1 Introduction

A decade has passed since the start of the Syrian conflict. This humanitarian crisis has severely affected agriculture and food production (FAO, 2014), leaving a nation that was once self-sufficient and food-producing under the immediate and persistent threat of food insecurity. Recent estimates suggest that over 12.4 million Syrians (about 60% of the population) are now food insecure (WFP, 2021). The nutritional status of the population has also been affected by the protracted war-situation, which has damaged agricultural infrastructure, disrupted food value chains (FAO & WFP, 2017; Jaafar et al., 2015), caused large fluctuations in food prices and exchange rates (Selimian et al., 2022), and massive internal population displacement (Selimian et al., 2022).

Several studies have explored the relationship between food security and protracted crisis (e.g., wars). Breisinger et al. (2014) state that “food security can be both a consequence and a cause of conflict.” Crises are generally associated with food insecurity, which often are related to factors such as low incomes, climate change, and food price shocks (Bandara & Cai, 2014; D’Souza & Jolliffe, 2013; Fjelde, 2015; Raleigh et al., 2015; Romano et al., 2019). Such conflicts generate considerable burdens for already vulnerable social groups, especially women and female-headed households (FHHs) (Jung et al., 2017).

Most published studies offer conceptual perspectives and descriptive analyses (e.g., Hendrix & Brinkman, 2013; Jaspars & Hilhorst, 2021; Mitchell et al., 2015; Sadiddin et al., 2019). However, few of these actually involve empirically (field) collected data (Brown et al., 2020; Brück et al., 2019; Dabalen & Paul, 2014; Maitre d’Hôtel et al., 2023; Malual & Mazur, 2022). Empirical field-based studies are necessary in documenting specific cases. This study examines the determinants of food (in)security in Syrian rural areas in the context of open conflict. It analyses the main factors driving food insecurity in countries suffering protracted crisis and the capacity of households to cope day-by-day with the consequences. To our knowledge, this is the first study to examine the factors affecting food (in)security at the household level in Syria. Previous studies have focused on the macro-level using Food Balance Sheets from National Budget Survey data (Jrad et al., 2013) or addressed the micro-level without using food security indicators to assess food security status (and often use “food sovereignty” or the “right to food” concepts; Galiè, 2013) within a limited geographic area (Doocy et al., 2017).

Few studies have investigated coping strategies as a means of adaptation among populations grappling with the effects of prolonged crisis. This study draws on the definition of Farzana et al. (2017) for coping strategies as desperate measures that are taken to face precarious economic circumstances. We also address the gender-specific dimension of food (in)security within war-affected regions to expand on the literature concerning the roles of gender and food security. This work may also contribute to debates over the links between food crises and violent conflict (Kemmerling et al., 2022) and the extent to which conflicts may prevent progress on SDG 1 (“No poverty”), SDG 2 (“Zero hunger”), and SDG 16 (“Peace, Justice and Strong Institutions”), and on interrelated Goals such as SDG 5 (“Gender equality”) and SDG 10 (“Reduced inequalities”). We report additional evidence on women’s roles in food production and acquisition, as well as their contributions to greater dietary diversity (see also Agarwal, 2018). Lastly, we forward recommendations to improve relief program designs that target Syria or other regions facing conflict or enduring protracted crises.

The article is structured as follows. Section 2 reviews the available literature and Section 3 reviews the methods used in this study. We then report our results and discuss them in Section 4, and provide some concluding remarks and policy implications in Section 5.

2 Conceptual framework

Food insecurity prevails where conflicts occur and can be both the cause and the consequence of a protracted crisis. Conflicts commonly destroy agricultural infrastructure, thereby reducing food production, restricting food availability, and preventing access to food markets (FAO et al., 2017). Conflicts are also usually accompanied by economic crises, leading to food price shocks, currency depreciation, and vulnerability to natural disasters (Teodosijević, 2003). These interconnected elements require urgent intervention to relieve affected people (FAO & WFP, 2019).

Armed conflicts also have specifically gendered impacts (FAO & WFP, 2010). In protracted crises, women's workloads increase, because they are compelled to take increased responsibilites in the absence of men (George et al., 2020). Several studies highlight how gender gaps in resource endowments and farming are linked to food security gaps (Agarwal, 2018; Aryal et al., 2019; Lewis, 2015). Studies conducted during food crises typically find that FHHs experience a greater decline in food security than MHHs (Agidew & Singh, 2018). However, this subject remains understudied, particularly where abrupt transitions occur as a result of conflict and/or mass population displacement.

The conceptual framework for this study (Fig. 1) considers different structural and socio-demographic factors influencing food security for both households and individuals (Martin-Shields & Stojetz, 2019). Figure 1 draws on previous work on food security in conflict areas in numerous examples: Palestine (Brück et al., 2019), Iraq (Rizk & Salemi, 2020), Côte d’Ivoire (Dabalen & Paul, 2014), Kenya (Kassie et al., 2014), Niger (Zakari et al., 2014), Pakistan (Abdullah et al., 2019), Ethiopia (Beyene & Muche, 2010), Malawi (Anderson, 2002), Sudan (Ibnouf, 2011), and Bangladesh (Farzana et al., 2017).

Fig. 1
figure 1

Conceptual framework

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Recurring and important factors include: (1) characteristics of the head of household (e.g., age, gender, and education), (2) household characteristics (e.g., size, demographic dependency, (3) diversity of incomes sources and access to off-farm income (employment and distribution of decision-making), and (4) farm characteristics (e.g., land and livestock endowment, size, planted crops and distance to water resource). Many authors use dichotomous food security – associated variables at the household level (Abdullah et al., 2019; Beyene & Muche, 2010; Zakari et al., 2014). However, few studies investigate the multiple dimensions of food (in)security (Brück et al., 2019; Rizk & Salemi, 2020). As a result, the variables that truly capture the effects of conflict on food security are not well-defined. Furthermore, little is known about household coping strategies and about the safety nets that may exist to overcome food security shocks. Therefore, this study uses three different food security indicators to address these different dimensions.

3 Methods

3.1 Study site characteristics and data collection

We used data from the Crop Production and Food Security Assessment in Syrian Rural Areas household survey, which was conducted by the National Agricultural Policy Centre of Syria. The survey was conducted in May 2017 as an aid programme assessment. A team of national researchers collected data from 12 of the 14 Syrian governorates,Footnote 1 including some war-affected zones. Figure 2 shows the study area. Our sample includes 296 villages in 143 sub-districts (Nahia) of 48 of 61 districts (Mantiqa). A total of 1,381 farmer households were chosen using a stratified sampling technique. Farmers in the agroecological zonesFootnote 2 (first stratum of sample selection) were grouped by dominant farming systems (second stratum). The subsequent selection of households was randomly conducted. We used available secondary data provided by the extension directorates, while also considering the reliability of enumerators. Following Grosh and Glewwe (2000), a household was defined as “a group of people who live together, pool their money and eat at least one meal together each day.”

Fig. 2
figure 2

Map of the administrative regions (Mantiqa) surveyed in 2017

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Interviews were carried out with household heads using a structured and pre-tested questionnaire. This questionnaire consisted of three parts: 1) the level of agricultural production during the 2016–2017 season, 2) the level of food security of rural households (i.e., the share of income dedicated to food, food consumption patterns, dietary diversity indicators, coping strategies during insecure situations), and 3) the emergency needs and assistance received by farmers.

3.2 Analytical approach

Data analysis was conducted in two phases. The first was descriptive. Since we could not assess the gender composition of each household,Footnote 3 we disaggregated the descriptive analysis based on the gender of the household head to explore gender roles in household livelihoods. Such disaggregation is common in food security studies (Felker-Kantor & Wood, 2012; Kassie et al., 2014; Mallick & Rafi, 2010; Nkomoki et al., 2019; Zakari et al., 2014). Mean differences between female-headed households (FHHs) and male-headed households (MHHs) were analysed using a t-test and chi-square (χ2) test to assess livelihoods differences (e.g., farming activities, income-generation, and strategies to access food).

The second phase used regression analysis to analyse the factors influencing food insecurity. We ran three models, in which food security is the dependent variableFootnote 4 and the following three indicators of food security.

  1. 1.

    The Food Consumption Score (FCS) or “The frequency weighted diet diversity score” is calculated using the consumption frequency of eight different food groups in the household during the seven-day recall period (WFP, 2008). The equation for calculating FCS (Jones et al., 2013) is:

    $$FCS= {a}_{1}{x}_{1}+ {a}_{2}{x}_{2}+\dots +{a}_{8}{x}_{8}$$

    where: 1–8: food groups.

    a:

    frequency (7-day recall)

    x:

    weight (meat, milk, and fish = 4, pulses = 3, staples = 2, vegetables and fruits = 1, sugar and oil = 0.5, condiments = 0).

  2. 2.

    The Dietary Diversity Score (DDS), which measures the number of different foods or food groups consumed over a seven-day recall period. DDS allows flexibility in determining how many foods (groups) to include, recall period length, and the cut-offs (Hoddinott & Yohannes, 2002; Ruel, 2002). These food groups are: 1. Cereals, roots and tubers; 2. Pulses and legumes; 3. Vegetables; 4. Fruit; 5. Meat, fish and seafood, and egg; 6. Dairy products; and 7. Oils and fats. A binomial variable was created with the value of 1 if the food group was consumed in the past week and 0 if otherwise. The sum of all food groups was calculated to define a DDS at the household level. The indicator value therefore ranges from 0 to 7.

  3. 3.

    The “reduced” Coping Strategies Index (rCSI), which is a behavioural index that measures household adjustments in consumption and livelihood when faced with shortages of food. The rCSI is comparative; it considers the five most common strategies with standardised weightsFootnote 5 (Maxwell & Caldwell, 2008). The respondent can choose from several strategies such as: i. eating less-preferred foods (scored as 1), ii. borrowing food/money from friends and relatives (scored as 2), iii. limiting portions at mealtime (scored as 1), iv. limiting adult intake (scored as 3), and v. reducing the number of meals per day (scored as 1). The sum was calculated for each household by multiplying the frequency of each option (i to v) of a household’s strategy (i.e., how many times that option was used in the past seven days) by its specific weight. Table 1 summarises the cut-off threshold for each indicator.

Table 1 Food security indicators and their cut-off point threshold values
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The food security indicators (as dependent variables) enable capturing multiple dimensions of food (in)security (Cafiero et al., 2014; Coates, 2013; Maxwell et al., 2014). These indicators are mainly linked with the access dimension of food security. These indicators are strongly correlated with calory intake (Hoddinott & Yohannes, 2002; WFP, 2008; Wiesmann et al., 2009).

The independent variables are grouped into three groups: 1) household head characteristics, 2) household characteristics and farm characteristics, and 3) conflict exposure. Conflict exposure was measured by assessing each household’s ability to cultivate their entire landholding despite armed conflict preventing field access. Household characteristics included size, diversity of income sources, access to different types of aid, and number of strategies used to cope with insecure food sources. Farm characteristics included total owned and cultivated land, the availability of livestock as the main source of self-production, and the availability of other productive assets that can increase production when land or livestock is limited. These variables are summarised in Table 2.

Table 2 Description of variables used in the instrumental variable regression model: variable names, descriptions, measurements, mean, and standard deviations
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The endogenous nature of conflict exposure to food security may lead to a biased model estimation. First, conflict can be both a cause and a result of food (in)security. Second, the conflict variable is self-reported by the household heads, and likely involves some measurement error. Third, unobserved variables may affect both food security and conflict, such as the location of the farming households. Importantly, armed conflict events were more severe in some areas during the period of data collection.

We used a two-stage least square techniques (2SLS) to address these problems and achieve a more consistent estimation of variable coefficients. We opted for farm location (i.e., governorate and geographic region) as the instrumental variables (see Brück et al., 2019; Miguel et al., 2004; Rizk & Salemi, 2020 on using instrumental variables to study conflict development outcomes). We follow the approach of Brück et al. (2019): the estimation begins with an OLS regression that examines the factors affecting household-level food security. The OLS regression is written as follows:

$${Y}_{i}\;=\; {\beta }_{0}\;+\;{CONFLICT}_{i}{\beta }_{1}\;+\;{X}_{i}{\beta }_{2}\;+\;{u}_{i}, i\;=\;1,\dots ,N$$
(1)

where:

Yi:

represents the outcome variables (food security indicators as scores)

CONFLICTi:

represents the exposure to conflict variable

Xi:

is a vector of households and farm characteristics

The results of OLS regression are given in Annex 1. The first stage of the instrumental variable equation thus becomes:

$${CONFLICT}_{i}\;=\; {\alpha }_{0}\;+\;{Location}_{ij}{\alpha }_{1}\;+\;{X}_{i}{\alpha }_{2}\;+ {\epsilon }_{i}$$
(2)

where:

Location:

is represented by the governorate j and household i.

Xi:

denotes the vector of the household i characteristics (predictors x1 to xn) except conflict

The following equation gives the second stage:

$${Y}_{i}\;=\;{\beta }_{0}^{\prime}\;+\;\widehat{{CONFLICT}_{i}}{\beta }_{2}^{\prime}\;+\;{X}_{i}{\beta }_{2}^{\prime}\;+\;{u}_{i}, i\;=\;1,\dots ,N$$
(3)

where

\({\widehat{CONFLICT}}_{i}\):

is the predicted exposure to conflict.

We ran Durbin and Wu-Hausman tests for endogeneity. Test results confirmed our assumption on the endogeneity of the ‘exposure to conflict’ (CONFLICT) as predictor. We then tested for the over-identifying restrictions to confirm the relevance of the ‘Location’ variable as instrument variable.

4 Results

4.1 Main characteristics of targeted households

Variables were grouped in four categories: (1) HH head characteristics (age), (2) HH characteristics (family size, income sources, off-farm income, remittances), (3) farm characteristics (land size, livestock ownership, productive asset ownership, the ability to cultivate the land), and (4) HH food security status and coping strategies. We summarise the main differences between FHHs and MHHs in Table 3.

Table 3 Descriptive statistics of household characteristics, disaggregated by the gender of household head
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4.1.1 Head of household characteristics

Our sample included 72 FHHs and 1,309 MHHs (5.2% and 94.8%, respectively). In 20 MHHs, women were the breadwinners. The heads of household had an average age of 53.4 years, with no significant difference in the mean age between FHHs and MHHs.

We identified fewer FHHs than estimated by the World Bank (i.e., around 12–17% in 2015; 2020). In this study, the household headFootnote 6 was the person who made key livelihood decisions, mainly in agricultural production, income use, and expenditure management. However, the security conditions and cultural factors may have made male family members more likely to approach the enumerator. This (alongside the inability to identify the household’s gender composition) was a data collection limitation.

4.1.2 Household characteristics

The mean household size was 7.35 members. Women-headed households had slightly larger household sizes (1.34). This difference was significant (p < 0.05), suggesting that FHHs need to support about one more person than MHHs.

FHHs have an average of 3.11 income sources, while MHHs have 2.52 sources, suggesting that FHHs might seek to diversify their income sources because their productive agricultural assets are smaller on average. Another significant factor was remittances: 26.4% of FHHs reported receiving remittances, compared to 9.1% of MHHs. Many households rely on remittances from their relatives, mainly adult men who migrated during the conflict. No significant differences were found between FHHs and MHHs in the proportion of off-farm income or share of income spent on food.

4.1.3 Farm characteristics

FHHs and MHHs had significantly different average land sizes, MHH-operated land being 2.8 times larger. This probably led to significant differences in agricultural income and, therefore, food security. Another significant difference was the ownership of production assets (machinery and agricultural tools) —only 6.9% of FHHs owned machinery, compared to 24% of MHHs, suggesting lower women’s ability to secure means to achieve food for the household.

Food security in rural areas appears closely dependent on agricultural production, since agriculture is both a source of food and income. Farmers seem to have abandoned high-maintenance cash-generating crops. We did not find any cotton or sugar beet production, two common industrial crops (Fig. 3). Farmers now focus on cereals, olive trees, and vegetables. This supports previous findings documenting shifts toward short-term, low-profitability crops or low-cost crops (e.g., olive cultivation) (Arias et al., 2019; Bozzoli & Brück, 2009).

Fig. 3
figure 3

Main cultivated crops in the sample

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Many of the respondents (39.6%) were unable to cultivate their total land holdings during the 2016-2017 season due to a lack of financial resources (65.5%), to unavailable inputs (59.8%), and to conflict-related safety conditions (53%).

The association between household head gender and the ability to fully cultivate lands was tested with a chi-square. We found an indication that significant associations exist between the household head gender and reasons for not cultivating the land: lack of financial sources (38.9% of FHHs; 24% of MHHs), lack of seeds (20.8% of FHHs; 9.9% in MHHs), and conflict-related reasons (29.2% of FHHs; 16% of MHHs).

4.1.4 Household food security: status and coping strategies

Most households exhibited acceptable levels of food security, as indicated by FCS and DDS (92.3% and 85.7%, respectively). Female-headed households were not represented in the poor categories of both indicators, yet this finding should be examined carefully due to the small number of FHHs. However, rCSI only classified 17.5% of households as food secure. A chi-square test suggested that the relationship between household head gender and food security status was not significant in FCS and DDS. There was a significant association between household head gender and rCSI (see discussion below).

Another important factor affecting food security was aid/assistance. Most (n = 798) households received at least one type of aid/assistance during the 12 months before the survey. This often was in the form of food aid (68.9%) or seeds (45.5%) as shown in Fig. 4.

Fig. 4
figure 4

Types of assistance received by the households

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Most households (n = 1,295) reported adopting strategies to cope with food insecurity (shortage of food or of money required to buy food) in the seven days before the survey. The most common strategies were: eating less expensive food (98%), reducing portion size (62%), and limiting the number of meals (50%). We used a chi-square test to identify three strategies with significant differences between FHHs and MHHs: borrowing food (26.4% of FHHs; 15.7% of MHHs), reducing the number of meals per day (66.7% of FHHs; 46% of MHHs), and reducing portion sizes (80.6% of FHHs; 56.9% of MHH). The last two strategies suggest that concerns about the health situation of FHHs need more investigation under the current conflict in Syria.

4.2 Determinants of household food security

The results of the second stage of the Instrumental Variable – Two Stage Least Square regression approach are reported in Table 4, where the estimated coefficients predict variations in food security (represented by FCS, DDS and rCSI) based on changes in the factors of interest.

Table 4 Instrumental variable regression results for household food security indicators
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MHHs were significantly more food secure according to rCSI. However, FHHs seemed to enjoy more nutritious and diversified diets, according to DDS. FCS model suggests that MHHs were more food secure, but this result was not significant. Younger household heads were more likely to secure higher FCS and DDS scores (p < 0.01 and p < 0.1, respectively). Household size was negatively associated with food security in FCS and DDS models, with a significant (p < 0.01) result from the DDS model. This contradicts findings on rCSI, but were not statistically significant.

Households with more than one income source were significantly less food secure according to FCS, but more food secure according to rCSI. This is reasonable during protracted crises, when income portfolio diversification becomes necessary to overcome adversities. Another contradiction is apparent from the share of off-farm income: this share did not have a significant (p > 0.1) impact on household food security, but significantly (p < 0.01) increased the severity of food insecurity, as reflected by the depletion of rCSI. Adopting several coping strategies was associated with a significant (p < 0.01) negative impact on food security status in all three models. Lastly, receiving aid and assistance positively impacted food security, although only significantly in FCS (p < 0.01) and DDS (p < 0.1).

Owning productive assets significantly enhanced food security, as indicated by FCS and DDS models. Similarly, livestock ownership had a significant (p < 0.01) positive impact on food security in FCS and rCSI. FCS and DDS models also determined that higher numbers of cultivated crops were associated with higher food security.

Cultivating an entire landholding had a significantly (p < 0.05) positive impact on household food security and dietary diversity. Vulnerabilities in accessing seeds, fertilisers, and water for irrigation negatively influenced food security. Lastly, and surprisingly, poor security status, as reflected in hardships accessing land due to conflict, was positively associated with higher food security with p < 0.01 in FCS and DDS models. However, being exposed to conflict negatively (p < 0.01) affected food security in rCSI.

5 Discussion

This study underscores the usefulness of considering multiple dimensions of food security to examine household vulnerabilities in countries experiencing prolonged crises. Several household and farm-related characteristics promote dietary diversity, consumption frequency, and adaptive strategies.

Large household size appears to face significant (p < 0.01) lower Dietary Diversity Score (DDS). This appears logical since dependency levels and unemployment rates are high in conflict zones (see also De Cock et al., 2013; Haile et al., 2005). Livelihood diversification appears beneficial to food security, with significant (p < 0.01) results in the model for the Reduced Coping Strategy Index (rCSI). This factor has consistently been found in previous work elsewhere (e.g., Maxwell et al., 2010), and has policy implications for food insecurity, especially during protracted crises (Breisinger et al., 2014). We find that a high proportion of off-farm income negatively affects household food security, especially when households reported difficulties in practicing agriculture. Under such circumstances, households must seek low-wage employment opportunities that barely cover their daily sustenance needs. The inability to cultivate land greatly impacted rCSI. However, access to financing and the effects of protracted crisis were stronger warning signs.

Owning productive assets significantly (p < 0.05) enhanced food security, as indicated by the Food Consumption Score (FCS) and the Dietary Diversity Score (DDS) models (Table 4). This conforms with other reports calling for prompt intervention to replace lost and damaged productive assets in areas affected by protracted crises (Maxwell et al., 2010). The availability of land, in conjunction with livestock availability and crop diversity, exerts a substantial influence on food security. A decrease in land use or the inability to utilize land diminishes household safety nets. These findings illustrate the importance of farm endowments and farming characteristics in a protracted ‘ecosystemic crisis’ (de Lattre-Gasquet & Clémence, 2018). In long-lasting crises, which are often marked by insecurity and population displacement, connections to agricultural resources become precarious. Households face intersecting vulnerabilities that strongly disrupt their farming practices and livelihoods, often with irreversible consequences.

Many farmers were unable to cultivate their total land, as a result of unavailable or high-priced inputs, lack of financial means, and/or inability to access their land. Access to inputs and insecurity were pivotal factors influencing all three dimensions of food security. This suggests that aid programs in the buffer zones surrounding conflict hotspots should therefore be prioritized.

Households that had to draw on multiple coping instruments to avoid food insecurity were more food insecure. This could suggest that the reported coping strategies were not effective (Farzana et al., 2017), but rather desperate measures to face precarious economic circumstances. This metric – the diversity of coping instruments – actually could serve as an indicator of household resilience (Ado et al., 2019). This study also found that assistance programs (e.g., farm inputs, food baskets, or the reconstruction of damaged infrastructure) have a significant (p < 0.01) positive impact on food security (Tranchant et al., 2019). Households receiving at least one type of aid in the last 12 months acquired an average of 3 additional points on the Food Consumption Score (FCS).

The head of household characteristics, namely gender and age, also played an important role. We found that younger (in the Food Consumption Score (FCS) model), and male-headed households (in the Reduced Coping Strategies Index (rCSI) model) were most food secure, an evidence found also in other studies (Jung et al., 2017; Kassie et al., 2014; Mason et al., 2015). Male-headed households (MHHs) could better adjust to food shortages due to increased access to assets, wider network relationships, and increased capacity to interact with other households. Unlike other food security indicators, dietary diversity was significantly better in female-headed households FHHs, although this effect was very small (p < 0.05). This finding concurs with previous research linking dietary diversity with women’s control over resources, access to credit, and decision-making power (Agarwal, 2018; Amugsi et al., 2016; Larson et al., 2019; Malapit et al., 2015; Yimer & Tadesse, 2015).

Household-level food security assessments suggest two significant issues that need further examination. As dietary diversity indices, FCS and DDS measure caloric sufficiency, but not other dimensions of food security such as food stability or social acceptability (Coates, 2013). Furthermore, household-level measurements, via a seven-day recall period, may not reflect the accurate quality and quantity of food consumed by individuals (Ruel, 2002).

6 Conclusions

This study underscores the importance of micro-level evidence in understanding factors impacting food security and nutritional outcomes. Improving access to financial resources and bolster the resilience of farmers to maintain access to inputs is critical if supply chains are disrupted leading to immediate shifts in market dynamics. This study in Syria indicates that measures to increase the access to agricultural inputs, financial support, and irrigation infrastructure maintenance are pivotal in transitioning to a more stable level of food security.

Future research should investigate how proximity to conflict disrupts the food value chain, and how crisis shocks food security. Survey data on the distance from conflict-affected areas could estimate both direct and indirect shock effects on agricultural operations and enable precise evaluations of the conflict’s impacts on farming and food security.

Despite being more vulnerable to food insecurity, female-headed households (FHHs) consistently outperform their male counterparts in facilitating diversified nutrition. We need however to stress here the large difference in sample sizes between FHHs and MHHs, and report this conclusion with caution. This difference in household resilience seems important in contributing to the current debates over women’s roles in promoting SDG 2 “Zero hunger” and SDG 5 “Gender equality” in countries facing protracted crisis. Intervention programs need to incorporate gender-sensitive components to enhance the overall well-being of households. Financial and non-financial support programs should be implemented with a priority for larger households and those headed by women, at least in the social and economic contexts similar to those of Syria. Such support might offer food aid, assistance in recovering productive machinery, and cash transfer initiatives. In such contexts, rural women should be the focus of aid, given their contributions to combatting food insecurity and their role as agents of rural livelihood change.

This study has notable limitations. First, the study does not consider food security of within-households individuals, and does not distinguish men and women’s specific needs. Future research should consider intra-household differences by collecting the caloric intake of each individual. Second, future work should analyse nutritional intake (adequacy) instead of dietary diversity. The Food Consumption Score (FCS) and Dietary Diversity Score (DDS) are helpful in the short term for addressing population needs in protracted crises. However, we call on scholars to use food security indicators based on household experiences, such as the Food Insecurity Experience Scale (FIES). Individual measures such as children’s nutrition, women’s dietary diversity, or Height-to-Weight and Height-to-Age Z-scores should also be considered. Future research should also investigate the consequences of gender roles in facilitating food security. Data should thus capture women and men’s contributions to household labour to take into account disparities within families and power dynamics within household to achieve food security.