Women are at a particular disadvantage by the stress that climate change poses on food systems in low- and middle-income countries (LMICs) as their adaptive capacity is hampered by unequal access to resources and services and constraints to their agency. This paper proposes a methodology to identify climate–agriculture–gender inequality hotspot LMICs and subnational areas where climate hazards converge with large concentrations of women participating in food systems and social conditions that disadvantage women. The methodology applies data reduction techniques on publicly available data to compute a hotspot index that forms the basis for ranking and mapping hotspots. Applying the methodology illustrates the hottest of 87 LMICs are located in Africa and identifies crop-specific hotspot subnational areas in four focus countries. Identifying hotspots can enable targeting populations at highest risk and render future efforts to support women’s agency for climate resilience and avert increasing gender inequalities more effective.
Research Article
Effectively targeting climate investments: A methodology for mapping climate–agriculture–gender inequality hotspots
https://doi.org/10.21203/rs.3.rs-1899046/v1
This work is licensed under a CC BY 4.0 License
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Climate change
gender inequality
women
agriculture
hotspot
low-and middle-income countries
- Five types of climate hazards relevant to changes in crop suitability are considered including drought, flood, climate variability, crop growing-season reductions, and high growing-season temperatures, as well as their combinations.
- Women and men can be differentially exposed to climate hazards affecting food systems because of contextual gender patterns in crop and commodity choices. It is assumed that women are highly exposed where they provide a large share of (crop- or category-specific) labor in agriculture and/or livestock production.
- Structural barriers to gender equality in food systems, agriculture and livestock production define the vulnerabilities of women exposed to climate hazards because they significantly limit women’s capacity to mitigate and adapt to climate change.
- Identifying subnational climate–agriculture–gender inequality hotspot areas within countries caters for the fact that women’s vulnerability to climate risk can be highly contextual.
- The methodology for identifying hotspots can be adapted to include additional or other relevant indicators of high climate change risk based on publicly available data representative at country or first administrative division level.
Climate change can undermine global efforts to transform food systems such that they enable the provision of affordable and nutritious food for all in environmentally sustainable ways. Food systems—including cropping, livestock, forestry, fishery and aquaculture sectors—face direct stress from climate-induced changes such as increases in temperature, variation in precipitation patterns and weather anomalies, and the increased frequency of extreme weather events. These trigger crop failure, pest and disease outbreak, and degradation of land and water resources—impeding the development of resilient food systems (Aggarwal et al. 2018; IPCC 2019; UNDP 2016a).
Climate change is also a potential threat to gender equality in food systems. Women play a crucial role in food systems as agents of food and nutritional security, smallholder farmers and livestock keepers, as well as primary managers of natural resources (Allen et al. 2018; Njuki et al. 2022). Women also make substantial contributions to climate change adaptation practices such as crop diversification, water harvesting, small-scale irrigation, improved livestock feeding and improved grain storage (Kristjanson et al. 2017; Bryan et al. 2021). Yet, gender relations and associated dynamics at work in food systems underlie gender differentiated climate change impact and resilience at the disadvantage of women.
Climate change hazards and gender differentiated exposure and vulnerabilities in food systems do not exist discretely and often overlap and interact with each other in complex ways specific to the local context and conditions. Understanding these locally specific interactions is essential to inform actions aiming to effectively improve coping mechanisms and mitigate risks for women and men as well as empower women and unlock gender agency.
The aim of this paper is to develop and apply a methodology to identify climate–agriculture–gender inequality hotspots. These are defined as geographical areas where significant physical and ecological effects of climate change coexist with large concentrations of women participating in food systems, particularly by providing labor in agriculture and/or livestock production, as well as with important gender inequalities which imply high levels of vulnerability of women to climate change adversities.
Identifying climate–agriculture–gender inequality hotspots can facilitate how future efforts are targeted to locations and populations where women are most at risk to climate change to make these more impactful. Ensuring that women are not left behind and can seize adaptation and mitigation opportunities that emerge as food systems transform against the background of climate change is essential—not only for the intrinsic value of gender equality, but also because this can enable women as agents of change for climate sustainability outcomes and climate resilience and can significantly contribute towards developing climate-resilient food systems.
The paper is structured as follows: In the next section, the climate, gender and food systems nexus is discussed as well as a framework for identifying climate–agriculture–gender inequality hotspots. The methodology for identifying hotspots is discussed in section three. In the subsequent result section, the application of the methodology is illustrated, which is followed by a conclusion.
2.1. Climate change and the vulnerabilities of food systems
Globally, over one billion people rely on food systems for food or to sustain their livelihoods. The instability of food systems is anticipated to increase with climate change, particularly with a two degree increase in temperature (IPCC 2019). Climate change-related challenges to food systems are projected to have a substantial negative impact on food production, food security, and livelihoods of many—particularly smallholder farmers in the global South (Bryan et al. 2017).
Climate change can be understood from two perspectives: long-term climate trends and shorter-term increased temporal and spatial variability in temperature, precipitation and winds, manifesting in higher incidence and magnitude of extreme events. Extreme events include heatwaves, extended dry spells and drought, heavy precipitation events and floods, tropical cyclone events, and high sea levels because of storm surges (Vermeulen et al. 2012). Figure 1 illustrates the predominant climate hazard types across the globe.
<figure 1>
Agricultural production is projected to experience declines in yield and reduced crop suitability under high temperatures. There is evidence that warming has negatively affected the yield of particular crops, including maize and wheat, in tropical and subtropical regions. Climate-induced changes are projected to affect livestock production and pastoralism by lowering pasture and animal productivity, damaging reproductive function, and leading to biodiversity loss (McGahey et al. 2014; IPCC 2019). Climate-induced pest and disease outbreak and degradation of land and water resources form additional challenges.
Climate-induced changes are more pronounced and acute in tropical and subtropical regions—including Asia and Africa. As agriculture accounts for an average of 18.2 percent of national Gross Domestic Product (GDP) in Africa and 15.5 percent in Asia (World Bank 2020) and these continents house a large population of smallholder farmers, who are dependent on their immediate environment for food and income and are vulnerable to poverty (Amjath-Babu et al. 2019), De Souza et al. (2015) called Africa and Asia climate change hotspots.
2.2 Gendered effects of climate change in food systems
The effects of climate change and related weather anomalies are locally specific and are experienced differently by social groups differentiated by gender, age, ethnicity, wealth, class and/or disability (Nelson et al. 2002; Vincent and Cull 2014). In many contexts, gender is an important determinant of exposure and vulnerability to climate change adversities affecting food systems.
First, women are highly exposed to climate change effects because of their high level of involvement in agriculture and food systems. In low-income countries, women are estimated to comprise up to 48 percent of the rural agricultural labor force (FAO 2020) and over 55 percent in parts of South Asia and Africa (UN Women 2020). Agricultural households depend highly on women’s labor in subsistence or cash-crop production or other agricultural activities—as producers, wage workers and providers of (unpaid) family labor (Nelson et al. 2002; Eastin 2018). Besides, socioeconomic context and cultural norms influence women’s preference for cultivating certain crops and the management practices they adopt. Evidence from sub-Sahara Africa shows that some crops are disproportionately grown by men or women and women are more likely to grow crops with less complicated production techniques (Doss 2002; de Brauw et al. 2015). Hence, this may imply a higher exposure of women to climate hazards than men if locally specific climate-induced challenges affect those crops more.
Climate-related changes in cropping patterns and livestock production have been observed to affect the gendered division of labor and increase the farm and household workload of women (Eastin 2018; Rao et al. 2019). Outmigration, a climate change adaptation strategy, adopted especially by men, tends to be associated with increased labor responsibilities for women but not necessarily with improved access to finance, social networks or knowledge (Rao et al. 2020). Faced with male outmigration, poor women in South Africa are observed to adopt diversification strategies and take on extra workloads (Babugura 2020). In India, poor women and women belonging to the lower castes or ethnic minorities not only face increased work burdens but also loss of support, hence, increased vulnerability (Rao and Mitra 2013).
Second, both women and men in LMICs can be vulnerable to climate risks because of the extent of their dependency on an exposed agricultural sector (or a stage of value chain) and capability to deal with the risks– but women are more likely to be vulnerable (Perez et al. 2015). There is evidence that women tend to be more dependent on natural resources as compared to men and have more limited options for livelihood diversification (Osman-Elasha 2012). Climate and weather shocks can directly negatively impact women’s assets which are critical for their resilience. For instance, drought in Uganda had an important negative impact on women’s non-land assets. In Bangladesh, however, drought impacts on women’s assets were more limited because women’s lower involvement in agriculture, lower levels of women’s asset ownership and more effective emergency assistance (Quisumbing et al. 2018).
Women and girls are also found to be more likely than men to go hungry following natural disasters linked to climate change (FAO 2017; Oxfam 2019). In India, for example, twice the number of women than men reported eating less in response to a drought (Lambrou and Nelson 2010). Women are more likely to be killed by natural disasters, especially in societies where women have a low socioeconomic status (Neumayer and Plümper 2007), although men face higher risk when overrepresented in high-risk occupations such as construction (Erman et al. 2021).
2.3. Gendered capabilities to deal with climate change
Women’s lower access to productive resources, technology, markets, finance and information; poverty, socioeconomic, cultural and political marginalization; as well as restrictive sociocultural norms and gender roles that disadvantage women cumulatively put women in a disadvantaged position in coping with and adapting to the adverse impacts of climate change (Chanana-Nag and Aggarwal 2018; Huyer 2016; IPCC 2019). This does not only make women more vulnerable but also less resilient to climate change in food systems (Yadav and Lal 2018; Acosta et al. 2019; Anugwa et al. 2020, Diouf et al. 2020; Evertsen and van der Geest 2020, Tsige et al. 2020; Nhat Lam Duyen et al. 2021).
Access, ownership and control over financial and productive resources, including land, are important for women farmers’ capabilities to cope with and adapt to the challenges of climate change in food systems. However, there is ample evidence that women have fewer and lower value assets, less access to capital and labor, limited land ownership and fewer agricultural inputs (Anugwa et al. 2020; Diouf et al. 2020; Nchu et al. 2019). Even where women have access, they may not have the agency to use and control the resources they need to adapt to climate change impacts (Acosta et al. 2019; Akter et al. 2016; Duffy et al. 2021).
Similarly, limited access to information results in lower awareness and knowledge of climate risk, making them less prepared than men (Gumucio et al. 2020; Partey et al. 2018), and less able to adopt strategies to mitigate or adapt to climate risks (Bernier et al. 2015; Bryan et al. 2021). In Bangladesh, for instance, women are less likely to diversify into viable livelihood options in the face of climate shocks due to restrictive social norms, lack of education, and limited access to productive resources (Rabbani et al. 2015). In Burundi, men were found to adopt technical innovations, such as disease management practices for banana crops, faster than women because they have more access to information and farmer learning groups (Iradukunda et al. 2019). In Uganda, Ghana and Bangladesh, Jost et al. (2016) found that women use fewer climate sustainable agricultural practices than men because of resource constraints, limited access to information and extension services, and sociocultural norms and practices that restrict women’s participation in training programs.
Various socioeconomic and cultural factors limit women’s ability to respond to or cope with climate change impacts causing women to be disproportionately affected by climate change (Chanana-Nag and Aggarwal 2018; Huyer 2016). Discriminatory social institutions, including, for instance, formal inheritance laws or informal customary rules disadvantaging women in land rights, set barriers to women’s equal access to resources. Such institutions, as well as discriminatory social norms and gender roles, can further restrict women’s agency and their ability to make decisions or participate in different food-system activities—both at household and community levels (Evertsen and van der Geest 2020; Yadav and Lal 2018).
Social norms also pose barriers to access supportive institutional mechanisms, including agricultural extension and other rural services and collective action. For instance, social norms limit women’s ability to participate in group activities, move freely, and use specific technologies or practices—reducing their capacity to respond to climate-related stresses (Bryan et al. 2017). In Africa, even if they are member of community groups, women are unable to benefit to the same extent as men due sociocultural factors determining who owns land, who is head of a household, who has domestic responsibilities (Acosta et al. 2019; Ngigi et al. 2017; Tsige et al. 2020).
2.4. A framework for identifying of hotspots where climate change affects women in food systems most
The framework of risk proposed by the Intergovernmental Panel on Climate Change (IPCC) (2020) forms the basis of the climate–agriculture–gender inequality hotspots defined in this paper. Risk or impact is derived from the interaction of social and environmental processes, from the combination of physical hazards and the vulnerabilities of exposed elements (IPCC 2014). Risk can “arise from potential impacts of climate change as well as human responses to climate change” (IPCC 2020).
In line with the IPCC climate risk framework, the climate–agriculture–gender inequality hotspots are defined as geographic areas that concurrently experience high levels of climate hazards, high levels of women’s exposure because of their engagement in crop production and/or livestock rearing, and high levels of women’s vulnerability due to prevailing gender inequalities.[1]
[1] Hazard refers to the occurrence of a natural or human-induced physical event that may cause health impacts, loss of life, physical damage and loss. Exposure in this case refers to the presence of people in settings that could be adversely affected. Vulnerability here refers to the propensity of human beings, their livelihoods, and assets to suffer adverse effects when impacted by hazard events, the level of which depends on susceptibilities, fragilities, or deficiencies that favor adverse effects.
The methodology to identify climate–agriculture–gender inequality hotspots in LMICs builds upon the work by Khatri-Chhetri et al. (2017) and Chanana-Nag and Aggarwal (2018).
For the purposes of this paper, hazards are based on the predominant ‘Climate Hazard Types’ data developed by the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) (figure 1). This comprises five types of climate hazards relevant to changes in crop suitability including drought, flood, climate variability, crop growing-season reductions, and high growing-season temperatures, as well as their combinations. Women’s exposure to climate hazards affecting food systems is proxied by the extent to which women farmers, compared to men farmers, are exposed to climate hazards due to their relatively heavy (labor) involvement in agriculture and/or livestock production. Gender inequality and structural constraints to gender equality in food systems, agriculture and livestock production define the vulnerabilities of women exposed to climate hazards because they favor adverse effects of hazards and significantly limit women’s capacity to mitigate and adapt to climate change.
The methodology for identifying climate–agriculture–gender inequality hotspots takes a two-step approach. First, a climate–agriculture–gender inequality hotspot index at the national level is computed, using geospatial information and publicly available large representative socioeconomic datasets, which reflects the overall risk (i.e., the combination of climate hazards, women’s exposure and women’s vulnerability due to gender inequalities) under warming scenario RCP 8.5 (or ‘business as usual’) by the year 2050 (Moss et al. 2008). Subsequently, LMICs are ranked based on the index value and the ranking is illustrated on a global map using color codes for relative ‘hotness’. Second, in a selection of hotspot countries in Africa and Asia where subnationally representative data is available, a subnational level climate–agriculture–gender inequality hotspot index that takes into account women’s exposure in specific crop and/or livestock production is computed. Subnational areas within countries are then ranked based on the crop- or category-specific index values and the within-country rankings illustrated in maps.
The method for identifying hotspot LMICs and subnational hotspot areas in selected hotspot countries follow the same framework and principles, but partly differ in the data and extent of detail in the data used for measuring the three components (hazards, exposure, and vulnerability). This is due to data limitations. The publicly available datasets used in this paper only allow computing indicators of women’s exposure for specific crops and livestock for subnational areas in selected countries. The indicator that proxies vulnerability due to gender inequalities is available for most countries but not available at subnational area level.
In what follows, we discuss the indicators and methods for defining, ranking and mapping climate–agriculture–gender inequality hotspot countries and subnational areas in more detail.
3.1. Indicators of and data sources for climate hazards
To estimate the rural population facing climatic hazards across LMICs, the gridded population headcount and the extent of urban and rural areas with the geospatial data layer of CCAFS Climate Hazard Types are overlaid at 10 kilometres grids resolution. This way an indicator of the share of the rural population facing climate hazards is generated at the country level, as well as at the first level of (subnational) administrative boundaries.
Table 1 includes an overview of the indicators for climate hazards (3.1.), women’s exposure to climate hazards affecting food systems (3.2.), and women’s vulnerability due to gender inequalities (3.3.) used for defining climate–agriculture–gender inequality hotspots at national and subnational levels.
Table 1
Indicators of hazard, exposure and vulnerability used to define climate–agriculture–gender inequality hotspots at national and subnational levels
3.2. Indicators of and data sources for women’s exposure to climate hazards
Given the presence of contextual gender patterns in crop and commodity choices, women and men can be differentially exposed to climate hazards affecting food systems. It is assumed that women are highly exposed where they provide a large share of labor in agriculture and/or livestock production.
At the national level, women’s exposure to climate hazards is identified by women’s relative participation in agricultural employment—that is, the share of female agricultural workers out of the total agricultural workers. Data for this indicator are obtained from the latest nationally representative Labor Force Survey (LFS) datasets retrieved from the World Development Indicators database (World Bank 2021).
To measure women’s exposure to climate hazards at the subnational level (first level of administrative division), a crop/category-specific index of women’s exposure to climate hazards is constructed by combining (a) the relative importance in the local economy of each of the six crop/categories classified in the LFS datasets measured by overall labor participation, regardless of sex; (b) the crop/category-specific women’s share of labor participation; and (c) the crop/category-specific share of hours worked by women (relative to men) capturing effort intensity.[2] The six crops/categories are: (i) cereals, leguminous crops and oilseeds; (ii) rice; (iii) vegetables, melons, roots and tubers; (iv) perennial crops; (v) livestock; and (vi) mixed farming.
3.3. Indicators of and data sources for women’s vulnerability due to gender inequalities
At the national level, vulnerability of women to climate hazards due to prevailing gender inequalities is proxied by the Social Institutions and Gender Index (SIGI) (OECD 2014). The SIGI 2014 was selected because it captures a range of gender-inequality elements relevant for women’s capabilities to mitigate and adapt to climate change effects and has limited missing data. It distinguishes five broad dimensions of discriminatory social institutions that restrict women’s access to opportunities, resources and power including discriminatory family code, restricted physical integrity, son bias, restricted resources and assets, and restricted civil liberties (OECD 2014). These capture gaps between women and men in rights and opportunities as reflected in legislation, practices and attitudes at formal and informal systemic levels as underlying factors leading to gender inequalities in economic and social outcomes.
At the subnational level, a composite index of a set of four sub-indicators is used to capture women’s vulnerability due to gender inequalities as a subnational level SIGI 2014 is not available. This composite index is constructed using Principal Component Analysis (PCA) and includes (i) the Subnational Gender Development Index (2019) and components of the SIGI 2014 that can be constructed using publicly available data including (ii) prevalence of child marriage; (iii) prevalence of gender-based violence; and (iv) missing women (son bias).[3]
The first sub-indicator, the Subnational Gender Development Index—representative at the first level of administrative division available for most countries—measures the gender gap in human development achievements by the disparity between women and men in three basic dimensions of human development, including education, health and standard of living (GDL 2020a). The data for the selected four focus hotspot countries—namely Mali, Zambia, Pakistan and Bangladesh (See section 4)—are extracted from the Subnational Gender Development Index database for the year 2019 (GDL 2020b).
The second sub-indicator is the prevalence of child marriage among girls aged 15–19 years reflecting the SIGI 2014 dimension of discriminatory family code. It is based on the 2018 LFSs for Mali, Zambia and Pakistan (PBS 2017); and the 2013 LFS for Bangladesh. The third sub-indicator is the prevalence of gender-based violence experienced by women reflecting the dimension of restricted physical integrity. It is based on the 2018 Demographic and Health Surveys for Mali, Zambia, and Pakistan, and the 2015 Bangladesh Integrated Household Survey. The fourth sub-indicator is the ratio of male/female children between zero and four years old reflecting the dimension of son bias (missing women). National Census data are used to construct this indicator (2010 for Zambia, 2009 for Mali, 2011 for Bangladesh, and 2017 for Pakistan).
3.4. Defining, ranking and mapping by climate–agriculture–gender inequality hotspot scores
Principal Component Analysis (PCA), a statistical technique for data reduction, is used to calculate an climate–agriculture–gender inequality hotspot index at the national level for LMICs in Latin America, Asia, and Africa. PCA allows combining the hazard, exposure and gender-inequality driven vulnerability indicators which are expressed in different metrics and measure diverse characteristics of risk in an ordinal index. PCA constructs a series of uncorrelated linear combinations of the variables that contain the largest share of the variance (principal components), i.e., explain the greatest share of the variation in risk. This strategy reduces the number of variables without imposing arbitrary weights. Subsequently, an index value of overall risk is calculated for each country using the estimated coefficients of the first principal component as weights (Filmer and Pritchett 2001). This index of risk—i.e., the climate–agriculture–gender inequality hotspot index—thus captures the convergence of hazard, women’s exposure and vulnerability due to gender inequality.
As the index is standardized, it provides a ranking of countries based on intensity of concurrent climate hazards, women’s exposure, and women’s vulnerability due to gender inequalities (See Appendix A). The ranking by the index can then be illustrated on a global map using color codes for relative hot-or coolness.
At the subnational level in the four selected hotspot countries, six crop/category-specific risk indices -i.e., climate–agriculture–gender inequality hotspot indices- are constructed using PCA. These are based on: (1) the climate hazards indicator; (2) the six crop/category-specific indicators of the relative importance of each crop/category in the subnational area; (3) the six crop/category-specific indicators of female share in labor participation; (4) the six crop/category-specific indicators of the share of hours worked by women; and (5) the composite index for women’s vulnerability due to gender inequalities. The components and weights of the six PCA-based crop/category-specific climate–agriculture–gender inequality hotspot indices are available in Appendix B.
The index values of each crop/category-specific climate–agriculture–gender inequality hotspot (risk) index are relative to all subnational areas across the four focus countries included in the PCA (See Appendix C). Subsequently, within each country and for each crop/category, subnational areas can be ranked based on the crop/category-specific climate–agriculture–gender inequality hotspot index values. This allows mapping relative crop/category-specific hotspot or cold-spot subnational areas using color codes in each focus country (where the maps of subnational areas per country reflect within-country ranking).[4]
Different criteria can be used to identify subnational hotspot areas. To illustrate the methodology, in this paper, we identify a crop/category-specific subnational hotspot area in each focus hotspot country based on its relatively high hotspot index value for that particular crop or category.
[2] Correlation among all exposure indicators was analyzed to check complementarity in cultivating specific crops/categories.
[3] Components of the SIGI 2014 not captured in our subnational level sub-indicators include formal laws which are unlikely to vary within countries, as well as informal laws, social norms, and restricted assets (including land) as such data, representative at the first level administrative division, are not readily available.
[4] Hotspot maps for vegetables, roots and tubers in Mali, rice in Zambia, perennial crops in Pakistan and mixed farming in Bangladesh are missing as the number of observations was insufficient to derive reliable statistics.
This section of the paper describes the results of the application of the climate–agriculture–gender inequality hotspot methodology both at the national and subnational levels. At the global level, 87 LMIC countries from Latin America, Asia, and Africa were ranked by hotspot index value and the ranking illustrated on a global map (figure 2; Appendix A). The countries which have a high hotspot index value and hence, hotter, are colored orange (decreasingly hot from dark to light orange) and the countries that have a low hotspot index value (and therefore, cooler) are colored blue (increasingly cool from light to dark blue).
<figure 2>
The global map shows that significant climate hazards, high women’s exposure to climate hazards affecting food systems, and high women’s vulnerability due to gender inequalities converge particularly in West-, Central- and East-Africa; in Western and South Asia; and in a few countries in Southeast Asia. The ‘hottest’ countries (global rank 1 to 15) are all situated in Africa.
Following this, two countries each from Africa and Asia were selected for a subnational hotspot analysis based on their ranking and data availability for the selected indicators. The two countries in Africa are Mali, ranked the fourth hottest LMIC country globally, and Zambia, the seventh hottest (Index values 2.028 and 1.639 respectively; Appendix A). The two countries in (South) Asia are Pakistan and Bangladesh, ranked 16 and 17 respectively (Index values 1.029 and 1.026; Appendix A).
In what follows, we describe the context of the focus countries, illustrate the subnational hotspot analysis and mapping and discuss a particular crop/category specific subnational hotspot area.
4.1. Mali
Mali, a Sahelian country, is majorly dependent on agriculture which contributes 37 percent to its the national GDP and is a source of income for more than 65 percent of its population. The rural economy of Mali is dominated by rainfed and subsistence crop and livestock production (FAO 2017). Increasingly variable and unpredictable rainfall, droughts and floods have led to multiple crop failures and food shortages. While women constitute half of the labor force in the agriculture sector, they have less control over agricultural and household assets (especially land) than men. Women have limited representation in decision-making spheres which is linked to restrictive customary laws (CIAT et al. 2021).
The subnational climate–agriculture–gender inequality hotspot analysis reveals the Tombouctou region in the northern part of Mali as a hotspot for livestock (M06 in figure 3; Appendix C), suggesting that women livestock keepers are experiencing high climate change risk.
The northern part of Mali, where Tombouctou region is located, is centered around a pastoralist livestock economy (Djoudi and Brockhaus 2011). Throughout the Sahel—including in Mali—climate change has made a pastoral livelihood strategy difficult as access to water and foraging resources have greatly reduced due to increasingly warm and dry weather patterns, and extended dry periods (McOmber 2020; Segnon et al. 2021).
Pastoralist livelihoods in the Sahel are also marked by specific gendered roles and responsibilities and associated norms. Women are less likely to control cattle than men, but more likely to control small livestock. Nevertheless, women contribute labor in both types of livestock rearing. For instance, women tend to be responsible for milking and dairy processing. Few women own agricultural land, and most have restricted access not only to land but also other productive resources. Heavy productive and reproductive work burdens, in addition to other structural and logistic barriers, constrain women’s access to livestock extension and information about raising new breeds of livestock more adaptive to ecological changes (McOmber 2020). While differences exist by community and class, women in Tombouctou are subject to restrictive norms and roles hindering their access to resources, extension and information, diverse livelihood portfolios, and mobility; as well as burdening them with heavy reproductive and productive workloads (Djoudi and Brockhaus 2011). The conflict in the region drove outmigration, particularly by men as women have less access to financial assets and transportation or face mobility restrictions (McOmber 2020). As a result of male outmigration, women’s tasks now include what have been traditionally male activities (SPRING 2016; Djoudi and Brockhaus 2011).
<figure 3>
4.2. Zambia
Close to 50 percent Zambia’s economically active population is employed in agriculture. Women constitute an estimated 55 percent of the agricultural labor force (ILO 2021). Smallholder farmers are majorly reliant on a few staple crops like maize and cassava. Climate hazards include drought and dry spells, seasonal and flash floods, extreme temperatures, and changes in season onset and cessation. Combined with relatively low yields, high deforestation rates and localized land degradation, these induce high risks of food security. (CIAT and World Bank 2017c). De jure, women in Zambia have equal rights to own land, but that is rarely practiced. Men usually receive land rights after marriage from the parents-in law and the wife receives the right to cultivation. Women have limited access to credit and financial services in Zambia and lack the support of policies and legal framework that can facilitate their economic empowerment (OECD 2019).
Within Zambia, Luapula province is identified as a hotspot area for perennial crops (Zo4 in figure 4; Appendix C). Luapula province witnesses heavy annual rainfall, which helps the growing season but also makes the region prone to flooding (USAID 2014). Seasonal flooding causes significant crop damage to the perennial crop of cassava which is extensively cultivated and the second most important staple food in the province (Curran et al., 2009; White et.al, 2015).
While farming is the key livelihood option in the province, 68 percent of the women in Luapula do not own land (Zambia Demographic and Health Survey 2018). Luapula province is also one of the most gender unequal regions in Zambia offering limited economic opportunities and experiencing high levels of unemployment (UNDP 2016b; GRZ 2021). Cassava is commonly labelled as a women’s crop and in the Samfya and Mansa districts of the province women are involved in 90 percent of the processing activities (Alamu et. al 2019). However, women’s access to inputs and tools to improve the efficiency of processing is restricted, contributing to their time poverty. Further, women working in cassava production lack access to relevant knowledge and information that can help them diversify production in the face of a changing climate (Alamu et. al 2019). Mobility restrictions and power relations limit women farmers' ability to cultivate different crops and adapt to flooding (Baidu-Forson JJ, et al, 2015).
<figure 4>
4.3. Pakistan
Agriculture in Pakistan, contributing 19 percent to its national GDP (Government of Pakistan 2021), is severely affected by a range of climate hazards including, for instance, droughts and floods in the Punjab province and extreme temperatures in the Sindh province. Women contribute labor to crop production as well as livestock management. Yet, they have limited access to technologies and income-generating assets due to weak extension systems, illiteracy and lack of legal ownership of land (CIAT and World Bank 2017b; Wilderspin et al. 2019).
In Pakistan, the Balochistan Province emerges as a hotspot region for cereals (P04 in figure 5; Appendix C). Balochistan is poor, rural, arid and water-scarce with only 2 percent of the province being cultivated at any time due to water scarcity. Drought is a recurring feature of the province, and people are heavily dependent on the monsoon for irrigation (Rafiq and Blaschke 2012; FAO 2015). Cereals like wheat, millet, and maize are extensively cultivated in the province (IPC, 2021).
Nearly none of the women in rural Balochistan own land (NIPS 2019). Women work and produce on land mostly owned by men who are the one’s selling the harvest in the market. Yet, women have a high participation rate in agricultural labor and tend to be solely responsible for weeding, seed cleaning and storing crops‒all manual and labor-intensive (FAO 2015). Other factors that limit women’s empowerment include lack of access to credit, gender bias in transfer of new technologies and required training, and lack of access to education (FAO 2015). A study in the Jaffarabad District of Balochistan illustrates how male dominance, sociocultural notions and traditions, limited land access, and time poverty constrain women’s knowledge of and access to agricultural extension services (Baloch 2015). The patriarchal ideology in Balochistan is maintained through people, cultural practices and discourses which overtly assert idealized notions of masculinity and honor (Drucza and Peveri 2018). This ideology excludes women from decision-making in their households and reinforces economic subordination of women (Baloch 2015; FAO 2015).
<figure 5>
4.4. Bangladesh
Agriculture in Bangladesh contributes 17 percent to the national GDP, and constitutes at least one source of livelihood for 87 percent of its rural households. Bangladesh faces multiple climate hazards, including frequent flooding, droughts, cyclones and rising salinity (Ferdushi et al. 2019; Ahmed and Eklund 2021).
Based on the subnational hotspot analysis, the cluster of Kishoreganj districts (in Dhaka Division) and Mymensingh and Netrokona districts (in Mymensingh Division) emerges as a hotspot for rice (B11 in figure 6; Appendix C). In these districts, where substantial areas are characterized by wetland ecosystems (Haor), the occurrence of flash floods before the harvesting season affects crop output and causes severe livelihood losses to farmers (GOB 2018; Rahaman et al. 2019; Hoq et al. 2021). Siltation and sedimentation of major rivers, as well as riverbank erosion, are other challenges in this region (CEGIS 2012).
Boro rice is the major crop cultivated in this region. It is grown using irrigation in waterlogged low-lying or medium lands (Hoq et al. 2021). Women in northern Bangladesh typically have a substantial role in rice farming and are increasingly involved in farm management, in part due to male outmigration. Yet, women’s role and contributions are not always acknowledged by their male counterparts (Rahman et al. 2020). While women and men daily spend similar amounts of time working in Bangladesh, women spend 86 percent of their working time engaged in unpaid domestic activities—and men only 25 percent (Seymour and Floro 2016). Women’s work burdens increase not only in times of acute climate crises, but also because of soil and riverbank erosion which necessitates moving fields. The climate-induced male outmigration leaves women with increased productive and domestic workloads. Besides, in the context of Bangladesh, patriarchal gender norms restrict women’s access to resources, their mobility, and decision-making power in their households. However, such normative restrictions may vary by women’s level of education, household wealth and non-governmental organization membership (Rahman et al. 2020). Studies in North-Western Bangladesh found that women have a lower adaptive capacity to climate shocks than men as they have limited choices for livelihood diversification (Tanny et al. 2017, Naz and Saqib 2021). In wetland areas, such as the Kishoreganj-Mymensingh-Netrakona districts, women’s mobility restrictions hinder their access to timely warnings about floods and other information enabling them to mitigate or adapt to the climate adversities (Rahman and Haider 2020).
<figure 6>
There is growing recognition and increasing evidence that, in some contexts, women in agriculture and food systems are more affected and vulnerable to the adverse impacts of climate change than men. Gender inequalities and structural constraints to equality in society can exacerbate negative impacts by limiting women’s adaptive capacity.
Climate hazards, women’s exposure to climate hazards, and women’s vulnerability due to gender inequalities influence the climate change risks that women are facing. Each of these components of climate change risks can differ by location and context and overlap each other in complex ways specific to location and context.
The methodology proposed and applied in this paper highlights geographical areas that can be considered climate–agriculture–gender inequality hotspots as they are characterized by the convergence of significant climate hazards, a large number of women exposed to and potentially impacted by such hazards because of their heavy involvement in agriculture and/or livestock production, and a high vulnerability of women to adverse effects thereof because of the social conditions that disadvantage women. The methodology uses geospatial information and publicly available large representative socioeconomic datasets and applies data reduction techniques to calculate climate–agriculture–gender inequality hotspot index values. This is done at national and subnational area levels and allows ranking countries and subnational areas within countries by the respective hotspot index values, hence, identifying and mapping hotspots – and ‘cold-spots’.
The hotspot mapping methodology could be fine-tuned further with the inclusion of additional variables for women’s exposure to climate hazards beyond agriculture and livestock production; and for women’s vulnerability due to gender inequalities by including measures of inequalities in land ownership, time poverty, access to finance, technology, information, and education, among others. The hotspot mapping methodology could be extended to identifying populations at highest climate change risk differentiated not only by gender and subnational area, but also by age, ethnicity, caste, dis(ability), and/or socioeconomic status. However, availability of data representative at country or first administrative subnational levels has proven to be a constraint.
Climate–agriculture–gender inequality hotspot ranking and mapping have multiple uses. First, hotspot mapping holds the potential to underpin decision-making. It can guide risk management and investments in mitigation or adaptation assistance and the allocation of scarce resources to populations at highest risk by identifying high-risk countries and subnational areas where climate change hazards, exposure and vulnerabilities most acutely converge (de Sherbinin 2014). Second, it can provide important contextual information for projects or studies that aim to address issues at the nexus of climate change, gender, agriculture and food systems. The hotspot analysis and its indicators can support identifying the various local challenges the population, and women in particular, face across countries and within identified national boundaries. Other possible uses of hotspot ranking and mapping include cross-country or cross-regional comparisons, tracking changes over time in climate–agriculture–gender inequality hotspot rankings, or monitoring correlations of hotspot rankings with changes in gender, climate and/or agricultural policies over longer-term periods.
Competing interest statement
On behalf of all the authors of the manuscript, as the corresponding author, I declare that none of the authors have competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Data availability statement
All data is sourced from publicly available datasets. Detailed data of sub-indicators on the basis of which the subnational crop/category-specific climate–agriculture–gender inequality hotspot indices are calculated are available as online supplementary materials.
Acknowledgements and funding
We are grateful for the financial support of the International Development Research Centre (IDRC) (https://www.idrc.ca/en), the CGIAR Trust Fund Contributors (www.cgiar.org/funders), and the United States Agency for International Development (USAID) under the Gender, Climate Change and Nutrition Integration Initiative (GCAN). We wish to thank Elizabeth Bryan and Bhim Reddy to review and provide helpful comments on earlier versions of this work and participants of the Cultivating Equality Conference, 12-15 October 2021, and the Women in SERVIR workshop, 23 September 2021, for useful feedback.
Data availability statement
All data is sourced from publicly available datasets. Detailed data of sub-indicators on the basis of which the subnational crop/category-specific climate–agriculture–gender inequality hotspot indices are calculated are available as online supplementary materials.
Ethics statement
This article does not contain any studies with human or animal participants performed by any of the authors.
Competing interest statement
On behalf of all the authors of the manuscript, as the corresponding author, I declare that none of the authors have competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Author contributions
Jawoo Koo: Defining the conceptual framework and methodology for mapping climate–agriculture–gender inequality hotspots; Statistics; Hotspot index computation and mapping
Carlo Azzarri: Defining the conceptual framework and methodology for mapping climate–agriculture–gender inequality hotspots; Statistics; Hotspot index computation and mapping; Indicators of exposure based on Labor Force Surveys
Avni Mishra: Defining the conceptual framework and methodology for mapping climate–agriculture–gender inequality hotspots; Literature; Conceptual framing of vulnerability due to gender inequalities; Indicators of vulnerability due to gender inequalities; Contextualization of results
Els Lecoutere: Defining the conceptual framework and methodology for mapping climate–agriculture–gender inequality hotspots; Literature; Conceptual framing of vulnerability due to gender inequalities; Indicators of vulnerability due to gender inequalities; Contextualization of results
Ranjitha Puskur: Defining the conceptual framework and methodology for mapping climate–agriculture–gender inequality hotspots
Nitya Chanana: Defining the conceptual framework and methodology for mapping climate–agriculture–gender inequality hotspots; Literature; Conceptual framing of vulnerability due to gender inequalities; Contextualization of results
Niyati Singaraju: Defining the conceptual framework and methodology for mapping climate–agriculture–gender inequality hotspots; Literature; Conceptual framing of vulnerability due to gender inequalities; Contextualization of results
Gianluigi Nico: Statistics; Hotspot index computation and mapping; Indicators of exposure based on Labor Force Surveys
Arun Khatri-Chhetri: Defining the conceptual framework and methodology for mapping climate–agriculture–gender inequality hotspots
Consent for publication
On behalf of all the authors of the manuscript, as the corresponding author, I declare that all authors agree with the content and consent to submit the article for publication.
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