi:10.1111/gcbb.12299.
  501. Santilli, G., C. Vendittozzi, C. Cappelletti, S. Battistini, and P. Gessini, 2018: CubeSat constellations for disaster management in remote areas. Acta Astronaut., 145, 11–17, doi:10.1016/j.actaastro.2017.12.050.
  502. Sanz-Sanchez, M.-J. et al., 2017: Sustainable Land Management Contribution to Successful Land-based Climate Change Adaptation and Mitigation. A Report of the Science-Policy Interface, United Nations Convention to Combat Desertification (UNCCD), Bonn, Germany, 170 pp.
  503. Schaeffer, M. et al.., 2015: Mid – and long-term climate projections for 1 fragmented and delayed-action scenarios. Technol. Forecast. Soc. Change, 90, 257–268, doi:10.1016/j.techfore.2013.09.013.
  504. Schanes, K., K. Dobernig and B. Gözet, 2018: Food waste matters – A systematic review of household food waste practices and their policy implications. J. Clean. Prod., 182, 978–991, doi:10.1016/J.JCLEPRO.2018.02.030.
  505. Schauberger, B. et al., 2017: Consistent negative response of US crops to high temperatures in observations and crop models. Nat. Commun., 8, doi:10.1038/ncomms13931.
  506. Scheidel, A. and C. Work, 2018: Forest plantations and climate change discourses: New powers of ‘green’ grabbing in Cambodia. Land use policy, 77, 9–18, doi:10.1016/j.landusepol.2018.04.057.
  507. Schepaschenko, D. et al., 2015: Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. Remote Sens. Environ., 162, 208–220, doi:10.1016/j.rse.2015.02.011.
  508. Schipper, L.A., R.L. Parfitt, S. Fraser, R.A. Littler, W.T. Baisden and C. Ross, 2014: Soil order and grazing management effects on changes in soil C and N in New Zealand pastures. Agric. Ecosyst. Environ., 184, 67–75, doi:10.1016/J. AGEE.2013.11.012.
  509. Schlenker, W. and D.B. Lobell, 2010: Robust negative impacts of climate change on African agriculture. Environ. Res. Lett., 5, 14010, doi:10.1186/ s13021-018-0095-3.
  510. Schlesinger, W.H., 2018: Are wood pellets a green fuel? Science, 359, 1328– 1329, doi:10.1126/science.aat2305.
  511. Schmidt, C.G., K. Foerstl, and B. Schaltenbrand, 2017: The supply chain position paradox: Green practices and firm performance. J. Supply Chain Manag., 53, 3–25, doi:10.1111/jscm.12113.
  512. Schneider, F. and T. Buser, 2018: Promising degrees of stakeholder interaction in research for sustainable development. Sustain. Sci., 13, 129–142, doi:10.1007/s11625-017-0507-4.
  513. Scholes, R. et al., 2018: IPBES: Summary for policymakers of the thematic assessment report on land degradation and restoration of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES secretariat, Bonn, Germany, 44 pp.
  514. Schröter, M. et al., 2018: Interregional flows of ecosystem services: Concepts, typology and four cases. Ecosyst. Serv., doi:10.1016/j.ecoser.2018.02.003. Schulte, R.P.O. et al., 2014: Functional land management: A framework for managing soil-based ecosystem services for the sustainable intensification of agriculture. Environ. Sci. Policy, 38, 45–58, doi:10.1016/J. ENVSCI.2013.10.002.
  515. Schut, M. et al., 2016: Sustainable intensification of agricultural systems in the Central African Highlands: The need for institutional innovation. Agric. Syst., 145, 165–176, doi:10.1016/J.AGSY.2016.03.005.
  516. Schweikert, A., P. Chinowsky, X. Espinet, and M. Tarbert, 2014: Climate change and infrastructure impacts: Comparing the impact on roads in ten countries through 2100. Procedia Eng., 78, 306–316, doi:10.1016/j. proeng.2014.07.072.
  517. Searchinger, T.D. et al., 2015: High carbon and biodiversity costs from converting Africa’s wet savannahs to cropland. Nat. Clim. Chang., 5, 481– 486, doi:10.1038/nclimate2584.
  518. Searchinger, T.D., T. Beringer, and A. Strong, 2017: Does the world have low-carbon bioenergy potential from the dedicated use of land? Energy Policy, 110, 434–446, doi:10.1016/j.enpol.2017.08.016.
  519. Searle, S. and C. Malins, 2015: A reassessment of global bioenergy potential in 2050. GCB Bioenergy, 7, 328–336, doi:10.1111/gcbb.12141.
  520. Seidl, R. et al., 2017: Forest disturbances under climate change. Nat. Clim. Chang., doi:10.1038/nclimate3303.
  521. Seneviratne, S.I. et al., 2018: Climate extremes, land-climate feedbacks and land-use forcing at 1.5 degrees C. Philos. Trans. R. Soc. a-Mathematical Phys. Eng. Sci., 376, doi:2016045010.1098/rsta.2016.0450.
  522. Seto, K.C. and A. Reenberg (eds.), 2014: Rethinking Global Land Use in an Urban Era. The MIT Press, Cambridge, Massachusetts, USA, 408 pp.
  523. Seto, K.C. and N. Ramankutty, 2016: Hidden linkages between urbanization and food systems. Science, 352, 943–945, doi:10.1126/science.aaf7439.
  524. Seto, K.C., B. Guneralp and L.R. Hutyra, 2012: Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl. Acad. Sci., 109, 16083–16088, doi:10.1073/pnas.1211658109.
  525. Settele, J., R. Scholes, R. Betts, S. Bunn, P. Leadley, D. Nepstad, J.T. Overpeck, and M.A. Taboada, 2014: Terrestrial and inland water systems. In: Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L.White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 271–359.
  526. Sheehy, T., F. Kolahdooz, C. Roache, and S. Sharma, 2015: Traditional food consumption is associated with better diet quality and adequacy among Inuit adults in Nunavut, Canada. Int. J. Food Sci. Nutr., 66, 445–451, doi: 10.3109/09637486.2015.1035232.
  527. Shi, S., W. Zhang, P. Zhang, Y. Yu, and and F. Ding, 2013: A synthesis of change in deep soil organic carbon stores with afforestation of agricultural soils. For. Ecol. Manage., 296, 53–63, doi:10.1016/j.foreco.2013.01.026.
  528. Shimamoto, C.Y., A.A. Padial, C.M. Da Rosa, and M.C.M.M. Marques, 2018: Restoration of ecosystem services in tropical forests:A global meta-analysis. PLoS One, 13, 1–16, doi:10.1371/journal.pone.0208523.
  529. Shoyama, K., 2008: Reforestation of abandoned pasture on Hokkaido, northern Japan: Effect of plantations on the recovery of conifer-broadleaved mixed forest. Landsc. Ecol. Eng., 4, 11–23, doi:10.1007/s11355-008-0034-7.
  530. Shtienberg, D., 2013: Will decision-support systems be widely used for the management of plant diseases? Annu. Rev. Phytopathol., 51, 1–16, doi:10.1146/annurev-phyto-082712-102244.
  531. Siebert, S., M. Kummu, M. Porkka, P. Döll, N. Ramankutty and B.R. Scanlon, 2015: A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci., doi:10.5194/hess-19-1521-2015.
  532. Silveira, L., P. Gamazo, J. Alonso and L. Martínez, 2016: Effects of afforestation on groundwater recharge and water budgets in the western region of Uruguay. Hydrol. Process., 30, 3596–3608, doi:10.1002/hyp.10952.
  533. Sivakumar, M.V.K., 2007: Interactions between climate and desertification. Agric. For. Meteorol.142, 143–155, doi:10.1016/j.agrformet.2006.03.025. Sloan, S. and J.A. Sayer, 2015: Forest Resources Assessment of 2015 shows
  534. positive global trends but forest loss and degradation persist in poor tropical countries. For. Ecol. Manage., 352, 134–145, doi:10.1016/j. foreco.2015.06.013.
  535. Smith, M.D., M.P. Rabbitt and A. Coleman – Jensen, 2017: Who are the world’s food insecure? New evidence from the food and agriculture organization’s food insecurity experience scale. World Dev., 93, 402–412, doi:10.1016/J. WORLDDEV.2017.01.006.
  536. Smith, P., 2016: Soil carbon sequestration and biochar as negative emission technologies. Glob. Chang. Biol., 22, 1315–1324, doi:10.1111/gcb.13178.
  537. Smith, P. and P.J. Gregory, 2013: Climate change and sustainable food production. Proceedings of the Nutrition Society, Vol. 72 of, 21–28, doi:10.1017/S0029665112002832.
  538. SmithP.,M.Bustamante,H.Ahammad,H.Clark,H.Dong,E.A.Elsiddig,H.Haberl, R. Harper, J. House, M. Jafari, O. Masera, C. Mbow, N.H. Ravindranath, C.W. Rice, C. Robledo Abad, A. Romanovskaya, F. Sperling, and F. Tubiello, 2014: Agriculture, Forestry and Other Land Use (AFOLU). In: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the
  539. Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Edenhofer, O., R. Pichs-Madruga, Y. Sokona, E. Farahani, S. Kadner, K. Seyboth, A. Adler, I. Baum, S. Brunner, P. Eickemeier, B. Kriemann, J. Savolainen, S. Schlömer, C. von Stechow, T. Zwickel and J.C. Minx (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
  540. Smith, P. et al., 2016: Biophysical and economic limits to negative CO2 emissions. Nat. Clim. Chang., 6, 42–50, doi:DOI: 10.1038/NCLIMATE2870. Song, X.-P., 2018: Global estimates of ecosystem service value and change: Taking into account uncertainties in satellite-based land cover data. Ecol. Econ., 143, 227–235, doi:10.1016/j.ecolecon.2017.07.019. Song, X.-P. et al., 2018: Global land change from 1982 to 2016. Nature, 560, 639–643, doi:10.1038/s41586-018-0411-9.
  541. Spennemann, P.C. et al., 2018: Land-atmosphere interaction patterns in southeastern South America using satellite products and climate models. Int. J. Appl. Earth Obs. Geoinf., 64, 96–103, doi:10.1016/j.jag.2017.08.016.
  542. Springmann, M. et al., 2018: Options for keeping the food system within environmental limits. Nature, 562, 1, doi:10.1038/s41586-018-0594-0. Ssmith, P. et al., 2013: How much land-based greenhouse gas mitigation can be achieved without compromising food security and environmental goals? Glob. Chang. Biol., 19, 2285–2302, doi:10.1111/gcb.12160.
  543. Stadler, K. et al., 2018: EXIOBASE 3: Developing a time series of detailed environmentally extended multi-regional input-output tables. Journal of Industrial Ecology, 22, 502–515, doi:10.1111/jiec.12715. Stavi, I. and R. Lal, 2015: Achieving zero net land degradation: Challenges and opportunities. J. Arid Environ., 112, 44–51, doi:10.1016/j. jaridenv.2014.01.016.
  544. Stavi, I., G. Bel and E. Zaady, 2016: Soil functions and ecosystem services in conventional, conservation and integrated agricultural systems. A review. Agron. Sustain. Dev., 36, 32, doi:10.1007/s13593-016-0368-8. Sterling, E. et al., 2017: Culturally grounded indicators of resilience in social-ecological systems. Environ. Soc., 8, 63–95, doi:10.3167/ares.2017.080104. Sterner, T. and Coria, J. (eds.), 2003: Policy Instruments for Environmental and Natural Resource Management. Resources for the Future Press, Washington, DC, USA, 504 pp.
  545. Stocker, T.F. et al., 2013b: Technical Summary. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 33–115 pp.
  546. Stockmann, U. et al., 2013: The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric. Ecosyst. Environ., 164, 80–99, doi:10.1016/J.AGEE.2012.10.001.
  547. Strack, M., 2008: Peatland and Climate Change. International Peat Society and Saarijärven Offset Oy, Jyväskylä, Finland, 223 pp.
  548. Strassburg, B.B.N. et al., 2017: Moment of truth for the Cerrado hotspot. Nat. Ecol. Evol., 1, 0099, doi:10.1038/s41559-017-0099.
  549. Sunil, N. and S.R. Pandravada, 2015: Alien Crop Resources and Underutilized Species for Food and Nutritional Security of India. In: Plant Biology and Biotechnology, Springer India, New Delhi, pp. 757–775.
  550. Surminski, S. and D. Oramas-Dorta, 2014: Flood insurance schemes and climate adaptation in developing countries. Int. J. Disaster Risk Reduct., 7, 154–164, doi: 10.1016/j.ijdrr.2013.10.005.
  551. Sutton, P.C., S.J. Anderson, R. Costanza, and I. Kubiszewski, 2016: The ecological economics of land degradation: Impacts on ecosystem service values. Ecol. Econ., 129, 182–192, doi:10.1016/j.ecolecon.2016.06.016.
  552. Swain, M., L. Blomqvist, J. McNamara, and W.J. Ripple, 2018: Reducing the environmental impact of global diets. Sci. Total Environ., 610–611, 1207– 1209, doi:10.1016/J.SCITOTENV.2017.08.125.
  553. Swart, R.O.B. and F. Raes, 2007: Making integration of adaptation and mitigation work: mainstreaming into sustainable development policies? Clim. Policy, 7, 288–303, doi:10.1080/14693062.2007.9685657.
  554. Tal, A., 2010: Desertification. In: The Turning Points of Environmental History [Uekoetter, F. (ed.)]. University of Pittsburgh Press, Pittsburgh, Pennsylvania, USA, pp. 146–161.
  555. Taylor, A., J. Downing, B. Hassan, F. Denton and T.E. Downing, 2007: Inter-relationships between adaptation and mitigation. Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden, and C.E. Hanson, (eds.)]. Cambridge University Press, Cambridge, UK, 745–777 pp.
  556. Terraube, J., A. Fernandez-Llamazares, and M. Cabeza, 2017: The role of protected areas in supporting human health: A call to broaden the assessment of conservation outcomes. Curr. Opin. Environ. Sustain., 25, 50–58, doi.org/10.1016/j.cosust.2017.08.005.
  557. Theriault, V., M. Smale, and H. Haider, 2017: How does gender affect sustainable intensification of cereal production in the West African Sahel? Evidence from Burkina Faso. World Dev., 92, 177–191, doi:10.1016/J. WORLDDEV.2016.12.003.
  558. Thompson-Hall, M., E.R. Carr, and U. Pascual, 2016: Enhancing and expanding intersectional research for climate change adaptation in agrarian settings. Ambio, 45, 373–382, doi:10.1007/s13280-016-0827-0.
  559. Thompson, I.D. et al., 2014: Biodiversity and ecosystem services: Lessons from nature to improve management of planted forests for REDD-plus. Biodivers. Conserv., 23, 2613–2635, doi:10.1007/s10531-014-0736-0.
  560. Thyberg, K.L. and D.J. Tonjes, 2016: Drivers of food waste and their implications for sustainable policy development. Resour. Conserv. Recycl., 106, 110–123, doi:10.1016/J.RESCONREC.2015.11.016.
  561. Tian, H. et al., 2019: Global soil nitrous oxide emissions since the preindustrial era estimated by an ensemble of terrestrial biosphere models: Magnitude, attribution and uncertainty. Glob. Chang. Biol., 25, 640–659, doi:10.1111/ gcb.14514.
  562. Tigchelaar, M., D.S. Battisti, R.L. Naylor and D.K. Ray, 2018: Future warming increases probability of globally synchronized maize production shocks. Proc. Natl. Acad. Sci., 115, 6644–6649, doi:10.1073/pnas.1718031115.
  563. Tilman, D. and M. Clark, 2014: Global diets link environmental sustainability and human health. Nature, 515, 518–522, doi:10.1038/nature13959.
  564. Tilman, D., C. Balzer, J. Hill, and B.L. Befort, 2011: Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci., 108, 20260–20264, doi:10.1073/pnas.1116437108.
  565. Tom Veldkamp, Nico Polman, Stijn Reinhard, M.S., 2011: From scaling to governance of the land system: Bridging ecological and economic perspectives. Ecol. Soc., 16, 1, doi: 10.5751/ES-03691-160101.
  566. Tubiello, F.N. et al., 2015: The contribution of agriculture, forestry and other land use activities to global warming, 1990–2012. Glob. Chang. Biol., 21, 2655–2660, doi:10.1111/gcb.12865.
  567. Turner, P.A., C.B. Field, D.B. Lobell, D.L. Sanchez, and K.J. Mach, 2018: Unprecedented rates of land-use transformation in modelled climate change mitigation pathways. Nature Sust., 1, 240–245, doi: 10.1038/ s41893-018-0063-7.
  568. UNCCD, 2014: Desertification: The Invisible Frontline, Secretariat of the United Nations Convention to Combat Desertification, United Nations Convention to Combat Desertification, Bonn, Germany.
  569. UNEP, 2016: Global Gender and Environment Outlook, UN Environment, Nairobi, Kenya, 222 pp.
  570. United Nations, 2015: Transforming Our World: The 2030 Agenda for Sustainable Development. United Nations, New York, NY, USA, 41 pp.
  571. United Nations, 2018: 2018 Revision of World Urbanization Prospects. http://www.un.org/development/desa/publications/2018-revision-of-world- urbanization-prospects.html.
  572. United Nations Department of Economic and Social Affairs, 2017: World Population Prospects: The 2017 Revision, DVD Edition.
  573. Urban, M.C. et al., 2016: Improving the forecast for biodiversity under climate change. Science, 353, aad8466, doi:10.1126/science.aad8466.
  574. USDA, 2007: Precision Agriculture: NRCS Support for Emerging Technologies. Agronomy Technical Note No. 1, Soil Quality National Technology Development Team, East National Technology Support Center, Natural Resources Conservation Service, Greensboro, North Carolina, USA, 9 pp.
  575. Vadell, E., S. De-Miguel, and J. Pemán, 2016: Large-scale reforestation and afforestation policy in Spain: A historical review of its underlying ecological, socioeconomic and political dynamics. Land use policy, 55, 37–48, doi:10.1016/J.LANDUSEPOL.2016.03.017. 1
  576. Valayamkunnath, P., V. Sridhar, W. Zhao, and R.G. Allen, 2018: Intercomparison of surface energy fluxes, soil moisture, and evapotranspiration from eddy covariance, large-aperture scintillometer, and modeling across three ecosystems in a semiarid climate. Agric. For. Meteorol., 248, 22–47, doi:10.1016/j.agrformet.2017.08.025.
  577. Valentin, C. et al., 2008: Agriculture, ecosystems and environment runoff and sediment losses from 27 upland catchments in Southeast Asia: Impact of rapid land use changes and conservation practices. Agric. Ecosyst. Environ., 128, 225–238, doi:10.1016/j.agee.2008.06.004.
  578. Vaughan, N.E. and C. Gough, 2016: Expert assessment concludes negative emissions scenarios may not deliver. Environ. Res. Lett., 11, 95003, doi:10.1088/1748-9326/11/9/095003.
  579. Veldman, J.W. et al., 2015: Where tree planting and forest expansion are bad for biodiversity and ecosystem services. Bioscience, 65, 1011–1018, doi:10.1093/biosci/biv118.
  580. Veldman, J.W., F.A.O. Silveira, F.D. Fleischman, N.L. Ascarrunz and G. Durigan, 2017: Grassy biomes: An inconvenient reality for large-scale forest restoration? A comment on the essay by Chazdon and Laestadius. Am. J. Bot., 104, 649–651, doi:10.3732/ajb.1600427.
  581. Venter, O. et al., 2016: Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun., 7, doi:10.1038/ncomms12558.
  582. van Vliet, J., D.A. Eitelberg, and P.H. Verburg, 2017: A global analysis of land take in cropland areas and production displacement from urbanization. Glob. Environ. Chang., 43, 107–115, doi:10.1016/j.gloenvcha.2017.02.001.
  583. De Vos, J.M., L.N. Joppa, J.L. Gittleman, P.R. Stephens, and S.L. Pimm, 2015: Estimating the normal background rate of species extinction. Conserv. Biol., 29, 452–462, doi:10.1111/cobi.12380.
  584. van Vuuren, D.P. and T.R. Carter, 2014: Climate and socio-economic scenarios for climate change research and assessment: reconciling the new with the old. Clim. Change, 122, 415–429, doi:10.1007/s10584-013-0974-2.
  585. van Vuuren, D.P. et al., 2017: Energy, land-use and greenhouse gas emissions trajectories under a green growth paradigm. Glob. Environ. Chang., 42, 237–250, doi:10.1016/J.GLOENVCHA.2016.05.008.
  586. Vuuren, D.P. Van et al., 2018: The need for negative emission technologies. Nat. Clim. Chang., 8, 391–397, doi:10.1038/s41558-018-0119-8.
  587. Walker, W.E., M. Haasnoot and J.H. Kwakkel, 2013: Adapt or perish: A review of planning approaches for adaptation under deep uncertainty. Sustainability, 5, 955–979, doi:10.3390/su5030955.
  588. Walters, M. and R.J. Scholes (eds.), 2017: The GEO handbook on biodiversity observation networks. Springer International Publishing, Cham, Switzerland, 326 pp. doi:10.1007/978-3-319-27288-7.
  589. Wang, X. et al., 2016: Taking account of governance: Implications for land-use dynamics, food prices and trade patterns. Ecol. Econ., 122, 12–24, doi:10.1016/j.ecolecon.2015.11.018.
  590. Wärlind, D. et al., 2014: Nitrogen feedbacks increase future terrestrial ecosystem carbon uptake in an individual-based dynamic vegetation model. Biogeosciences, 11, 6131–6146, doi:10.5194/bg-11-6131-2014.
  591. Warren, D.D. and D.C. and N.R. and J.P. and R., 2014: Global crop yield response to extreme heat stress under multiple climate change futures. Environ. Res. Lett., 9, 34011.
  592. Warszawski, L., K. Frieler, V. Huber, F. Piontek, O. Serdeczny and J. Schewe, 2014: The inter-sectoral impact model intercomparison project (ISI–MIP): Project framework. Proc. Natl. Acad. Sci., 111, 3228–3232, doi:10.1073/ pnas.1312330110.
  593. Watmuff, G., D.J. Reuter and S.D. Speirs, 2013: Methodologies for assembling and interrogating N, P, K, and S soil test calibrations for Australian cereal, oilseed and pulse crops. Crop Pasture Sci., 64, 424, doi:10.1071/CP12424.
  594. Weindl, I. et al., 2017: Livestock and human use of land: Productivity trends and dietary choices as drivers of future land and carbon dynamics. Glob. Planet. Change, 159, 1–10, doi:10.1016/j.gloplacha.2017.10.002.
  595. Weitzman, M.L., 2014: Can negotiating a uniform carbon price help to internalize the global warming externality? J. Assoc. Environ. Resour. Econ., 1, 29–49, doi:10.3386/w19644.
  596. van der Werf, P. and J.A. Gilliland, 2017: A systematic review of food losses and food waste generation in developed countries. Proc. Inst. Civ. Eng. – Waste Resour. Manag., 170, 66–77, doi:10.1680/jwarm.16.00026.
  597. West, T.A.P., 2016: Indigenous community benefits from a de-centralized approach to REDD+ in Brazil. Clim. Policy, 16, 924–939, doi:10.1080/14 693062.2015.1058238.
  598. Wichelns, D., 2017: The water-energy-food nexus: Is the increasing attention warranted, from either a research or policy perspective? Environ. Sci. Policy, 69, 113–123, doi:10.1016/J.ENVSCI.2016.12.018.
  599. Widener, M.J., L. Minaker, S. Farber, J. Allen, B. Vitali, P.C. Coleman and B. Cook, 2017: How do changes in the daily food and transportation environments affect grocery store accessibility? Appl. Geogr., 83, 46–62, doi:10.1016/J. APGEOG.2017.03.018.
  600. Wiedmann, T. and M. Lenzen, 2018: Environmental and social footprints of international trade. Nat. Geosci., 11, 314–321, doi:10.1038/s41561-018- 0113-9.
  601. Williamson, P., 2016: Emissions reduction: Scrutinize CO2 removal methods. Nature, 530, 153–155, doi:10.1038/530153a.
  602. Wilson, S.J., J. Schelhas, R. Grau, A.S. Nanni and S. Sloan, 2017: Forest ecosystem-service transitions: The ecological dimensions of the forest transition. Ecol. Soc., 22, doi:10.5751/es-09615-220438.
  603. Wilting, H.C., A.M. Schipper, M. Bakkenes, J.R. Meijer and M.A.J. Huijbregts, 2017: Quantifying biodiversity losses due to human consumption: A global-scale footprint analysis. Environ. Sci. Technol., 51, 3298–3306, doi:10.1021/acs.est.6b05296.
  604. Wise, R.M., I. Fazey, M.S. Smith, S.E. Park, H.C. Eakin, E.R.M.A. Van Garderen and B. Campbell, 2014: Reconceptualising adaptation to climate change as part of pathways of change and response. Glob. Environ. Chang., 28, 325–336, doi:10.1016/j.gloenvcha.2013.12.002.
  605. Wisser, D., S. Frolking, E.M. Douglas, B.M. Fekete, C.J. Vörösmarty and A.H. Schumann, 2008: Global irrigation water demand: Variability and uncertainties arising from agricultural and climate data sets. Geophys. Res. Lett., doi:10.1029/2008GL035296.
  606. Wolff, S., E.A. Schrammeijer, C. Schulp and P.H. Verburg, 2018: Meeting global land restoration and protection targets: What would the world look like in 2050? Glob. Environ. Chang., 52, 259–272, doi:10.1016/j. gloenvcha.2018.08.002.
  607. Wood, S.A., M.R. Smith, J. Fanzo, R. Remans and R.S. DeFries, 2018: Trade and the equitability of global food nutrient distribution. Nat. Sustain., 1, 34–37, doi:10.1038/s41893-017-0008-6.
  608. Wu, X., Y. Lu, S. Zhou, L. Chen and B. Xu, 2016: Impact of climate change on human infectious diseases: Empirical evidence and human adaptation. Environ. Int., 86, 14–23, doi:10.1016/J.ENVINT.2015.09.007.
  609. Wunder, S., 2015: Revisiting the concept of payments for environmental services. Ecol. Econ., 117, 234–243, doi:10.1016/J.ECOLECON.2014.08.016. Wynes, S. and K.A. Nicholas, 2017: The climate mitigation gap: Education and government recommendations miss the most effective individual actions.
  610. Environ. Res. Lett., 12, 74024, doi:10.1088/1748-9326/aa7541.
  611. Xu, Y., 2018: Political economy of land grabbing inside China involving foreign investors. Third World Q., 39(11), 2069–2084, doi:10.1080/0143 6597.2018.1447372. Xue, L. et al., 2017: Missing food, missing data? A critical review of global
  612. food losses and food waste data. Environ. Sci. Technol., 51, 6618–6633, doi:10.1021/acs.est.7b00401.
  613. Yang, L., L. Chen, W. Wei, Y. Yu and H. Zhang, 2014: Comparison of deep soil moisture in two re-vegetation watersheds in semi-arid regions. J. Hydrol., 513, 314–321, doi:10.1016/j.jhydrol.2014.03.049.
  614. Yang, Y., D. Tilman, G. Furey and C. Lehman, 2019: Soil carbon sequestration accelerated by restoration of grassland biodiversity. Nat. Commun., 10, 718, doi:10.1038/s41467-019-08636-w.
  615. Yirdaw, E., M. Tigabu and A. Monge, 2017: Rehabilitation of degraded dryland ecosystems – Review. Silva Fenn., 51, doi:10.14214/sf.1673.
  616. Yohe, G.W., 2001: Mitigative capacity – The mirror image of adaptive capacity on the emissions side. Clim. Change, 49, 247–262, doi:10.1023/A:1010677916703.
  617. Yu, L. et al., 2014: Meta-discoveries from a synthesis of satellite-based land-cover mapping research. Int. J. Remote Sens., 35, 4573–4588, doi:10. 1080/01431161.2014.930206.
  618. Yu, Y., K. Feng and K. Hubacek, 2013: Tele-connecting local consumption to global land use. Glob. Environ. Chang., 23, 1178–1186, doi:10.1016/J. GLOENVCHA.2013.04.006.
  619. Zaloumis, N.P. and W.J. Bond, 2015: Reforestation of afforestation? The attributes of old growth grasslands in South Africa. Philos. Trans. R. Soc. B, 371, 1–9, doi:10.1098/rstb.2015.0310.
  620. Zhang, M. et al., 2014: Response of surface air temperature to small-scale land clearing across latitudes. Environ. Res. Lett., 9, 34002, doi:10.1088/1748- 9326/9/3/034002.
  621. Zhang, X. et al., 2015: Managing nitrogen for sustainable development. Nature, 528, 51–59, doi:10.1038/nature15743.
  622. Zhao, L., A. Dai and B. Dong, 2018: Changes in global vegetation activity and its driving factors during 1982–2013. Agric. For. Meteorol., doi:10.1016/j. agrformet.2017.11.013.
  623. Zheng, H., Y. Wang, Y. Chen and T. Zhao, 2016: Effects of large-scale afforestation project on the ecosystem water balance in humid areas: An example for southern China. Ecol. Eng., 89, 103–108, doi:10.1016/j. ecoleng.2016.01.013.
  624. Zhu, Z. et al., 2016: Greening of the Earth and its drivers. Nat. Clim. Chang., 6, 791–795, doi:10.1038/nclimate3004.
  625. Ziadat, F., S. Bunning, S. Corsi, and R. Vargas, 2018: Sustainable soil and land management for climate smart agriculture. In: Climate Smart Agriculture Sourcebook [Ziadat, F., S. Bunning, S. Corsi and R. Vargas (eds.)]. Food and Agriculture Organization of the United Nations, Rome, Italy, pp 1–33.
  626. Ziese, M. et al., 2014: The GPCC Drought Index – A new, combined and gridded global drought index. Earth Syst. Sci. Data, 6, 285–295, doi:10.5194/essd- 6-285-2014.
  627. Ziska, L.H. et al., 2016: Rising atmospheric CO2 is reducing the protein concentration of a floral pollen source essential for North American bees. Proceedings. Biol. Sci., 283, 20160414, doi:10.1098/rspb.2016.0414.
  628. Zorya, S. et al., 2011: Missing food: The Case of Postharvest Grain Losses in Sub-Saharan Africa. The International Bank for Reconstruction and Development/The World Bank Report No. 60371-AFR, Washington, DC, USA, 96 pp.

Land–Climate interactions