The role of product diversification in enhancing market vendor adaptability and food-system resilience in Senegal, West Africa Cyrus Muriithi a,* , Christine Kiria Chege a, Issa Ouedraogo b, Caroline Mwongera c a International Center for Tropical Agriculture (CIAT), Duduville Campus Off Kasarani Road P.O. Box 823-00621, Nairobi, Kenya b International Center for Tropical Agriculture (CIAT), Almadies, Parcelles 22, Zone 10 Lot 227 Zip/Postal Code: 24063 Dakar, Senegal c International Fund for Agricultural Development (IFAD), Addis Ababa, Ethiopia A R T I C L E I N F O Keywords: Market vendors Adaptive capacity Food security Product diversity Senegal A B S T R A C T Severe food insecurity in Senegal, exacerbated by climate shocks and weak infrastructure, underscores the need to understand the role of market vendors in food system resilience. Unlike producers, vendors remain under studied despite their central role in food access. This mixed-methods study examines how product diversity, measured using the Shannon-Wiener index, influences Market Vendor Adaptive Capacity (MVAC) among 691 vendors in Sedhiou and Tambacounda. Survey and interview data reveal that diversity enhances MVAC, particularly for small retail and open-air vendors offering both staple foods and nutrient-rich products. Vendor characteristics such as employing staff, extending credit, and participating in training further strengthen adaptability, while systemic constraints like poor infrastructure and high transport costs limit benefits, especially in rural areas. Results indicate that diversity functions less as an independent driver and more as a strategic outcome of vendor capacity, reframing its role within resilience theory. The study contributes by (1) linking product diversity to adaptive capacity, (2) identifying enabling and constraining factors, and (3) outlining policy directions, including infrastructure investment, financial support, and vendor training. Strengthening these areas can expand food access, bolster resilience, and advance Sustainable Development Goal 2 (Zero Hunger) in Senegal with implication for West Africa. 1. Introduction Food insecurity remains a critical challenge in Sub-Saharan Africa (SSA), with 342 million people facing severe food insecurity in 2022 [35]. Achieving food and nutrition security is a cornerstone of the Sus tainable Development Goals (SDGs) and national policies across SSA [15]. However, the region’s vulnerability to climate change, extreme weather events, and limited access to yield-boosting technologies con tinues to undermine efforts to ensure sustainable and resilient food systems [1,6,48]. In Senegal, widespread food insecurity, driven by climate variability, weak rural infrastructure, and persistent economic constraints, continue to undermine the resilience and sustainability of food systems [35,59]. Food systems encompass the entire process of food production, dis tribution, and consumption, involving all actors and activities from farm, to table, and waste removal [46,103]. The food environment - defined as the context in which consumers interact with food and make choices - plays a critical role in shaping food access, availability, and consumption [52]. Market vendors, as key actors in the food environ ment, play a vital role in shaping food access, affordability, and di versity, influencing household food security [52,101]. Despite their critical role in local food systems, research on vendor-level resilience is limited compared to upstream actors like farmers, leaving a gap in un derstanding how vendors adapt to market challenges [13,100]. Market vendors in traditional food markets are pivotal to food system resilience [21,42]. By shaping food access and dietary diversity, they influence household nutrition and community adaptability [23,52]. Market food variety supports diets [55], while improved access and vendor diversity enhance dietary outcomes [41]. Recognizing vendors as resilience agents underscores policy priorities in infrastructure, finance, and training [28]. Adaptive capacity refers to the ability to adjust to disruptions such as economic shocks or supply chain issues [40]. Market vendors’ adaptive capacity (MVAC) is defined as their ability to respond to changing * Corresponding author. E-mail addresses: karokicyrus@gmail.com (C. Muriithi), C.Chege@CGIAR.ORG (C.K. Chege), I.Ouedraogo@cgiar.org (I. Ouedraogo), c.mwongera@ifad.org (C. Mwongera). Contents lists available at ScienceDirect Sustainable Futures journal homepage: www.sciencedirect.com/journal/sustainable-futures https://doi.org/10.1016/j.sftr.2025.101593 Received 30 June 2025; Received in revised form 3 November 2025; Accepted 5 December 2025 Sustainable Futures 11 (2026) 101593 Available online 12 December 2025 2666-1888/© 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC license ( http://creativecommons.org/licenses/by- nc/4.0/ ). https://orcid.org/0000-0001-5043-3996 https://orcid.org/0000-0001-5043-3996 mailto:karokicyrus@gmail.com mailto:C.Chege@CGIAR.ORG mailto:I.Ouedraogo@cgiar.org mailto:c.mwongera@ifad.org www.sciencedirect.com/science/journal/26661888 https://www.sciencedirect.com/journal/sustainable-futures https://doi.org/10.1016/j.sftr.2025.101593 https://doi.org/10.1016/j.sftr.2025.101593 http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/ market conditions, including price fluctuations and consumer demand shifts [5,57,77]. Previous studies identified several key drivers of vendor adaptability, including vendors capacity, institutional support, access to capital, and experience, while reduced exposure to climate shocksfur ther enhances vendors’ adaptive capacity [4,20,68]. Product diversity i.e., offering a range of foods like staples (e.g., cereals) and nutrient-rich items (e.g., nuts) can enhance MVAC by improving vendor competitiveness and consumer access to varied diets [80,102]. However, vendors face barriers such as high transportation costs, limited funds, and poor infrastructure, which hinder adaptability and exacerbate food insecurity [38]. To conceptualize MVAC, this study draws on two complementary theoretical perspectives: Social-Ecological Systems Theory (SEST) and Entrepreneurial Resilience Theory (ERT). The SEST posits that human societies and natural ecosystems func tion as interconnected, co-evolving, and adaptive systems, shaped by dynamic feedback loops and interactions across multiple spatial and temporal scales [3,72,83,105]. Within this framework, adaptive capac ity is shaped by the interaction between assets, agency, institutions, and ecological conditions [72,83]. Applying SEST to MVAC underscores that vendors operate within an interconnected system rather than in isola tion and their resilience is shaped by complex interactions with local producers, off-farm suppliers, consumers, and institutional structures that regulate markets, resource and information flows [40,104]. Product diversification, in this context, is not just a business choice but an adaptive strategy shaped by ecological variability (e.g., seasonal avail ability) and institutional dynamics (e.g., credit access, infrastructure). SEST thus allows us to situate vendor adaptability within the broader food system, recognizing markets as nodes in a larger web of social and ecological interdependencies [93,104]. This study aims at integrating the ERT with the SEST to capture both systemic and individual dimensions of market vendor adaptability. While SEST emphasizes the interconnectedness of human and ecological systems, the ERT provides a complementary lens by focusing on the individual-level adaptive capacity of entrepreneurs operating under uncertainty [18,91,106]. It conceptualizes resilience not as a static personality trait, but as a dynamic and iterative process through which entrepreneurs mobilize resources, devise coping strategies, and trans form adversity into opportunity [18,65,91]. In the context of market vendors, this perspective recognizes resilience as emerging from capa bilities (e.g., skills, networks), situational factors (e.g., market compe tition, customer demand), and intentional actions (e.g., diversification, innovation, resource management) [18,65]. By applying ERT to informal food market systems, this study advances a novel analytical approach that situates vendor resilience within both contextual de pendencies and entrepreneurial agency, thereby filling a critical gap in food system resilience research that has largely overlooked distribution actors and micro-entrepreneurial dynamics [63]. By blending SEST and ERT, this study conceptualizes MVAC as both a systemic property and an entrepreneurial process [8,19,71]. From the ERT perspective, adaptability is expressed through vendors’ capacity to leverage resources, diversify products, and innovate in response to shocks [62,88,95]. Together, these frameworks highlight that MVAC is not merely about coping with risks, but about actively navigating and shaping opportunities within complex socio-ecological systems. Theoretically, this study advances understanding of MVAC by inte grating SEST and ERT into a unified framework that explains how product diversity and vendor characteristics interact to shape resilience, thereby extending food systems resilience research beyond production to distribution actors. It also introduces an innovative mixed-methods approach that combines ecological diversity indices (e.g., Shannon- Wiener) with advanced techniques such as Factorial Analysis of Mixed Data (FAMD) and Multiple Indicators Multiple Causes (MIMIC) model ling, operationalizing MVAC as a measurable latent construct and providing a replicable tool for future studies. Data was collected from 691 market vendors in Senegal’s Sedhiou and Tambacounda regions. Results revealed that while product diversity supports adaptability, vendor type, business scale, and access to resources (employees, training, credit) are equally critical, reframing diversity as a strategic outcome of vendor capacity rather than a standalone driver. The study provides new theoretical grounding, methodological innovation, and empirical insights that inform policies to strengthen market resilience, enhance food security, and advance progress toward Sustainable Development Goal 2 (Zero Hunger) in Senegal and comparable West African contexts. 2. Materials and methods 2.1. Study area This study was conducted in Senegal’s Sedhiou and Tambacounda regions (Fig. 1). Sédhiou, with its lowlands and favorable soil and climate, is a key area for traditional subsistence rice farming largely managed by women. However, declining rainfall, frequent droughts, and soil degradation through salinization, acidification, and silting pose serious threats to rice production [78]. Tambacounda, Senegal’s largest region, lies in the eastern Sahelian plains and faces low agricultural productivity, limited agro-processing, and poor transport and storage infrastructure. These challenges contribute to widespread malnutrition and high youth out-migration to urban areas and Europe in search of better opportunities [69,78]. These contrasting contexts provide a unique opportunity to examine MVAC in diverse food environments. 2.2. Sampling and data The study used a mixed-methods design combining quantitative surveys with qualitative key informant interviews conducted at commune, village, and market levels. In Senegalese administrative sys tem, villages are small rural settlements led by traditional chiefs, while communes represent larger administrative units, comparable to mu nicipalities, governed by elected mayors who oversee local governance, planning, and development initiatives [79]. A stratified three-stage random sampling strategy was employed to ensure diversity, representativeness, and statistical robustness across study sites and respondent categories (Table 1). Stratified probability sampling was used in vendor surveys to ensure proportional represen tation of subgroups (e.g., commune and village markets). A sample probability of 0.0185 was applied, meaning 1 out of every 52 markets was selected, resulting in 28 markets. This approach balanced propor tionality across vendor types and locations, ensuring statistical robust ness and generalizability. The sample size was calculated using the Cochran, [25] formula: n = z2.p. (1 − p) E2 (1) n is the required sample size. z is the Z-score corresponding to the desired confidence level (1.96 for a 95 % confidence level). p is the estimated probability of an event occurring. This is often based on the recruitment and training databases. While (1-p) is the probability of the event not happening. E is the desired margin of error i.e., 5 %, which represents the maximum acceptable difference between the sample estimate and the true population proportion. In the first stage, thirty-five communes per region were randomly selected from a purposively constructed sampling frame, considering distance, market frequency, and product categories. Two knowledgeable representatives (purposively selected) from each commune, familiar with markets and the local food environment, were interviewed, yielding 70 interviews. In the second stage, forty-two villages (24 in Tambacounda and 18 in Sédhiou) were randomly drawn from the selected communes. Seven key random respondents per village provided insights on food availability, C. Muriithi et al. Sustainable Futures 11 (2026) 101593 2 diversity, and access, generating 294 interviews. In the third stage, twenty-eight food markets were randomly selected from the selected communes based on operating frequency (daily or weekly). From each, 25 vendors were randomly selected and inter viewed, representing diverse types such as street hawkers, stallholders, kiosk operators, retailers, and supermarket vendors. Where markets had fewer vendors, replacements were drawn from nearby markets of similar type. This produced a final sample of 700 vendors (262 in Sédhiou and 436 in Tambacounda), with 691 retained after cleaning, achieving a 98.7 % response rate. The study received ethical approval from the Alliance of Bioversity International and CIAT Institutional Review Board (IRB) (authorization #2022-IRB41), ensuring compliance with ethical guidelines for data collection and participant interaction. Key informant interviews and surveys were conducted from October to December 2023, aligning with peak market activity to enhance data reliability [69]. 2.3. Empirical analysis The empirical analysis was designed to operationalize and test the relationship between product diversity and MVAC. To ensure robust ness, we employed a sequential strategy that combined product diversity indices, dimensionality reduction, structural modelling, and regression analysis. This sequential approach allowed us to test whether product diversity operates as an independent driver of adaptability or as a strategy conditioned by vendor capacities. 2.3.0. Conceptual framework The conceptual framework below (Fig. 2) illustrates the hypothe sized relationships between product diversity and MVAC within the context of traditional markets. Product diversity measured through Shannon-Wiener Index, variety of goods sold, and seasonal flexibility is posited to enhance vendors’ adaptive capacity by improving income stability, market resilience, and access to alternative livelihood strate gies. This relationship is mediated by factors such as asset ownership, market access, and institutional support, while socioeconomic charac teristics and environmental shocks serve as moderating variables. The framework also recognizes feedback effects, where enhanced adaptive capacity can, in turn, enable greater product diversification, creating a reinforcing loop that strengthens overall food system resilience [2,49, 74,86]. Notes: A. Drivers of Adaptive Capacity: Vendor traits, business capacity, support systems, and market context jointly shape both product diversity and MVAC.B. MVAC: A composite measure of vendors’ ability to learn, innovate, and adapt to shocks. C. Product Diversity: Indicating the range and balance of goods sold. D. Food System Resilience: The ultimate outcome reflects stable markets, improved food security, and dietary diversity. E. Feedback Loop: A resilient food system strengthens vendor adaptability, reinforcing long-term market resilience and inclusivity. 2.3.1. Product diversity indices To assess the diversity of food products offered by market vendors, we employed three indices: the Shannon-Wiener index, the Simpson index, and the inverse Simpson index. These indices provide comple mentary insights into richness (the number of food categories) and evenness (the distribution of quantities within categories), both of which are critical for understanding diversity, adaptability and resilience. The Shannon-Wiener index is especially robust for small samples and sen sitive to rare items, making it particularly suited for vendor-level anal ysis [96,97]. Higher values reflect balanced and diversified offerings Fig.1. Distribution of market vendors in the study areas. The black dot indicates the location of market vendor during food market study. Table 1 Number of respondents and data collected by study type. Study level Type of study Type of data collected Sample size Tambacounda Sedhiou Commune (Administrative unit) Key informants Qualitative & quantitative 40 30 Villages (low-level administrative unit within commune) Key informants Qualitative & quantitative 143 109 Food Markets Survey Qualitative & quantitative 436 262 C. Muriithi et al. Sustainable Futures 11 (2026) 101593 3 [58]. By contrast, the Simpson index emphasizes dominance, identifying when a few products account for most of a vendor’s portfolio [50,58]. The inverse Simpson index, meanwhile, highlights even distributions among food items, with higher values denoting greater diversity [58]. These indices, calculated at the individual vendor level, enabled us to evaluate how product offerings shape adaptive capacity within markets. The Eq. (2) below shows how the index is calculated: Shannon Weiner (H) = − ∑n i=1 (pi ∗ lnpi) (2) where pi is the number of commodities within each food group i, lnpi is the natural log of pi,n is the number of food groups reported, and ∑ is the sum from food group(species) 1 to n. In contrast, the Simpson index emphasizes dominance within food groups, with lower values showing higher diversity [50,58]. This index is useful for identifying the concentration of specific food items within a vendor’s offerings. Eq. (3) shows how it is calculated: Simpson Index(D) = 1 − ∑ ni(ni − 1) N(N − 1) (3) Where ni is the number of commodities that belong to food group i for each vendor and N is the total number of food groups for each vendor. The inverse Simpson index builds on the Simpson index by high lighting dominant food groups and their importance. Higher values of this index indicate greater diversity, as it emphasizes even distributions among food items [58]. It is calculated as in Eq. (4): inverse Simpson index = ( 1 Simpson indexD ) (4) Although three diversity indices were computed to capture comple mentary aspects of richness and evenness, their very high correlations (r > 0.90; Appendix Fig. A3) indicated redundancy. To streamline inter pretation, the Shannon-Wiener index was prioritized as the primary measure in the main analysis, given its strong theoretical grounding in resilience studies and sensitivity to both common and rare products [37, 87]. The Simpson and Inverse Simpson indices were retained only for robustness checks, with results remaining consistent, thereby confirm ing that the choice of Shannon-Wiener does not affect the substantive findings. 2.3.2. Dimensionality reduction using factor analysis of mixed data (FAMD) Given the multidimensional nature of vendor characteristics, we applied FAMD for dimensionality reduction. FAMD integrates the prin cipal component analysis for quantitative variables and multiple cor respondence analysis for categorical variables, making it particularly suitable for our dataset [84,85]. This reduction allowed us to identify broad patterns of variation such as the contrast between experienced, diversified vendors and younger, specialized traders that would other wise be obscured in high-dimensional data (Table 2). By retaining components based on greater eigenvalues and cumulative variance explained, we ensured that the most informative dimensions were car ried forward into subsequent regression and MIMIC analyses. This step improved interpretability and reduced risks of multicollinearity. 2.3.3. Constructing MVAC: the mimic model To capture adaptability as a multidimensional construct, MVAC was estimated using a MIMIC model. The MIMIC framework constructs a latent variable - here, MVAC- derived from observable indicators (Table 2) and simultaneously modeled as a function of external drivers. This dual specification captures both direct effects and underlying re lationships between vendor attributes and adaptability [34,70]. In dicators reflected coping strategies, business continuity, and investment behaviors, while causes included vendor type, access to credit, employment, training, and product diversity, consistent with Estoque et al., [33] and the ERT [94]. As illustrated in Fig. 3, MVAC represents vendors’ ability to adjust to market, economic, and environmental changes. Derived from four Fig. 2. Conceptual Framework for Market Vendor Adaptive Capacity. C. Muriithi et al. Sustainable Futures 11 (2026) 101593 4 domains i.e., demographics, business capacity, market characteristics, and knowledge; the model links these measurable indicators to the Shannon–Wiener diversity index. This approach offers a comprehensive, theory-driven measure of adaptability that moves beyond single- variable proxies [89]. The mathematical Eq. (5) expresses the measurement model (Ap pendix Fig. A2), where yi is the coefficient for latent indicators related to each of the components. ληi is the coefficient of the latent variable for each component i, and ∈ is the random error. yi = ληi + ∈ (5) Eq. (6) indicates that MVAC (n) is influenced by the components (x) error term (ε) and Bi as the coefficient from the measurement model in Eq. (5). MVAC (n) = [Bi] ∗ X ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ Demographics Business Market Knowledge ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ + ϵ (6) 2.3.4. Regression models To test the association between product diversity and MVAC, we estimated ordinary least squares (OLS) regression models. The first specification regressed MVAC on the Shannon-Wiener index alone, capturing the bivariate relationship. The second specification intro duced additional controls derived from the FAMD dimensions, including vendor demographics, business characteristics, market factors, and knowledge-related attributes. In Eq. (7) we present the OLS regression model used to estimate the relationship between diversity and MVAC. yi = Xʹ iβ + ε (7) Here, yi represents the composite index of MVAC, derived from Eqs. (5) and (6). Xʹ i is a vector of explanatory variables, including gender, education, business characteristics, market characteristics, and knowl edge/training levels, among others. The term β denotes the slope or coefficient for each explanatory variable in the model while ε is the error term to account for other factors that could influence this relationship outside the model. 2.3. 5 validation and sensitivity analysis Several validation checks were conducted to ensure the reliability of results. For regression models, multicollinearity was assessed through variance inflation factors (all VIF < 3), and heteroskedasticity was addressed using robust standard errors [30,82]. For the FAMD, retained components were validated by eigenvalue criteria and clear interpret ability of loadings [85,90]. For the MIMIC model, construct validity was confirmed through strong and significant factor loadings, while fit indices exceeded conventional thresholds (CFI > 0.95, RMSEA < 0.05, SRMR < 0.05) [10,70]. As robustness checks, we substituted the Shannon-Wiener index with the Simpson diversity index, re-estimated models excluding the top 5 % of vendors and tested alternative MIMIC specifications excluding one indicator at a time. Across all checks, the Table 2 Grouped latent factors from factor analysis for mixed data (FAMD) analysis. Group Latent factors from FAMD Variables discarded by FAMD Demographics • Business owner education • Business owner gender • Vendors own the business • ⋅ Who runs the business • Department or location • Household size of the vendor • ⋅ Age of the business owner Business • Does the business have employees? • Is business formal or informal? • Does the business offer sales on credit? • Type of business • Cost of purchases (investment) • Product pricing based on purchase cost • Product pricing (considering all costs / business acumen, through cost of purchases, by observing supply and demand forces, through government price cap) • Revenue from sales (income) • Age of business • Number of sellers in the market • Does the business offer discounts? • Advertisement • Does business train on importance of food safety? • Seasonality of fruits, vegetables, and fish Market • Type of markets • Is business environmentally sustainable? • Business challenges • Availability of cold and dry storage • Source of energy for lighting and cooking • Number of vendors in the market Knowledge • The number of years since the vendor benefited from nutrition project • Knowledge about access to funding • Knowledge about climate change • Knowledge about gender equality • Knowledge about access and use of information about climate • Knowledge about social learning • Knowledge about gender equality Fig. 3. Two steps employed in estimating market vendors adaptive capacity. C. Muriithi et al. Sustainable Futures 11 (2026) 101593 5 results remained stable, underscoring the robustness of our analytical framework. 3. Results 3.1. Vendor characteristics The analysis covered 691 vendors representing six major types: open-air, home-based, roadside, small retail, street hawkers, and res taurants (Table 3). Vendor adaptability, measured through the MVAC index, showed significant heterogeneity. Small retail vendors exhibited the highest MVAC (0.62), followed by open-air vendors (0.58), whereas street hawkers recorded the lowest capacity (0.48), underscoring structural differences in vendors’ ability to respond to shocks (Table 3). The Product diversity, measured primarily by the Shannon-Wiener index, averaged 0.30 across vendors but varied significantly by vendor type. Small retail vendors offered the most diverse portfolios (0.56), while home-based vendors (0.18) and street hawkers (0.26) were least diversified. Alternative indices, such as Simpson’s and Inverse Simp son’s, revealed similar patterns, reinforcing robust consistency. This distribution indicates that adaptive capacity is closely tied to product diversity, aligning with SEST’s emphasis on diversity as a resource that buffers shocks and enhances resilience. Demographic and business characteristics also reflected systematic differences (Table 3). Women accounted for 52 % of vendors overall, with high participation in open-air, roadside, and restaurant markets, while men dominated small retail and home-based vending. Vendors averaged 40 years of age, with household sizes of nine members and eight years of business operation. Education patterns varied: 59 % had formal education, but informal training was especially prevalent among small retail and restaurant vendors. These structural and human capital variations illustrate how entrepreneurial resources intersect with prod uct diversity to shape vendors’ adaptive capacity. 3.2. Commune and village characteristics Food insecurity was widespread across the study sites (Fig. 4). Using the Household Food Insecurity Access Scale (HFIAS) [24,99], 88.6 % of commune households and 78.6 % of village households were classified as severely food insecure. Despite the challenges in food insecurity (access), communes exhibited relatively better market and service access and higher access to safe drinking water and markedly greater access to health facilities than villages. Nonetheless, infrastructure constraints remain a signifi cant barrier. Nearly one-fifth (21 %) of commune areas and almost half (46 %) of village areas were inaccessible by road, underscoring mobility challenges and structural inequalities between the two settings (Table 4). 3.3. Market vendor challenges Market vendors encountered a diverse set of operational constraints that both reflected and reinforced their adaptive capacity within local food systems (Table 5). Transportation difficulties were the most frequently reported challenge, disproportionately affecting open-air vendors, small retailers, and street hawkers. Such barriers restrict the timely flow of goods, undermining efficiency and limiting the reliability of food supply chains for an outcome consistent with theories of market accessibility and spatial inequality [53]. High inventory costs placed additional financial pressure on restaurants and small retailers, high lighting the burden of working capital constraints in informal econo mies. Liquidity shortages were also evident, with restaurants and home vendors reporting insufficient funds to sustain operations, a finding that aligns with capital scarcity theories in small enterprise development [45]. Beyond financial and logistical pressures, seasonality, low sales, and competition emerged as recurrent risks, particularly for home vendors (19.7 % citing seasonality) and small retailers (11.0 % reporting low sales). These structural and cyclical constraints illustrate how vendor type mediates exposure to market risks, shaping resilience and adaptive strategies in Senegal’s food environment. 3.4. Product diversity and adaptive capacity Product diversity across food groups revealed important patterns for market resilience (Appendix Table B2). Nuts and seeds (0.69) and milk/ dairy products (0.64) exhibited high diversity, reflecting wide assort ments that reduce dependency on single items. In contrast, fish (0.28) and other fruits (0.17) showed limited variety, suggesting vulnerability to supply shocks. Cereals (0.49) and spices/condiments (0.58) displayed moderate diversity with balanced availability. Thus, product categories embody different resilience potentials, consistent with SEST’s emphasis on redundancy and ERT’s framing of diversification as a strategy to spread risk. An OLS regression confirmed that product diversity was positively associated with MVAC, implying that a one-unit increase in the Shannon-Wiener index raised MVAC by 7.6 %. This supports the Table 3 Descriptive Statistics of Market Vendors. Variable Overall, N = 6911 Open Air, N = 1391 Home, N = 271 Roadside, N = 2121 Small retail, N = 1361 Street hawker, N = 571 Restaurant, N = 601 Others, N = 601 p-value2 Adaptive Capacity & Diversity ​ ​ ​ ​ ​ ​ ​ ​ ​ MVAC Index 0.55 (0.12) 0.58 (0.10) 0.53 (0.15) 0.51 (0.10) 0.62 (0.13) 0.48 (0.07) 0.55 (0.12) 0.56 (0.16) <0.001 Shannon-Wiener (H) 0.30 (0.30) 0.25 (0.27) 0.18 (0.22) 0.21 (0.27) 0.56 (0.23) 0.26 (0.26) 0.38 (0.31) 0.23 (0.30) <0.001 Simpson (D) 0.40 (0.37) 0.35 (0.36) 0.25 (0.30) 0.29 (0.35) 0.71 (0.24) 0.26 (0.33) 0.50 (0.36) 0.29 (0.38) <0.001 Inverse Simpson (1/D) 0.17 (0.21) 0.14 (0.18) 0.08 (0.12) 0.11 (0.18) 0.34 (0.19) 0.10 (0.18) 0.24 (0.26) 0.14 (0.20) <0.001 Demographics ​ ​ ​ ​ ​ ​ ​ ​ ​ Household size 9 (7) 9 (5) 12 (10) 9 (6) 8 (8) 8 (6) 10 (8) 9 (9) 0.012 Business owner age 40 (12) 39 (11) 43 (14) 41 (12) 40 (12) 37 (11) 37 (10) 37 (10) 0.013 Business owner gender ​ ​ ​ ​ ​ ​ ​ ​ <0.001 Female 358 (52 %) 86 (62 %) 6 (22 %) 151 (71 %) 24 (18 %) 33 (58 %) 49 (82 %) 9 (15 %) ​ Male 333 (48 %) 53 (38 %) 21 (78 %) 61 (29 %) 112 (82 %) 24 (42 %) 11 (18 %) 51 (85 %) ​ Business owner education ​ ​ ​ ​ ​ ​ ​ ​ <0.001 Informal 283 (41 %) 17 (28 %) 63 (45 %) 6 (22 %) 110 (52 %) 33 (24 %) 29 (51 %) 25 (42 %) ​ Formal 408 (59 %) 43 (72 %) 76 (55 %) 21 (78 %) 102 (48 %) 103 (76 %) 28 (49 %) 35 (58 %) ​ Business Characteristics ​ ​ ​ ​ ​ ​ ​ ​ ​ Business age (years) 8 (7) 9 (7) 9 (11) 7 (6) 8 (7) 7 (6) 7 (6) 9 (7) 0.10 Number of sellers in market 86 (248) 136 (464) 43 (32) 67 (169) 72 (149) 59 (110) 89 (126) 108 (160) <0.001 Notes: Means are presented with standard deviation (SD) or frequency (%) in parenthesis. 2Kruskal-Wallis rank sum test; Pearson’s Chi-squared test. 9 market vendor observations were dropped row-wise because of missing information. C. Muriithi et al. Sustainable Futures 11 (2026) 101593 6 argument that vendors offering a broader range of products are better positioned to absorb market shocks and sustain operations (Table 6). However, when additional structural and behavioral factors were controlled for, the effect of diversity became statistically insignificant. This attenuation indicates that while diversity initially appears central, its impact is mediated by deeper institutional and resource-based en ablers consistent with the notion in SEST that resilience stems from interacting capacities rather than single factors. Diversity enhances adaptive capacity but is most effective when embedded within broader vendor strategies and resource endowments, aligning with ERT focus on multiple, overlapping pathways of adapta tion (Appendix Fig. A2). Small and open-air vendors, those employing workers, and those offering credit to customers demonstrated higher adaptive capacity. Additionally, access to nutrition training and awareness of funding opportunities further strengthened their adaptability. The model explained 45.2 % of the variance in MVAC (R² = 0.452, adjusted R² = 0.440, F = 39.79, p < 0.001), demonstrating fair explanatory power. Assumptions of homoscedasticity were largely met, with only minor deviations (Appendix Fig. A1). 4. Discussion 4.1. Key findings This study shows that product diversity measured by the Shannon- Wiener index enhances MVAC in Senegal. Vendors with more diverse offerings, particularly small retail and open-air traders, were better able to manage supply disruptions and economic shocks, consistent with findings that operational flexibility fosters resilience [22]. However, once control variables were introduced, the effect of diversity weakened, indicating that diversity functions more as a manifestation of resilience than as an independent determinant of it. This aligns with SEST, where adaptive capacity arises from the interaction between resources and context rather than any single attribute. Despite persistent food insecurity, vendors with higher MVAC and diverse portfolios expanded dietary options for consumers, reinforcing the role of local markets in nutritional resilience [12,67]. Yet, structural barriers such as poor infrastructure and high operating costs limit ven dors’ ability to translate diversity into adaptability and improved food security [17,60]. 4.2. Reframing diversity as an enabler This study reconceptualizes product diversity as a conditional enabler of resilience rather than a universal determinant. Although di versity initially appeared to be positively associated with MVAC, its explanatory power diminished once vendor resources and business structures were taken into consideration. This reflects the livelihood diversification perspective, which views diversification as an adaptive response to constraints rather than an intrinsic driver of resilience [11, 32,81]. The ERT framework further emphasizes that diversification is a strategic choice accessible to those with sufficient resources and agency [18,94]. Hence, diversity emerges as an outcome of underlying capac ities such as access to credit, labor, and training rather than a stand-alone resilience factor [31]. For resource-constrained vendors, diversification without enabling support offers limited adaptive benefit [7]. Policies should therefore prioritize building capacities that empower vendors to leverage diversification effectively. 4.3. Structural challenges and barriers Market vendors face multiple structural constraints including poor infrastructure, limited finance, and inadequate services that hinder adaptive capacity [27,76]. Weak transport networks, high transaction costs, and limited access to water and sanitation reduce vendors’ ability to sustain operations and diversify products [14,43]. These barriers disproportionately affect small-scale vendors who lack the buffer re sources of larger enterprises [64]. Addressing such systemic inequities is critical for enabling inclusive market participation and strengthening food system resilience [98]. 4.4. Contribution to food systems resilience and security Market vendors contribute to food system resilience by combining Fig. 4. Food insecurity at commune and village level. C. Muriithi et al. Sustainable Futures 11 (2026) 101593 7 product diversity with adaptive capacity, stabilizing food access and improving dietary quality. Diversity buffers markets against shocks and expands consumer options [16,56,75]. This supports multi-scalar resil ience thinking where micro-level adaptations aggregate into broader food security outcomes [51,54,61]. However, diversity is effective only when supported by vendor re sources such as credit, employees, and training [31,92]. The benefits remain uneven across urban and rural markets due to persistent infra structural and financial barriers [39,66]. Interestingly, cost pressures such as transport and inventory expenses were positively associated with MVAC, reflecting adaptive stress dynamics [9,47,73]. Yet, without institutional investment in infrastructure, finance, and capacity-building, these adaptive responses cannot overcome systemic inequities. Similar findings in Zambia highlight how vendors strengthen food system linkages and planning through flexible governance and local innovation [44]. 4.5. Limitations and future research This study is limited by its cross-sectional design and reliance on self- reported data, which may introduce recall bias and limit understanding of long-term adaptation. Future longitudinal studies should assess how vendor capacities evolve following interventions such as training or infrastructure investments. Comparative research across West African regions could test the generalizability of these findings. Incorporating consumer perspectives would further clarify how market diversity in fluences household nutrition within broader food systems. 4.6. Policy recommendations Strengthening food security through market systems requires addressing both vendor capacities and structural constraints. In vestments in rural infrastructure can reduce costs and improve supply chain efficiency [29]. Expanding financial inclusion via microcredit programs would enhance liquidity for diversification and business continuity [107]. Complementary training in financial management, nutrition, and market practices can further enhance adaptability and improve food quality [38]. These strategies align with SEST’s multi-scalar view of resilience, linking individual adaptability with supportive institutional structures. Together, they support Senegal’s goal of ensuring sustainable, equitable food access and align with global commitments to end hunger [26]. 5. Conclusion This study demonstrates that market vendors in Senegal contribute to food system resilience by offering diverse food products, particularly through small retail and open-air markets. Vendors with greater Table 4 Commune and Village Characteristics. Commune (N = 70) Village (N = 252) Overall (N = 322) P value Average distance to the market in kilometers ​ ​ ​ ​ Mean (SD) 8.19 (14.4) 0.978 (3.92) 4.91 (11.5) 0.001611** Median [Min, Max] 1.50 [0.0100, 70.0] 0.200 [0,25.0] 0.500 [0,70.0] ​ Average distance to the other nearest market in kilometers ​ ​ ​ 0.9935 Mean (SD) 27.5 (34.2) 27.4 (30.2) 27.4 (30.5) ​ Median [Min, Max] 16.5 [2.00, 145] 15.0 [0.600, 190] 15.0 [0.600, 190] ​ Proportion (%) of households with access to drinking water for domestic use ​ ​ ​ ​ Mean (SD) 35.4 (23.6) 24.5 (33.5) 26.8 (31.9) 0.002358** Median [Min, Max] 30.0 [0,90.0] 2.00 [0100] 10.0 [0100] ​ Types of roads ​ ​ ​ 1.002e-08*** Paved roads everywhere, tracks 4 (5.7 %) 0 (0 %) 4 (1.2 %) ​ All-weather roads 17 (24.3 %) 26 (10.3 %) 43 (13.4 %) ​ More paved roads and fewer all-weather roads 17 (24.3 %) 31 (12.3 %) 48 (14.9 %) ​ More weather roads and less paved roads 8 (11.4 %) 67 (26.6 %) 75 (23.3 %) ​ Not accessible at all 15 (21.4 %) 115 (45.6 %) 130 (40.4 %) ​ Other categories 9 (12.9 %) 13 (5.2 %) 22 (6.8 %) ​ Access to health facility ​ ​ ​ 2.2e-16*** No 1 (1.4 %) 178 (70.6 %) 179 (55.6 %) ​ Yes 69 (98.6 %) 74 (29.4 %) 143 (44.4 %) ​ Access to external assistance (food and non-food products) ​ ​ ​ 5.034e-05** No 49 (70.0 %) 227 (90.1 %) 276 (85.7 %) ​ Yes 21 (30.0 %) 25 (9.9 %) 46 (14.3 %) ​ Note: . p < 0.1; * p < 0.05; ** p < 0.01; *** p < 0.001. Table 5 Key Challenges Faced by Vendors. Challenge Open air (393) Home (66) Roadside (596) Small retail (392) Street hawker (163) Restaurant (175) Other vendors (172) High inventory cost 15.8 % 1.5 % 15.4 % 18.1 % 16.6 % 19.4 % 18.6 % Inaccessibility of credit facilities 4.8 % 7.6 % 6.9 % 7.4 % 8.0 % 14.3 % 2.9 % Insufficient funds to run the business 16.5 % 19.7 % 18.5 % 16.8 % 16.6 % 25.7 % 18.6 % Low sales 6.6 % 10.6 % 10.1 % 11.0 % 4.3 % 9.7 % 7.6 % Seasonality issues 7.9 % 19.7 % 6.9 % 3.3 % 9.2 % 2.3 % 4.1 % Set an appropriate price for my products to attract customers and profits. 5.3 % 9.1 % 6.9 % 8.2 % 8.6 % 8.6 % 10.5 % Strong competition from other sellers 9.7 % 4.5 % 7.0 % 7.7 % 5.5 % 7.4 % 11.0 % Supply of fresh products 10.4 % 6.1 % 10.1 % 4.8 % 9.8 % 4.6 % 6.4 % Transportation of goods between suppliers and the market 22.9 % 21.2 % 18.3 % 22.7 % 21.5 % 8.0 % 20.3 % C. Muriithi et al. Sustainable Futures 11 (2026) 101593 8 adaptive capacity supported by employees, credit provision, and training enhance food security by stabilizing supply and broadening consumer access to nutritious foods. Yet, systemic barriers such as weak infrastructure, high transportation costs, and limited financial resources constrain their capacity to adapt. The findings extend ERT by showing that diversity is not an autonomous source of resilience but a strategic resource that becomes effective when combined with structural capac ities. Within a SEST perspective, adaptive capacity emerges as a dynamic interaction between vendor agency and broader institutional and infrastructural environments. Thus, diversity acts as both a signal of resilience potential and a lever for adaptive strategies when enabling conditions exist. Policy efforts should therefore focus not only on encouraging prod uct diversification but also on strengthening the enabling environment such as improving infrastructure, expanding access to finance, and investing in vendor training. Such interventions can amplify the adap tive role of vendors, ensuring markets remain reliable sources of affordable, diverse, and nutritious foods. Strengthening these di mensions will not only enhance market stability and food access but also advance Sustainable Development Goal 2 (Zero Hunger) across Senegal and similar West African contexts. By linking diversity to resilience through SEST and ERT, this study highlights practical pathways for strengthening food systems in Senegal with implications in West Africa. Disclosure statement The views expressed by the authors in this paper are their own and do not reflect or represent the views of any government or institutions with which they are associated. CRediT authorship contribution statement Cyrus Muriithi: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptu alization. Christine Kiria Chege: Writing – review & editing, Project administration, Methodology, Investigation, Conceptualization. Issa Ouedraogo: Writing – review & editing, Project administration, Fund ing acquisition, Conceptualization. Caroline Mwongera: Writing – re view & editing, Funding acquisition, Conceptualization. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements This paper is part of the AVENIR project’s activities (Adaptation and Valorization of Entrepreneurship in Irrigated Agriculture) in Tamba counda and Sedhiou Regions of Senegal, funded by Global Affairs Can ada (GAC), Project No. P005390 and implemented by Mennonite Economic Development Associates (MEDA) in collaboration with In ternational Center for Tropical Agriculture (CIAT). The authors also acknowledge the editorial support provided by Glenn Hyman and Olga Spellman of the Alliance of Bioversity International and CIAT Science- Writing Service. Appendix 1. Model diagnostics This section provides diagnostic plots to validate the OLS regression model used in Results Section 3.2 (Table 6) to examine the relationship between product diversity and MVAC. The plots confirm the model’s robustness. • Model Fit: Observed MVAC values align closely with predicted values, peaking at 0.6–1.2, indicating good model performance. • Linearity: Residuals versus fitted values show a flat trend, confirming linear relationships between predictors and MVAC. • Homogeneity of Variance: Residual spread is consistent, supporting equal variance across predictions. • Collinearity: Variance Inflation Factors (VIF < 5) indicate low multicollinearity, ensuring stable estimates. • Normality of Residuals: The Q-Q plot shows residuals follow a normal distribution, validating model assumptions. Table 6 OLS Regression Results for MVAC. Predictors Estimates (β) Std. Error CI Statistic p-value Model 1: Diversity Only ​ ​ ​ ​ ​ Intercept 0.530 0.0065 0.517–0.543 81.77 <0.001*** Shannon-Wiener Index (H) 0.076 0.0154 0.046–0.107 4.97 <0.001*** R2 / R2 adjusted 0.035 / 0.033 ​ ​ ​ ​ F Statistic 24.666*** (df = 1; 689) ​ ​ ​ ​ Model 2: With Controls ​ ​ ​ ​ ​ Intercept 0.398 0.0393 0.321–0.475 10.13 <0.001*** Shannon-Wiener Index (H) 0.009 0.0135 − 0.018–0.035 0.65 0.514 Small Retail Vendor 0.083 0.0115 0.060–0.105 7.20 <0.001*** Open-Air Vendor 0.060 0.0098 0.041–0.079 6.15 <0.001*** Employees [Yes] 0.171 0.0101 0.151–0.191 16.94 <0.001*** Credit Sales [Yes] 0.031 0.0118 0.008–0.054 2.64 0.008** Nutrition Training [Years] − 0.002 0.0009 − 0.004–0.000 − 2.33 0.020* Funding Knowledge 0.001 0.0004 0.001–0.002 3.02 0.003** Observations 691 ​ ​ ​ ​ R² / Adjusted R² 0.452 / 0.440 (Model 2) ​ ​ ​ ​ F Statistic 39.79*** (df = 14; 676) ​ ​ ​ ​ Notes: Model 2: The effect of diversity attenuates and becomes non-significant once controls are included, suggesting that diversity is mediated by vendor capacity (e.g., access to credit, employees, training). ***p < 0.001, **p < 0.01, *p < 0.05. C. Muriithi et al. Sustainable Futures 11 (2026) 101593 9 2. Food Group Diversity This section details the food groups offered by 691 market vendors from 28 markets. Results Section 3.2’s analysis of product diversity using the Shannon-Wiener index. Commodities are categorized based on [36] guidelines. Spices/condiments (217 vendors) and cereals (200 vendors) were the most common, followed by other vegetables (197 vendors), oils/fats (156 vendors), and milk/dairy (116 vendors). Fish (109 vendors), tuber s/plantains (96 vendors), and vitamin A-rich vegetables (93 vendors) were also prominent. Less common groups included nuts/seeds (34 vendors), mixed food groups (24 vendors), and organ meats (8 vendors) (Appendix Table B1). Table B1 Commodities Offered by Vendors. Food Group Commodities Number of Vendors Cereals Rice, Corn (dry/flour), Wheat flour, Bread, Millet, Sorghum, Spaghetti 200 Vegetables and Tubers (Vitamin A-rich) Carrot, Orange Fleshed Sweet Potatoes, Squash, Red bell pepper, Butternut 93 Dark Green Leafy Vegetables Kale, Sweet potato leaves, Cassava leaves, Lettuce, Pumpkin leaves 39 Other Vegetables Tomato, Eggplant, Onion, Cabbage, Okra, Green beans, Cucumber 197 Dried Vegetables Beans, Peas, Lentils 62 Tubers and White Roots, Plantains Irish potatoes, Sweet potatoes (white), Cassava, Yam, Plantain 96 Fruits (Vitamin A-rich) Peach (fresh/dried), Papaya (ripe) 12 Other Fruits Watermelon, Banana, Orange, Lemon, Pineapple, Apple 87 Organ Meats (Iron-rich) Liver 8 Meat Goat, Beef, Lamb, Chicken, Duck 77 Eggs Chicken eggs 61 Fish Tilapia, Nile Perch, Catfish, Sardines, Shellfish 109 Nuts and Seeds Peanuts, Peanut Butter, Baobab seeds 34 Milk and Dairy Milk, Cream, Fermented milk, Cheese, Yogurt 116 Oils and Fats Cooking oil, Margarine, Palm oil, Butter 156 Treats/Sweets Candy, Sugar, Cakes, Cookies 73 Spices, Condiments, Beverages Black pepper, Soft drinks, Coffee, Garlic, Salt, Tea, Soy sauce 217 Mixed Food Groups Fruit juice, Cooked beans/corn, Vegetable salad 24 Appendix Table B2 summarizes the diversity of food groups offered by 691 market vendors, with cereals (200 vendors) and spices/condiments (217 vendors) being the most common. The Shannon-Wiener index (H), calculated at the individual vendor level, measures product diversity within food groups (Table 6). Nuts/seeds (H = 0.69) and milk/dairy products (H = 0.64) showed high diversity, indicating a wide variety of items, while fish (H = 0.28) and other fruits (H = 0.17) had low diversity, reflecting limited variety. Cereals (H = 0.49) and spices/condiments (H = 0.58) exhibited moderate diversity with balanced offerings. Table B2 Diversity of Commodities Offered by Vendors. Food Group Shannon Wiener (H) Diversity or richness (rare) Simpson (D) Dominance (common) Spices, condiments and beverages 0.58 0.73 Cereals 0.49 0.61 Dark green leafy vegetables 0.55 0.71 Dried vegetables 0.56 0.71 Eggs 0.62 0.76 Fish 0.28 0.34 Fruits rich in vitamin A 0.32 0.43 Meat in the flesh 0.26 0.32 Milk and dairy products 0.64 0.79 Mixed Food Groups 0.30 0.42 Nuts and seeds 0.69 0.81 Oils and fats 0.65 0.80 Organ meats / (rich in iron) 0.53 0.72 Other fruits 0.17 0.22 Other vegetables 0.45 0.59 Others 0.38 0.51 Tubers and white roots, plantains 0.59 0.75 Vegetables and tubers rich in vitamin A 0.53 0.69 Treats/ sweets 0.65 0.79 Note: The Shannon-Wiener Index (H) measures the diversity or richness of food items within a vendor’s offerings for a specific food group. A higher H value indicates greater diversity, meaning the vendor offers a wide variety of items within that food group, with no single item dominating. For example, a high H value for "Nuts and seeds" (0.69) suggests vendors offer many different types of nuts and seeds in relatively balanced proportions. Conversely, a lower H value, such as for "Other fruits" (0.17), indicates lower di versity, meaning fewer types of items or one or two items dominate the offerings. The Simpson Index (D) measures dominance, or how much one or a few items dominate a vendor’s of ferings within a food group. A higher D value indicates greater dominance, meaning one or a few items make up most of the offerings, with less variety. For instance, a high D value for "Nuts and seeds" (0.81) suggests that one or a few types of nuts or seeds dominate the vendor’s stock. A lower D value, such as for "Other fruits" (0.22), indicates less dominance, meaning the offerings are more evenly distributed among different items. C. Muriithi et al. Sustainable Futures 11 (2026) 101593 10 3. Supplementary analyses This section presents additional analyses referenced in Results Sections 3.2 and 3.3, including the MIMIC model and challenge-MVAC relation ships, which provide context for the primary findings (Fig. A1). 3.1 MIMIC and MVAC model results The path diagram Fig. A2 illustrates the relationships between MVAC, product diversity (Shannon-Wiener index), and underlying factors. Arrows show how business factors (e.g., employees, credit sales), market factors (e.g., vendor type), and knowledge (e.g., funding access) positively influence MVAC. Pricing based on purchase cost slightly reduces MVAC, while demographic factors (e.g., education) show mixed effects. Appendix Table B3 presents regression results examining how vendor challenges relate to MVAC. Counterintuitively, high inventory costs and transportation difficulties (β = 0.038, p = 0.006) are associated with higher MVAC, indicating that these challenges may encourage vendors to innovate and adapt. Setting appropriate prices shows a marginally positive relationship, while other challenges, such as insufficient funds and low sales, are not significantly linked to MVAC. The model explains 6.3 % of MVAC variance. Table B3 OLS Model on Challenges and MVAC. Predictors Estimates Std. Error CI Statistic p-value Intercept 0.515 0.032 0.453–0.577 16.33 <0.001*** High Inventory Cost 0.032 0.014 0.005–0.059 2.36 0.019* Inaccessibility of Credit 0.007 0.016 − 0.023–0.038 0.47 0.638 Insufficient Funds − 0.016 0.013 − 0.042–0.010 − 1.20 0.232 Low Sales − 0.012 0.015 − 0.041–0.017 − 0.78 0.435 Seasonality Issues 0.013 0.016 − 0.017–0.044 0.85 0.397 Setting Appropriate Prices 0.025 0.015 − 0.004–0.054 1.68 0.094 Strong Competition 0.016 0.015 − 0.014–0.045 1.03 0.302 Supply of Fresh Products − 0.007 0.015 − 0.036–0.023 − 0.43 0.664 Transportation of Goods 0.038 0.014 0.011–0.066 2.76 0.006** Observations 691 ​ ​ ​ ​ R² / Adjusted R² 0.063 / 0.051 ​ ​ ​ ​ F Statistic 5.292*** (df = 9; 681) ​ ​ ​ ​ Notes: The model examines how challenges affect MVAC (Results Section 3.3). High inventory costs and transportation difficulties show positive associations, sug gesting adaptive responses. ***p < 0.001, **p < 0.01, *p < 0.05. 3.2 Correlation of Indices As shown in Fig. A3, the high correlations among indices (r > 0.90) indicate redundancy; therefore, the Shannon-Wiener index is prioritized for analysis, given its balanced sensitivity to richness and evenness. Fig. A1. OLS Model Validation Diagnostics. C. Muriithi et al. Sustainable Futures 11 (2026) 101593 11 Fig. A2. Path diagram: MIMIC modelling results indicating the relationships of latent and adaptive capacity and how they link to the diversity index. M2=Type of market, M3=Shannon Wiener diversity index, d2=Business owner gender,d4=Vendors own the business, d5=Business owner education, b1=Does the business employ employee, b2=Is the business formal or informal,b3=Does the business offer sales on credit,b4=Type of business, b6=Product pricing based on purchase cost,b10=Investment (total cost of purchases), k1=The number of years since the vendor benefited from nutrition project,k3=Knowledge about access to funding,k4=Knowledge about climate change, AC_score=MVAC. Fig. A3. Correlation between the indices. C. Muriithi et al. Sustainable Futures 11 (2026) 101593 12 Data availability Data will be made available on request. References [1] A. Abdul-Rahaman, G. Issahaku, Y.A. Zereyesus, Improved rice variety adoption and farm production efficiency: accounting for unobservable selection bias and technology gaps among smallholder farmers in Ghana, Technol. Soc. 64 (2021), https://doi.org/10.1016/j.techsoc.2020.101471. [2] Abebe, M.G. (2025). 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Sustainable Futures 11 (2026) 101593 15 https://doi.org/10.1186/s40100-021-00190-8 https://doi.org/10.1186/s40100-021-00190-8 http://refhub.elsevier.com/S2666-1888(25)01153-0/sbref0103 http://refhub.elsevier.com/S2666-1888(25)01153-0/sbref0103 https://doi.org/10.5751/ES-00650-090205 https://doi.org/10.5751/ES-00650-090205 https://doi.org/10.5751/ES-05072-180327 https://doi.org/10.3390/admsci15060224 https://doi.org/10.1108/IJMF-07-2014-0110 https://doi.org/10.1108/IJMF-07-2014-0110 The role of product diversification in enhancing market vendor adaptability and food-system resilience in Senegal, West Africa 1 Introduction 2 Materials and methods 2.1 Study area 2.2 Sampling and data 2.3 Empirical analysis 2.3.0 Conceptual framework 2.3.1 Product diversity indices 2.3.2 Dimensionality reduction using factor analysis of mixed data (FAMD) 2.3.3 Constructing MVAC: the mimic model 2.3.4 Regression models 2.3 5 validation and sensitivity analysis 3 Results 3.1 Vendor characteristics 3.2 Commune and village characteristics 3.3 Market vendor challenges 3.4 Product diversity and adaptive capacity 4 Discussion 4.1 Key findings 4.2 Reframing diversity as an enabler 4.3 Structural challenges and barriers 4.4 Contribution to food systems resilience and security 4.5 Limitations and future research 4.6 Policy recommendations 5 Conclusion Disclosure statement CRediT authorship contribution statement Declaration of competing interest Acknowledgements Appendix 1 Model diagnostics 2 Food Group Diversity 3 Supplementary analyses 3.1 MIMIC and MVAC model results 3.2 Correlation of Indices Data availability References