Editing IPCC:AR6/SRCCL/Chapter-3 (section)

=== 3.6.2 Socio-economic responses ===

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Socio-economic and policy responses are often crucial in enhancing the adoption of SLM practices (Cordingley et al. 2015 <sup>[[#fn:r1143|1143]]</sup> ; Fleskens and Stringer 2014 <sup>[[#fn:r1144|1144]]</sup> ; Nyanga et al. 2016 <sup>[[#fn:r1145|1145]]</sup> ) and for assisting agricultural households to diversify their sources of income (Barrett et al. 2017 <sup>[[#fn:r1146|1146]]</sup> ; Shiferaw and Djido 2016 <sup>[[#fn:r1147|1147]]</sup> ). Technology and socio-economic responses are not independent, but continuously interact.

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==== 3.6.2.1 Socio-economic responses for combating desertification under climate change ====

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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 <sup>[[#fn:r1148|1148]]</sup> ; Engdawork and Bork 2016 <sup>[[#fn:r1149|1149]]</sup> ). Using indigenous and local knowledge for combating desertification could contribute to climate change adaptation strategies (Belfer et al. 2017 <sup>[[#fn:r1150|1150]]</sup> ; Codjoe et al. 2014 <sup>[[#fn:r1151|1151]]</sup> ; Etchart 2017 <sup>[[#fn:r1152|1152]]</sup> ; Speranza et al. 2010 <sup>[[#fn:r1153|1153]]</sup> ; Makondo and Thomas 2018 <sup>[[#fn:r1154|1154]]</sup> ; Maldonado et al. 2016 <sup>[[#fn:r1155|1155]]</sup> ; Nyong et al. 2007 <sup>[[#fn:r1156|1156]]</sup> ). There are abundant examples of how indigenous and local knowledge, which are an important part of broader agroecological knowledge (Altieri 2018 <sup>[[#fn:r1157|1157]]</sup> ), 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 <sup>[[#fn:r1158|1158]]</sup> ) (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 <sup>[[#fn:r1159|1159]]</sup> ). 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 <sup>[[#fn:r1160|1160]]</sup> ) as well as to manage forests in the Moroccan High Atlas Mountains (Auclair et al. 2011 <sup>[[#fn:r1161|1161]]</sup> ). Across the Arabian Peninsula and North Africa, a rotational grazing system, ''hema'' , was historically practiced by the Bedouin communities (Hussein 2011 <sup>[[#fn:r1162|1162]]</sup> ; Louhaichi and Tastad 2010 <sup>[[#fn:r1163|1163]]</sup> ). The Beni-Amer herders in the Horn of Africa have developed complex livestock breeding and selection systems (Fre 2018 <sup>[[#fn:r1164|1164]]</sup> ). 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 <sup>[[#fn:r1165|1165]]</sup> ; Engdawork and Bork 2016 <sup>[[#fn:r1166|1166]]</sup> ). Moreover, there is ''robust evidence'' documenting the marginalisation or loss of indigenous and local knowledge (Dominguez 2014 <sup>[[#fn:r1167|1167]]</sup> ; Fernández-Giménez and Fillat Estaque 2012 <sup>[[#fn:r1168|1168]]</sup> ; Hussein 2011 <sup>[[#fn:r1169|1169]]</sup> ; Kodirekkala 2017 <sup>[[#fn:r1170|1170]]</sup> ; Moreno-Calles et al. 2012 <sup>[[#fn:r1171|1171]]</sup> ). 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 <sup>[[#fn:r1172|1172]]</sup> ; Guzman et al. 2018 <sup>[[#fn:r1173|1173]]</sup> ).

'''Collective action''' has the potential to contribute to SLM and climate change adaptation ( ''medium confidence'' ) (Adger 2003 <sup>[[#fn:r1174|1174]]</sup> ; Engdawork and Bork 2016 <sup>[[#fn:r1175|1175]]</sup> ; Eriksen and Lind 2009 <sup>[[#fn:r1176|1176]]</sup> ; Ostrom 2009 <sup>[[#fn:r1177|1177]]</sup> ; Rodima-Taylor et al. 2012 <sup>[[#fn:r1178|1178]]</sup> ). 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 <sup>[[#fn:r1179|1179]]</sup> ; Woolcock and Narayan 2000 <sup>[[#fn:r1180|1180]]</sup> ). 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 <sup>[[#fn:r1181|1181]]</sup> ). 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 <sup>[[#fn:r1182|1182]]</sup> ; Infante 2017 <sup>[[#fn:r1183|1183]]</sup> ; Kassie et al. 2013 <sup>[[#fn:r1184|1184]]</sup> ; Nyangena 2008 <sup>[[#fn:r1185|1185]]</sup> ; Willy and Holm-Müller 2013 <sup>[[#fn:r1186|1186]]</sup> ; Wossen et al. 2015 <sup>[[#fn:r1187|1187]]</sup> ). However, there are also cases when they did not improve SLM where they were not strictly enforced (Teshome et al. 2016 <sup>[[#fn:r1188|1188]]</sup> ). Collective action for implementing responses to dryland degradation is often hindered by local asymmetric power relations and ‘elite capture’ (Kihiu 2016 <sup>[[#fn:r1189|1189]]</sup> ; Stringer et al. 2007 <sup>[[#fn:r1190|1190]]</sup> ). 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 <sup>[[#fn:r1191|1191]]</sup> ). 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 <sup>[[#fn:r1192|1192]]</sup> ).

'''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 <sup>[[#fn:r1193|1193]]</sup> ; Tambo and Wünscher 2015 <sup>[[#fn:r1194|1194]]</sup> ; Waters-Bayer et al. 2009 <sup>[[#fn:r1195|1195]]</sup> ). SLM technologies co-generated through direct participation of agricultural households have higher chances of being accepted by them ( ''medium confidence'' ) (Bonney et al. 2016 <sup>[[#fn:r1196|1196]]</sup> ; Vente et al. 2016 <sup>[[#fn:r1197|1197]]</sup> ). Usually farmer-driven innovations are more frugal and better adapted to their resource scarcities than externally introduced technologies (Gupta et al. 2016 <sup>[[#fn:r1198|1198]]</sup> ). Farmer-to-farmer sharing of their own innovations and mutual learning positively contribute to higher technology adoption rates (Dey et al. 2017 <sup>[[#fn:r1199|1199]]</sup> ). 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 <sup>[[#fn:r1200|1200]]</sup> ; Herrick et al. 2017 <sup>[[#fn:r1201|1201]]</sup> ; McKinley et al. 2017 <sup>[[#fn:r1202|1202]]</sup> ; Steger et al. 2017 <sup>[[#fn:r1203|1203]]</sup> ).

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 <sup>[[#fn:r1204|1204]]</sup> ; Cordingley et al. 2015 <sup>[[#fn:r1205|1205]]</sup> ; Lokonon and Mbaye 2018 <sup>[[#fn:r1206|1206]]</sup> ; Mulinge et al. 2016 <sup>[[#fn:r1207|1207]]</sup> ; Wildemeersch et al. 2015 <sup>[[#fn:r1208|1208]]</sup> ) (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 <sup>[[#fn:r1209|1209]]</sup> ; Safriel and Adeel 2008 <sup>[[#fn:r1210|1210]]</sup> ).

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==== 3.6.2.2 Socio-economic responses for economic diversification ====

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'''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 <sup>[[#fn:r1211|1211]]</sup> ; Bryan et al. 2009 <sup>[[#fn:r1212|1212]]</sup> ; Dumenu and Obeng 2016 <sup>[[#fn:r1213|1213]]</sup> ; Salik et al. 2017 <sup>[[#fn:r1214|1214]]</sup> ; Shiferaw et al. 2009 <sup>[[#fn:r1215|1215]]</sup> ). 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 <sup>[[#fn:r1216|1216]]</sup> ; Lybbert et al. 2004 <sup>[[#fn:r1217|1217]]</sup> ). 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 <sup>[[#fn:r1218|1218]]</sup> ).

'''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 <sup>[[#fn:r1219|1219]]</sup> ). 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 <sup>[[#fn:r1220|1220]]</sup> ); during droughts in Burkina Faso in the 2000s (Barbier et al. 2009 <sup>[[#fn:r1221|1221]]</sup> ); in Mexico in the 1990s (Nawrotzki et al. 2016 <sup>[[#fn:r1222|1222]]</sup> ); 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 <sup>[[#fn:r1223|1223]]</sup> ). 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 <sup>[[#fn:r1224|1224]]</sup> ) (Section 3.4.2). Barrios et al. (2006) <sup>[[#fn:r1225|1225]]</sup> 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 <sup>[[#fn:r1226|1226]]</sup> ), 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 <sup>[[#fn:r1227|1227]]</sup> ).

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 <sup>[[#fn:r1228|1228]]</sup> ), 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 <sup>[[#fn:r1229|1229]]</sup> ; Salik et al. 2017 <sup>[[#fn:r1230|1230]]</sup> ; Sikder and Higgins 2017 <sup>[[#fn:r1231|1231]]</sup> ). 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 <sup>[[#fn:r1232|1232]]</sup> ; Liu et al. 2016a <sup>[[#fn:r1233|1233]]</sup> ).

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