Climate and Development
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tcld20
Differential household vulnerability to climatic and
non-climatic stressors in semi-arid areas of Mali,
West Africa
Alcade C. Segnon, Edmond Totin, Robert B. Zougmoré, Jourdain C. Lokossou,
Mary Thompson-Hall, Benjamin O. Ofori, Enoch G. Achigan-Dako &
Christopher Gordon
To cite this article: Alcade C. Segnon, Edmond Totin, Robert B. Zougmoré, Jourdain C. Lokossou,
Mary Thompson-Hall, Benjamin O. Ofori, Enoch G. Achigan-Dako & Christopher Gordon (2021)
Differential household vulnerability to climatic and non-climatic stressors in semi-arid areas of Mali,
West Africa, Climate and Development, 13:8, 697-712, DOI: 10.1080/17565529.2020.1855097
To link to this article:  https://doi.org/10.1080/17565529.2020.1855097
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CLIMATE AND DEVELOPMENT
2021, VOL. 13, NO. 8, 697–712
https://doi.org/10.1080/17565529.2020.1855097
RESEARCH ARTICLE
Differential household vulnerability to climatic and non-climatic stressors in semi-
arid areas of Mali, West Africa
Alcade C. Segnon a,b,c,d, Edmond Totin e, Robert B. Zougmoré a,b, Jourdain C. Lokossou b, Mary Thompson-
Hallf, Benjamin O. Oforid, Enoch G. Achigan-Dako c and Christopher Gordon d
aCGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), International Crops Research Institute for the Semi-Arid Tropics
(ICRISAT), Bamako, Mali; bInternational Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Bamako, Mali; cFaculty of Agronomic Sciences,
University of Abomey-Calavi, Cotonou, Republic of Benin; dInstitute for Environment and Sanitation Studies, University of Ghana, Accra, Ghana;
eUniversité Nationale d’Agriculture du Bénin, Kétou, Republic of Benin; fInternational START Secretariat, Washington, DC, USA
ABSTRACT ARTICLE HISTORY
Semi-Arid Regions (SARs) of West Africa are considered climate change “hotspots” where strong Received 7 December 2019
ecological, economic and social impacts converge to make socio-ecological systems particularly Accepted 17 November 2020
vulnerable. While both climatic and non-climatic drivers interact across scales to influence
vulnerability, traditionally, this inter-connectedness has received little attention in vulnerability KEYWORDSvulnerability patterns;
assessments in the region. This study adopted the vulnerability patterns framework, operationalized archetype analysis;
using the Multidimensional Livelihood Vulnerability approach to include both climatic and non- vulnerability assessment;
climatic stressors to analyze differential household vulnerability in SARs of Mali. Findings showed that multiple stressors;
while drought was the most mentioned climate-related stressor, households were also exposed to a heterogeneity; adaptive
diversity of environmental and socio-economic stressors, including food scarcity, livestock disease, capacity; semi-arid regions
labour unavailability, crop damage, and erratic rainfall patterns. The typology revealed three
vulnerability archetypes differentiated by adaptive capacity and sensitivity. Availability of productive
household members, household resource endowments, livelihood diversification and social networks
were the main discriminant factors of household adaptive capacity, while challenges relating to food
and water security make households more sensitive to stressors. The analysis highlighted the
heterogeneity in household vulnerability patterns within and across communities. Failing to account
for this heterogeneity in adaptation planning might result in a mismatch between adaptation needs
and interventions, and potentially in maladaptation.
1. Introduction considered in the global arena (Bathiany et al., 2018; Diffen-
baugh & Giorgi, 2012; Turco et al., 2015). This underscores
In Semi-Arid Regions (SARs), global warming is particularly the urgency of concerted efforts to reduce vulnerability and
enhanced, with local temperatures increasing faster than the enhance the resilience of communities and ecosystems to
global average (Huang et al., 2017). A global warming of 2°C increasing and potentially irreversible climatic challenges.
rather than 1.5°C would mean a warming of 3.2–4.0°C over Effectively achieving the aims of these efforts will require a bet-
semi-arid drylands, with disastrous impacts including ter understanding of who, why and how human systems are
decreased crop yields and runoff, increases of more severe vulnerable to inform effective adaptation planning.
and longer drought, andmore favourable conditions formalaria There has been extensive empirical research conducted over
transmission (Hoegh-Guldberg et al., 2018; Huang et al., 2017). the last few decades exploring who, where, how, and why
The situation in SARs of West Africa presents significant human systems are vulnerable to the changing climate (Ford
and urgent challenges that call for rapid attention. These et al., 2018; McDowell et al., 2016; Räsänen et al., 2016; Ton-
areas are not only climate change exposure hotspots (Bathiany moy et al., 2014; Williams et al., 2018). Vulnerability assess-
et al., 2018; de Sherbinin, 2014; Turco et al., 2015), but also ment has gained importance for policy purposes and become
hotspots of climate change impacts because of the unique bio- a necessity for informing policy and decision making (Hinkel,
logical, environmental and socio-economic attributes of the 2011; McDowell et al., 2016; Tonmoy et al., 2014). In sub-
region (de Sherbinin, 2014; Diffenbaugh & Giorgi, 2012; Mül- Saharan Africa, the number of vulnerability assessments has
ler et al., 2014). These increased climate change impacts will be increased over time, with the agricultural sector being the
persistent over time, independent of emissions pathways most common focus (McDowell et al., 2016; Williams et al.,
CONTACT Alcade C. Segnon alcadese@gmail.com CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), International Crops
Research Institute for the Semi-Arid Tropics (ICRISAT), BP 320 Bamako, Mali; International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), BP 320 Bamako,
Mali; Faculty of Agronomic Sciences, University of Abomey-Calavi, 01 BP 526 Cotonou, Republic of Benin; Institute for Environment and Sanitation Studies, University of
Ghana, P.O. Box LG 209, Accra, Ghana
Supplemental data for this article can be accessed https://doi.org/10.1080/17565529.2020.1855097
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tribution, and reproduction in any medium, provided the original work is properly cited.
698 A. C. SEGNON ET AL.
2018). Recognition that early investigations privileged climatic (Kok et al., 2016; Sietz et al., 2011) to construct household vul-
factors over social ones has, over time, pushed vulnerability nerability archetypes and explore intra-community heterogen-
research beyond a sole focus on climate in vulnerability assess- eity and diversity in household vulnerability patterns. We
ment, with vulnerability now being conceived as a condition or hypothesized that households within community are not hom-
state embedded in socio-economic processes (Bohle et al., ogenous in terms of their vulnerability to climatic and non-cli-
1994; Cardona, 2004; Chambers, 1989; Ford et al., 2018; Räsä- matic stressors and using a pattern or archetype analysis will
nen et al., 2016). In SARs of West Africa, vulnerability of local reveal the differential vulnerability.
communities is driven not only by climatic factors but also by a
diversity of biophysical, socio-economic, cultural and political
factors (Antwi-Agyei et al., 2017; Nyantakyi-Frimpong & Bez- 2. Conceptual framework
ner-Kerr, 2015; Ouédraogo et al., 2016). The most common
climatic stressors in the region include droughts, erratic rain- Rooted in natural hazard and poverty studies, the concept of
fall, floods, and winds (Antwi-Agyei et al., 2017; Epule et al., vulnerability has gradually entered into research on adaptation
2018; Nyantakyi-Frimpong & Bezner-Kerr, 2015). Non-cli- of people to socio-ecological changes (Adger, 2006; Cutter,
matic biophysical and socio-economic stressors frequently 1996; Füssel, 2007). Although there are diverse interpretations
reported include lack of money, high cost of farm inputs, cattle of vulnerability, according to different fields of study and tra-
destruction of crops, livestock pest, limited access to markets ditions, vulnerability is most often conceptualized as the sus-
and lack of agricultural equipment (Antwi-Agyei et al., 2017; ceptibility of a system to perturbations or stresses shaped by
Nyantakyi-Frimpong & Bezner-Kerr, 2015; Segnon, 2019). its exposure and sensitivity to perturbations or stresses, and
Inequality in decision making processes and access to the capacity to adapt (Adger, 2006; Füssel, 2007; Nelson et al.,
resources resulting from patriarchal local culture and insti- 2007; Smit &Wandel, 2006; Tonmoy et al., 2014). Increasingly,
tutions also shape the differential vulnerability of social groups vulnerability is conceptualized as a condition, including
(Ahmed et al., 2016; Antwi-Agyei et al., 2017). characteristics of exposure, sensitivity, and adaptive capacity,
While climatic stressors and accompanying risks are a domi- determined by social, economic, physical and environmental
nant driver of vulnerability in SARs ofWest Africa (Epule et al., factors or processes, which increase the susceptibility of a sys-
2018; Gautier et al., 2016), complex interconnections between tem to the impact of hazards, rather than as a direct outcome
multiple climatic and non-climatic stressors, operating across of a perturbation or stress (Adger, 2006; Füssel & Klein, 2006;
different scales, are also important influences (Antwi-Agyei Miller et al., 2010; O’Brien et al., 2007). Exposure is the nature
et al., 2017; Nyantakyi-Frimpong & Bezner-Kerr, 2015; Oué- and degree to which a system experiences environmental or
draogo et al., 2016; Räsänen et al., 2016). While there are socio-political stress (Adger, 2006; Füssel & Klein, 2006). Sen-
increasing studies pointing at the multiple stressors (Antwi- sitivity is the degree to which a system is modified or affected,
Agyei et al., 2017; Nyantakyi-Frimpong & Bezner-Kerr, 2015; either adversely or beneficially, by perturbations, such as cli-
Räsänen et al., 2016) driving differential vulnerability, the mate-related stress (Adger, 2006; Füssel & Klein, 2006). Adap-
way in which these multiple climatic and non-climatic stressors tive capacity is “the ability of a system to evolve in order to
interact to shape vulnerability is not well documented in this accommodate environmental hazards or policy change and to
region. Furthermore, conventional vulnerability assessments expand the range of variability with which it can cope”
based on indicators typically use aggregation methods to com- (Adger, 2006; Brooks et al., 2005; Engle, 2011; Smit & Wandel,
pute a composite index at community level (Hahn et al., 2009; 2006). It represents the preconditions necessary to enable adap-
Hinkel, 2011; Tonmoy et al., 2014; Vincent & Cull, 2014), an tation, including physical and social resources, and the ability
approach that often fails at capturing intra-community hetero- to mobilize these resources to respond to perceived or current
geneity and diversity. Indicator-based vulnerability assessment stresses (Brooks et al., 2005; Engle, 2011; Nelson et al., 2007).
and mapping approaches are most often conducted at commu- Measuring vulnerability is notoriously challenging as assess-
nity, district or national scale, missing crucial details about ments suppose theoretically specified connections or linkages
variation at household level (Antwi-Agyei et al., 2017). Finding between often non-observable elements (Crane et al., 2017).
ways to capture these intra-community dynamics is crucial for These non-observable elements are assumed to constitute vul-
effective targeting of adaptation interventions and adaptation nerability, supported by theoretical models that are then
planning. Capturing intra- or within community heterogeneity measured through sets of proxy indicators (Crane et al., 2017;
could also be vital to enhancing the relevance of vulnerability Hinkel, 2011; Tonmoy et al., 2014; Vincent &Cull, 2014). Asses-
assessments (Antwi-Agyei et al., 2017). sing vulnerability entails the operationalization of the vulner-
To fill this gap, this study analysed the diversity of climatic ability frameworks selected (Crane et al., 2017; Hinkel, 2011).
and non-climatic stressors facing smallholder farmers in Kou- Indicators are commonly used to operationalize vulnerability,
tiala, a SAR of Mali, and assessed household vulnerability to either deductively, based on theoretical frameworks, or induc-
both climatic and non-climatic stressors. Specifically, the fol- tively based on data used to build statistical models, or using a
lowing questions were addressed: (i) what are the climatic normative approach based on (individual or collective) value
and non-climatic stressors driving household vulnerability?, judgments (Crane et al., 2017; Hinkel, 2011; Tonmoy et al.,
(ii) what additional factors shape household vulnerability?, 2014; Vincent & Cull, 2014). Indicator-based vulnerability
(iii) are there patterns in household vulnerability?, and how assessments have been widely applied because they provide
similar are households in terms of vulnerability patterns?. opportunities to incorporate socio-economic and biophysical
The study adopted the Vulnerability Patterns framework components of vulnerability and are relatively easy to perform
CLIMATE AND DEVELOPMENT 699
and simple to communicate to the policymakers and the general or unit of analysis exposed or more sensitive to climate change
public (Tonmoy et al., 2014; Vincent & Cull, 2014). impacts and influence their ability to cope with socio-ecological
This study adopted the Vulnerability Patterns framework change (Hahn et al., 2009; Reed et al., 2013).
(Kok et al., 2016; Sietz et al., 2011) to explore the diversity in The MLVI modifies and extends the initial seven com-
household vulnerability patterns in SARs of Mali. The Vulner- ponents of the LVI, resulting in twelve components so that it
ability Patterns framework is similar to the IPCC framework, addresses factors that are relevant to the authors’ specific con-
but provides a considerable elaboration of adaptive capacity, text (Figure 1) (Gerlitz et al., 2017). As in the initial LVI, each
specifically on coping capacity to adjust to climate-related component is composed of several measurable and specific
risks, manage loss and damage or explore alternative opportu- vulnerability indicators (Gerlitz et al., 2017; Hahn et al., 2009).
nities (Crane et al., 2017). This framework applies the cluster
analysis methodology as a way to deliver useful insights into
recurrent combinations of measurements based on similarities 3. Materials and methods
among systems of analysis, in cases where such a clustering
3.1. Study area
exists (Crane et al., 2017; Kok et al., 2016; Sietz et al., 2011).
The method helps identify specific constellations or groups of This study was undertaken as part of a broader research pro-
indicator values with recognizable patterns (Kok et al., 2016). gramme “Adaptation at Scale in Semi-Arid Regions of Africa
To operationalize the framework, we adopted and modified and Asia” (ASSAR). ASSAR’s overarching research objective
the Multidimensional Livelihood Vulnerability Index (MLVI), is to use insights from multiple-scale, interdisciplinary work
developed by Gerlitz et al. (2017). The MLVI assesses multidi- to improve the understanding of the barriers, enablers and
mensional livelihood vulnerability to environmental (including limits to effective, sustained and widespread adaptation to cli-
climatic) and socio-economic changes with household as unit of matic and non-climatic risks in semi-arid regions of Asia and
analysis (Gerlitz et al., 2017). The MLVI is an expansion and Africa. The study was carried out in the Cercle of Koutiala, a
modification of the Livelihood Vulnerability Index (LVI) semi-arid area in southeastern Mali (Figure 2). The climate
(Hahn et al., 2009) using multidimensional index construction is typical of the Sudano-Sahelian region, with annual rainfall
approach (Gerlitz et al., 2017). Developed based on the Sustain- ranging from 400 to 800 mm, with high inter-annual and
able Livelihood (SL) framework, the LVI offers a pragmatic and intra-seasonal variability. The rainy season starts in June and
flexible tool for vulnerability assessment and has been widely ends in October with rainfall peaking in August. The dry sea-
used in the literature (Gerlitz et al., 2017; Hahn et al., 2009). son is made up of a relatively cold period (November to Feb-
The SL framework has been widely used as a theoretical basis ruary) and a hot period (March to May). Daily average
for deductively selecting indicators for vulnerability assessment. temperatures range between 22°C (during cold period of
The SL framework relies on the understanding of how people November to February) and 35°C (hot period of March to
access the social, human, financial, natural and physical capital June), with an average maximum temperature of 34°C during
assets (Reed et al., 2013) and has been shown to be relevant for the rainy season and 40°C during the hot dry season. The natu-
assessing the capacity of households to withstand various socio- ral vegetation is a tree and shrub savannah with an understory
ecological shocks (Hahn et al., 2009). The SL framework is par- of annual and perennial grasses in a complex mosaic. Soils are
ticularly pertinent to explore vulnerability to climate-related poor, of sandy or sand–loamy texture and often gravelly.
shocks since it offers a space for analysing not only key com- Koutiala belongs to the administrative region of Sikasso, and
ponents that make up livelihoods but also contextual factors is 140 km north of Sikasso town. It covers an area of 8740 km2
that affect livelihoods (Reed et al., 2013). These components (about 12.17% of the total area of Sikasso region and 0.7% of
are also intimately related to the elements that make a system the country’s total area). According to the latest population
Figure 1. Conceptual framework of the study.
700 A. C. SEGNON ET AL.
Figure 2. Location of the study area.
census of 2009, Koutiala is home to 575,253 people, with an et al., 2016; Sanogo et al., 2015). Rather than a rainfall recov-
average density of 52.32 inhabitants per km2. Sikasso region ery, the SARs of West Africa is experiencing a new era of cli-
is known to be Mali’s breadbasket and supplies a considerable mate extremes (Biasutti, 2019; Bichet & Diedhiou, 2018;
amount of food to the rest of the country as well as incomes Panthou et al., 2018). An analysis of rainfall data from Kou-
through cotton cultivation. Koutiala, in particular, is con- tiala weather station confirmed the strong spatio-temporal
sidered as “white gold” district because of the significant contri- variability in rainfall patterns than a clear significant change
bution of cotton production to agricultural systems and food (Segnon, 2019).
production (Laris et al., 2015; Sidibé et al., 2018). Based on The new rainfall conditions termed “hybrid rainy seasons”
the latest population census, cotton production occupies 90% induced by global warming and characterized by false start and
of Koutiala population (Laris et al., 2015; Laris & Foltz, 2014; early cessation of rainy seasons and increased frequency of
Sidibé et al., 2018). In addition to cotton, Koutiala is also an intense daily rainfall (Salack et al., 2015; Salack et al., 2016)
important maize production area and rank second in terms have serious implications for agricultural production, which
of maize production in Sikasso region, contribution to about is mainly rain-fed in the SARs of West Africa (Gautier et al.,
14% (56,714 t) to the total maize output (Diallo et al., 2020). 2016; Salack et al., 2015; Sultan et al., 2013; Sultan et al.,
While a diversity of crops is cultivated, cotton, maize, sorghum, 2014; Sultan & Gaetani, 2016; You et al., 2011). In the Sahel,
pearl millet and groundnut are the main crops in Koutiala (Seg- variability and changes in temperature and precipitation
non, 2019). After cotton and cereal cultivations, livestock pro- have led to a significant decline in tree density and species rich-
duction is the second main livelihood activity in Koutiala. ness (Gonzalez et al., 2012). It is very likely that services and
Over the last 50 years, a gradual and spatially variable functions derived from tree-based ecosystems were also
increase in the annual mean temperature has been observed affected/reduced. Changes in the number of rainy days and
in West African region (Daron, 2014; Riede et al., 2016; Sylla, the timing of the rainy season negatively affected vegetation
Nikiema, et al., 2016), with the highest (0.6–3.0°C) tempera- growth in the semi-arid Sahel (Zhang et al., 2018). Climate
ture increases observed in northern parts of the region, par- change and variability also led to a southward shift of the cli-
ticularly in Mali (Daron, 2014). This increase has been faster matic zones inWest Africa (Gonzalez et al., 2012), with serious
in SARs of the region and faster than global warming (Klutse consequences for agricultural output and human livelihoods
et al., 2018; Sarr, 2012). In Koutiala, minimum temperature (Sissoko et al., 2011). In addition, the strong spatio-temporal
has been increasing faster than maximum temperature (Seg- variability in rainfall patterns has far-reaching impacts on
non, 2019). There has been a substantial spatial and temporal water quality and availability, on crop yield and production,
(multi-decadal, annual and seasonal) variability in rainfall and thus on food security in the region (Ben Mohamed,
patterns over the past 50 years in the West Africa region, 2011; Gautier et al., 2016; Sissoko et al., 2011; Sultan & Gae-
especially in the SARs, with a very dry period in the 70s tani, 2016; Zougmoré et al., 2016). The late twenty-first century
and 80s (Biasutti, 2019; Daron, 2014; Nicholson, 2013; projections reveal an extension of torrid, arid and semi-arid
Riede et al., 2016; Sanogo et al., 2015; Sarr, 2012). After climate regime throughout West Africa, with the recession of
the severe drought periods, rainfall seems to be recovering, moist and wet zones (Sylla et al., 2016). The current Sahel,
but this recovery is characterized by new rainfall features mainly semi-arid in present-day conditions, is expected to
including false start and early cessation of rainy seasons, face a moderately persistent future arid climate (Sylla et al.,
increased frequency of rainy days along with increased pre- 2016). In Mali, projected cereal crops yield reductions are
cipitation intensity, more frequent dry spells, increasing expected to reduce food availability and food self-sufficiency,
number of hot nights and warm days (Biasutti, 2019; Salack especially of smallholder farmers, who are already food
CLIMATE AND DEVELOPMENT 701
Table 1. Vulnerability dimensions, components, and indicators.
Dimensions Components Indicators Description
Adaptive Socio-demographic Education of household head Educational attainment of household head
capacity status Dependency ratio Ratio of No. of household member under 15 and over 65 years of age to
household member between 19 and 64 years of age
Household productive members No. of household member under 15 and over 65 years of age
Livelihood Agricultural livelihood diversity No. of primary livelihood strategies
strategies Non-agricultural livelihood diversity No. of secondary or tertiary livelihood strategies
Total livelihood diversity No. of all livelihood strategies
Resources Agricultural land Household total farmland size (ha)
Livestock (TLU) No. of livestock (TLU – Tropical Livestock Unit)
Diversity of livestock types Number of livestock types
Agricultural equipment (plough, cart, seed No. agricultural equipment (plough, cart, seed drill, sprayer, draught animal,
drill, sprayer, draught animal, donkey) donkey) owned by the household
Social networks CBOs membership Does the household belong to a community-based group (Yes/No)?
Diversity of CBOs membership No. of CBOs in which household belong to
Political access How easy is it to access political powers in the community (5-point Likert scale
response from 1 – Not easy all to 5 – Very easy)
Physical Road practicability Is the road leading from household to feeder or tarred roads practicable all
accessibility year-round (1 – Not practicable, 2 – Fairly practicable, 3 – Practicable all year-
round)
Market orientation Does household sell part or entire production (Yes/No)
Sensitivity Food security Source of household food Where does the household get most of its food (1 – Own production only; 2 –
Own production [2/3] & purchase [1/3]; 3 – Own production [1/3] & purchase
[2/3])
Food self-sufficiency Food self-sufficiency
Number of month with insufficient food No. of month household struggles to get sufficient food to cover its needs
Crop diversity No. of crops grown by household
Health & Sanitation Drinking water quality Does household use a source that, by nature of its construction, adequately
protects the water from outside contamination, in particular from faecal
matter
Toilet facility quality Does household use sanitation facilities that hygienically separate human
excreta from human contact
Illness (Health case number) No. health case in household
Household member with chronic illness No. household member with chronic illness
Average time to closest health facility Average time to closest health facility
Availability of health facility in the village Availability of health facility in the village
Water security Diversity of water source Number of water source used by household
Water sufficiency Water sufficiency
Number of month with insufficient water No. of month household struggles to get sufficient water to cover its needs
Exposure Environmental Environmental shocks experienced over the Environmental shocks experienced over the past 12 months
shocks past 12 months
Socio-economic Socio-economic shocks experienced over Socio-economic shocks experienced over the past 12 months
shocks the past 12 months
insecure (Butt et al., 2005; Traore et al., 2017). In Sikasso where s is the required sample size, i.e. the number of farm
region, southern Mali, white (Irish) potatoes, a key cash crop household to be surveyed; X2 is the table value of chi-square
for households, will be the most affected by changing climatic for one degree of freedom at the confidence level fixed at 95%;
conditions by 2060, with yields decreasing, under both dry and N is the population size, i.e. the total population of household
wet conditions, by almost 25% (Ebi et al., 2011). in Koutiala based on the latest population census; p is the popu-
lation proportion, i.e. the proportion of farm household. In
3.2. Data collection Sikasso region, where the study area is located, about 60% of
households are farm households; d is the degree of accuracy
To assess household vulnerability to climatic and non-climatic and fixed at 0.05 (5%). The sample size following the above for-
stressors, a household survey was designed and conducted in mula is 368 households. Oversampling was done to increase the
the 10 villages selected within a watershed in Koutiala (see sample to 501 households. Roughly 50 households were ran-
Figure 2). The communities were randomly selected from a domly selected in each community. A household was defined
list of communities located within the watershed. The selected as “a group of people living in the same dwelling space who
communities were Danzana, Dentiola, M’Pessoba (in M’Pes- have at least one common plot together or one income-generat-
soba district), Fonfona (Tao district), Oudiala, Zansoni (in ing activity together (for example, herding, business, or fishing)
Fakolo-Kou district), N’Tossoni, Diéla (in N’Tossoni district), and who acknowledge the authority of a man or woman who is
Ntiesso (in Koutiala district), and Zangorola (in N’Golonia- the head of household” (Beaman & Dillon, 2012).
nasso district). The household survey sample size was deter- The MLVI framework (vulnerability as a function of
mined following Krejcie and Morgan (1970): exposure, sensitivity, and adaptive capacity) (Gerlitz et al.,
= X
2Np(1− p) 2017) was adopted and modified to suit the context of the
s
2 − + 2 − semi-arid study area to assess household vulnerability to cli-d (N 1) X p(1 p)
matic and non-climatic factors. The MLVI is composed of
702 A. C. SEGNON ET AL.
12 components which address factors that are relevant to square test. All the statistical analyses were performed in R stat-
authors’ specific context (Gerlitz et al., 2017). In this study, istical environment (R Core Team, 2019).
components that were not relevant to the study area context
(e.g. environmental stability was linked to the mountain spe-
cificities of the Hindu Kush Himalayas region where the 4. Results
MLVI was developed) were removed, resulting in 10 com- 4.1. Household socio-economic profiles
ponents (Table 1 and Figure 1). Each component is operatio-
nalized by a number of measurable and specific indicators Over 98% of households interviewed were headed by a male.
(Gerlitz et al., 2017). A preliminary list of indicators was ident- Household head ages ranged from 19 to 84 years, with mean
ified based on a review of literature in the SARs of West Africa age of 48.33 (± 12.28) years. More than 86% of the households
(Achigan-Dako et al., 2013; Segnon, 2019). The list of indi- interviewed belonged to Minianka ethnic groups, which is the
cators was revised and refined during participatory workshops dominant sociolinguistic group in the study area. The rest of
with local stakeholders, including communities and local households interviewed were either Peulh (about 4%) or belonged
experts in the study area (Table 1). The indicators were docu- to other groups (6%) which were Bambara, Sonike and Senoufo.
mented through the household survey using a semi-structured About 34% of household heads had no formal education,
questionnaire. The survey questionnaire was pre-tested with while about 21% and 5% of them attained primary and second-
20 households (within Koutiala cercle, but not in study vil- ary educations respectively. About 11% of the household heads
lages; these households did not participate in this research) attained Islamic or Koranic education and 28% had been
to ensure that the questions were understandable. trained to read and write in a local language. Islam is the domi-
nant religion in the study area and was practiced by more than
95% of the households interviewed. On average, households
3.3. Data analysis interviewed had about nine productive members (number of
The vulnerability pattern approach (Kok et al., 2016; Sietz et al., household members between 19 and 64 years of age), but
2011) was adopted in analysing household vulnerability pat- this varied largely (from 1 to 58 productive members). Depen-
terns. To identify and analyse household vulnerability patterns, dency ratio (number of household members under 15 and over
typologies or archetypes of vulnerability were constructed 65 years of age to number of household members between 19
using Factor Analysis of Mixed Data (FAMD) (Pagès, 2014) and 64 years of age) ranged from 0.17 to 6.5, with an average of
combined with a Hierarchical Cluster Analysis using the pack- 1.42. More than 63% of the households had a dependency ratio
age FactoMineR (Lê et al., 2008). FAMD is similar to Principal greater than one and 39% of them had a value greater or equal
Component Analysis but allowed to simultaneously quantify to 1.5. A summary of the descriptive statistics of household
categorical and quantitative variables while reducing the socio-economic and demographic characteristics is presented
dimensionality of the data (Akohoue et al., 2018; Pagès, in Supplementary Material Table S1.
2014). Since we have both categorical and quantitative indi- Farming was the main livelihood activity of all the surveyed
cators in our dataset, we used FAMD. To identify the relevant households. Figure 3 illustrates the diversity and importance of
number of components to retain, the eigenvalues of the com- cultivated crops in the study area. Cultivated crops per house-
ponents were analysed and components with eigenvalue hold ranged from two to 22, with an average of 10.42 ± 3.65. In
greater than one were kept (Abdi & Williams, 2010). total, 25 different crops were cultivated by the surveyed house-
Hierarchical Clustering on Principal Component (HCPC) holds. While there was a diversity of crops (see Figure 3), maize
analysis (Husson et al., 2017) was performed on factors con- (cultivated by more than 99% of the surveyed households), sor-
structed fromFAMDto identify homogenous archetypes. Before ghum (about 97% of the surveyed households), pearl millet
performing the HCPC analysis, the clustering tendency of the (about 95% of the surveyed households), cotton (about 92%
data was assessed using the Visual Assessment of cluster Ten- of the surveyed households), groundnut (about 89% of the sur-
dency (VAT) algorithm (Bezdek & Hathaway, 2002). Clustering veyed households) and cowpea (88% of the surveyed house-
tendency was assessed to evaluate whether the dataset contains holds) were respectively the most cultivated crops by the
meaningful clusters (i.e. non-random structures) or not, in surveyed households. Crops cultivated by few households
other word the feasibility or validity of the clustering analysis were soybean, Taro, Fonio, banana and watermelon (Figure 3).
on the data (Bezdek &Hathaway, 2002). To evaluate the cluster-
ing validity and identify the optimal number of clusters, the sil-
4.2. Diversity of climatic and non-climatic stressors
houette method (Rousseeuw, 1987) was used. It measures the
quality of a clustering by determining how well each object lies Stressors to which household were exposed to in the study area
within its cluster (Rousseeuw, 1987). A high average silhouette were of both climatic and non-climatic nature. Figure 4 illus-
width indicates a good clustering and the optimal number of trates the diversity of stressors to which households were
clusters k is the one that maximizes the average silhouette over exposed. Drought was the most experienced stressors by the
a range of possible values for k (Rousseeuw, 1987). Descriptive majority of surveyed households (77.25%). Other stressors
statistics was used to describe the archetypes identified and sum- included food scarcity (42.32%), livestock disease (41.72%),
marize the climatic and non-climatic stressors. We used Chi- erratic rainfall patterns (21.16%) and floods (9.58%). Other
square test to test if the types of stressors were archetype-depen- key non-climatic stressors experienced by households were
dent. To test whether vulnerability archetypes were independent damage to crops and farms by livestock (25.55%), scarcity of
of study communities (or research sites), we also used Chi- labour to perform farm activities (24.95%), inability to access
CLIMATE AND DEVELOPMENT 703
Figure 3. Diversity of cultivated plants and their importance.
fertilizer (10.98%), and death (9.98%) or sickness of a house- one (mean 0.81 ± 0.93) for socio-economic shocks and amedian
hold member (9.78%). number of two (mean 1.92 ± 0.83) for environmental shocks.
Over the past 12 months prior to the survey, the total num-
ber of shocks experienced by households ranged between one
and seven, with a median number of three per household 4.3. Household vulnerability patterns
(mean 2.73 ± 1.35). It ranged from zero to five for both environ- The Factor Analysis of Mixed Data (FAMD) analysis showed
mental and socio-economic shocks, and a median number of that the first 12 Principal Components (PC) have eigenvalues
Figure 4. Diversity of stressors/shocks experienced by surveyed households.
704 A. C. SEGNON ET AL.
greater than one and accounted for 61.20% of the total variance the indicators, see Table 2), water security (diversity of water
(Figure S1). In addition to these first 12 PCs, the next four com- source and water sufficiency), social network (membership of
ponents with eigenvalues close to 1 (above 0.9) were included CBOs and diversity of CBOs), physical accessibility (road prac-
in order to increase the total variance to be used in the further ticability), and health and sanitation (drinking water quality,
analysis. The first 16 components accounted for 71.61% of the toilet facility quality, illness and average time to health facility).
total variance and were considered in the clustering analysis. Indicators that were not discriminant included variables related
Supplementary Material Figure S1 presented the eigenvalues to environmental and socio-economic stressors. In addition, a
and the percentage of variance explained by the first 16 PCs Chi-square test of independence indicated that there was no
included in the cluster analysis. Supplemental Material Table dependence between vulnerability archetypes and either overall
S2 presented the correlation of the indicator variables with stressors (Chi-squared = 20.845, df = 18, p-value = 0.287),
the first five principal components. Supplemental Material environmental stressors (Chi-squared = 13.802, df = 8, p-value
Figure S2 illustrated the quality of representation of these vari- = 0.087) or socio-economic stressors (Chi-squared = 6.2593, df
ables on the principal components. = 8, p-value = 0.618). This result indicated that stressors were
The results of the feasibility of clustering analysis per- not specific to any archetypes and that households were simi-
formed confirmed that there is a cluster structure in the data larly exposed to stressors across communities.
(Supplementary Material Figure S3). The dark diagonal blocks Table 2 presents the description of the three vulnerability
in Supplementary Material Figure S3 clearly indicate the pres- archetypes according to the discriminant vulnerability indicator
ence of clusters, as well as the isolated singleton in data. The variables.Vulnerability archetype 1was composedof households
Hierarchical Clustering on Principal Component (HCPC) with low resource endowments and limited livelihood activities
analysis showed three clusters or archetypes of household vul- and comprised 43.31% of surveyed households. Households in
nerability patterns (Figure 5). This was supported by the analy- this vulnerability archetype had smaller land holdings (9.40 ±
sis of the dendrogram (Figure 6), which clearly revealed three 3.66 ha, p < 0.001), fewer livestock (4.38 ± 4.18 TLU, p < 0.001)
classes. Cluster validity analysis performed using the silhouette and less agricultural equipment (number of ploughs, drought
method indicated the average silhouette width was maximized animals, carts, donkeys, seed drills, and pesticide sprayers; p <
when the number of clusters was three (Supplementary 0.001, Table 2) than average. They had limited productive house-
Material Figure S4). The highest average silhouette width is holdmembers (6.88 ± 3.58, p < 0.001), were involved in few live-
0.4, indicating that households have been acceptably clustered lihood activities (2.68 ± 0.88, p < 0.001), especially agricultural
in each archetype. livelihood activities (1.91 ± 0.71, p = 0.023), cultivated lower
The most discriminant indicators of household vulnerability numbers of crops (9.22 ± 2.93, p < 0.001), had limited water
patterns were related to socio-demographic status (education sources to satisfy household needs (1.04 ± 0.19, p < 0.001) and
and number of household productive members), livelihood belonged to fewer farmer- or community-based organization
strategies (all the three indicators, see Table 2), household (1.11±0.49, p < 0.001).About 75%ofhouseholds in vulnerability
resources (all the indicators, see Table 2), food security (all archetype 1 also did not have access to an improved drinking
Figure 5. Archetypes of household vulnerability patterns.
CLIMATE AND DEVELOPMENT 705
Figure 6. Dendrogram showing the grouping of households into vulnerability archetypes.
water source (p < 0.001, Table 2). About 28% of households that archetypes, vulnerability archetype 2 was composed of less edu-
buy more food to supplement their own production to satisfy cated households: about 48% of them received no education
household needs belong to vulnerability archetype 1 (p < 0.001, and almost 50% of households with no education belong to
Table 2). This archetype can be characterized as a mildly vulner- this archetype, while only about 12% of them received primary
able archetype. education. Households in this vulnerability archetype can be
Vulnerability archetype 2, which is composed of 35.13% of characterized as highly vulnerable.
surveyed households, was also composed of households with Vulnerability archetype 3 included 21.56% of surveyed
low resource endowments and limited livelihood activities households and was composed of households with high
(Table 2) but was characterized by prevalent food insecurity con- resource endowments and livelihood activities, and a diverse
ditions. Households in vulnerability archetype 2 had smaller land social network, and food secure households (Table 2). House-
holdings (9.71 ± 4.49 ha, p < 0.001), fewer livestock (3.79 ± 3.75 holds in this vulnerability archetype had larger land holdings
TLU, p < 0.001) and less agricultural equipment (number of (18.88 ± 6.39 ha, p < 0.001), larger (14.34 ± 8.68 TLU, p <
ploughs, drought animals, carts, donkeys, seed drills, and pesti- 0.001) and diverse livestock resources (3.96 ± 0.79 livestock
cide sprayers; p < 0.001, Table 2) than average households. type, p < 0.001), and more agricultural equipment (number
They had limited productive household members (7.45 ± 4.22, of ploughs, drought animals, carts, donkeys, seed drills, and
p < 0.001), and were involved in fewer livelihood activities (2.65 pesticide sprayers; p < 0.001, Table 2) than average. They had
± 0.87, p < 0.001), particularly non-agricultural livelihoods activi- more productive household members (15.83 ± 9.10, p <
ties (0.66 ± 0.64, p < 0.001). More than 81% of households in vul- 0.001), and were involved in a larger variety of livelihood
nerability archetype 2 were not food secure and were unable to activities (3.23 ± 1.02, p < 0.001), both agricultural (2.15 ±
feed their households with their own production all the months 0.80, p = 0.023) and non-agricultural (1.08 ± 0.72, p < 0.001).
of the year (p < 0.001). The number of months in which house- About 99% of households in vulnerability archetype 3 were
holds of this vulnerability archetype did not have sufficient members of at least one farmer- or community-based organiz-
food to satisfy household needs was higher than the average ation (p = 0.041, Table 2), with most of them having more
(2.39 ± 1.71 vs. 1.97 ± 1.64, p < 0.001). About 58% of households diverse membership than average (1.44 ± 0.60, p < 0.001).
that bought more food to supplement their own production to They cultivated a higher number of crops (12.68 ± 3.55, p <
satisfy household needs belong to vulnerability archetype 2 (p 0.001) and reported fewer months than average (1.37 ± 1.44,
< 0.001, Table 2). Only about 14% of households in this vulner- p < 0.001) in which households did not have sufficient food
ability archetype produced enough food to satisfy household to satisfy household needs. Households in this archetype either
needs (p < 0.001). More than 98% of households in this vulner- produced enough food to satisfy household needs year-round
ability archetype used both improved and non-improved drink- (about 38% of households in the archetype) or supplemented
ing water sources and more than 69% of them did not have an amount of food lower than their own production from
access to improved toilet facilities (p < 0.001). Households in external sources (about 55% of households in the archetype).
this archetype had, however, more water sources than average About 43% of households in this archetype were food self-
households or households in other archetypes (2.07 ± 0.26, p < sufficient (p < 0.001, Table 2). However, more than 66% of
0.001) and about 53% of them had enough water to satisfy house- households in this archetype did not have enough water to
hold needs year-round (p = 0.001, Table 2). Compared to other satisfy household consumption needs year-round (p = 0.001,
706 A. C. SEGNON ET AL.
Table 2. Characterization of household vulnerability patterns based on discriminant indicators.
Cluster 1 (43.31% Cluster 2 (35.12% Cluster 3 (21.56% Global average or p-
Indicators Modalities HH) HH) HH) percentage value
Education None 49.71 (48.30) 12.87 (20.37) 34.13 <0.001
Literacy 28.28 (23.30) 28.28 (37.96) 28.94
Primary 22.86 (13.64) 20.96
Household productive 6.88 (3.58) 7.45 (4.22) 15.83 (9.10) 9.01 (6.52) <0.001
members
Agricultural livelihood 1.91 (0.71) 2.15 (0.80) 1.99 (0.74) 0.023
diversity
Non-agricultural livelihood 0.66 (0.64) 1.08 (0.72) 0.80 (0.68) <0.001
diversity
Livelihood diversity 2.68 (0.88) 2.65 (0.87) 3.23 (1.02) 2.79 (0.94) <0.001
Agricultural land 9.40 (3.66) 9.71 (4.49) 18.88 (6.39) 11.55 (6.04) <0.001
Livestock (TLU) 4.38 (4.18) 3.79 (3.75) 14.34 (8.63) 6.32 (6.80) <0.001
Diversity of livestock type 3.03 (1.43) 2.99 (1.47) 3.96 (0.79) 3.22 (1.39) <0.001
Number of plough 1.86 (1.07) 1.81 (1.08) 3.58 (1.79) 2.21 (1.45) <0.001
Number of draught animal 2.29 (1.25) 2.23 (1.35) 4.74 (1.73) 2.79 (1.73) <0.001
Number of donkey 1.22 (0.63) 1.19 (0.83) 2.45 (1.18) 1.48 (0.99) <0.001
Number of cart 1.04 (0.36) 1.01 (0.35) 1.84 (1.06) 1.20 (0.67) <0.001
Number of pesticide sprayer 0.52 (0.55) 0.64 (0.66) 1.28 (0.65) 0.72 (0.68) <0.001
Number of seed drill 0.47 (0.52) 0.49 (0.51) 1.04 (0.53) 0.60 (0.57)
CBOs membership Yes 22.67 (99.07) 94.21 0.041
No 3.45 (0.93) 5.79
Diversity of CBOs membership 1.11 (0.49) 1.44 (0.60) 1.19 (0.56) <0.001
Road practicability Not practicable year-round 66.67 (10.14) 12.12 (2.27) 6.59 0.015
Practicable year-round 38.53 (74.43) 67.86
Source of household food Own production [1/3] + 28.07 (7.37) 57.89 (18.75) 11.38 <0.001
Purchase [2/3]
Own Production 18.90 (13.64) 32.28 (37.96) 25.35
Own production [2/3] + 18.61 (54.63) 63.27
Purchase [1/3]
Food self-sufficiency Not food self-sufficient 39.67 (81.82) 17.08 (57.41) 72.46 <0.001
Food self-sufficient 23.19 (18.18) 33.33 (42.59) 27.54
Number of month with 2.39 (1.71) 1.37 (1.44) 1.97 (1.64) <0.001
insufficient food
Crop diversity 9.22 (2.93) 12.68 (3.55) 10.42 (3.64) <0.001
Drinking water quality Improved 77.46 (25.35) 2.82 (1.14) 14.17 <0.001
Not Improved 78.26 (74.65) 0.00 41.32
Both 0.00 78.03 (98.86) 44.51
Toilet facility quality Not Improved 40.13 (69.32) 14.14 (39.81) 60.68 <0.001
Improved 28.76 (40.74) 30.54
Both 11.36 (2.84) 47.73 (19.44) 8.78
Illness (Health case number) 1.45 (1.76) 2.50 (2.80) 1.99 (2.28) <0.001
Average time to health facility 13.27 (11.47) 17.03 (14.77) 14.61 (13.02) 0.009
Diversity of water source 1.04 (0.19) 2.07 (0.26) 1.52 (0.57) <0.001
Water sufficiency Water sufficient 44.34 (53.41) 16.98 (33.33) 42.32 0.001
Not water sufficient 28.37 (46.59) 24.91 (66.67) 57.68
Table 2); only 33% of them were water secure. This vulner- (Chi-squared = 144.12, df = 18, p-value < 0.001). High vulnerable
ability archetype was composed of fewer households with no households were more represented respectively in Danzana (70%
education (20.37%) and more literate households (37.97%). of the surveyed households), Diela (63%), Ntossini (51%) and
Households in this vulnerability archetype can be character- Mpessoba (48%). Zankorala seems to be better off with only
ized as less vulnerable. medium (75%) and less (25%) vulnerable households. In
addition to Zankorala, medium vulnerable households were
more represented in Oudiala (78%), Zanzoni (64%) andDentiola
4.4. Distribution of vulnerability archetypes across
(48%). In Ntiesso, the three archetypes are fairly distributed with
community
respectively 43%, 29% and 27% of high, low andmedium vulner-
Figure 7 showed the distribution of the three vulnerability arche- ability households. A similar observation is also made in Fonfana
types across the study communities. Except in one community with respectively 46%, 30% and 24% of high, low and vulner-
(Zankorola) where only two archetypes were identified, all the ability households (Figure 7).
three vulnerability archetypes were represented in all the 10
study communities. While the medium vulnerability archetype
is the most represented (with 43.31% of surveyed households) 5. Discussions
followed respectively by high (35.13% of surveyed households)
5.1. Diversity of climatic and non-climatic stressors
and low (21.56% of surveyed households) vulnerability arche-
types, their distribution varied across community (see Figure Our findings highlight the diversity of stressors to which house-
7). Chi-square test of independence indicated that there was a holds are exposed to in the study area. Drought was the most
significant association between archetypes and community mentioned stressor in the study area, confirming previous
CLIMATE AND DEVELOPMENT 707
Figure 7. Distribution of vulnerability archetypes across the study communities.
studies that have reported drought as the most important cli- scale (country, region, sub-region or community) (Hahn
matic stressor in SARs of West Africa (Epule et al., 2018; Gau- et al., 2009; Hinkel, 2011; Tonmoy et al., 2014; Vincent &
tier et al., 2016). The second most mentioned stressor was food Cull, 2014). This fails to capture intra- or within scale differ-
insecurity. This is consistent with previous studies highlighting ential vulnerability and treats community or sub-region as
the association between food insecurity and high vulnerability homogeneous. The vulnerability pattern approach used in
in Africa (McDowell et al., 2016; Williams et al., 2018). While this study to construct vulnerability archetypes illustrated
climatic stressors, namely drought, were the most mentioned, the heterogeneity in household vulnerability patterns within
households were also exposed to a wide range of climatic and and across communities (see Figure 7). All the three vulner-
non-stressors, including food scarcity, livestock disease, labour ability archetypes identified were represented in all the 10
unavailability, crop damage, inaccessibility to fertilizer, erratic study communities. This implies that households are not
rainfall patterns, and death or sickness of a household member. uniform or homogeneous in terms of vulnerability to cli-
Climatic stressors were just one among many challenges. Indeed, matic and non-climatic risks both within and across commu-
a recent systematic review of climatic stressors in the SARs of nities. Within the same community, high, medium and low
West Africa has shown that while climatic drivers are dominant, vulnerability household can be present. By revealing and
there is, however, an increasing recognition of the influential describing patterns in household vulnerability, our study
role of non-climatic stressors (Epule et al., 2018). Our findings contributes to the growing bodies of studies that operationa-
emphasized the interconnections between multiple climatic lize vulnerability pattern approach through archetype
and non-climatic stressors to which households are vulnerable analysis in various contexts across the world (see Akohoue
in this region and contribute to the increasing and growing et al. (2018); Kok et al. (2016); Nazari Nooghabi et al.
‘multiple stressors’ studies in sub-Saharan Africa (Ahmed (2019); Sietz et al. (2011); Sietz et al. (2012); Vidal
et al., 2016; Antwi-Agyei et al., 2017; Nyantakyi-Frimpong & Merino et al. (2019)).
Bezner-Kerr, 2015; Räsänen et al., 2016; Williams et al., 2018).
Multiple non-climatic stressors operating at different scales
drive vulnerability (Räsänen et al., 2016) and climate change 5.3. Drivers of household vulnerability patterns
intersects with many socio-ecological challenges that smallholder
We found that key determinants of household vulnerability
farmers are facing in SARs (Ahmed et al., 2016; Nyantakyi-
patterns were socio-demographic status, livelihood strategies,
Frimpong & Bezner-Kerr, 2015; Ouédraogo et al., 2016).
household resource endowments, food security, water security,
social network, physical accessibility, and health and sani-
tation. Exposure to stressors was not discriminant. This
5.2. Heterogeneity in household vulnerability patterns
implies that household vulnerability patterns in the study
Indicator-based vulnerability assessments typically use aggre- area were mainly shaped by household adaptive capacity and
gation methods to compute a composite index at a given sensitivity. The non-association between vulnerability
708 A. C. SEGNON ET AL.
archetypes and stressors implies that household exposure to stres- where female-headed households are culturally accepted and
sors was similar across the archetypes and communities. These common might provide better insights regarding gender and
findings confirmed that differential vulnerability can be explained household vulnerability patterns. Consistently with previous
not just by the difference in exposure to climate-related and studies highlighting the association between food insecurity
environmental hazards but largely by social and economic pro- with high vulnerability in Africa (McDowell et al., 2016; Wil-
cesses (Thomas et al., 2019). Our findings are, however, inconsist- liams et al., 2018), our findings also showed that food insecur-
ent with Nazari Nooghabi et al. (2019) results, where drought was ity was a key determinant of vulnerability patterns.
a key vulnerability-driving factor for one of the three wheat
farmer vulnerability archetypes in Northeast Iran. Socio-econ-
omic drivers were key vulnerability factors for the two other 6. Conclusions
archetypes (Nazari Nooghabi et al., 2019). This study analysed the diversity of stressors experienced by
Thomas et al. (2019) identified four broad themes – resource smallholder farmers in SARs of Mali and provided a typology
access, governance, culture, and knowledge – as useful in or archetype of household vulnerability. The combined vulner-
explaining differential local-scale vulnerability. In our study, ability patterns and MLVI approaches used in this study to con-
resource access and knowledge were key determinants of differ- struct archetypes of vulnerability offer a pragmatic way to reveal
ential vulnerability patterns. Resource endowments (agricul- the heterogeneity in household vulnerability to both climatic
tural land, livestock and agricultural equipment in this study) and non-climatic stressors within and across communities.
are one crucial factor that shapes people’s ability to plan for The findings showed that while climatic stressor, mainly
and respond to climate change challenges (Thomas et al., drought, was the most reported stressor, households in the
2019). Resource endowments influence vulnerability by redu- study were exposed to a diversity of environmental and socio-
cing or increasing exposure, sensitivity, and adaptive capacity economic shocks, including food scarcity, livestock disease,
(Thomas et al., 2019). Marginalization and deprivation arising labour unavailability, crop damage, inaccessibility of fertilizer,
from social stratification play important roles in unequal access erratic rainfall patterns, death or sickness of a household mem-
to resources, which in turn explains differential sensitivity to ber. Climatic stressors or risks were just one amongmany socio-
and capacity to respond to climate impacts (Thomas et al., ecological challenges facing smallholder farmers in SARs. The
2019). Knowledge and information interact, directly and typology revealed three household vulnerability archetypes
indirectly, with vulnerability to climate change by shaping differentiated by adaptive capacity and sensitivity dimensions,
peoples’ adaptive capacity, exposure, and sensitivity (Thomas implying that households were similarly exposed to stressors.
et al., 2019). Different types and sources of information and Availability of productive household members, household
modes of knowledge transmission affect how people under- resource endowments (agricultural land, livestock and agricul-
stand, perceive, and act on information (Thomas et al., 2019). tural equipment), livelihood diversification and social networks
While information is necessary, it is not alone sufficient for are the main discriminant factors of household adaptive
reducing vulnerability (Thomas et al., 2019). capacity. Challenges to get sufficient food and water to satisfy
In the study area, household socio-demographic character- household needs throughout the year make households more
istics such as household size or number of productive house- sensitive to stressors. The analysis also highlighted the hetero-
hold members determined household ability to respond to geneity in household vulnerability patterns within and across
various socio-ecological challenges such as labour availability, communities and the context-specific driving forces of vulner-
ability to produce enough food (in terms of diversity and ability in SARs. Failing to account for this heterogeneity in vul-
quantity) to ensure food security, household members’ sick- nerability patterns across scales and nuanced understanding of
ness or death. In fact, family labour, especially child labour, context-specific drivers in adaptation planning might result in
is still playing an important role in West African farming sys- a mismatch between adaptation needs and interventions, and
tems, and elsewhere in Africa. Indeed, according to the 2016 potentially in maladaptation.
Global Estimates of Child Labor, the agriculture sector
accounts for 85% of all child labour in Africa (ILO, 2017).
For instance, one of the main drivers of farmers’ decisions to Acknowledgements
adopt, as well as to intensify the use of soil and water conser- This research was carried out as part of the Adaptation at Scale in Semi-
vation practices to adapt to climate change in SARs of West Arid Regions (ASSAR) project. ASSAR is one of four research programs
Africa is the presence of children in the household (Kpadonou funded under the Collaborative Adaptation Research Initiative in Africa
et al., 2017). The household labour force is also one of the key and Asia (CARIAA), with financial support from the UK Government’s
drivers of the adoption of climate-smart technologies and Department for International Development (DFID) and the International
Development Research Centre (IDRC), Canada. The views expressed in
practices in southern Mali (Ouédraogo et al., 2019). These this work are those of the creators and do not necessarily represent
highlight how crucial household size and labour are for house- those of DFID, IDRC or its Board of Governors. This work got additional
hold adaptive capacity in the region. support through the CGIAR Research Program on Climate Change, Agri-
Previous research has shown that vulnerability in SARs of culture and Food Security (CCAFS), which is carried out with support
Africa is gendered differentiated (Rao et al., 2019). As from the CGIAR Fund Donors and through several bilateral funding
agreements (the CGIAR Fund Council, Australia-ACIAR, European
female-headed households were rare in our study area context, Union, International Fund for Agricultural Development-IFAD, Ireland,
our analysis was unable to reveal gender-differentiated vulner- New Zealand, Netherlands, Switzerland, USAID, UK, and Thailand). For
ability patterns. A replication of our approach in a context details, please visit https://ccafs.cgiar.org/donors.
CLIMATE AND DEVELOPMENT 709
Disclosure statement in 2015 as a Program Specialist working primarily on the Adaptation at
Scale in Semi-Arid Regions (ASSAR) project, part of the broader Colla-
No potential conflict of interest was reported by the author(s). borative Adaptation Research Initiative in Africa and Asia (CARIAA).
Previously, Mary worked with World Bank and African Development
Bank experts on participatory adaptation fieldwork for the Zambian
Funding Pilot Program for Climate Resilience (PPCR), and also co-authored a
USAID commissioned report on gender and climate change adaptation.
This work was supported by International Development Research Centre Most recently, she worked as a post-doctoral researcher at the Basque
[Grant Number 107640-001] Center for Climate Change (BC3) in Bilbao, Spain on topics relating to
agricultural biodiversity and climate change adaptation.
ORCID Dr. Benjamin O. Ofori is a Senior Research Fellow at the Institute forEnvironment and Sanitation Studies (IESS) of the University of Ghana.
Alcade C. Segnon http://orcid.org/0000-0001-9751-120X He holds a PhD and MSc in Geography and Resource Development
Edmond Totin http://orcid.org/0000-0003-3377-6190 and a BA (Hons) in Geography with Sociology all from the University
Robert B. Zougmoré http://orcid.org/0000-0002-6215-4852 of Ghana. His current research areas include Rural Resource Utilization
Jourdain C. Lokossou http://orcid.org/0000-0001-5365-8517 and Marketing, and Community Livelihoods, especially in Dam-affected
Enoch G. Achigan-Dako http://orcid.org/0000-0002-5493-0516 Areas.
Christopher Gordon http://orcid.org/0000-0003-2734-851X Prof. Dr. Enoch G. Achigan-Dako is Geneticist and Plant Breeder. He is
Associate Professor and Head of the Laboratory of Genetics, Horticulture
and Seed Science of the Faculty of Agronomic Sciences, University of
Notes on contributors Abomey-Calavi. He holds a PhD from the Martin-Luther-University,
Halle-Wittenberg (Germany). Prof. Achigan-Dako is an alumni of the
Dr Alcade C. Segnon is a Postdoctoral Fellow at the CGIAR Research Pro- African Plant Breeding Academy of the University of California, Davis
gram on Climate Change, Agriculture and Food Security (CCAFS) based and the Leibniz Institute of Plant Genetics and Crop Plant Research
at the International Crops Research Institute for the Semi-Arid Tropics (Germany). He worked with the University of Wageningen as scientific
(ICRISAT), Bamako, Mali. Dr Segnon holds a BSc and MSc in Agronomy editor of the programme Plant Resources of Tropical Africa (PROTA)
from the University of Abomey-Calavi (Benin) and a PhD in Environ- in Kenya until 2012. His research focuses on the sustainable use of orphan
mental Science from the University of Ghana. His research interest is at and underutilized species and crop wild relatives.
the intersection of climate change adaptation, agrobiodiversity utilization
and traditional knowledge systems, and agroecosystem sustainability. He Prof. Christopher Gordon is based at the University of Ghana’s Institute
is particularly interested in how and to what extent agrobiodiversity and for Environment and Sanitation Studies. He graduated from the Univer-
knowledge that local people share on it can be used to improve small- sity of Ghana with BSc (Hons.) in 1980 and MSc, 1986 both in Zoology,
holders' livelihoods and resilience to climate and environmental change. and PhD from King’s College, University of London (1995), in Human
Recently, he has been exploring climate change adaptation e ectiveness, Environmental Science. Prof Gordon has supervised more than 60 MScff
especially agrobiodiversity-based adaptation. Dr Segnon is involved in and PhD students in areas of Environmental Science, Climate Change
the Global Adaptation Mapping Initiative (GAMI) and is a Contributing and Sustainable Development, Chemistry, Agricultural Extension and
Author (Chapter 16) of the IPCC's 6th Assessment Report, WGII. Communication Studies. He has contributed to national and international
policy in disciplines including Water Quality, Freshwater Biodiversity,
Dr Edmond Totin is an Assistant Professor at the National University of Fisheries, Wetland Ecology. He has served as Vice-President for both
Agriculture (Benin). He holds a PhD in Knowledge Technology and Inno- Wetlands International and the International Society of Limnology. Cur-
vation fromWageningen University, the Netherlands (2013). He works as rently he is member of the World Adaption Research Programme of UN
a rural sociologist in the field of management of agricultural innovation, Environment as well as an Earth Commissioner for Future Earth. In 2016
climate adaptation and governance. He focusses on processes of socio- he was invested as Member Order of the Volta, for services in research,
technical innovation and transformation and approaches to stimulate education and development.
science-informed policy agendas.
Dr Robert B. Zougmoré is an agronomist and soil scientist with a PhD in
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