Research, part of a special feature on Enhancing Social-Ecological Resilience and Equity in Southern Africa: insights from Research and Practice

INTRODUCTION

Food and nutrition security are salient concerns in Southern Africa (FAO et al. 2018, FAO, UNECA and AU Commission 2021), which also experiences some of the highest rates of urbanization in the world (Zimmer et al. 2020). Most urban households in the region purchase their food rather than grow it (Davies et al. 2021), which means that urban food systems (UFS) bridge important divides between sites of production and consumption (High Level Panel on Experts on Food Security and Nutrition [HLPE] 2020, Haysom and Battersby 2023). In this paper, we use the 2020 HLPE on Food and Nutrition Security’s definition of UFS, where UFS comprise a hybrid mix of household food sourcing opportunities from retailers, including open-air markets, street vendors, local shops, and supermarkets, as well as household-level strategies, such as urban agriculture and food sharing.

UFS in Southern Africa contend with multiple challenges that affect the provision of sufficient, affordable, nutritious, safe, and culturally appropriate food to urban consumers, including challenges related to climate change, conflict, socioeconomic inequities, and public health (Ziervogel and Frayne 2011, Clapp and Moseley 2020, Josephson et al. 2021, Abu Hatab 2022). For instance, the COVID-19 pandemic highlighted and exacerbated already existing and longstanding inequities across public and private sectors, societies, and geographies (Laborde et al. 2020, Moseley and Battersby 2020, Joshi et al. 2022). Lockdowns across the globe, including in Africa, presented new challenges to food availability and access, particularly for lower income consumers who lacked the capacity to adapt to shocks and stressors (Dombroski et al. 2020, Bisoffi et al. 2021, Carey et al. 2021). The Russia-Ukraine conflict further contributed to increased food, fertilizer, and energy prices, which placed pressures on global food supply chains with adverse outcomes in sub-Saharan African countries (Abu Hatab 2022, Alexander et al. 2023). At the same time, climate change continued to exacerbate inequalities via a combination of slow-onset pressures (e.g., drought) and sudden-onset disasters (e.g., flash flooding) (Rusca et al. 2023). These pressures and disasters can have adverse impacts on urban food retailers, depending on where the event(s) occur and the type of agri-food supply chain that these retailers rely on (Blekking et al. 2022). Compounding each of these challenges are ongoing high rates of poverty and food insecurity that co-occur in an expanding urban Southern Africa (FAO et al. 2018, UNECA and AU Commission 2021), where urban populations across the region are expected to grow from about 79 million in 2015 to 204 million people by 2050 (UN-DESA 2018).

A nutrition transition affecting food security also underlies these exogenous shocks and pressures to UFS in Southern Africa, with implications for equity. Across the world and in Africa, scholars have observed an increase in dietary diversity and a rise in “Western style” diets (e.g., rich in fat and refined carbohydrates) (Reardon et al. 2003, Dixon et al. 2007). In Southern Africa, nutrition trends depend on the specific urban location and the diversity of food retailers present. Some urban households still purchase most of their food from open-air markets (Tuholske et al. 2020, Hannah et al. 2022), whereas those in other cities purchase at a greater rate from supermarkets, which tend to offer more processed foods, animal products, and other “Western style” foods (Khonje et al. 2020, Otterbach et al. 2021). Although open-air markets traditionally sell whole foods (e.g., fresh produce), these retailers are increasingly adapting to consumer needs by selling processed foods, sometimes produced by the growing food processing sectors in the countries where these markets are located (Reardon et al. 2021). The nutrition transition of Southern African UFS has specifically occurred alongside a transformation of the food retail environment in cities, which is largely driven by the confluence of the economic interests of real estate developers, private finance, and investors (Battersby 2017). This financialization of UFS has expanded the role of investors and larger commercial food retailers, like supermarkets, leading to higher food prices and high price volatility with disproportionate implications for food security among poor and vulnerable populations (Isakson et al. 2023). The result is an UFS development process that is less inclusive of the diverse socioeconomic populations who rely on these systems for food provisioning.

Given the compounding shocks and stresses to UFS and an underlying urban nutrition transition in Southern African UFS, what opportunities exist to address uneven access to food and improve urban food security outcomes?

From a theoretical angle, cultivating a resilience perspective is one approach for identifying pathways to cope with shocks and pressures in a variety of social-ecological systems contexts (Biggs et al. 2015, Hamann et al. 2018, Reyers et al. 2022, Rockström et al. 2023). Seven specific core principles are proposed to help build resilience: (1) maintain diversity and redundancy, (2) manage connectivity, (3) manage slow and fast variables, (4) foster complex adaptive systems thinking, (5) encourage learning, (6) broaden participation, and (7) promote polycentric systems (Biggs et al. 2015). These resilience principles have been applied to core challenges that often arise in food systems (i.e., environmental and nutritional decline, food insecurity and trade, inequity and governance, food systems illiteracy) to illustrate pathways to future food systems resilience (Wood et al. 2023). However, the applicability of resilience principles to the day-to-day governance of UFS remains formidable for planners and policymakers who need swift, actionable solutions to context-specific UFS challenges, like those described above, as well as the financial backing to implement these solutions.

From the resilience development programming angle, which focuses on household food security, applied research efforts have focused on evaluating the absorptive, adaptive, and transformative capacities of households to mitigate the adverse development consequences of future shocks and stresses (Constas et al. 2014). Across resilience theory and development practice, significant efforts have developed ways of theorizing and measuring these enabling resilience capacities among individuals and households (Barrett et al. 2021, Constas et al. 2022, Smith and Frankenberger 2022). Recent efforts in the resilience measurement space have reviewed a suite of principles, frameworks, and caveats to a resilience measurement agenda of food systems (Béné et al. 2023). Even with these variable measurement tools, common themes and findings persist. For instance, households of lower socioeconomic standing and female-headed households tend to be more food insecure relative to higher income and male-headed households (Modirwa 2011, Caesar and Riley 2018, Bulawayo et al. 2019, Akalu and Wang 2023), but further research is needed to identify pathways for addressing these inequities in UFS.

Despite significant contributions to the theory and measurement of resilience and food security in the context of food systems (Béné and Devereux 2023), explicit considerations of equity and resilience and how to govern UFS in recognition of widespread inequities are nascent in the academic literature. Moreover, it is not yet clear how UFS in the Southern African context can equitably support urban populations to meet their nutrition and food security needs. To address this knowledge gap, we pose the following research questions:

1. How do socioeconomic inequities occur among urban households in Southern African cities?
2. How do low- to middle-income urban households in Southern Africa navigate their local UFS in the context of the COVID-19 pandemic, and do household approaches vary across different cities?
3. What role can policy and planning play in promoting equitable and resilient UFS in a Southern African context?

We first consider the existing literature on equity and UFS with a focus on Southern Africa’s smaller urban areas. Secondary cities are growing rapidly on the African continent, with cities of fewer than 500,000 people now hosting two-thirds of all urban population growth (UN-DESA 2014, Pieterse et al. 2015). The UFS of these smaller cities are under increasing pressure to ensure their local constituents are food secure, but there is a relative lack of data on UFS in secondary cities compared to larger primary cities or capital cities (Riley and Crush 2023). Then, we provide an empirical case study of how households in Zambia’s smaller urban areas navigated their UFS in the initial stages of the COVID-19 pandemic using household survey data that was collected across 14 secondary cities over the three time periods of 2019, 2020, and 2021. Rather than operationalizing equity and resilience in the data analysis, we focus instead on presenting a portrait of these diverse UFS, including how households across socioeconomic groups perceive challenges to maintaining food security and how they navigate their UFS over time. In the Discussion section, we use the theoretical concepts on equity and resilience with local knowledge of Zambia’s UFS (and UFS in the wider Southern African region) to consider the implications of the empirical case study for promoting household and UFS resilience.

Finally, in our discussion we examine how UFS policy and planning could address these inequities. Our investigation is a collaborative research effort between partners from multiple Southern African countries, including Zambia, which supports a resilience-action agenda for urban resilience. Co-production of research findings is essential for identifying context-specific solutions and relevant pathways for promoting equity and resilience in UFS (Sitas et al. 2021). Thus, this research paper was co-produced amongst researchers with food systems expertise in the African context and colleagues in the Ministry of Agriculture in Zambia as part of the 2022–2023 Southern African Resilience Academy. Results of the study were shared in outreach campaigns with relevant urban planners and policymakers in the 14 cities where the research was conducted. Co-produced communications materials included a policy toolkit for Zambian urban planners and policymakers and a policy note for the wider Southern African region. The entire process of co-producing research and engaging with relevant planners and policymakers to identify and discuss contextually relevant solutions was especially formative to the information presented in the Discussion and Conclusion sections.

UFS in Southern Africa

Food systems encompass a suite of human-environment interactions related to food production, processing, packaging, distribution, retail, and waste management (Ericksen 2008). As countries urbanize, the food supply chains that food systems rely on become longer and increasingly complex as they connect agricultural production areas with UFS (Barrett et al. 2022). UFS are geographically situated in areas of highly concentrated consumer demand and therefore shape the dynamics of the wider food system via consumption demands and higher amounts of purchasing power (Reardon et al. 2012, Barrett et al. 2022). Because of this widespread reach and influence of UFS, changes to their functionality and complexity, from either external shocks or an underlying nutrition transition, can substantially alter urban and peri-urban human and environmental well-being, as well as social and political outcomes. These changes can affect how urban residents navigate and acquire food in their UFS, which are often characterized by intersecting socioeconomic, gender, health, and political inequities (Hawkes 2006, Hawkes et al. 2022).

Food systems are also sites of historical, dynamic, and cross-scalar interactions among many actors, ranging from producers, market traders, informal vendors, wholesalers, modern retailers, consumers, policymakers, and so forth (Ericksen 2008). For UFS in Southern Africa, colonial legacies, local planning efforts, and globalization and financialization trends shape the interactions among these actors (Haysom 2021). Colonial era conceptualizations of how a modern city should function are often pursued to this day, which can further marginalize some populations (Fox 2014, Kiaka et al. 2021). For example, urban cleanliness and order tend to characterize idealist visions of a contemporary African city. This concept of modernity is viewed positively and supports the formal food retail industry in urban development. Conversely, informality is considered unacceptable due to concerns like overcrowding, hygiene, and illegality (Battersby 2017, Battersby and Watson 2018a, 2018b). This marginalizes informal food system actors, including street vendors, despite many lower income households relying on these vendors for daily food provisioning.

To understand these UFS inequities in a Southern African context, we can draw theoretically on Fraser’s work on justice (Fraser 2000, 2005, 2009) and Leach et al.’s (2018) adaptations of Fraser’s framework of equity in a social-ecological system. In short, equity in UFS can manifest in three forms. First, distributional equity refers to how resources, costs, and benefits are allocated or shared amongst people and groups. For example, urban agriculture has commonly been promoted as a solution to household food insecurity and poverty in African cities (Lee-Smith 2010, Nkrumah 2018). Yet recent evidence from Southern Africa highlights various barriers that can exclude lower income households from growing or raising their own food, such as a lack of access to land, property rights, and farming inputs (Davies et al. 2021).These barriers are unevenly distributed, as households in more affluent suburbs have greater capacity to engage in urban agriculture because of better access to resources (Davies et al. 2021, Matamanda et al. 2022).

Second, recognitional equity refers to the acknowledgment of and respect for identity, values, and associated rights. The Food and Agriculture Organization’s HLPE on Food and Nutrition Security recently proposed including agency as a dimension of food security, in addition to availability, accessibility, utilization, and stability (HLPE 2020). Reflective of recognitional equity, agency within the context of food systems refers to the ability of people to make choices about what to eat; how the food they consume is produced, processed, and distributed; and where they purchase that food (Clapp et al. 2022). The UFS environment can especially impact household agency around food access when local food retailers are displaced by commercial chains and food systems dynamics prioritize processed foods (Dixon et al. 2007).

Third, procedural equity highlights how decisions are made, and the extent to which different people and groups can influence these decisions or ensure that their perspectives are represented or incorporated in decision-making processes. Procedural equities can be compromised, alongside food security and limiting market participation in the UFS, if forms of localized feedback on UFS policy and planning are not incorporated. Procedural equity is especially pertinent in consideration of the increasing concentration of power by food system actors, such as large, industrialized food retailers (e.g., see Battersby 2017). Yet, informal actors, such as street vendors, often play an important role in the UFS of Southern Africa but are not always incorporated into UFS policy and planning decisions (Toriro 2019, Giroux et al. 2021). Politics and municipal governance in some African cities can influence which actors are recognized or not in an UFS. In cities like Accra, Dakar, and Lusaka, the safety and well-being of informal vendors, who are often vulnerable and working in unsafe conditions, have been forcibly excluded from the UFS with extreme cases of crackdowns and violence (Resnick 2019). Overlooking the important contribution of diverse actors to the UFS can have devastating consequences for urban household employment and food security.

Although it is helpful to have a conceptualization of equity in UFS, the greater challenge is to develop systems of UFS governance that incorporate the three forms of equity described above. Although UFS serve urban consumers from across the socioeconomic spectrum, UFS in Southern Africa are often not developed with the intention of ensuring equitability in all its forms. For instance, urban planners often endorse investment by large chain supermarkets and shopping malls, viewing them as symbols of urban modernization that stimulate local economic growth (Skinner 2016). However, the pricing and quantities of food in formal retail outlets like supermarkets often cater to middle- and upper-income urban residents who can afford to buy in bulk, inadvertently excluding lower income households that rely more on traditional markets and informal vendors for their food purchases (Peyton et al. 2015, Berger and van Helvoirt 2018). UFS in Southern Africa thus face ongoing equity challenges across household, neighborhood, retail, and city scales.

A further challenge to addressing equity challenges in UFS is that the development and coordination of UFS are not always explicit mandates for urban planners. Urban development plans are also often created independently of UFS consideration, even though urban planning impacts food systems in several ways (Haysom 2021). For instance, a study of 91 policies in South Africa found that policies relevant to food systems governance were often developed in silos and the diverse stakeholders did not often coordinate in the implementation of policies (Kushitor et al. 2022). Unfortunately, if urban policymakers and planners are indifferent toward food-related planning, then food-related outcomes may be negative, not neutral (Pothukuchi and Kaufman 1999). Thus, UFS policy and planning may be central to fostering the resilience of households to equitably recover from and adapt to the impacts of shocks and stresses.

METHODS

We use equity and resilience as the theoretical lens to consider challenges and opportunities for maintaining food security in Southern African UFS. Our approach centers on the presentation of an empirical case study based on household survey data of how socioeconomic inequities occur among households in UFS in 14 smaller, secondary cities in Zambia, how these inequities shape perceptions of sustaining household food security, and how these different households navigated their UFS environment under the shock of COVID-19. Although equities can present in a myriad of ways in UFS (Hawkes 2006, Hawkes et al. 2022), we focus on socioeconomic inequities at the household-level as the principal framing of equity for presenting information on the localized perspectives of food security and resilience in our Zambia UFS case study.

We present an empirical portrait of UFS in Zambia’s secondary cities. Due to limitations with the availability of consistent data in the 2020 and 2021 survey periods and the nascent state of theory on how equity and resilience are relevant to UFS governance, we do not operationalize equity or resilience in our analysis. Instead, we use an inductive, theory-building approach (Eisenhardt 1989, Volmar and Eisenhardt 2020). We observed patterns among households and cities in our case study and coupled these observations with additional insights from the authors’ existing knowledge and the academic literature to sharpen existing theoretical constructs of equity and resilience in UFS. We use the Discussion section to reflect on our research in the context of our case study and offer theoretical observations that can be further explored and tested in future studies on UFS.

Urban household survey

Our survey dataset consists of 657 household surveys collected in June–July 2019, February 2021, and August 2021 from 14 secondary cities in Zambia (Fig. 1). We discuss these surveys based on when they were designed, using the years 2019, 2020, and 2021, respectively. The secondary cities in our sample were Batoka, Choma, Chongwe, Itezhi-Tezhi, Kapiri Mposhi, Maamba, Mazabuka, Mbabala, Mkushi, Mpongwe, Namwala, Nyimba, Pemba, and Petauke. We identified these specific cities using the Global Human Settlement Population (GHS-POP) dataset (Schiavina et al. 2019). We focused on secondary cities with populations ranging from 5000 to 200,000 people because half of Africa’s urban population is concentrated in urban areas with fewer than 300,000 inhabitants (UN-DESA 2018). Local enumerators who were fluent in local languages conducted the surveys in person in 2019 and over the phone during the 2020 and 2021 surveys. We obtained permission from municipal-level authorities and community leaders, as well as Institutional Review Board approval from the lead author’s home institution, to conduct the surveys based on ethical human subjects-based research protocols (#1804499759).

The 2019 survey was designed to support a larger project on the impacts of agricultural decision-making and adaptive management on food security. The subsequent 2020 and 2021 surveys were implemented opportunistically to accommodate data collection constraints during the first two years of the COVID-19 pandemic. In-person surveys lasted approximately 45–60 min and phone surveys lasted approximately 20–30 min. Questions asked in the phone survey only included a subset of the questions from the in-person survey to more easily accommodate the limited time availability and personal convenience that an individual would have over the phone versus in person. In the 2019 survey, respondents were asked an extensive series of questions on the following topics: household characteristics, demographics, migration and labor, social capital, community support and food transfers, food consumption scores, food security indices, farming, urban agriculture, food purchasing behaviors, household expenditures, and perceptions of climate and food security challenges.

However, the 2020 and 2021 surveys asked only a subset of the questions that were posed in the 2019 survey. Thus, the multi-year surveys do not host the types of data that would be appropriate for a longitudinal analysis specific for this paper’s topic. Data relevant to our research that remained consistent across all three survey periods included engagement in urban agriculture and food purchasing behaviors. Household characteristics and demographic data were only collected for households that moved locations between the survey periods. Additionally, questions about the perceptions of food security challenges were only asked in 2019. For this paper, we use household characteristics, demographic, and perceptions of food security challenges data from the 2019 survey and urban agriculture and food purchasing behavior data from all three 2019, 2020, and 2021 survey periods.

The household sampling approach was informed by the authors’ prior familiarity with the cities and consultations with each city’s local government officials. These officials shared estimates of the total numbers of households and estimated population size in each residential area or each city, as well as contextual information about the city. On the basis of this information, households were purposely selected from low- and middle-income residential areas in the 14 secondary cities in 2019. Compared to higher income households, lower income households are more dependent on UFS for maintaining food security as a function of poverty and other inequalities that are associated with a range of social, economic, political, and environmental drivers (e.g., extreme weather, conflict, price shocks, governance failures, and health-related challenges) (Ziervogel and Frayne 2011, Clapp and Moseley 2020, FAO et al. 2021). In other words, we would expect lower income households to be more vulnerable to UFS shocks. Thus, with an aim to identify how UFS can best support the resilience and equity of those households most susceptible to food insecurity, we focused on low- to middle-income residential areas as our targeted population of interest and sample population.

Within each residential area of each city, we used a systematic random-sampling approach. A defined number of households relative to the size of the sampling area were surveyed each day. This defined number was determined on the basis of the size of the residential area, which ensured a representative and spatially distributed sample for each residential area. Additionally, households next to one another were not sampled. After sampling a household, an enumerator would skip a pre-defined number of households (based on the daily defined sampling goals) until sampling the next household. If, for example, a residential area was estimated to have 100 households, and the daily quota was 10 surveys, then the enumerators would skip every 10 households. For larger cities, if a residential area was estimated to have 500 households, and the daily quota was 20 household surveys, the enumerators would skip every 25 households. Enumerators were encouraged to work together with the fieldwork team and urban officials to ensure that household sampling was spatially distributed evenly across the residential areas.

If a household had no available or eligible respondent, then the enumerator continued to each successive house until they were able to carry out the survey and then the process of skipping a set number of households was repeated. Eligible respondents included only individuals over the age of 18 from each household with knowledge of household and household member characteristics. Respondents who did not have in-depth knowledge of the employment and income of other household members were not interviewed. The number of surveys conducted ranged from five to thirty per residential area in each city, depending on the density of the area. Sample sizes ranged from 30 to 300 total households in each secondary city, with the smallest cities (e.g., Batoka) consisting of just one residential area, whereas larger cities (e.g., Choma) had many. This data collection approach allowed for the spatially stratified sampling of households, where the household number sample was proportional to city size.

Data preparation

Our final dataset consisted of 657 households that participated in all three surveys. The 2019 dataset consists of 2040 households that participated in the in-person survey. Of these households, 1572 provided a phone number(s), indicating their willingness to be contacted again in subsequent surveys. This set of 1572 households from 2019 served as the sampling population for the 2020 survey. We used a proportional random sample based on the 2019 sub-sample sizes per city to ensure that the same proportion of surveys were collected in each city, while also maintaining enough observations to conduct statistical analysis. The 2020 household surveys were conducted via phone call because of travel restrictions and social distancing policies introduced at the start of the COVID-19 pandemic. All reasonable attempts were made to interview the same household member that was first interviewed in the 2019 survey. In total, 950 households engaged in the 2020 survey, of which 865 households participated in the third survey in 2021. Fewer numbers of households participated in the 2021 survey because of natural attrition rates. Survey participation attrition rates were the highest between the 2019 and 2020 surveys, but between each period there were no clear patterns resulting from this attrition. This allowed us to maintain a proportional sample across study cities.

To prepare the data for analysis, households with duplicate identification numbers or without any identification numbers were excluded from the combined dataset. Data cleaning and analyses were conducted by using RStudio version 2023.3.0.386. We used an initial listwise deletion approach to remove household observations with missing household identification numbers so that we could work from a complete dataset, resulting in a set of 740 households. The dataset was then reduced by 11 households to 729 because of the exclusion of the outliers of incomes greater than 25,000 Zambian Kwacha per month (i.e., US $1787 at the time of data collection in 2019) and households with more than 14 household members. We determined these threshold numbers in consultation with our Zambian authors as being unexpectedly extreme outliers and not representative of the typical urban Zambian household. In the regression analysis, we used the log transformation of the income variable to account for its non-linear and highly skewed distribution. Taking the logarithm of income is a common analytical approach to stabilize the variance and improve model interpretation of highly skewed variables.

Then, we excluded the observations for 39 households that moved between the 2019 and 2020 survey periods and 33 additional households that moved between the 2020 and 2021 survey periods. Following this data cleaning, the results presented in this paper are based on 657 observations in the 14 Zambian cities, unless otherwise stated in the Results section. The description of the variables for this final dataset that we used in this paper are presented in Tables 1 and 2. Appendix 1 presents figures with descriptive statistics of the distributions of some household variables by secondary city.

Urban food systems context in Zambia’s smaller secondary cities

Like other cities in sub-Saharan Africa (Hannah et al. 2022, Blekking et al. 2023, Fobi et al. 2024), open-air markets are an important food sourcing option in the UFS of our case study (Fig. 2). Based on data collected in 2019, local shops and vendors are also important food sourcing options in most cities, followed by supermarkets (Hannah et al. 2022; Fig. 2). Supermarkets tended to be more prominent as a food sourcing option in larger cities, as well as some smaller cities that are near larger cities (e.g., Batoka and Mbabala, located close to Choma), where households may prefer shopping at supermarkets (Figs. 2 and 3).

Given this diversity across UFS, we would expect households to exhibit variations in food acquisition patterns in response to any given shock, because no single UFS in Zambia is representative of all UFS in the country or across Southern Africa. Households situated in the UFS of smaller cities (e.g., Batoka, Mbabala, Namwala, and Pemba), with no or fewer supermarkets and local shops, could face significant constraints in food purchases if open-air markets were required to close. Households in these cities could then rely more on urban agriculture or street vendors as sources of food. Households in larger sized cities (e.g., Choma, Kapiri Mposhi, and Mazabuka) may have access to a greater diversity of food sourcing options within their UFS, hence shifts in food acquisition behaviors would be contingent on which retailers closed.

During the early period of the COVID-19 pandemic (2020–2021), laws in Zambia that prohibited street vendors and informal markets from operating in undesignated areas were strictly enforced. However, most businesses dealing with essential goods and services, including more formal open-air markets and supermarkets, were allowed to continue operating (Matenga and Hichambwa 2022, Mudenda et al. 2022, Manda 2023). Although laws mandated certain restrictions and behaviors during the pandemic, insights from our Zambian authors noted that enforcement varied across UFS and even within UFS throughout the pandemic, where some locations were stricter (e.g., Choma) and others more ambiguous, such as in the smaller, more remote cities.

There were also disruptions in food supply chains due to reduced reliance on imports (e.g., from Zimbabwe and South Africa), as well as health screenings for truck drivers and the disinfection of vehicles that slowed transportation of food (Manda 2023). Supply chain issues resulted in higher food prices, which affected urban consumers’ ability to afford food and maintain food security, especially among households that lost employment in the informal sectors (Matenga and Hichambwa 2022). All of these challenges would likely increase the need for people to rely on alternative food sources, such as engaging in urban agriculture or depending on in-country agricultural systems. Despite these challenges, however, and according to the authors’ perspective, there did not seem to be any clearly available and widespread systematic support during the COVID-pandemic from government programs to adequately target improvements in the UFS.

Analysis

Using the survey dataset from 2019, we delineated response groupings based on socioeconomic standing defined by income quartiles. Because urban households most often rely on food purchases to meet their food and nutrition security, we used income quartiles as a socioeconomic lens to guide our analysis. These income quartiles were calculated on the basis of households’ adjusted household income. The total income from all household members were aggregated from their respective incoming salaries, rents, remittances, informal business revenues, and social grants. Then, we divided this sum by the square root of the total number of household members. This approach distributes the incomes across the sampled households in a way that income is not disproportionately affected by differing household sizes (OECD 2011). Households were most often headed by males (488 households, 74%) compared to females (169, 26%).

First, we examined the relationship between socioeconomic standing and food security in our dataset. Across all households, we assessed the association of household socioeconomic standing via income quartiles and food security using the reduced Coping Strategy Index (rCSI) scores, where a higher rCSI score suggests greater food insecurity. With significant existing evidence that shows that food insecurity is most often associated with socioeconomic standing, we disaggregated our data by gender of the household head to illustrate the intersectionality of female-headed households with socioeconomic standing in the context of household food security. We conducted this analysis for only the baseline year of 2019, because that was the only year for which the rCSI was collected.

We used rCSI as a proxy measure for household food security because rCSI evaluates household reactions to limited food availability and accessibility during a seven-day period. The rCSI is calculated on the basis of the number of days a household has experienced five coping strategies in the prior seven days from when the household was surveyed. The coping strategies include relying on less preferred or less expensive foods, borrowing food or relying on help from friends or relatives, limiting portion size at mealtime, restricting consumption by adults for small children to eat, and reducing the number of meals eaten in a day. A weighted rCSI score ranges from 0 to 56, where scores ranging from 0 to 3 indicate no to low coping, 4 to 9 indicate medium coping, and greater or equal to 10 indicate high coping and high food insecurity.

Second, we investigated how urban households perceive challenges as they relate to food security from a disaggregated perspective of socioeconomic standing via the 2019 income quartiles. In the 2019 survey, we asked households, “What are your three biggest challenges with regard to maintaining this household’s food security?” Enumerators posed the question so that respondents had an open-ended response. The enumerator then recorded the key topics, which were post coded as follows: food is too expensive / cannot afford; socioeconomic security (e.g., loss of employment, no income, low income, not enough money, lack of job opportunities, poor economy, school fees, high prices, household demographics - large household, not in labor force); health and food concerns (e.g., there is not enough diversity of food available that I both enjoy and can afford and the food that I have access to is not safe / poor quality); climate- or weather-related challenges (e.g., drought, flood, storms, high temperatures); and do not have any challenges related to food security.

Finally, we considered the ways in which households across the 14 secondary cities in Zambia bought their food from retailers and produced their own food via urban agriculture in the context of the COVID-19 shock to these UFS. Because of the multi-year nature of our data, we can use COVID-19 as an example of an acute shock to the UFS, understanding that similar other shocks or pressures could similarly impact UFS. Urban household food security is largely contingent on access to the food retail sector or households’ ability to grow their own food. When we assessed the overall food purchasing patterns of sampled households across the survey period, we saw a general decrease in the total number of purchases made in a 14-day period from open-air markets, street vendors, and small retail shops. Thus, to assess the variation of how households navigate UFS across different cities, we aggregated household visits to urban retailers and engagement in urban agriculture by city.

Then, we conducted regression analyses to assess the relationship between what types of households engaged in urban agriculture and frequented open-air markets, vendors, local shops, and supermarkets over the 2019, 2020, and 2021 survey periods. All models serve as a correlation analysis to explore relationships between variables rather than to establish causation. We employed binary logistic regression models for investigating which types of households engage in urban agriculture (1 = engaged in urban agriculture, 0 = not engaged). Then, we used multiple multivariate regression for assessing which types of households frequented open-air markets, vendors, local shops, and supermarkets in the past two weeks. For all models, household characteristics and demographics were obtained from the 2019 survey as the independent variables. Data on these elements were not collected from our sample population of households that did not move over the survey periods. Thus, the significant underlying assumption of our models is that these 2019 household variables remain the same or similar over the three 2019, 2020, and 2021 survey periods.

We additionally included binary control variables on the secondary cities to further link our household analysis with the UFS and consider which types of cities tend to be related to greater or lesser engagement in urban agriculture and frequenting of various food retailers. By situating our analysis across different-sized secondary cities, we can consider how a shock or pressure, like COVID-19, could affect households’ food acquisition behaviors at the UFS-level. We adopt the city cluster types that were established in Hannah et al. (2022) using a Principal Component Analysis as follows: Mbabala, Batoka, and Pemba are considered smaller cities; Mpongwe, Namwala, Maamba, and Itezhi-Tezhi are remote mid-sized cities; Nyimba, Petauke, Chongwe, and Mkushi are connected mid-sized cities; and Choma, Kapiri Mposhi, and Mazabuka are larger secondary cities. In our models, we used the larger secondary cities grouping as the reference category for comparing results. All models were run with robust standard errors and tested for multicollinearity. Observations containing missing data were excluded from the regression analysis by using a listwise deletion approach. Regression results were presented using the stargazer package (Hlavac 2022).

RESULTS

We first present the baseline results from our 2019 household survey by highlighting food insecurity differences between household types and income quartiles, disaggregating our data by the gender of the household head. Then we present the results from the same survey regarding self-reported challenges for maintaining household food security. Finally, we provide results using all three household surveys aggregated at the city level and the multivariate multiple regression analyses that investigate how household-retailer purchasing patterns and engagement with urban agriculture changed between 2019 and 2021.

Socioeconomic standing and food security

We found important differences between socioeconomic standing and food security coping strategies. Households with lower incomes had, on average, higher rCSI scores than households with higher incomes, according to our baseline data from 2019. When disaggregated by gender of the head of household (Fig. 3), we find that female-headed households in the first income quartile have a median rCSI score of 18, whereas male-headed households in that same quartile have a median rCSI score of 10. The other three income quartile groups have relatively similar median rCSI scores between female- and male-headed households, with each increasing quartile representing a decrease in median rCSI values, indicating increasing food security as incomes rise.

Although other studies have noted similar results, it remains important to focus on inequities that female-headed households often face compared to male-headed households for two reasons. First, gender-based inequities often disproportionately hamper the ability of women to improve their livelihoods (Dodson and Chiweza 2016). Second, this finding may also indicate that these households contend with fewer income-earning household members, which in an urban setting creates a unique challenge for households to access food, because most food is purchased in the urban context (Davies et al. 2021).

Household perceptions of maintaining food security

Figure 4 shows that households perceived similar challenges for maintaining their households’ food security regardless of their income quartile. Although households in the highest income quartile reported no challenges compared to the other income quartiles, the distribution of perceived challenges across all categories follows a similar order. Across all income quartiles, households reported that food prices are the biggest challenge, followed by issues related to the socioeconomic security of the household as a secondary challenge, and climate- or weather-related challenges as a tertiary challenge. Fewer lower income households reported no challenges in maintaining their households’ food security compared to the other income quartiles, which suggests that lower income households were more likely to engage in coping strategies in the face of challenges (e.g., Fig. 3). At the same time, however, some households in the highest income quartile were not immune to food insecurity.

Regardless of income levels, the perceived barriers to maintaining food security across different types of households were similar (Fig. 4). However, lower income households perceived greater challenges compared to households with higher incomes. Food price and socioeconomic security concerns were the most cited challenges by all households, regardless of their income quartile. These results make intuitive sense as urban household food security is largely the result of food access (Crush and Frayne 2011), which in the urban context is correlated with purchasing power (Reardon et al. 2012, Barrett et al. 2022). Climate was consistently cited among household respondents as the third most common concern for maintaining food security with little to no variation across income quartiles. This suggests a consensus among households in secondary cities that climate remains a persistent challenge to food security, a finding that warrants further investigation.

Household navigation of UFS in the context of COVID-19

From 2019 to 2021, we found changes at the city scale regarding households’ food purchasing behavior for four different retailers (Fig. 5). In 2019, the most visited food retailer was open-air markets, with an average of 6.5 visits per 14 days. Specifically, open-air markets were most frequented in Mbabala, Mamba, and Pemba (approximately 11.8, 9.6, and 9.1 visits per 14 days, respectively). Households visited street vendors most in Batoka, Mbabala, and Pemba (5.2, 4.1, and 3.6 times, respectively), whereas households visited local shops the most in Choma, Pemba, and Mpongwe (4.0, 3.2, and 3.0 times, respectively). Supermarket use averaged less than 1 visit per 14 days in all secondary cities, with the highest use occurring in Mazabuka, Kapiri-Mposhi, and Choma. These larger cities are also the only cities within our sample that had a supermarket at the time of data collection.

With the onset of the COVID-19 pandemic in early 2020, our results indicate an overall decrease in the average number of visits to all retailer types overall, and only a handful of increases in the number of visits. For instance, Mkushi, Mpongwe, and Petauke each had increased purchases from roadside vendors in 2020, which was not the case for open-air markets and local shops. These trends may correspond with local government efforts to manage the spread of COVID-19, efforts by households to minimize their exposure to the virus by visiting markets and shops less frequently, and an overall diminished household income available to purchase food. These households may have sought out roadside vendors in the absence of these other retailers, or their lack of income to purchase food from these retailers may have contributed to the decline in visits.

On the other hand, supermarkets also had decreased visitation during this time, but this decrease was not as severe as our data show for open-air markets and local shops, which may be a result of fewer households from low- to middle-income neighborhoods going to supermarkets in general. Wealthier households that do go to supermarkets would likely have the income to continue to purchase food from supermarkets despite the socioeconomic pressures associated with the COVID-19 pandemic.

Our data also indicate a return to pre-pandemic use of some retailers in 2021, while for others the decline was greater. In general, local shop purchasing patterns appear to have rebounded to 2019 values after the 2020 decrease. For open-air markets some cities saw a return to 2019 values for households purchasing food from open-air markets, whereas others remained lower than 2019 values. For instance, households in Chongwe and Kapiri-Mposhi both visited open-air markets at an average of about 4.3 times per 14 days in 2020, but in 2021 that frequency increased to approximately 5.5 times per 14 days, nearly the same number of visits as reported in 2019. On the other hand, data from Maamba and Mbabala show more visits in 2021 compared to 2020, yet the number of visits in 2021 were far below the 2019 values.

For roadside vendors, Mpongwe households visited roadside vendors approximately 2.9 times per 14 days in 2020, but in 2021 that frequency dropped to about 1 visit per 14 days: slightly lower than the 2019 frequency of 1.5 times per 14 days, on average. Likewise, Choma, Maamba, and Petauke all had substantial decreases in the frequency with which households visited local shops between 2020 and 2021.

Results from the multivariate multiple regression analysis further highlight the variations between smaller and mid-sized secondary cities in the context of the relationship between household characteristics and food purchasing frequency at urban food retailers (Tables 3 and 4). These regression models are used to assess correlations between the independent and dependent variables rather than to establish causation. Additionally, low adjusted R-squared values across all models suggest that the proportion of variance in the response variables are not well explained by independent variables. Thus, we present these models in an effort to explore possible relationships between variables that can be more carefully investigated in future empirical research.

Food purchasing behaviors were diverse across all city types and shifted across the survey periods. Reflecting patterns observed in Figure 5, frequent shopping at open-air markets before the pandemic held a significantly positive relationship with both smaller urban areas and remote mid-sized cities in 2019 (relative to larger secondary cities). This relationship was negative in 2020 and positive in 2021 (Table 3). We also observed a positive relationship between connected mid-sized cities (relative to larger secondary cities) and frequency of food purchases at open-air markets and vendors in 2019. This relationship also flipped to a positive relationship in 2021 (Table 3). These shifting trends over time may be attributed to open-air market and vendor regulations during the early stages of the pandemic.

In terms of household characteristics, there was a positive relationship between female-headed households and purchasing food from vendors following the initial stages of the COVID-19 restrictions (Table 3), which is the only instance where we observed a significant difference between male- and female-headed households in terms of the number of times they went to different food retailers. We also found a significant negative relationship between the log of income and purchasing from these informal food retailers in 2020 for open-air markets and 2019 for vendors (Table 3), which suggests that the locations where households purchase food is dependent upon household income. Increased distance to bus stops was also negatively associated with purchases from open-air markets (Table 3).

For shopping at more formal food retail vendors, our regression results presented a statistically significant positive relationship between the log of income and frequency of food purchases at both local shops and supermarkets across all survey periods (except for shopping at supermarkets in 2019). Converse to the patterns observed for shopping at more informal retailers, these results suggest that households with more wealth are more likely to shop at supermarkets, which is consistent with the existing literature (Blekking et al. 2023, Fobi et al. 2024). No other household characteristic showed a significant relationship to shopping except for the 2019 survey period for purchasing food at local shops. In this case, increased household size, decreased distance to bus stop, and renting households all presented a negative relationship to purchasing food at local shops.

Similar to observations for the informal food retail sector, results showed that diverse relationships between different types of cities and purchasing food at local shops and supermarkets. Smaller urban areas were not significantly associated with making frequent purchases at local shops or supermarkets, with the only exception being during the 2021 survey period. Remote mid-sized urban areas were only found to be positively associated with purchasing more frequently at local shops in the 2020 survey, whereas connected mid-sized cities (i.e., cities located in closer proximity to other cities) were found to be negatively associated with purchasing at local shops in 2019. Yet, there was a persisting negative relationship between both remote and connected urban areas and purchasing food at supermarkets across all time horizons. These results are supported by the existing literature that supermarkets do not feature prominently in smaller to mid-sized secondary cities (Hannah et al. 2022, Fobi et al. 2024), even in the context of a shock like the COVID-19 pandemic, where most low- to middle-income households would likely not be able to afford supermarket food prices.

For these same 14 secondary cities, the use of urban agriculture varied during the study years, and our results suggest both decreases and increases following the onset of the COVID-19 pandemic (Fig. 6). Batoka, Itezhi-Tezhi, Kapiri-Mposhi, Mazabuka, Mkushi, Mpongwe, Nyimba, and Pemba all feature an increase in the percentage of households practicing urban agriculture from 2019 to the 2020 survey. For example, in 2019, about 20 percent of households in Batoka reported growing their own food at home or in their residential area, but after the start of the pandemic, 100 percent reported growing some amount of their own food at home or in their residential area. Apart from Mbabala, which is in a rural area, households shifted significantly to urban agriculture in the smaller sized urban areas (e.g., Batoka, Mpongwe, Namwala, Nyimba, and Pemba) compared to larger sized urban areas that have a greater number of food retail options.

Of the households that reported practicing urban agriculture in the 2019 survey (42% of all households), most (81%) cultivated food in household gardens and several others (18%) cultivated food at a garden or other location beyond the household. Most households (67%) reported planting dark green and leafy vegetables, followed by maize (38%) and various other plants, such as Irish potatoes, sweet potatoes, cassava, carrots, tomatoes, mangos, bananas, nuts, seeds, and other fruits and vegetables. Very few households reported raising livestock; those included chicken (only 48 households), goat (5 households), rabbit (1 household), and cow (1 household). In terms of scale, only 4 households reported using pots or sacks for planting food and less than a fifth of households (17%) shared that they only have a few plants. Approximately a quarter of households (26%) planted food in an area of less than 6 m² and a fifth of households (20%) cultivated food in an area of 6 to 12 m². Over a third (35%) planted food in an area greater than 12 m², of which 31 households cultivated plants on an area of land greater than 30 m². Based on the authors’ familiarity with the region and field observations, larger plots of land are typically located in the smaller and remote secondary cities that are less densely populated. These larger areas of land typically allow for greater opportunities to plant diverse crops and support livestock.

Binary logistic regression results further show diverse household engagement in urban agriculture, both across survey time periods and city types (Table 5). In these regressions, additional household variables most relevant to supporting urban agriculture were included (e.g., private water source on the property). Odds ratios are used to interpret the logistic regressions, where ratios greater than one are associated with a greater likelihood of the dependent event occurring as the independent variables increase (e.g., urban agriculture engagement as income increases). Ratios less than one suggest that the event is less likely to occur.

Overall, we found that those households with greater socioeconomic entitlements (e.g., not renting, larger houses, private water source on property) were associated with engagement in urban agriculture when there was a significant relationship. In 2019, before the pandemic, households in planned settlements, renting households, and households on the electric grid were significantly less likely to engage in urban agriculture. Although not significant, the likelihood of engaging in urban agriculture flipped for renting households in 2020 but then returned to being significantly less likely to engage in 2021, which is consistent with some of the trends observed in Figure 6.

As the income wealth of households increased, there was a greater likelihood to engage in urban agriculture in 2020. This finding was the only emergent household characteristic related to engaging in urban agriculture following the pandemic, which may be attributed to the ability of wealthier households to absorb shocks and adapt to alternative food production sources within a short timeline compared to lower income households. Across all survey time periods, larger households (e.g., those with an increased number of rooms) and those with a private water source on their property were more likely to engage in urban agriculture. These findings are consistent with some identified barriers to engagement in urban agriculture among lower income households, which are less likely to own land, have access to production resources, and be able to afford the inputs to grow food (Davies et al. 2022).

Variations in urban agriculture engagement were also observed across city types. Smaller urban areas (relative to larger secondary cities) were less likely to engage in urban agriculture only in the pre-pandemic period. Connected mid-sized cities (relative to larger secondary cities) were also less likely to engage in urban agriculture across all survey periods, which is likely due to households having greater access to a more diverse set of food retail options than smaller and remote cities. Remote mid-sized cities (relative to larger secondary cities) were significantly less likely to engage in urban agriculture before the pandemic in 2019 but then were significantly more likely to engage in urban agriculture during the 2020 and 2021 survey periods. This result may be attributed to being less connected with the wider food system to source food, especially during the pandemic when food supply chains were compromised. These variations in household engagement in urban agriculture, both across time and city types, show how diverse UFS are in the context of a shock like the COVID-19 pandemic.

DISCUSSION

Our results regarding the observed socioeconomic inequities across urban households in Zambia align with observations in other parts of Africa (Modirwa 2011, Caesar and Riley 2018, Bulawayo et al. 2019, Akalu and Wang 2023). Lower income households tend to rely on more coping strategies in response to food insecurity, and lower income female-headed households disproportionately engage in more coping strategies compared to all other households. This highlights the heightened vulnerability of lower income and female-headed households to the impacts of different shocks or pressures compared to higher income and male-headed households. Despite these disparities, we found that households from different socioeconomic backgrounds perceived similar overarching concerns, highlighting shared vulnerabilities in the shock context of the COVID-19 pandemic. Beyond these well-established trends, our findings emphasize the role of diverse UFS in offering different food acquisition options to households in different types of cities. Our discussion emphasizes these more novel insights to explore how policy and planning can promote equitable and resilient UFS in a Southern African context.

A case for equity in UFS planning and policy: Zambia’s Constituency Development Fund

Across the households included in our study, food prices were perceived as the predominant concern for maintaining food security, even though more vulnerable households are likely to struggle with the rising cost of food to a greater degree. Moreover, fluctuating food prices are a concern for urban households regardless of the driver or origin of that fluctuation. Coupled with the nutrition transition and the financialization of UFS (Battersby 2017), the COVID-19 pandemic, economic effects of the Russia-Ukraine conflict on global food chains, and regional drought impacts on crop production have all recently impacted food prices in Zambia and Southern Africa more broadly (Isakson et al. 2023). Aside from food prices, urban households commonly identified general socioeconomic security and climate to be significant concerns for household food security.

Despite common concerns about the types of shocks to food security across urban households, and regardless of the nature or origin of a given shock, a more equitable approach for mitigating the impacts of shocks would require UFS policy and planning to provide greater support to lower income and female-headed households to ensure that they have as much capacity to cope with and adapt to shocks as households with higher socioeconomic status. UFS policy and planning can consider Fraser’s three dimensions of distributional, recognitional, and procedural equity (Fraser 2000, 2005, 2009) and Leach et al.’s (2018) adaptation of this equity framing, whereby resources are allocated to support diverse populations (distributional equity), diverse households and food retailers are afforded the authority and agency to engage in their UFS (recognitional equity), and mechanisms are established to design an UFS environment that facilitates food accessibility, affordability, and availability for diverse households in diverse cities (procedural equity).

For UFS in Zambia, the Constituency Development Fund (CDF) is an example of a national effort that has specific implications for recognitional, distributional, and procedural equity opportunities in UFS. Established under the Zambia Constitution, the CDF Act (2018) sets up the governance mechanisms, such as CDF committees, to manage, disburse, utilize, and maintain accountability of government-appropriated funds for local development initiatives (i.e., distributional equity). The CDF provides opportunities for service delivery to be more responsive to public needs, strengthens decentralized local institutions, and promotes economic development (Casey et al. 2021) (i.e., recognitional equity). The Ministry of Local Government and Rural Development is mandated to coordinate the implementation of the CDF projects and programs through the CDF Committees, Members of Parliament, local authorities, cooperating partners, and members of the community (i.e., procedural equity).

Although the CDF Act was passed in 2018, the CDF only increased to K28.3 million per constituency in the 2023 national budget to account for inclusive development. Prior to 2023, funding was not sufficient to support the wide range of community development initiatives. Thus, in the 14 cities in our study for the period of 2019–2021, when we collected data, we did not identify any evidence of CDF support that would have improved the UFS. Future studies on UFS in Zambia could investigate the impacts of CDF-supported projects on household and UFS resilience.

The CDF in Zambia has thus far been used to address several distributional inequities that women especially face in acquiring food safely and engaging economically in their local UFS as producers and vendors. These efforts simultaneously promote women’s agency and ability to participate productively in the UFS, which is a critical component of food security (Clapp et al. 2022a). In Kitwe, for example, a market was constructed specifically for women by using CDF funds, with the aim of providing women with a safe and secure space to sell their goods and access clean water, sanitation, and other essential services. In Ndola, the CDF was used to provide loans to women who are starting food-related businesses. In Kabwe, the CDF supported a project that is training women in urban agriculture and providing them with access to land, seeds, and other agricultural inputs. In Lusaka, the CDF was used to provide training in business management, marketing, and financial literacy to women who are market vendors. Financial support from this CDF has been used to purchase goods, rent stalls, and pay for other business expenses.

Different city types host diverse UFS and households require diverse UFS

Our findings further show that different cities require different UFS planning and policy responses to deal with shocks. At the intercity level, we found that the shock of the COVID-19 pandemic had variable impacts on different UFS depending on city type. These differences were evident based on the changes in the frequency of household visits to food retailers over the pandemic period, as well as the reported changes in engagement in urban agriculture in these households. Several characteristics may inform differences in UFS. For instance, some urban areas that are smaller and more remote (e.g., Mbabala, Mpongwe, and Batoka) are less directly engaged with the global food supply chain than larger cities and would therefore be comparatively insulated from global-scale shocks. On the other hand, larger cities (e.g., Choma, Kapiri Mposhi, and Mazabuka) tend to be more connected to global food supply chains and would thus be more sensitive to global shocks, such as COVID-19 and the conflict in Ukraine.

These dynamics are further influenced by the varying characteristics and structures of UFS in different-sized cities, which shape their resilience to shocks and stressors. For instance, larger secondary cities often feature more opportunities for households to earn cash income that can be used toward food purchases, as well as more diverse food retail systems that include formal supermarkets, local shops, open-air markets, and street vendors. This diversity enhances resilience by providing households with multiple food sourcing options, enabling them to adapt more easily to disruptions in any single supply chain. In contrast, smaller secondary cities may rely more heavily on informal markets and urban agriculture, which allows for closer networks to local agricultural producers. These networks can contribute to resilience by offering a more localized and potentially stable food supply during external shocks.

Whereas the proximity of smaller secondary cities to rural agricultural areas may buffer food insecurity risks during large-scale economic shocks, smaller, more remote urban areas would be more sensitive to localized shocks, including climate extremes such as droughts, floods, and heatwaves. Because these smaller cities would likely have a more limited diversity of food sourcing options, households would need to seek out alternative food acquisition strategies, such as engaging more in urban agriculture. Larger cities also have a comparative advantage because of their capacity to host a greater diversity of food acquisition options, which may enable households of average or greater socioeconomic standing to adapt and be resilient to these kinds of shocks. The resilience of UFS in different-sized secondary cities is thus not determined by a single factor, such as retail diversity or connectivity to rural agricultural networks. Rather, UFS resilience emerges from the interplay of various elements, including the diversity of food sources along with infrastructure quality, governance, and the adaptive capacities of households.

Diverse UFS require diverse UFS planning and policy approaches

Given the diversity of cities and their UFS in Zambia, and Southern Africa broadly, urban policymakers and planners will need to draw from a variety of options to respond to a given shock in an equitable manner. Complementary to diversity, UFS and household resilience to food-related shocks is not contingent on access to food, in general, but rather sustained access to healthy and nutritious foods over time (Clapp et al. 2022). To some degree, the nutrition transition has been occurring due to a lack of equitable planning (Battersby 2017), where larger agri-food actors do not always take into consideration the importance of nutrition and balanced diets. This nutrition transition also likely varies across cities. With compounding challenges to local UFS globally and the underlying nutrition transformation, maintaining a diverse food system to meet diverse households is critical (Béné and Devereux 2023).

In Zambia, there are several policies that may be relevant for helping strengthen the resilience of UFS, including some that may not be explicitly designed with UFS in mind. For instance, the Markets and Bus Stations Act is a national policy with important local implications. The Act sets up the governance structures for open-air markets, which play a critical role in the UFS of most urban areas in Zambia (Hannah et al. 2022). Supporting investment in these markets and including market committees in city-level policy and planning processes can help address the recognitional equity aspect of markets that is often missing in UFS planning. The Zambia National Lands Policy supports government agencies, such as the Department of Town and Country Planning, to promote sustainable land management and equitable access to land. This includes making provisions for land tenure, water accessibility, and the availability of agricultural inputs, all of which are necessary for households to engage effectively in urban agriculture (Davies et al. 2020). Further relevant policies in Zambia are presented in Table B1 of Appendix 2.

As each UFS is unique, our survey data alone do not allow us to draw precise conclusions about the variability in UFS across cities, nor about shifting household behaviors, such as changes in visits to food retailers or levels of urban agriculture engagement. However, the variation in changes that we observed across different cities from 2019 to 2021 suggests a need for flexible, context-specific responses to shocks and pressures, whereby interventions can be adapted to meet the needs of diverse household types and different cities. To achieve this approach, it is important to consider the unique strengths and vulnerabilities of each city, including how decision-makers support cities in response to shocks, which can affect how households respond to food insecurity challenges.

Policy tools such as the CDF can help address equity challenges by serving as a reinforcement system to support households in times of need, thereby strengthening the resilience of households and UFS to future shocks. Simultaneously, policy tools should aim to incorporate the perspectives and voices of all relevant stakeholders to ensure recognitional and procedural equity in UFS governance. In some cities, street vending and urban agriculture contribute significantly to food accessibility, affordability, and availability. Establishing clear guidelines and providing resources and training to vendors and urban farmers and fostering collaboration between local government authorities and relevant national ministries are essential actions for promoting safe and organized street vending and supporting urban agriculture. These collective efforts can build more resilient and equitable UFS that ensure food security and well-being for all residents.

Finally, policies aimed at strengthening UFS must consider household-level capacities to ensure equity in resilience-building efforts, recognizing that the resilience of UFS and the resilience of individual households are interconnected and can be mutually reinforcing. Our findings demonstrate that the diversity within UFS served as a buffer for some urban households during the COVID-19 pandemic, thereby enhancing household resilience. To assess these relationships, we examined household coping mechanisms alongside UFS dynamics, which included shifts in food acquisition patterns that reveal an interplay between household- and system-level resilience. Strengthening UFS resilience through strategies like supporting diverse food retailers, promoting inclusivity in urban planning, and investing in robust infrastructure expands the ability of households to cope with shocks. System-level approaches, such as the CDF play a critical role by directly funding community-based initiatives that address localized food insecurity and strengthen UFS infrastructure, which builds household resilience. In turn, resilient households contribute to the broader resilience of UFS by maintaining stable demand across diverse food sources and adopting adaptive practices like urban agriculture, which strengthen local food systems. These dynamics underscore the importance of an integrated approach to resilience-building that simultaneously addresses household needs and the functioning of the system as a whole.

CONCLUSION

The need for resilient and equitable UFS is critical as Southern Africa faces compounding shocks and stresses alongside an underlying nutrition transition. We focus on socioeconomic inequities at the household-level, which we use as a lens to understand localized perspectives of food security and resilience in the UFS of 14 secondary cities in Zambia. Our findings highlight three key patterns related to resilience and equity in UFS. First, lower income, female-headed households in Zambia are particularly vulnerable to the impacts of shocks and pressures, including those leading to food insecurity, aligning with similar studies from Southern Africa. Second, households across different socioeconomic backgrounds perceive similar challenges, with fluctuations in food prices, socioeconomic security, and climate-related impacts being major concerns. An extension of our research, which focuses only on a subset population of low- to medium-income households, should expand the sample population to also include higher income households to see how they would perceive these challenges. Third, UFS in diverse urban areas provided varying food acquisition options to households during the challenges of the COVID-19 pandemic, suggesting that some households may have coped better based on their city type.

Policy and planning are central to building UFS resilience and mitigating inequities across households and UFS patterns across cities. Resilient and equitable UFS exhibit diversity and the ability to withstand and recover from shocks and stresses throughout the entire value chain, from production to consumption, while ensuring fair and sustainable access to safe, nutritious, affordable, and culturally appropriate food for all residents within an urban area. Equitable resilience, achieved through building resilience in an equitable manner, enables all UFS users to absorb shocks, recover, and adapt to new conditions. Incorporating equity in all its forms, including distributional, recognitional, and procedural aspects, contributes significantly to resilience building in UFS.

Creating more resilient and equitable UFS in Zambia and Southern Africa necessitates a comprehensive and adaptable approach to urban policy and planning that recognizes the interconnected and mutually reinforcing relationship between UFS and household resilience. This approach should consider both similarities and differences at household and city scales, encompass various policy areas (including those that may not be explicitly designed to manage UFS), and engage multiple stakeholders in planning and decision-making processes. Aligning and integrating key policies at national and local levels can further address urban food insecurity and enhance UFS resilience. Ultimately, promoting equitable UFS resilience requires an approach that simultaneously addresses household needs and systems-level functionality, wherein policy and planning are central to addressing urban food security challenges more equitably.

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