Editing IPCC:AR6/SRCCL/Chapter-1 (section)

== 1.2 Key challenges related to land use change ==

<span id="land-system-change-land-degradation-desertification-and-food-security"></span>
=== 1.2.1 Land system change, land degradation, desertification and food security ===

<div id="section-1-2-1-1-future-trends-in-the-global-land-system"></div>

<span id="future-trends-in-the-global-land-system"></span>
==== 1.2.1.1 Future trends in the global land system ====

<div id="section-1-2-1-1-future-trends-in-the-global-land-system-block-1"></div>

Human population is projected to increase to nearly 9.8 (± 1) billion people by 2050 and 11.2 billion by 2100 (United Nations 2018 <sup>[[#fn:r241|241]]</sup> ). More people, a growing global middle class (Crist et al. 2017 <sup>[[#fn:r242|242]]</sup> ), economic growth, and continued urbanisation (Jiang and O’Neill 2017 <sup>[[#fn:r243|243]]</sup> ) increase the pressures on expanding crop and pasture area and intensifying land management. Changes in diets, efficiency and technology could reduce these pressures (Billen et al. 2015 <sup>[[#fn:r244|244]]</sup> ; Popp et al. 2016 <sup>[[#fn:r245|245]]</sup> ; Muller et al. 2017 <sup>[[#fn:r246|246]]</sup> ; Alexander et al. 2015 <sup>[[#fn:r247|247]]</sup> ; Springmann et al. 2018 <sup>[[#fn:r248|248]]</sup> ; Myers et al. 2017 <sup>[[#fn:r249|249]]</sup> ; Erb et al. 2016c <sup>[[#fn:r250|250]]</sup> ; FAO 2018b <sup>[[#fn:r251|251]]</sup> ) (Sections 5.3 and 6.2.2).

Given the large uncertainties underlying the many drivers of land use, as well as their complex relation to climate change and other biophysical constraints, future trends in the global land system are explored in scenarios and models that seek to span across these uncertainties (Cross-Chapter Box 1 in Chapter 1). Generally, these scenarios indicate a continued increase in global food demand, owing to population growth and increasing wealth. The associated land area needs are a key uncertainty, a function of the interplay between production, consumption, yields, and production efficiency (in particular for livestock and waste) (FAO 2018b; van Vuuren et al. 2017 <sup>[[#fn:r252|252]]</sup> ; Springmann et al. 2018 <sup>[[#fn:r253|253]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r254|254]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r255|255]]</sup> ; Ramankutty et al. 2018 <sup>[[#fn:r256|256]]</sup> ; Erb et al. 2016b <sup>[[#fn:r257|257]]</sup> ; Popp et al. 2016 <sup>[[#fn:r258|258]]</sup> ) (Section 1.3 and Cross-Chapter Box 1 in Chapter 1). Many factors, such as climate change, local contexts, education, human and social capital, policy-making, economic framework conditions, energy availability, degradation, and many more, affect this interplay, as discussed in all chapters of this report.

Global telecouplings in the land system, the distal connections and multidirectional flows between regions and land systems, are expected to increase, due to urbanisation (Seto et al. 2012 <sup>[[#fn:r259|259]]</sup> ; van Vliet et al. 2017 <sup>[[#fn:r260|260]]</sup> ; Jiang and O’Neill 2017 <sup>[[#fn:r261|261]]</sup> ; Friis et al. 2016 <sup>[[#fn:r262|262]]</sup> ), and international trade (Konar et al. 2016 <sup>[[#fn:r263|263]]</sup> ; Erb et al. 2016b; Billen et al. 2015 <sup>[[#fn:r264|264]]</sup> ; Lassaletta et al. 2016 <sup>[[#fn:r265|265]]</sup> ). Telecoupling can support efficiency gains in production, but can also lead to complex cause–effect chains and indirect effects such as land competition or leakage (displacement of the environmental impacts; see Glossary), with governance challenges (Baldos and Hertel 2015 <sup>[[#fn:r266|266]]</sup> ; Kastner et al. 2014 <sup>[[#fn:r267|267]]</sup> ; Liu et al. 2013 <sup>[[#fn:r268|268]]</sup> ; Wood et al. 2018 <sup>[[#fn:r269|269]]</sup> ; Schröter et al. 2018 <sup>[[#fn:r270|270]]</sup> ; Lapola et al. 2010 <sup>[[#fn:r271|271]]</sup> ; Jadin et al. 2016 <sup>[[#fn:r272|272]]</sup> ; Erb et al. 2016b; Billen et al. 2015 <sup>[[#fn:r273|273]]</sup> ; Chaudhary and Kastner 2016 <sup>[[#fn:r274|274]]</sup> ; Marques et al. 2019 <sup>[[#fn:r275|275]]</sup> ; Seto and Ramankutty 2016 <sup>[[#fn:r276|276]]</sup> ) (Section 1.2.1.5). Furthermore, urban growth is anticipated to occur at the expense of fertile (crop)land, posing a food security challenge, in particular in regions of high population density and agrarian-dominated economies, with limited capacity to compensate for these losses (Seto et al. 2012 <sup>[[#fn:r277|277]]</sup> ; Güneralp et al. 2013 <sup>[[#fn:r278|278]]</sup> ; Aronson et al. 2014 <sup>[[#fn:r279|279]]</sup> ; Martellozzo et al. 2015 <sup>[[#fn:r280|280]]</sup> ; Bren d’Amour et al. 2016 <sup>[[#fn:r281|281]]</sup> ; Seto and Ramankutty 2016 <sup>[[#fn:r282|282]]</sup> ; van Vliet et al. 2017 <sup>[[#fn:r283|283]]</sup> ).

Future climate change and increasing atmospheric CO <sub>2</sub> concentration are expected to accentuate existing challenges by, for example, shifting biomes or affecting crop yields (Baldos and Hertel 2015 <sup>[[#fn:r284|284]]</sup> ; Schlenker and Lobell 2010 <sup>[[#fn:r285|285]]</sup> ; Lipper et al. 2014 <sup>[[#fn:r286|286]]</sup> ; Challinor et al. 2014 <sup>[[#fn:r287|287]]</sup> ; Myers et al. 2017 <sup>[[#fn:r288|288]]</sup> ) (Section 5.2.2), as well as through land-based climate change mitigation. There is ''high confidence'' that large-scale implementation of bioenergy or afforestation can further exacerbate existing challenges (Smith et al. 2016 <sup>[[#fn:r289|289]]</sup> ) (Section 1.3.1 and Cross-Chapter Box 7 in Chapter 6).

<div id="section-1-2-1-2-land-degradation"></div>

<span id="land-degradation"></span>
==== 1.2.1.2 Land degradation ====

<div id="section-1-2-1-2-land-degradation-block-1"></div>

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 <sup>[[#fn:r291|291]]</sup> ; Mirzabaev et al. 2015 <sup>[[#fn:r292|292]]</sup> ; FAO and ITPS 2015 <sup>[[#fn:r293|293]]</sup> ; Cerretelli et al. 2018 <sup>[[#fn:r294|294]]</sup> ). 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 <sup>[[#fn:r295|295]]</sup> ; Lal 2009 <sup>[[#fn:r296|296]]</sup> ; Beinroth et al. 1994 <sup>[[#fn:r297|297]]</sup> ; Abu Hammad and Tumeizi 2012 <sup>[[#fn:r298|298]]</sup> ; Ferreira et al. 2018 <sup>[[#fn:r299|299]]</sup> ; Franco and Giannini 2005 <sup>[[#fn:r300|300]]</sup> ; Abahussain et al. 2002 <sup>[[#fn:r301|301]]</sup> ).

Global estimates of the total degraded area vary from less than 10 million km <sup>2</sup> to over 60 million km <sup>2</sup> , with additionally large disagreement regarding the spatial distribution (Gibbs and Salmon 2015 <sup>[[#fn:r302|302]]</sup> ) (Section 4.3). The annual increase in the degraded land area has been estimated as 50,000–100,000 million km <sup>2</sup> yr <sup>–1</sup> (Stavi and Lal 2015 <sup>[[#fn:r303|303]]</sup> ), and the loss of total ecosystem services equivalent to about 10% of the world’s GDP in the year 2010 (Sutton et al. 2016 <sup>[[#fn:r304|304]]</sup> ). 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 <sup>[[#fn:r305|305]]</sup> ; Lal 2015 <sup>[[#fn:r306|306]]</sup> ), as well as the rapid degradation of primary forests through fragmentation (Haddad et al. 2015 <sup>[[#fn:r307|307]]</sup> ). 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 CO <sub>2</sub> emissions and degradation ( ''high confidence'' ) (Strack 2008 <sup>[[#fn:r308|308]]</sup> ; Limpens et al. 2008 <sup>[[#fn:r309|309]]</sup> ; Aich et al. 2014 <sup>[[#fn:r310|310]]</sup> ; Murdiyarso et al. 2015 <sup>[[#fn:r311|311]]</sup> ; Kauffman et al. 2016 <sup>[[#fn:r312|312]]</sup> ; Dohong et al. 2017 <sup>[[#fn:r313|313]]</sup> ; Arifanti et al. 2018 <sup>[[#fn:r314|314]]</sup> ; Evans et al. 2019 <sup>[[#fn:r315|315]]</sup> ). Land degradation is an important factor contributing to uncertainties in the mitigation potential of land-based ecosystems (Smith et al. 2014 <sup>[[#fn:r316|316]]</sup> ). Furthermore, degradation that reduces forest (and agricultural) biomass and soil organic carbon leads to higher rates of runoff ( ''high confidence'' ) (Molina et al. 2007 <sup>[[#fn:r317|317]]</sup> ; Valentin et al. 2008 <sup>[[#fn:r318|318]]</sup> ; Mateos et al. 2017 <sup>[[#fn:r319|319]]</sup> ; Noordwijk et al. 2017 <sup>[[#fn:r320|320]]</sup> ) and hence to increasing flood risk ( ''low confidence'' ) (Bradshaw et al. 2007 <sup>[[#fn:r321|321]]</sup> ; Laurance 2007 <sup>[[#fn:r322|322]]</sup> ; van Dijk et al. 2009 <sup>[[#fn:r323|323]]</sup> ).

<div id="section-1-2-1-3-desertification"></div>

<span id="desertification"></span>
==== 1.2.1.3 Desertification ====

<div id="section-1-2-1-3-desertification-block-1"></div>

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 <sup>[[#fn:r930|930]]</sup> ; Bai et al. 2008 <sup>[[#fn:r931|931]]</sup> ). A critical challenge in the assessment of desertification is to identify a ‘non-desertified’ reference state (Bestelmeyer et al. 2015 <sup>[[#fn:r324|324]]</sup> ). 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 <sup>[[#fn:r325|325]]</sup> ).

As a consequence of varying definitions and different methodologies, the area of desertification varies widely (D’Odorico et al. 2013 <sup>[[#fn:r326|326]]</sup> ; Bestelmeyer et al. 2015 <sup>[[#fn:r327|327]]</sup> ; and references therein). Arid regions of the world cover up to about 46% of the total terrestrial surface (about 60 million km <sup>2</sup> ) (Pravalie 2016 <sup>[[#fn:r328|328]]</sup> ; Koutroulis 2019 <sup>[[#fn:r329|329]]</sup> ). Around 3 billion people reside in dryland regions (D’Odorico et al. 2013 <sup>[[#fn:r330|330]]</sup> ; Maestre et al. 2016 <sup>[[#fn:r331|331]]</sup> ) (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 <sup>[[#fn:r332|332]]</sup> ; Cherlet et al. 2018 <sup>[[#fn:r333|333]]</sup> ).

<div id="section-1-2-1-4-food-security-food-systems-and-linkages-to-land-based-ecosystems"></div>

<span id="food-security-food-systems-and-linkages-to-land-based-ecosystems"></span>
==== 1.2.1.4 Food security, food systems and linkages to land-based ecosystems ====

<div id="section-1-2-1-4-food-security-food-systems-and-linkages-to-land-based-ecosystems-block-1"></div>

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 <sup>[[#fn:r334|334]]</sup> ). 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 <sup>[[#fn:r335|335]]</sup> ). 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 <sup>[[#fn:r336|336]]</sup> ; 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 <sup>[[#fn:r337|337]]</sup> ) (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 <sup>[[#fn:r338|338]]</sup> ).

Food insecurity most notably occurs in situations of conflict, and conflict combined with droughts or floods (Cafiero et al. 2018 <sup>[[#fn:r339|339]]</sup> ; Smith et al. 2017 <sup>[[#fn:r340|340]]</sup> ). 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 <sup>[[#fn:r341|341]]</sup> ; Tigchelaar et al. 2018 <sup>[[#fn:r342|342]]</sup> ; Iizumi and Ramankutty 2015 <sup>[[#fn:r343|343]]</sup> ), the nutritional quality of food (Loladze 2014 <sup>[[#fn:r344|344]]</sup> ; Myers et al. 2014 <sup>[[#fn:r345|345]]</sup> ; Ziska et al. 2016 <sup>[[#fn:r346|346]]</sup> ; Medek et al. 2017 <sup>[[#fn:r347|347]]</sup> ), water supply (Nkhonjera 2017 <sup>[[#fn:r348|348]]</sup> ), and incidence of pests and diseases (Curtis et al. 2018 <sup>[[#fn:r349|349]]</sup> ). These factors also impact on human health, increasing morbidity and affecting human ability to process ingested food (Franchini and Mannucci 2015 <sup>[[#fn:r350|350]]</sup> ; Wu et al. 2016 <sup>[[#fn:r351|351]]</sup> ; Raiten and Aimone 2017 <sup>[[#fn:r352|352]]</sup> ). 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 <sup>[[#fn:r353|353]]</sup> ; Thyberg and Tonjes 2016 <sup>[[#fn:r354|354]]</sup> ; Borsato et al. 2018 <sup>[[#fn:r355|355]]</sup> ; Kibler et al. 2018 <sup>[[#fn:r356|356]]</sup> ), water quality (Malone et al. 2014 <sup>[[#fn:r357|357]]</sup> ; Norse and Ju 2015 <sup>[[#fn:r358|358]]</sup> ), and ecosystem services loss (Schipper et al. 2014 <sup>[[#fn:r359|359]]</sup> ; Eeraerts et al. 2017 <sup>[[#fn:r360|360]]</sup> ) 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 <sup>[[#fn:r361|361]]</sup> ; Salmon et al. 2018 <sup>[[#fn:r362|362]]</sup> ).

<div id="section-1-2-1-4-food-security-food-systems-and-linkages-to-land-based-ecosystems-block-2"></div>

<span id="figure-1.4"></span>
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<span id="food-system-and-its-relations-to-land-and-climatethe-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"></span>
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'''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 […]'''
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[[File:9de221615672da9f46259732059f7d27 Figure-1.4-1024x699.jpg]]

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).

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<div id="section-1-2-1-5-challenges-arising-from-land-governance"></div>

<span id="challenges-arising-from-land-governance"></span>
==== 1.2.1.5 Challenges arising from land governance ====

<div id="section-1-2-1-5-challenges-arising-from-land-governance-block-1"></div>

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 <sup>[[#fn:r363|363]]</sup> ). 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 <sup>[[#fn:r364|364]]</sup> ). 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 <sup>[[#fn:r365|365]]</sup> ; Ravnborg et al. 2016 <sup>[[#fn:r366|366]]</sup> ). 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 <sup>[[#fn:r367|367]]</sup> ).

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 <sup>[[#fn:r368|368]]</sup> ; Lambin 2012 <sup>[[#fn:r369|369]]</sup> ; Venter et al. 2016 <sup>[[#fn:r370|370]]</sup> ). 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 <sup>[[#fn:r371|371]]</sup> ; Foley et al. 2011 <sup>[[#fn:r372|372]]</sup> ; Lambin 2012 <sup>[[#fn:r373|373]]</sup> ; Popp et al. 2016 <sup>[[#fn:r374|374]]</sup> ) ( ''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 <sup>[[#fn:r375|375]]</sup> ) 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 <sup>[[#fn:r376|376]]</sup> ). Since 2000, almost 50 million hectares of land have been acquired, and there are no signs of stagnation in the foreseeable future (Land Matrix 2018 <sup>[[#fn:r377|377]]</sup> ).The LSLA phenomenon, which largely targets agriculture, is widespread, including Sub-Saharan Africa, Southeast Asia, Eastern Europe and Latin America (Rulli et al. 2012 <sup>[[#fn:r378|378]]</sup> ; Nolte et al. 2016 <sup>[[#fn:r379|379]]</sup> ; Constantin et al. 2017 <sup>[[#fn:r380|380]]</sup> ). LSLAs are promoted by investors and host governments on economic grounds (infrastructure, employment, market development) (Deininger et al. 2011 <sup>[[#fn:r381|381]]</sup> ), but their social and environmental impacts can be negative and significant (Dell’Angelo et al. 2017a <sup>[[#fn:r382|382]]</sup> ).

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 <sup>[[#fn:r383|383]]</sup> ) and displaced communities creating secondary land expansion (Messerli et al. 2014 <sup>[[#fn:r384|384]]</sup> ; Davis et al. 2015 <sup>[[#fn:r385|385]]</sup> ). The promises that LSLAs would develop efficient agriculture on non-forested, unused land (Deininger et al. 2011 <sup>[[#fn:r386|386]]</sup> ) has so far not been fulfilled. However, LSLA is not the only outcome of weak land governance structures (Wang et al. 2016 <sup>[[#fn:r387|387]]</sup> ): other forms of inequitable or irregular land acquisition can also be home-grown, pitting one community against a more vulnerable group (Xu 2018 <sup>[[#fn:r388|388]]</sup> ) or land capture by urban elites (McDonnell 2017 <sup>[[#fn:r389|389]]</sup> ). 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).

<span id="progress-in-dealing-with-uncertainties-in-assessing-land-processes-in-the-climate-system"></span>
=== 1.2.2 Progress in dealing with uncertainties in assessing land processes in the climate system ===

<div id="section-1-2-2-1-concepts-related-to-risk-uncertainty-and-confidence"></div>

<span id="concepts-related-to-risk-uncertainty-and-confidence"></span>
==== 1.2.2.1 Concepts related to risk, uncertainty and confidence ====

<div id="section-1-2-2-1-concepts-related-to-risk-uncertainty-and-confidence-block-1"></div>

In context of the SRCCL, risk refers to the potential for the adverse consequences for human or (land-based) ecological systems, arising from climate change or responses to climate change. Risk related to climate change impacts integrates across the hazard itself, the time of exposure and the vulnerability of the system; the assessment of all three of these components, their interactions and outcomes, is uncertain (see Glossary for expanded definition, and Section 7.1.2). For instance, a risk to human society is the continued loss of productive land which might arise from climate change, mismanagement, or a combination of both factors. However, risk can also arise from the potential for adverse consequences from responses to climate change, such as widespread deployment of bioenergy which is intended to reduce GHG emissions and thus limit climate change, but can present its own risks to food security (Chapters 5–7).

Demonstrating with some statistical certainty that the climate or the land system affected by climate or land use has changed (detection),

and evaluating the relative contributions of multiple causal factors to that change (with a formal assessment of confidence (attribution); see Glossary) remain challenging aspects in both observations and models (Rosenzweig and Neofotis 2013 <sup>[[#fn:r390|390]]</sup> ; Gillett et al. 2016 <sup>[[#fn:r391|391]]</sup> ; Lean 2018 <sup>[[#fn:r392|392]]</sup> ). Uncertainties arising for example, from missing or imprecise data, ambiguous terminology, incomplete process representation in models, or human decision-making contribute to these challenges, and some examples are provided in this subsection. In order to reflect various sources of uncertainties in the state of scientific understanding, IPCC assessment reports provide estimates of confidence (Mastrandrea et al. 2011 <sup>[[#fn:r393|393]]</sup> ). This confidence language is also used in the SRCCL (Figure 1.5).

<div id="section-1-2-2-1-concepts-related-to-risk-uncertainty-and-confidence-block-2"></div>

<span id="figure-1.5"></span>
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'''Figure 1.5'''

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'''Use of confidence language.'''
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[[File:55b66f2e798c07918f09fffeaf51f20b Figure-1.5-1024x511.jpg]]

Use of confidence language.

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<div id="section-1-2-2-2-nature-and-scope-of-uncertainties-related-to-land-use"></div>

<span id="nature-and-scope-of-uncertainties-related-to-land-use"></span>
==== 1.2.2.2 Nature and scope of uncertainties related to land use ====

<div id="section-1-2-2-2-nature-and-scope-of-uncertainties-related-to-land-use-block-1"></div>

Identification and communication of uncertainties is crucial to support decision making towards sustainable land management. Providing a robust, and comprehensive understanding of uncertainties in observations, models and scenarios is a fundamental first step in the IPCC confidence framework (see above). This will remain a challenge in future, but some important progress has been made over recent years.

Uncertainties in observations

The detection of changes in vegetation cover and structural properties underpins the assessment of land-use change, degradation and desertification. It is continuously improving by enhanced Earth observation capacity (Hansen et al. 2013 <sup>[[#fn:r394|394]]</sup> ; He et al. 2018 <sup>[[#fn:r395|395]]</sup> ; Ardö et al. 2018 <sup>[[#fn:r396|396]]</sup> ; Spennemann et al. 2018 <sup>[[#fn:r397|397]]</sup> ) (see also Table SM.1.1 in Supplementary Material). Likewise, the picture of how soil organic carbon, and GHG and water fluxes, respond to land-use change and land management continues to improve through advances in methodologies and sensors (Kostyanovsky et al. 2018 <sup>[[#fn:r398|398]]</sup> ; Brümmer et al. 2017 <sup>[[#fn:r399|399]]</sup> ; Iwata et al. 2017 <sup>[[#fn:r400|400]]</sup> ; Valayamkunnath et al. 2018 <sup>[[#fn:r401|401]]</sup> ). In both cases, the relative shortness of the record, data gaps, data treatment algorithms and – for remote sensing – differences in the definitions of major vegetation-cover classes limit the detection of trends (Alexander et al. 2016a <sup>[[#fn:r402|402]]</sup> ; Chen et al. 2014 <sup>[[#fn:r403|403]]</sup> ; Yu et al. 2014 <sup>[[#fn:r404|404]]</sup> ; Lacaze et al. 2015 <sup>[[#fn:r405|405]]</sup> ; Song 2018 <sup>[[#fn:r406|406]]</sup> ; Peterson et al. 2017 <sup>[[#fn:r407|407]]</sup> ). In many developing countries, the cost of satellite remote sensing remains a challenge, although technological advances are starting to overcome this problem (Santilli et al. 2018 <sup>[[#fn:r408|408]]</sup> ), while ground-based observations networks are often not available.

Integration of multiple data sources in model and data assimilation schemes reduces uncertainties (Li et al. 2017 <sup>[[#fn:r409|409]]</sup> ; Clark et al. 2017 <sup>[[#fn:r410|410]]</sup> ; Lees et al. 2018 <sup>[[#fn:r411|411]]</sup> ), which might be important for the advancement of early warning systems. Early warning systems are a key feature of short-term (i.e. seasonal) decision-support systems and are becoming increasingly important for sustainable land management and food security (Shtienberg 2013 <sup>[[#fn:r412|412]]</sup> ; Jarroudi et al. 2015 <sup>[[#fn:r413|413]]</sup> ) (Sections 6.2.3 and 7.4.3). Early warning systems can help to optimise fertiliser and water use, aid disease suppression, and/or increase the economic benefit by enabling strategic farming decisions on when and what to plant (Caffi et al. 2012 <sup>[[#fn:r414|414]]</sup> ; Watmuff et al. 2013 <sup>[[#fn:r415|415]]</sup> ; Jarroudi et al. 2015 <sup>[[#fn:r416|416]]</sup> ; Chipanshi et al. 2015 <sup>[[#fn:r417|417]]</sup> ). Their suitability depends on the capability of the methods to accurately predict crop or pest developments, which in turn depends on expert agricultural knowledge, and the accuracy of the weather data used to run phenological models (Caffi et al. 2012 <sup>[[#fn:r418|418]]</sup> ; Shtienberg 2013 <sup>[[#fn:r419|419]]</sup> ).

Uncertainties in models

Model intercomparison is a widely used approach to quantify some sources of uncertainty in climate change, land-use change and ecosystem modelling, often associated with the calculation of model-ensemble medians or means (see e.g., Sections 2.2 and 5.2). Even models of broadly similar structure differ in their projected outcome for the same input, as seen for instance in the spread in climate change projections from Earth System Models (ESMs) to similar future anthropogenic GHG emissions (Parker 2013 <sup>[[#fn:r932|932]]</sup> ; Stocker et al. 2013a <sup>[[#fn:r933|933]]</sup> ). These uncertainties arise, for instance, from different parameter values, different processes represented in models, or how these processes are mathematically described. If the outputs of ESM simulations are used as input to impact models, these uncertainties can propagate to projected impacts (Ahlstrom et al. 2013 <sup>[[#fn:r420|420]]</sup> ).

Thus, the increased quantification of model performance in benchmarking exercises (the repeated confrontation of models with observations to establish a track-record of model developments and performance) is an important development to support the design and the interpretation of the outcomes of model ensemble studies (Randerson et al. 2009 <sup>[[#fn:r421|421]]</sup> ; Luo et al. 2012 <sup>[[#fn:r422|422]]</sup> ; Kelley et al. 2013 <sup>[[#fn:r423|423]]</sup> ). Since observational datasets in themselves are uncertain, benchmarking benefits from transparent information on the observations that are used, and the inclusion of multiple, regularly updated data sources (Luo et al. 2012 <sup>[[#fn:r424|424]]</sup> ; Kelley et al. 2013 <sup>[[#fn:r425|425]]</sup> ). Improved benchmarking approaches and the associated scoring of models may support weighted model means contingent on model performance. This could be an important step forward when calculating ensemble means across a range of models (Buisson et al. 2009 <sup>[[#fn:r426|426]]</sup> ; Parker 2013 <sup>[[#fn:r427|427]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r428|428]]</sup> ).

Uncertainties arising from unknown futures

Large differences exist in projections of future land-cover change, both between and within scenario projections (Fuchs et al. 2015 <sup>[[#fn:r429|429]]</sup> ; Eitelberg et al. 2016 <sup>[[#fn:r430|430]]</sup> ; Popp et al. 2016 <sup>[[#fn:r431|431]]</sup> ; Krause et al. 2017 <sup>[[#fn:r432|432]]</sup> ; Alexander et al. 2016a <sup>[[#fn:r433|433]]</sup> ). These differences reflect the uncertainties associated with baseline data, thematic classifications, different model structures and model parameter estimation (Alexander et al. 2017a <sup>[[#fn:r434|434]]</sup> ; Prestele et al. 2016 <sup>[[#fn:r435|435]]</sup> ; Cross-Chapter Box 1 in Chapter 1). Likewise, projections of future land-use change are also highly uncertain, reflecting – among other factors – the absence of important crop, pasture and management processes in Integrated Assessment Models (Rose 2014 <sup>[[#fn:r436|436]]</sup> ) (Cross-Chapter Box 1 in Chapter 1 ) and in models of the terrestrial carbon cycle (Arneth et al. 2017 <sup>[[#fn:r437|437]]</sup> ). These processes have been shown to have large impacts on carbon stock changes (Arneth et al. 2017 <sup>[[#fn:r438|438]]</sup> ). Common scenario frameworks are used to capture the range of future uncertainties in scenarios. The most commonly used recent framework in climate change studies is based on the Representative Concentration Pathways (RCPs) and the Shared Socio-economic Pathways (SSPs) (Popp et al. 2016 <sup>[[#fn:r439|439]]</sup> ; Riahi et al. 2017 <sup>[[#fn:r440|440]]</sup> ). The RCPs prescribe levels of radiative forcing (W m <sup>–2</sup> ) arising from different atmospheric concentrations of GHGs that lead to different levels of climate change. For example, RCP2.6 (2.6 W m <sup>–2</sup> ) is projected to lead to global mean temperature changes of about 0.9°C–2.3°C, and RCP8.5 (8.5 W m <sup>–2</sup> ) to global mean temperature changes of about 3.2°C–5.4°C (van Vuuren et al. 2014 <sup>[[#fn:r441|441]]</sup> ).

The SSPs describe alternative trajectories of future socio-economic development with a focus on challenges to climate mitigation and challenges to climate adaptation (O’Neill et al. 2014 <sup>[[#fn:r442|442]]</sup> ). SSP1 represents a sustainable and cooperative society with a low-carbon economy and high capacity to adapt to climate change. SSP3 has social inequality that entrenches reliance on fossil fuels and limits adaptive capacity. SSP4 has large differences in income within and across world regions; it facilitates low-carbon economies in places, but limits adaptive capacity everywhere. SSP5 is a technologically advanced world with a strong economy that is heavily dependent on fossil fuels, but with high adaptive capacity. SSP2 is an intermediate case between SSP1 and SSP3 (O’Neill et al. 2014 <sup>[[#fn:r443|443]]</sup> ). The SSPs are commonly used with models to project future land-use change (Cross-Chapter Box 1 in Chapter 1).

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