Agricultural livelihoods, adaptation, and environmental migration in sub-Saharan drylands: a meta-analytical review

The literature search for this study was conducted in two stages—a first stage in June 2017 and a second stage in March 2020 when newer studies were added to the initial selection (Wiederkehr et al 2018). We followed a stepwise procedure to identify relevant studies for our review. In a first step, a systematic search of studies was carried out on the scientific search platform 'Web of Science', which provides comprehensive citation data for various academic fields. We used relevant keywords, including the names of all countries in SSA, terms related to environmental change and associated hazards, and the terms 'adapt' and 'cope'. More information on the specific search terms can be found in supplementary material B.

The entire search process yielded a preliminary selection of 3625 papers, which were further examined and filtered (see supplementary figure S2 for a PRISMA diagram). The filtering involved two steps. First, the relevance of papers was assessed based on their titles, keywords, and abstracts. In a second step, the full texts were thoroughly assessed for eligibility based on predefined inclusion criteria. We only considered peer-reviewed articles published in English.

The following inclusion criteria were used to determine the eligibility of studies. A paper was considered eligible, if it (i) comprised primary data from a local case study, (ii) focused on populations in predominantly rural and (semi-)arid areas characterized by subsistence livelihoods or small-scale agricultural activities, (iii) reported an environmental change process ⁷ , (iv) presented data at the household level, (v) provided information on multiple coping and adaptation strategies (excluding preferred or planned actions), and (vi) provided information on the percentage of households in a study population adopting specific strategies.

The search and selection procedure resulted in a total sample of 89 individual case studies covering more than 21 thousand households in 22 countries (see supplementary table S1 for a full list of studies). Information on the case studies were retrieved from 53 distinct articles, which were published between 2004 and 2019. The studies were conducted in different regions in SSA and used a variety of research designs and methods (figure 2(c)). Household surveys, focus groups, and key informant or expert interviews were the most common data collection methods, often complemented by semi-structured interviews and field or participant observations. Sample sizes ranged from 15 to 623 participants, with most studies based on fewer than 200 participants (figure 2(b)).

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The dryland locations covered by our sample of case studies are exposed to a range of environmental changes and hazards. Most commonly, studies reported changes ⁸ in the amount of rainfall (71.9% of studies) as well as more unpredictable and erratic rainfall (48.3%), increasing temperature levels (67.4%), and drought (41.5%) as major sources of environmental stress. Also, risks related to the degradation of land (58.4%) and water bodies (31.5%) were frequently mentioned. These include issues related to soil erosion (10 study cases), lake drying (4 cases), and the desiccation of floodplains (1 case). Other mentions relate to risks of flooding (18.0%) and stress related to wind (10.1%), especially increasing wind speeds (7 study cases) or the occurrence of dust storms (4 cases).

From each article, we extracted information about the study location(s), period of data collection, other study characteristics and results, which became the basis of our meta-data. We use this information to compare result patterns across studies and explore whether these were influenced by contextual factors at the household and community level. We are primarily interested in the adaptation strategies used by households, which we classify in 14 broad categories. In each category, we calculated the percentage of households among the study population who reported the adoption of a specific type of strategy. Table S1 in the supplementary materials gives examples of adaptation measures reported in each of the 14 categories. Typically, the households reported the use of several measures belonging to one category at a time. This means that in certain categories several percentage values were recorded, the sum of which may exceed the value of 100%.

Based on the information retrieved from the studies, we constructed two main outcome variables for each of the 14 adaptation categories: (i) A dummy variable taking the value of one if any measures belonging to a specific category were reported in the study population and zero otherwise, and (ii) a continuous variable which corresponds to the share of households using the most common measure in a category. The latter measure provides a conservative lower bound estimate of the share of households using measures in each of the 14 adaptation categories. For our main analyses, we rely on the continuous outcome. Results using the dummy variable are reported in the supplementary materials (supplementary figure S3). Importantly, both the dummy and the continuous outcome variables are comparable across categories and across studies, which allows us to investigate what the most common forms of adaptation are, and in which context they are most likely to be used.

In addition to the measures described above, we retrieved information from each article about the mode of data collection, characteristics of the interviewed households, indications of environmental change processes, and other environmental and non-environmental factors relevant for the local context. One key piece of information which was retrieved from the studies is the predominant livelihood strategy among interviewed households. We used this information to categorize study populations in three types: sedentary farmers, (agro-)pastoralists, and mixed populations who use a combination of both farming and (agro-)pastoralism (figure 2(a)). Among the different livelihood strategies, farming was the most common across study populations, with nearly 80% of households on average involved in some form of farming activities.

We aimed to collect the precise global positioning system (GPS) coordinates (latitude and longitude) of the study populations. Some studies did not report GPS coordinates, but we managed to determine the approximate location of study sites based on available information, for example the name of the district in which the study was carried out. Once the GPS coordinates of each study location were determined, we were able to join the meta-data retrieved from the articles with external data sources. In particular, we added information about the overall socioeconomic conditions in the region, which may influence the adaptation options available to households.

We retrieved information about the overall socioeconomic conditions from Demographic and Health Surveys (DHS)—large and nationally representative household surveys conducted in over 90 low- and middle-income countries, including most countries in SSA. The DHS data are representative at the subnational level, which is usually based on the census enumeration areas in the respective country (for example region or district). Various contextual information were obtained from DHS and aggregated at the lowest representative enumeration area for the respective DHS survey. In particular, we calculated: (i) the percentage of households employed in agriculture, including both farmers and (agro-) pastoralists, as a proxy for agricultural dependence, (ii) the percentage of households with access to electricity and with a finished (non-natural) floor as wealth proxies, and (iii) the percentage of households with secondary education and higher as a proxy for adaptive capacity related to education. To assign accurate background information to the case studies, we used DHS data from the same period when the data for the original studies were collected. If studies were missing information about the exact data collection period, we used the publication year as reference point.

We employed a range of analytical tools to identify adaptation patterns across the studies included in the analysis. The texts were comprehensively screened and relevant data concerning study context, methods, and findings were extracted into a spreadsheet. The distribution of adaptation patterns across studies was explored and described using quantitative methods and descriptive graphs for illustration. In addition, we reviewed and coded the texts to contextualize the findings from the studies and to provide an in-depth perspective and examples on how the study populations responded to environmental changes. Here, we primarily identified common protective and adaptive activities employed by households as well as challenges reported.

In this study, we are particularly interested in migration and how it is used in combination with other strategies depending on the ecological and socioeconomic context. The primary goal is not to consider migration in isolation, but to understand its links to adaptation in a community more broadly. To this end, we consider differences in response patterns conditional on characteristics of the local populations represented by the studies. Matching case study locations with external socioeconomic data derived from DHS allows us to complement the information provided in the studies with wider contextual measures and effectively compare the different study contexts with each other.

Cluster analysis is used to identify adaptation clusters within the case study sample, i.e. populations using similar strategies, and to determine the socioeconomic conditions associated with certain combinations of adaptation activities. For the clustering, we use a centroid-based k-means clustering approach, which partitions the observed study cases x into a set C of k different clusters so as to minimize the within-cluster sum of squares (WCSS), i.e. the difference of each observation from the mean ${\mu _i}$ within each cluster ${C_i}$.

$$\begin{equation}\mathop {{\text{arg min}}}\limits_S \mathop \sum \limits_{i = 1}^k \mathop \sum \limits_{x \in {C_i}} {\left| {\left| {x - {\mu _i}} \right|} \right|^2}\end{equation} \tag{ 1 }$$

The number of clusters k is defined prior to the clustering and we employ various tests to identify the optimal number in our application (see supplementary materials E). x represents a vector of the percentage of households in a study sample using the different adaptation strategies. The cluster analysis is performed in R using the cluster package (Maechler et al 2021) and the Hartigan and Wong algorithm (1979). The algorithm assigns data points to a randomly created initial set of clusters based on their difference to the cluster centroids and calculates changes in the WCSS (1) if the data point were to be included in another cluster. If the resulting WCSS is smaller than in the original assignment, the data point is assigned to this new cluster. This iteration continues until the assignment is stable, i.e. no more improvements in the WCSS criterion can be reached by changing cases between clusters. In this situation, any change would make the clusters more internally variable or more externally similar and thus increase the WCSS.

Households use a diverse range of strategies to cope with and adapt to the environmental changes observed (figure 3). Agricultural strategies, including measures related to crop cultivation (41.2%), livestock keeping (24.4%), and soil and water management (21.5%), are clearly the most common, likely reflecting the predominance of sedentary farmers in our sample of studies. This also indicates a tendency of households to adapt in-situ and continue with agriculture rather than completely abandon cropping or herding. The reliance on in-situ activities could be due to socio-cultural reasons that induce people to stay in their current place of residence, such as place attachment (Vinke et al 2020), or it could be due to barriers and constraints in mobility (Cattaneo and Peri 2016).

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The agricultural strategies cover a broad range of activities further highlighting the diversity of adaptation measures used by rural households. As part of the most commonly reported category, crop management, households report changes in their use of fertilizer and compost (e.g. Barbier et al 2009, in Burkina Faso; Mertz et al 2009b, in Senegal), the use of more resistant crop varieties (e.g. Tambo and Abdoulaye 2013, in Nigeria; Okpara et al 2016, in Chad), the diversification of crops (e.g. Gebrehiwot and Van Der Veen 2013, in Ethiopia; Antwi-Agyei et al 2014, in Ghana), changes in crop rotation (e.g. Padonou et al 2014, in Benin; Yila and Resurreccion 2014, in Nigeria), and modifications in the farmland (e.g. Snorek et al 2014, in Niger; Tesfaye and Seifu 2016, in Ethiopia).

A broad variety of other (non-agricultural) strategies are also used by the households. These include short-term strategies, such as the search for aid (8.7%) or support within the social network (13.7%), or financial measures, such as taking loans or dissolving savings (7.6%). Changes in food consumption and provision are also often reported (15%). In many study contexts, these changes reflect adaptation challenges and heightened levels of vulnerability of affected populations. Some studies report changes in diets, such as an increase in the consumption of wild plants or fruits (Osbahr et al 2010, in South Africa; Bola et al 2014, in Zimbabwe) or low quality famine foods (McKune and Silva 2013, in Nigeria).

Others find that households reduce their food consumption to cope with environmental stress, e.g. by eating less (Silvestri et al 2012, in Kenya), eating fewer meals per day or skipping meals (Hänke et al 2017 in Madagascar; Mubaya and Mafongoya 2017, in Zimbabwe; Pauline et al 2017, in Tanzania), or refraining from eating for a whole day (McKune and Silva 2013, in Nigeria). In some communities, households were forced to sell their assets and livestock to buy food (Chianu et al 2004, in Nigeria; Yaffa 2013, in Gambia) or relied on external support and food aid (Silvestri et al 2012, in Kenya; Ariti et al 2015, in Ethiopia).

Of all households considered in the studies, on average 7.8% reported to not have used any adaptation strategy. While this might reflect lower levels of exposure to hazards, it could also indicate the presence of adaption barriers. For the case of Ethiopia, Gebrehiwot and Van Der Veen (2013) show that lack of information on adaptation measures and insufficient access to finance are two important factors inhibiting adaptation to climate change (see also Tambo and Abdoulaye 2013, for Ghana, Gbetibouo et al 2010, for South Africa).

The availability of information and experiences influence whether and how people perceive environmental risks and how they respond to them. At the same time, adaptation requires financial and other resources, which might not be accessible to all households, e.g. because of insufficient access to agricultural inputs, technologies or credit markets. To better understand household responses to environmental changes, there is a need to consider the wider context in which they take decisions, including their perception of environmental hazards and potential resource constraints (Koubi et al 2016).

Besides agricultural strategies, migration is among the most common strategies reported by the interviewed households. Across all case studies, nearly one out of four households stated to have used some sort of migration as a means to deal with environmental change. As such, it constitutes an integral part of the adaptation options used by rural households in SSA. The in-situ diversification of income sources, which often goes hand in hand with migration (Wuepper et al 2018), was reported by 22.1% of households. In our classification, we treated in-situ diversification and migration as two separate categories to distinguish them in the analysis. Migration can serve the purpose to diversify income sources ex situ, which was also mentioned as an important motive for migration by several of the reviewed studies (Osbahr et al 2010, Dumenu and Obeng 2016).

Of the 17 case studies with very high levels of migration (mentioned by >50% of all households), 12 (70.6%) were conducted in the Sahel region. Typically, the migration patterns reported by households in these studies were internal within the same country or region. For example, vulnerable communities in Northern Ghana had a long history of migration to Southern Ghana, especially by young men, to engage in farm or off-farm wage labor (Antwi-Agyei et al 2014, Ngwese et al 2018). A similar pattern was observed in a study from Northern Burkina Faso, where household members primarily migrated to the South of the country where land was still available or to neighboring Ivory Coast to work on cocoa plantations (Barbier et al 2009). In Niger, households reported the neighboring countries of Libya and Nigeria as primary migration destinations (McKune and Silva 2013).

Seasonal migration for a restricted time was very common among the study populations. In Chad, Botswana, and Burkina Faso, for example, households were found to travel to resource-abundant areas for collective livestock grazing or fishing (Motsholapheko et al 2012, Okpara et al 2016), highlighting the importance of mobility for communities depending on herding and transhumance (see also section 4.3). In Kenya, Turkana herders frequently moved across borders, especially to Uganda, South Sudan, or Ethiopia, to access resources and markets (Opiyo et al 2015). Similar patterns of livestock-related seasonal mobility were reported for Ethiopia (Berhanu and Beyene 2015) and Zimbabwe (Mubaya and Mafongoya 2017).

While temporal forms of mobility were found to be common in the considered sample of studies, also more permanent forms of outmigration were used. In Burkina Faso, for example, the majority of study participants considered permanent migration to other parts of Burkina Faso or to Ghana/Ivory coast a viable strategy if climate change impacts worsen (Zampaligré et al 2014). New international destinations are also becoming more attractive for migration from the region. For example, international labor migration from Ethiopia and other Eastern African countries to the Arab states of the Persian Gulf is a growing trend (Mersha and Van Laerhoven 2016).

If migration is used, it is typically only individual household members who migrate and not the entire household (Smucker and Wisner 2008, Barbier et al 2009, Dumenu and Obeng 2016). In the sub-Saharan context, migrants are usually young men who migrate for economic reasons to find work or to engage in seasonal jobs. In search for better opportunities, many migrate internally toward urban centers (Clement et al 2021), which is also reflected in several of the case studies (e.g. Yaffa 2013, in Gambia; Dumenu and Obeng 2016, Kumasi et al 2019, in Ghana). This contributes to the rapid urban growth observed in SSA, which is the world region with some of the fastest growing cities worldwide (Hoffmann and Muttarak 2021).

Remittances play an important role for mobility in the area and migrants usually maintain close ties to their home regions. Even if migration in the considered studies is not explicitly framed as 'economic', it is often mentioned in association with job search or remittances (Snorek et al 2014, Yila and Resurreccion 2014, Ngwese et al 2018). This also highlights the multicausal nature of mobility, which is typically influenced not only by one, but a range of different economic, sociopolitical, demographic, and environmental factors (Black et al 2011a).

The relationship between environmental factors and migration is not uniform but depends on the local livelihood and ecological conditions (figure 4). While in-situ adaptation strategies, such as changes in cultivation practices or soil and water management, are most often reported in studies focusing on farmer populations, migration is clearly most common among (agro-)pastoralists. In those highly mobile communities, the role of environmental change processes has to be understood against the background of existing forms of mobility (e.g. transhumance), which are influenced by a range of factors (Boas et al 2019).

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These populations are also more likely to report diversification (38.8%) and changes in food consumption (37.3%) as ways to cope and adapt when confronted with environmental changes and hazards (figure 4(a)). In many instances, migration in search for better economic opportunities were also reported. In Ngamiland, Botswana, for instance, high percentages of respondents stated to migrate to towns for wage labor in the context of pastoral rangeland degradation (Basupi et al 2019). Similarly, a study from Kenya mentions out-migration of agro-pastoralists in search of paid work (Smucker and Wisner 2008). There might be several reasons for the higher prevalence of migration among (agro-)pastoralist households, which we discuss in greater detail in section 5.

In rare cases, households reported the reduction of mobility (4.2%) as a way to deal with an environmental hardship (McKune and Silva 2013). This strategy was again most commonly reported by pastoralist populations, who might have had to settle temporarily if they were unable to sustain their livelihood with a nomadic lifestyle. This is also exemplified by Basupi and colleagues (Basupi et al 2019), who report an increasing dependency of pastoralists on social welfare programs due to the high uncertainty of pastoral income (resulting i.a. from livestock diseases and restricted access to productive rangelands) and lacking alternative income sources.

Whether or not migration is used by communities in the drylands is found to also depend on the reported environmental changes (figure 4(b)). Most commonly, households listed migration as a strategy in the context of land degradation (28.4%), rainfall variability (27.8%), flooding (24.7%) and drought (23.8%). All of these stressors are directly linked to agricultural production and can be particularly destructive in dryland areas characterized by overall low precipitation and high temperatures (Falco et al 2019).

Typically, communities in the studies were exposed not only to one, but to multiple changes and hazards in their environments. Environmental impacts are not independent of each other, but are closely correlated and can co-occur (Hoffmann et al 2021). In additional analyses, we find that certain types of environmental stressors were often jointly reported by the case studies (supplementary table S4 and figure S4). For example, changes in rainfall amounts were often mentioned together with challenges arising from changing temperature levels, and drought hazards with changes in rainfall variability, land degradation, and flooding. Especially the co-occurrence of different stressors requires a combination of strategies to adapt to the changes (adaptation mix). With increasing levels of environmental stress, a threshold may be reached beyond which in-situ adaptation may no longer be sustainable for local populations (Warner et al 2012, Dow et al 2013, Xu et al 2020).

The individual adaptation strategies are rarely used in isolation. Instead, households use a combination of different strategies to adapt to environmental changes and hazards (figure 5). Interviewed communities in Botswana, Benin, South Africa, or Kenya reported a mix of eight or nine different strategies to deal with experienced environmental changes (Osbahr et al 2010, Opiyo et al 2015, Oyerinde et al 2015, Basupi et al 2019).

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In one community in Burkina Faso, for example, a large share of households used combinations of changes in cultivation practices, livestock, and soil and water management (Mertz et al 2012), whereas another community in Ghana more heavily relied on income diversification, social contacts, adjustments in food consumption and external aid in combination with migration (Antwi-Agyei et al 2014). Although the papers reviewed here indicate how many strategies were reported in total, they do not provide specific information on how many households actually applied several or all of them simultaneously. This still constitutes a missing piece of information to better understand the dynamics between different strategies, including migration (see also Wiederkehr et al 2018).

To understand households' responses to environmental stress, strategies have to be considered in a wider context of adaptation behaviors as these are closely linked to each other and correlated. In the literature, there is evidence that households tend to adopt several strategies simultaneously (Silvestri et al 2012, Mogotsi et al 2013, Hooli 2016, Tesfaye and Seifu 2016, Hermans and Garbe 2019). These strategies can either complement or substitute each other.

Figure 5(a) shows the correlation of the share of households using different adaptation strategies across the sample of studies. Some of the strategies are highly positively correlated and seem to complement each other. For example, changes in crop management are often named in combination with soil and water conservation measures (but not livestock), and the search for social support is often named together with the diversification of income sources, changes in food consumption and provision, and external aid.

In our explorative analysis, strategies are found to be in most cases either uncorrelated or positively correlated suggesting positive complementarities. Likewise, based on data from surveyed households in Ghana, Tambo (2016) states that the majority of analyzed strategies act as complements, not substitutes. For Kenya, Tongruksawattana and Wainaina (2019) identified farm adjustments as a substitution strategy for selling assets and borrowing for drought adaptation, whereas a complementary reduction in consumption was observed for households who were forced to borrow money. Eriksen et al (2005) conclude that during periods of environmental distress households usually adopt one principal adaptation strategy that is complemented by other secondary measures.

Migration, like the other strategies, is only one of many potential responses to environmental stress and has to be analyzed against the background of other strategies and activities of the households (McLeman and Smit 2006, Perch-Nielsen et al 2008, Piguet 2010). Figure 5(b) shows the average percentage of households mentioning migration conditional on whether other strategies were used simultaneously by the households. Migration is commonly reported with other strategies, such as the search for social support, relying on humanitarian aid, changes in food consumption and provision, search for information, the diversification of income sources, and livestock management. On the other hand, we do not find a strong correlation between migration and the use of other more sedentary forms of agricultural adaptation, such as changes in cultivation and soil and water management. This also mirrors our findings on livelihood-specific differences in adaptation choices addressed in section 4.3, with migration being more common among (agro-)pastoralist households.

Focusing on the nine most common adaptation strategies, we conducted a cluster analysis to identify clusters of study populations using similar strategies when confronted with environmental change and hazards (figure 6). For this, we follow a stepwise procedure to determine the optimal number of clusters and explore the resulting cluster assignment (supplementary materials E). Our cluster analysis reveals five distinct adaptation clusters from a total of 89 cases. The analysis allows us to explore cases with a similar adaptation profile and to reveal the inter-connectedness of different strategies employed in the surveyed communities. It also illustrates patterns and differences in adaptation across the studied settings.

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Figure 6(a) shows the distribution of adaptation strategies across the clusters with higher values representing a greater share of households in a cluster using a strategy. Figure 6(b) shows the corresponding narrative and size of each cluster and figure 6(c) combines information about the location of the clusters with background information about the local socioeconomic conditions to explore under which conditions certain clusters are most likely to occur. Here, we consider the average percentage of the population in a cluster with at least secondary education, a finished floor, access to electricity, and an agricultural occupation. The latter measure accounts for both the share of farmers and (agro-) pastoralists in the population, which are not distinguished in the DHS.

Three of the considered clusters (1–3) are migration clusters with an increased share (median share > 25%) of households using migration as an adaptation strategy. The geographic location of the migration clusters is shown in figure 7. As can be seen from this figure, clusters with high levels of migration (filled symbols) can be primarily found in the western and central Sahel region as well as in selected locations in East and Southern Africa. While all three migration clusters can be characterized by increased levels of human mobility, they are distinct in their use of complementary strategies. A form of adaptation that is closely related to migration are activities related to the diversification of income sources. For all migration clusters, increased levels for this strategy can be observed, indicating a complementary relationship between both strategies.

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The important role of context and socioeconomic influences is apparent in all results. Whereas migration is found in both areas with a higher and lower level of development, differences are visible in the combination of strategies used. In particular, high levels of poverty, limited access to education and information, lacking social protection, inequality and uneven development processes, weak institutions, and limited access to financial, labor, and agricultural markets have been identified in the case studies as major inhibitors to achieving climate resilience at large (Ng'ang'a et al 2016, Pauline et al 2017).

The first 'Mixed Strategies' cluster (n = 20 case study populations) uses migration in combination with a wide variety of other strategies, ranging from attempts to diversify income sources, to search for external support via social networks or aid organizations, or to engage in other adaptation activities. Also changes in food consumption, e.g. changes in diets or reductions of food intake, are reported by a considerable share of households in this cluster (median > 25%), which we consider as a sign of populations facing challenges in their adaptation, potentially contributing to increased vulnerability. Indeed, considering the socioeconomic profile of this cluster, it is most commonly found in more deprived areas where only a very low percentage of the population has access to basic infrastructures, such as education and electricity, and where livelihoods strongly depend on agricultural occupations.

The second 'Agriculture & Migration' cluster (n = 17) uses migration in combination with different agricultural strategies, both relating to changes in cultivation, soil and water management, and livestock keeping. Also attempts to diversify income sources play a role, but to a lesser extent. Unlike the first cluster, this cluster is characterized by a relatively higher socioeconomic status with on average more than 50% of populations in the regions having completed secondary education, having access to electricity, and having a finished floor. Also, only a comparably low share of the communities in this cluster directly depend on agriculture, suggesting greater opportunities to diversify income sources to adapt to environmental change. In this context, migration is often described as a complementary strategy used to support the household income, including mobility in search of wage labor (Smucker and Wisner 2008 in Kenya), seasonal mobility (Rasmussen 2018 in Burkina Faso), and herding-related mobility (Berhanu and Beyene 2015, Ng'ang'a et al 2016, in Ethiopia).

The third 'Mainly Migration' (n = 7) cluster represents populations where a large share of households report using primarily migration as a form of adaptation. While in some communities also agricultural strategies are employed, the majority of communities in this cluster do not use any of these strategies (median = 0). The only notable exception reported by the households are strategies related to food and nutrition. On average, more than 60% of households indicated that they changed their diets or made additional efforts to obtain food to react to environmental changes, which might reflect limited in-situ adaptive capacities and increased levels of stress. Like the first cluster, this cluster is characterized by a weaker socioeconomic profile, suggesting a close relationship between human development, vulnerability, and adaptation. Unlike the second cluster, agricultural dependence is high and migration and changing food consumption seem to serve as strategies of last resort here. While this observed pattern can be found in only few of the considered study populations, it hints toward a reduced ability of certain populations to withstand environmental pressures and a lack of viable adaptation options, making migration the only suitable strategy to deal with environmental hardships. While migration is described as an adaptation mechanism by study populations, it can also come with significant long-term repercussions for migrants and their families, for example negative health impacts (Yaffa 2013, in Gambia) or increased divorce rates (McKune and Silva 2013, in Niger).

The fourth 'Mainly Agriculture' (n = 35) cluster is the biggest of all clusters. Households in this group primarily rely on in-situ agricultural adaptation strategies, such as changes in cultivation and soil and water management. Migration, on the other hand, is not found to play a major role. The focus on agriculture in adaptation is also reflected in the background variables, which reveal that regions belonging to this cluster are characterized by a high agricultural dependence with on average more than 50% of the populations having an agricultural occupation. In general, the cluster is characterized by a weaker socioeconomic profile with less than 30% of the populations having completed secondary education and having access to electricity. Most commonly, households in this cluster use a combination of crop and soil and water management techniques to adapt to environmental changes. For example, households in a study in Burkina Faso (Mertz et al 2012) were found to use a combination of manure application and fertilization methods together with soil and water conservation, reforestation, improved irrigation, and vegetation protection (e.g. stone bunds, fallow periods) methods.

The final 'No Adaptation' cluster (n = 10) is a cluster where only very few households make use of the considered adaptation strategies. Communities belonging to this cluster are characterized by a relatively high level of education (>50% with secondary), comparably good access to electricity (>40%), and well-developed housing structures (>70% with finished floor). While all case studies included in this cluster were primarily focused on crop and livestock farmers, the considered communities were located in regions with an overall low agricultural dependence. All of the above suggests a relatively low vulnerability to environmental stressors and, hence, a reduced need to take action in terms of coping and adaptation. In addition, the environmental change processes reported in these studies were mostly related to perceived gradual changes in temperature and rainfall patterns, which were addressed by farmers—if at all—by using in-situ adaptation responses, such as modifications in farming practices (e.g. Gbetibouo et al 2010, in South Africa; Tambo and Abdoulaye 2013, in Nigeria). Migration as an adaptation strategy was rarely reported. While again only few of the considered communities show this adaptation pattern, it is worthwhile to emphasize that there are also communities in the surveyed sub-Saharan drylands that have no need to adapt to environmental changes, either because of a limited exposure to stressors or reduced susceptibility to their impacts.

The different combinations of strategies used by the five clusters shows the diversity of adaptation approaches in sub-Saharan drylands. Despite this diversity between clusters, similarities can be found for communities belonging to the same cluster. Common adaptation patterns exist, where households are found to employ a combination of certain complementary strategies to adapt to environmental changes. Our indicative findings suggest that these patterns vary by the socioeconomic conditions faced by the populations and possibly also the nature of the environmental change experienced. While a more rigorous analysis of contextual influences is challenging here given the limited scope of our data, we believe that a more careful exploration of these impacts and their role for the evolution of adaptation patterns would be a fruitful direction for future research.