Editing IPCC:AR6/SRCCL/Chapter-4 (section)

=== 4.2.2 Drivers of land degradation ===

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Drivers of land degradation and land improvement are many and they interact in multiple ways. Figure 4.2 illustrates how some of the most important drivers interact with the land users. It is important to keep in mind that natural and human factors can drive both degradation and improvement (Kiage 2013 <sup>[[#fn:r208|208]]</sup> ; Bisaro et al. 2014 <sup>[[#fn:r209|209]]</sup> ).

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'''Figure 4.2'''

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'''Schematic representation of the interactions between the human (H) and environmental (E) components of the land system showing decision-making and ecosystem services as the key linkages between the components (moderated by an effective system of local and scientific knowledge), and indicating how the rates of change and the way these linkages operate must be kept […]'''
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[[File:e0f1f8ac699bbfa5ad615b2d1c345fd9 Figure-4.2-1024x589.jpg]]

Schematic representation of the interactions between the human (H) and environmental (E) components of the land system showing decision-making and ecosystem services as the key linkages between the components (moderated by an effective system of local and scientific knowledge), and indicating how the rates of change and the way these linkages operate must be kept broadly in balance for functional coevolution of the components. Modified with permission from Stafford Smith et al. (2007) <sup>[[#fn:r1643|1643]]</sup> .

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Land degradation is driven by the entire spectrum of factors, from very short and intensive events, such as individual rain storms of 10 minutes removing topsoil or initiating a gully or a landslide (Coppus and Imeson 2002 <sup>[[#fn:r210|210]]</sup> ; Morgan 2005b <sup>[[#fn:r211|211]]</sup> ) to century-scale slow depletion of nutrients or loss of soil particles (Johnson and Lewis 2007, pp. 5–6). But, instead of focusing on absolute temporal variations, the drivers of land degradation can be assessed in relation to the rates of possible recovery. Unfortunately, this is impractical to do in a spatially explicit way because rates of soil formation are difficult to measure due to the slow rate, usually &lt;5mm/century (Delgado and Gómez 2016 <sup>[[#fn:r212|212]]</sup> ). Studies suggest that erosion rates of conventionally tilled agricultural fields exceed the rate at which soil is generated by one to two orders of magnitude (Montgomery 2007a <sup>[[#fn:r213|213]]</sup> ).

The landscape effects of gully erosion from one short intensive rainstorm can persist for decades and centuries (Showers 2005 <sup>[[#fn:r214|214]]</sup> ). Intensive agriculture under the Roman Empire in occupied territories in France is still leaving its marks and can be considered an example of irreversible land degradation (Dupouey et al. 2002 <sup>[[#fn:r215|215]]</sup> ).

The climate-change-related drivers of land degradation are gradual changes of temperature, precipitation and wind, as well as changes of the distribution and intensity of extreme events (Lin et al. 2017 <sup>[[#fn:r216|216]]</sup> ). Importantly, these drivers can act in two directions: land improvement and land degradation. Increasing CO <sub>2</sub> level in the atmosphere is a driver of land improvement, even if the net effect is modulated by other factors, such as the availability of nitrogen (Terrer et al. 2016 <sup>[[#fn:r217|217]]</sup> ) and water (Gerten et al. 2014 <sup>[[#fn:r218|218]]</sup> ; Settele et al. 2015 <sup>[[#fn:r219|219]]</sup> ; Girardin et al. 2016 <sup>[[#fn:r220|220]]</sup> ).

The gradual and planetary changes that can cause land degradation/ improvement have been studied by global integrated models and Earth observation technologies. Studies of global land suitability for agriculture suggest that climate change will increase the area suitable for agriculture by 2100 in the Northern high latitudes by 16% (Ramankutty et al. 2002 <sup>[[#fn:r221|221]]</sup> ) or 5.6 million km <sup>2</sup> (Zabel et al. 2014 <sup>[[#fn:r222|222]]</sup> ), while tropical regions will experience a loss (Ramankutty et al. 2002 <sup>[[#fn:r223|223]]</sup> ; Zabel et al. 2014 <sup>[[#fn:r224|224]]</sup> ).

Temporal and spatial patterns of tree mortality can be used as an indicator of climate change impacts on terrestrial ecosystems. Episodic mortality of trees occurs naturally even without climate change, but more widespread spatio-temporal anomalies can be a sign of climate-induced degradation (Allen et al. 2010 <sup>[[#fn:r225|225]]</sup> ). In the absence of systematic data on tree mortality, a comprehensive meta-analysis of 150 published articles suggests that increasing tree mortality around the world can be attributed to increasing drought and heat stress in forests worldwide (Allen et al. 2010 <sup>[[#fn:r226|226]]</sup> ).

Other and more indirect drivers can be a wide range of factors such as demographic changes, technological change, changes of consumption patterns and dietary preferences, political and economic changes, and social changes (Mirzabaev et al. 2016 <sup>[[#fn:r227|227]]</sup> ). It is important to stress that there are no simple or direct relationships between underlying drivers and land degradation, such as poverty or high population density, that are necessarily causing land degradation (Lambin et al. 2001 <sup>[[#fn:r228|228]]</sup> ). However, drivers of land degradation need to be studied in the context of spatial, temporal, economic, environmental and cultural aspects (Warren 2002 <sup>[[#fn:r229|229]]</sup> ). Some analyses suggest an overall negative correlation between population density and land degradation (Bai et al. 2008 <sup>[[#fn:r230|230]]</sup> ) but we find many local examples of both positive and negative relationships (Brandt et al. 2018a, 2017 <sup>[[#fn:r231|231]]</sup> ). Even if there are correlations in one or the other direction, causality is not always the same.

Land degradation is inextricably linked to several climate variables, such as temperature, precipitation, wind, and seasonality. This means that there are many ways in which climate change and land degradation are linked. The linkages are better described as a web of causality rather than a set of cause–effect relationships.

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