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Desertification - Sahel case study

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Desertification in the Sahel region is a pressing environmental issue with far-reaching consequences. In this article, we will explore the causes, effects, and potential solutions to combat desertification, using a case study from the Sahel region. By examining the unique challenges faced in this area, we can gain insights into the broader fight against desertification and the importance of sustainable land management practices. The Sahel is a semi-arid zone stretching from the Atlantic Ocean in West Africa to the Red Sea in the East, through northern Senegal, southern Mauritania, the great bend of the Niger River in Mali, Burkina Faso, southern Niger, northeastern Nigeria, south-central Chad, and into Sudan ( Brittanica ).

It is a biogeographical transition between the arid Sahara Desert to the North and the more humid savanna systems on its Southern side.

Desertification in the Sahel has increased over the last number of years.  It has been increasingly impacted by desertification, especially during the second half of the twentieth century. The whole Sahel region in Africa has been affected by devastating droughts, bordering the Sahara Desert and the Savannas.

During this period, the Sahara desert area grew by roughly 10% , most of which in the Southward direction into the semi-arid steppes of the Sahel. 

Understanding desertification in the Sahel

The Sahel region, stretching across Africa from the Atlantic Ocean to the Red Sea, is characterized by fragile ecosystems and vulnerable communities. The combination of climate change, overgrazing, deforestation , and improper agricultural practices has resulted in extensive land degradation and desertification. The consequences of desertification in the Sahel are severe, including food insecurity, loss of biodiversity, and displacement of communities.

in the region, for around 8 months of the year, the weather is dry. The rainy season only happens for a few short months and only produces around 4-8 inches of water. The population growth over the years has caused illegal farming to take place over the last few years and has resulted in major soil erosion and desertification to take place. 

Examining a specific case study in the Sahel region sheds light on the complexities and impacts of desertification. In a particular community, unsustainable farming methods and drought have led to soil erosion and degradation. The once-fertile land has turned into arid, unproductive soil, forcing farmers to abandon their livelihoods and seek alternative means of survival. This case study highlights the urgent need for intervention and sustainable land management practices in the region.

Addressing the challenges

To combat desertification effectively, a multi-faceted approach is necessary. First and foremost, raising awareness about the issue and its consequences is crucial. Governments, NGOs, and local communities must collaborate to implement sustainable land management practices. This involves promoting agroforestry, conservation farming, and reforestation initiatives to restore degraded land and improve soil health. Additionally, supporting alternative income-generating activities and providing access to water resources can help alleviate pressure on the land and reduce vulnerability to drought.

Read more: Preventing desertification: Top 5 success stories

The impact of humans on the Sahel

The impact of humans on the Sahel region is a critical factor contributing to its current challenges and environmental changes. Human activities, including armed violence, climate change, deforestation, and overgrazing, have had significant consequences for both the ecosystem and the local communities. While the area of the Sahel region is already considered to be a dry place, the impact of the human population in the area has really affected how the area continues to evolve. Towns are popping up all over the place, and because of this, more land is being used than ever before. The ground that they are building their lives on quickly began to die and became extremely unhealthy for any type of growth. This has made headlines everywhere and even caught the attention of the United Nations. In 1994, the United Nations declared that June 17th would be known as the World Day to Combat Desertification and Drought. . This was a result of the large-scale droughts and famines that had been taking place and were at their height between 1968 and 1974.

In conclusion, the impact of humans on the Sahel is a multifaceted issue. The region faces a humanitarian crisis alongside security concerns, with climate change and human activities playing significant roles. Desertification caused by climate change, deforestation, and overgrazing has resulted in land degradation, loss of vegetation, and increased vulnerability to droughts and food insecurity. Implementing sustainable land management strategies is essential to mitigate the impact and promote the resilience of the Sahel's ecosystems and communities.

Droughts, grazing, and recharging aquifers

The Sahel’s natural climate cycles make it vulnerable to droughts throughout the year. But, during the second half of the twentieth century, the region also experienced significant increases in human population and resulting in increases in the exploitation of the lands through (cattle) grazing, wood- and bush consumption for firewood, and crop growth where possible.

These anthropogenic processes accelerated during the 1960s when relatively high rainfall amounts were recorded in the region for short periods of time, and grazing and agricultural expansion were promoted by the governments of the Sahel countries, seeing a good opportunity to use the region’s ecosystem for maximizing economic returns.

This resulted in the removal of large parts of the natural vegetation, including shrubs, grasses, and trees, and replacing them with crops and grass types that were suitable for (short-term) grazing.

The world effort for the Sahel:

Natural aquifers, which were previously able to replenish their groundwater stocks during the natural climate cycles, were no longer able to do so, and the regions closest to the Sahara desert were increasingly desertified.

Removing the natural vegetation removed plant roots that bound the soil together, with over-exploitation by grazing eating away much of the grass.

Agricultural activity disrupted the natural system, forcing significant parts of the Sahel region to become dry and barren. Before the particularly bad famine of 1984, desertification was solely put down to climatic causes.

As the Sahel dries, the Sahara advances : and it is estimated to advance with a rate of 60 kilometres the Sahel lost and the Sahara desert gained per year.  Human influence is an important factor in the Sahel’s desertification, but not all can be attributed to human behaviour, says Sumant Nigam, a climate scientist at the University of Maryland.

'There is an important anthropogenic influence there, but it is also being met with natural cycles of climate variability that add and subtract in different periods', Nigam said. 'Understanding both is important for both attribution and prediction.' Ecologists have been meeting all over the world to discuss the desertification of the Sahel at length. While many possible solutions have been proposed, a few goals have been established and are being worked on. The Food and Agricultural Organization of the United Nations has not become involved and is working to create a long-lasting impact on the Sahel Region. However, after the mid-1980s , human-caused contributions were identified and taken seriously by the United Nations and many non-governmental organizations. Severe and long-lasting droughts followed throughout the 1960s-1980s, and impacted the human settlements in the forms of famine and starvation, allowing the Sahara desert to continue to expand southward. As a result, a barren and waterless landscape has emerged, with the northernmost sections of the Sahel transformed into new sections of the Sahara Desert. Even though the levels of drought have decreased since the 1990s, other significant reductions in rainfall have been recorded in the region, including a severe drought in 2012. It is estimated that over 23 million people in the Sahel region are facing severe food insecurity in 2022, and the European Commission projects that the crisis will worsen further amidst rising social security struggles. Now, the goal is to see change take place by   2063,  a year that seems far away but is a start in the efforts to rebuild the Sahel Region. 

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Special Report: Special Report on Climate Change and Land

Desertification, coordinating lead authors.

• Alisher Mirzabaev (Germany, Uzbekistan)
• Jianguo Wu (China)

Lead Authors

• Jason Evans (Australia)
• Felipe Garcia Oliva (Mexico)
• Ismail Abdel Galil Hussein (Egypt)
• Muhammad Mohsin Iqbal (Pakistan)
• Joyce Kimutai (Kenya)
• Tony Knowles (South Africa)
• Francisco Meza (Chile)
• Dalila Nedjraoui (Algeria)
• Fasil Tena (Ethiopia)
• Murat Türkeş (Turkey)
• Ranses José Vázquez (Cuba)
• Mark Weltz (United States)

Contributing Authors

• Mansour Almazroui (Saudi Arabia)
• Hamda Aloui (Tunisia)
• Hesham El-Askary (Egypt)
• Abdul Rasul Awan (Pakistan)
• Céline Bellard (France)
• Arden Burrell (Australia)
• Stefan van der Esch (Netherlands)
• Robyn Hetem (South Africa)
• Kathleen Hermans (Germany)
• Margot Hurlbert (Canada)
• Jagdish Krishnaswamy (India)
• Zaneta Kubik (Poland)
• German Kust (Russia)
• Eike Lüdeling (Germany)
• Johan Meijer (Netherlands)
• Ali Mohammed (Egypt)
• Katerina Michaelides (Cyprus, United Kingdom)
• Lindsay Stringer (United Kingdom)
• Stefan Martin Strohmeier (Austria)
• Grace Villamor (Philippines)

Review Editors

• Mariam Akhtar-Schuster (Germany)
• Fatima Driouech (Morocco)
• Mahesh Sankaran (India)

Chapter Scientists

• Chuck Chuan Ng (Malaysia)
• Helen Berga Paulos (Ethiopia)

FAQ 3.1 | How does climate change affect desertification?

Desertification is land degradation in drylands. Climate change and desertification have strong interactions. Desertification affects climate change through loss of fertile soil and vegetation. Soils contain large amounts of carbon, some of which could be released to the atmosphere due to desertification, with important repercussions for the global climate system. The impacts of climate change on desertification are complex and knowledge on the subject is still insufficient. On the one hand, some dryland regions will receive less rainfall and increases in temperatures can reduce soil moisture, harming plant growth. On the other hand, the increase of CO2 in the atmosphere can enhance plant growth if there are enough water and soil nutrients available.

FAQ 3.2 | How can climate change induced desertification be avoided, reduced or reversed?

Managing land sustainably can help avoid, reduce or reverse desertification, and contribute to climate change mitigation and adaptation. Such sustainable land management practices include reducing soil tillage and maintaining plant residues to keep soils covered, planting trees on degraded lands, growing a wider variety of crops, applying efficient irrigation methods, improving rangeland grazing by livestock and many others.

FAQ 3.3 | How do sustainable land management practices affect ecosystem services and biodiversity?

Sustainable land management practices help improve ecosystems services and protect biodiversity. For example, conservation agriculture and better rangeland management can increase the production of food and fibres. Planting trees on degraded lands can improve soil fertility and fix carbon in soils. Sustainable land management practices also support biodiversity through habitat protection. Biodiversity protection allows for the safeguarding of precious genetic resources, thus contributing to human well-being.

Executive Summary

Desertification is land degradation in arid, semi-arid, and dry sub-humid areas, collectively known as drylands, resulting from many factors, including human activities and climatic variations. The range and intensity of desertification have increased in some dryland areas over the past several decades ( high confidence ). Drylands currently cover about 46.2% (±0.8%) of the global land area and are home to 3 billion people. The multiplicity and complexity of the processes of desertification make its quantification difficult. Desertification hotspots, as identified by a decline in vegetation productivity between the 1980s and 2000s, extended to about 9.2% of drylands (±0.5%), affecting about 500 (±120) million people in 2015. The highest numbers of people affected are in South and East Asia, the circum Sahara region including North Africa and the Middle East including the Arabian Peninsula ( low confidence ). Other dryland regions have also experienced desertification. Desertification has already reduced agricultural productivity and incomes (high confidence) and contributed to the loss of biodiversity in some dryland regions ( medium confidence ). In many dryland areas, spread of invasive plants has led to losses in ecosystem services ( high confidence ), while over-extraction is leading to groundwater depletion ( high confidence ). Unsustainable land management, particularly when coupled with droughts, has contributed to higher dust-storm activity, reducing human well-being in drylands and beyond ( high confidence ). Dust storms were associated with global cardiopulmonary mortality of about 402,000 people in 2005. Higher intensity of sand storms and sand dune movements are causing disruption and damage to transportation and solar and wind energy harvesting infrastructures ( high confidence ). {3.1.1, 3.1.4, 3.2.1, 3.3.1, 3.4.1, 3.4.2, 3.4.2, 3.7.3, 3.7.4}

Attribution of desertification to climate variability and change, and to human activities, varies in space and time ( high confidence ). Climate variability and anthropogenic climate change, particularly through increases in both land surface air temperature and evapotranspiration, and decreases in precipitation, are likely to have played a role, in interaction with human activities, in causing desertification in some dryland areas. The major human drivers of desertification interacting with climate change are expansion of croplands, unsustainable land management practices and increased pressure on land from population and income growth. Poverty is limiting both capacities to adapt to climate change and availability of financial resources to invest in sustainable land management (SLM) ( high confidence ). {3.1.4, 3.2.2, 3.4.2}

Climate change will exacerbate several desertification processes ( medium confidence ). Although CO 2 fertilisation effect is enhancing vegetation productivity in drylands ( high confidence ), decreases in water availability have a larger effect than CO 2 fertilisation in many dryland areas. There is high confidence that aridity will increase in some places, but no evidence for a projected global trend in dryland aridity ( medium confidence ). The area at risk of salinisation is projected to increase in the future ( limited evidence, high agreement ). Future climate change is projected to increase the potential for water driven soil erosion in many dryland areas ( medium confidence ), leading to soil organic carbon decline in some dryland areas. {3.1.1, 3.2.2, 3.5.1, 3.5.2, 3.7.1, 3.7.3}

Risks from desertification are projected to increase due to climate change ( high confidence ). Under shared socio-economic pathway SSP2 (‘Middle of the Road’) at 1.5°C, 2°C and 3°C of global warming, the number of dryland population exposed (vulnerable) to various impacts related to water, energy and land sectors (e.g., water stress, drought intensity, habitat degradation) is projected to reach 951 (178) million, 1152 (220) million and 1285 (277) million, respectively. While at global warming of 2°C, under SSP1 (‘Sustainability’), the exposed (vulnerable) dryland population is 974 (35) million, and under SSP3 (‘Fragmented World’) it is 1267 (522) million. Around half of the vulnerable population is in South Asia, followed by Central Asia, West Africa and East Asia. {2.2, 3.1.1, 3.2.2, 3.5.1, 3.5.2, 7.2.2}

Desertification and climate change, both individually and in combination, will reduce the provision of dryland ecosystem services and lower ecosystem health, including losses in biodiversity ( high confidence ). Desertification and changing climate are projected to cause reductions in crop and livestock productivity ( high confidence ), modify the composition of plant species and reduce biological diversity across drylands ( medium confidence ). Rising CO 2 levels will favour more rapid expansion of some invasive plant species in some regions. A reduction in the quality and quantity of resources available to herbivores can have knock-on consequences for predators, which can potentially lead to disruptive ecological cascades ( limited evidence, low agreement ). Projected increases in temperature and the severity of drought events across some dryland areas can increase chances of wildfire occurrence ( medium confidence ). {3.1.4, 3.4.1, 3.5.2, 3.7.3}

Increasing human pressures on land, combined with climate change, will reduce the resilience of dryland populations and constrain their adaptive capacities ( medium confidence ). The combination of pressures coming from climate variability, anthropogenic climate change and desertification will contribute to poverty, food insecurity, and increased disease burden ( high confidence ), as well as potentially to conflicts ( low confidence ). Although strong impacts of climate change on migration in dryland areas are disputed ( medium evidence, low agreement ), in some places, desertification under changing climate can provide an added incentive to migrate ( medium confidence ). Women will be impacted more than men by environmental degradation, particularly in those areas with higher dependence on agricultural livelihoods ( medium evidence, high agreement ). {3.4.2, 3.6.2}

Desertification exacerbates climate change through several mechanisms such as changes in vegetation cover, sand and dust aerosols and greenhouse gas fluxes (high confidence). The extent of areas in which dryness (rather than temperature) controls CO 2 exchange has increased by 6% between 1948 and 2012, and is projected to increase by at least another 8% by 2050 if the expansion continues at the same rate. In these areas, net carbon uptake is about 27% lower than in other areas ( low confidence ). Desertification also tends to increase albedo, decreasing the energy available at the surface and associated surface temperatures, producing a negative feedback on climate change ( high confidence ). Through its effect on vegetation and soils, desertification changes the absorption and release of associated greenhouse gases (GHGs). Vegetation loss and drying of surface cover due to desertification increases the frequency of dust storms ( high confidence ). Arid ecosystems could be an important global carbon sink, depending on soil water availability ( medium evidence, high agreement ). {3.3.3, 3.4.1, 3.5.2}

Site and regionally-specific technological solutions, based both on new scientific innovations and indigenous and local knowledge (ILK), are available to avoid, reduce and reverse desertification, simultaneously contributing to climate change mitigation and adaptation ( high confidence ). SLM practices in drylands increase agricultural productivity and contribute to climate change adaptation with mitigation co-benefits ( high confidence ). Integrated crop, soil and water management measures can be employed to reduce soil degradation and increase the resilience of agricultural production systems to the impacts of climate change ( high confidence ). These measures include crop diversification and adoption of drought-resilient econogically appropriate plants, reduced tillage, adoption of improved irrigation techniques (e.g., drip irrigation) and moisture conservation methods (e.g., rainwater harvesting using indigenous and local practices), and maintaining vegetation and mulch cover. Conservation agriculture increases the capacity of agricultural households to adapt to climate change ( high confidence ) and can lead to increases in soil organic carbon over time, with quantitative estimates of the rates of carbon sequestration in drylands following changes in agricultural practices ranging between 0.04 and 0.4 t ha– 1 ( medium confidence ). Rangeland management systems based on sustainable grazing and re-vegetation increase rangeland productivity and the flow of ecosystem services ( high confidence ). The combined use of salt-tolerant crops, improved irrigation practices, chemical remediation measures and appropriate mulch and compost is effective in reducing the impact of secondary salinisation ( medium confidence ). Application of sand dune stabilisation techniques contributes to reducing sand and dust storms ( high confidence ). Agroforestry practices and shelterbelts help reduce soil erosion and sequester carbon. Afforestation programmes aimed at creating windbreaks in the form of ‘green walls’ and ‘green dams’ can help stabilise and reduce dust storms, avert wind erosion, and serve as carbon sinks, particularly when done with locally adapted native and other climate resilient tree species ( high confidence ). {3.4.2, 3.6.1, 3.7.2}

Investments into SLM, land restoration and rehabilitation in dryland areas have positive economic returns ( high confidence ). Each USD invested into land restoration can have social returns of about 3–6 USD over a 30-year period. Most SLM practices can become financially profitable within 3 to 10 years ( medium evidence, high agreement ). Despite their benefits in addressing desertification, mitigating and adapting to climate change, and increasing food and economic security, many SLM practices are not widely adopted due to insecure land tenure, lack of access to credit and agricultural advisory services, and insufficient incentives for private land-users ( robust evidence, high agreement ). {3.6.3}

ILK often contributes to enhancing resilience against climate change and combating desertification ( medium confidence ). Dryland populations have developed traditional agroecological practices which are well adapted to resource-sparse dryland environments. However, there is robust evidence documenting losses of traditional agroecological knowledge. Traditional agroecological practices are also increasingly unable to cope with growing demand for food. Combined use of ILK and new SLM technologies can contribute to raising the resilience to the challenges of climate change and desertification ( high confidence ). {3.1.3, 3.6.1, 3.6.2}

Policy frameworks promoting the adoption of SLM solutions contribute to addressing desertification as well as mitigating and adapting to climate change, with co-benefits for poverty eradication and food security among dryland populations ( high confidence ). Implementation of Land Degradation Neutrality policies allows populations to avoid, reduce and reverse desertification, thus contributing to climate change adaptation with mitigation co-benefits ( high confidence ). Strengthening land tenure security is a major factor contributing to the adoption of soil conservation measures in croplands (high confidence). On-farm and off-farm livelihood diversification strategies increase the resilience of rural households against desertification and extreme weather events, such as droughts ( high confidence ). Strengthening collective action is important for addressing causes and impacts of desertification, and for adapting to climate change ( medium confidence ). A greater emphasis on understanding gender-specific differences over land use and land management practices can help make land restoration projects more successful ( medium confidence ). Improved access to markets raises agricultural profitability and motivates investment into climate change adaptation and SLM ( medium confidence ). Payments for ecosystem services give additional incentives to land users to adopt SLM practices ( medium confidence ). Expanding access to rural advisory services increases the knowledge on SLM and facilitates their wider adoption ( medium confidence ). Developing, enabling and promoting access to cleaner energy sources and technologies can contribute to reducing desertification and mitigating climate change through decreasing the use of fuelwood and crop residues for energy ( medium confidence ). Policy responses to droughts based on proactive drought preparedness and drought risk mitigation are more efficient in limiting drought-caused damages than reactive drought relief efforts ( high confidence ). {3.4.2, 3.6.2, 3.6.3, Cross-Chapter Box 5 in this chapter}

The knowledge on limits of adaptation to the combined effects of climate change and desertification is insufficient. However, the potential for residual risks and maladaptive outcomes is high ( high confidence ). Empirical evidence on the limits to adaptation in dryland areas is limited. Potential limits to adaptation include losses of land productivity due to irreversible forms of desertification. Residual risks can emerge from the inability of SLM measures to fully compensate for yield losses due to climate change impacts. They also arise from foregone reductions in ecosystem services due to soil fertility loss even when applying SLM measures could revert land to initial productivity after some time. Some activities favouring agricultural intensification in dryland areas can become maladaptive due to their negative impacts on the environment ( medium confidence ) Even when solutions are available, social, economic and institutional constraints could pose barriers to their implementation ( medium confidence ). {3.6.4}

Improving capacities, providing higher access to climate services, including local-level early warning systems, and expanding the use of remote sensing technologies are high-return investments for enabling effective adaptation and mitigation responses that help address desertification ( high confidence ). Reliable and timely climate services, relevant to desertification, can aid the development of appropriate adaptation and mitigation options reducing, the impact of desertification on human and natural systems ( high confidence ), with quantitative estimates showing that every USD invested in strengthening hydro-meteorological and early warning services in developing countries can yield between 4 and 35 USD ( low confidence ). Knowledge and flow of knowledge on desertification is currently fragmented. Improved knowledge and data exchange and sharing will increase the effectiveness of efforts to achieve Land Degradation Neutrality ( high confidence ). Expanded use of remotely sensed information for data collection helps in measuring progress towards achieving Land Degradation Neutrality ( low evidence, high agreement ). {3.2.1, 3.6.2, 3.6.3, Cross-Chapter Box 5 in this chapter}

The nature of desertification

Introduction.

In this report, desertification is defined as land degradation in arid, semi-arid, and dry sub-humid areas resulting from many factors, including climatic variations and human activities (United Nations Convention to Combat Desertification (UNCCD) 1994). Land degradation is 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 (Section 4.1.3). Arid, semi-arid, and dry sub-humid areas, together with hyper-arid areas, constitute drylands (UNEP 1992 1 ), home to about 3 billion people (van der Esch et al. 2017 2 ). The difference between desertification and land degradation is not process-based but geographic. Although land degradation can occur anywhere across the world, when it occurs in drylands, it is considered desertification (FAQ 1.3). Desertification is not limited to irreversible forms of land degradation, nor is it equated to desert expansion, but represents all forms and levels of land degradation occurring in drylands.

The geographic classification of drylands is often based on the aridity index (AI) – the ratio of average annual precipitation amount (P) to potential evapotranspiration amount (PET, see Glossary) (Figure 3.1). Recent estimates, based on AI, suggest that drylands cover about 46.2% (±0.8%) of the global land area (Koutroulis 2019 3 ; Prăvălie 2016 4 ) ( low confidence ). Hyper-arid areas, where the aridity index is below 0.05, are included in drylands, but are excluded from the definition of desertification (UNCCD 1994 5 ). Deserts are valuable ecosystems (UNEP 2006 6 ; Safriel 2009 7 ) geographically located in drylands and vulnerable to climate change. However, they are not considered prone to desertification. Aridity is a long-term climatic feature characterised by low average precipitation or available water (Gbeckor-Kove 1989 8 ; Türkeş 1999 9 ). Thus, aridity is different from drought, which is a temporary climatic event (Maliva and Missimer 2012 10 ). Moreover, droughts are not restricted to drylands, but occur both in drylands and humid areas (Wilhite et al. 2014 11 ). Following the Synthesis Report (SYR) of the IPCC Fifth Assessment Report (AR5), drought is defined here as “a period of abnormally dry weather long enough to cause a serious hydrological imbalance” (Mach et al. 2014 12 ) (Cross-Chapter Box 5 in this chapter).

AI is not an accurate proxy for delineating drylands in an increasing CO 2 environment (Section 3.2.1). The suggestion that most of the world has become more arid, since the AI has decreased, is not supported by changes observed in precipitation, evaporation or drought (Sheffield et al. 2012 13 ; Greve et al. 2014 14 ). While climate change is expected to decrease the AI due to increases in potential evaporation, the assumptions that underpin the potential evaporation calculation are not consistent with a changing CO 2 environment and the effect this has on transpiration rates (Roderick et al. 2015 15 ; Milly and Dunne 2016 16 ; Greve et al. 2017 17 ) (Section 3.2.1). Given that future climate is characterised by significant increases in CO 2 , the usefulness of currently applied AI thresholds to estimate dryland areas is limited under climate change. If instead of the AI, other variables such as precipitation, soil moisture, and primary productivity are used to identify dryland areas, there is no clear indication that the extent of drylands will change overall under climate change (Roderick et al. 2015 18 ; Greve et al. 2017 19 ; Lemordant et al. 2018 20 ). Thus, some dryland borders will expand, while some others will contract ( high confidence ).

Approximately 70% of dryland areas are located in Africa and Asia (Figure 3.2). The biggest land use/cover in terms of area in drylands, if deserts are excluded, are grasslands, followed by forests and croplands (Figure 3.3). The category of ‘other lands’ in Figure 3.3 includes bare soil, ice, rock, and all other land areas that are not included within the other five categories (FAO 2016 21 ). Thus, hyper-arid areas contain mostly deserts, with some small exceptions, for example, where grasslands and croplands are cultivated under oasis conditions with irrigation (Section 3.7.4). Moreover, FAO (2016) 1786 defines grasslands as permanent pastures and meadows used continuously for more than five years. In drylands, transhumance, i.e. seasonal migratory grazing, often leads to non-permanent pasture systems, thus some of the areas under the ‘other land’ category are also used as non-permanent pastures (Ramankutty et al. 2008 22 ; Fetzel et al. 2017 23 ; Erb et al. 2016 24 ).

Geographical distribution of drylands, delimited based on the aridity index (AI). The classification of AI is: Humid AI > 0.65, Dry sub-humid 0.50 < AI ≤ 0.65, Semi-arid 0.20 < AI ≤ 0.50, Arid 0.05 < AI ≤ 0.20, Hyper-arid AI < 0.05. Data: TerraClimate precipitation and potential evapotranspiration (1980–2015) (Abatzoglou et al. 2018).

Geographical distribution of drylands, delimited based on the aridity index (AI). The classification of AI is: Humid AI > 0.65, Dry sub-humid 0.50 < AI ≤ 0.65, Semi-arid 0.20 < AI ≤ 0.50, Arid 0.05 < AI ≤ 0.20, Hyper-arid AI < 0.05. Data: TerraClimate precipitation and potential evapotranspiration (1980–2015) (Abatzoglou et al. 2018 1787 ).

Dryland categories across geographical areas (continents and Pacific region). Data: TerraClimate precipitation and potential evapotranspiration (1980–2015) (Abatzoglou et al. 2018).

Dryland categories across geographical areas (continents and Pacific region). Data: TerraClimate precipitation and potential evapotranspiration (1980–2015) (Abatzoglou et al. 2018 1788 ).

In the earlier global assessments of desertification (since the 1970s), which were based on qualitative expert evaluations, the extent of desertification was found to range between 4% and 70% of the area of drylands (Safriel 2007 25 ). More recent estimates, based on remotely sensed data, show that about 24–29% of the global land area experienced reductions in biomass productivity between the 1980s and 2000s (Bai et al. 2008 26 ; Le et al. 2016 27 ), corresponding to about 9.2% of drylands (±0.5%) experiencing declines in biomass productivity during this period ( low confidence ), mainly due to anthropogenic causes. Both of these studies consider rainfall dynamics, thus, accounting for the effect of droughts. While less than 10% of drylands is undergoing desertification, it is occurring in areas that contain around 20% of dryland population (Klein Goldewijk et al. 2017 28 ). In these areas the population has increased from approximately 172 million in 1950 to over 630 million today (Figure 1.1).

Available assessments of the global extent and severity of desertification are relatively crude approximations with considerable uncertainties, for example, due to confounding effects of invasive bush encroachment in some dryland regions. Different indicator sets and approaches have been developed for monitoring and assessment of desertification from national to global scales (Imeson 2012 29 ; Sommer et al. 2011 30 ; Zucca et al. 2012 31 ; Bestelmeyer et al. 2013 32 ). Many indicators of desertification only include a single factor or characteristic of desertification, such as the patch size distribution of vegetation (Maestre and Escudero 2009 33 ; Kéfi et al. 2010 34 ), Normalized Difference Vegetation Index (NDVI) (Piao et al. 2005 35 ), drought-tolerant plant species (An et al. 2007), grass cover (Bestelmeyer et al. 2013 36 ), land productivity dynamics (Baskan et al. 2017 37 ), ecosystem net primary productivity (Zhou et al. 2015 38 ) or Environmentally Sensitive Land Area Index (Symeonakis et al. 2016 39 ). In addition, some synthetic indicators of desertification have also been used to assess desertification extent and desertification processes, such as climate, land use, soil, and socio-economic parameters (Dharumarajan et al. 2018 40 ), or changes in climate, land use, vegetation cover, soil properties and population as the desertification vulnerability index (Salvati et al. 2009 41 ). Current data availability and methodological challenges do not allow for accurately and comprehensively mapping desertification at a global scale (Cherlet et al. 2018 42 ). However, the emerging partial evidence points to a lower global extent of desertification than previously estimated ( medium confidence ) (Section 3.2).

This assessment examines the socio-ecological links between drivers (Section 3.1) and feedbacks (Section 3.3) that influence desertification–climate change interactions, and then examines associated observed and projected impacts (Sections 3.4 and 3.5) and responses (Section 3.6). Moreover, this assessment highlights that dryland populations are highly vulnerable to desertification and climate change (Sections 3.2 and 3.4). At the same time, dryland populations also have significant past experience and sources of resilience embodied in indigenous and local knowledge and practices in order to successfully adapt to climatic changes and address desertification (Section 3.6). Numerous site-specific technological response options are also available for SLM in drylands that can help increase the resilience of agricultural livelihood systems to climate change (Section 3.6). However, continuing environmental degradation combined with climate change is straining the resilience of dryland populations. Enabling policy responses for SLM and livelihoods diversification can help maintain and strengthen the resilience and adaptive capacities in dryland areas (Section 3.6). The assessment finds that policies promoting SLM in drylands will contribute to climate change adaptation and mitigation, with co-benefits for broader sustainable development ( high confidence ) (Section 3.4).

Land use and land cover in drylands and share of each dryland category in global land area. Source: FAO (2016).

Land use and land cover in drylands and share of each dryland category in global land area. Source: FAO (2016) 1789 .

Desertification in previous IPCC and related reports

The IPCC Fifth Assessment Report (AR5) and Special Report on Global Warming of 1.5°C include a limited discussion of desertification. In AR5 Working Group I desertification is mentioned as a forcing agent for the production of atmospheric dust (Myhre et al. 2013 43 ). The same report had low confidence in the available projections on the changes in dust loadings due to climate change (Boucher et al. 2013 44 ). In AR5 Working Group II desertification is identified as a process that can lead to reductions in crop yields and the resilience of agricultural and pastoral livelihoods (Field et al. 2014 45 ; Klein et al. 2015 46 ). AR5 Working Group II notes that climate change will amplify water scarcity, with negative impacts on agricultural systems, particularly in semi-arid environments of Africa ( high confidence ), while droughts could exacerbate desertification in southwestern parts of Central Asia (Field et al. 2014). AR5 Working Group III identifies desertification as one of a number of often overlapping issues that must be dealt with when considering governance of mitigation and adaptation (Fleurbaey et al. 2014 47 ). The IPCC Special Report on Global Warming of 1.5°C noted that limiting global warming to 1.5°C instead of 2°C is strongly beneficial for land ecosystems and their services ( high confidence ) such as soil conservation, contributing to avoidance of desertification (Hoegh-Guldberg et al. 2018 48 ).

The recent Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Land Degradation and Restoration Assessment report (IPBES 2018a 49 ) is also of particular relevance. While acknowledging a wide variety of past estimates of the area undergoing degradation, IPBES (2018a) pointed at their lack of agreement about where degradation is taking place. IPBES (2018a) also recognised the challenges associated with differentiating the impacts of climate variability and change on land degradation from the impacts of human activities at a regional or global scale.

The third edition of the World Atlas of Desertification (Cherlet et al. 2018 50 ) indicated that it is not possible to deterministically map the global extent of land degradation – and its subset, desertification – pointing out that the complexity of interactions between social, economic, and environmental systems make land degradation not amenable to mapping at a global scale. Instead, Cherlet et al. (2018) presented global maps highlighting the convergence of various pressures on land resources.

Dryland populations: Vulnerability and resilience

Drylands are home to approximately 38.2% (±0.6%) of the global population (Koutroulis 2019 51 ; van der Esch et al. 2017 52 ), that is about 3 billion people. The highest number of people live in the drylands of South Asia (Figure 3.4), followed by Sub-Saharan Africa and Latin America (van der Esch et al. 2017 53 ). In terms of the number of people affected by desertification, Reynolds et al. (2007) indicated that desertification was directly affecting 250 million people. More recent estimates show that 500 (±120) million people lived in 2015 in those dryland areas which experienced significant loss in biomass productivity between the 1980s and 2000s (Bai et al. 2008 54 ; Le et al. 2016 55 ). The highest numbers of affected people were in South and East Asia, North Africa and the Middle East (l ow confidence ). The population in drylands is projected to increase about twice as rapidly as non-drylands, reaching 4 billion people by 2050 (van der Esch et al. 2017 56 ). This is due to higher population growth rates in drylands. About 90% of the population in drylands live in developing countries (UN-EMG 2011 57 ).

Current and projected population (under SSP2) in drylands, in billions. Source: van der Esch et al. (2017).

Current and projected population (under SSP2) in drylands, in billions. Source: van der Esch et al. (2017) 1790 .

Dryland populations are highly vulnerable to desertification and climate change because their livelihoods are predominantly dependent on agriculture, one of the sectors most susceptible to climate change (Rosenzweig et al. 2014 58 ; Schlenker and Lobell 2010 59 ). Climate change is projected to have substantial impacts on all types of agricultural livelihood systems in drylands (CGIAR-RPDS 2014 60 ) (Sections 3.4.1 and 3.4.2).

One key vulnerable group in drylands are pastoral and agropastoral households 1 . There are no precise figures about the number of people practicing pastoralism globally. Most estimates range between 100 million and 200 million (Rass 2006 61 ; Secretariat of the Convention on Biological Diversity 2010 62 ), of whom 30–63 million are nomadic pastoralists (Dong 2016 63 ; Carr-Hill 2013 64 ) 2

Pastoral production systems represent an adaptation to high seasonal climate variability and low biomass productivity in dryland ecosystems (Varghese and Singh 2016 65 ; Krätli and Schareika 2010 66 ), which require large areas for livestock grazing through migratory pastoralism (Snorek et al. 2014 67 ). Grazing lands across dryland environments are being degraded, and/or being converted to crop production, limiting the opportunities for migratory livestock systems, and leading to conflicts with sedentary crop producers (Abbass 2014 68 ; Dimelu et al. 2016 69 ). These processes, coupled with ethnic differences, perceived security threats, and misunderstanding of pastoral rationality, have led to increasing marginalisation of pastoral communities and disruption of their economic and cultural structures (Elhadary 2014 70 ; Morton 2010 71 ). As a result, pastoral communities are not well prepared to deal with increasing weather/climate variability and weather/climate extremes due to changing climate (Dong 2016 72 ; López-i-Gelats et al. 2016 73 ), and remain amongst the most food insecure groups in the world (FAO 2018).

There is an increasing concentration of poverty in the dryland areas of Sub-Saharan Africa and South Asia (von Braun and Gatzweiler 2014 74 ; Barbier and Hochard 2016) 75 , where 41% and 12% of the total populations live in extreme poverty, respectively (World Bank 2018 76 ). For comparison, the average share of global population living in extreme poverty is about 10% (World Bank 2018 77 ). Multidimensional poverty, prevalent in many dryland areas, is a key source of vulnerability (Safriel et al. 2005 78 ; Thornton et al. 2014 79 ; Fraser et al. 2011 80 ; Thomas 2008 81 ). Multidimensional poverty incorporates both income-based poverty, and also other dimensions such as poor healthcare services, lack of education, lack of access to water, sanitation and energy, disempowerment, and threat from violence (Bourguignon and Chakravarty 2003 82 ; Alkire and Santos 2010 83 , 2014 84 ). Contributing elements to this multidimensional poverty in drylands are rapid population growth, fragile institutional environment, lack of infrastructure, geographic isolation and low market access, insecure land tenure systems, and low agricultural productivity (Sietz et al. 2011 85 ; Reynolds et al. 2011 86 ; Safriel and Adeel 2008 87 ; Stafford Smith 2016 88 ). Even in high-income countries, those dryland areas that depend on agricultural livelihoods represent relatively poorer locations nationally, with fewer livelihood opportunities, for example in Italy (Salvati 2014 89 ). Moreover, in many drylands areas, female-headed households, women and subsistence farmers (both male and female) are more vulnerable to the impacts of desertification and climate change (Nyantakyi-Frimpong and Bezner-Kerr 2015 90 ; Sultana 2014 91 ; Rahman 2013 92 ). Some local cultural traditions and patriarchal relationships were found to contribute to higher vulnerability of women and female-headed households through restrictions on their access to productive resources (Nyantakyi-Frimpong and Bezner-Kerr 2015 94 ; Sultana 2014 95 ; Rahman 2013 1791 ) (Sections 3.4.2 and 3.6.3, and Cross-Chapter Box 11 in Chapter 7).

Despite these environmental, socio-economic and institutional constraints, dryland populations have historically demonstrated remarkable resilience, ingenuity and innovations, distilled into ILK to cope with high climatic variability and sustain livelihoods (Safriel and Adeel 2008 96 ; Davis 2016 97 ; Davies 2017 98 ) (Sections 3.6.1 and 3.6.2, and Cross-Chapter Box 13 in Chapter 7). For example, across the Arabian Peninsula and North Africa, informal community by-laws were successfully used for regulating grazing, collection and cutting of herbs and wood, and which limited rangeland degradation (Gari 2006 99 ; Hussein 2011 100 ). Pastoralists in Mongolia developed indigenous classifications of pasture resources which facilitated ecologically optimal grazing practices (Fernandez-Gimenez 2000 101 ) (Section 3.6.2). Currently, however, indigenous and local knowledge and practices are increasingly lost or can no longer cope with growing demands for land-based resources (Dominguez 2014 102 ; Fernández-Giménez and Fillat Estaque 2012 103 ; Hussein 2011 104 ; Kodirekkala 2017 105 ; Moreno-Calles et al. 2012 106 ) (Section 3.4.2). Unsustainable land management is increasing the risks from droughts, floods and dust storms (Sections 3.4.2 and 3.5). Policy actions promoting the adoption of SLM practices in dryland areas, based on both indigenous and local knowledge and modern science, and expanding alternative livelihood opportunities outside agriculture can contribute to climate change adaptation and mitigation, addressing desertification, with co-benefits for poverty eradication and food security ( high confidence ) (Cowie et al. 2018 107 ; Liniger et al. 2017 108 ; Safriel and Adeel 2008 109 ; Stafford-Smith et al. 2017 110 ).

Processes and drivers of desertification under climate change

Processes of desertification and their climatic drivers.

Processes of desertification are mechanisms by which drylands are degraded. Desertification consists of both biological and non-biological processes. These processes are classified under broad categories of degradation of physical, chemical and biological properties of terrestrial ecosystems. The number of desertification processes is large and they are extensively covered elsewhere (IPBES 2018a 111 ; Lal 2016 112 ; Racine 2008 113 ; UNCCD 2017 114 ). Section 4.2.1 and Tables 4.1 and 4.2 in Chapter 4 highlight those which are particularly relevant for this assessment in terms of their links to climate change and land degradation, including desertification.

Drivers of desertification are factors which trigger desertification processes. Initial studies of desertification during the early-to-mid 20th century attributed it entirely to human activities. In one of the influential publications of that time, Lavauden (1927) 115 stated that: “Desertification is purely artificial. It is only the act of the man…” However, such a uni-causal view of desertification was shown to be invalid (Geist et al. 2004 116 ; Reynolds et al. 2007 117 ) (Sections 3.1.4.2 and 3.1.4.3). Tables 4.1 and 4.2 in Chapter 4 summarise the drivers, linking them to the specific processes of desertification and land degradation under changing climate.

Erosion refers to removal of soil by the physical forces of water, wind, or often caused by farming activities such as tillage (Ginoux et al. 2012 118 ). The global estimates of soil erosion differ significantly, depending on scale, study period and method used (García-Ruiz et al. 2015 119 ), ranging from approximately 20 Gt yr– 1 to more than 200 Gt yr– 1 (Boix-Fayos et al. 2006 120 ; FAO 2015 121 ). There is a significant potential for climate change to increase soil erosion by water, particularly in those regions where precipitation volumes and intensity are projected to increase (Panthou et al. 2014 122 ; Nearing et al. 2015 123 ). On the other hand, while it is a dominant form of erosion in areas such as West Asia and the Arabian Peninsula (Prakash et al. 2015 124 ; Klingmüller et al. 2016 125 ), there is limited evidence concerning climate change impacts on wind erosion (Tables 4.1 and 4.2 in Chapter 4, and Section 3.5).

Saline and sodic soils (see Glossary) occur naturally in arid, semi-arid and dry sub-humid regions of the world. Climate change or hydrological change can cause soil salinisation by increasing the mineralised groundwater level. However, secondary salinisation occurs when the concentration of dissolved salts in water and soil is increased by anthropogenic processes, mainly through poorly managed irrigation schemes. The threat of soil and groundwater salinisation induced by sea level rise and seawater intrusion are amplified by climate change (Section 4.9.7).

Global warming is expected to accelerate soil organic carbon (SOC) turnover, since the decomposition of the soil organic matter by microbial activity begins with low soil water availability, but this moisture is insufficient for plant productivity (Austin et al. 2004 126 ) (Section 3.4.1.1). SOC is also lost due to soil erosion (Lal 2009 127 ); therefore, in some dryland areas leading to SOC decline (Sections 3.3.3 and 3.5.2) and the transfer of carbon (C) from soil to the atmosphere (Lal 2009 128 ).

Sea surface temperature (SST) anomalies can drive rainfall changes, with implications for desertification processes. North Atlantic SST anomalies are positively correlated with Sahel rainfall anomalies (Knight et al. 2006 129 ; Gonzalez-Martin et al. 2014 130 ; Sheen et al. 2017 131 ). While the eastern tropical Pacific SST anomalies have a negative correlation with Sahel rainfall (Pomposi et al. 2016 132 ), a cooler North Atlantic is related to a drier Sahel, with this relationship enhanced if there is a simultaneous relative warming of the South Atlantic (Hoerling et al. 2006 133 ). Huber and Fensholt (2011) 134 explored the relationship between SST anomalies and satellite observed Sahel vegetation dynamics, finding similar relationships but with substantial west–east variations in both the significant SST regions and the vegetation response. Concerning the paleoclimatic evidence on aridification after the early Holocene ‘Green Sahara’ period (11,000 to 5000 years ago), Tierney et al. (2017) 135 indicate that a cooling of the North Atlantic played a role (Collins et al. 2017 136 ; Otto-Bliesner et al. 2014 137 ; Niedermeyer et al. 2009 138 ) similar to that found in modern observations. Besides these SST relationships, aerosols have also been suggested as a potential driver of the Sahel droughts (Rotstayn and Lohmann 2002 139 ; Booth et al. 2012 140 ; Ackerley et al. 2011 141 ). For eastern Africa, both recent droughts and decadal declines have been linked to human-induced warming in the western Pacific (Funk et al. 2018 142 ).

Invasive plants contributed to desertification and loss of ecosystem services in many dryland areas in the last century ( high confidence ) (Section 3.7.3). Extensive woody plant encroachment altered runoff and soil erosion across much of the drylands, because the bare soil between shrubs is very susceptible to water erosion, mainly in high-intensity rainfall events (Manjoro et al. 2012 143 ; Pierson et al. 2013 144 ; Eldridge et al. 2015 145 ). Rising CO 2 levels due to global warming favour more rapid expansion of some invasive plant species in some regions. An example is the Great Basin region in western North America where over 20% of ecosystems have been significantly altered by invasive plants, especially exotic annual grasses and invasive conifers, resulting in loss of biodiversity. This land-cover conversion has resulted in reductions in forage availability, wildlife habitat, and biodiversity (Pierson et al. 2011, 2013 146 ; Miller et al. 2013 147 ).

The wildfire is a driver of desertification, because it reduces vegetation cover, increases runoff and soil erosion, reduces soil fertility and affects the soil microbial community (Vega et al. 2005 148 ; Nyman et al. 2010 149 ; Holden et al. 2013 150 ; Pourreza et al. 2014 151 ; Weber et al. 2014 152 ; Liu and Wimberly 2016 153 ). Predicted increases in temperature and the severity of drought events across some dryland areas (Section 2.2) can increase chances of wildfire occurrence ( medium confidence ) (Jolly et al. 2015 154 ; Williams et al. 2010 155 ; Clarke and Evans 2018 156 ) (Cross-Chapter Box 3 in Chapter 2). In semi-arid and dry sub-humid areas, fire can have a profound influence on observed vegetation and particularly the relative abundance of grasses to woody plants (Bond et al. 2003 157 ; Bond and Keeley 2005 158 ; Balch et al. 2013 159 ).

While large uncertainty exists concerning trends in droughts globally (AR5) (Section 2.2), examining the drought data by Ziese et al. (2014) 160 for drylands only reveals a large inter-annual variability combined with a trend toward increasing dryland area affected by droughts since the 1950s (Figure 1.1). Thus, over the period 1961–2013, the annual area of drylands in drought has increased, on average, by slightly more than 1% per year, with large inter-annual variability.

Anthropogenic drivers of desertification under climate change

The literature on the human drivers of desertification is substantial (e.g., D’Odorico et al. 2013 161 ; Sietz et al. 2011 162 ; Yan and Cai 2015 163 ; Sterk et al. 2016 164 ; Varghese and Singh 2016 165 ) and there have been several comprehensive reviews and assessments of these drivers very recently (Cherlet et al. 2018 166 ; IPBES 2018a 167 ; UNCCD 2017 168 ). IPBES (2018a) identified cropland expansion, unsustainable land management practices including overgrazing by livestock, urban expansion, infrastructure development, and extractive industries as the main drivers of land degradation. IPBES (2018a) also found that the ultimate driver of land degradation is high and growing consumption of land-based resources, e.g., through deforestation and cropland expansion, escalated by population growth. What is particularly relevant in the context of the present assessment is to evaluate if, how and which human drivers of desertification will be modified by climate change effects.

Growing food demand is driving conversion of forests, rangelands, and woodlands into cropland (Bestelmeyer et al. 2015 169 ; D’Odorico et al. 2013 170 ). Climate change is projected to reduce crop yields across dryland areas (Sections 3.4.1 and 5.2.2), potentially reducing local production of food and feed. Without research breakthroughs mitigating these productivity losses through higher agricultural productivity, and reducing food waste and loss, meeting the increasing food demands of growing populations will require expansion of cropped areas to more marginal areas (with most prime areas in drylands already being under cultivation) (Lambin 2012 171 ; Lambin et al. 2013 172 ; Eitelberg et al. 2015 173 ; Gutiérrez-Elorza 2006 174 ; Kapović Solomun et al. 2018 175 ). Borrelli et al. (2017) 176 showed that the primary driver of soil erosion in 2012 was cropland expansion. Although local food demands could also be met by importing from other areas, this would mean increasing the pressure on land in those areas (Lambin and Meyfroidt 2011 177 ). The net effects of such global agricultural production shifts on land condition in drylands are not known.

Climate change will exacerbate poverty among some categories of dryland populations (Sections 3.4.2 and 3.5.2). Depending on the context, this impact comes through declines in agricultural productivity, changes in agricultural prices and extreme weather events (Hertel and Lobell 2014 178 ; Hallegatte and Rozenberg 2017 179 ). There is high confidence that poverty limits both capacities to adapt to climate change and availability of financial resources to invest into SLM (Gerber et al. 2014 180 ; Way 2016 181 ; Vu et al. 2014 182 ) (Sections 3.5.2, 3.6.2 and 3.6.3).

Labour mobility is another key human driver that will interact with climate change. Although strong impacts of climate change on migration in dryland areas are disputed, in some places, it is likely to provide an added incentive to migrate (Section 3.4.2.7). Out-migration will have several contradictory effects on desertification. On one hand, it reduces an immediate pressure on land if it leads to less dependence on land for livelihoods (Chen et al. 2014 183 ; Liu et al. 2016a). Moreover, migrant remittances could be used to fund the adoption of SLM practices. Labour mobility from agriculture to non-agricultural sectors could allow land consolidation, gradually leading to mechanisation and agricultural intensification (Wang et al. 2014 184 , 2018 185 ). On the other hand, this can increase the costs of labour-intensive SLM practices due to lower availability of rural agricultural labour and/or higher rural wages. Out-migration increases the pressure on land if higher wages that rural migrants earn in urban centres will lead to their higher food consumption. Moreover, migrant remittances could also be used to fund land-use expansion to marginal areas (Taylor et al. 2016 186 ; Gray and Bilsborrow 2014 187 ). The net effect of these opposite mechanisms varies from place to place (Qin and Liao 2016 188 ). There is very little literature evaluating these joint effects of climate change, desertification and labour mobility (Section 7.3.2).

There are also many other institutional, policy and socio-economic drivers of desertification, such as land tenure insecurity, lack of property rights, lack of access to markets, and to rural advisory services, lack of technical knowledge and skills, agricultural price distortions, agricultural support and subsidies contributing to desertification, and lack of economic incentives for SLM (D’Odorico et al. 2013 189 ; Geist et al. 2004 190 ; Moussa et al. 2016 191 ; Mythili and Goedecke 2016 192 ; Sow et al. 2016 193 ; Tun et al. 2015 194 ; García-Ruiz 2010 195 ). There is no evidence that these factors will be materially affected by climate change, however, serving as drivers of unsustainable land management practices, they do play a very important role in modulating responses for climate change adaptation and mitigation (Section 3.6.3).

Interaction of drivers: Desertification syndrome versus drylands development paradigm

Two broad narratives have historically emerged to describe responses of dryland populations to environmental degradation. The first is ‘desertification syndrome’ which describes the vicious cycle of resource degradation and poverty, whereby dryland populations apply unsustainable agricultural practices leading to desertification, and exacerbating their poverty, which then subsequently further limits their capacities to invest in SLM (MEA 2005 196 ; Safriel and Adeel 2008 197 ). The alternative paradigm is one of ‘drylands development’, which refers to social and technical ingenuity of dryland populations as a driver of dryland sustainability (MEA 2005; Reynolds et al. 2007 198 ; Safriel and Adeel 2008 199 ). The major difference between these two frameworks is that the ‘drylands development’ paradigm recognises that human activities are not the sole and/or most important drivers of desertification, but there are interactions of human and climatic drivers within coupled social-ecological systems (Reynolds et al. 2007 200 ). This led Behnke and Mortimore (2016) 201 , and earlier Swift (1996) 202 , to conclude that the concept of desertification as irreversible degradation distorts policy and governance in dryland areas. Mortimore (2016) 203 suggested that instead of externally imposed technical solutions, what is needed is for populations in dryland areas to adapt to this variable environment which they cannot control. All in all, there is high confidence that anthropogenic and climatic drivers interact in complex ways in causing desertification. As discussed in Section 3.2.2, the relative influence of human or climatic drivers on desertification varies from place to place ( high confidence ) (Bestelmeyer et al. 2018 204 ; D’Odorico et al. 2013 205 ; Geist and Lambin 2004 206 ; Kok et al. 2016 207 ; Polley et al. 2013 208 ; Ravi et al. 2010 209 ; Scholes 2009 210 ; Sietz et al. 2017 211 ; Sietz et al. 2011 212 ).

Observations of desertification

Status and trends of desertification.

Current estimates of the extent and severity of desertification vary greatly due to missing and/or unreliable information (Gibbs and Salmon 2015 213 ). The multiplicity and complexity of the processes of desertification make its quantification difficult (Prince 2016 214 ; Cherlet et al. 2018 215 ). The most common definition for the drylands is based on defined thresholds of the AI (Figure 3.1; UNEP 1992 216 ). While past studies have used the AI to examine changes in desertification or extent of the drylands (Feng and Fu 2013 217 ; Zarch et al. 2015 218 ; Ji et al. 2015 219 ; Spinoni et al. 2015 220 ; Huang et al. 2016 221 ; Ramarao et al. 2018 222 ), this approach has several key limitations: (i) the AI does not measure desertification, (ii) the impact of changes in climate on the land surface and systems is more complex than assumed by AI, and (iii) the relationship between climate change and changes in vegetation is complex due to the influence of CO 2 . Expansion of the drylands does not imply desertification by itself, if there is no long-term loss of at least one of the following: biological productivity, ecological integrity, or value to humans.

The use of the AI to define changing aridity levels and dryland extent in an environment with changing atmospheric CO 2 has been strongly challenged (Roderick et al. 2015 223 ; Milly and Dunne 2016 224 ; Greve et al. 2017 225 ; Liu et al. 2017 226 ). The suggestion that most of the world has become more arid, since the AI has decreased, is not supported by changes observed in precipitation, evaporation or drought ( medium confidence ) (Sheffield et al. 2012 227 ; Greve et al. 2014 228 ). A key issue is the assumption in the calculation of potential evapotranspiration that stomatal conductance remains constant, which is invalid if atmospheric CO 2 changes. Given that atmospheric CO 2 has been increasing over the last century or more, and is projected to continue increasing, this means that AI with constant thresholds (or any other measure that relies on potential evapotranspiration) is not an appropriate way to estimate aridity or dryland extent (Donohue et al. 2013 229 ; Roderick et al. 2015 230 ; Greve et al. 2017 231 ). This issue helps explain the apparent contradiction between the drylands becoming more arid according to the AI and also becoming greener according to satellite observations (Fensholt et al. 2012 232 ; Andela et al. 2013 233 ) (Figure 3.5). Other climate type classifications based on various combinations of temperature and precipitation (Köppen-Trewartha, Köppen-Geiger) have also been used to examine historical changes in climate zones, finding a tendency toward drier climate types (Feng et al. 2014 234 ; Spinoni et al. 2015 235 ).

The need to establish a baseline when assessing change in the land area degraded has been extensively discussed in Prince et al. (2018). Desertification is a process, not a state of the system, hence an ‘absolute’ baseline is not required; however, every study uses a baseline defined by the start of their period of interest.

Depending on the definitions applied and methodologies used in evaluation, the status and extent of desertification globally and regionally still show substantial variations ( high confidence ) (D’Odorico et al. 2013 236 ). There is high confidence that the range and intensity of desertification has increased in some dryland areas over the past several decades (Sections 3.2.1.1 and 3.2.1.2). The three methodological approaches applied for assessing the extent of desertification: expert judgement, satellite observation of net primary productivity, and use of biophysical models, together provide a relatively holistic assessment but none on its own captures the whole picture (Gibbs and Salmon 2015 237 ; Vogt et al. 2011 238 ; Prince 2016 239 ) (Section 4.2.4).

Mean annual maximum NDVI 1982–2015 (Global Inventory Modelling and Mapping Studies NDVI3g v1). Non-dryland regions (aridity index >0.65) are masked in grey.

Global scale

Complex human–environment interactions, coupled with biophysical, social, economic and political factors unique to any given location, render desertification difficult to map at a global scale (Cherlet et al. 2018 240 ). Early attempts to assess desertification focused on expert knowledge in order to obtain global coverage in a cost-effective manner. Expert judgement continues to play an important role because degradation remains a subjective feature whose indicators are different from place to place (Sonneveld and Dent 2007 241 ). GLASOD (Global Assessment of Human-induced Soil Degradation) estimated nearly 2000 million hectares (Mha) (15.3% of the total land area) had been degraded by the early 1990s since the mid-20th century. GLASOD was criticised for perceived subjectiveness and exaggeration (Helldén and Tottrup 2008 242 ; Sonneveld and Dent 2007 243 ). Dregne and Chou (1992) 244 found 3000 Mha in drylands (i.e. about 50% of drylands) were undergoing degradation. Significant improvements have been made through the efforts of WOCAT (World Overview of Conservation Approaches and Technologies), LADA (Land Degradation Assessment in Drylands) and DESIRE (Desertification Mitigation and Remediation of Land) who jointly developed a mapping tool for participatory expert assessment, with which land experts can estimate current area coverage, type and trends of land degradation (Reed et al. 2011 245 ).

A number of studies have used satellite-based remote sensing to investigate long-term changes in the vegetation and thus identify parts of the drylands undergoing desertification. Satellite data provides information at the resolution of the sensor, which can be relatively coarse (up to 25 km), and interpretations of the data at sub-pixel levels are challenging. The most widely used remotely sensed vegetation index is the NDVI, providing a measure of canopy greenness that is related to the quantity of standing biomass (Bai et al. 2008 246 ; de Jong et al. 2011 247 ; Fensholt et al. 2012 248 ; Andela et al. 2013 249 ; Fensholt et al. 2015 250 ; Le et al. 2016 251 ) (Figure 3.5). A main challenge associated with NDVI is that although biomass and productivity are closely related in some systems, they can differ widely when looking across land uses and ecosystem types, giving a false positive in some instances (Pattison et al. 2015 252 ; Aynekulu et al. 2017 253 ). For example, bush encroachment in rangelands and intensive monocropping with high fertiliser application gives an indication of increased productivity in satellite data though these could be considered as land degradation. According to this measure there are regions undergoing desertification, however the drylands are greening on average (Figure 3.6).

Trend in the annual maximum NDVI 1982–2015 (Global Inventory Modelling and Mapping Studies NDVI3g v1) calculated using the Theil–Sen estimator which is a median based estimator, and is robust to outliers. Non-dryland regions (aridity index >0.65) are masked in grey

A simple linear trend in NDVI is an unsuitable measure for dryland degradation for several reasons (Wessels et al. 2012 254 ; de Jong et al. 2013 255 ; Higginbottom and Symeonakis 2014 256 ; Le et al. 2016 257 ). NDVI is strongly coupled to precipitation in drylands where precipitation has high inter-annual variability. This means that NDVI trend can be dominated by any precipitation trend and is sensitive to wet or dry periods, particularly if they fall near the beginning or end of the time series. Degradation may only occur during part of the time series, while NDVI is stable or even improving during the rest of the time series. This reduces the strength and representativeness of a linear trend. Other factors such as CO 2 fertilisation also influence the NDVI trend. Various techniques have been proposed to address these issues, including the residual trends (RESTREND) method to account for rainfall variability (Evans and Geerken 2004 258 ), time-series break point identification methods to find major shifts in the vegetation trends (de Jong et al. 2013 259 ; Verbesselt et al. 2010a 260 ), and methods to explicitly account for the effect of CO 2 fertilisation (Le et al. 2016 261 ).

Using the RESTREND method, Andela et al. (2013) 262 found that human activity contributed to a mixture of improving and degrading regions in drylands. In some locations these regions differed substantially from those identified using the NDVI trend alone, including an increase in the area being desertified in southern Africa and northern Australia, and a decrease in southeast and western Australia and Mongolia. De Jong et al. (2013) 263 examined the NDVI time series for major shifts in vegetation activity and found that 74% of drylands experienced such a shift between 1981 and 2011. This suggests that monotonic linear trends are unsuitable for accurately capturing the changes that have occurred in the majority of the drylands. Le et al. (2016) 264 explicitly accounted for CO 2 fertilisation effect and found that the extent of degraded areas in the world is 3% larger when compared to the linear NDVI trend.

Besides NDVI, there are many vegetation indices derived from satellite data in the optical and infrared wavelengths. Each of these datasets has been derived to overcome some limitation in existing indices. Studies have compared vegetation indices globally (Zhang et al. 2017 265 ) and specifically over drylands (Wu 2014 266 ). In general, the data from these vegetation indices are available only since around 2000, while NDVI data is available since 1982. With less than 20 years of data, the trend analysis remains problematic with vegetation indices other than NDVI. However, given the various advantages in terms of resolution and other characteristics, these newer vegetation indices will become more useful in the future as more data accumulates.

A major shortcoming of these studies based on vegetation datasets derived from satellite sensors is that they do not account for changes in vegetation composition, thus leading to inaccuracies in the estimation of the extent of degraded areas in drylands. For example, drylands of eastern Africa currently face growing encroachment of invasive plant species, such as Prosopis juliflora (Ayanu et al. 2015 267 ), which constitutes land degradation since it leads to losses in economic productivity of affected areas but appears as a greening in the satellite data. Another case study in central Senegal found degradation manifested through a reduction in species richness despite satellite observed greening (Herrmann and Tappan 2013 268 ). A number of efforts to identify changes in vegetation composition from satellites have been made (Brandt et al. 2016a 269 , b 270 ; Evans and Geerken 2006 271 ; Geerken 2009 272 ; Geerken et al. 2005 273 ; Verbesselt et al. 2010a 274 , b 275 ). These depend on well-identified reference NDVI time series for particular vegetation groupings, can only differentiate vegetation types that have distinct spectral phenology signatures, and require extensive ground observations for validation. A recent alternative approach to differentiating woody from herbaceous vegetation involves the combined use of optical/infrared-based vegetation indices, indicating greenness, with microwave based Vegetation Optical Depth (VOD) which is sensitive to both woody and leafy vegetation components (Andela et al. 2013 276 ; Tian et al. 2017 277 ).

Vegetation Optical Depth (VOD) has been available since the 1980s. VOD is based on microwave measurements and is related to total above-ground biomass water content. Unlike NDVI, which is only sensitive to green canopy cover, VOD is also sensitive to water in woody parts of the vegetation and hence provides a view of vegetation changes that can be complementary to NDVI. Liu et al. (2013) 278 used VOD trends to investigate biomass changes and found that VOD was closely related to precipitation changes in drylands. To complement their work with NDVI, Andela et al. (2013) 279 also applied the RESTREND method to VOD. By interpreting NDVI and VOD trends together they were able to differentiate changes to the herbaceous and woody components of the biomass. They reported that many dryland regions are experiencing an increase in the woody fraction often associated with shrub encroachment and suggest that this was aided by CO 2 fertilisation.

Biophysical models use global datasets that describe climate patterns and soil groups, combined with observations of land use, to define classes of potential productivity and map general land degradation (Gibbs and Salmon 2015 280 ). All biophysical models have their own set of assumptions and limitations that contribute to their overall uncertainty, including: model structure; spatial scale; data requirements (with associated errors); spatial heterogeneities of socio-economic conditions; and agricultural technologies used. Models have been used to estimate the vegetation productivity potential of land (Cai et al. 2011 281 ) and to understand the causes of observed vegetation changes. Zhu et al. (2016) 282 used an ensemble of ecosystem models to investigate causes of vegetation changes from 1982–2009, using a factorial simulation approach. They found CO 2 fertilisation to be the dominant effect globally, though climate and land-cover change were the dominant effects in various dryland locations. Borrelli et al. (2017) 283 modelled that about 6.1% of the global land area experienced very high soil erosion rates (exceeding 10 Mg ha− 1 yr− 1 ) in 2012, particularly in South America, Africa, and Asia.

Overall, improved estimation and mapping of areas undergoing desertification are needed. This requires a combination of rapidly expanding sources of remotely sensed data, ground observations and new modelling approaches. This is a critical gap, especially in the context of measuring progress towards achieving the land degradation-neutrality target by 2030 in the framework of SDGs.

Regional scale

While global-scale studies provide information for any region, there are numerous studies that focus on sub-continental scales, providing more in-depth analysis and understanding. Regional and local studies are important to detect location-specific trends in desertification and heterogeneous influences of climate change on desertification. However, these regional and local studies use a wide variety of methodologies, making direct comparisons difficult. For details of the methodologies applied by each study refer to the individual papers.

It is estimated that 46 of the 54 countries in Africa are vulnerable to desertification, with some already affected (Prăvălie 2016 284 ). Moderate or higher severity degradation over recent decades has been identified in many river basins including the Nile (42% of area), Niger (50%), Senegal (51%), Volta (67%), Limpopo (66%) and Lake Chad (26%) (Thiombiano and Tourino-Soto 2007 285 ).

The Horn of Africa is getting drier (Damberg and AghaKouchak 2014 286 ; Marshall et al. 2012 287 ) exacerbating the desertification already occurring (Oroda 2001 288 ). The observed decline in vegetation cover is diminishing ecosystem services (Pricope et al. 2013 289 ). Based on NDVI residuals, Kenya experienced persistent negative (positive) trends over 21.6% (8.9%) of the country, for the period 1992–2015 (Gichenje and Godinho 2018 290 ). Fragmentation of habitats, reduction in the range of livestock grazing, and higher stocking rates are considered to be the main drivers for vegetation structure loss in the rangelands of Kenya (Kihiu 2016 291 ; Otuoma et al. 2009 292 ).

Despite desertification in the Sahel being a major concern since the 1970s, wetting and greening conditions have been observed in this region over the last three decades (Anyamba and Tucker 2005 294 ; Huber et al. 2011 295 ; Brandt et al. 2015 296 ; Rishmawi et al. 2016 297 ; Tian et al. 2016 298 ; Leroux et al. 2017 299 ; Herrmann et al. 2005 300 ; Damberg and AghaKouchak 2014 301 ). Cropland areas in the Sahel region of West Africa have doubled since 1975, with settlement area increasing by about 150% (Traore et al. 2014 302 ). Thomas and Nigam (2018) 303 found that the Sahara expanded by 10% over the 20th century based on annual rainfall. In Burkina Faso, Dimobe et al. (2015) 304 estimated that from 1984 to 2013, bare soils and agricultural lands increased by 18.8% and 89.7%, respectively, while woodland, gallery forest, tree savannahs, shrub savannahs and water bodies decreased by 18.8%, 19.4%, 4.8%, 45.2% and 31.2%, respectively. In Fakara region in Niger, a 5% annual reduction in herbaceous yield between 1994 and 2006 was largely explained by changes in land use, grazing pressure and soil fertility (Hiernaux et al. 2009 305 ). Aladejana et al. (2018) 306 found that between 1986 and 2015, 18.6% of the forest cover around the Owena River basin was lost. For the period 1982–2003, Le et al. (2012) 307 found that 8% of the Volta River basin’s landmass had been degraded, with this increasing to 65% after accounting for the effects of CO 2 (and NOx) fertilisation.

Greening has also been observed in parts of southern Africa but it is relatively weak compared to other regions of the continent (Helldén and Tottrup 2008 308 ; Fensholt et al. 2012 309 ). However, greening can be accompanied by desertification when factors such as decreasing species richness, changes in species composition and shrub encroachment are observed (Smith et al. 2013 310 ; Herrmann and Tappan 2013 311 ; Kaptué et al. 2015 312 ; Herrmann and Sop 2016 313 ; Saha et al. 2015) 314 (Sections 3.1.4 and 3.7.3). In the Okavango river Basin in southern Africa, conversion of land towards higher utilisation intensities, unsustainable agricultural practises and overexploitation of the savanna ecosystems have been observed in recent decades (Weinzierl et al. 2016 315 ).

In the arid Algerian High Plateaus, desertification due to both climatic and human causes led to the loss of indigenous plant biodiversity between 1975 and 2006 (Hirche et al. 2011 316 ). Ayoub (1998) 317 identified 64 Mha in Sudan as degraded, with the Central North Kordofan state being most affected. However, reforestation measures in the last decade sustained by improved rainfall conditions have led to low-medium regrowth conditions in about 20% of the area (Dawelbait and Morari 2012 318 ). In Morocco, areas affected by desertification were predominantly on plains with high population and livestock pressure (del Barrio et al. 2016 319 ; Kouba et al. 2018 320 ; Lahlaoi et al. 2017 321 ). The annual costs of soil degradation were estimated at about 1% of Gross Domestic Product (GDP) in Algeria and Egypt, and about 0.5% in Morocco and Tunisia (Réquier-Desjardins and Bied-Charreton 2006 322 ).

Prăvălie (2016) 323 found that desertification is currently affecting 38 of 48 countries in Asia. The changes in drylands in Asia over the period 1982–2011 were mixed, with some areas experiencing vegetation improvement while others showed reduced vegetation (Miao et al. 2015a 324 ). Major river basins undergoing salinisation include: Indo-Gangetic Basin in India (Lal and Stewart 2012 325 ), Indus Basin in Pakistan (Aslam and Prathapar 2006 326 ), Yellow River Basin in China (Chengrui and Dregne 2001 327 ), Yinchuan Plain in China (Zhou et al. 2013 328 ), Aral Sea Basin of Central Asia (Cai et al. 2003 329 ; Pankova 2016 330 ; Qadir et al. 2009 331 ).

Helldén and Tottrup (2008) 332 highlighted a greening trend in East Asia between 1982 and 2003. Over the past several decades, air temperature and the rainfall increased in the arid and hyper-arid region of Northwest China (Chen et al. 2015 333 ; Wang et al. 2017 334 ). Within China, rainfall erosivity has shown a positive trend in dryland areas between 1961 and 2012 (Yang and Lu 2015 335 ). While water erosion area in Xinjiang, China, has decreased by 23.2%, erosion considered as severe or intense was still increasing (Zhang et al. 2015 336 ). Xue et al. (2017) 337 used remote sensing data covering 1975 to 2015 to show that wind-driven desertified land in northern Shanxi in China had expanded until 2000, before contracting again. Li et al. (2012) 338 used satellite data to identify desertification in Inner Mongolia, China and found a link between policy changes and the locations and extent of human-caused desertification. Several oasis regions in China have seen increases in cropland area, while forests, grasslands and available water resources have decreased (Fu et al. 2017 339 ; Muyibul et al. 2018 340 ; Xie et al. 2014 341 ). Between 1990 and 2011 15.3% of Hogno Khaan nature reserve in central Mongolia was subjected to desertification (Lamchin et al. 2016 342 ). Using satellite data Liu et al. (2013) 343 found the area of Mongolia undergoing non-climatic desertification was associated with increases in goat density and wildfire occurrence.

In Central Asia, drying up of the Aral Sea is continuing to have negative impacts on regional microclimate and human health (Issanova and Abuduwaili 2017 344 ; Lioubimtseva 2015 345 ; Micklin 2016 346 ; Xi and Sokolik 2015 347 ). Half of the region’s irrigated lands, especially in the Amudarya and Syrdarya river basins, were affected by secondary salinisation (Qadir et al. 2009 349 ). Le et al. (2016) 1792 showed that about 57% of croplands in Kazakhstan and about 20% of croplands in Kyrgyzstan had reductions in their vegetation productivity between 1982 and 2006. Chen et al. (2019) 350 indicated that about 58% of the grasslands in the region had reductions in their vegetation productivity between 1999 and 2015. Anthropogenic factors were the main driver of this loss in Turkmenistan and Uzbekistan, while the role of human drivers was smaller than that of climate-related factors in Tajikistan and Kyrgyzstan (Chen et al. 2019). The total costs of land degradation in Central Asia were estimated to equal about 6 billion USD annually (Mirzabaev et al. 2016 351 ).

Damberg and AghaKouchak (2014) 352 found that parts of South Asia experienced drying over the last three decades. More than 75% of the area of northern, western and southern Afghanistan is affected by overgrazing and deforestation (UNEP-GEF 2008 353 ). Desertification is a serious problem in Pakistan with a wide range of human and natural causes (Irshad et al. 2007 354 ; Lal 2018 355 ). Similarly, desertification affects parts of India (Kundu et al. 2017 356 ; Dharumarajan et al. 2018 357 ; Christian et al. 2018 358 ). Using satellite data to map various desertification processes, Ajai et al. (2009) 359 found that 81.4 Mha were subject to various processes of desertification in India in 2005, while salinisation affected 6.73 Mha in the country (Singh 2009 360 ).

Saudi Arabia is highly vulnerable to desertification (Ministry of Energy Industry and Mineral Resources 2016 361 ), with this vulnerability expected to increase in the north-western parts of the country in the coming decades. Yahiya (2012) 362 found that Jazan, south-western Saudi Arabia, lost about 46% of its vegetation cover from 1987 to 2002. Droughts and frequent dust storms were shown to impose adverse impacts over Saudi Arabia, especially under global warming and future climate change (Hasanean et al. 2015 363 ). In north-west Jordan, 18% of the area was prone to severe to very severe desertification (Al-Bakri et al. 2016 364 ). Large parts of the Syrian drylands have been identified as undergoing desertification (Evans and Geerken 2004 365 ; Geerken and Ilaiwi 2004 366 ). Moridnejad et al. (2015) 367 identified newly desertified regions in the Middle East based on dust sources, finding that these regions accounted for 39% of all detected dust source points. Desertification has increased substantially in Iran since the 1930s. Despite numerous efforts to rehabilitate degraded areas, it still poses a major threat to agricultural livelihoods in the country (Amiraslani and Dragovich 2011 368 ).

Damberg and AghaKouchak (2014) 369 found that wetter conditions were experienced in northern Australia over the last three decades with widespread greening observed between 1981 and 2006 over much of Australia, except for eastern Australia where large areas were affected by droughts from 2002 to 2009 based on Advanced High Resolution Radiometer (AVHRR) satellite data (Donohue et al. 2009) 370 . For the period 1982–2013, Burrell et al. (2017) 371 also found widespread greening over Australia including eastern Australia over the post-drought period. This dramatic change in the trend found for eastern Australia emphasises the dominant role played by precipitation in the drylands. Degradation due to anthropogenic activities and other causes affects over 5% of Australia, particularly near the central west coast. Jackson and Prince (2016) used a local NPP scaling approach applied with MODIS derived vegetation data to quantify degradation in a dryland watershed in Northern Australia from 2000 to 2013. They estimated that 20% of the watershed was degraded. Salinisation has also been found to be degrading parts of the Murray-Darling Basin in Australia (Rengasamy 2006 372 ). Eldridge and Soliveres (2014) 373 examined areas undergoing woody encroachment in eastern Australia and found that rather than degrading the landscape, the shrubs often enhanced ecosystem services.

Drylands cover 33.8% of northern Mediterranean countries: approximately 69% of Spain, 66% of Cyprus, and between 16% and 62% in Greece, Portugal, Italy and France (Zdruli 2011 374 ). The European Environment Agency (EEA) indicated that 14 Mha, that is 8% of the territory of the European Union (mostly in Bulgaria, Cyprus, Greece, Italy, Romania, Spain and Portugal), had a ‘very high’ and ‘high sensitivity’ to desertification (European Court of Auditors 2018 375 ). This figure increases to 40 Mha (23% of the EU territory) if ‘moderately’ sensitive areas are included (Prăvălie et al. 2017 376 ; European Court of Auditors 2018 377 ). Desertification in the region is driven by irrigation developments and encroachment of cultivation on rangelands (Safriel 2009 378 ) caused by population growth, agricultural policies, and markets. According to a recent assessment report (ECA 2018 379 ), Europe is increasingly affected by desertification leading to significant consequences on land use, particularly in Portugal, Spain, Italy, Greece, Malta, Cyprus, Bulgaria and Romania. Using the Universal Soil Loss Equation, it was estimated that soil erosion can be as high as 300 t ha– 1 yr– 1 (equivalent to a net loss of 18 mm yr– 1 ) in Spain (López-Bermúdez 1990 380 ). For the badlands region in south-east Spain, however, it was shown that biological soil crusts effectively prevent soil erosion (Lázaro et al. 2008 381 ). In Mediterranean Europe, Guerra et al. (2016) 382 found a reduction of erosion due to greater effectiveness of soil erosion prevention between 2001 and 2013. Helldén and Tottrup (2008) 383 observed a greening trend in the Mediterranean between 1982 and 2003, while Fensholt et al. (2012) 384 also show a dominance of greening in Eastern Europe.

In Russia, at the beginning of the 2000s, about 7% of the total area (that is, approximately 130 Mha) was threatened by desertification (Gunin and Pankova 2004 385 ; Kust et al. 2011 386 ). Turkey is considered highly vulnerable to drought, land degradation and desertification (Türkeş 1999 387 , 2003 388 ). About 60% of Turkey’s land area is characterised with hydro-climatological conditions favourable for desertification (Türkeş 2013 389 ). ÇEMGM (2017) 390 estimated that about half of Turkey’s land area (48.6%) is prone to moderate-to-high desertification.

North America

Drylands cover approximately 60% of Mexico. According to Pontifes et al. (2018) 391 , 3.5% of the area was converted from natural vegetation to agriculture and human settlements between 2002 and 2011. The region is highly vulnerable to desertification due to frequent droughts and floods (Méndez and Magaña 2010 392 ; Stahle et al. 2009 393 ; Becerril-Pina Rocio et al. 2015 394 ).

For the period 2000–2011 the overall difference between potential and actual NPP in different land capability classes in the south-western United States was 11.8% (Noojipady et al. 2015 395 ); reductions in grassland-savannah and livestock grazing area and forests were the highest. Bush encroachment is observed over a fairly wide area of grasslands in the western United States, including Jornada Basin within the Chihuahuan Desert, and is spreading at a fast rate despite grazing restrictions intended to curb the spread (Yanoff and Muldavin 2008 396 ; Browning and Archer 2011 397 ; Van Auken 2009 398 ; Rachal et al. 2012 399 ). In comparing sand dune migration patterns and rates between 1995 and 2014, Potter and Weigand (2016) 400 established that the area covered by stable dune surfaces, and sand removal zones, decreased, while sand accumulation zones increased from 15.4 to 25.5 km 2 for Palen Dunes in the Southern California desert, while movement of Kelso Dunes is less clear (Lam et al. 2011 401 ). Within the United States, average soil erosion rates on all croplands decreased by about 38% between 1982 and 2003 due to better soil management practices (Kertis 2003 402 ).

Central and South America

Morales et al. (2011) 403 indicated that desertification costs between 8% and 14% of gross agricultural product in many Central and South American countries. Parts of the dry Chaco and Caldenal regions in Argentina have undergone widespread degradation over the last century (Verón et al. 2017 404 ; Fernández et al. 2009 405 ). Bisigato and Laphitz (2009) 406 identified overgrazing as a cause of desertification in the Patagonian Monte region of Argentina. Vieira et al. (2015) 407 found that 94% of northeast Brazilian drylands were susceptible to desertification. It is estimated that up to 50% of the area was being degraded due to frequent prolonged droughts and clearing of forests for agriculture. This land-use change threatens the extinction of around 28 native species (Leal et al. 2005 408 ). In Central Chile, dryland forest and shrubland area was reduced by 1.7% and 0.7%, respectively, between 1975 and 2008 (Schulz et al. 2010 409 ).

Attribution of desertification

Desertification is a result of complex interactions within coupled social-ecological systems. Thus, the relative contributions of climatic, anthropogenic and other drivers of desertification vary depending on specific socio-economic and ecological contexts. The high natural climate variability in dryland regions is a major cause of vegetation changes but does not necessarily imply degradation. Drought is not degradation as the land productivity may return entirely once the drought ends (Kassas 1995 410 ). However, if droughts increase in frequency, intensity and/or duration they may overwhelm the vegetation’s ability to recover (ecosystem resilience, Prince et al. 2018), causing degradation. Assuming a stationary climate and no human influence, rainfall variability results in fluctuations in vegetation dynamics which can be considered temporary, as the ecosystem tends to recover with rainfall, and desertification does not occur (Ellis 1995 411 ; Vetter 2005 412 ; von Wehrden et al. 2012 413 ). Climate change on the other hand, exemplified by a non-stationary climate, can gradually cause a persistent change in the ecosystem through aridification and CO 2 changes. Assuming no human influence, this ‘natural’ climatic version of desertification may take place rapidly, especially when thresholds are reached (Prince et al. 2018 414 ), or over longer periods of time as the ecosystems slowly adjust to a new climatic norm through progressive changes in the plant community composition. Accounting for this climatic variability is required before attributions to other causes of desertification can be made.

For attributing vegetation changes to climate versus other causes, rain use efficiency (RUE – the change in net primary productivity (NPP) per unit of precipitation) and its variations in time have been used (Prince et al. 1998 415 ). Global applications of RUE trends to attribute degradation to climate or other (largely human) causes have been performed by Bai et al. (2008) 416 and Le et al. (2016) 417 (Section 3.2.1.1). The RESTREND (residual trend) method analyses the correlation between annual maximum NDVI (or other vegetation index as a proxy for NPP) and precipitation by testing accumulation and lag periods for the precipitation (Evans and Geerken 2004 418 ). The identified relationship with the highest correlation represents the maximum amount of vegetation variability that can be explained by the precipitation, and corresponding RUE values can be calculated. Using this relationship, the climate component of the NDVI time series can be reconstructed, and the difference between this and the original time series (the residual) is attributed to anthropogenic and other causes.

The RESTREND method, or minor variations of it, have been applied extensively. Herrmann and Hutchinson (2005) 419 concluded that climate was the dominant causative factor for widespread greening in the Sahel region from 1982–2003, and anthropogenic and other factors were mostly producing land improvements or no change. However, pockets of desertification were identified in Nigeria and Sudan. Similar results were also found from 1982–2007 by Huber et al. (2011) 420 . Wessels et al. (2007) 421 applied RESTREND to South Africa and showed that RESTREND produced a more accurate identification of degraded land than RUE alone. RESTREND identified a smaller area undergoing desertification due to non-climate causes compared to the NDVI trends. Liu et al. (2013) 430 extended the climate component of RESTREND to include temperature and applied this to VOD observations of the cold drylands of Mongolia. They found the area undergoing desertification due to non-climatic causes is much smaller than the area with negative VOD trends. RESTREND has also been applied in several other studies to the Sahel (Leroux et al. 2017 422 ), Somalia (Omuto et al. 2010) 423 , West Africa (Ibrahim et al. 2015) 424 , China (Li et al. 2012 425 ; Yin et al. 2014 426 ), Central Asia (Jiang et al. 2017 427 ), Australia (Burrell et al. 2017 428 ) and globally (Andela et al. 2013 429 ). In each of these studies the extent to which desertification can be attributed to climate versus other causes varies across the landscape.

These studies represent the best regional, remote-sensing based attribution studies to date, noting that RESTREND and RUE have some limitations (Higginbottom and Symeonakis 2014 431 ). Vegetation growth (NPP) changes slowly compared to rainfall variations and may be sensitive to rainfall over extended periods (years), depending on vegetation type. Detection of lags and the use of weighted antecedent rainfall can partially address this problem, though most studies do not do this. The method addresses changes since the start of the time series; it cannot identify whether an area is already degraded at the start time. It is assumed that climate, particularly rainfall, is a principal factor in vegetation change which may not be true in more humid regions.

Another assumption in RESTREND is that any trend is linear throughout the period examined. That is, there are no discontinuities (break points) in the trend. Browning et al. (2017) 432 have shown that break points in NDVI time series reflect vegetation changes based on long-term field sites. To overcome this limitation, Burrell et al. (2017) 433 introduced the Time Series Segmentation-RESTREND (TSS-RESTREND) which allows a breakpoint or turning point within the period examined (Figure 3.7). Using TSS-RESTREND over Australia they identified more than double the degrading area than could be identified with a standard RESTREND analysis. The occurrence and drivers of abrupt change (turning points) in ecosystem functioning were also examined by Horion et al. (2016) 434 over the semi-arid Northern Eurasian agricultural frontier. They combined trend shifts in RUE, field data and expert knowledge, to map environmental hotspots of change and attribute them to climate and human activities. One-third of the area showed significant change in RUE, mainly occurring around the fall of the Soviet Union (1991) or as the result of major droughts. Recent human-induced turning points in ecosystem functioning were uncovered around Volgograd (Russia) and around Lake Balkhash (Kazakhstan), attributed to recultivation, increased salinisation, and increased grazing.

Attribution of vegetation changes to human activity has also been done within modelling frameworks. In these methods ecosystem models are used to simulate potential natural vegetation dynamics, and this is compared to the observed state. The difference is attributed to human activities. Applied to the Sahel region during the period of 1982–2002, it showed that people had a minor influence on vegetation changes (Seaquist et al. 2009 435 ). Similar model/observation comparisons performed globally found that CO 2 fertilisation was the strongest forcing at global scales, with climate having regionally varying effects (Mao et al. 2013 436 ; Zhu et al. 2016 437 ). Land-use/ land-cover change was a dominant forcing in localised areas. The use of this method to examine vegetation changes in China (1982–2009) attributed most of the greening trend to CO 2 fertilisation and nitrogen (N) deposition (Piao et al. 2015). However in some parts of northern and western China, which includes large areas of drylands, Piao et al. (2015) 438 found climate changes could be the dominant forcing. In the northern extratropical land surface, the observed greening was consistent with increases in greenhouse gases (notably CO 2 ) and the related climate change, and not consistent with a natural climate that does not include anthropogenic increase in greenhouse gases (Mao et al. 2016 439 ). While many studies found widespread influence of CO 2 fertilisation, it is not ubiquitous; for example, Lévesque et al. (2014) found little response to CO 2 fertilisation in some tree species in Switzerland/northern Italy.

Using multiple extreme-event attribution methodologies, Uhe et al. (2018) 440 shows that the dominant influence for droughts in eastern Africa during the October–December ‘short rains’ season is the prevailing tropical SST patterns, although temperature trends mean that the current drought conditions are hotter than they would have been without climate change. Similarly, Funk et al. (2019) 441 found that 2017 March–June East African drought was influenced by Western Pacific SST, with high SST conditions attributed to climate change.

There are numerous local case studies on attribution of desertification, which use different periods, focus on different land uses and covers, and consider different desertification processes. For example, two-thirds of the observed expansion of the Sahara Desert from 1920–2003 has been attributed to natural climate cycles (the cold phase of Atlantic Multi-Decadal Oscillation and Pacific Decadal Oscillation) (Thomas and Nigam 2018 442 ). Some studies consider drought to be the main driver of desertification in Africa (e.g., Masih et al. 2014 443 ). However, other studies suggest that although droughts may contribute to desertification, the underlying causes are human activities (Kouba et al. 2018 444 ). Brandt et al. (2016a) found that woody vegetation trends are negatively correlated with human population density. Changes in land use, water pumping and flow diversion have enhanced drying of wetlands and salinisation of freshwater aquifers in Israel (Inbar 2007 445 ). The dryland territory of China has been found to be very sensitive to both climatic variations and land-use/land-cover changes (Fu et al. 2000 446 ; Liu and Tian 2010 447 ; Zhao et al. 2013, 2006 448 ). Feng et al. (2015) shows that socio-economic factors were dominant in causing desertification in north Shanxi, China, between 1983 and 2012, accounting for about 80% of desertification expansion. Successful grass establishment has been impeded by overgrazing and nutrient depletion leading to the encroachment of shrubs into the northern Chihuahuan Desert (USA) since the mid-19th century (Kidron and Gutschick 2017 449 ). Human activities led to rangeland degradation in Pakistan and Mongolia during 2000–2011 (Lei et al. 2011 450 ). More equal shares of climatic (temperature and precipitation trends) and human factors were attributed for changes in rangeland condition in China (Yang et al. 2016 451 ).

This kaleidoscope of local case studies demonstrates how attribution of desertification is still challenging for several reasons. Firstly, desertification is caused by an interaction of different drivers which vary in space and time. Secondly, in drylands, vegetation reacts closely to changes in rainfall so the effect of rainfall changes on biomass needs to be ‘removed’ before attributing desertification to human activities. Thirdly, human activities and climatic drivers impact vegetation/ ecosystem changes at different rates. Finally, desertification manifests as a gradual change in ecosystem composition and structure (e.g., woody shrub invasion into grasslands). Although initiated at a limited location, ecosystem change may propagate throughout an extensive area via a series of feedback mechanisms. This complicates the attribution of desertification to human and climatic causes, as the process can develop independently once started.

The drivers of dryland vegetation change. The mean annual change in NDVImax between 1982 and 2015 (see Figure 3.6 for total change using Global Inventory Modelling and Mapping Studies NDVI3g v1 dataset) attributable to(a)CO2 fertilisation(b)climate and (c) land use. The change attributable to CO2 fertilisation was calculated using the CO2 fertilisation relationship described in Franks […]

The drivers of dryland vegetation change. The mean annual change in NDVImax between 1982 and 2015 (see Figure 3.6 for total change using Global Inventory Modelling and Mapping Studies NDVI3g v1 dataset) attributable to(a)CO 2 fertilisation(b)climate and (c) land use. The change attributable to CO 2 fertilisation was calculated using the CO 2 fertilisation relationship described in Franks et al. 2013 1793 . The Time Series Segmented Residual Trends (TSS-RESTREND) method (Burrell et al. 2017 1794 ) applied to the CO 2 -adjusted NDVI was used to separate Climate and Land Use. A multi-climate dataset ensemble was used to reduce the impact of dataset errors (Burrell et al. 2018 1795 ). Non-dryland regions (aridity index >0.65) are masked in dark grey. Areas where the change did not meet the multi-run ensemble significance criteria, or are smaller than the error in the sensors (±0.00001) are masked in white

Rasmussen et al. (2016) 452 studied the reasons behind the overall lack of scientific agreement in trends of environmental changes in the Sahel, including their causes. The study indicated that these are due to differences in conceptualisations and choice of indicators, biases in study site selection, differences in methods, varying measurement accuracy, differences in time and spatial scales. High-resolution, multi-sensor airborne platforms provide a way to address some of these issues (Asner et al. 2012 453 ).

The major conclusion of this section is that, with all the shortcomings of individual case studies, relative roles of climatic and human drivers of desertification are location-specific and evolve over time ( high confidence ). Biophysical research on attribution and socio-economic research on drivers of land degradation have long studied the same topic, but in parallel, with little interdisciplinary integration. Interdisciplinary work to identify typical patterns, or typologies, of such interactions of biophysical and human drivers of desertification (not only of dryland vulnerability), and their relative shares, done globally in comparable ways, will help in the formulation of better informed policies to address desertification and achieve land degradation neutrality.

Desertification feedbacks to climate

While climate change can drive desertification (Section 3.1.4.1), the process of desertification can also alter the local climate, providing a feedback (Sivakumar 2007 454 ). These feedbacks can alter the carbon cycle, and hence the level of atmospheric CO 2 and its related global climate change, or they can alter the surface energy and water budgets, directly impacting the local climate. While these feedbacks occur in all climate zones (Chapter 2), here we focus on their effects in dryland regions and assess the literature concerning the major desertification feedbacks to climate. The main feedback pathways discussed throughout Section 3.3 are summarised in Figure 3.8.

Drylands are characterised by limited soil moisture compared to humid regions. Thus, the sensible heat (heat that causes the atmospheric temperature to rise) accounts for more of the surface net radiation than latent heat (evaporation) in these regions (Wang and Dickinson 2013 455 ). This tight coupling between the surface energy balance and the soil moisture in semi-arid and dry sub-humid zones makes these regions susceptible to land–atmosphere feedback loops that can amplify changes to the water cycle (Seneviratne et al. 2010 456 ). Changes to the land surface caused by desertification can change the surface energy budget, altering the soil moisture and triggering these feedbacks.

Schematic of main pathways through which desertification can feed back on climate, as discussed in Section 3.4. Note: Red arrows indicate a positive effect. Blue arrows indicate a negative effect. Grey arrows indicate an indeterminate effect (potentially both positive and negative). Solid arrows are direct while dashed arrows are indirect.

Sand and dust aerosols

Sand and mineral dust are frequently mobilised from sparsely vegetated drylands forming ‘sand storms’ or ‘dust storms’ (UNEP et al. 2016 457 ). The African continent is the most important source of desert dust; perhaps 50% of atmospheric dust comes from the Sahara (Middleton 2017 458 ). Ginoux et al. (2012) 459 estimated that 25% of global dust emissions have anthropogenic origins, often in drylands. These events can play an important role in the local energy balance. Through reducing vegetation cover and drying the surface conditions, desertification can increase the frequency of these events. Biological or structural soil crusts have been shown to effectively stabilise dryland soils. Thus their loss due to intense land use and/ or climate change can be expected to cause an increase in sand and dust storms ( high confidence ) (Rajot et al. 2003 460 ; Field et al. 2010 461 ; Rodriguez-Caballero et al. 2018 462 ). These sand and dust aerosols impact the regional climate in several ways (Choobari et al. 2014 463 ). The direct effect is the interception, reflection and absorption of solar radiation in the atmosphere, reducing the energy available at the land surface and increasing the temperature of the atmosphere in layers with sand and dust present (Kaufman et al. 2002 464 ; Middleton 2017 465 ; Kok et al. 2018 466 ). The heating of the dust layer can alter the relative humidity and atmospheric stability, which can change cloud lifetimes and water content. This has been referred to as the semi-direct effect (Huang et al. 2017 467 ). Aerosols also have an indirect effect on climate through their role as cloud condensation nuclei, changing cloud radiative properties as well as the evolution and development of precipitation (Kaufman et al. 2002 468 ). While these indirect effects are more variable than the direct effects, depending on the types and amounts of aerosols present, the general tendency is toward an increase in the number, but a reduction in the size of cloud droplets, increasing the cloud reflectivity and decreasing the chances of precipitation. These effects are referred to as aerosol-radiation and aerosol–cloud interactions (Boucher et al. 2013 469 ).

There is high confidence that there is a negative relationship between vegetation green-up and the occurrence of dust storms (Engelstaedter et al. 2003 470 ; Fan et al. 2015 471 ; Yu et al. 2015 472 ; Zou and Zhai 2004 473 ). Changes in groundwater can affect vegetation and the generation of atmospheric dust (Elmore et al. 2008 474 ). This can occur through groundwater processes such as the vertical movement of salt to the surface causing salinisation, supply of near-surface soil moisture, and sustenance of groundwater dependent vegetation. Groundwater dependent ecosystems have been identified in many dryland regions around the world (Decker et al. 2013 475 ; Lamontagne et al. 2005 476 ; Patten et al. 2008 477 ). In these locations declining groundwater levels can decrease vegetation cover. Cook et al. (2009) 478 found that dust aerosols intensified the ‘Dust Bowl’ drought in North America during the 1930s.

By decreasing the amount of green cover and hence increasing the occurrence of sand and dust storms, desertification will increase the amount of shortwave cooling associated with the direct effect ( high confidence ). There is medium confidence that the semi-direct and indirect effects of this dust would tend to decrease precipitation and hence provide a positive feedback to desertification (Huang et al. 2009 479 ; Konare et al. 2008 480 ; Rosenfeld et al. 2001 481 ; Solmon et al. 2012 482 ; Zhao et al. 2015 483 ). However, the combined effect of dust has also been found to increase precipitation in some areas (Islam and Almazroui 2012 484 ; Lau et al. 2009 485 ; Sun et al. 2012 486 ). The overall combined effect of dust aerosols on desertification remains uncertain with low agreement between studies that find positive (Huang et al. 2014 487 ), negative (Miller et al. 2004 488 ) or no feedback on desertification (Zhao et al. 2015 489 ).

Off-site feedbacks

Aerosols can act as a vehicle for the long-range transport of nutrients to oceans (Jickells et al. 2005 490 ; Okin et al. 2011 491 ) and terrestrial land surfaces (Das et al. 2013 492 ). In several locations, notably the Atlantic Ocean, the west of northern Africa, and the Pacific Ocean east of northern China, a considerable amount of mineral dust aerosols, sourced from nearby drylands, reaches the oceans. It was estimated that 60% of dust transported off Africa is deposited in the Atlantic Ocean (Kaufman et al. 2005 493 ), while 50% of the dust generated in Asia reaches the Pacific Ocean or further (Uno et al. 2009 494 ; Zhang et al. 1997 495 ). The Sahara is also a major source of dust for the Mediterranean basin (Varga et al. 2014 496 ). The direct effect of atmospheric dust over the ocean was found to be a cooling of the ocean surface ( limited evidence, high agreement ) (Evan and Mukhopadhyay 2010 497 ; Evan et al. 2009 498 ) with the tropical North Atlantic mixed layer cooling by over 1°C (Evan et al. 2009 499 ).

It has been suggested that dust may act as a source of nutrients for the upper ocean biota, enhancing the biological activity and related carbon sink ( medium evidence, low agreement ) (Lenes et al. 2001 500 ; Shaw et al. 2008 501 ; Neuer et al. 2004 502 ). The overall response depends on the environmental controls on the ocean biota, the type of aerosols including their chemical constituents, and the chemical environment in which they dissolve (Boyd et al. 2010 503 ).

Dust deposited on snow can increase the amount of absorbed solar radiation leading to more rapid melting (Painter et al. 2018 504 ), impacting a region’s hydrological cycle ( high confidence ). Dust deposition on snow and ice has been found in many regions of the globe (e.g., Painter et al. 2018; Kaspari et al. 2014 505 ; Qian et al. 2015 506 ; Painter et al. 2013 507 ), however quantification of the effect globally and estimation of future changes in the extent of this effect remain knowledge gaps.

Changes in surface albedo

Increasing surface albedo in dryland regions will impact the local climate, decreasing surface temperature and precipitation, and provide a positive feedback on the albedo ( high confidence ) (Charney et al. 1975 508 ). This albedo feedback can occur in desert regions worldwide (Zeng and Yoon 2009 509 ). Similar albedo feedbacks have also been found in regional studies over the Middle East (Zaitchik et al. 2007 510 ), Australia (Evans et al. 2017 511 ; Meng et al. 2014a 512 , b 513 ), South America (Lee and Berbery 2012 514 ) and the USA (Zaitchik et al. 2013 515 ).

Recent work has also found albedo in dryland regions can be associated with soil surface communities of lichens, mosses and cyanobacteria (Rodriguez-Caballero et al. 2018 516 ). These communities compose the soil crust in these ecosystems and due to the sparse vegetation cover, directly influence the albedo. These communities are sensitive to climate changes, with field experiments indicating albedo changes greater than 30% are possible. Thus, changes in these communities could trigger surface albedo feedback processes ( limited evidence, high agreement ) (Rutherford et al. 2017 517 ).

A further pertinent feedback relationship exists between changes in land-cover, albedo, carbon stocks and associated GHG emissions, particularly in drylands with low levels of cloud cover. One of the first studies to focus on the subject was Rotenberg and Yakir (2010) 518 , who used the concept of ‘radiative forcing’ to compare the relative climatic effect of a change in albedo with a change in atmospheric GHGs due to the presence of forest within drylands. Based on this analysis, it was estimated that the change in surface albedo due to the degradation of semi-arid areas has decreased radiative forcing in these areas by an amount equivalent to approximately 20% of global anthropogenic GHG emissions between 1970 and 2005.

Changes in vegetation and greenhouse gas fluxes

Terrestrial ecosystems have the ability to alter atmospheric GHGs through a number of processes (Schlesinger et al. 1990 519 ). This may be through a change in plant and soil carbon stocks, either sequestering atmospheric CO 2 during growth or releasing carbon during combustion and respiration, or through processes such as enteric fermentation of domestic and wild ruminants that leads to the release of methane and nitrous oxide (Sivakumar 2007 520 ). It is estimated that 241–470 GtC is stored in dryland soils (top 1 m) (Lal 2004 521 ; Plaza et al. 2018 522 ). When evaluating the effect of desertification, the net balance of all the processes and associated GHG fluxes needs to be considered.

Desertification usually leads to a loss in productivity and a decline in above – and below-ground carbon stocks (Abril et al. 2005 523 ; Asner et al. 2003 524 ). Drivers such as overgrazing lead to a decrease in both plant and SOC pools (Abdalla et al. 2018 525 ). While dryland ecosystems are often characterised by open vegetation, not all drylands have low biomass and carbon stocks in an intact state (Lechmere-Oertel et al. 2005 526 ; Maestre et al. 2012 527 ). Vegetation types such as the subtropical thicket of South Africa have over 70 tC ha– 1 in an intact state, greater than 60% of which is released into the atmosphere during degradation through overgrazing (Lechmere-Oertel et al. 2005 528 ; Powell 2009 529 ). In comparison, semi-arid grasslands and savannahs with similar rainfall, may have only 5–35 tC ha– 1 (Scholes and Walker 1993 530 ; Woomer et al. 2004 531 ).

At the same time, it is expected that a decline in plant productivity may lead to a decrease in fuel loads and a reduction in CO 2 , nitrous oxide and methane emissions from fire. In a similar manner, decreasing productivity may lead to a reduction in numbers of ruminant animals that in turn would decrease methane emissions. Few studies have focussed on changes in these sources of emissions due to desertification and it remains a field that requires further research.

In comparison to desertification through the suppression of primary production, the process of woody plant encroachment can result in significantly different climatic feedbacks. Increasing woody plant cover in open rangeland ecosystems leads to an increase in woody carbon stocks both above – and below-ground (Asner et al. 2003 532 ; Hughes et al. 2006 533 ; Petrie et al. 2015 534 ; Li et al. 2016 535 ). Within the drylands of Texas, USA, shrub encroachment led to a 32% increase in aboveground carbon stocks over a period of 69 years (3.8 tC ha– 1 to 5.0 tC ha– 1 ) (Asner et al. 2003 536 ). Encroachment by taller woody species can lead to significantly higher observed biomass and carbon stocks. For example, encroachment by Dichrostachys cinerea and several Vachellia species in the sub-humid savannahs of north-west South Africa led to an increase of 31–46 tC ha– 1 over a 50–65 year period (1936–2001) (Hudak et al. 2003 537 ). In terms of potential changes in SOC stocks, the effect may be dependent on annual rainfall and soil type. Woody cover generally leads to an increase in SOC stocks in drylands that have less than 800 mm of annual rainfall, while encroachment can lead to a loss of soil carbon in more humid ecosystems (Barger et al. 2011 538 ; Jackson et al. 2002 539 ).

The suppression of the grass layer through the process of woody encroachment may lead to a decrease in carbon stocks within this relatively small carbon pool (Magandana 2016 540 ). Conversely, increasing woody cover may lead to a decrease and even halt in surface fires and associated GHG emissions. In their analysis of drivers of fire in southern Africa, Archibald et al. (2009) 541 note that there is a potential threshold around 40% canopy cover, above which surface grass fires are rare. Whilst there have been a number of studies on changes in carbon stocks due to desertification in North America, southern Africa and Australia, a global assessment of the net change in carbon stocks – as well as fire and ruminant GHG emissions due to woody plant encroachment – has not been done yet.

Desertification impacts on natural and socio-economic systems under climate change

Impacts on natural and managed ecosystems, impacts on ecosystems and their services in drylands.

The Millenium Ecosystem Assessement (2005) 542 proposed four classes of ecosystem services: provisioning, regulating, supporting and cultural services (Cross-Chapter Box 8 in Chapter 6). These ecosystem services in drylands are vulnerable to the impacts of climate change due to high variability in temperature, precipitation and soil fertility (Enfors and Gordon 2008 543 ; Mortimore 2005 544 ). There is high confidence that desertification processes such as soil erosion, secondary salinisation, and overgrazing have negatively impacted provisioning ecosystem services in drylands, particularly food and fodder production (Majeed and Muhammad 2019 545 ; Mirzabaev et al. 2016 546 ; Qadir et al. 2009 547 ; Van Loo et al. 2017 548 ; Tokbergenova et al. 2018 549 ) (Section 3.4.2.2). Zika and Erb (2009) 550 reported an estimation of NPP losses between 0.8 and 2.0 GtC yr– 1 due to desertification, comparing the potential NPP and the NPP calculated for the year 2000. In terms of climatic factors, although climatic changes between 1976 and 2016 were found to be favourable for crop yields overall in Russia (Ivanov et al. 2018 551 ), yield decreases of up to 40–60% in dryland areas were caused by severe and extensive droughts (Ivanov et al. 2018 552 ). Increase in temperature can have a direct impact on animals in the form of increased physiological stress (Rojas-Downing et al. 2017 553 ), increased water requirements for drinking and cooling, a decrease in the production of milk, meat and eggs, increased stress during conception and reproduction (Nardone et al. 2010 554 ) or an increase in seasonal diseases and epidemics (Thornton et al. 2009 555 ; Nardone et al. 2010 556 ). Furthermore, changes in temperature can indirectly impact livestock through reducing the productivity and quality of feed crops and forages (Thornton et al. 2009 557 ; Polley et al. 2013 558 ). On the other hand, fewer days with extreme cold temperatures during winter in the temperate zones are associated with lower livestock mortality. The future projection of impacts on ecosystems is presented in Section 3.5.2.

Over-extraction is leading to groundwater depletion in many dryland areas ( high confidence ) (Mudd 2000 559 ; Mays 2013 560 ; Mahmod and Watanabe 2014 561 ; Jolly et al. 2008 562 ). Globally, groundwater reserves have been reduced since 1900, with the highest rate of estimated reductions of 145 km 3 yr– 1 between 2000 and 2008 (Konikow 2011 563 ). Some arid lands are very vulnerable to groundwater reductions, because the current natural recharge rates are lower than during the previous wetter periods (e.g., the Atacama Desert, and Nubian aquifer system in Africa) (Squeo et al. 2006 564 ; Mahmod and Watanabe 2014 565 ; Herrera et al. 2018 566 ).

Among regulating services, desertification can influence levels of atmospheric CO 2 . In drylands, the majority of carbon is stored below ground in the form of biomass and SOC (FAO 1995 567 ) (Section 3.3.3). Land-use changes often lead to reductions in SOC and organic matter inputs into soil (Albaladejo et al. 2013 568 ; Almagro et al. 2010 569 ; Hoffmann et al. 2012 570 ; Lavee et al. 1998 571 ; Rey et al. 2011 572 ), increasing soil salinity and soil erosion (Lavee et al. 1998 573 ; Martinez-Mena et al. 2008 574 ). In addition to the loss of soil, erosion reduces soil nutrients and organic matter, thereby impacting land’s productive capacity. To illustrate, soil erosion by water is estimated to result in the loss of 23–42 Mt of nitrogen and 14.6–26.4 Mt of phosphorus from soils globally each year (Pierzynski et al. 2017 575 ).

Precipitation, by affecting soil moisture content, is considered to be the principal determinant of the capacity of drylands to sequester carbon (Fay et al. 2008 576 ; Hao et al. 2008 577 ; Mi et al. 2015 578 ; Serrano-Ortiz et al. 2015 579 ; Vargas et al. 2012 580 ; Sharkhuu et al. 2016 581 ). Lower annual rainfall resulted in the release of carbon into the atmosphere for a number of sites located in Mongolia, China and North America (Biederman et al. 2017 582 ; Chen et al. 2009 583 ; Fay et al. 2008 584 ; Hao et al. 2008 585 ; Mi et al. 2015 586 ; Sharkhuu et al. 2016 587 ). Low soil water availability promotes soil microbial respiration, yet there is insufficient moisture to stimulate plant productivity (Austin et al. 2004 588 ), resulting in net carbon emissions at an ecosystem level. Under even drier conditions, photo degradation of vegetation biomass may often constitute an additional loss of carbon from an ecosystem (Rutledge et al. 2010 589 ). In contrast, years of good rainfall in drylands resulted in the sequestration of carbon (Biederman et al. 2017 590 ; Chen et al. 2009 591 ; Hao et al. 2008 592 ). In an exceptionally rainy year (2011) in the southern hemisphere, the semi-arid ecosystems of this region contributed 51% of the global net carbon sink (Poulter et al. 2014 593 ). These results suggest that arid ecosystems could be an important global carbon sink, depending on soil water availability (medium evidence, high agreement). However, drylands are generally predicted to become warmer with an increasing frequency of extreme drought and high rainfall events (Donat et al. 2016 594 ).

When desertification reduces vegetation cover, this alters the soil surface, affecting the albedo and the water balance (Gonzalez-Martin et al. 2014 595 ) (Section 3.3). In such situations, erosive winds have no more obstacles, which favours the occurrence of wind erosion and dust storms. Mineral aerosols have an important influence on the dispersal of soil nutrients and lead to changes in soil characteristics (Goudie and Middleton 2001 596 ; Middleton 2017 597 ). Thereby, the soil formation as a supporting ecosystem service is negatively affected (Section 3.3.1). Soil erosion by wind results in a loss of fine soil particles (silt and clay), reducing the ability of soil to sequester carbon (Wiesmeier et al. 2015 598 ). Moreover, dust storms reduce crop yields by loss of plant tissue caused by sandblasting (resulting in loss of plant leaves and hence reduced photosynthetic activity (Field et al. 2010 599 ), exposing crop roots, crop seed burial under sand deposits, and leading to losses of nutrients and fertiliser from topsoil (Stefanski and Sivakumar 2009 600 )). Dust storms also impact crop yields by reducing the quantity of water available for irrigation; they can decrease the storage capacity of reservoirs by siltation, and block conveyance canals (Middleton 2017 601 ; Middleton and Kang 2017 602 ; Stefanski and Sivakumar 2009 603 ). Livestock productivity is reduced by injuries caused by dust storms (Stefanski and Sivakumar 2009 604 ). Additionally, dust storms favour the dispersion of microbial and plant species, which can make local endemic species vulnerable to extinction and promote the invasion of plant and microbial species (Asem and Roy 2010 605 ; Womack et al. 2010 606 ). Dust storms increase microbial species in remote sites ( high confidence ) (Kellogg et al. 2004 607 ; Prospero et al. 2005 608 ; Griffin et al. 2006 609 ; Schlesinger et al. 2006 610 ; Griffin 2007 611 ; De Deckker et al. 2008 612 ; Jeon et al. 2011 613 ; Abed et al. 2012 614 ; Favet et al. 2013 615 ; Woo et al. 2013 616 ; Pointing and Belnap 2014 617 ).

Impacts on biodiversity: Plant and wildlife

Plant biodiversity

Over 20% of global plant biodiversity centres are located within drylands (White and Nackoney 2003 618 ). Plant species located within these areas are characterised by high genetic diversity within populations (Martínez-Palacios et al. 1999 619 ). The plant species within these ecosystems are often highly threatened by climate change and desertification (Millennium Ecosystem Assessment 2005b 620 ; Maestre et al. 2012 621 ). Increasing aridity exacerbates the risk of extinction of some plant species, especially those that are already threatened due to small populations or restricted habitats (Gitay et al. 2002 622 ). Desertification, including through land-use change, already contributed to the loss of biodiversity across drylands ( medium confidence ) (Newbold et al. 2015 623 ; Wilting et al. 2017 624 ). For example, species richness decreased from 234 species in 1978 to 95 in 2011 following long periods of drought and human driven degradation on the steppe land of south-western Algeria (Observatoire du Sahara et du Sahel 2013 625 ). Similarly, drought and overgrazing led to loss of biodiversity in Pakistan to the point that only drought-adapted species can now survive on the arid rangelands (Akhter and Arshad 2006 626 ). Similar trends were observed in desert steppes of Mongolia (Khishigbayar et al. 2015 627 ). In contrast, the increase in annual moistening of southern European Russia from the late 1980s to the beginning of the 21st century caused the restoration of steppe vegetation, even under conditions of strong anthropogenic pressure (Ivanov et al. 2018 628 ). The seed banks of annual species can often survive over the long term, germinating in wet years, suggesting that these species could be resilient to some aspects of climate change (Vetter et al. 2005 629 ). Yet, Hiernaux and Houérou (2006) 630 showed that overgrazing in the Sahel tended to decrease the seed bank of annuals, which could make them vulnerable to climate change over time. Perennial species, considered as the structuring element of the ecosystem, are usually less affected as they have deeper roots, xeromorphic properties and physiological mechanisms that increase drought tolerance (Le Houérou 1996 631 ). However, in North Africa, long-term monitoring (1978–2014) has shown that important plant perennial species have also disappeared due to drought ( Stipa tenacissima and Artemisia herba alba ) (Hirche et al. 2018 633 ; Observatoire du Sahara et du Sahel 2013 634 ). The aridisation of the climate in the south of Eastern Siberia led to the advance of the steppes to the north and to the corresponding migration of steppe mammal species between 1976 and 2016 (Ivanov et al. 2018 635 ). The future projection of impacts on plant biodiversity is presented in Section 3.5.2.

Wildlife biodiversity

Dryland ecosystems have high levels of faunal diversity and endemism (MEA 2005 636 ; Whitford 2002 637 ). Over 30% of the endemic bird areas are located within these regions, which is also home to 25% of vertebrate species (Maestre et al. 2012 638 ; MEA 2005 639 ). Yet, many species within drylands are threatened with extinction (Durant et al. 2014 640 ; Walther 2016 641 ). Habitat degradation and desertification are generally associated with biodiversity loss (Ceballos et al. 2010 642 ; Tang et al. 2018 643 ; Newbold et al. 2015 644 ). The ‘grazing value’ of land declines with both a reduction in vegetation cover and shrub encroachment, with the former being more detrimental to native vertebrates (Parsons et al. 2017 645 ). Conversely, shrub encroachment may buffer desertification by increasing resource and microclimate availability, resulting in an increase in vertebrate species abundance and richness observed in the shrub-encroached arid grasslands of North America (Whitford 1997 646 ) and Australia (Parsons et al. 2017 647 ). However, compared to historically resilient drylands, these encroached habitats and their new species assemblages may be more sensitive to droughts, which may become more prevalent with climate change (Schooley et al. 2018 648 ). Mammals and birds may be particularly sensitive to droughts because they rely on evaporative cooling to maintain their body temperatures within an optimal range (Hetem et al. 2016 649 ) and risk lethal dehydration in water limited environments (Albright et al. 2017 650 ). The direct effects of reduced rainfall and water availability are likely to be exacerbated by the indirect effects of desertification through a reduction in primary productivity. A reduction in the quality and quantity of resources available to herbivores due to desertification under changing climate can have knock-on consequences for predators and may ultimately disrupt trophic cascades ( limited evidence, low agreement ) (Rey et al. 2017 651 ; Walther 2010 652 ). Reduced resource availability may also compromise immune response to novel pathogens, with increased pathogen dispersal associated with dust storms (Zinabu et al. 2018 653 ). Responses to desertification are species-specific and mechanistic models are not yet able to accurately predict individual species’ responses to the many factors associated with desertification (Fuller et al. 2016 654 ).

Impacts on socio-economic systems

Combined impacts of desertification and climate change on socio-economic development in drylands are complex. Figure 3.9 schematically represents our qualitative assessment of the magnitudes and the uncertainties associated with these impacts on attainment of the SDGs in dryland areas (UN 2015 655 ). The impacts of desertification and climate change are difficult to isolate from the effects of other socio-economic, institutional and political factors (Pradhan et al. 2017 656 ). However, there is high confidence that climate change will exacerbate the vulnerability of dryland populations to desertification, and that the combination of pressures coming from climate change and desertification will diminish opportunities for reducing poverty, enhancing food and nutritional security, empowering women, reducing disease burden, and improving access to water and sanitation. Desertification is embedded in SDG 15 (Target 15.3) and climate change is under SDG 13. The high confidence and high magnitude impacts depicted for these SDGs (Figure 3.9) indicate that the interactions between desertification and climate change strongly affect the achievement of the targets of SDGs 13 and 15.3, pointing at the need for the coordination of policy actions on land degradation neutrality and mitigation and adaptation to climate change. The following subsections present the literature and assessments which serve as the basis for Figure 3.9.

Socio-economic impacts of desertification and climate change with the SDG framework.

Impacts on poverty

Climate change has a high potential to contribute to poverty particularly through the risks coming from extreme weather events (Olsson et al. 2014 657 ). However, the evidence rigourously attributing changes in observed poverty to climate change impacts is currently not available. On the other hand, most of the research on links between poverty and desertification (or more broadly, land degradation) focused on whether or not poverty is a cause of land degradation (Gerber et al. 2014 658 ; Vu et al. 2014 659 ; Way 2016 660 ) (Section 4.7.1). The literature measuring the extent to which desertification contributed to poverty globally is lacking: the related literature remains qualitative or correlational (Barbier and Hochard 2016 661 ). At the local level, on the other hand, there is limited evidence and high agreement that desertification increased multidimensional poverty. For example, Diao and Sarpong (2011) 662 estimated that land degradation lowered agricultural incomes in Ghana by 4.2 billion USD between 2006 and 2015, increasing the national poverty rate by 5.4% in 2015. Land degradation increased the probability of households becoming poor by 35% in Malawi and 48% in Tanzania (Kirui 2016 663 ). Desertification in China was found to have resulted in substantial losses in income, food production and jobs (Jiang et al. 2014 664 ). On the other hand, Ge et al. (2015) 665 indicated that desertification was positively associated with growing incomes in Inner Mongolia in China in the short run since no costs were incurred for SLM, while in the long run higher incomes allowed allocation of more investments to reduce desertification. This relationship corresponds to the Environmental Kuznets Curve, which posits that environmental degradation initially rises and subsequently falls with rising income (e.g., Stern 2017 666 ). There is limited evidence on the validity of this hypothesis regarding desertification.

Impacts on food and nutritional insecurity

About 821 million people globally were food insecure in 2017, of whom 63% in Asia, 31% in Africa and 5% in Latin America and the Caribbean (FAO et al. 2018 667 ). The global number of food insecure people rose by 37 million since 2014. Changing climate variability, combined with a lack of climate resilience, was suggested as a key driver of this increase (FAO et al. 2018 668 ). Sub-Saharan Africa, East Africa and South Asia had the highest share of undernourished populations in the world in 2017, with 28.8%, 31.4% and 33.7% respectively (FAO et al. 2018 669 ). The major mechanism through which climate change and desertification affect food security is through their impacts on agricultural productivity. There is robust evidence pointing to negative impacts of climate change on crop yields in dryland areas ( high agreement ) (Hochman et al. 2017 670 ; Nelson et al. 2010 671 ; Zhao et al. 2017 672 ) (Sections 3.4.1, 5.2.2 and 4.7.2). There is also robust evidence and high agreement on the losses in agricultural productivity and incomes due to desertification (Kirui 2016 673 ; Moussa et al. 2016 674 ; Mythili and Goedecke 2016 675 ; Tun et al. 2015 676 ). Nkonya et al. (2016a) 677 estimated that cultivating wheat, maize, and rice with unsustainable land management practices is currently resulting in global losses of 56.6 billion USD annually, with another 8.7 billion USD of annual losses due to lower livestock productivity caused by rangeland degradation. However, the extent to which these losses affected food insecurity in dryland areas is not known. Lower crop yields and higher agricultural prices worsen existing food insecurity, especially for net food-buying rural households and urban dwellers. Climate change and desertification are not the sole drivers of food insecurity, but especially in the areas with high dependence on agriculture, they are among the main contributors.

Impacts on human health through dust storms

The frequency and intensity of dust storms are increasing due to land-use and land-cover changes and climate-related factors (Section 2.4) particularly in some regions of the world such as the Arabian Peninsula (Jish Prakash et al. 2015 678 ; Yu et al. 2015 679 ; Gherboudj et al. 2017 680 ; Notaro et al. 2013 681 ; Yu et al. 2013 682 ; Alobaidi et al. 2017 683 ; Maghrabi et al. 2011 684 ; Almazroui et al. 2018 685 ) and broader Middle East (Rashki et al. 2012 686 ; Türkeş 2017 687 ; Namdari et al. 2018 688 ) as well as Central Asia (Indoitu et al. 2015 689 ; Xi and Sokolik 2015 690 ), with growing negative impacts on human health ( high confidence ) (Díaz et al. 2017 691 ; Goudarzi et al. 2017 692 ; Goudie 2014 693 ; Samoli et al. 2011 694 ). Dust storms transport particulate matter, pollutants, pathogens and potential allergens that are dangerous for human health over long distances (Goudie and Middleton 2006 695 ; Sprigg 2016 696 ). Particulate matter (PM; that is, the suspended particles in the air of up to 10 micrometres (PM10) or less in size), have damaging effects on human health (Díaz et al. 2017 697 ; Goudarzi et al. 2017 698 ; Goudie 2014 699 ; Samoli et al. 2011 700 ). The health effects of dust storms are largest in areas in the immediate vicinity of their origin, primarily the Sahara Desert, followed by Central and eastern Asia, the Middle East and Australia (Zhang et al. 2016 701 ), however, there is robust evidence showing that the negative health effects of dust storms reach a much wider area (Bennett et al. 2006 702 ; Díaz et al. 2017 703 ; Kashima et al. 2016 704 ; Lee et al. 2014 705 ; Samoli et al. 2011 706 ; Zhang et al. 2016 707 ). The primary health effects of dust storms include damage to the respiratory and cardiovascular systems (Goudie 2013 708 ). Dust particles with a diameter smaller than 2.5μm were associated with global cardiopulmonary mortality of about 402,000 people in 2005, with 3.47 million years of life lost in that single year (Giannadaki et al. 2014 709 ). Although globally only 1.8% of cardiopulmonary deaths were caused by dust storms, in the countries of the Sahara region, Middle East, South and East Asia, dust storms were suggested to be the cause of 15–50% of all cardiopulmonary deaths (Giannadaki et al. 2014 710 ). A 10 μgm- 3 increase in PM10 dust particles was associated with mean increases in non-accidental mortality from 0.33% to 0.51% across different calendar seasons in China, Japan and South Korea (Kim et al. 2017 711 ). The percentage of all-cause deaths attributed to fine particulate matter in Iranian cities affected by Middle Eastern dust storms (MED) was 0.56–5.02%, while the same percentage for non-affected cities was 0.16–4.13% (Hopke et al. 2018 712 ). Epidemics of meningococcal meningitis occur in the Sahelian region during the dry seasons with dusty conditions (Agier et al. 2012 713 ; Molesworth et al. 2003 714 ). Despite a strong concentration of dust storms in the Sahel, North Africa, the Middle East and Central Asia, there is relatively little research on human health impacts of dust storms in these regions. More research on health impacts and related costs of dust storms, as well as on public health response measures, can help in mitigating these health impacts.

Impacts on gender equality

Environmental issues such as desertification and impacts of climate change have been increasingly investigated through a gender lens (Bose (n.d.) 715 ; Broeckhoven and Cliquet 2015 716 ; Kaijser and Kronsell 2014 717 ; Kiptot et al. 2014 718 ; Villamor and van Noordwijk 2016 719 ). There is medium evidence and high agreement that women will be impacted more than men by environmental degradation (Arora-Jonsson 2011 720 ; Gurung et al. 2006 721 ) (Cross-Chapter Box 11 in Chapter 7). Socially structured gender-specific roles and responsibilities, daily activities, access and control over resources, decision-making and opportunities lead men and women to interact differently with natural resources and landscapes. For example, water scarcity affected women more than men in rural Ghana as they had to spend more time in fetching water, which has implications on time allocations for other activities (Ahmed et al. 2016 722 ). Despite the evidence pointing to differentiated impact of environmental degradation on women and men, gender issues have been marginally addressed in many land restoration and rehabilitation efforts, which often remain gender-blind. Although there is robust evidence on the location-specific impacts of climate change and desertification on gender equality, there is l imited evidence on the gender-related impacts of land restoration and rehabilitation activities. Women are usually excluded from local decision-making on actions regarding desertification and climate change. Socially constructed gender-specific roles and responsibilities are not static because they are shaped by other factors such as wealth, age, ethnicity and formal education (Kaijser and Kronsell 2014 723 ; Villamor et al. 2014 724 ). Hence, women’s and men’s environmental knowledge and priorities for restoration often differ (Sijapati Basnett et al. 2017 725 ). In some areas where sustainable land options (e.g., agroforestry) are being promoted, women were not able to participate due to culturally embedded asymmetries in power relations between men and women (Catacutan and Villamor 2016 726 ). Nonetheless women, particularly in the rural areas, remain heavily involved in securing food for their households. Food security for them is associated with land productivity and women’s contribution to address desertification is crucial.

Impacts on water scarcity and use

Reduced water retention capacity of degraded soils amplifies floods (de la Paix et al. 2011 727 ), reinforces degradation processes through soil erosion, and reduces annual intake of water to aquifers, exacerbating existing water scarcities (Le Roux et al. 2017 728 ; Cano et al. 2018 729 ). Reduced vegetation cover and more intense dust storms were found to intensify droughts (Cook et al. 2009 730 ). Moreover, secondary salinisation in the irrigated drylands often requires leaching with considerable amounts of water (Greene et al. 2016 731 ; Wichelns and Qadir 2015 732 ). Thus, different types of soil degradation increase water scarcity both through lower water quantity and quality (Liu et al. 2017 733 ; Liu et al. 2016c 734 ). All these processes reduce water availability for other needs. In this context, climate change will further intensify water scarcity in some dryland areas and increase the frequency of droughts ( medium confidence ) (IPCC 2013 735 ; Zheng et al. 2018 736 ) (Section 2.2). Higher water scarcity may imply growing use of wastewater effluents for irrigation (Pedrero et al. 2010 737 ). The use of untreated wastewater exacerbates soil degradation processes (Tal 2016 738 ; Singh et al. 2004 739 ; Qishlaqi et al. 2008 740 ; Hanjra et al. 2012 741 ), in addition to negative human health impacts (Faour-Klingbeil and Todd 2018 742 ; Hanjra et al. 2012 743 ). Climate change will thus amplify the need for integrated land and water management for sustainable development.

Impacts on energy infrastructure through dust storms

Desertification leads to conditions that favour the production of dust storms ( high confidence ) (Section 3.3.1). There is robust evidence and high agreement that dust storms negatively affect the operational potential of solar and wind power harvesting equipment through dust deposition, reduced reach of solar radiation and increasing blade-surface roughness, and can also reduce effective electricity distribution in high-voltage transmission lines (Zidane et al. 2016 744 ; Costa et al. 2016 745 ; Lopez-Garcia et al. 2016 746 ; Maliszewski et al. 2012 747 ; Mani and Pillai 2010 748 ; Mejia and Kleissl 2013 749 ; Mejia et al. 2014 750 ; Middleton 2017 751 ; Sarver et al. 2013 752 ; Kaufman et al. 2002 753 ; Kok et al. 2018 754 ). Direct exposure to desert dust storm can reduce energy generation efficiency of solar panels by 70–80% in one hour (Ghazi et al. 2014 755 ). (Saidan et al. 2016 756 ) indicated that in the conditions of Baghdad, Iraq, one month’s exposure to weather reduced the efficiency of solar modules by 18.74% due to dust deposition. In the Atacama desert, Chile, one month’s exposure reduced thin-film solar module performance by 3.7–4.8% (Fuentealba et al. 2015 757 ). This has important implications for climate change mitigation efforts using the expansion of solar and wind energy generation in dryland areas for substituting fossil fuels. Abundant access to solar energy in many dryland areas makes them high-potential locations for the installation of solar energy generating infrastructure. Increasing desertification, resulting in higher frequency and intensity of dust storms imposes additional costs for climate change mitigation through deployment of solar and wind energy harvesting facilities in dryland areas. Most frequently used solutions to this problem involve physically wiping or washing the surface of solar devices with water. These result in additional costs and excessive use of already scarce water resources and labour (Middleton 2017 758 ). The use of special coatings on the surface of solar panels can help prevent the deposition of dusts (Costa et al. 2016 759 ; Costa et al. 2018 760 ; Gholami et al. 2017 761 ).

Impacts on transport infrastructure through dust storms and sand movement

Dust storms and movement of sand dunes often threaten the safety and operation of railway and road infrastructure in arid and hyper-arid areas, and can lead to road and airport closures due to reductions in visibility. For example, the dust storm on 10 March 2009 over Riyadh was assessed to be the strongest in the previous two decades in Saudi Arabia, causing limited visibility, airport shutdown and damages to infrastructure and environment across the city (Maghrabi et al. 2011 762 ). There are numerous historical examples of how moving sand dunes led to the forced decommissioning of early railway lines built in Sudan, Algeria, Namibia and Saudi Arabia in the late 19th and early 20th century (Bruno et al. 2018 763 ). Currently, the highest concentrations of railways vulnerable to sand movements are located in north-western China, Middle East and North Africa (Bruno et al. 2018 764 ; Cheng and Xue 2014 765 ). In China, sand dune movements are periodically disrupting the railway transport on the Linhai–Ceke line in north-western China and on the Lanzhou–Xinjiang High-speed Railway in western China, with considerable clean-up and maintenance costs (Bruno et al. 2018 766 ; Zhang et al. 2010 767 ). There are large-scale plans for expansion of railway networks in arid areas of China, Central Asia, North Africa, the Middle East, and eastern Africa. For example, The Belt and Road Initiative promoted by China, the Gulf Railway project by the Cooperation Council for the Arab States of the Gulf or Lamu Port–South Sudan–Ethiopia Transport (LAPSSET) Corridor in Eastern Africa. These investments have long-term return and operation periods. Their construction and associated engineering solutions will therefore benefit from careful consideration of potential desertification and climate change effects on sand storms and dune movements.

Impacts on conflicts

There is low confidence in climate change and desertification leading to violent conflicts. There is medium evidence and low agreement that climate change and desertification contribute to already existing conflict potentials (Herrero 2006 768 ; von Uexkull et al. 2016 769 ; Theisen 2017 770 ; Olsson 2017 771 ; Wischnath and Buhaug 2014 772 ) (Section 4.7.3). To illustrate, Hsiang et al. (2013) 773 found that each one standard deviation increase in temperature or rainfall was found to increase interpersonal violence by 4% and intergroup conflict by 14% (Hsiang et al. 2013 774 ). However, this conclusion was disputed by Buhaug et al. (2014) 775 , who found no evidence linking climate variability to violent conflict after replicating Hsiang et al. (2013) 776 by studying only violent conflicts. Almer et al. (2017) 777 found that a one standard deviation increase in dryness raised the likelihood of riots in Sub-Saharan African countries by 8.3% during the 1990–2011 period. On the other hand, Owain and Maslin (2018) 778 found that droughts and heatwaves were not significantly affecting the level of regional conflict in East Africa. Similarly, it was suggested that droughts and desertification in the Sahel played a relatively minor role in the conflicts in the Sahel in the 1980s, with the major reasons for the conflicts during this period being political, especially the marginalisation of pastoralists (Benjaminsen 2016 779 ), corruption and rent-seeking (Benjaminsen et al. 2012 780 ). Moreover, the role of environmental factors as the key drivers of conflicts was questioned in the case of Sudan (Verhoeven 2011 781 ) and Syria (De Châtel 2014 782 ). Selection bias, when the literature focuses on the same few regions where conflicts occurred and relates them to climate change, is a major shortcoming, as it ignores other cases where conflicts did not occur (Adams et al. 2018 783 ) despite degradation of the natural resource base and extreme weather events.

Impacts on migration

Environmentally induced migration is complex and accounts for multiple drivers of mobility as well as other adaptation measures undertaken by populations exposed to environmental risk ( high confidence ). There is medium evidence and low agreement that climate change impacts migration. The World Bank (2018) 784 predicted that 143 million people would be forced to move internally by 2050 if no climate action is taken. Focusing on asylum seekers alone, rather than the total number of migrants, Missirian and Schlenker (2017) 785 predict that asylum applications to the European Union will increase from 28% (98,000 additional asylum applications per year) up to 188% (660,000 additional applications per year) depending on the climate scenario by 2100. While the modelling efforts have greatly improved over the years (Hunter et al. 2015 786 ; McLeman 2011 787 ; Sherbinin and Bai 2018 788 ) and in particular, these recent estimates provide an important insight into potential future developments, the quantitative projections are still based on the number of people exposed to risk rather than the number of people who would actually engage in migration as a response to this risk (Gemenne 2011 789 ; McLeman 2013 790 ) and they do not take into account individual agency in migration decision nor adaptive capacities of individuals (Hartmann 2010 791 ; Kniveton et al. 2011 792 ; Piguet 2010 793 ) (see Section 3.6.2 discussing migration as a response to desertification). Accordingly, the available micro-level evidence suggests that climate-related shocks are one of the many drivers of migration (Adger et al. 2014 794 ; London Government Office for Science and Foresight 2011 795 ; Melde et al. 2017 796 ), but the individual responses to climate risk are more complex than commonly assumed (Gray and Mueller 2012a 797 ). For example, despite strong focus on natural disasters, neither flooding (Gray and Mueller 2012b 798 ; Mueller et al. 2014 799 ) nor earthquakes (Halliday 2006 800 ) were found to induce long-term migration; but instead, slow-onset changes, especially those provoking crop failures and heat stress, could affect household or individual migration decisions (Gray and Mueller 2012a 801 ; Missirian and Schlenker 2017 802 ; Mueller et al. 2014 803 ). Out-migration from drought-prone areas has received particular attention (de Sherbinin et al. 2012 804 ; Ezra and Kiros 2001 805 ). A substantial body of literature suggests that households engage in local or internal migration as a response to drought (Findlay 2011 806 ; Gray and Mueller 2012a 807 ), while international migration decreases with drought in some contexts (Henry et al. 2004 808 ), but might increase in contexts where migration networks are well established (Feng et al. 2010 809 ; Nawrotzki and DeWaard 2016 810 ; Nawrotzki et al. 2015 811 , 2016 812 ). Similarly, the evidence is not conclusive with respect to the effect of environmental drivers, in particular desertification, on mobility. While it has not consistently entailed out-migration in the case of Ecuadorian Andes (Gray 2009, 2010 813 ), environmental and land degradation increased mobility in Kenya and Nepal (Gray 2011 814 ; Massey et al. 2010 815 ), but marginally decreased mobility in Uganda (Gray 2011 816 ). These results suggest that in some contexts, environmental shocks actually undermine households’ financial capacity to undertake migration (Nawrotzki and Bakhtsiyarava 2017 817 ), especially in the case of the poorest households (Barbier and Hochard 2018 818 ; Koubi et al. 2016 819 ; Kubik and Maurel 2016 820 ; McKenzie and Yang 2015 821 ). Adding to the complexity, migration, especially to frontier areas, by increasing pressure on land and natural resources, might itself contribute to environmental degradation at the destination (Hugo 2008 822 ; IPBES 2018a 823 ; McLeman 2017 824 ). The consequences of migration can also be salient in the case of migration to urban or peri-urban areas; indeed, environmentally induced migration can add to urbanisation (Section 3.6.2.2), often exacerbating problems related to poor infrastructure and unemployment.

Impacts on pastoral communities

Pastoral production systems occupy a significant portion of the world (Rass 2006 825 ; Dong 2016 826 ). Food insecurity among pastoral households is often high (Gomes 2006 827 ) (Section 3.1.3). The Sahelian droughts of the 1970s–1980s provided an example of how droughts could affect livestock resources and crop productivity, contributing to hunger, out-migration and suffering for millions of pastoralists (Hein and De Ridder 2006 828 ; Molua and Lambi 2007 829 ). During these Sahelian droughts low and erratic rainfall exacerbated desertification processes, leading to ecological changes that forced people to use marginal lands and ecosystems. Similarly, the rate of rangeland degradation is now increasing because of environmental changes and overexploitation of resources (Kassahun et al. 2008 830 ; Vetter 2005 831 ). Desertification coupled with climate change is negatively affecting livestock feed and grazing species (Hopkins and Del Prado 2007 832 ), changing the composition in favour of species with low forage quality, ultimately reducing livestock productivity (D’Odorico et al. 2013 833 ; Dibari et al. 2016 834 ) and increasing livestock disease prevalence (Thornton et al. 2009 849 ). There is robust evidence and high agreement that weak adaptive capacity, coupled with negative effects from other climate-related factors, are predisposing pastoralists to increased poverty from desertification and climate change globally (López-i-Gelats et al. 2016 835 ; Giannini et al. 2008 836 ; IPCC 2007 837 ). On the other hand, misguided policies such as enforced sedentarisation, and in certain cases protected area delineation (fencing), which restrict livestock mobility have hampered optimal use of grazing land resources (Du 2012 838 ). Such policies have led to degradation of resources and out-migration of people in search of better livelihoods (Gebeye 2016 839 ; Liao et al. 2015 840 ). Restrictions on the mobile lifestyle are reducing the resilient adaptive capacity of pastoralists to natural hazards including extreme and variable weather conditions, drought and climate change (Schilling et al. 2014 841 ). Furthermore, the exacerbation of the desertification phenomenon due to agricultural intensification (D’Odorico et al. 2013 842 ) and land fragmentation caused by encroachment of agriculture into rangelands (Otuoma et al. 2009 843 ; Behnke and Kerven 2013 844 ) is threatening pastoral livelihoods. For example, commercial cotton ( Gossypium hirsutum ) production is crowding out pastoral systems in Benin (Tamou et al. 2018 845 ). Food shortages and the urgency to produce enough crop for public consumption are leading to the encroachment of agriculture into productive rangelands and those converted rangelands are frequently prime lands used by pastoralists to produce feed and graze their livestock during dry years (Dodd 1994 846 ). The sustainability of pastoral systems is therefore coming into question because of social and political marginalisation of those systems (Davies et al. 2016 847 ) and also because of the fierce competition they are facing from other livelihood sources such as crop farming (Haan et al. 2016 848 ).

Future projections

Future projections of desertification.

Assessing the impact of climate change on future desertification is difficult as several environmental and anthropogenic variables interact to determine its dynamics. The majority of modelling studies regarding the future evolution of desertification rely on the analysis of specific climate change scenarios and Global Climate Models (GCMs) and their effect on a few processes or drivers that trigger desertification (Cross-Chapter Box 1 in Chapter 1).

With regards to climate impacts, the analysis of global and regional climate models concludes that under all representative concentration pathways (RCPs) potential evapotranspiration (PET) would increase worldwide as a consequence of increasing surface temperatures and surface water vapour deficit (Sherwood and Fu 2014 850 ). Consequently, there would be associated changes in aridity indices that depend on this variable ( high agreement, robust evidence ) (Cook et al. 2014a 851 ; Dai 2011 852 ; Dominguez et al. 2010 853 ; Feng and Fu 2013 854 ; Ficklin et al. 2016 855 ; Fu et al. 2016 856 ; Greve and Seneviratne 1999 857 ; Koutroulis 2019 858 ; Scheff and Frierson 2015 859 ). Due to the large increase in PET and decrease in precipitation over some subtropical land areas, aridity index will decrease in some drylands (Zhao and Dai 2015 860 ), with one model estimating approximately 10% increase in hyper-arid areas globally (Zeng and Yoon 2009 861 ). Increases in PET are projected to continue due to climate change (Cook et al. 2014a 862 ; Fu et al. 2016 863 ; Lin et al. 2015 864 ; Scheff and Frierson 2015 865 ). However, as noted in Sections 3.1.1 and 3.2.1, these PET calculations use assumptions that are not valid in an environment with changing CO 2 . Evidence from precipitation, runoff or photosynthetic uptake of CO 2 suggest that a future warmer world will be less arid (Roderick et al. 2015 866 ). Observations in recent decades indicate that the Hadley cell has expanded poleward in both hemispheres (Fu et al. 2006 867 ; Hu and Fu 2007 868 ; Johanson et al. 2009 869 ; Seidel and Randel 2007 870 ), and under all RCPs would continue expanding (Johanson et al. 2009 871 ; Lu et al. 2007 872 ). This expansion leads to the poleward extension of subtropical dry zones and hence an expansion in drylands on the poleward edge (Scheff and Frierson 2012 873 ). Overall, this suggests that while aridity will increase in some places ( high confidence ), there is insufficient evidence to suggest a global change in dryland aridity ( medium confidence ).

Regional modelling studies confirm the outcomes of Global Climate Models (Africa: Terink et al. 2013 874 ; China: Yin et al. 2015 875 ; Brazil: Marengo and Bernasconi 2015 876 ; Cook et al. 2012 877 ; Greece: Nastos et al. 2013 878 ; Italy: Coppola and Giorgi 2009 879 ). According to the IPCC AR5 (IPCC 2013) 880 , decreases in soil moisture are detected in the Mediterranean, southwest USA and southern African regions. This is in line with alterations in the Hadley circulation and higher surface temperatures. This surface drying will continue to the end of this century under the RCP8.5 scenario ( high confidence ). Ramarao et al. (2015) 881 showed that a future climate projection based on RCP4.5 scenario indicated the possibility for detecting the summer-time soil drying signal over the Indian region during the 21st century in response to climate change. The IPCC Special Report on Global Warming of 1.5°C (SR15) (Chapter 3; Hoegh-Guldberg et al. 2018 882 ) concluded with ‘ medium confidence ’ that global warming by more than 1.5°C increases considerably the risk of aridity for the Mediterranean area and southern Africa. Miao et al. (2015b) 883 showed an acceleration of desertification trends under the RCP8.5 scenario in the middle and northern part of Central Asia and some parts of north-western China. It is also useful to consider the effects of the dynamic–thermodynamical feedback of the climate. Schewe and Levermann (2017) 884 show increases of up to 300% in the Central Sahel rainfall by the end of the century due to an expansion of the West African monsoon. Warming could trigger an intensification of monsoonal precipitation due to increases in ocean moisture availability.

The impacts of climate change on dust storm activity are not yet comprehensively studied and represent an important knowledge gap. Currently, GCMs are unable to capture recent observed dust emission and transport (Evan 2018 885 ; Evan et al. 2014 886 ), limiting confidence in future projections. Literature suggests that climate change decreases wind erosion/dust emission overall, with regional variation ( low confidence ). Mahowald et al. (2006) 887 and Mahowald (2007) 888 found that climate change led to a decrease in desert dust source areas globally using CMIP3 GCMs. Wang et al. (2009) 889 found a decrease in sand dune movement by 2039 (increasing thereafter) when assessing future wind-erosion-driven desertification in arid and semi-arid China using a range of SRES scenarios and HadCM3 simulations. Dust activity in the Southern Great Plains in the USA was projected to increase, while in the Northern Great Plains it was projected to decrease under RCP8.5 climate change scenario (Pu and Ginoux 2017 890 ). Evan et al. (2016) 891 project a decrease in African dust emission associated with a slowdown of the tropical circulation in the high CO 2 RCP8.5 scenario.

Global estimates of the impact of climate change on soil salinisation show that under the IS92a emissions scenario (a scenario prepared in 1992 that contains ‘business as usual’ assumptions) (Leggett et al. 1992 892 ) the area at risk of salinisation would increase in the future ( limited evidence, high agreement ) (Schofield and Kirkby 2003 893 ). Climate change has an influence on soil salinisation that induces further land degradation through several mechanisms that vary in their level of complexity. However, only a few examples can be found to illustrate this range of impacts, including the effect of groundwater table depletion (Rengasamy 2006 894 ) and irrigation management (Sivakumar 2007 895 ), salt migration in coastal aquifers with decreasing water tables (Sherif and Singh 1999 896 ) (Section 4.10.7), and surface hydrology and vegetation that affect wetlands and favour salinisation (Nielsen and Brock 2009 897 ).

Future vulnerability and risk of desertification

Following the conceptual framework developed in the Special Report on extreme events (SREX) (IPCC 2012 898 ), future risks are assessed by examining changes in exposure (that is, presence of people; livelihoods; species or ecosystems; environmental functions, service, and resources; infrastructure; or economic, social or cultural assets; see Glossary), changes in vulnerability (that is, propensity or predisposition to be adversely affected; see Glossary) and changes in the nature and magnitude of hazards (that is, potential occurrence of a natural or human-induced physical event that causes damage; see Glossary). Climate change is expected to further exacerbate the vulnerability of dryland ecosystems to desertification by increasing PET globally (Sherwood and Fu 2014 899 ). Temperature increases between 2°C and 4°C are projected in drylands by the end of the 21st century under RCP4.5 and RCP8.5 scenarios, respectively (IPCC 2013 900 ). An assessment by Carrão et al. 2017 901 showed an increase in drought hazards by late-century (2071–2099) compared to a baseline (1971–2000) under high RCPs in drylands around the Mediterranean, south-eastern Africa, and southern Australia. In Latin America, Morales et al. (2011) 902 indicated that areas affected by drought will increase significantly by 2100 under SRES scenarios A2 and B2. The countries expected to be affected include Guatemala, El Salvador, Honduras and Nicaragua. In CMIP5 scenarios, Mediterranean types of climate are projected to become drier (Alessandri et al. 2014 903 ; Polade et al. 2017 904 ), with the equatorward margins being potentially replaced by arid climate types (Alessandri et al. 2014 905 ). Globally, climate change is predicted to intensify the occurrence and severity of droughts ( medium confidence ) (Dai 2013 906 ; Sheffield and Wood 2008 907 ; Swann et al. 2016 908 ; Wang 2005 909 ; Zhao and Dai 2015 910 ; Carrão et al. 2017 911 ; Naumann et al. 2018 912 ) (Section 2.2). Ukkola et al. (2018) 913 showed large discrepancies between CMIP5 models for all types of droughts, limiting the confidence that can be assigned to projections of drought.

Drylands are characterised by high climatic variability. Climate impacts on desertification are not only defined by projected trends in mean temperature and precipitation values but are also strongly dependent on changes in climate variability and extremes (Reyer et al. 2013 914 ). The responses of ecosystems depend on diverse vegetation types. Drier ecosystems are more sensitive to changes in precipitation and temperature (Li et al. 2018 915 ; Seddon et al. 2016 916 ; You et al. 2018 917 ), increasing vulnerability to desertification. It has also been reported that areas with high variability in precipitation tend to have lower livestock densities and that those societies that have a strong dependence on livestock that graze natural forage are especially affected (Sloat et al. 2018 918 ). Social vulnerability in drylands increases as a consequence of climate change that threatens the viability of pastoral food systems (Dougill et al. 2010 919 ; López-i-Gelats et al. 2016 920 ). Social drivers can also play an important role with regards to future vulnerability (Máñez Costa et al. 2011 921 ). In the arid region of north-western China, Liu et al. (2016b) 922 estimated that under RCP4.5 areas of increased vulnerability to climate change and desertification will surpass those with decreased vulnerability.

Using an ensemble of global climate, integrated assessment and impact models, Byers et al. (2018) 923 investigated 14 impact indicators at different levels of global mean temperature change and socio-economic development. The indicators cover water, energy and land sectors. Of particular relevance to desertification are the water (e.g., water stress, drought intensity) and the land (e.g., habitat degradation) indicators. Under shared socio-economic pathway SSP2 (‘Middle of the Road’) at 1.5°C, 2°C and 3°C of global warming, the numbers of dryland populations exposed (vulnerable) to various impacts related to water, energy and land sectors (e.g., water stress, drought intensity, habitat degradation) are projected to reach 951 (178) million, 1152 (220) million and 1285 (277) million, respectively. While at global warming of 2°C, under SSP1 (‘Sustainability’), the exposed (vulnerable) dryland population is 974 (35) million, and under SSP3 (‘Fragmented World’) it is 1267 (522) million. Steady increases in the exposed and vulnerable populations are seen for increasing global mean temperatures. However much larger differences are seen in the vulnerable population under different SSPs. Around half the vulnerable population is in South Asia, followed by Central Asia, West Africa and East Asia.

Future projections of impacts

Future climate change is expected to increase the potential for increased soil erosion by water in dryland areas ( medium confidence ). Yang et al. (2003) 924 use a Revised Universal Soil Loss Equation (RUSLE) model to study global soil erosion under historical, present and future conditions of both cropland and climate. Soil erosion potential has increased by about 17%, and climate change will increase this further in the future. In northern Iran, under the SRES A2 emission scenario the mean erosion potential is projected to grow by 45%, comparing the period 1991–2010 with 2031–2050 (Zare et al. 2016 925 ).

A strong decrease in precipitation for almost all parts of Turkey was projected for the period 2021–2050 compared to 1971–2000 using Regional Climate Model, RegCM4.4 of the International Centre for Theoretical Physics (ICTP) under RCP4.5 and RCP8.5 scenarios (Türkeş et al. 2019 926 ). The projected changes in precipitation distribution can lead to more extreme precipitation events and prolonged droughts, increasing Turkey’s vulnerability to soil erosion. In Portugal, a study comparing wet and dry catchments under A1B and B1 emission scenarios showed an increase in erosion in dry catchments (Serpa et al. 2015 927 ). In Morocco an increase in sediment load is projected as a consequence of reduced precipitation (Simonneaux et al. 2015 928 ). WGII AR5 concluded the impact of increases in heavy rainfall and temperature on soil erosion will be modulated by soil management practices, rainfall seasonality and land cover (Jiménez Cisneros et al. 2014 929 ). Ravi et al. (2010) 930 predicted an increase in hydrologic and aeolian soil erosion processes as a consequence of droughts in drylands. However, there are some studies that indicate that soil erosion will be reduced in Spain (Zabaleta et al. 2013 931 ), Greece (Nerantzaki et al. 2015 932 ) and Australia (Klik and Eitzinger 2010 933 ), while others project changes in erosion as a consequence of the expansion of croplands (Borrelli et al. 2017 934 ).

Potential dryland expansion implies lower carbon sequestration and higher risk of desertification (Huang et al. 2017 935 ), with severe impacts on land usability and threats to food security. At the level of biomes (global-scale zones, generally defined by the type of plant life that they support in response to average rainfall and temperature patterns; see Glossary), soil carbon uptake is determined mostly by weather variability. The area of the land in which dryness controls CO 2 exchange has risen by 6% since 1948 and is projected to expand by at least another 8% by 2050. In these regions net carbon uptake is about 27% lower than elsewhere (Yi et al. 2014 936 ). Potential losses of soil carbon are projected to range from 9% to 12% of the total carbon stock in the 0–20 cm layer of soils in southern European Russia by end of this century (Ivanov et al. 2018 937 ).

Desertification under climate change will threaten biodiversity in drylands ( medium confidence ). Rodriguez-Caballero et al. (2018) 938 analysed the cover of biological soil crusts under current and future environmental conditions utilising an environmental niche modelling approach. Their results suggest that biological soil crusts currently cover approximately 1600 Mha in drylands. Under RCP scenarios 2.6 to 8.5, 25–40% of this cover will be lost by 2070 with climate and land use contributing equally. The predicted loss is expected to substantially reduce the contribution of biological soil crusts to nitrogen cycling (6.7–9.9 TgN yr− 1 ) and carbon cycling (0.16–0.24 PgC yr− 1 ) (Rodriguez-Caballero et al. 2018 939 ). A study in Colorado Plateau, USA showed that changes in climate in drylands may damage the biocrust communities by promoting rapid mortality of foundational species (Rutherford et al. 2017 940 ), while in the Southern California deserts climate change-driven extreme heat and drought may surpass the survival thresholds of some desert species (Bachelet et al. 2016 941 ). In semi-arid Mediterranean shrublands in eastern Spain, plant species richness and plant cover could be reduced by climate change and soil erosion (García-Fayos and Bochet 2009 942 ). The main drivers of species extinctions are land-use change, habitat pollution, over-exploitation, and species invasion, while climate change is indirectly linked to species extinctions (Settele et al. 2014 943 ). Malcolm et al. (2006) 944 found that more than 2000 plant species located within dryland biodiversity hotspots could become extinct within 100 years, starting 2004 (within the Cape Floristic Region, Mediterranean Basin and southwest Australia). Furthermore, it is suggested that land use and climate change could cause the loss of 17% of species within shrublands and 8% within hot deserts by 2050 ( low confidence ) (van Vuuren et al. 2006 945 ). A study in the semi-arid Chinese Altai Mountains showed that mammal species richness will decline, rates of species turnover will increase, and more than 50% of their current ranges will be lost (Ye et al. 2018 946 ).

Changing climate and land use have resulted in higher aridity and more droughts in some drylands, with the rising role of precipitation, wind and evaporation on desertification (Fischlin et al. 2007 947 ). In a 2°C world, annual water discharge is projected to decline, and heatwaves are projected to pose risk to food production by 2070 (Waha et al. 2017 948 ). However, Betts et al. (2018) 949 found a mixed response of water availability (runoff) in dryland catchments to global temperature increases from 1.5°C to 2°C. The forecasts for Sub-Saharan Africa suggest that higher temperatures, increase in the number of heatwaves, and increasing aridity, will affect the rainfed agricultural systems (Serdeczny et al. 2017 950 ). A study by Wang et al. (2009) 951 in arid and semi-arid China showed decreased livestock productivity and grain yields from 2040 to 2099, threatening food security. In Central Asia, projections indicate a decrease in crop yields, and negative impacts of prolonged heat waves on population health (Reyer et al. 2017 952 ) (Section 3.7.2). World Bank (2009) 953 projected that, without the carbon fertilisation effect, climate change will reduce the mean yields for 11 major global crops – millet, field pea, sugar beet, sweet potato, wheat, rice, maize, soybean, groundnut, sunflower and rapeseed – by 15% in Sub-Saharan Africa, 11% in Middle East and North Africa, 18% in South Asia, and 6% in Latin America and the Caribbean by 2046–2055, compared to 1996–2005. A separate meta-analysis suggested a similar reduction in yields in Africa and South Asia due to climate change by 2050 (Knox et al. 2012 954 ). Schlenker and Lobell (2010) 955 estimated that in sub-Saharan Africa, crop production may be reduced by 17–22% due to climate change by 2050. At the local level, climate change impacts on crop yields vary by location (Section 5.2.2). Negative impacts of climate change on agricultural productivity contribute to higher food prices. The imbalance between supply and demand for agricultural products is projected to increase agricultural prices in the range of 31% for rice, to 100% for maize by 2050 (Nelson et al. 2010 956 ), and cereal prices in the range between a 32% increase and a 16% decrease by 2030 (Hertel et al. 2010 957 ). In southern European Russia, it is projected that the yields of grain crops will decline by 5–10% by 2050 due to the higher intensity and coverage of droughts (Ivanov et al. 2018 958 ).

Climate change can have strong impacts on poverty in drylands ( medium confidence ) (Hallegatte and Rozenberg 2017 959 ; Hertel and Lobell 2014 960 ). Globally, Hallegatte et al. (2015) 961 project that without rapid and inclusive progress on eradicating multidimensional poverty, climate change could increase the number of the people living in poverty by between 35 million and 122 million people by 2030. Although these numbers are global and not specific to drylands, the highest impacts in terms of the share of the national populations being affected are projected to be in the drylands areas of the Sahel region, eastern Africa and South Asia (Stephane Hallegatte et al. 2015 962 ). The impacts of climate change on poverty vary depending on whether the household is a net agricultural buyer or seller. Modelling results showed that poverty rates would increase by about one-third among the urban households and non-agricultural self-employed in Malawi, Uganda, Zambia and Bangladesh due to high agricultural prices and low agricultural productivity under climate change (Hertel et al. 2010) 963 . On the contrary, modelled poverty rates fell substantially among agricultural households in Chile, Indonesia, the Philippines and Thailand, because higher prices compensated for productivity losses (Hertel et al. 2010 964 ).

Responses to desertification under climate change

Achieving sustainable development of dryland livelihoods requires avoiding dryland degradation through SLM and restoring and rehabilitating the degraded drylands due to their potential wealth of ecosystem benefits and importance to human livelihoods and economies (Thomas 2008 965 ). A broad suite of on-the-ground response measures exists to address desertification (Scholes 2009 966 ), be it in the form of improved fire and grazing management, the control of erosion; integrated crop, soil and water management, among others (Liniger and Critchley 2007 967 ; Scholes 2009 968 ). These actions are part of the broader context of dryland development and long-term SLM within coupled socio-economic systems (Reynolds et al. 2007 969 ; Stringer et al. 2017 970 ; Webb et al. 2017 971 ). Many of these response options correspond to those grouped under ‘land transitions’ in the IPCC Special Report on Global Warming of 1.5°C (Coninck et al. 2018 972 ) (Table 6.4). It is therefore recognised that such actions require financial, institutional and policy support for their wide-scale adoption and sustainability over time (Sections 3.6.3, 4.8.5 and 6.4.4).

SLM technologies and practices: On-the-ground actions

A broad range of activities and measures can help avoid, reduce and reverse degradation across the dryland areas of the world. Many of these actions also contribute to climate change adaptation and mitigation, with further sustainable development co-benefits for poverty eradication and food security ( high confidence ) (Section 6.3). As preventing desertification is strongly preferable and more cost-effective than allowing land to degrade and then attempting to restore it (IPBES 2018b 973 ; Webb et al. 2013 974 ), there is a growing emphasis on avoiding and reducing land degradation, following the Land Degradation Neutrality framework (Cowie et al. 2018 975 ; Orr et al. 2017 976 ) (Section 4.8.5).

An assessment is made of six activities and measures practicable across the biomes and anthromes of the dryland domain (Figure 3.10). This suite of actions is not exhaustive, but rather a set of activities that are particularly pertinent to global dryland ecosystems. They are not necessarily exclusive to drylands and are often implemented across a range of biomes and anthromes (Figure 3.10; for afforestation, see Section 3.7.2, Cross-Chapter Box 2 in Chapter 1, and Chapter 4 (Section 4.8.3)). The use of anthromes as a structuring element for response options is based on the essential role of interactions between social and ecological systems in driving desertification within coupled socio-ecological systems (Cherlet et al. 2018 977 ). The concept of the anthromes is defined in the Glossary and explored further in Chapters 1, 4 and 6.

The assessment of each action is twofold: firstly, to assess the ability of each action to address desertification and enhance climate change resilience, and secondly, to assess the potential impact of future climate change on the effectiveness of each action.

Figure 3.10

The typical distribution of on-the-ground actions across global biomes and anthromes.

Integrated crop–soil–water management

Forms of integrated cropland management have been practiced in drylands for thousands of years (Knörzer et al. 2009 978 ). Actions include planting a diversity of species including drought-resilient ecologically appropriate plants, reducing tillage, applying organic compost and fertiliser, adopting different forms of irrigation and maintaining vegetation and mulch cover. In the contemporary era, several of these actions have been adopted in response to climate change.

In terms of climate change adaptation , the resilience of agriculture to the impacts of climate change is strongly influenced by the underlying health and stability of soils as well as improvements in crop varieties, irrigation efficiency and supplemental irrigation, for example, through rainwater harvesting (medium evidence, high agreement) (Altieri et al. 2015 979 ; Amundson et al. 2015 980 ; Derpsch et al. 2010 981 ; Lal 1997 982 ; de Vries et al. 2012 983 ). Desertification often leads to a reduction in ground cover that in turn results in accelerated water and wind erosion and an associated loss of fertile topsoil that can greatly reduce the resilience of agriculture to climate change (medium evidence, high agreement) (Touré et al. 2019 984 ; Amundson et al. 2015 985 ; Borrelli et al. 2017 986 ; Pierre et al. 2017 987 ). Amadou et al. (2011) 988 note that even a minimal cover of crop residues (100 kg ha– 1 ) can substantially decrease wind erosion.

Compared to conventional (flood or furrow) irrigation, drip irrigation methods are more efficient in supplying water to the plant root zone, resulting in lower water requirements and enhanced water use efficiency ( robust evidence, high agreement ) (Ibragimov et al. 2007 989 ; Narayanamoorthy 2010 990 ; Niaz et al. 2009 991 ). For example, in the rainfed area of Fetehjang, Pakistan, the adoption of drip methods reduced water usage by 67–68% during the production of tomato, cucumber and bell peppers, resulting in a 68–79% improvement in water use efficiency compared to previous furrow irrigation (Niaz et al. 2009 992 ). In India, drip irrigation reduced the amount of water consumed in the production of sugarcane by 44%, grapes by 37%, bananas by 29% and cotton by 45%, while enhancing yields by up to 29% (Narayanamoorthy 2010 993 ). Similarly, in Uzbekistan, drip irrigation increased the yield of cotton by 10–19% while reducing water requirements by 18–42% (Ibragimov et al. 2007 994 ).

A prominent response that addresses soil loss, health and cover is altering cropping methods. The adoption of intercropping (inter – and intra-row planting of companion crops) and relay cropping (temporally differentiated planting of companion crops) maintains soil cover over a larger fraction of the year, leading to an increase in production, soil nitrogen, species diversity and a decrease in pest abundance ( robust evidence, medium agreement ) (Altieri and Koohafkan 2008 995 ; Tanveer et al. 2017 996 ; Wilhelm and Wortmann 2004 997 ). For example, intercropping maize and sorghum with Desmodium (an insect repellent forage legume) and Brachiaria (an insect trapping grass), which is being promoted in drylands of East Africa, led to a two-to-three-fold increase in maize production and an 80% decrease in stem boring insects (Khan et al. 2014 998 ). In addition to changes in cropping methods, forms of agroforestry and shelterbelts are often used to reduce erosion and improve soil conditions (Section 3.7.2). For example, the use of tree belts of mixed species in northern China led to a reduction of surface wind speed and an associated reduction in soil temperature of up to 40% and an increase in soil moisture of up to 30% (Wang et al. 2008 999 ).

A further measure that can be of increasing importance under climate change is rainwater harvesting (RWH), including traditional zai (small basins used to capture surface runoff), earthen bunds and ridges (Nyamadzawo et al. 2013 1001 ), fanya juus infiltration pits (Nyagumbo et al. 2019 1002 ), contour stone bunds (Garrity et al. 2010 1003 ) and semi-permeable stone bunds (often referred to by the French term digue filtrante ) (Taye et al. 2015 1004 ). RWH increases the amount of water available for agriculture and livelihoods through the capture and storage of runoff, while at the same time reducing the intensity of peak flows following high-intensity rainfall events. It is therefore often highlighted as a practical response to dryness (i.e., long-term aridity and low seasonal precipitation) and rainfall variability, both of which are projected to become more acute over time in some dryland areas (Dile et al. 2013 1005 ; Vohland and Barry 2009 1006 ). For example, for drainage in Wadi Al-Lith, Saudi Arabia, the use of rainwater harvesting was suggested as a key climate change adaptation action (Almazroui et al. 2017 1007 ). There is robust evidence and high agreement that the implementation of RWH systems leads to an increase in agricultural production in drylands (Biazin et al. 2012 1008 ; Bouma and Wösten 2016 1009 ; Dile et al. 2013 1010 ). A global meta-analysis of changes in crop production due to the adoption of RWH techniques noted an average increase in yields of 78%, ranging from –28% to 468% (Bouma and Wösten 2016 1011 ). Of particular relevance to climate change in drylands is that the relative impact of RWH on agricultural production generally increases with increasing dryness. Relative yield improvements due to the adoption of RWH were significantly higher in years with less than 330 mm rainfall, compared to years with more than 330 mm (Bouma and Wösten 2016 1012 ). Despite delivering a clear set of benefits, there are some issues that need to be considered. The impact of RWH may vary at different temporal and spatial scales (Vohland and Barry 2009 1013 ). At a plot scale, RWH structures may increase available water and enhance agricultural production, SOC and nutrient availability, yet at a catchment scale, they may reduce runoff to downstream uses (Meijer et al. 2013 1014 ; Singh et al. 2012 1015 ; Vohland and Barry 2009 1016 ; Yosef and Asmamaw 2015 1017 ). Inappropriate storage of water in warm climes can lead to an increase in water related diseases unless managed correctly, for example, schistosomiasis and malaria (Boelee et al. 2013 1018 ).

Integrated crop–soil–water management may also deliver climate change mitigation benefits through avoiding, reducing and reversing the loss of SOC (Table 6.5). Approximately 20–30 Pg of SOC have been released into the atmosphere through desertification processes, for example, deforestation, overgrazing and conventional tillage (Lal 2004 1019 ). Activities, such as those associated with conservation agriculture (minimising tillage, crop rotation, maintaining organic cover and planting a diversity of species), reduce erosion, improve water use efficiency and primary production, increase inflow of organic material and enhance SOC over time, contributing to climate change mitigation and adaptation ( high confidence ) (Plaza-Bonilla et al. 2015 1020 ; Lal 2015 1021 ; Srinivasa Rao et al. 2015 1022 ; Sombrero and de Benito 2010 1023 ). Conservation agriculture practices also lead to increases in SOC ( medium confidence ). However, sustained carbon sequestration is dependent on net primary productivity and on the availability of crop-residues that may be relatively limited and often consumed by livestock or used elsewhere in dryland contexts (Cheesman et al. 2016 1024 ; Plaza-Bonilla et al. 2015 1025 ). For this reason, expected rates of carbon sequestration following changes in agricultural practices in drylands are relatively low (0.04–0.4 tC ha –1 ) and it may take a protracted period of time, even several decades, for carbon stocks to recover if lost ( medium confidence ) (Farage et al. 2007 1026 ; Hoyle et al. 2013 1027 ; Lal 2004 1028 ). This long recovery period enforces the rationale for prioritising the avoidance and reduction of land degradation and loss of C, in addition to restoration activities.

Grazing and fire management in drylands

Rangeland management systems such as sustainable grazing approaches and re-vegetation increase rangeland productivity ( high confidence ) (Table 6.5). Open grassland, savannah and woodland are home to the majority of world’s livestock production (Safriel et al. 2005 1029 ). Within these drylands areas, prevailing grazing and fire regimes play an important role in shaping the relative abundance of trees versus grasses (Scholes and Archer 1997 1030 ; Staver et al. 2011 1031 ; Stevens et al. 2017 1032 ), as well as the health of the grass layer in terms of primary production, species richness and basal cover (the propotion of the plant that is in the soil) (Plaza-Bonilla et al. 2015 1033 ; Short et al. 2003 1034 ). This in turn influences levels of soil erosion, soil nutrients, secondary production and additional ecosystem services (Divinsky et al. 2017 1035 ; Pellegrini et al. 2017 1036 ). A further set of drivers, including soil type, annual rainfall and changes in atmospheric CO 2 may also define observed rangeland structure and composition (Devine et al. 2017 1037 ; Donohue et al. 2013 1038 ), but the two principal factors that pastoralists can manage are grazing and fire, by altering their frequency, type and intensity.

The impact of grazing and fire regimes on biodiversity, soil nutrients, primary production and further ecosystem services is not constant and varies between locations (Divinsky et al. 2017 1039 ; Fleischner 1994 1040 ; van Oijen et al. 2018 1041 ). Trade-offs may therefore need to be considered to ensure that rangeland diversity and production are resilient to climate change (Plaza-Bonilla et al. 2015 1042 ; van Oijen et al. 2018 1043 ). In certain locations, even light to moderate grazing has led to a significant decrease in the occurrence of particular species, especially forbs (O’Connor et al. 2011 1044 ; Scott-shaw and Morris 2015 1045 ). In other locations, species richness is only significantly impacted by heavy grazing and is able to withstand light to moderate grazing (Divinsky et al. 2017 1046 ). A context specific evaluation of how grazing and fire impact particular species may therefore be required to ensure the persistence of target species over time (Marty 2005 1047 ). A similar trade-off may need to be considered between soil carbon sequestration and livestock production. As noted by Plaza-Bonilla et al. (2015) 1048 increasing grazing pressure has been found to increase SOC stocks in some locations, and decrease them in others. Where it has led to a decrease in soil carbon stocks, for example in Mongolia (Han et al. 2008 1049 ) and Ethiopia (Bikila et al. 2016 1050 ), trade-offs between carbon sequestration and the value of livestock to local livelihoods need be considered.

Although certain herbaceous species may be unable to tolerate grazing pressure, a complete lack of grazing or fire may not be desired in terms of ecosystems health. It can lead to a decrease in basal cover and the accumulation of moribund, unpalatable biomass that inhibits primary production (Manson et al. 2007 1051 ; Scholes 2009 1052 ). The utilisation of the grass sward through light to moderate grazing stimulates the growth of biomass and basal cover, and allows water services to be sustained over time (Papanastasis et al. 2017 1053 ; Scholes 2009 1054 ). Even moderate to heavy grazing in periods of higher rainfall may be sustainable, but constant heavy grazing during dry periods, and especially droughts, can lead to a reduction in basal cover, SOC, biological soil crusts, ecosystem services and an accelerated erosion ( high agreement, robust evidence ) (Archer et al. 2017 1055 ; Conant and Paustian 2003 1056 ; D’Odorico et al. 2013 1057 ; Geist and Lambin 2004 1058 ; Havstad et al. 2006 1059 ; Huang et al. 2007 1060 ; Manzano and Návar 2000 1061 ; Pointing and Belnap 2012 1062 ; Weber et al. 2016 1063 ). For this reason, the inclusion of drought forecasts and contingency planning in grazing and fire management programmes is crucial to avoid desertification (Smith and Foran 1992 1064 ; Torell et al. 2010 1065 ). It is an important component of avoiding and reducing early degradation. Although grasslands systems may be relatively resilient and can often recover from a moderately degraded state (Khishigbayar et al. 2015 1066 ; Porensky et al. 2016 1067 ), if a tipping point has been exceeded, restoration to a historic state may not be economical or ecologically feasible (D’Odorico et al. 2013 1068 ).

Together with livestock management (Table 6.5), the use of fire is an integral part of rangeland management, which can be applied to remove moribund and unpalatable forage, exotic weeds and woody species (Archer et al. 2017 1069 ). Fire has less of an effect on SOC and soil nutrients in comparison to grazing (Abril et al. 2005 1070 ), yet elevated fire frequency has been observed to lead to a decrease in soil carbon and nitrogen (Abril et al. 2005 1071 ; Bikila et al. 2016 1072 ; Bird et al. 2000 1073 ; Pellegrini et al. 2017 1074 ). Although the impact of climate change on fire frequency and intensity may not be clear due to its differing impact on fuel accumulation, suitable weather conditions and sources of ignition (Abatzoglou et al. 2018 1075 ; Littell et al. 2018 1076 ; Moritz et al. 2012 1077 ), there is an increasing use of prescribed fire to address several global change phenomena, for example, the spread of invasive species and bush encroachment, as well as the threat of intense runaway fires (Fernandes et al. 2013 1078 ; McCaw 2013 1079 ; van Wilgen et al. 2010 1080 ). Cross-Chapter Box 3 in Chapter 2 provides a further review of the interaction between fire and climate change.

There is often much emphasis on reducing and reversing the degradation of rangelands due to the wealth of benefits they provide, especially in the context of assisting dryland communities to adapt to climate change (Webb et al. 2017 1081 ; Woollen et al. 2016 1082 ). The emerging concept of ecosystem-based adaptation has highlighted the broad range of important ecosystem services that healthy rangelands can provide in a resilient manner to local residents and downstream economies (Kloos and Renaud 2016 1083 ; Reid et al. 2018 1084 ). In terms of climate change mitigation, the contribution of rangelands, woodland and sub-humid dry forest (e.g., Miombo woodland in south-central Africa) is often undervalued due to relatively low carbon stocks per hectare. Yet due to their sheer extent, the amount of carbon sequestered in these ecosystems is substantial and can make a valuable contribution to climate change mitigation (Lal 2004 1085 ; Pelletier et al. 2018 1086 ).

Clearance of bush encroachment

The encroachment of open grassland and savannah ecosystems by woody species has occurred for at least the past 100 years (Archer et al. 2017 1087 ; O’Connor et al. 2014 1088 ; Schooley et al. 2018 1089 ). Dependent on the type and intensity of encroachment, it may lead to a net loss of ecosystem services and be viewed as a form of desertification (Dougill et al. 2016 1090 ; O’Connor et al. 2014 1091 ). However, there are circumstances where bush encroachment may lead to a net increase in ecosystem services, especially at intermediate levels of encroachment, where the ability of the landscape to produce fodder for livestock is retained, while the production of wood and associated products increases (Eldridge et al. 2011 1092 ; Eldridge and Soliveres 2014 1093 ). This may be particularly important in regions such as southern Africa and India where over 65% of rural households depend on fuelwood from surrounding landscapes as well as livestock production (Komala and Prasad 2016 1094 ; Makonese et al. 2017 1095 ; Shackleton and Shackleton 2004 1096 ).

This variable relationship between the level of encroachment, carbon stocks, biodiversity, provision of water and pastoral value (Eldridge and Soliveres 2014 1097 ) can present a conundrum to policymakers, especially when considering the goals of three Rio Conventions: UNFCCC, UNCCD and UNCBD. Clearing intense bush encroachment may improve species diversity, rangeland productivity, the provision of water and decrease desertification, thereby contributing to the goals of the UNCBD and UNCCD as well as the adaptation aims of the UNFCCC. However, it would lead to the release of biomass carbon stocks into the atmosphere and potentially conflict with the mitigation aims of the UNFCCC.

For example, Smit et al. (2015) 1098 observed an average increase in above-ground woody carbon stocks of 44 tC ha –1 in savannahs in northern Namibia. However, since bush encroachment significantly inhibited livestock production, there are often substantial efforts to clear woody species (Stafford-Smith et al. 2017 1099 ). Namibia has a national programme, currently in its early stages, aimed at clearing woody species through mechanical measures (harvesting of trees) as well as the application of arboricides (Smit et al. 2015 1100 ). However, the long-term success of clearance and subsequent improved fire and grazing management remains to be evaluated, especially restoration back towards an ‘original open grassland state’. For example, in northern Namibia, the rapid reestablishment of woody seedlings has raised questions about whether full clearance and restoration is possible (Smit et al. 2015 1101 ). In arid landscapes, the potential impact of elevated atmospheric CO 2 (Donohue et al. 2013 1102 ; Kgope et al. 2010 1103 ) and opportunity to implement high-intensity fires that remove woody species and maintain rangelands in an open state has been questioned (Bond and Midgley 2000 1104 ). If these drivers of woody plant encroachment cannot be addressed, a new form of ‘emerging ecosystem’ (Milton 2003 1105 ) may need to be explored that includes both improved livestock and fire management as well as the utilisation of biomass as a long-term commodity and source of revenue (Smit et al. 2015 1106 ). Initial studies in Namibia and South Africa (Stafford-Smith et al. 2017 1107 ) indicate that there may be good opportunity to produce sawn timber, fencing poles, fuelwood and commercial energy, but factors such as the cost of transport can substantially influence the financial feasibility of implementation.

The benefit of proactive management that prevents land from being degraded (altering grazing systems or treating bush encroachment at early stages before degradation has been initiated) is more cost-effective in the long term and adds more resistance to climate change than treating lands after degradation has occurred (Webb et al. 2013 1108 ; Weltz and Spaeth 2012 1109 ). The challenge is getting producers to alter their management paradigm from short-term objectives to long-term objectives.

Combating sand and dust storms through sand dune stabilisation

Dust and sand storms have a considerable impact on natural and human systems (Sections 3.4.1 and 3.4.2). Application of sand dune stabilisation techniques contributes to reducing sand and dust storms ( high confidence ). Using a number of methods, sand dune stabilisation aims to avoid and reduce the occurrence of dust and sand storms (Mainguet and Dumay 2011 1110 ). Mechanical techniques include building palisades to prevent the movement of sand and reduce sand deposits on infrastructure. Chemical methods include the use of calcium bentonite or using silica gel to fix mobile sand (Aboushook et al. 2012 1111 ; Rammal and Jubair 2015 1112 ). Biological methods include the use of mulch to stabilise surfaces (Sebaa et al. 2015 1113 ; Yu et al. 2004 1114 ) and establishing permanent plant cover using pasture species that improve grazing at the same time (Abdelkebir and Ferchichi 2015 1115 ; Zhang et al. 2015 1116 ) (Section 3.7.1.3). When the dune is stabilised, woody perennials are introduced that are selected according to climatic and ecological conditions (FAO 2011 1117 ). For example, such re-vegetation processes have been implemented on the shifting dunes of the Tengger Desert in northern China leading to the stabilisation of sand and the sequestration of up to 10 tC ha –1 over a period of 55 years (Yang et al. 2014 1118 ).

Use of halophytes for the re-vegetation of saline lands

Soil salinity and sodicity can severely limit the growth and productivity of crops (Jan et al. 2017 1119 ) and lead to a decrease in available arable land. Leaching and drainage provides a possible solution, but can be prohibitively expensive. An alternative, more economical option, is the growth of halophytes (plants that are adapted to grow under highly saline conditions) that allow saline land to be used in a productive manner (Qadir et al. 2000 1120 ). The biomass produced can be used as forage, food, feed, essential oils, biofuel, timber, or fuelwood (Chughtai et al. 2015 1121 ; Mahmood et al. 2016 1122 ; Sharma et al. 2016 1123 ). A further co-benefit is the opportunity to mitigate climate change through the enhancement of terrestrial carbon stocks as land is re-vegetated (Dagar et al. 2014 1124 ; Wicke et al. 2013 1125 ). The combined use of salt-tolerant crops, improved irrigation practices, chemical remediation measures and appropriate mulch and compost is effective in reducing the impact of secondary salinisation ( medium confidence ).

In Pakistan, where about 6.2 Mha of agricultural land is affected by salinity, pioneering work on utilising salt-tolerant plants for the re-vegetation of saline lands (biosaline agriculture) was done in the early 1970s (NIAB 1997 1796 ). A number of local and exotic varieties were initially screened for salt tolerance in lab – and greenhouse-based studies, and then distributed to similar saline areas (Ashraf et al. 2010 1126 ). These included tree species ( Acacia ampliceps, Acacia nilotica, Eucalyptus camaldulensis, Prosopis juliflora, Azadirachta indica ) (Awan and Mahmood 2017 1127 ), forage plants ( Leptochloa fusca, Sporobolus arabicus, Brachiaria mutica, Echinochloa sp., Sesbania and Atriplex spp.) and crop species including varieties of barley ( Hordeum vulgare ), cotton, wheat ( Triticum aestivum ) and Brassica spp. (Mahmood et al. 2016 1128 ) as well as fruit crops in the form of date palm ( Phoenix dactylifera ) that has high salt tolerance with no visible adverse effects on seedlings (Yaish and Kumar 2015 1129 ; Al-Mulla et al. 2013 1130 ; Alrasbi et al. 2010 1131 ). Pomegranate ( Punica granatum L. ) is another fruit crop of moderate to high salt tolerance. Through regulating growth form and nutrient balancing, it can maintain water content, chlorophyll fluorescence and enzyme activity at normal levels (Ibrahim 2016 1132 ; Okhovatian-Ardakani et al. 2010 1133 ).

In India and elsewhere, tree species including Prosopis juliflora, Dalbergia sissoo , and Eucalyptus tereticornis have been used to re-vegetate saline land. Certain biofuel crops in the form of Ricinus communis (Abideen et al. 2014 1134 ), Euphorbia antisyphilitica (Dagar et al. 2014 1135 ), Karelinia caspia (Akinshina et al. 2016 1797 ) and Salicornia spp. (Sanandiya and Siddhanta 2014 1136 ) are grown in saline areas, and Panicum turgidum (Koyro et al. 2013 1137 ) and Leptochloa fusca (Akhter et al. 2003 1138 ) have been grown as fodder crop on degraded soils with brackish water. In China, intense efforts are being made on the use of halophytes (Sakai et al. 2012 1139 ; Wang et al. 2018 1140 ). These examples reveal that there is great scope for saline areas to be used in a productive manner through the utilisation of halophytes. The most productive species often have yields equivalent to conventional crops, at salinity levels matching even that of seawater.

Socio-economic responses

Socio-economic and policy responses are often crucial in enhancing the adoption of SLM practices (Cordingley et al. 2015 1143 ; Fleskens and Stringer 2014 1144 ; Nyanga et al. 2016 1145 ) and for assisting agricultural households to diversify their sources of income (Barrett et al. 2017 1146 ; Shiferaw and Djido 2016 1147 ). Technology and socio-economic responses are not independent, but continuously interact.

Socio-economic responses for combating desertification under climate change

Desertification limits the choice of potential climate change mitigation and adaptation response options by reducing climate change adaptive capacities. Furthermore, many additional factors, for example, a lack of access to markets or insecurity of land tenure, hinder the adoption of SLM. These factors are largely beyond the control of individuals or local communities and require broader policy interventions (Section 3.6.3). Nevertheless, local collective action and ILK are still crucial to the ability of households to respond to the combined challenge of climate change and desertification. Raising awareness, capacity building and development to promote collective action and indigenous and local knowledge contribute to avoiding, reducing and reversing desertification under changing climate.

The use of indigenous and local knowledge enhances the success of SLM and its ability to address desertification (Altieri and Nicholls 2017 1148 ; Engdawork and Bork 2016 1149 ). Using indigenous and local knowledge for combating desertification could contribute to climate change adaptation strategies (Belfer et al. 2017 1150 ; Codjoe et al. 2014 1151 ; Etchart 2017 1152 ; Speranza et al. 2010 1153 ; Makondo and Thomas 2018 1154 ; Maldonado et al. 2016 1155 ; Nyong et al. 2007 1156 ). There are abundant examples of how indigenous and local knowledge, which are an important part of broader agroecological knowledge (Altieri 2018 1157 ), have allowed livelihood systems in drylands to be maintained despite environmental constraints. An example is the numerous traditional water harvesting techniques that are used across the drylands to adapt to dry spells and climate change. These include creating planting pits ( zai, ngoro ) and micro-basins, contouring hill slopes and terracing (Biazin et al. 2012 1158 ) (Section 3.6.1). Traditional ndiva water harvesting systems in Tanzania enable the capture of runoff water from highland areas to downstream community-managed micro-dams for subsequent farm delivery through small-scale canal networks (Enfors and Gordon 2008 1159 ). A further example are pastoralist communities located in drylands who have developed numerous methods to sustainably manage rangelands. Pastoralist communities in Morocco developed the agdal system of seasonally alternating use of rangelands to limit overgrazing (Dominguez 2014 1160 ) as well as to manage forests in the Moroccan High Atlas Mountains (Auclair et al. 2011 1161 ). Across the Arabian Peninsula and North Africa, a rotational grazing system, hema , was historically practiced by the Bedouin communities (Hussein 2011 1162 ; Louhaichi and Tastad 2010 1163 ). The Beni-Amer herders in the Horn of Africa have developed complex livestock breeding and selection systems (Fre 2018 1164 ). Although well adapted to resource-sparse dryland environments, traditional practices are currently not able to cope with increased demand for food and environmental changes (Enfors and Gordon 2008 1165 ; Engdawork and Bork 2016 1166 ). Moreover, there is robust evidence documenting the marginalisation or loss of indigenous and local knowledge (Dominguez 2014 1167 ; Fernández-Giménez and Fillat Estaque 2012 1168 ; Hussein 2011 1169 ; Kodirekkala 2017 1170 ; Moreno-Calles et al. 2012 1171 ). Combined use of indigenous and local knowledge and new SLM technologies can contribute to raising resilience to the challenges of climate change and desertification (high confidence) (Engdawork and Bork 2016 1172 ; Guzman et al. 2018 1173 ).

Collective action has the potential to contribute to SLM and climate change adaptation ( medium confidence ) (Adger 2003 1174 ; Engdawork and Bork 2016 1175 ; Eriksen and Lind 2009 1176 ; Ostrom 2009 1177 ; Rodima-Taylor et al. 2012 1178 ). Collective action is a result of social capital. Social capital is divided into structural and cognitive forms: structural corresponding to strong networks (including outside one’s immediate community); and cognitive encompassing mutual trust and cooperation within communities (van Rijn et al. 2012 1179 ; Woolcock and Narayan 2000 1180 ). Social capital is more important for economic growth in settings with weak formal institutions, and less so in those with strong enforcement of formal institutions (Ahlerup et al. 2009 1181 ). There are cases throughout the drylands showing that community by-laws and collective action successfully limited land degradation and facilitated SLM (Ajayi et al. 2016 1182 ; Infante 2017 1183 ; Kassie et al. 2013 1184 ; Nyangena 2008 1185 ; Willy and Holm-Müller 2013 1186 ; Wossen et al. 2015 1187 ). However, there are also cases when they did not improve SLM where they were not strictly enforced (Teshome et al. 2016 1188 ). Collective action for implementing responses to dryland degradation is often hindered by local asymmetric power relations and ‘elite capture’ (Kihiu 2016 1189 ; Stringer et al. 2007 1190 ). This illustrates that different levels and types of social capital result in different levels of collective action. In a sample of East, West and southern African countries, structural social capital in the form of access to networks outside one’s own community was suggested to stimulate the adoption of agricultural innovations, whereas cognitive social capital, associated with inward-looking community norms of trust and cooperation, was found to have a negative relationship with the adoption of agricultural innovations (van Rijn et al. 2012 1191 ). The latter is indirectly corroborated by observations of the impact of community-based rangeland management organisations in Mongolia. Although levels of cognitive social capital did not differ between them, communities with strong links to outside networks were able to apply more innovative rangeland management practices in comparison to communities without such links (Ulambayar et al. 2017 1192 ).

Farmer-led innovations. Agricultural households are not just passive adopters of externally developed technologies, but are active experimenters and innovators (Reij and Waters-Bayer 2001 1193 ; Tambo and Wünscher 2015 1194 ; Waters-Bayer et al. 2009 1195 ). SLM technologies co-generated through direct participation of agricultural households have higher chances of being accepted by them ( medium confidence ) (Bonney et al. 2016 1196 ; Vente et al. 2016 1197 ). Usually farmer-driven innovations are more frugal and better adapted to their resource scarcities than externally introduced technologies (Gupta et al. 2016 1198 ). Farmer-to-farmer sharing of their own innovations and mutual learning positively contribute to higher technology adoption rates (Dey et al. 2017 1199 ). This innovative ability can be given a new dynamism by combining it with emerging external technologies. For example, emerging low-cost phone applications (‘apps’) that are linked to soil and water monitoring sensors can provide farmers with previously inaccessible information and guidance (Cornell et al. 2013 1200 ; Herrick et al. 2017 1201 ; McKinley et al. 2017 1202 ; Steger et al. 2017 1203 ).

Currently, the adoption of SLM practices remains insufficient to address desertification and contribute to climate change adaptation and mitigation more extensively. This is due to the constraints on the use of indigenous and local knowledge and collective action, as well as economic and institutional barriers for SLM adoption (Banadda 2010 1204 ; Cordingley et al. 2015 1205 ; Lokonon and Mbaye 2018 1206 ; Mulinge et al. 2016 1207 ; Wildemeersch et al. 2015 1208 ) (Section 3.1.4.2; 3.6.3). Sustainable development of drylands under these socio-economic and environmental (climate change, desertification) conditions will also depend on the ability of dryland agricultural households to diversify their livelihoods sources (Boserup 1965 1209 ; Safriel and Adeel 2008 1210 ).

Socio-economic responses for economic diversification

Livelihood diversification through non-farm employment increases the resilience of rural households against desertification and extreme weather events by diversifying their income and consumption (high confidence). Moreover, it can provide the funds to invest into SLM (Belay et al. 2017 1211 ; Bryan et al. 2009 1212 ; Dumenu and Obeng 2016 1213 ; Salik et al. 2017 1214 ; Shiferaw et al. 2009 1215 ). Access to non-agricultural employment is especially important for poorer pastoral households as their small herd sizes make them less resilient to drought (Fratkin 2013 1216 ; Lybbert et al. 2004 1217 ). However, access to alternative opportunities is limited in the rural areas of many developing countries, especially for women and marginalised groups who lack education and social networks (Reardon et al. 2008 1218 ).

Migration is frequently used as an adaptation strategy to environmental change ( medium confidence ). Migration is a form of livelihood diversification and a potential response option to desertification and increasing risk to agricultural livelihoods under climate change (Walther et al. 2002 1219 ). Migration can be short-term (e.g., seasonal) or long-term, internal within a country or international. There is medium evidence showing rural households responding to desertification and droughts through all forms of migration, for example: during the Dust Bowl in the USA in the 1930s (Hornbeck 2012 1220 ); during droughts in Burkina Faso in the 2000s (Barbier et al. 2009 1221 ); in Mexico in the 1990s (Nawrotzki et al. 2016 1222 ); and by the Aymara people of the semi-arid Tarapacá region in Chile between 1820 and 1970, responding to declines in rainfall and growing demands for labour outside the region (Lima et al. 2016 1223 ). There is robust evidence and high agreement showing that migration decisions are influenced by a complex set of different factors, with desertification and climate change playing relatively lesser roles (Liehr et al. 2016 1224 ) (Section 3.4.2). Barrios et al. (2006) 1225 found that urbanisation in Sub-Saharan Africa was partially influenced by climatic factors during the 1950–2000 period, in parallel to liberalisation of internal restrictions on labour movements: each 1% reduction in rainfall was associated with a 0.45% increase in urbanisation. This migration favoured more industrially diverse urban areas in Sub-Saharan Africa (Henderson et al. 2017 1226 ), because they offer more diverse employment opportunities and higher wages. Similar trends were also observed in Iran in response to water scarcity (Madani et al. 2016 1227 ).

However, migration involves some initial investments. For this reason, reductions in agricultural incomes due to climate change or desertification have the potential to decrease out-migration among the poorest agricultural households, who become less able to afford migration (Cattaneo and Peri 2016 1228 ), thus increasing social inequalities. There is medium evidence and high agreement that households with migrant worker members are more resilient against extreme weather events and environmental degradation compared to non-migrant households, who are more dependent on agricultural income (Liehr et al. 2016 1229 ; Salik et al. 2017 1230 ; Sikder and Higgins 2017 1231 ). Remittances from migrant household members potentially contribute to SLM adoptions, however, substantial out-migration was also found to constrain the implementation of labour-intensive land management practices (Chen et al. 2014 1232 ; Liu et al. 2016a 1233 ).

Policy responses

The adoption of SLM practices depends on the compatibility of the technology with prevailing socio-economic and biophysical conditions (Sanz et al. 2017 1798 ). Globally, it was shown that every USD invested into restoring degraded lands yields social returns, including both provisioning and non-provisioning ecosystem services, in the range of 3–6 USD over a 30-year period (Nkonya et al. 2016a 1234 ). A similar range of returns from land restoration activities was found in Central Asia (Mirzabaev et al. 2016 1235 ), Ethiopia (Gebreselassie et al. 2016 1236 ), India (Mythili and Goedecke 2016 1237 ), Kenya (Mulinge et al. 2016 1238 ), Niger (Moussa et al. 2016 1239 ) and Senegal (Sow et al. 2016 1240 ) ( medium confidence ). Despite these relatively high returns, there is robust evidence that the adoption of SLM practices remains low (Cordingley et al. 2015 1241 ; Giger et al. 2015 1242 ; Lokonon and Mbaye 2018 1243 ). Part of the reason for these low adoption rates is that the major share of the returns from SLM are social benefits, namely in the form of non-provisioning ecosystem services (Nkonya et al. 2016a 1244 ). The adoption of SLM technologies does not always provide implementers with immediate private benefits (Schmidt et al. 2017 1245 ). High initial investment costs, institutional and governance constraints and a lack of access to technologies and equipment may inhibit their adoption further (Giger et al. 2015 1246 ; Sanz et al. 2017 1247 ; Schmidt et al. 2017 1248 ). However, not all SLM practices have high upfront costs. Analysing the World Overview of Conservation Approaches and Technologies (WOCAT) database, a globally acknowledged reference database for SLM, Giger et al. (2015) 1249 found that the upfront costs of SLM technologies ranged from about 20 USD to 5000 USD, with the median cost being around 500 USD. Many SLM technologies are profitable within 3 to 10 years ( medium confidence ) (Djanibekov and Khamzina 2016 1250 ; Giger et al. 2015 1251 ; Moussa et al. 2016 1252 ; Sow et al. 2016 1253 ). About 73% of 363 SLM technologies evaluated were reported to become profitable within three years, while 97% were profitable within 10 years (Giger et al. 2015 1254 ). Similarly, it was shown that social returns from investments in restoring degraded lands will exceed their costs within six years in many settings across drylands (Nkonya et al. 2016a 1255 ). However, even with affordable upfront costs, market failures – in the form of lack of access to credit, input and output markets, and insecure land tenure (Section 3.1.3) – result in the lack of adoption of SLM technologies (Moussa et al. 2016 1256 ). Payments for ecosystem services, subsidies for SLM, and encouragement of community collective action can lead to a higher level of adoption of SLM and land restoration activities ( medium confidence ) (Bouma and Wösten 2016 1257 ; Lambin et al. 2014 1258 ; Reed et al. 2015 1259 ; Schiappacasse et al. 2012 1260 ; van Zanten et al. 2014 1261 ) (Section 3.6.3). Enabling the policy responses discussed in this section will contribute to overcoming these market failures.

Many socio-economic factors shaping individual responses to desertification typically operate at larger scales. Individual households and communities do not exercise control over these factors, such as land tenure insecurity, lack of property rights, lack of access to markets, availability of rural advisory services, and agricultural price distortions. These factors are shaped by national government policies and international markets. As is the case with socio-economic responses, policy responses are classified below in two ways: those which seek to combat desertification under changing climate; and those which seek to provide alternative livelihood sources through economic diversification. These options are mutually complementary and contribute to all the three hierarchical elements of the Land Degradation Neutrality (LDN) framework, namely, avoiding, reducing and reversing land degradation (Cowie et al. 2018 1262 ; Orr et al. 2017 1263 ) (Sections 4.8.5 and 7.4.5, and Table 7.2). An enabling policy environment is a critical element for the achievement of LDN (Chasek et al. 2019 1264 ). Implementation of LDN policies can contribute to climate change adaptation and mitigation ( high confidence ) (Sections 3.6.1 and 3.7.2).

Policy responses towards combating desertification under climate change

Policy responses to combat desertification take numerous forms (Marques et al. 2016 1265 ). Below we discuss major policy responses consistently highlighted in the literature in connection with SLM and climate change, because these response options were found to strengthen adaptation capacities and to contribute to climate change mitigation. They include improving market access, empowering women, expanding access to agricultural advisory services, strengthening land tenure security, payments for ecosystem services, decentralised natural resource management, investing into research and monitoring of desertification and dust storms, and investing into modern renewable energy sources.

Policies aiming at improving market access, that is the ability to access output and input markets at lower costs, help farmers and livestock producers earn more profit from their produce. Increased profits both motivate and enable them to invest more in SLM. Higher access to input, output and credit markets was consistently found as a major factor in the adoption of SLM practices in a wide number of settings across the drylands ( medium confidence ) (Aw-Hassan et al. 2016 1266 ; Gebreselassie et al. 2016 1267 ; Mythili and Goedecke 2016 1268 ; Nkonya and Anderson 2015 1269 ; Sow et al. 2016). Lack of access to credit limits adjustments and agricultural responses to the impacts of desertification under changing climate, with long-term consequences for the livelihoods and incomes, as was shown during the North American Dust Bowl of the 1930s (Hornbeck 2012 1271 ). Government policies aimed at improving market access usually involve constructing and upgrading rural–urban transportation infrastructure and agricultural value chains, such as investments into construction of local markets, abattoirs and cold storage warehouses, as well as post-harvest processing facilities (McPeak et al. 2006). However, besides infrastructural constraints, providing improved access often involves relieving institutional constraints to market access (Little 2010 1272 ), such as improved coordination of cross-border food safety and veterinary regulations (Ait Hou et al. 2015 1273 ; Keiichiro et al. 2015 1274 ; McPeak et al. 2006; Unnevehr 2015 1275 ), and availability and access to market information systems (Bobojonov et al. 2016 1276 ; Christy et al. 2014 1277 ; Nakasone et al. 2014 1278 ).

Women’s empowerment. A greater emphasis on understanding gender-specific differences over land use and land management practices as an entry point can make land restoration projects more successful ( medium confidence ) (Broeckhoven and Cliquet 2015 1279 ; Carr and Thompson 2014 1280 ; Catacutan and Villamor 2016 1281 ; Dah-gbeto and Villamor 2016 1282 ). In relation to representation and authority to make decisions in land management and governance, women’s participation remains lacking particularly in the dryland regions. Thus, ensuring women’s rights means accepting women as equal members of the community and citizens of the state (Nelson et al. 2015 1283 ). This includes equitable access of women to resources (including extension services), networks, and markets. In areas where socio-cultural norms and practices devalue women and undermine their participation, actions for empowering women will require changes in customary norms, recognition of women’s (land) rights in government policies, and programmes to assure that their interests are better represented (Section 1.4.2 and Cross-Chapter Box 11 in Chapter 7). In addition, several novel concepts are recently applied for an in-depth understanding of gender in relation to science–policy interface. Among these are the concepts of intersectionality, that is, how social dimensions of identity and gender are bound up in systems of power and social institutions (Thompson-Hall et al. 2016 1284 ), bounded rationality for gendered decision-making, related to incomplete information interacting with limits to human cognition leading to judgement errors or objectively poor decision making (Villamor and van Noordwijk 2016 1285 ), anticipatory learning for preparing for possible contingencies and consideration of long-term alternatives (Dah-gbeto and Villamor 2016 1286 ) and systematic leverage points for interventions that produce, mark, and entrench gender inequality within communities (Manlosa et al. 2018 1287 ), which all aim to improve gender equality within agroecological landscapes through a systems approach.

Education and expanding access to agricultural services.  Providing access to information about SLM practices facilitates their adoption ( medium confidence ) (Kassie et al. 2015 1288 ; Nkonya et al. 2015 1289 ; Nyanga et al. 2016 1291 ). Moreover, improving the knowledge of climate change, capacity building and development in rural areas can help strengthen climate change adaptive capacities (Berman et al. 2012 1292 ; Chen et al. 2018 1293 ; Descheemaeker et al. 2018 1294 ; Popp et al. 2009 1296 ; Tambo 2016 1297 ; Yaro et al. 2015 1298 ). Agricultural initiatives to improve the adaptive capacities of vulnerable populations were more successful when they were conducted through reorganised social institutions and improved communication, for example, in Mozambique (Osbahr et al. 2008 1299 ). Improved communication and education could be facilitated by wider use of new information and communication technologies (ICTs) (Peters et al. 2015 1300 ). Investments into education were associated with higher adoption of soil conservation measures, for example, in Tanzania (Tenge et al. 2004 1301 ). Bryan et al. (2009) found that access to information was the prominent facilitator of climate change adaptation in Ethiopia. However, resource constraints of agricultural services, and disconnects between agricultural policy and climate policy can hinder the dissemination of climate-smart agricultural technologies (Morton 2017 1302 ). Lack of knowledge was also found to be a significant barrier to implementation of soil rehabilitation programmes in the Mediterranean region (Reichardt 2010 1303 ). Agricultural services will be able to facilitate SLM best when they also serve as platforms for sharing indigenous and local knowledge and farmer innovations (Mapfumo et al. 2016 1304 ). Participatory research initiatives conducted jointly with farmers have higher chances of resulting in technology adoption (Bonney et al. 2016 1305 ; Rusike et al. 2006 1306 ; Vente et al. 2016). Moreover, rural advisory services are often more successful in disseminating technological innovations when they adopt commodity/value chain approaches, remain open to engagement in input supply, make use of new opportunities presented by ICTs, facilitate mutual learning between multiple stakeholders (Morton 2017 1307 ), and organise science and SLM information in a location-specific manner for use in education and extension (Bestelmeyer et al. 2017 1308 ).

Strengthening land tenure security. Strengthening land tenure security is a major factor contributing to the adoption of soil conservation measures in croplands ( high confidence ) (Bambio and Bouayad Agha 2018 1309 ; Higgins et al. 2018 1310 ; Holden and Ghebru 2016 1311 ; Paltasingh 2018 1312 ; Rao et al. 2016; Robinson et al. 2018 1313 ), thus contributing to climate change adaptation and mitigation. Moreover, land tenure security can lead to more investment in trees (Deininger and Jin 2006 1314 ; Etongo et al. 2015 1315 ). Land tenure recognition policies were found to lead to higher agricultural productivity and incomes, although with inter-regional variations, requiring an improved understanding of overlapping formal and informal land tenure rights (Lawry et al. 2017 1316 ). For example, secure land tenure increased investments into SLM practices in Ghana, but without affecting farm productivity (Abdulai et al. 2011 1317 ). Secure land tenure, especially for communally managed lands, helps reduce arbitrary appropriations of land for large-scale commercial farms (Aha and Ayitey 2017; Baumgartner 2017 1318 ; Dell’Angelo et al. 2017 1319 ). In contrast, privatisation of rangeland tenures in Botswana and Kenya led to the loss of communal grazing lands and actually increased rangeland degradation (Basupi et al. 2017 1320 ; Kihiu 2016 1321 ) as pastoralists needed to graze livestock on now smaller communal pastures. Since food insecurity in drylands is strongly affected by climate risks, there is robust evidence and high agreement that resilience to climate risks is higher with flexible tenure for allowing mobility for pastoralist communities, and not fragmenting their areas of movement (Behnke 1994 1323 ; Holden and Ghebru 2016 1324 ; Liao et al. 2017 1325 ; Turner et al. 2016 1326 ; Wario et al. 2016 1327 ). More research is needed on the optimal tenure mix, including low-cost land certification, redistribution reforms, market-assisted reforms and gender-responsive reforms, as well as collective forms of land tenure such as communal land tenure and cooperative land tenure (see Section 7.6.5 for a broader discussion of land tenure security under climate change).

Payment for ecosystem services (PES) provides incentives for land restoration and SLM ( medium confidence ) (Lambin et al. 2014 1328 ; Li et al. 2018; Reed et al. 2015 1329 ; Schiappacasse et al. 2012 1330 ). Several studies illustrate that the social costs of desertification are larger than its private cost (Costanza et al. 2014 1331 ; Nkonya et al. 2016a 1332 ). Therefore, although SLM can generate public goods in the form of provisioning ecosystem services, individual land custodians underinvest in SLM as they are unable to reap these benefits fully. Payment for ecosystem services provides a mechanism through which some of these benefits can be transferred to land users, thereby stimulating further investment in SLM. The effectiveness of PES schemes depends on land tenure security and appropriate design, taking into account specific local conditions (Börner et al. 2017 1333 ). However, PES has not worked well in countries with fragile institutions (Karsenty and Ongolo 2012 1334 ). Equity and justice in distributing the payments for ecosystem services were found to be key for the success of the PES programmes in Yunnan, China (He and Sikor 2015). Yet, when reviewing the performance of PES programmes in the tropics, Calvet-Mir et al. (2015), found that they are generally effective in terms of environmental outcomes, despite being sometimes unfair in terms of payment distribution. It is suggested that the implementation of PES will be improved through decentralised approaches giving local communities a larger role in the decision-making process (He and Lang 2015).

Empowering local communities for decentralised natural resource management. Local institutions often play a vital role in implementing SLM initiatives and climate change adaptation measures ( high confidence ) (Gibson et al. 2005 1335 ; Smucker et al. 2015 1336 ). Pastoralists involved in community-based natural resource management in Mongolia had greater capacity to adapt to extreme winter frosts, resulting in less damage to their livestock (Fernandez-Gimenez et al. 2015 1337 ). Decreasing the power and role of traditional community institutions, due to top-down public policies, resulted in lower success rates in community-based programmes focused on rangeland management in Dirre, Ethiopia (Abdu and Robinson 2017 1338 ). Decentralised governance was found to lead to improved management in forested landscapes (Dressler et al. 2010 1339 ; Ostrom and Nagendra 2006 1340 ). However, there are also cases when local elites were placed in control and this decentralised natural resource management negatively impacted the livelihoods of the poorer and marginalised community members due to reduced access to natural resources (Andersson and Ostrom 2008 1341 ; Cullman 2015 1343 ; Dressler et al. 2010 1344 ).

The success of decentralised natural resource management initiatives depends on increased participation and empowerment of a diverse set of community members, not only local leaders and elites, in the design and management of local resource management institutions (Kadirbeyoglu and Özertan 2015 1345 ; Umutoni et al. 2016 1346 ), while considering the interactions between actors and institutions at different levels of governance (Andersson and Ostrom 2008 1347 ; Carlisle and Gruby 2017 1349 ; McCord et al. 2017 1351 ). An example of such programmes where local communities played a major role in land restoration and rehabilitation activities is the cooperative project on The National Afforestation and Erosion Control Mobilization Action Plan in Turkey, initiated by the Turkish Ministry of Agriculture and Forestry (Çalişkan and Boydak 2017 1352 ), with the investment of 1.8 billion USD between 2008 and 2012. The project mobilised local communities in cooperation with public institutions, municipalities, and non-governmental organisations, to implement afforestation, rehabilitation and erosion control measures, resulting in the afforestation and reforestation of 1.5 Mha (Yurtoglu 2015 1353 ). Moreover, some 1.75 Mha of degraded forest and 37,880 ha of degraded rangelands were rehabilitated. Finally, the project provided employment opportunities for 300,000 rural residents for six months every year, combining land restoration and rehabilitation activities with measures to promote socio-economic development in rural areas (Çalişkan and Boydak 2017 1354 ).

Investing in research and development. Desertification has received substantial research attention over recent decades (Turner et al. 2007 1355 ). There is also a growing research interest on climate change adaptation and mitigation interventions that help address desertification (Grainger 2009 1356 ). Agricultural research on SLM practices has generated a significant number of new innovations and technologies that increase crop yields without degrading the land, while contributing to climate change adaptation and mitigation (Section 3.6.1). There is robust evidence that such technologies help improve the food security of smallholder dryland farming households (Harris and Orr 2014 1357 ) (Section 6.3.5). Strengthening research on desertification is of high importance not only to meet SDGs but also to manage ecosystems effectively, based on solid scientific knowledge. More investment in research institutes and training the younger generation of researchers is needed for addressing the combined challenges of desertification and climate change (Akhtar-Schuster et al. 2011 1358 ; Verstraete et al. 2011 1359 ). This includes improved knowledge management systems that allow stakeholders to work in a coordinated manner by enhancing timely, targeted and contextualised information sharing (Chasek et al. 2011 1360 ). Knowledge and flow of knowledge on desertification is currently highly fragmented, constraining the effectiveness of those engaged in assessing and monitoring the phenomenon at various levels (Reed et al. 2011 1361 ). Improved knowledge and data exchange and sharing increase the effectiveness of efforts to address desertification ( high confidence ).

Developing modern renewable energy sources. Transitioning to renewable energy resources contributes to reducing desertification by lowering reliance on traditional biomass in dryland regions ( medium confidence ). This can also have socioeconomic and health benefits, especially for women and children ( high confidence ). Populations in most developing countries continue to rely on traditional biomass, including fuelwood, crop straws and livestock manure, for a major share of their energy needs, with the highest dependence in Sub-Saharan Africa (Amugune et al. 2017 1363 ; IEA 2013). Use of biomass for energy, mostly fuelwood (especially as charcoal), was associated with deforestation in some dryland areas (Iiyama et al. 2014 1364 ; Mekuria et al. 2018 1365 ; Neufeldt et al. 2015 1366 ; Zulu 2010 1367 ), while in some other areas there was no link between fuelwood collection and deforestation (Simon and Peterson 2018 1368 ; Swemmer et al. 2018 1369 ; Twine and Holdo 2016 1370 ). Moreover, the use of traditional biomass as a source of energy was found to have negative health effects through indoor air pollution (de la Sota et al. 2018 1371 ; Lim and Seow 2012), while also being associated with lower female labour force participation (Burke and Dundas 2015 1372 ). Jiang et al. (2014) indicated that providing improved access to alternative energy sources such as solar energy and biogas could help reduce the use of fuelwood in south-western China, thus alleviating the spread of rocky desertification. The conversion of degraded lands into cultivation of biofuel crops will affect soil carbon dynamics (Albanito et al. 2016 1374 ; Nair et al. 2011 1375 ) (Cross-Chapter Box 7 in Chapter 6). The use of biogas slurry as soil amendment or fertiliser can increase soil carbon (Galvez et al. 2012; Negash et al. 2017 1376 ). Large-scale installation of wind and solar farms in the Sahara Desert was projected to create a positive climate feedback through increased surface friction and reduced albedo, doubling precipitation over the neighbouring Sahel region with resulting increases in vegetation (Li et al. 2018). Transition to renewable energy sources in high-income countries in dryland areas primarily contributes to reducing GHG emissions and mitigating climate change, with some other co-benefits such as diversification of energy sources (Bang 2010 1377 ), while the impacts on desertification are less evident. The use of renewable energy has been proposed as an important mitigation option in dryland areas as well (El-Fadel et al. 2003 1378 ). Transitions to renewable energy are being promoted by governments across drylands (Cancino-Solórzano et al. 2016 1379 ; Hong et al. 2013 1380 ; Sen and Ganguly 2017) including in fossil-fuel rich countries (Farnoosh et al. 2014 1381 ; Dehkordi et al. 2017; Stambouli et al. 2012 1382 ; Vidadili et al. 2017 1383 ), despite important social, political and technical barriers to expanding renewable energy production (Afsharzade et al. 2016; Baker et al. 2014 1384 ; Elum and Momodu 2017 1385 ; Karatayev et al. 2016 1386 ). Improving social awareness about the benefits of transitioning to renewable energy resources, and access to hydro-energy, solar and wind energy contributes to their improved adoption (Aliyu et al. 2017 1387 ; Katikiro 2016).

Developing and strengthening climate services relevant for desertification. Climate services provide climate, drought and desertification-related information in a way that assists decision-making by individuals and organisations. Monitoring desertification, and integrating biogeophysical (climate, soil, ecological factors, biodiversity) and socio-economic (use of natural resources by local population) issues provide a basis for better vulnerability prediction and assessment (OSS, 2012; Vogt et al. 2011 1388 ). Examples of relevant services include: drought monitoring and early warning systems, often implemented by national climate and meteorological services but also encompassing regional and global systems (Pozzi et al. 2013 1389 ); and the Sand and Dust Storm Warning Advisory and Assessment System (SDS-WAS), created by WMO in 2007, in partnership with the World Health Organization (WHO) and the United Nations Environment Program (UNEP). Currently, there is also a lack of ecological monitoring in arid and semi-arid regions to study surface winds, dust and sand storms, and their impacts on ecosystems and human health (Bergametti et al. 2018 1390 ; Marticorena et al. 2010 1391 ). Reliable and timely climate services, relevant to desertification, can aid the development of appropriate adaptation and mitigation options, reducing the impact of desertification under changing climate on human and natural systems ( high confidence ) (Beegum et al. 2016 1392 ; Beegum et al. 2018; Cornet 2012 1393 ; Haase et al. 2018 1395 ; Sergeant et al. 2012 1396 ).

Policy responses supporting economic diversification

Despite policy responses for combating desertification, other factors will put strong pressures on the land, including climate change and growing food demands, as well as the need to reduce poverty and strengthen food security (Cherlet et al. 2018 1397 ) (Sections 6.1.4 and 7.2.2). Sustainable development of drylands and their resilience to combined challenges of desertification and climate change will thus also depend on the ability of governments to promote policies for economic diversification within agriculture and in non-agricultural sectors in order make dryland areas less vulnerable to desertification and climate change.

Investing into irrigation. Investments into expanding irrigation in dryland areas can help increase the resilience of agricultural production to climate change, improve labour productivity and boost production and income revenue from agriculture and livestock sectors (Geerts and Raes 2009 1399 ; Olayide et al. 2016 1400 ; Oweis and Hachum 2006 1401 ). This is particularly true for Sub-Saharan Africa, where currently only 6% of the cultivated areas are irrigated (Nkonya et al. 2016b 1402 ). While renewable groundwater resources could help increase the share of irrigated land to 20.5–48.6% of croplands in the region (Altchenko and Villholth 2015 1403 ). On the other hand, over-extraction of groundwaters, mainly for irrigating crops, is becoming an important environmental problem in many dryland areas (Cherlet et al. 2018 1404 ), requiring careful design and planning of irrigation expansion schemes and use of water-efficient irrigation methods (Bjornlund et al. 2017 1405 ; Woodhouse et al. 2017 1406 ). For example, in Saudi Arabia, improving the efficiency of water management, for example through the development of aquifers, water recycling and rainwater harvesting, is part of a suite of policy actions to combat desertification (Bazza, et al. 2018 1407 ; Kingdom of Saudi Arabia 2016 1408 ). The expansion of irrigation to riverine areas, crucial for dry season grazing of livestock, needs to consider the income from pastoral activities, which is not always lower than income from irrigated crop production (Behnke and Kerven 2013 1409 ). Irrigation development could be combined with the deployment of clean-energy technologies in economically viable ways (Chandel et al. 2015 1410 ). For example, solar-powered drip irrigation was found to increase household agricultural incomes in Benin (Burney et al. 2010 1411 ). The sustainability of irrigation schemes based on solar-powered extraction of groundwaters depends on measures to avoid over-abstraction of groundwater resources and associated negative environmental impacts (Closas and Rap 2017 1412 ).

Expanding agricultural commercialisation. Faster poverty rate reduction and economic growth enhancement is realised when countries transition into the production of non-staple, high-value commodities and manage to build a robust agro-industry sector (Barrett et al. 2017 1413 ). Ogutu and Qaim (2019) found that agricultural commercialisation increased incomes and decreased multidimensional poverty in Kenya. Similar findings were earlier reported by Muriithi and Matz (2015) for commercialisation of vegetables in Kenya. Commercialisation of rice production was found to have increased smallholder welfare in Nigeria (Awotide et al. 2016 1414 ). Agricultural commercialisation contributed to improved household food security in Malawi, Tanzania and Uganda (Carletto et al. 2017 1415 ). However, such a transition did not improve farmers’ livelihoods in all cases (Reardon et al. 2009). High-value cash crop/animal production can be bolstered by wide-scale use of technologies, for example, mechanisation, application of inorganic fertilisers, crop protection and animal health products. Market oriented crop/animal production facilitates social and economic progress, with labour increasingly shifting out of agriculture into non-agricultural sectors (Cour 2001). Modernised farming, improved access to inputs, credit and technologies enhances competitiveness in local and international markets (Reardon et al. 2009 1417 ).

Facilitating structural transformations in rural economies implies that the development of non-agricultural sectors encourages the movement of labour from land-based livelihoods, vulnerable to desertification and climate change, to non-agricultural activities (Haggblade et al. 2010 1420 ). The movement of labour from agriculture to non-agricultural sectors is determined by relative labour productivities in these sectors (Shiferaw and Djido 2016 1421 ). Given already high underemployment in the farm sector, increasing labour productivity in the non-farm sector was found as the main driver of labour movements from farm sector to non-farm sector (Shiferaw and Djido 2016 1422 ). More investments into education can facilitate this process (Headey et al. 2014 1423 ). However, in some contexts, such as pastoralist communities in Xinjiang, China, income diversification was not found to improve the welfare of pastoral households (Liao et al. 2015 1424 ). Economic transformations also occur through urbanisation, involving the shift of labour from rural areas into gainful employment in urban areas (Jedwab and Vollrath 2015 1425 ). The majority of world population will be living in urban centres in the 21st century and this will require innovative means of agricultural production with minimum ecological footprint and less dependence on fossil fuels (Revi and Rosenzweig 2013 1426 ), while addressing the demand of cities (see Section 4.9.1 for discussion on urban green infrastructure). Although there is some evidence of urbanisation leading to the loss of indigenous and local ecological knowledge, however, indigenous and local knowledge systems are constantly evolving, and are also being integrated into urban environments (Júnior et al. 2016 1427 ; Reyes-García et al. 2013 1429 ; van Andel and Carvalheiro 2013 1430 ). Urban areas are attracting an increasing number of rural residents across the developing world (Angel et al. 2011 1431 ; Cour 2001 1432 ; Dahiya 2012 1433 ). Urban development contributes to expedited agricultural commercialisation by providing market outlet for cash crops, high-value crops, and livestock products. At the same time, urbanisation also poses numerous challenges in the form of rapid urban sprawl and pressures on infrastructure and public services, unemployment and associated social risks, which have considerable implications on climate change adaptive capacities (Bulkeley 2013 1434 ; Garschagen and Romero-Lankao 2015 1435 ).

Limits to adaptation, maladaptation, and barriers for mitigation

Chapter 16 in the IPCC Fifth Assessment Report (AR5) (Klein et al. 2015 1799 ) discusses the existence of soft and hard limits to adaptation, highlighting that values and perspectives of involved agents are relevant to identify limits (Sections 4.8.5.1 and 7.4.9). In that sense, adaptation limits vary from place to place and are difficult to generalise (Barnett et al. 2015 1486 ; Dow et al. 2013 1800 ; Klein et al. 2015 1801 ). Currently, there is a lack of knowledge on adaptation limits and potential maladaptation to combined effects of climate change and desertification (see Section 4.8.6 for discussion on resilience, thresholds, and irreversible land degradation, also relevant for desertification). However, the potential for residual risks (those risks which remain after adaptation efforts were taken, irrespective of whether they are tolerable or not, tolerability being a subjective concept) and maladaptive outcomes is high ( high confidence ). Some examples of residual risks are illustrated below in this section. Although SLM measures can help lessen the effects of droughts, they cannot fully prevent water stress in crops and resulting lower yields (Eekhout and de Vente 2019 1487 ). Moreover, although in many cases SLM measures can help reduce and reverse desertification, there would still be short-term losses in land productivity. Irreversible forms of land degradation (for example, loss of topsoil, severe gully erosion) can lead to the complete loss of land productivity. Even when solutions are available, their costs could be prohibitive, presenting the limits to adaptation (Dixon et al. 2013 1488 ). If warming in dryland areas surpasses human thermal physiological thresholds (Klein et al. 2015; Waha et al. 2013 1489 ), adaptation could eventually fail (Kamali et al. 2018 1490 ). Catastrophic shifts in ecosystem functions and services (for example coastal erosion (Chen et al. 2015; Schneider and Kéfi 2016 1491 ) (Section 4.9.8)) and economic factors can also result in adaptation failure (Evans et al. 2015). Despite the availability of numerous options that contribute to combating desertification, climate change adaptation and mitigation, there are also chances of maladaptive actions ( medium confidence ) (see Glossary). Some activities favouring agricultural intensification in dryland areas can become maladaptive due to their negative impacts on the environment ( medium confidence ). Agricultural expansion to meet food demands can come through deforestation and consequent diminution of carbon sinks (Godfray and Garnett 2014 1492 ; Stringer et al. 2012 1493 ). Agricultural insurance programmes encouraging higher agricultural productivity and measures for agricultural intensification can result in detrimental environmental outcomes in some settings (Guodaar et al. 2019 1494 ; Müller et al. 2017 1495 ) (Table 6.12). Development of more drought-tolerant crop varieties is considered as a strategy for adaptation to shortening rainy seasons, but this can also lead to a loss of local varieties (Al Hamndou and Requier-Desjardins 2008 1496 ). Livelihood diversification to collecting and selling firewood and charcoal production can exacerbate deforestation (Antwi-Agyei et al. 2018 1497 ). Avoiding maladaptive outcomes can often contribute both to reducing the risks from climate change and combating desertification (Antwi-Agyei et al. 2018 1498 ). Avoiding, reducing and reversing desertification would enhance soil fertility, increase carbon storage in soils and biomass, thus reducing carbon emissions from soils to the atmosphere (Section 3.7.2 and Cross-Chapter Box 2 in Chapter 1). In specific locations, there may be barriers for some of these activities. For example, afforestation and reforestation programmes can contribute to reducing sand storms and increasing carbon sinks in dryland regions (Chu et al. 2019) (Sections 3.6.1 and 3.7.2). However, implementing agroforestry measures in arid locations can be constrained by lack of water (Apuri et al. 2018 1499 ), leading to a trade-off between soil carbon sequestration and other water uses (Cao et al. 2018). Thus, even when solutions are available, social, economic and institutional constraints could post barriers to their implementation ( medium confidence ).

Hotspots and case studies

The challenges of desertification and climate change in dryland areas across the world often have very location-specific characteristics. The five case studies in this section present rich experiences and lessons learnt on: (i) soil erosion, (ii) afforestation and reforestation through ‘green walls’, (iii) invasive plant species, (iv) oases in hyper-arid areas, and (v) integrated watershed management. Although it is impossible to cover all hotspots of desertification and on-the-ground actions from all dryland areas, these case studies present a more focused assessment of these five issues, which emerged as salient in the group discussions and several rounds of review of this chapter. The choice of these case studies was also motivated by the desire to capture a wide diversity of dryland settings.

Climate change and soil erosion

Soil erosion under changing climate in drylands.

Soil erosion is a major form of desertification occurring in varying degrees in all dryland areas across the world (Section 3.2), with negative effects on dryland ecosystems (Section 3.4). Climate change is projected to increase soil erosion potential in some dryland areas through more frequent heavy rainfall events and rainfall variability (see Section 3.5.2 for a more detailed assessment) (Achite and Ouillon 2007 1500 ; Megnounif and Ghenim 2016 1501 ; Vachtman et al. 2013 1502 ; Zhang and Nearing 2005 1503 ). There are numerous soil conservation measures that can help reduce soil erosion (Section 3.6.1). Such soil management measures include afforestation and reforestation activities, rehabilitation of degraded forests, erosion control measures, prevention of overgrazing, diversification of crop rotations, and improvement in irrigation techniques, especially in sloping areas (Anache et al. 2018 1504 ; ÇEMGM 2017; Li and Fang 2016; Poesen 2018 1505 ; Ziadat and Taimeh 2013 1506 ). Effective measures for soil conservation can also use spatial patterns of plant cover to reduce sediment connectivity, and the relationships between hillslopes and sediment transfer in eroded channels (García-Ruiz et al. 2017 1507 ). The following three examples present lessons learnt from the soil erosion problems and measures to address them in different settings of Chile, Turkey and the Central Asian countries.

No-till practices for reducing soil erosion in central Chile

Soil erosion by water is an important problem in Chile. National assessments conducted in 1979, which examined 46% of the continental surface of the country, concluded that very high levels of soil erosion affected 36% of the territory. The degree of soil erosion increases from south to north. The leading locations in Chile are the region of Coquimbo with 84% of eroded soils (Lat. 29°S, semi-arid climate), the region of Valparaíso with 57% of eroded soils (Lat. 33°S, Mediterranean climate) and the region of O’Higgins with 37% of eroded soils (Lat. 34°S, Mediterranean climate). The most important drivers of soil erosion are soil, slope, climate erosivity (i.e., precipitation, intensity, duration and frequency) due to a highly concentrated rainy season, and vegetation structure and cover. In the region of Coquimbo, goat and sheep overgrazing have aggravated the situation (CIREN 2010 1508 ). Erosion rates reach up to 100 t ha –1 annually, having increased substantially over the last 50 years (Ellies 2000). About 10.4% of central Chile exhibits high erosion rates (greater than 1.1 t ha –1 annually) (Bonilla et al. 2010 1509 ).

Over the last few decades there has been an increasing interest in the development of no-till (also called zero tillage) technologies to minimise soil disturbance, reduce the combustion of fossil fuels and increase soil organic matter. No-till, in conjunction with the adoption of strategic cover crops, has positively impacted soil biology with increases in soil organic matter. Early evaluations by Crovetto, (1998) showed that no-till application (after seven years) had doubled the biological activity indicators compared to traditional farming and even surpassed those found in pasture (grown for the previous 15 years). Besides erosion control, additional benefits are an increase of water-holding capacity and reduction in bulk density. Currently, the above no-till farm experiment has lasted for 40 years and continues to report benefits to soil health and sustainable production (Reicosky and Crovetto 2014 1510 ). The influence of this iconic farm has resulted in the adoption of soil conservation practices – and especially no-till – in dryland areas of the Mediterranean climate region of central Chile (Martínez et al. 2011 1511 ). Currently, it has been estimated that the area under no-till farming in Chile varies between 0.13 and 0.2 Mha (Acevedo and Silva 2003 1512 ).

Combating wind erosion and deflation in Turkey: The greening desert of Karapınar

In Turkey, the amount of sediment recently released through erosion into seas was estimated to be 168 Mt yr -1 , which is considerably lower than the 500 Mt yr –1 that was estimated to be lost in the 1970s. The decrease in erosion rates is attributed to an increase in spatial extent of forests, rehabilitation of degraded forests, erosion control, prevention of overgrazing, and improvement in irrigation technologies. Soil conservation measures conducted in the Karapınar district, Turkey, exemplify these activities. The district is characterised by a semi-arid climate and annual average precipitation of 250–300 mm (Türkeş 2003 1513 ; Türkeş and Tatlı 2011 1514 ). In areas where vegetation was overgrazed or inappropriately tilled, the surface soil horizon was removed through erosion processes resulting in the creation of large drifting dunes that threatened settlements around Karapınar (Groneman 1968 1515 ). Such dune movement had begun to affect the Karapınar settlement in 1956 (Kantarcı et al. 2011 1516 ). Consequently, by the early 1960s, Karapınar town and nearby villages were confronted with the danger of abandonment due to out-migration in the early 1960s (Figure 3.11(1)). The reasons for increasing wind erosion in the Karapınar district can be summarised as follows: sandy material was mobilised following drying of the lake; hot and semi-arid climate conditions; overgrazing and use of pasture plants for fuel; excessive tillage; and strong prevailing winds.

Figure 3.11a

(1) a general view of a nearby village of karapınar town in the early 1960s (çarkaci 1999)..

(1) A general view of a nearby village of Karapınar town in the early 1960s (Çarkaci 1999) 1802 .

Figure 3.11b

(2)a view of the karapınar wind erosion area in 2013 (photo: murat türkeş, 17 june 2019)..

(2)A view of the Karapınar wind erosion area in 2013 (Photo: Murat Türkeş 1803 , 17 June 2019).

Figure 3.11c

(3) construction of cane screens in the early 1960s in order to decrease wind speed and prevent movement of the sand accumulations and dunes; this was one of the physical measures during the prevention and mitigation period (çarkaci 1999)..

(3) Construction of cane screens in the early 1960s in order to decrease wind speed and prevent movement of the sand accumulations and dunes; this was one of the physical measures during the prevention and mitigation period (Çarkaci 1999 1804 ).

Figure 3.11d

(4) a view of mixed vegetation, which now covers most of the karapınar wind erosion area in 2013, the main tree species of which were selected for afforestation with respect to their resistance to the arid continental climate conditions along with a warm/hot temperature regime over the district (photo: murat türkeş, 17 june 2013)..

(4) A view of mixed vegetation, which now covers most of the Karapınar wind erosion area in 2013, the main tree species of which were selected for afforestation with respect to their resistance to the arid continental climate conditions along with a warm/hot temperature regime over the district (Photo: Murat Türkeş 1805 , 17 June 2013).

Restoration and mitigation strategies were initiated in 1959, and today 4300 ha of land have been restored (Akay and Yildirim 2010 1517 ) (Figure 3.11 (2)), using specific measures: (i) physical measures: construction of cane screens to decrease wind speed and prevent sand movement (Figure 3.11(3)); (ii) restoration of cover: increasing grass cover between screens using seeds collected from local pastures or the cultivation of rye ( Secale sp.) and wheat grass ( Agropyron elongatum ) that are known to grow in arid and hot conditions; and (iii) afforestation: saplings obtained from nursery gardens were planted and grown between these screens. Main tree species selected were oleaster ( Eleagnus sp.), acacia ( Robinia pseudeaccacia ), ash ( Fraxinus sp.), elm ( Ulmus sp.) and maple (Acer sp.) (Figure 3.11 (4)). Economic growth occurred after controlling erosion and new tree nurseries have been established with modern irrigation. Potential negative consequences through the excessive use of water can be mitigated through engagement with local stakeholders and transdisciplinary learning processes, as well as by restoring the traditional land uses in the semi-arid Konya closed basin (Akça et al. 2016 1518 ).

Soil erosion in Central Asia under changing climate

Soil erosion is widely acknowledged to be a major form of degradation of Central Asian drylands, affecting a considerable share of croplands and rangelands. However, up-to-date information on the actual extent of eroded soils at the regional or country level is not available. The estimates compiled by Pender et al. (2009), based on the Central Asian Countries Initiative for Land Management (CACILM), indicate that about 0.8 Mha of the irrigated croplands were subject to high degree of soil erosion in Uzbekistan. In Turkmenistan, soil erosion was indicated to be occurring in about 0.7 Mha of irrigated land. In Kyrgyzstan, out of 1 Mha of irrigated land in the foothill zones, 0.76 Mha were subject to soil erosion by water, leading to losses in crop yields of 20–60% in these eroded soils. About 0.65 Mha of arable land were prone to soil erosion by wind (Mavlyanova et al. 2017 1519 ). Soil erosion is widespread in rainfed and irrigated areas in Kazakhstan (Saparov 2014). About 5 Mha of rainfed croplands were subject to high levels of soil erosion (Pender et al. 2009 1520 ). Soil erosion by water was indicated to be a major concern in sloping areas in Tajikistan (Pender et al. 2009 1521 ).

The major causes of soil erosion in Central Asia are related to human factors, primarily excessive water use in irrigated areas (Gupta et al. 2009 1522 ), deep ploughing and lack of maintenance of vegetative cover in rainfed areas (Suleimenov et al. 2014 1523 ), and overgrazing in rangelands (Mirzabaev et al. 2016 1524 ). Lack of good maintenance of watering infrastructure for migratory livestock grazing, and fragmentation of livestock herds led to overgrazing near villages, increasing the soil erosion by wind (Alimaev et al. 2008 1526 ). Overgrazing in the rangeland areas of the region (e.g., particularly in Kyzylkum) contributes to dust storms, coming primarily from the Ustyurt Plateau, desertified areas of Amudarya and Syrdarya rivers’ deltas, the dried seabed of the Aral Sea (now called Aralkum), and the Caspian Sea (Issanova and Abuduwaili 2017 1527 ; Xi and Sokolik 2015). Xi and Sokolik (2015) estimated that total dust emissions in Central Asia were 255.6 Mt in 2001, representing 10–17% of the global total.

Central Asia is one of the regions highly exposed to climate change, with warming levels projected to be higher than the global mean (Hoegh-Guldberg et al. 2018 1528 ), leading to more heat extremes (Reyer et al. 2017 1529 ). There is no clear trend in precipitation extremes, with some potential for moderate rise in occurrence of droughts. The diminution of glaciers is projected to continue in the Pamir and Tian Shan mountain ranges, a major source of surface waters along with seasonal snowmelt. Glacier melting will increase the hazards from moraine-dammed glacial lakes and spring floods (Reyer et al. 2017 1530 ). Increased intensity of spring floods creates favourable conditions for higher soil erosion by water, especially in the sloping areas in Kyrgyzstan and Tajikistan. The continuation of some of the current unsustainable cropland and rangeland management practices may lead to elevated rates of soil erosion, particularly in those parts of the region where climate change projections point to increases in floods (Kyrgyzstan, Tajikistan) or increases in droughts (Turkmenistan, Uzbekistan) (Hijioka et al. 2014 1531 ). Increasing water use to compensate for higher evapotranspiration due to rising temperatures and heat waves could increase soil erosion by water in the irrigated zones, especially in sloping areas and crop fields with uneven land levelling (Bekchanov et al. 2010 1532 ). The desiccation of the Aral Sea resulted in a hotter and drier regional microclimate, adding to the growing wind erosion in adjacent deltaic areas and deserts (Kust 1999 1533 ).

There are numerous sustainable land and water management practices available in the region for reducing soil erosion (Abdullaev et al. 2007 1534 ; Gupta et al. 2009 1535 ; Kust et al. 2014 1536 ; Nurbekov et al. 2016 1537 ). These include: improved land levelling and more efficient irrigation methods such as drip, sprinkler and alternate furrow irrigation (Gupta et al. 2009 1538 ); conservation agriculture practices, including no-till methods and maintenance of crop residues as mulch in the rainfed and irrigated areas (Kienzler et al. 2012 1539 ; Pulatov et al. 2012 1540 ); rotational grazing; institutional arrangements for pooling livestock for long-distance mobile grazing; reconstruction of watering infrastructure along the livestock migratory routes (Han et al. 2016; Mirzabaev et al. 2016 1541 ); afforesting degraded marginal lands (Djanibekov and Khamzina 2016 1543 ; Khamzina et al. 2009 1545 ; Khamzina et al. 2016 1546 ); integrated water resource management (Dukhovny et al. 2013 1547 ; Kazbekov et al. 2009 1548 ); and planting salt – and drought-tolerant halophytic plants as windbreaks in sandy rangelands (Akinshina et al. 2016 1549 ; Qadir et al. 2009 1550 ; Toderich et al. 2009 1551 ; Toderich et al. 2008 1552 ), and potentially the dried seabed of the former Aral Sea (Breckle 2013 1553 ). The adoption of enabling policies, such as those discussed in Section 3.6.3, can facilitate the adoption of these sustainable land and water management practices in Central Asia ( high confidence ) (Aw-Hassan et al. 2016 1554 ; Bekchanov et al. 2016 1555 ; Bobojonov et al. 2013 1556 ; Djanibekov et al. 2016 1557 ; Hamidov et al. 2016 1559 ; Mirzabaev et al. 2016 1560 ).

Green walls and green dams

This case study evaluates the experiences of measures and actions implemented to combat soil erosion, decrease dust storms, and to adapt to and mitigate climate change under the Green Wall and Green Dam programmes in East Asia (e.g., China) and Africa (e.g., Algeria, Sahara and the Sahel region). These measures have also been implemented in other countries, such as Mongolia (Do and Kang 2014; Lin et al. 2009), Turkey (Yurtoglu 2015 1562 ; Çalişkan and Boydak 2017 1563 ) and Iran (Amiraslani and Dragovich 2011 1564 ), and are increasingly considered as part of many national and international initiatives to combat desertification (Goffner et al. 2019 1565 ) (Cross-Chapter Box 2 in Chapter 1). Afforestation and reforestation programmes can contribute to reducing sand storms and increasing carbon sinks in dryland regions ( high confidence ). On the other hand, green wall and green dam programmes also decrease the albedo and hence increase the surface absorption of radiation, increasing the surface temperature. The net effect will largely depend on the balance between these and will vary from place to place depending on many factors.

The experiences of combating desertification in China

Arid and semi-arid areas of China, including north-eastern, northern and north-western regions, cover an area of more than 509 Mha, with annual rainfall of below 450 mm. Over the past several centuries, more than 60% of the areas in arid and semi-arid regions were used as pastoral and agricultural lands. The coupled impacts of past climate change and human activity have caused desertification and dust storms to become a serious problem in the region (Xu et al. 2010). In 1958, the Chinese government recognised that desertification and dust storms jeopardised the livelihoods of nearly 200 million people, and afforestation programmes for combating desertification have been initiated since 1978. China is committed to go beyond the Land Degradation Neutrality objective, as indicated by the following programmes that have been implemented. The Chinese Government began the Three North’s Forest Shelterbelt programme in Northeast China, North China, and Northwest China, with the goal to combat desertification and to control dust storms by improving forest cover in arid and semi-arid regions. The project is implemented in three stages (1978–2000, 2001–2020 and 2021–2050). In addition, the Chinese government launched the Beijing and Tianjin Sandstorm Source Treatment Project (2001–2010), Returning Farmlands to Forest Project (2003–present), and the Returning Grazing Land to Grassland Project (2003–present) to combat desertification, and for adaptation and mitigation of climate change (State Forestry Administration of China 2015 1566 ; Wang 2014 1567 ; Wang et al. 2013 1568 ).

The results of the fifth monitoring period (2010–2014) showed: (i) compared with 2009, the area of degraded land decreased by 12,120 km 2 over a five-year period; (ii) in 2014, the average coverage of vegetation in the sand area was 18.33%, an increase of 0.7% compared with 17.63% in 2009, and the carbon sequestration increased by 8.5%; (iii) compared with 2009, the amount of wind erosion decreased by 33%, the average annual occurrence of sandstorms decreased by 20.3% in 2014; (iv) as of 2014, 203,700 km 2 of degraded land were effectively managed, accounting for 38.4% of the 530,000 km 2 of manageable desertified land; (v) the restoration of degraded land has created an annual output of 53.63 Mt of fresh and dried fruits, accounting for 33.9% of the total national annual output of fresh and dried fruits (State Forestry Administration of China 2015 1570 ). This has become an important pillar for economic development and a high priority for peasants as a method to eradicate poverty (State Forestry Administration of China 2015 1571 ).

Stable investment mechanisms for combating desertification have been established along with tax relief policies and financial support policies for guiding the country in its fight against desertification. The investments in scientific and technological innovation for combating desertification have been improved, the technologies for vegetation restoration under drought conditions have been developed, the popularisation and application of new technologies has been accelerated, and the training of technicians to assist farmers and herdsmen has been strengthened. To improve the monitoring capability and technical level of desertification studies, the monitoring network system has been strengthened, and the popularisation and application of modern technologies have been intensified (e.g., information technology and remote sensing) (Wu et al. 2015). Special laws on combating desertification have been decreed by the government. The provincial government’s responsibilities for desertification prevention and controlling objectives and laws have been strictly implemented.

Many studies showed that these projects generally played an active role in combating desertification and fighting against dust storms in China over the past several decades ( high confidence ) (Cao et al. 2018; State Forestry Administration of China 2015; Wang et al. 2013 1573 ; Wang et al. 2014 1574 ; Yang et al. 2013 1576 ). At the beginning of the projects, some problems appeared in some places due to lack of enough knowledge and experience ( low confidence ) (Jiang 2016 1578 ; Wang et al. 2010 1579 ). For example, some tree species selected were not well suited to local soil and climatic conditions (Zhu et al. 2007), and there was inadequate consideration of the limitation of the amount of available water on the carrying capacity of trees in some arid regions (Dai 2011; Feng et al. 2016 1580 ) (Section 3.6.4). In addition, at the beginning of the projects, there was an inadequate consideration of the effects of climate change on combating desertification (Feng et al. 2015 1581 ; Tan and Li 2015). Indeed, climate change and human activities over past years have influenced the desertification and dust storm control effects in China (Feng et al. 2015 1582 ; Wang et al. 2009 1583 ; Tan and Li 2015), and future climate change will bring new challenges for combating desertification in China (Wang et al. 2017 1584 ; Yin et al. 2015; Xu et al. 2019). In particular, the desertification risk in China will be enhanced at 2°C compared to 1.5°C global temperature rise (Ma et al. 2018). Adapting desertification control to climate change involves: improving the adaptation capacity to climate change for afforestation and grassland management by executing SLM practices; optimising the agricultural and animal husbandry structure; and using big data to meet the water resources regulation (Zhang and Huisingh 2018 1588 ). In particular, improving scientific and technological supports in desertification control is crucial for adaptation to climate change and combating desertification, including protecting vegetation in desertification-prone lands by planting indigenous plant species, facilitating natural restoration of vegetation to conserve biodiversity, employing artificial rain or snow, water-saving irrigation and water storage technologies (Jin et al. 2014; Yang et al. 2013 1589 ).

The Green Dam in Algeria

After independence in 1962, the Algerian government initiated measures to replant forests destroyed by the war, and the steppes affected by desertification, among its top priorities (Belaaz 2003 1591 ).

In 1972, the government invested in the Green Dam ( Barrage vert ) project. This was the first significant experiment to combat desertification, influence the local climate and decrease the aridity by restoring a barrier of trees. The Green Dam extends across arid and semi-arid zones between the isohyets 300 mm and 200 mm. It is a 3 Mha band of plantation running from east to west (Figure 3.12). It is over 1200 km long (from the Algerian–Moroccan border to the Algerian–Tunisian border) and has an average width of about 20 km. The soils in the area are shallow, low in organic matter and susceptible to erosion. The main objectives of the project were to conserve natural resources, improve the living conditions of local residents and avoid their exodus to urban areas. During the first four decades (1970–2000) the success rate was low (42%) due to lack of participation by the local population and the choice of species (Bensaid 1995 1592 ).

The Green Dam did not have the desired effects. Despite tree-planting efforts, desertification intensified on the steppes, especially in south-western Algeria, due to the prolonged drought during the 1980s. Rainfall declined in the range from 18% to 27%, and the dry season has increased by two months in the last century (Belala et al. 2018 1593 ). Livestock numbers in the Green Dam regions, mainly sheep, grew exponentially, leading to severe overgrazing, causing trampling and soil compaction, which greatly increased the risk of erosion. Wind erosion, very prevalent in the region, is due to climatic conditions and the strong anthropogenic action that reduced the vegetation cover. The action of the wind carries fine particles such as sands and clays and leaves on the soil surface a lag-gravel pavement, which is unproductive. Water erosion is largely due to torrential rains in the form of severe thunderstorms that disintegrate the bare soil surface from raindrop impact (Achite et al. 2016 1594 ). The detached soil and nutrients are transported offsite via runoff, resulting in loss of fertility and water holding capacity. The risk of and severity of water erosion is a function of human land-use activities that increase soil loss through removal of vegetative cover. The National Soil Sensitivity to Erosion Map (Salamani et al. 2012 1595 ) shows that more than 3 Mha of land in the steppe provinces are currently experiencing intense wind activity (Houyou et al. 2016 1596 ) and that these areas are at particular risk of soil erosion. Mostephaoui et al. (2013), estimates that each year there is a loss of 7 t ha –1 of soils due to erosion. Nearly 0.6 Mha of land in the steppe zone are fully degraded without the possibility of biological recovery.

To combat the effects of erosion and desertification, the government has planned to relaunch the rehabilitation of the Green Dam by incorporating new concepts related to sustainable development, and adaptation to climate change. The experience of previous years has led to integrated rangeland management, improved tree and fodder shrub plantations and the development of water conservation techniques. Reforestation is carried out using several species, including fruit trees, to increase and diversify the sources of income for the population.

The evaluation of the Green Dam from 1972 to 2015 (Merdas et al. 2015 1597 ) shows that 0.3 Mha of forest plantation have been planted, which represents 10% of the project area. Estimates of the success rate of reforestation vary considerably between 30% and 75%, depending on the region. Through demonstration, the Green Dam has inspired several African nations to work together to build a Great Green Wall to combat land degradation, mitigate climate change effects, loss of biodiversity and poverty in a region that stretches from Senegal to Djibouti (Sahara and Sahel Observatory (OSS) 2016) (Section 3.7.2.3).

Figure 3.12b

Location of the green dam in algeria (saifi et al. 2015). note: the green coloured band represents the location of the green dam..

Location of the Green Dam in Algeria (Saifi et al. 2015 1806 ). Note: The green coloured band represents the location of the Green Dam.

The Great Green Wall of the Sahara and the Sahel Initiative

The Great Green Wall is an initiative of the Heads of State and Government of the Sahelo-Saharan countries to mitigate and adapt to climate change, and to improve the food security of the Sahel and Saharan peoples (Sacande 2018 1598 ; Mbow 2017 1599 ). Launched in 2007, this regional project aims to restore Africa’s degraded arid landscapes, reduce the loss of biodiversity and support local communities to sustainable use of forests and rangelands. The Great Green Wall focuses on establishing plantations and neighbouring projects, covering a distance of 7775 km from Senegal on the Atlantic coast to Eritrea on the Red Sea coast, with a width of 15 km (Figure 3.13). The wall passes through Djibouti, Eritrea, Ethiopia, Sudan, Chad, Niger, Nigeria, Mali, Burkina Faso, Mauritania and Senegal.

The choice of woody and herbaceous species that will be used to restore degraded ecosystems is based on biophysical and socio-economic criteria, including socio-economic value (food, pastoral, commercial, energetic, medicinal, cultural); ecological importance (carbon sequestration, soil cover, water infiltration); and resilience to climate change and variability. The Pan-African Agency of the Great Green Wall (PAGGW) was created in 2010 under the auspices of the African Union and CEN-SAD to manage the project. The initiative is implemented at the level of each country by a national structure. A monitoring and evaluation system has been defined, allowing nations to measure outcomes and to propose the necessary adjustments.

In the past, reforestation programmes in the arid regions of the Sahel and North Africa that have been undertaken to stop desertification were poorly studied and cost a lot of money without significant success (Benjaminsen and Hiernaux 2019 1600 ). Today, countries have changed their strategies and opted for rural development projects that can be more easily funded. Examples of scalable practices for land restoration include managing water bodies for livestock and crop production, and promoting fodder trees to reduce runoff (Mbow 2017 1601 ).

The implementation of the initiative has already started in several countries. For example, the FAO’s Action Against Desertification project was restoring 18,000 hectares of land in 2018 through planting native tree species in Burkina Faso, Ethiopia, The Gambia, Niger, Nigeria and Senegal (Sacande 2018 1602 ). Berrahmouni et al. (2016) 1807 estimated that 166 Mha can be restored in the Sahel, requiring the restoration of 10 Mha per year to achieve Land Degradation Neutrality targets by 2030. Despite these early implementation actions on the ground, the achievement of the planned targets is questionable, and will be challenging without significant additional funding.

Figure 3.13

The great green wall of the sahara and the sahel. source for the data layer: this dataset is an extract from the globcover 2009 land cover map, covering africa and the arabian peninsula. the globcover 2009 land cover map is derived by an automatic and regionally tuned classification of a time series of global meris […].

The Great Green Wall of the Sahara and the Sahel. Source for the data layer: This dataset is an extract from the GlobCover 2009 land cover map, covering Africa and the Arabian Peninsula. The GlobCover 2009 land cover map is derived by an automatic and regionally tuned classification of a time series of global MERIS (MEdium Resolution Imaging Spectrometer) FR mosaics for the year 2009. The global land cover map counts 22 land cover classes defined with the United Nations (UN) Land Cover Classification System (LCCS)

Invasive plant species

The spread of invasive plants can be exacerbated by climate change (Bradley et al. 2010 1603 ; Davis et al. 2000 1604 ). In general, it is expected that the distribution of invasive plant species with high tolerance to drought or high temperatures may increase under most climate change scenarios ( medium to high confidence ) (Bradley et al. 2010 1605 ; Settele et al. 2014 1606 ; Scasta et al. 2015 1607 ). Invasive plants are considered a major risk to native biodiversity and can disturb the nutrient dynamics and water balance in affected ecosystems (Ehrenfeld 2003 1608 ). Compared to more humid regions, the number of species that succeed in invading dryland areas is low (Bradley et al. 2012 1609 ), yet they have a considerable impact on biodiversity and ecosystem services (Le Maitre et al. 2015, 2011; Newton et al. 2011 1610 ). Moreover, human activities in dryland areas are responsible for creating new invasion opportunities (Safriel et al. 2005 1611 ).

Current drivers of species introductions include expanding global trade and travel, land degradation and changes in climate (Chytrý et al. 2012 1612 ; Richardson et al. 2011 1613 ; Seebens et al. 2018 1614 ). For example, Davis et al. (2000) suggests that high rainfall variability promotes the success of alien plant species – as reported for semi-arid grasslands and Mediterranean-type ecosystems (Cassidy et al. 2004 1615 ; Reynolds et al. 2004 1616 ; Sala et al. 2006 1617 ). Furthermore, Panda et al. (2018) demonstrated that many invasive species could withstand elevated temperature and moisture scarcity caused by climate change. Dukes et al. (2011) observed that the invasive plant yellow-star thistle ( Centaurea solstitialis ) grew six time larger under the elevated atmospheric CO 2 expected in future climate change scenarios.

Climate change is likely to aggravate the problem as existing species continue to spread unabated and other species develop invasive characteristics (Hellmann et al. 2008 1619 ). Although the effects of climate change on invasive species distributions have been relatively well explored, the greater impact on ecosystems is less well understood (Bradley et al. 2010 1620 ; Eldridge et al. 2011 1621 ).

Due to the time lag between the initial release of invasive species and their impact, the consequence of invasions is not immediately detected and may only be noticed centuries after introduction (Rouget et al. 2016 1622 ). Climate change and invading species may act in concert (Bellard et al. 2013 1623 ; Hellmann et al. 2008 1625 ; Seebens et al. 2015 1626 ). For example, invasion often changes the size and structure of fuel loads, which can lead to an increase in the frequency and intensity of fire (Evans et al. 2015). In areas where the climate is becoming warmer, an increase in the likelihood of suitable weather conditions for fire may promote invasive species, which in turn may lead to further desertification. Conversely, fire may promote plant invasions via several mechanisms (by reducing cover of competing vegetation, destroying native vegetation and clearing a path for invasive plants or creating favourable soil conditions) (Brooks et al. 2004 1627 ; Grace et al. 2001 1628 ; Keeley and Brennan 2012 1629 ).

Figure 3.14

Difference between the number of invasive alien species (n=99, from bellard et al. (2013)) predicted to occur by 2050 (under a1b scenario) and current period ‘2000’ within the dryland areas.

Difference between the number of invasive alien species (n=99, from Bellard et al. (2013) 1808 ) predicted to occur by 2050 (under A1B scenario) and current period ‘2000’ within the dryland areas

At a regional scale, Bellard et al. (2013) 1809 predicted increasing risk in Africa and Asia, with declining risk in Australia (Figure 3.14). This projection does not represent an exhaustive list of invasive alien species occurring in drylands.

A set of four case studies in Ethiopia, Mexico, the USA and Pakistan is presented below to describe the nuanced nature of invading plant species, their impact on drylands and their relationship with climate change.

The two invasive plants that inflict the heaviest damage to ecosystems, especially biodiversity, are the annual herbaceous weed, Parthenium hysterophorus ( Asteraceae ) also known as Congress weed; and the tree species, Prosopis juliflora (Fabaceae ) also called Mesquite, both originating from the southwestern United States to Central/South America (Adkins and Shabbir 2014 1630 ). Prosopis was introduced in the 1970s and has since spread rapidly. Prosopis , classified as the highest priority invader in Ethiopia, is threatening livestock production and challenging the sustainability of the pastoral systems. Parthenium is believed to have been introduced along with relief aid during the debilitating droughts of the early 1980s, and a recent study reported that it has spread into 32 out of 34 districts in Tigray, the northernmost region of Ethiopia (Teka 2016 1631 ). A study by Etana et al. (2011) indicated that Parthenium caused a 69% decline in the density of herbaceous species in Awash National Park within a few years of introduction. In the presence of Parthenium, the growth and development of crops is suppressed due to its allelopathic properties. McConnachie et al. (2011) estimated a 28% crop loss across the country, including a 40–90% reduction in sorghum yield in eastern Ethiopia alone (Tamado et al. 2002 1632 ). The weed is a substantial agricultural and natural resource problem and constitutes a significant health hazard (Fasil 2011). Parthenium causes acute allergic respiratory problems, skin dermatitis, and reportedly mutagenicity both in humans and livestock (Mekonnen 2017; Patel 2011 1633 ). The eastern belt of Africa – including Ethiopia – presents a very suitable habitat, and the weed is expected to spread further in the region in the future (Mainali et al. 2015 1635 ).

There is neither a comprehensive intervention plan nor a clear institutional mandate to deal with invasive weeds, however, there are fragmented efforts involving local communities even though they are clearly inadequate. The lessons learned, related to actions that have contributed to the current scenario, are several. First, lack of coordination and awareness – mesquite was introduced by development agencies as a drought-tolerant shade tree with little consideration of its invasive nature. If research and development institutions had been aware, a containment strategy could have been implemented early on. The second major lesson is the cost of inaction. When research and development organisations did sound the alarm, the warnings went largely unheeded, resulting in the spread and buildup of two of the worst invasive plant species in the world (Fasil 2011 1636 ).

Buffelgrass ( Cenchrus ciliaris L.), a native species from southern Asia and East Africa, was introduced into Texas and northern Mexico in the 1930s and 1940s, as it is highly productive in drought conditions (Cox et al. 1988; Rao et al. 1996). In the Sonoran desert of Mexico, the distribution of buffelgrass has increased exponentially, covering 1 Mha in Sonora State (Castellanos-Villegas et al. 2002 1637 ). Furthermore, its potential distribution extended to 53% of Sonora State and 12% of semi-arid and arid ecosystems in Mexico (Arriaga et al. 2004 1638 ). Buffelgrass has also been reported as an aggressive invader in Australia and the USA, resulting in altered fire cycles that enhance further spread of this plant and disrupt ecosystem processes (Marshall et al. 2012 1639 ; Miller et al. 2010 1641 ; Schlesinger et al. 2013 1642 ).

Castellanos et al. (2016) reported that soil moisture was lower in the buffelgrass savannah cleared 35 years ago than in the native semi-arid shrubland, mainly during the summer. The ecohydrological changes induced by buffelgrass can therefore displace native plant species over the long term. Invasion by buffelgrass can also affect landscape productivity, as it is not as productive as native vegetation (Franklin and Molina-Freaner 2010 1643 ). Incorporation of buffelgrass is considered a good management practice by producers and the government. For this reason, no remedial actions are undertaken.

United States of America

Sagebrush ecosystems have declined from 25 Mha to 13 Mha since the late 1800s (Miller et al. 2011 1644 ). A major cause is the introduction of non-native cheatgrass ( Bromus tectorum ), which is the most prolific invasive plant in the USA. Cheatgrass infests more than 10 Mha in the Great Basin and is expanding every year (Balch et al. 2013 1645 ). It provides a fine-textured fuel that increases the intensity, frequency and spatial extent of fire (Balch et al. 2013). Historically, wildfire frequency was 60 to 110 years in Wyoming big sagebrush communities and has increased to five years following the introduction of cheatgrass (Balch et al. 2013 1646 ; Pilliod et al. 2017 1648 ).

The conversion of the sagebrush steppe biome to annual grassland with higher fire frequencies has severely impacted livestock producers, as grazing is not possible for a minimum of two years after fire. Furthermore, cheatgrass and wildfires reduce critical habitat for wildlife and negatively impact species richness and abundance – for example, the greater sage-grouse ( Centocercus urophasianus ) and pygmy rabbit ( Brachylagus idahoensis ) which are on the verge of being listed for federal protection (Crawford et al. 2004 1649 ; Larrucea and Brussard 2008 1650 ; Lockyer et al. 2015 1651 ).

Attempts to reduce cheatgrass impacts through reseeding of both native and adapted introduced species have occurred for more than 60 years (Hull and Stewart 1949 1652 ) with little success. Following fire, cheatgrass becomes dominant and recovery of native shrubs and grasses is improbable, particularly in relatively low-elevation sites with minimal annual precipitation (less than 200 mm yr –1 ) (Davies et al. 2012 1653 ; Taylor et al. 2014 1654 ). Current rehabilitation efforts emphasise the use of native and non-native perennial grasses, forbs and shrubs (Bureau of Land Management 2005 1655 ). Recent literature suggests that these treatments are not consistently effective at displacing cheatgrass populations or re-establishing sage-grouse habitat, with success varying with elevation and precipitation (Arkle et al. 2014 1656 ; Knutson et al. 2014 1657 ). Proper post-fire grazing rest, season-of-use, stocking rates, and subsequent management are essential to restore resilient sagebrush ecosystems before they cross a threshold and become an annual grassland (Chambers et al. 2014 1658 ; Miller et al. 2011 1659 ; Pellant et al. 2004 1660 ). Biological soil crust protection may be an effective measure to reduce cheatgrass germination, as biocrust disturbance has been shown to be a key factor promoting germination of non-native grasses (Hernandez and Sandquist 2011). Projections of increasing temperature (Abatzoglou and Kolden 2011 1662 ), and observed reductions in and earlier melting of snowpack in the Great Basin region (Harpold and Brooks 2018 1663 ; Mote et al. 2005 1664 ) suggest that there is a need to understand current and past climatic variability as this will drive wildfire variability and invasions of annual grasses.

The alien plants invading local vegetation in Pakistan include Brossentia papyrifera (found in Islamabad Capital territory), Parthenium hysterophorus (found in Punjab and Khyber Pakhtunkhwa provinces), Prosopis juliflora (found all over Pakistan), Eucalyptus camaldulensis (found in Punjab and Sindh provinces), Salvinia (aquatic plant widely distributed in water bodies in Sindh), Cannabis sativa (found in Islamabad Capital Territory), Lantana camara and Xanthium strumarium (found in upper Punjab and Khyber Pakhtunkhwa provinces) (Khan et al. 2010 1665 ; Qureshi et al. 2014 1666 ). Most of these plants were introduced by the Forest Department decades ago for filling the gap between demand and supply of timber, fuelwood and fodder. These non-native plants have some uses but their disadvantages outweigh their benefits (Marwat et al. 2010 1667 ; Rashid et al. 2014 1668 ).

Besides being a source of biological pollution and a threat to biodiversity and habitat loss, the alien plants reduce the land value and cause huge losses to agricultural communities (Rashid et al. 2014 1810 ). Brossentia papyrifera , commonly known as Paper Mulberry, is the root cause of inhalant pollen allergy for the residents of lush green Islamabad during spring. From February to April, the pollen allergy is at its peak, with symptoms of severe persistent coughing, difficulty in breathing, and wheezing. The pollen count, although variable at different times and days, can be as high as 55,000 m -3 .

Early symptoms of the allergy include sneezing, itching in the eyes and skin, and blocked nose. With changing climate, the onset of disease is getting earlier, and pollen count is estimated to cross 55,000 m –3 (Rashid et al. 2014 1670 ). About 45% of allergic patients in the twin cities of Islamabad and Rawalpindi showed positive sensitivity to the pollens (Marwat et al. 2010 1671 ). Millions of rupees have been spent by the Capital Development Authority on pruning and cutting of Paper Mulberry trees but because of its regeneration capacity growth is regained rapidly (Rashid et al. 2014 1672 ). Among other invading plants, Prosopis juliflora has allelopathic properties, and Eucalyptus is known to transpire huge amounts of water and deplete the soil of its nutrient elements (Qureshi et al. 2014 1673 ).

Although a Biodiversity Action Plan exists in Pakistan, it is not implemented in letter or spirit. The Quarantine Department focuses only on pests and pathogens but takes no notice of plant and animal species being imported. Also, there is no provision for checking the possible impacts of imported species on the environment (Rashid et al. 2014 1674 ) or for carrying out bioassays of active allelopathic compounds of alien plants.

Oases in hyper-arid areas in the Arabian Peninsula and northern Africa

Oases are isolated areas with reliable water supply from lakes and springs, located in hyper-arid and arid zones (Figure 3.15). Oasis agriculture has long been the only viable crop production system throughout the hot and arid regions of the Arabian Peninsula and North Africa. Oases in hyper-arid climates are usually subject to water shortage as evapotranspiration exceeds rainfall. This often causes salinisation of soils. While many oases have persisted for several thousand years, many others have been abandoned, often in response to changes in climate or hydrologic conditions (Jones et al. 2019 1675 ), providing testimony to societies’ vulnerability to climatic shifts and raising concerns about similarly severe effects of anthropogenic climate change (Jones et al. 2019 1676 ).

On the Arabian Peninsula and in North Africa, climate change is projected to have substantial and complex effects on oasis areas (Abatzoglou and Kolden 2011 1677 ; Ashkenazy et al. 2012 1678 ; Bachelet et al. 2016 1679 ; Guan et al. 2018 1680 ; Iknayan and Beissinger 2018 1681 ; Ling et al. 2013 1682 ). To illustrate, by the 2050s, the oases in southern Tunisia are expected to be affected by hydrological and thermal changes, with an average temperature increase of 2.7°C, a 29% decrease in precipitation and a 14% increase in evapotranspiration rate (Ministry of Agriculture and Water Resources of Tunisia and GIZ 2007 1683 ). In Morocco, declining aquifer recharge is expected to impact the water supply of the Figuig oasis (Jilali 2014 1684 ), as well as for the Draa Valley (Karmaoui et al. 2016 1685 ). Saudi Arabia is expected to experience a 1.8°C–4.1°C increase in temperatures by 2050, which is forecast to raise agricultural water demand by 5–15% in order to maintain production levels equal to those of 2011 (Chowdhury and Al-Zahrani 2013 1686 ). The increase of temperatures and variable pattern of rainfall over the central, north and south-western regions of Saudi Arabia may pose challenges for sustainable water resource management (Tarawneh and Chowdhury 2018 1687 ). Moreover, future climate scenarios are expected to increase the frequency of floods and flash floods, such as in the coastal areas along the central parts of the Red Sea and the south-southwestern areas of Saudi Arabia (Almazroui et al. 2017 1688 ).

While many oases are cultivated with very heat-tolerant crops such as date palms, even such crops eventually have declines in their productivity when temperatures exceed certain thresholds or hot conditions prevail for extended periods. Projections so far do not indicate severe losses in land suitability for date palm for the Arabian Peninsula (Aldababseh et al. 2018 1689 ; Shabani et al. 2015 1690 ). It is unclear, however, how reliable the climate response parameters in the underlying models are, and actual responses may differ substantially.

Figure 3.15a

Oases across the Arabian Peninsula and North Africa (alphabetically by country). (a) Masayrat ar Ruwajah oasis, Ad Dakhiliyah Governorate, Oman (Photo: Eike Lüdeling).

Figure 3.15b

(b) Tasselmanet oasis, Ouarzazate Province, Morocco (Photo: Abdellatif Khattabi).

Figure 3.15c

c) Al-Ahsa oasis, Al-Ahsa Governarate, Saudi Arabia (Photo: Shijan Kaakkara).

Figure-3.15d

Zarat oasis, Governorate of Gabes, Tunisia (Photo: Hamda Aloui). The use rights for (a), (b) and (d) were granted by copyright holders; (c) is licensed under the Creative Commons Attribution 2.0 Generic license.

Date palms are routinely assumed to be able to endure very high temperatures, but recent transcriptomic and metabolomic evidence suggests that heat stress reactions already occur at 35°C (Safronov et al. 2017 1691 ), which is not exceptionally warm for many oases in the region. Given current assumptions about the heat-tolerance of date palm, however, adverse effects are expected to be small (Aldababseh et al. 2018 1692 ; Shabani et al. 2015 1693 ). For some other perennial oasis crops, impacts of temperature increases are already apparent. Between 2004/2005 and 2012/2013, high-mountain oases of Al Jabal Al Akhdar in Oman lost almost all fruit and nut trees of temperate-zone origin, with the abundance of peaches, apricots, grapes, figs, pears, apples, and plums dropping by between 86% and 100% (Al-Kalbani et al. 2016 1694 ). This implies that that the local climate may not remain suitable for species that depend on cool winters to break their dormancy period (Luedeling et al. 2009 1695 ). A similar impact is very probable in Tunisia and Morocco, as well as in other oasis locations in the Arabian Peninsula and North Africa (Benmoussa et al. 2007 1811 ). All these studies expect strong decreases in winter chill, raising concerns that many currently well-established species will no longer be viable in locations where they are grown today. The risk of detrimental chill shortfalls is expected to increase gradually, slowly diminishing the economic prospects to produce such species. Without adequate adaptation actions, the consequences of this development for many traditional oasis settlements and other plantations of similar species could be highly negative.

At the same time, population growth and agricultural expansion in many oasis settlements are leading to substantial increases in water demand for human consumption (Al-Kalbani et al. 2014 1696 ). For example, a large unmet water demand has been projected for future scenarios in the valley of Seybouse in East Algeria (Aoun-Sebaiti et al. 2014 1697 ), and similar conclusions were drawn for Wadi El Natrun in Egypt (Switzman et al. 2018 1698 ). Modelling studies have indicated long-term decline in available water and increasing risk of water shortages – for example, for oases in Morocco (Johannsen et al. 2016 1699 ; Karmaoui et al. 2016 1700 ), the Dakhla oasis in Egypt’s Western Desert (Sefelnasr et al. 2014 1701 ) and for the large Upper Mega Aquifer of the Arabian Peninsula (Siebert et al. 2016 1702 ). Mainly due to the risk of water shortages, Souissi et al. (2018) classified almost half of all farmers in Tunisia as non-resilient to climate change, especially those relying on tree crops, which limit opportunities for short-term adaptation actions.

The maintenance of the oasis systems and the safeguarding of their population’s livelihoods are currently threatened by continuous water degradation, increasing soil salinisation, and soil contamination (Besser et al. 2017 1703 ). Waterlogging and salinisation of soils due to rising saline groundwater tables coupled with inefficient drainage systems have become common to all continental oases in Tunisia, most of which are concentrated around saline depressions, known locally as chotts (Ben Hassine et al. 2013 1704 ). Similar processes of salinisation are also occurring in the oasis areas of Egypt due to agricultural expansion, excessive use of water for irrigation and deficiency of the drainage systems (Abo-Ragab 2010 1705 ; Masoud and Koike 2006 1706 ). A prime example for this is Siwa oasis (Figure 3.16), a depression extending over 1050 km 2 in the north-western desert of Egypt in the north of the sand dune belt of the Great Sand Sea (Abo-Ragab and Zaghloul 2017 1707 ). Siwa oasis has been recognised as a Globally Important Agricultural Heritage Site (GIAHS) by the FAO for being an in situ repository of plant genetic resources, especially of uniquely adapted varieties of date palm, olive and secondary crops that are highly esteemed for their quality and continue to play a significant role in rural livelihoods and diets (FAO 2016).

The population growth in Siwa is leading rapid agricultural expansion and land reclamation.The Siwan farmers are converting the surrounding desert into reclaimed land by applying their old inherited traditional practices. Yet, agricultural expansion in the oasis mainly depends on non-renewable groundwaters. Soil salinisation and vegetation loss have been accelerating since 2000 due to water mismanagement and improper drainage systems (Masoud and Koike 2006 1708 ). Between 1990 and 2008, the cultivated area increased from 53 to 88 km , lakes from 60 to 76 km 2 , sabkhas (salt flats) from 335 to 470 km 2 , and the urban area from 6 to 10 km 2 (Abo-Ragab 2010 1709 ). The problem of rising groundwater tables was exacerbated by climatic changes (Askri et al. 2010 1710 ; Gad and Abdel-Baki 2002; Marlet et al. 2009 1711 ).

Water supply is likely to become even scarcer for oasis agriculture under changing climate in the future than it is today, and viable solutions are difficult to find. While some authors stress the possibility to use desalinated water for irrigation (Aldababseh et al. 2018 1712 ), the economics of such options, especially given the high evapotranspiration rates in the Arabian Peninsula and North Africa, are debatable. Many oases are located far from water sources that are suitable for desalination, adding further to feasibility constraints. Most authors therefore stress the need to limit water use (Sefelnasr et al. 2014 1713 ), for example, by raising irrigation efficiency (Switzman et al. 2018 1714 ), reducing agricultural areas (Johannsen et al. 2016 1715 ) or imposing water use restrictions (Odhiambo 2017 1716 ), and to carefully monitor desertification (King and Thomas 2014 1717 ). Whether adoption of crops with low water demand, such as sorghum ( Sorghum bicolor (L.) Moench) or jojoba ( Simmondsia chinensis (Link) C. K. Schneid.) (Aldababseh et al. 2018 1718 ), can be a viable option for some oases remains to be seen, but given their relatively low profit margins compared to currently grown oasis crops, there are reasons to doubt the economic feasibility of such proposals. While it is currently unclear to what extent oasis agriculture can be maintained in hot locations of the region, cooler sites offer potential for shifting towards new species and cultivars, especially for tree crops, which have particular climatic needs across seasons. Resilient options can be identified, but procedures to match tree species and cultivars with site climate need to be improved to facilitate effective adaptation.

There is high confidence that many oases of North Africa and the Arabian Peninsula are vulnerable to climate change. While the impacts of recent climate change are difficult to separate from the consequences of other change processes, it is likely that water resources have already declined in many places and the suitability of the local climate for many crops, especially perennial crops, has already decreased. This decline of water resources and thermal suitability of oasis locations for traditional crops is very likely to continue throughout the 21st century. In the coming years, the people living in oasis regions across the world will face challenges due to increasing impacts of global environmental change (Chen et al. 2018 1719 ). Hence, efforts to increase their adaptive capacity to climate change can facilitate the sustainable development of oasis regions globally. In particular this will mean addressing the trade-offs between environmental restoration and agricultural livelihoods (Chen et al. 2018). Ultimately, sustainability in oasis regions will depend on policies integrating the provision of ecosystem services and social and human welfare needs (Wang et al. 2017 1724 ).

Figure 3.16

Satellite image of the Siwa Oasis, Egypt. Source: Google Maps

Integrated watershed management

Desertification has resulted in significant loss of ecosystem processes and services, as described in detail in this chapter. The techniques and processes to restore degraded watersheds are not linear and integrated watershed management (IWM) must address physical, biological and social approaches to achieve SLM objectives (German et al. 2007 1726 ).

Population growth, migration into Jordan and changes in climate have resulted in desertification of the Jordan Badia region. The Badia region covers more than 80% of the country’s area and receives less than 200 mm of rainfall per year, with some areas receiving less than 100 mm (Al-Tabini et al. 2012 1727 ). Climate analysis has indicated a generally increasing dryness over the West Asia and Middle East region (AlSarmi and Washington 2011 1728 ; Tanarhte et al. 2015 1729 ), with reduction in average annual rainfall in Jordan’s Badia area (De Pauw et al. 2015 1730 ). The incidence of extreme rainfall events has not declined over the region. Locally increased incidence of extreme events over the Mediterranean region has been proposed (Giannakopoulos et al. 2009 1731 ).

The practice of intensive and localised livestock herding, in combination with deep ploughing and unproductive barley agriculture, are the main drivers of severe land degradation and depletion of the rangeland natural resources. This affected both the quantity and the diversity of vegetation as native plants with a high nutrition value were replaced with invasive species with low palatability and nutritional content (Abu-Zanat et al. 2004 1732 ). The sparsely covered and crusted soils in Jordan’s Badia area have a low rainfall interception and infiltration rate, which leads to increased surface runoff and subsequent erosion and gullying, speeding up the drainage of rainwater from the watersheds, which can result in downstream flooding in Amman, Jordan (Oweis 2017 1733 ).

Figure 3.17a

(a)Newly prepared micro water harvesting catchment, using the Vallerani system.

Figure-3.17b

(b) Aerial imaging showing micro water harvesting catchment treatment after planting

Figure 3.17c

(c) one year after treatment. Source: Stefan Strohmeier.

Figure 3.18

Illustration of enhanced soil water retention in the mechanized micro rainwater harvesting compared to untreated badia rangelands in jordan, showing precipitation (pcp), sustained stress level resulting in decreased production, field capacity and wilting point for available soil moisture, and then measured soil moisture content between the two treatments (degraded rangeland and the restored rangeland with […].

Illustration of enhanced soil water retention in the Mechanized Micro Rainwater Harvesting compared to untreated Badia rangelands in Jordan, showing precipitation (PCP), sustained stress level resulting in decreased production, field capacity and wilting point for available soil moisture, and then measured soil moisture content between the two treatments (degraded rangeland and the restored rangeland with the Vallerani plough).

To restore the desertified Badia an IWM plan was developed using hillslope-implemented water harvesting micro catchments as a targeted restoration approach (Tabieh et al. 2015 1734 ). Mechanized Micro Rainwater Harvesting (MIRWH) technology using the ‘Vallerani plough’ (Antinori and Vallerani 1994 1735 ; Gammoh and Oweis 2011 1736 ; Ngigi 2003 1737 ) is being widely applied for rehabilitation of highly degraded rangeland areas in Jordan. A tractor digs out small water harvesting pits on the contour of the slope (Figure 3.17) allowing the retention, infiltration and local storage of surface runoff in the soil (Oweis 2017 1739 ). The micro catchments are planted with native shrub seedlings, such as saltbush ( Atriplex halimus ), with enhanced survival as a function of increased soil moisture (Figure 3.18) and increased dry matter yields (>300 kg ha –1 ) that can serve as forage for livestock (Oweis 2017 1738 ; Tabieh et al. 2015 1740 ).

Simultaneously to MIRWH upland measures, the gully erosion is being treated through intermittent stone plug intervention (Figure 3.19), stabilising the gully beds, increasing soil moisture in proximity of the plugs, dissipating the surface runoff’s energy, and mitigating further back-cutting erosion and quick drainage of water. Eventually, the treated gully areas silt up and dense vegetation cover can re-establish. In addition, grazing management practices are implemented to increase the longevity of the treatment. Ultimately, the recruitment processes and re-vegetation shall control the watershed’s hydrological regime through rainfall interception, surface runoff deceleration and filtration, combined with the less erodible and enhanced infiltration characteristics of the rehabilitated soils.

Figure 3.19a

(a) Gully plug development in September 2017.

Figure 3.19b

(b) Post-rainfall event (March 2018). Near Amman, Jordan. Source: Stefan Strohmeier.

In-depth understanding of the Badia’s rangeland status transition, coupled with sustainable rangeland management, are still subject to further investigation, development and adoption; a combination of all three is required to mitigate the ongoing degradation of the Middle Eastern rangeland ecosystems.

Oweis (2017) 1813 indicated that the cost of the fully automated Vallerani technique was approximately 32 USD ha-1. The total cost of the restoration package included the production, planting and maintenance of the shrub seedlings (11 USD ha –1) . Tabieh et al. (2015) 1812 calculated a benefit-cost ratio (BCR) of above 1.5 for re-vegetation of degraded Badia areas through MIRWH and saltbush. However, costs vary based on the seedling’s costs and availability of trained labour.

Water harvesting is not a recent scientific advancement. Water harvesting is known to have been developed during the Bronze Age and was widely practiced in the Negev Desert during the Byzantine time period (1300–1600 years ago) (Fried et al. 2018 1741 ; Stavi et al. 2017 1742 ). Through construction of various structures made of packed clay and stone, water was either held on site in half-circular dam structures ( hafir) that faced up-slope to capture runoff, or on terraces that slowed water allowing it to infiltrate and to be stored in the soil profile. Numerous other systems were designed to capture water in below-ground cisterns to be used later to provide water to livestock or for domestic use. Other water harvesting techniques divert runoff from hillslopes or wadis and spread the water in a systematic manner across playas and the toe-slope of a hillslope. These systems allow production of crops in areas with 100 mm of average annual precipitation by harvesting an additional 300+ mm of water (Beckers et al. 2013 1743 ). Water harvesting is a proven technology to mitigate or adapt to climate change where precipitation may be reduced, and allow for small-scale crop and livestock production to continue supporting local needs.

The second great challenge after the Green Revolution in India was the low productivity in the rain-fed and semi-arid regions where land degredation and drought were serious concerns. In response to this challenge IWM projects were implemented over large areas in semi-arid biomes over the past few decades. IWM was meant to become a key factor in meeting a range of social development goals in many semi-arid rainfed agrarian landscapes in India (Bouma et al. 2007 1744 ; Kerr et al. 2002 1745 ). Over the years, watershed development has become the fulcrum of rural development, and has the potential to achieve the twin objectives of ecosystem restoration and livelihood assurance in the drylands of India (Joy et al. 2004).

Many reports indicate significant improvements in mitigation of drought impacts, raising crops and fodder, livestock productivity, expanding the availability of drinking water and increasing incomes as a result of IWM (Rao 2000), but in some cases overall the positive impact of the programme has been questioned and, except in a few cases, the performance has not lived up to expectations (Joy et al. 2004; JM Kerr et al. 2002). Comparisons of catchments with and without IWM projects using remotely sensed data have sometimes shown no significant enhancement of biomass, in part due to methodological challenges of space for time comparisons (Bhalla et al. 2013 1746 ). The factors contributing to the successful cases were found to include effective participation of stakeholders in management (Rao 2000; Ratna Reddy et al. 2004 1747 ).

Attribution of success in soil and water conservation measures was confounded by inadequate monitoring of rainfall variability and lack of catchment hydrologic indicators (Bhalla et al. 2013 1748 ). Social and economic trade-offs included bias of benefits to downstream crop producers at the expense of pastoralists, women and upstream communities. This biased distribution of IWM benefits could potentially be addressed by compensation for environmental services between communities (Kerr et al. 2002 1749 ). The successes in some areas also led to increased demand for water, especially groundwater, since there has been no corresponding social regulation of water use after improvement in water regime (Samuel et al. 2007 1750 ). Policies and management did not ensure water allocation to sectors with the highest social and economic benefits (Batchelor et al. 2003 1751 ). Limited field evidence of the positive impacts of rainwater harvesting at the local scale is available, but there are several potential negative impacts at the watershed scale (Glendenning et al. 2012 1752 ). Furthermore, watershed projects are known to have led to more water scarcity, and higher expectations for irrigation water supply, further exacerbating water scarcity (Bharucha et al. 2014 1753 ).

In summary, the mixed performance of IWM projects has been linked to several factors. These include: inequity in the distribution of benefits (Kerr et al. 2002); focus on institutional aspects rather than application of appropriate watershed techniques and functional aspects of watershed restoration (Joy et al. 2006; Vaidyanathan 2006 1755 ); mismatch between scales of focus and those that are optimal for catchment processes (Kerr 2007 1756 ); inconsistencies in criteria used to select watersheds for IWM projects (Bhalla et al. 2011 1757 ); and in a few cases additional costs and inefficiencies of local non-governmental organisations (Chandrasekhar et al. 2006 1758 ; Deshpande 2008 1759 ). Enabling policy responses for improvement of IWM performance include: a greater emphasis on ecological restoration rather than civil engineering; sharper focus on sustainability of livelihoods than just conservation; adoption of ‘water justice’ as a normative goal and minimising externalities on non-stakeholder communities; rigorous independent biophysical monitoring, with feedback mechanisms and integration with larger schemes for food and ecological security, and maintenance of environmental flows for downstream areas (Bharucha et al. 2014 1760 ; Calder et al. 2008 1761 ; Joy et al. 2006). Successful adaptation of IWM to achieve land degradation neutrality would largely depend on how IWM creatively engages with dynamics of large-scale land use and hydrology under a changing climate, involvement of livelihoods and rural incomes in ecological restoration, regulation of groundwater use, and changing aspirations of rural population ( robust evidence, high agreement ) (O’Brien et al. 2004 1762 ; Samuel et al. 2007 1763 ; Samuel and Joy 2018 1764 ).

Limpopo River Basin

Covering an area of 412,938 km 2 , the Limpopo River basin spans parts of Botswana, South Africa, Zimbabwe and Mozambique, eventually entering into the Mozambique Channel. It has been selected as a case study as it provides a clear illustration of the combined effect of desertification and climate change, and why IWM may be a crucial component of reducing exposure to climate change. It is predominantly a semi-arid area with an average annual rainfall of 400 mm (Mosase and Ahiablame 2018 1765 ). Rainfall is both highly seasonal and variable, with the prominent impact of the El Niño/ La Niña phenomena and the Southern Oscillation leading to severe droughts (Jury 2016 1766 ). It is also exposed to tropical cyclones that sweep in from the Mozambique Channel often leading to extensive casualties and the destruction of infrastructure (Christie and Hanlon 2001 1767 ). Furthermore, there is good agreement across climate models that the region is going to become warmer and drier, with a change in the frequency of floods and droughts (Engelbrecht et al. 2011 1768 ; Zhu and Ringler 2012). Seasonality is predicted to increase, which in turn may increase the frequency of flood events in an area that is already susceptible to flooding (Spaliviero et al. 2014 1769 ).

A clear need exists to both address exposure to flood events as well as predicted decreases in water availability, which are already acute. Without the additional impact of climate change, the basin is rapidly reaching a point where all available water has been allocated to users (Kahinda et al. 2016 1770 ; Zhu and Ringler 2012). The urgency of the situation was identified several decades ago (FAO 2004), with the countries of the basin recognising that responses are required at several levels, both in terms of system governance and the need to address land degradation.

Recent reviews of the governance and implementation of IWM within the basin recognise that an integrated approach is needed and that a robust institutional, legal, political, operational, technical and support environment is crucial (Alba et al. 2016 1771 ; Gbetibouo et al. 2010 1773 ; Machethe et al. 2004 1774 ; Spaliviero et al. 2011 1775 ; van der Zaag and Savenije 1999 1776 ). Within the scope of emerging lessons, two principal ones emerge. The first is capacity and resource constraints at most levels. Limited capacity within Limpopo Watercourse Commission (LIMCOM) and national water management authorities constrains the implementation of IWM planning processes (Kahinda et al. 2016 1777 ; Spaliviero et al. 2011 1778 ). Whereas strategy development is often relatively well-funded and resourced through donor funding, long-term implementation is often limited due to competing priorities. The second is adequate representation of all parties in the process in order to address existing inequalities and ensure full integration of water management. For example, within Mozambique, significant strides have been made towards the decentralisation of river basin governance and IWM. Despite good progress, Alba et al. (2016) found that the newly implemented system may enforce existing inequalities as not all stakeholders, particularly smallholder farmers, are adequately represented in emerging water management structures and are often inhibited by financial and institutional constraints. Recognising economic and socio-political inequalities, and explicitly considering them to ensure the representation of all participants, can increase the chances of successful IWM implementation.

Knowledge gaps and key uncertainties

• Desertification has been studied for decades and different drivers of desertification have been described, classified, and are generally understood (e.g., overgrazing by livestock or salinisation from inappropriate irrigation) (D’Odorico et al. 2013 1779 ). However, there are knowledge gaps on the extent and severity of desertification at global, regional, and local scales (Zhang and Huisingh 2018 1780 ; Zucca et al. 2012 1781 ). Overall, improved estimation and mapping of areas undergoing desertification is needed. This requires a combination of rapidly expanding sources of remotely sensed data, ground observations and new modelling approaches. This is a critical gap, especially in the context of measuring progress towards achieving the Land Degradation Neutrality target by 2030 in the framework of SDGs.
• Despite numerous relevant studies, consistent indicators for attributing desertification to climatic and/or human causes are still lacking due to methodological shortcomings.
• Climate change impacts on dust and sand storm activity remain a critical gap. In addition, the impacts of dust and sand storms on human welfare, ecosystems, crop productivity and animal health are not measured, particularly in the highly affected regions such as the Sahel, North Africa, the Middle East and Central Asia. Dust deposition on snow and ice has been found in many regions of the globe (e.g., Painter et al. 2018; Kaspari et al. 2014 1782 ; Qian et al. 2015 1783 ; Painter et al. 2013 1784 ), however, the quantification of the effect globally, and estimation of future changes in the extent of this effect, remain knowledge gaps.
• Future projections of combined impacts of desertification and climate change on ecosystem services, fauna and flora, are lacking, even though this topic is of considerable social importance. Available information is mostly on separate, individual impacts of either (mostly) climate change or desertification. Responses to desertification are species-specific and mechanistic models are not yet able to accurately predict individual species responses to the many factors associated with desertification under changing climate.
• Previous studies have focused on the general characteristics of past and current desertification feedbacks to the climate system. However, the information on the future interactions between climate and desertification (beyond changes in the aridity index) are lacking. The knowledge of future climate change impacts on such desertification processes as soil erosion, salinisation, and nutrient depletion remains limited both at the global and at the local levels.
• Further research to develop the technologies and innovations needed to combat desertification is required, but it is also important to gain a better understanding of the reasons for the observed poor adoption of available innovations, to improve adoption rates.
• Desertification under changing climate has a high potential to increase poverty, particularly through the risks coming from extreme weather events (Olsson et al. 2014 1785 ). However, the evidence rigorously attributing changes in observed poverty to climate change impacts is currently not available.
• The knowledge on the limits to adaptation to the combined effects of climate change and desertification is insufficient. This is an important gap since the potential for residual risks and maladaptive outcomes is high.
• Filling these gaps involves considerable investments in research and data collection. Using Earth observation systems in a standardised approach could help fill some of these gaps. This would increase data comparability and reduce uncertainty in approaches and costs. Systematically collected data would provide far greater insights than incomparable fragmented data.
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Land Degradation

Summary for policymakers.

• 1 Introduction
• A People, land and climate in a warming world
• B Adaptation and mitigation response options
• C Enabling response options
• D Action in the near-term
• + Acknowledgements
• + SPM in UN Languages

Technical Summary

Framing and context.

• ES Executive Summary
• 1.1.1 Objectives and scope of the assessment
• 1.1.2.1 1.1.2.1 Land ecosystems and climate change
• 1.1.2.2 Current patterns of land use and land cover
• 1.1.2.3 Past and ongoing trends
• 1.2.1.1 Future trends in the global land system
• 1.2.1.2 Land degradation
• 1.2.1.3 Desertification
• 1.2.1.4 Food security, food systems and linkages to land-based ecosystems
• 1.2.1.5 Challenges arising from land governance
• 1.2.2.1 Concepts related to risk, uncertainty and confidence
• 1.2.2.2 Nature and scope of uncertainties related to land use
• 1.2.2.3 Uncertainties in decision-making
• 1.3.1 Targeted decarbonisation relying on large land-area need
• 1.3.2.1 Agricultural, forest and soil management
• 1.3.3.1 Supply management
• 1.3.3.2 Demand management
• 1.3.4 Risk management
• 1.3.5 Economics of land-based mitigation pathways: Costs versus benefits of early action under uncertainty
• 1.3.6 Adaptation measures and scope for co-benefits with mitigation
• 1.4.1 Governance to enable the response
• 1.4.2 Gender agency as a critical factor in climate and land sustainability outcomes
• 1.4.3.1 Legal and regulatory instruments
• 1.4.3.2 Economic and financial instruments
• 1.4.3.3 Rights-based instruments and customary norms
• 1.4.3.4 Social and cultural norms
• 1.5 The interdisciplinary nature of the SRCCL

Land–Climate interactions

• 2.1.1 Recap of previous IPCC and other relevant reports as baselines
• 2.1.2 Introduction to the chapter structure
• 2.2.1.1 Climate drivers of land form and function
• 2.2.1.2 Changes in global land surface air temperature
• 2.2.2 Climate-driven changes in aridity
• 2.2.3 The influence of climate change on food security
• 2.2.4 Climate-driven changes in terrestrial ecosystems
• 2.2.5.1 Changes in extreme temperatures, heatwaves and drought
• 2.2.5.2 Impacts of heat extremes and drought on land
• 2.2.5.3 Changes in heavy precipitation
• 2.2.5.4 Impacts of precipitation extremes on different land cover types
• 2.3.1.1 The total net flux of CO2 between land and atmosphere
• 2.3.1.2 Separation of the total net land flux into AFOLU fluxes and the land sink
• 2.3.1.3 Gross emissions and removals contributing to AFOLU emissions
• 2.3.1.4 Gross emissions and removals contributing to the non-anthropogenic land sink
• 2.3.1.5 Potential impact of mitigation on atmospheric CO 2 concentrations
• 2.3.2.1 Atmospheric trends
• 2.3.2.2 Land use effects
• 2.3.3.1 Atmospheric trends
• 2.3.3.2 Land use effects
• 2.4.1.1 Mineral dust as a short-lived climate forcer from land
• 2.4.1.2 Effects of past climate change on dust emissions and feedbacks
• 2.4.1.3 Future changes of dust emissions
• 2.4.2.1 Carbonaceous aerosol precursors of short-lived climate forcers from land
• 2.4.2.2 Effects of past climate change on carbonaceous aerosols emissions and feedbacks
• 2.4.2.3 Future changes of carbonaceous aerosol emissions
• 2.4.3.1 BVOC precursors of short-lived climate forcers from land
• 2.4.3.2 Historical changes of BVOCs and contribution to climate change
• 2.4.3.3 Future changes of BVOCs
• 2.5.1.1 Impacts of global historical land cover changes on climate
• 2.5.1.2 Impacts of future global land cover changes on climate
• 2.5.2.1 Impacts of deforestation and forestation
• 2.5.2.2 Impacts of changes in land management
• 2.5.3.1 Effects of changes in land cover and productivity resulting from global warming
• 2.5.3.2 Feedbacks to climate from high-latitude land-surface changes
• 2.5.3.3 Feedbacks related to changes in soil moisture resulting from global warming
• 2.5.4 Non-local and downwind effects resulting from changes in land cover
• 2.6.1.1 Land management in agriculture
• 2.6.1.2 Land management in forests
• 2.6.1.3 Land management of soils
• 2.6.1.4 Land management in other ecosystems
• 2.6.1.5 Bioenergy and bioenergy with carbon capture and storage
• 2.6.1.6 Enhanced weathering
• 2.6.1.7 Demand management in the food sector (diet change, waste reduction)
• 2.6.2 Integrated pathways for climate change mitigation
• 2.6.3 The contribution of response options to the Paris Agreement
• 2.7.1 Temperature responses of plant and ecosystem production
• 2.7.2 Water transport through soil-plant-atmosphere continuum and drought mortality
• 2.7.3 Soil microbial effects on soil nutrient dynamics and plant responses to elevated CO2
• 2.7.4 Vertical distribution of soil organic carbon
• 2.7.5 Soil carbon responses to warming and changes in soil moisture
• 2.7.6 Soil carbon responses to changes in organic matter inputs by plants
• 3.1.1 Introduction
• 3.1.2 Desertification in previous IPCC and related reports
• 3.1.3 Dryland populations: Vulnerability and resilience
• 3.1.4.1 Processes of desertification and their climatic drivers
• 3.1.4.2 Anthropogenic drivers of desertification under climate change
• 3.1.4.3 Interaction of drivers: Desertification syndrome versus drylands development paradigm
• 3.2.1.1 Global scale
• 3.2.1.2 Regional scale
• 3.2.2 Attribution of desertification
• 3.3.1.1 Off-site feedbacks
• 3.3.2 Changes in surface albedo
• 3.3.3 Changes in vegetation and greenhouse gas fluxes
• 3.4.1.1 Impacts on ecosystems and their services in drylands
• 3.4.1.2 Impacts on biodiversity: Plant and wildlife
• 3.4.2.1 Impacts on poverty
• 3.4.2.2 Impacts on food and nutritional insecurity
• 3.4.2.3 Impacts on human health through dust storms
• 3.4.2.4 Impacts on gender equality
• 3.4.2.5 Impacts on water scarcity and use
• 3.4.2.6 Impacts on energy infrastructure through dust storms
• 3.4.2.7 Impacts on transport infrastructure through dust storms and sand movement
• 3.4.2.8 Impacts on conflicts
• 3.4.2.9 Impacts on migration
• 3.4.2.10 Impacts on pastoral communities
• 3.5.1.1 Future vulnerability and risk of desertification
• 3.5.2 Future projections of impacts
• 3.6.1.1 Integrated crop–soil–water management
• 3.6.1.2 Grazing and fire management in drylands
• 3.6.1.3 Clearance of bush encroachment
• 3.6.1.4 Combating sand and dust storms through sand dune stabilisation
• 3.6.1.5 Use of halophytes for the re-vegetation of saline lands
• 3.6.2.1 Socio-economic responses for combating desertification under climate change
• 3.6.2.2 Socio-economic responses for economic diversification
• 3.6.3.1 Policy responses towards combating desertification under climate change
• 3.6.3.2 Policy responses supporting economic diversification
• 3.6.4 Limits to adaptation, maladaptation, and barriers for mitigation
• 3.7.1.1 Soil erosion under changing climate in drylands
• 3.7.1.2 No-till practices for reducing soil erosion in central Chile
• 3.7.1.3 Combating wind erosion and deflation in Turkey: The greening desert of Karapınar
• 3.7.1.4 Soil erosion in Central Asia under changing climate
• 3.7.2.1 The experiences of combating desertification in China
• 3.7.2.2 The Green Dam in Algeria
• 3.7.2.3 The Great Green Wall of the Sahara and the Sahel Initiative
• 3.7.3.1 Introduction
• 3.7.3.2 Ethiopia
• 3.7.3.3 Mexico
• 3.7.3.4 United States of America
• 3.7.3.5 Pakistan
• 3.7.4 Oases in hyper-arid areas in the Arabian Peninsula and northern Africa
• 3.7.5.1 Jordan
• 3.7.5.2 India
• 3.7.5.3 Limpopo River Basin
• 3.8 Knowledge gaps and key uncertainties
• 4.1.1 Scope of the chapter
• 4.1.2 Perspectives of land degradation
• 4.1.3 Definition of land degradation
• 4.1.4 Land degradation in previous IPCC reports
• 4.1.5 Sustainable land management (SLM) and sustainable forest management (SFM)
• 4.1.6 The human dimension of land degradation and forest degradation
• 4.2.1.1 Types of land degradation processes
• 4.2.1.2 Land degradation processes and climate change
• 4.2.2 Drivers of land degradation
• 4.2.3.1 Direct linkages with climate change
• 4.2.3.2 Indirect and complex linkages with climate change
• 4.2.4 Approaches to assessing land degradation
• 4.3.1 Land degradation
• 4.3.2 Forest degradation
• 4.4.1.1 Changes in water erosion risk due to precipitation changes
• 4.4.1.2 Climate-induced vegetation changes, implications for land degradation
• 4.4.1.3 Coastal erosion
• 4.4.2 Indirect impacts on land degradation
• 4.5.1 Potential scale of bioenergy and land-based CDR
• 4.5.2 Risks of land degradation from expansion of bioenergy and land-based CDR
• 4.5.3 Potential contributions of land-based CDR to reducing and reversing land degradation
• 4.5.4 Traditional biomass provision and land degradation
• 4.6.1 Impact on greenhouse gases (GHGs)
• 4.6.2 Physical impacts
• 4.7.1 Relationships between land degradation, climate change and poverty
• 4.7.2 Impacts of climate-related land degradation on food security
• 4.7.3 Impacts of climate-related land degradation on migration and conflict
• 4.8.1.1 4.8.1.1 Agronomic and soil management measures
• 4.8.1.2 Mechanical soil and water conservation
• 4.8.1.3 Agroforestry
• 4.8.1.4 Crop–livestock interaction as an approach to managing land degradation
• 4.8.2 Local and indigenous knowledge for addressing land degradation
• 4.8.3 Reducing deforestation and forest degradation and increasing afforestation
• 4.8.4 Sustainable forest management (SFM) and CO2 removal (CDR) technologies
• 4.8.5.1 Limits to adaptation
• 4.8.6 Resilience and thresholds
• 4.8.7 Barriers to implementation of sustainable land management (SLM)
• 4.9.1 Urban green infrastructure
• 4.9.2 Perennial grains and soil organic carbon (SOC)
• 4.9.3.1 South Korea case study on reforestation success
• 4.9.3.2 China case study on reforestation success
• 4.9.4 Degradation and management of peat soils
• 4.9.5.1 Role of biochar in climate change mitigation
• 4.9.5.2 Role of biochar in management of land degradation
• 4.9.6.1 Management of coastal wetlands
• 4.9.7 Saltwater intrusion
• 4.9.8 Avoiding coastal maladaptation
• 4.10 Knowledge gaps and key uncertainties

Food Security

• 5.1.1.1 Food security as an outcome of the food system
• 5.1.1.2 Effects of climate change on food security
• 5.1.2.1 Trends in the global food system
• 5.1.2.2 Food insecurity status and trends
• 5.1.3 Climate change, gender and equity
• 5.1.4.1 Food systems in AR5 and SR15
• 5.1.4.2 Food systems and the Paris Agreement
• 5.1.4.3 Charting the future of food security
• 5.2.1.1 Short-lived climate pollutants
• 5.2.2.1 Impacts on crop production
• 5.2.2.2 Impacts on livestock production systems
• 5.2.2.3 Impacts on pests and diseases
• 5.2.2.4 Impacts on pollinators
• 5.2.2.5 Impacts on aquaculture
• 5.2.2.6 Impacts on smallholder farming systems
• 5.2.3.1 Impacts on prices and risk of hunger
• 5.2.3.2 Impacts on land use
• 5.2.4.1 Impacts on food safety and human health
• 5.2.4.2 Impacts on food quality
• 5.2.5.1 Impacts of extreme events
• 5.2.5.2 Food aid
• 5.3.1 Challenges and opportunities
• 5.3.2.1 Autonomous, incremental, and transformational adaptation
• 5.3.2.2 Risk management
• 5.3.2.3 Role of agroecology and diversification
• 5.3.2.4 Role of cultural values
• 5.3.3.1 Crop production
• 5.3.3.2 Livestock production systems
• 5.3.3.3 Aquaculture, fisheries, and agriculture interactions
• 5.3.3.4 Transport and storage
• 5.3.3.5 Trade and processing
• 5.3.4 Demand-side adaptation
• 5.3.5.1 Global initiatives
• 5.3.5.2 National policies
• 5.3.5.3 Community-based adaptation
• 5.3.6.1 Early warning systems
• 5.3.6.2 Financial resources
• 5.4.1 Greenhouse gas emissions from food systems
• 5.4.2 Greenhouse gas emissions from croplands and soils
• 5.4.3 Greenhouse gas emissions from livestock
• 5.4.4 Greenhouse gas emissions from aquaculture
• 5.4.5 5.4.5 Greenhouse gas emissions from inputs, processing, storage and transport
• 5.4.6 Greenhouse gas emissions associated with different diets
• 5.5.1.1 Greenhouse gas mitigation in croplands and soils
• 5.5.1.2 Greenhouse gas mitigation in livestock systems
• 5.5.1.3 Greenhouse gas mitigation in agroforestry
• 5.5.1.4 Integrated approaches to crop and livestock mitigation
• 5.5.1.5 Greenhouse gas mitigation in aquaculture
• 5.5.1.6 Cellular agriculture
• 5.5.2.1 Mitigation potential of different diets
• 5.5.2.2 Role of dietary preferences
• 5.5.2.3 Uncertainties in demand-side mitigation potential
• 5.5.2.4 Insect-based diets
• 5.5.2.5 Food loss and waste, food security, and land use
• 5.5.2.6 Shortening supply chains
• 5.6.1 Land-based carbon dioxide removal (CDR) and bioenergy
• 5.6.2 Mitigation, food prices, and food security
• 5.6.3.1 Can dietary shifts provide significant benefits?
• 5.6.4.1 Agroecology
• 5.6.4.2 Climate-smart agriculture
• 5.6.4.3 Conservation agriculture
• 5.6.4.4 Sustainable intensification
• 5.6.5 Role of urban agriculture
• 5.6.6 Links to the Sustainable Development Goals
• 5.7.1.1 Agriculture and trade policy
• 5.7.1.2 Scope for expanded policies
• 5.7.1.3 Health-related policies and cost savings
• 5.7.1.4 Multiple policy pathways
• 5.7.2.1 Capital markets
• 5.7.2.2 Insurance and re-insurance
• 5.7.3 Just Transitions to sustainability
• 5.7.4.1 Indigenous and local knowledge
• 5.7.4.2 Citizen science
• 5.7.4.3 Capacity building and education
• 5.7.5.1 Impacts and adaptation
• 5.7.5.2 Emissions and mitigation
• 5.7.5.3 Synergies and trade-offs
• 5.8.1 Food price spikes
• 5.8.2.1 Migration
• 5.8.2.2 Conflict
• SM Supplementary Material

Interlinkages between desertification, land degradation, food security and GHG fluxes: synergies, trade-offs and integrated response options

• 6.1.1 Context of this chapter
• 6.1.2.1 Enabling conditions
• 6.1.3 Challenges and response options in current and historical interventions
• 6.1.4 Challenges represented in future scenarios
• 6.2.1.1 Integrated response options based on land management in agriculture
• 6.2.1.2 Integrated response options based on land management in forests
• 6.2.1.3 Integrated response options based on land management of soils
• 6.2.1.4 Integrated response options based on land management of all/other ecosystems
• 6.2.1.5 Integrated response options based on land management specifically for carbon dioxide removal (CDR)
• 6.2.2.1 Integrated response options based on value chain management through demand management
• 6.2.2.2 Integrated response options based on value chain management through supply management
• 6.2.3.1 Risk management options
• 6.3.1.1 Integrated response options based on land management
• 6.3.1.2 Integrated response options based on value chain management
• 6.3.1.3 Integrated response options based on risk management
• 6.3.2.1 Integrated response options based on land management
• 6.3.2.2 Integrated response options based on value chain management
• 6.3.2.3 Integrated response options based on risk management
• 6.3.3.1 Integrated response options based on land management
• 6.3.3.2 Integrated response options based on value chain management
• 6.3.3.3 Integrated response options based on risk management
• 6.3.4.1 Integrated response options based on land management
• 6.3.4.2 Integrated response options based on value chain management
• 6.3.4.3 Integrated response options based on risk management
• 6.3.5.1 Integrated response options based on land management
• 6.3.5.2 Integrated response options based on value chain management
• 6.3.5.3 Integrated response options based on risk management
• 6.3.6 Summarising the potential of the integrated response options across mitigation, adaptation, desertification land degradation and food security
• 6.4.1 Feasibility of the integrated response options with respect to costs, barriers, saturation and reversibility
• 6.4.2 Sensitivity of the integrated response options to climate change impacts
• 6.4.3.2 Impacts of integrated response options on the UNSDGs
• 6.4.3.1 Impacts of integrated response options on NCP
• 6.4.4.1 Where can the response options be applied?
• 6.4.4.2 Interlinkages and response options in future scenarios
• 6.4.4.3 Resolving challenges in response option implementation
• 6.4.5 Potential consequences of delayed action

Risk management and decision making in relation to sustainable development

• ES Executive summary
• 7.1.1 Findings of previous IPCC assessments and reports
• 7.1.2 Treatment of key terms in the chapter
• 7.1.3 Roadmap to the chapter
• 7.2.1 Assessing risk
• 7.2.2.1 Crop yield in low latitudes
• 7.2.2.2 Food supply instability
• 7.2.2.3 Soil erosion
• 7.2.2.4 Dryland water scarcity
• 7.2.2.5 Vegetation degradation
• 7.2.2.6 Fire damage
• 7.2.2.7 Permafrost
• 7.2.2.8 Risks of desertification, land degradation and food insecurity under different Future Development Pathways
• 7.2.3.1 Risk associated with land-based adaptation
• 7.2.3.2 Risk associated with land-based mitigation
• 7.2.4 Risks arising from hazard, exposure and vulnerability
• 7.3.1 What is at stake for food security?
• 7.3.2 Risks to where and how people live: Livelihood systems and migration
• 7.3.3 Risks to humans from disrupted ecosystems and species
• 7.3.4.1 Windows of opportunity
• 7.4.1 Multi-level policy instruments
• 7.4.2.1 Policies to ensure availability, access, utilisation and stability of food
• 7.4.2.2 Policies to secure social protection
• 7.4.3.1 Risk management instruments
• 7.4.3.2 Drought-related risk minimising instruments
• 7.4.3.3 Fire-related risk minimising instruments
• 7.4.3.4 Flood-related risk minimising instruments
• 7.4.4.1 GHG fluxes and climate change mitigation
• 7.4.4.2 Mitigation instruments
• 7.4.4.3 Market-based instruments
• 7.4.4.4 Technology transfer and land-use sectors
• 7.4.4.5 International cooperation under the Paris Agreement
• 7.4.5 Policies responding to desertification and degradation – Land Degradation Neutrality (LDN)
• 7.4.6.1 Land-use zoning
• 7.4.6.2 Conserving biodiversity and ecosystem services (ES)
• 7.4.6.3 Standards and certification for sustainability of biomass and land-use sectors
• 7.4.6.4 Energy access and biomass use
• 7.4.7.1 Financing mechanisms for land mitigation and adaptation
• 7.4.7.2 Instruments to manage the financial impacts of climate and land change disruption
• 7.4.7.3 Innovative financing approaches for transition to low-carbon economies
• 7.4.8 Enabling effective policy instruments – policy portfolio coherence
• 7.4.9.1 Barriers to adaptation
• 7.4.9.2 Barriers to land-based climate mitigation
• 7.4.9.3 Inequality
• 7.4.9.4 Corruption and elite capture
• 7.4.9.5 Overcoming barriers
• 7.5.1.1 Formal Decision Making
• 7.5.1.2 Informal decision-making
• 7.5.2.1 Problem structuring
• 7.5.2.2 Decision-making tools
• 7.5.2.3 Cost and timing of action
• 7.5.3 Best practices of decision-making toward sustainable land management (SLM)
• 7.5.4 Adaptive management
• 7.5.5 Performance indicators
• 7.5.6.1 Trade-offs and synergies between ecosystem services (ES)
• 7.5.6.2 Sustainable Development Goals (SDGs): Synergies and trade-offs
• 7.5.6.3 Forests and agriculture
• 7.5.6.4 Water, food and aquatic ecosystem services (ES)
• 7.5.6.5 Considering synergies and trade-offs to avoid maladaptation
• 7.6.1 Institutions building adaptive and mitigative capacity
• 7.6.2 Integration – Levels, modes and scale of governance for sustainable development
• 7.6.3 Adaptive climate governance responding to uncertainty
• 7.6.4 Participation
• 7.6.5 Land tenure
• 7.6.6 Institutional dimensions of adaptive governance
• 7.6.7 Inclusive governance for sustainable development
• 7.7 Key uncertainties and knowledge gaps

Annex-I Glossary

Annex-ii acronyms, annex-iii contributors, annex-iv reviewers, annex-v index.

Case Study: Sahel Desertification

What is desertification: It is the term used to describe the changing of semi arid (dry) areas into desert. It is severe in Sudan, Chad, Senegal and Burkina Faso

What are the causes:

• Overcultivation: the land is continually used for crops and does not have time to recover eventually al the nutrients are depleted (taken out) and the ground eventually turns to dust.
• Overgrazing: In some areas animals have eaten all the vegetation leaving bare soil.
• Deforestation: Cutting down trees leaves soil open to erosion by wind and rain.
• Climate Change: Decrease in rainfall and rise in temperatures causes vegetation to die

What is being done to solve the problem?

 Over the past twelve years Oxfam has worked with local villagers in Yatenga (Burkina Faso) training them in the process of BUNDING. This is building lines of stones across a slope to stop water and soil running away. This method preserves the topsoil and has improved farming and food production in the village.

Burkina Faso - desertification

This video shows the Sahel region south of the Sahara is at risk of becoming desert. Elders in a village in Burkina Faso describe how the area has changed from a fertile area to a drought-prone near-desert. The area experiences a dry season which can last up to eight or nine months. During this time rivers dry up and people, animals and crops are jeopardised.

This video showcases the Sahel region

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A Case Study of the Desertification of Haiti

2011, Journal of Sustainable Development

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Nathan McClintock

Andrew Tarter

ANETE PEREIRA

Ose Pauleus

Estimates of forest cover have important political, conservation, and funding implications, but methods vary greatly. Haiti has often been cited as one of the most deforested countries in the world, yet estimates of forest cover range from <1% to 33%. Here, we analyze land change for seven land cover classes (forest, shrub land, agriculture/pasture, plantation, urban/infrastructure, barren land, and water) between 2000 and 2015 using Landsat imagery (30 m resolution) in the Google Earth Engine platform. Forest cover was estimated at 26% in 2000 and 21% in 2015. Although forest cover is declining in Haiti, our quantitative analysis resulted in considerably higher forest cover than what is usually reported by local and international institutions. Our results determined that areas of forest decline were mainly converted to shrubs and mixed agriculture/pasture. An important driver of forest loss and degradation could be the high demand for charcoal, which is the principal source of cooking fuel. Our results differ from other forest cover estimates and forest reports from national and international institutions, most likely due to differences in forest definition, data sources, spatial resolution, and methods. In the case of Haiti, this work demonstrates the need for clear and functional definitions and classification methods to accurately represent land use/cover change. Regardless of how forests are defined, forest cover in Haiti will continue to decline unless corrective actions are taken to protect remaining forest patches. This can serve as a warning of the destructive land use patterns and can help us target efforts for better planning, management, and conservation.

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Desertification is a serious problem that threatens the livelihoods and the lives of nearly a billion people in more than 100 countries. The total area affected covers one-third of the Earth's land surface. The people living in these areas are at risk of having to abandon their homes and migrate because the land can no longer sustain them. Though significant efforts have been initiated to combat desertification, the problem is worsening: each year, according to the Worldwatch Institute, the continents lose 24 billion tons of topsoil, creating a condition that often results in severe desertification. Desertification does not, as many think, mean the expansion of deserts. It is a process of land degradation in the drylands where previously stable environments are degraded by humans through erosion, overgrazing, overcropping, poor irrigation practices and deforestation, combined with variations in climate. Desertification is an environmental problem that is both the reason behind and the consequence of numerous other ecological concerns, including the loss of biological diversity and the depletion of water resources. As such, it contributes to an environmental spiral that could get progressively worse unless drastic and immediate efforts are taken to correct it. Similarly, it stems from and leads to extreme poverty.

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Rocky desertification and its causes in karst areas: a case study in Yongshun County, Hunan Province, China

• Original Article
• Published: 27 June 2008
• Volume 57 , pages 1481–1488, ( 2009 )

Cite this article

• Y. J. Xiong 1 ,
• G. Y. Qiu 1 , 2 ,
• D. K. Mo 3 ,
• Q. X. Wang 4 ,
• S. H. Zhao 1 &

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Rocky desertification, a process of land degradation characterized by soil erosion and bedrock exposure, is one of the most serious land degradation problems in karst areas, and is regarded as an obstacle to local sustainable development. It is well known that human activities can accelerate rocky desertification; however, the effects of climate change on rocky desertification in karst areas are still unclear. This study focused on the effects of temperature and precipitation changes and human activities on rocky desertification in karst areas to determine the impacts of climate change and human disturbances on rocky desertification. Areas of different level of rocky desertification were obtained from Landsat TM (1987) and Landsat ETM+ (2000) images. The results show that, although the total desertification area increased by only 1.27% between 1987 and 2000, 17.73% of the slightly desertified land had degraded to a moderate or intense level, 2.01 and 15.71%, respectively. Meanwhile, between 1987 and 2000, the air temperature increased by 0.7°C, and precipitation increased by 170 mm. Statistical results indicate that the increase in precipitation was caused by heavy rainfall. In addition, under the interactive influences of heavy rainfall and temperature, the average karst dissolution rate was about 87 m 3 km −2 a −1 during the 14 years in the study area. Further analysis indicated that rocky desertification was positively related with the increase in temperature and precipitation and especially with the heavy rainfall events. Climate change accelerated rocky desertification in the karst areas.

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Acknowledgments

This study was supported by the National Science Foundation for Distinguished Young Scholars (Project 40425008) and by the Scientific Research Fund of Central South University of Forestry and Technology (Project 07005B). Thanks go to F. Che, X. P. Xue, H. Wang, Y. L. Sun, X. Li, Doctor C. Y. He, and Professor J. Chen, for their advice, to the Forestry Department of Hunan Province for providing forest inventory data of Yongshun County, to the China Meteorological Administration for providing meteorological data, to the National Bureau of Statistics of China for providing socio-economic data, and to the University of Maryland, for providing Landsat data.

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College of Resources Science and Technology, Beijing Normal University, Xinjiekouwai St. 19th, 100875, Beijing, People’s Republic of China

Y. J. Xiong, G. Y. Qiu, S. H. Zhao & J. Yin

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, 100875, Beijing, People’s Republic of China

Research Center of Forestry Remote Sensing and Information Engineering, Central South University of Forestry and Technology, Shaoshan Road 498th, 410004, Changsha, Hunan, People’s Republic of China

D. K. Mo, H. Lin & H. Sun

Asia Water Environment Section, National Institute for Environmental Studies, 16-2 Onogawa, 305-8506, Tsukuba, Japan

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254_2008_1425_MOESM1_ESM.tif

MOESM1 Figure 1 Landsat data (path 125 and row 40) coverage and its location (a) and false color images (R (4) B (3) G (2)) of Yongshun County: TM in October 26th, 1987 (b) and ETM+ in May 14th, 2000 (c) (TIFF 1244 kb)

MOESM2 Figure 2 Annual population and its growth rate in Yongshun County (TIFF 340 kb)

Moesm3 figure 3 the area of farmland in yongshun county (tiff 268 kb), moesm4 figure 4 livestock density in yongshun county (tiff 239 kb), moesm5 figure 5 gdp and the average per capita net income of peasants in yongshun county (tiff 361 kb), rights and permissions.

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Xiong, Y.J., Qiu, G.Y., Mo, D.K. et al. Rocky desertification and its causes in karst areas: a case study in Yongshun County, Hunan Province, China. Environ Geol 57 , 1481–1488 (2009). https://doi.org/10.1007/s00254-008-1425-7

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Received : 30 May 2007

Revised : 19 May 2008

Accepted : 09 June 2008

Published : 27 June 2008

Issue Date : June 2009

DOI : https://doi.org/10.1007/s00254-008-1425-7

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Land productivity dynamics in Socotra Island (Yemen)

Effects of preservation policy on land use changes in iranian northern zagros forests, the challenge and future of rocky desertification control in karst areas in southwest china, land degradation as a security threat amplifier: the new global frontline, landscape factors and restoration practices associated with initial reforestation success in haiti, research, part of a special feature on sustaining ecosystem services in cultural landscapes: analysis and management options the hedgification of maizescapes scalability and multifunctionality of jatropha curcas hedges in a mixed farming landscape in zambia, characteristics of changes in karst rocky desertification in southtern and western china and driving mechanisms, a not so cool harlley's legacy: the catastrophic human-made trindade island devastation and its recovery, 7 references, alley cropping as a sustainable agricultural technology for the hillsides of haiti: experience of an agroforestry outreach project, unep – united nations environment programme, world development report 1983, land degradation: development and breakdown of terrestrial environments: c.j. barrow, 1991, cambridge university press, 295 pp., us$ 80, isbn 0521-35-3335, desertification: financial support for the biosphere, global desertification: do humans cause deserts, handbook on desertification indicators., related papers.

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Sand dunes show the increasing desertification of the Tibetan Plateau, as land dries out and vegetation cover vanishes due to human activity.

• ENVIRONMENT

Desertification, explained

Humans are driving the transformation of drylands into desert on an unprecedented scale around the world, with serious consequences. But there are solutions.

As global temperatures rise and the human population expands, more of the planet is vulnerable to desertification, the permanent degradation of land that was once arable.

While interpretations of the term desertification vary, the concern centers on human-caused land degradation in areas with low or variable rainfall known as drylands: arid, semi-arid, and sub-humid lands . These drylands account for more than 40 percent of the world's terrestrial surface area.

While land degradation has occurred throughout history, the pace has accelerated, reaching 30 to 35 times the historical rate, according to the United Nations . This degradation tends to be driven by a number of factors, including urbanization , mining, farming, and ranching. In the course of these activities, trees and other vegetation are cleared away , animal hooves pound the dirt, and crops deplete nutrients in the soil. Climate change also plays a significant role, increasing the risk of drought .

All of this contributes to soil erosion and an inability for the land to retain water or regrow plants. About 2 billion people live on the drylands that are vulnerable to desertification, which could displace an estimated 50 million people by 2030.

Where is desertification happening, and why?

The risk of desertification is widespread and spans more than 100 countries , hitting some of the poorest and most vulnerable populations the hardest, since subsistence farming is common across many of the affected regions.

More than 75 percent of Earth's land area is already degraded, according to the European Commission's World Atlas of Desertification , and more than 90 percent could become degraded by 2050. The commission's Joint Research Centre found that a total area half of the size of the European Union (1.61 million square miles, or 4.18 million square kilometers) is degraded annually, with Africa and Asia being the most affected.

The drivers of land degradation vary with different locations, and causes often overlap with each other. In the regions of Uzbekistan and Kazakhstan surrounding the Aral Sea , excessive use of water for agricultural irrigation has been a primary culprit in causing the sea to shrink , leaving behind a saline desert. And in Africa's Sahel region , bordered by the Sahara Desert to the north and savannas to the south, population growth has caused an increase in wood harvesting, illegal farming, and land-clearing for housing, among other changes.

The prospect of climate change and warmer average temperatures could amplify these effects. The Mediterranean region would experience a drastic transformation with warming of 2 degrees Celsius, according to one study , with all of southern Spain becoming desert. Another recent study found that the same level of warming would result in "aridification," or drying out, of up to 30 percent of Earth's land surface.

A herder family tends pastures beside a growing desert.

When land becomes desert, its ability to support surrounding populations of people and animals declines sharply. Food often doesn't grow, water can't be collected, and habitats shift. This often produces several human health problems that range from malnutrition, respiratory disease caused by dusty air, and other diseases stemming from a lack of clean water.

Desertification solutions

In 1994, the United Nations established the Convention to Combat Desertification (UNCCD), through which 122 countries have committed to Land Degradation Neutrality targets, similar to the way countries in the climate Paris Agreement have agreed to targets for reducing carbon pollution. These efforts involve working with farmers to safeguard arable land, repairing degraded land, and managing water supplies more effectively.

The UNCCD has also promoted the Great Green Wall Initiative , an effort to restore 386,000 square miles (100 million hectares) across 20 countries in Africa by 2030. A similar effort is underway in northern China , with the government planting trees along the border of the Gobi desert to prevent it from expanding as farming, livestock grazing , and urbanization , along with climate change, removed buffering vegetation.

However, the results for these types of restoration efforts so far have been mixed. One type of mesquite tree planted in East Africa to buffer against desertification has proved to be invasive and problematic . The Great Green Wall initiative in Africa has evolved away from the idea of simply planting trees and toward the idea of " re-greening ," or supporting small farmers in managing land to maximize water harvesting (via stone barriers that decrease water runoff, for example) and nurture natural regrowth of trees and vegetation.

"The absolute number of farmers in these [at-risk rural] regions is so large that even simple and inexpensive interventions can have regional impacts," write the authors of the World Atlas of Desertification, noting that more than 80 percent of the world's farms are managed by individual households, primarily in Africa and Asia. "Smallholders are now seen as part of the solution of land degradation rather than a main problem, which was a prevailing view of the past."

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Situating china in the global effort to combat desertification.

1. Introduction

2.1. before the unccd (1977–1991): the first international political will, 2.2. unccd during 1992–1996: new approach, new focus, 2.3. first 10 years of the unccd (1997–2006): institutions matter, 2.4. unccd before the sustainable development goals (2007–2014): channeling science to policymakers, 2.5. unccd in the era of sdgs (2015-present): the approach matters, 3.1. before 1977: how to fix the problem, 3.2. before the unccd (1977–1991): china’s perspective on desertification, 3.3. china during 1992–1996: joining the effort, 3.4. china during the first 10 years of the unccd (1997–2006), 3.5. china before the sdgs (2007–2014): continuing the effort, 3.6. china in the era of sdgs (2015-present): advancing the effort.

Click here to enlarge figure

4. Discussion

4.1. political will and financial support matter, 4.2. “bottom-up” or “top-down”, 4.3. institutions matter, 4.4. channel science to policy makers, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Kong, Z.-H.; Stringer, L.; Paavola, J.; Lu, Q. Situating China in the Global Effort to Combat Desertification. Land 2021 , 10 , 702. https://doi.org/10.3390/land10070702

Kong Z-H, Stringer L, Paavola J, Lu Q. Situating China in the Global Effort to Combat Desertification. Land . 2021; 10(7):702. https://doi.org/10.3390/land10070702

Kong, Zheng-Hong, Lindsay Stringer, Jouni Paavola, and Qi Lu. 2021. "Situating China in the Global Effort to Combat Desertification" Land 10, no. 7: 702. https://doi.org/10.3390/land10070702

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Hot deserts - AQA Desertification - causes and prevention strategies

Hot deserts are an important ecosystem with distinct characteristics and adaptations. They provide opportunities for development but also cause challenges such as desertification.

Part of Geography The living world

Desertification - causes and prevention strategies

Desertification close desertification The spread of desert conditions in arid regions due to human activities, drought or climate change. is the process of land turning into desert as the quality of the soil declines over time. The main causes of desertification include:

• Population growth - the population in some desert areas is increasing. In places where there are developments in mining and tourism, people are attracted by jobs. An increased population is putting greater pressure on the environment for resources such as wood and water.
• Removal of wood - in developing countries, people use wood for cooking. As the population in desert areas increases, there is a greater need for fuel wood. When the land is cleared of trees, the roots of the trees no longer hold the soil together so it is more vulnerable to soil erosion close soil erosion When earth is washed or blown away. .
• Overgrazing close overgrazing When land cannot sustain the number of animals that are feeding from it. - an increasing population results in larger desert areas being farmed. Sheep, cattle and goats are overgrazing the vegetation. This leaves the soil exposed to erosion.
• Soil erosion - this is made worse by overgrazing and the removal of wood. Population growth is the primary cause of soil erosion.
• Climate change close climate change The long-term alteration of weather patterns. - the global climate is getting warmer. In desert regions conditions are not only getting warmer but drier too. On average there is less rain now in desert regions than there was 50 years ago.

Strategies to reduce desertification

Desertification can be reduced by adopting the following strategies:

• Planting more trees - the roots of trees hold the soil together and help to reduce soil erosion from wind and rain.
• Improving the quality of the soil - this can be managed by encouraging people to reduce the number of grazing animals they have and grow crops instead. The animal manure can be used to fertilise the crops grown. Growing crops in this way can improve the quality of the soil as it is held together by the roots of plants and protected from erosion. This type of farming is more sustainable close sustainable An activity which does not consume or destroy resources or the environment. .
• Water management - water can be stored in earth dams close earth dam A dam made of earth. Earth is used to create a circular hollow to store rain water. in the wet season and used to irrigate crops during the dry season. This is an example of using appropriate technology close appropriate technology Simple equipment and technology that the local people are able to use easily and without much cost. to manage water supplies in the desert environment.

More guides on this topic

• Ecosystems - AQA
• Tropical rainforests - AQA
• Cold environments - AQA

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FAO Regional Office for Near East and North Africa

Desertification and drought day 2024: united for land, our legacy, our future.

The future of our land faces critical challenges, with up to 40 percent of the world's land already degraded. Healthy land, crucial for providing 95 percent of our food, clothing, shelter, jobs, and protection from natural disasters, is disappearing at an alarming rate—equivalent to four football fields every second, or 100 million ha annually. The Near East and North Africa (NENA) region, covering 14.9 percent of the Earth's surface and home to nearly 420 million people, faces significant issues with arable land, fertile soils, and water resources. Only 6.8 percent of NENA's land is suitable for farming , about 0.21 ha per person, with countries like the United Arab Emirates, Mauritania, Oman, and Saudi Arabia having less than 1 percent arable land , posing substantial challenges for agricultural productivity and food security.  

NENA's arid climate, extreme temperatures, and limited precipitation result in soils with low organic content and poor structure. Agricultural production in the region is dominated by rainfed systems typical of arid and semi-arid areas receiving less than 400 mm of annual precipitation. These areas cover 85 percent of the land and support around 60 percent of the population. Conversely, irrigated agriculture, which relies increasingly on groundwater, uses 85 percent of renewable water resources and is confined to 30 percent of arable land, except in Egypt. Small-scale family farming, responsible for over 80 percent of crops and livestock products, faces numerous challenges, including water scarcity, land degradation, and limited institutional support. Around 45 percent of the total agricultural area is exposed to salinity, soil nutrient depletion, and wind-water erosion. In 2012, an estimated 20 percent of the population lived on these degraded lands, found mostly in the marginal and so-called lagging areas of the MENA region.                             

Addressing soil health and degradation  

Unsustainable land management practices have led to significant soil degradation in NENA. Studies on land degradation in MENA over the past two decades reveal overall land degradation of 40 percent to 70 percent. Issues such as deforestation, excessive use of agro-chemicals, frequent tillage, and overgrazing contribute to this degradation. Soil health is vital for sustainable agriculture, and degradation has far-reaching impacts on food security and human health. Therefore, promoting sustainable soil management practices is crucial to restoring soil health, enhancing agricultural productivity, and improving food security in the NENA region.  

Effective strategies include improving irrigation methods to minimize water waste and salinization, managing soil cover to prevent erosion and maintain moisture, and retaining crop residues to enrich the soil with organic matter. Optimizing crop selection and timing ensures that crops are well-suited to the climatic and soil conditions, reducing the need for chemical inputs and enhancing resilience to climate variability. Monitoring the quality of irrigation water helps prevent soil contamination and degradation. Additionally, practices such as crop rotation, agroforestry, and the use of organic fertilizers can improve soil structure and fertility, promoting long-term sustainability. Implementing these strategies requires a combination of technological innovation, farmer education, and supportive policies to create an enabling environment for sustainable land management.  

FAO's work in capacity development for sustainable soil management in the NENA region  

On this Desertification and Drought Day, we highlight the critical efforts of FAO in combating soil degradation in the NENA region. The FAO project, "Capacity Development for the Sustainable Management of Soil Resources in the NENA Region to Achieve the Sustainable Development Goals," is a cornerstone initiative aimed at addressing the pressing challenges of desertification, soil degradation, and drought. By aligning with the Sustainable Development Goals (SDGs), particularly SDG 2 (Zero Hunger) and SDG 15 (Life on Land), FAO has implemented a comprehensive strategy to enhance soil health and agricultural productivity. This initiative has assessed and fortified the capacities of 34 soil laboratories under the Ministries of Agriculture in countries such as Iraq, Jordan, Lebanon, Morocco, Oman, Palestine, Sudan, Syria, Tunisia, and Yemen. Through tailored training sessions for 227 laboratory technicians and managers, the project has focused on key aspects like standard operating procedures, quality assurance, equipment calibration, and soil result interpretation. These efforts ensure that national experts are well-equipped to tackle soil degradation and promote sustainable soil management practices.    

Furthermore, FAO has significantly strengthened regional and inter-regional collaboration on sustainable soil management through the NENA Soil Partnership . This collaboration has been instrumental in facilitating the exchange of knowledge and strategies among beneficiary countries. The project organized several decision-making meetings, including the 6th, 7th, and 8th plenary meetings of the NENA Soil Partnership, which were crucial in developing and reviewing regional work plans and strategies for 2023-2024. These meetings, alongside participation in the 9th and 10th sessions of the Global Soil Partnership Plenary Assembly, have ensured that regional priorities align with global soil management frameworks. By hosting soil mapping products on the Global Soil Information System (GloSIS) discovery hub and making all project outputs freely accessible, FAO has ensured technological and economic sustainability. This initiative not only addresses immediate soil degradation but also fosters long-term resilience against desertification and drought, ensuring a sustainable future for the NENA region. 

The role of FAO and collaborative initiatives  

Sand and dust storms (SDS) significantly impact the NENA region, exacerbating environmental degradation, climate issues, health problems, agricultural decline, and socio-economic challenges. SDS has been identified as one of the most critical challenges in the region, as highlighted during COP28 . 

Factors such as poor land management, desertification, and climate change have increased the frequency and intensity of these storms, threatening food security and livelihoods. Addressing SDS requires comprehensive strategies, including sustainable land and water management, disaster risk reduction, and regional cooperation. Through regional and country-specific projects, FAO enhances NENA countries' capacity to adopt climate-smart agriculture, improve early warning systems, and build resilience against SDS impacts. Through initiatives like the Coalition on Combating Sand and Dust Storms (SDS) , FAO provides technical expertise, facilitates knowledge exchange, and promotes collaborative projects aimed at enhancing land and water management practices, climate resilience, and disaster risk reduction.   The Contingency planning process for catalysing investments and actions to enhance resilience against sand and dust storms in agriculture in the Islamic Republic of Iran report outlines a conceptual framework for assessing and mapping sand and dust storm (SDS) hazards, risks, and vulnerabilities in agriculture, specifically in the Islamic Republic of Iran. It aims to establish an actionable procedure using web-based data, remote sensing imagery, and GIS modelling to reduce SDS impacts on agriculture. The report introduces agriculture-specific indicators for SDS risk assessment and details steps for their development. It also describes the legal and institutional frameworks in Iran relevant to SDS intervention, delineates organizational responsibilities, and highlights main action areas and challenges in SDS contingency planning, with a focus on Ahvaz County. Given Iran's significant exposure to SDS due to its location in the global dust belt, and the exacerbating effects of climate change, the report emphasizes the need for integrating short-term responses with long-term development actions to build agricultural resilience against SDS. 

The Sustainable Land Management for Improved Livelihoods in Degraded Areas of Iraq project exemplifies FAO's commitment to combating desertification and drought in the NENA region. This initiative aligns with FAO's broader strategy to enhance sustainable land management (SLM) practices, which are critical for reversing land degradation and promoting resilience against climate impacts. The project, despite facing delays due to political instability and the COVID-19 pandemic, achieved substantial milestones by training 103 decision and policy makers 500 farm households on conservation agriculture and agroecology, and is currently in the process of establishing a National SLM strategy. These efforts directly contribute to FAO's objectives by enhancing policy frameworks, improving agricultural productivity on 30 000 ha of land, and engaging 60 extension officers in SLM practices. The project also hopes to create a national knowledge management platform to further ensure that best practices are disseminated, supporting FAO's goal of building resilient ecosystems and improving livelihoods across the region. 

The Enhancing the resilience of agriculture and livestock producers through improved watershed management and development of environmentally-positive value chains in South East Mauritania project, currently being facilitated by FAO, will also be discussed. This project targets the highly vulnerable rural populations in the southeastern regions of Mauritania, where climate change exacerbates existing challenges. With over 3 400 Dimitra community listener clubs (CLCs) established across sub-Saharan Africa by FAO, built on successful experiences for women and youth in Mauritania since 2012, FAO brings a wealth of grassroots engagement expertise to the initiative. Leveraging lessons learned from past APFS (Agro-Pastoral Field Schools) projects, the initiative aims to equip local communities with the knowledge and skills needed to address climate change impacts effectively. FAO's intervention is significant given the alarming trends of desertification and drought in the NENA region, where Mauritania is particularly susceptible. By promoting sustainable land management practices, such as dune fixation and the restoration of eroded banks, FAO's approach not only enhances agricultural productivity but also mitigates the adverse effects of desertification, which threatens the livelihoods of millions in the region. This project underscores FAO's commitment to empowering communities to adapt to climate change while fostering environmentally sustainable agricultural practices, essential for the long-term resilience of vulnerable populations in the NENA region.   

More on this topic

• Desertification and Drought Day and relevant publications