Determinants of Household Food Security in Rural Uganda: Perceived Needs as Key Predictors

Determinants of Household Food Security in Rural Uganda: Perceived Needs as Key Predictors | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of Household Food Security in Rural Uganda: Perceived Needs as Key Predictors Joaquín Solano-Jiménez, Ricardo Abadía, Rafael Pinilla Palleja, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7261208/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Food insecurity remains a persistent challenge in Sub-Saharan Africa, particularly in rural areas where households depend on subsistence agriculture and face increasing vulnerability to climate shocks. Despite growing research on the topic, many studies rely on isolated or context-specific indicators, limiting comparability and policy relevance. This study addresses that gap by using two widely recognized indicators—the Food Consumption Score (FCS) and the Months of Adequate Household Food Provisioning (MAHFP)—to assess food security among rural households in Uganda’s Sembabule district. It had two main objectives: ( 1 ) to compare household classifications based on FCS and MAHFP, and ( 2 ) to identify key determinants of food security using binary logistic regression models. Data were collected from 167 households through a two-phase approach combining structured questionnaires and open-ended interviews. Results The two indicators produced contrasting results: while 41.5% of households were classified as food insecure by FCS, 68.9% were considered food insecure by MAHFP. Binary logistic regression models identified perceived minimum necessary income, rather than actual income, as a stronger predictor of food security. In addition, household-reported challenges such as hunger, insufficient clothing, lack of farmland, and dissatisfaction with water access emerged as significant determinants. These qualitative variables consistently showed stronger explanatory power than many traditional socioeconomic factors. Conclusions Findings highlight the multidimensional nature of food insecurity and the value of combining standardized indicators with open-ended, perception-based data. Improving access to cultivable land and safe water, as well as integrating local perspectives into rural development strategies, may enhance the effectiveness of food security interventions. The strong predictive power of perceived needs over objective income measures suggests that policies should go beyond income generation to include financial literacy and resource management support. Additionally, agricultural diversification—particularly through integrated crop-livestock systems—can help households strengthen resilience. This study supports the use of participatory approaches that give voice to lived experiences and help uncover priority needs often overlooked in conventional assessments. Food security FCS MAHFP Logit model Household survey Africa Uganda Livelihoods Rural development Determinants 1. Background Ensuring universal access to safe, nutritious, and sufficient food for proper nutrition is a key objective of the United Nations' Sustainable Development Goal "Zero Hunger," which was established in 2015. While significant efforts have been made in recent decades to develop strategies and policies aimed at achieving global food security, approximately one in ten people worldwide currently experiences severe levels of food insecurity ( 1 ). Many of the undernourished people in Sub-Saharan Africa and worldwide are smallholder farmers who rely on agriculture for their livelihoods ( 2 ). According to Saridakis et al. ( 3 ) Uganda is a country where 74.83% of the population identifies as farmers, meaning that improvements in this sector have a direct impact on nearly three-quarters of the population. Communities in rural areas rely heavily on subsistence farming and livestock rearing. In particular, the agricultural practices in the dry corridor region are marked by simplicity and limited resilience to adverse climatic conditions, such as unexpected droughts, which disproportionately affect the area ( 4 ). Maize is the primary crop grown in the region, serving as a dietary staple and occupying extensive farmlands alongside beans and plantains. It is well known that climate change, through increased frequency and intensity of extreme weather events like droughts and floods, as well as changes in pest prevalence, can negatively impact crop production and yields ( 5 – 7 ). With climate projections for Sub-Saharan Africa indicating further disruptions in rainfall patterns ( 8 ), the future presents significant challenges for these communities. Food security is a complex and multidimensional issue that requires careful measurement to understand the nuances of the problem. Past studies have found that relying on a single metric can overlook critical aspects of food insecurity ( 9 ). While some definitions focus solely on food availability, research has shown that other traits like access and affordability are equally crucial determinants ( 10 ). Despite growing attention, access to adequate and nutritious food remains a major challenge across many developing countries, particularly for rural populations, who often face the greatest barriers to food security ( 11 ). In rural Uganda, where the majority of the population relies on subsistence farming, understanding its determinants is essential for designing effective policy interventions ( 12 ). Previous research has highlighted the dynamic relationship between chronic poverty and environmental challenges in shaping food insecurity patterns among rural communities in sub-Saharan Africa ( 13 ). This implies that the vulnerability to food insecurity in these settings is often deeply rooted in the complex interplay of socioeconomic and ecological factors. In this regard, Nuvey et al. ( 14 ) provide further evidence from Ghana, showing that food insecurity was significantly more prevalent among livestock-dependent households located in drier districts and among those affected by a higher number of adverse agricultural events. Their study illustrates how agroecological vulnerability and farm-level shocks can reinforce each other in undermining household food access. The challenge of food insecurity in this region is a multifaceted phenomenon, rooted in factors such as poverty, limited agricultural productivity, climate change, and lack of access to resources ( 15 ). In Uganda, the rural population, which makes up the majority of the country's population, is particularly susceptible to food insecurity due to their heavy reliance on subsistence agriculture and limited access to markets and social services, among other variables ( 13 , 15 ). Prior studies have explored several key factors contributing to food insecurity in rural communities, including household characteristics, socioeconomic status and environmental conditions ( 15 – 18 ) (Appiah-Twumasi & Asale, 2024; Kim et al., 2011; McIntyre & Hendriks, 2018; Usman & Haile, 2022). In this regard, in order to effectively identify the determinants of food security, selecting appropriate indicators is a key step in obtaining meaningful results. Various scientific studies have explored the determinants of food security in rural Uganda, but the indicators used to assess the population's food security status are often tailored specifically to the context of each investigation. While this approach offers advantages, such as a high degree of contextual adaptation, it significantly limits the comparability of findings across studies. For instance, the research conducted in Uganda’s Gomba district by Semazzi & Kakungulu ( 12 ) exemplifies this limitation. Rather than employing established, widely-used indicators, they developed a unique monetary-based approach, converting food harvested and income into monetary equivalents and setting a benchmark value for annual household food security. Although innovative, this methodology significantly hampers the comparability of their results with those of other researchers. Indicators like the Food Consumption Score (FCS) and the Months of Adequate Household Food Provisioning (MAHFP), as proposed by the World Food Programme ( 19 ) and Bilinsky & Swindale ( 20 ), solve this issue. The widespread use of both standardized indicators across diverse geographical contexts underscores their validity and reliability in capturing the multifaceted nature of food security. These indicators have been extensively used by other researchers to examine the determinants of food security. In this regard, Matavel et al. ( 21 ) used MAHFP together with FCS and HDDS (Household Dietary Diversity Score) indicators to assess the food security situation in Mozambique during the pre-harvest and harvest periods and identify its key drivers. Similarly, Harris-Fry et al. ( 22 ) used MAHFP and WDDS (Women's Dietary Diversity Score) to identify determinants of household food security in Bangladesh. Furthermore, Nkomoki et al. ( 23 ) examined the determinants of food security among smallholder farmers in southern Zambia combining the use of FCS and HHS (Household Hunger Scale). The present study builds on existing knowledge by using a logistic regression model based on socioeconomic variables to investigate the determinants of food insecurity in the rural Ugandan district of Sembabule, employing standardized and widely adopted food security indicators—the Food Consumption Score (FCS) and Months of Adequate Household Food Provisioning (MAHFP)—to identify key influencing factors. Therefore, the objectives of this study are twofold: first, to analyze the differences between food-secure and food-insecure households according to the classification based on the two employed indicators (FCS and MAHFP); and second, to identify the key determinants of household food security in rural Sembabule by developing a regression model for both indicators. 2. Methods 2.1. Data Collection The study was conducted in Sembabule district, a predominantly rural area located in the central region of Uganda. The study surveyed 167 households that were selected from families with children attending Rafiki Primary School, which belongs to a local NGO (Non-Governmental Organization) called Kukorra Hamu Uganda, located in Kenziga village. Data was collected on various household characteristics, including socioeconomic status, agricultural practices, and access to resources. All procedures performed in this research involving human participants have been approved by the ethics committee of the University Miguel Hernández (Spain) [Reference: DEA.LMM.02.22] and were in accordance with the 1964 Helsinki declaration and its later amendments. Respondents received an explanation of the objective of the study, emphasizing that the information requested would be exclusively used for research and that confidentiality is absolutely guaranteed. Informed consent was obtained from all participants prior to data collection. The study employed a two-part interview process. Lengthy, in-depth interviews were conducted with the assistance of a social worker from Kukorra Hamu Uganda, who was fluent in both Luganda and English and well-known and trusted within the community. To gather the necessary data, the research team travelled to each household to conduct the interviews. The initial set of interviews, conducted during February and March 2022, focused on gathering comprehensive information about the household members, including their education, health, and economic status. Additionally, the interviews included open-ended qualitative questions, providing respondents with a rare opportunity to share their problems and perceptions of their needs in an open and precise way—a practice that is often avoided in similar research due to the significant effort required to categorize and analyze such responses. In the second phase of the study, carried out from September to October 2022, additional interviews were conducted to assess the food security status of the participating households. During these interviews, the household members were asked about their agricultural practices, food sources, and other relevant factors used to calculate the food security scores through the proxy indicators, namely the FCS and the MAHFP. The indicators selected are straightforward to implement, extensively validated, supported by readily available data for comparison, and capable of capturing diverse aspects of food security to enhance and complement the research findings. The studied independent variables were either scaled or categorical in nature. Table 1 shows how the information was collected, resulting in 29 original independent variables. Table 1 Original independent variables studied Variable Type Scale Data Format Possible Responses/Range Distance to School Quantitative Ratio Numeric 0 to infinite (whole numbers) People living in the household Quantitative Ratio Numeric 0 to infinite (whole numbers) Main source of drinking water in the household Qualitative Nominal Closed-Ended Options: 1. Piped water, 2. Tube well or borehole, 3. Dug well, 4. Spring water, 5. Rain water, 6. Tanker truck, 7. Car with small tank, 8. Surface water (ponds), 9. Bottled water, 10. Other Minutes walking for water Quantitative Ratio Numeric 0 to infinite (whole numbers) Method for making water drinkable Qualitative Nominal Closed-Ended Options: 1. Boil, 2. Ceramic filter, 3. Cloth filter, 4. Other Toilet facility Qualitative Nominal Closed-Ended Options: 1. Pit latrine with slab, 2. Ventilated improved pit latrine, 3. Pit latrine without slab, 4. Open pit, 5. Hanging latrine, 6. Other Share toilet facility Qualitative Nominal Closed-Ended Yes, No Cooking fuel Qualitative Nominal Closed-Ended Options: 1. Electricity, 2. LPG, 3. Natural gas, 4. Biogas, 5. Kerosene, 6. Coal/lignite, 7. Charcoal, 8. Wood, 9. Straw/bushes/herbs, 10. Farm crops, 11. Manure, 12. No cooking at household, 13. Other Household equipment Qualitative Nominal Multiple-response Options: 1. Electricity, 2. Radio, 3. Television Household members asset ownership Qualitative Nominal Multiple-response Options: 1. Watch, 2. Phone, 3. Bicycle, 4. Motorbike Water available for washing hands Qualitative Nominal Closed-Ended Yes, No Soap available for washing hands Qualitative Nominal Closed-Ended Yes, No House floor type Qualitative Nominal Closed-Ended Options: 1. Cement, 2. Dirt, 3. Tile, 4. Other House roof type Qualitative Nominal Closed-Ended Options: 1. Natural roof, 2. Constructed roof Minimum Necessary Household Income Quantitative Ratio Numeric Any non-negative number (e.g., 0, 100, 5000) Real income Quantitative Ratio Numeric Any non-negative number (e.g., 0, 100, 5000) Mentioned problems Qualitative Nominal Open-ended Free-text responses (paragraphs) Land tenure Qualitative Nominal Closed-Ended 1. Own property, 2. Rented property Size of farmland Qualitative Ordinal Closed-Ended 1: Less than 1 acre, 2: Exactly 1 acre, 3: Bigger than 1 acre Crops grown Qualitative Nominal Open-ended Any crop name (e.g., maize, beans, cassava) Yield satisfaction Qualitative Nominal Closed-Ended Yes, No Farming obstacles Qualitative Nominal Open-ended Free-text responses (paragraphs) Home garden Qualitative Nominal Closed-Ended Yes, No Livestock ownership Qualitative Nominal Closed-Ended Yes, No Livestock type Qualitative Nominal Open-ended Any animal name (e.g., sheep, chickens, pigs) Selling of agricultural surplus Qualitative Nominal Closed-Ended Yes, No Farmers group membership Qualitative Nominal Closed-Ended Yes, No Food source Qualitative Nominal Closed-Ended Options: 1. We buy at shop/market, 2. We grow it ourselves, 3. Work for food, 4. People help the family ( Table 1 placed at the end of the manuscript due to size constraints.) Food Security Analysis The calculation of the FCS and the MAHFP of each variable analysed has been carried out following the methodology proposed by the World Food Programme (WFP) and the Food and Nutrition Technical Assistance (FANTA) project, respectively. On the one hand, the FCS is a composite metric used to assess household-level food security by measuring the diversity and frequency of food groups consumed over a seven-day recall period. As described by the World Food Programme ( 19 ), foods are categorized into groups, with each group assigned a weight based on its relative nutritional value. The FCS is calculated by multiplying the number of days of consumption of each food group (Fi) by its assigned weight (Wi) and then summing the values to produce a total score ( 24 ), as is shown in equation [1]. It provides insights into both the quality and quantity of a household's diet, with higher scores reflecting better food consumption. This score serves as a key indicator for identifying households with inadequate diets and monitoring broader food security trends within a population ( 25 , 26 ) (Antwi & Lyford, 2021; Buzigi & Onakuse, 2023). \(\:FCS={\sum\:}_{i=1}^{n}\left({F}_{i}x{W}_{i}\right)\) [1] Where: \(\:{F}_{i}=\) Number of days a particular food group \(\:i\) was consumed over a seven-day recall period. \(\:{W}_{i}=\) Weighting factor assigned to each food group based on its nutritional value. \(\:n=\) Total number of food groups considered (in this case, 8 food groups). On the other hand, the MAHFP measures household food security by tracking the number of months in the past year during which a household was able to meet its food requirements. The calculation, explained by Bilinsky & Swindale ( 20 ), involves recording the number of months when a household did not experience food shortages and summing them to determine the total adequate months. This indicator provides insights into temporal trends in food security, highlighting periods of vulnerability and identifying patterns of seasonal or chronic food shortages. A higher number of adequate months indicates greater food security, reflecting a household's sustained access to food throughout the year. Widely adopted and validated —used, for example, in rural Kenya( 9 ) and among farming households in Zambia( 27 ) (Mofya-Mukuka & Hichaambwa, 2018)— the MAHFP complements other indicators like the FCS, allowing for comparability across studies and contexts. Its computation is summarized in equation [2]. \(\:MAHFP=12-{\sum\:}_{i=1}^{12}{M}_{i}\) [2] Where: \(\:{M}_{i}=\) Binary value for month 𝑖, where: \(\:{M}_{i}=1\) if the household experienced food shortage during month 𝑖 \(\:{M}_{i}=0\) if the household did not experience food shortage during month 𝑖 The collected data was analysed using SPSS v26.0. To ensure meaningful variability, independent variables were excluded if they were highly polarized—defined as having 85% or more of respondents selecting the same response. Only variables with sufficient distributional variation were retained for further analysis of food insecurity determinants. Categorical variables comprising more than two categories were transformed into "dummy" variables (1: presence/0: absence). The distributions of the independent variables were then examined in relation to the FCS and MAHFP results. FCS range extended from 0 to 112, with higher scores indicating better food security. MAHFP ranged from 0 to 12 months, with a higher number of adequate months also reflecting greater food security. The Shapiro-Wilk test was conducted at a significance level of α = 0.05 to assess the normality of the distribution of the independent variables. Subsequently, the population was divided into two groups -"food secure" and "food insecure"- based on the indicators used. Notably, the use of the two different food security indicators resulted in two distinct classifications of the population. For the FCS indicator, the official thresholds were used to create these two categories. In accordance with WFP guidelines ( 19 ), households classified as “poor” (scores 0 to 21) or “borderline” (scores 21.5 to 35) were grouped together and considered food insecure. Households with scores above 35, categorized as “acceptable,” were deemed food secure. Regarding the MAHFP indicator, thresholds established in prior studies were employed to ensure comparability. In this framework, authors like Matavel et al. ( 21 ) have considered individuals with a score of 5 months or less as the most food insecure (category 1), those with 6 to 9 months as moderately food insecure (category 2), and those with 10 months or more as the least food insecure (category 3). In the present study, categories 1 and 2 were merged and designated as "food insecure," representing households with a MAHFP score of 9 months or less. Households with a score of 10 months or more were classified as "food secure." A contingency table analysis and ANOVA (Analysis of Variance) test were conducted to systematically organize and analyze the categorical and continuous data respectively. This approach focused on the independent variables and the classification of households as either food secure or food insecure, as determined by the FCS and MAHFP indicators. Additionally, bivariate correlations were examined to assess the strength and direction of the dependent variables and the food security indicators, to prevent potential multicollinearity issues. Only the predictor variables identified as significantly associated with each of the food security indicators through ANOVA and contingency analysis were selected for inclusion in the regression model. 2.3. Logistic Regression Analysis A binary logistic regression model was determined as the most appropriate method to further explore the factors influencing food security, due to its suitability for dichotomous dependent variables, allowing the classification of households as “food secure” (coded as 1) or “food insecure” (coded as 0). This approach estimates the probability of food security outcomes based on independent variables without assuming linear relationships, utilizing the logit transformation to accommodate non-linear patterns typical in socioeconomic data. It provides interpretable odds ratios, offering clear insight into the effects of predictor variables, and supports both categorical and scaled predictors for comprehensive analysis. This method has also been effectively applied in similar contexts, such as in Northern Ethiopia to identify key household food security determinants ( 28 ), reinforcing its relevance for generating actionable insights for policymakers and stakeholders aiming to improve food security outcomes. The logistic regression model estimates the probability that a given household 𝑖 is food secure. This probability, 𝑝𝑖, is expressed by the equation [3]. \(\:{p}_{i}=\frac{{e}^{{\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+{\beta\:}_{2}{X}_{2}+...+{\beta\:}_{k}{X}_{k}}}{1+{e}^{{\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+{\beta\:}_{2}{X}_{2}+...+{\beta\:}_{k}{X}_{k}}}\) [3] Where: 𝑝 𝑖 = Probability that the 𝑖-th household is food secure (𝑌=1). \(\:{\beta\:}_{0}\) = Intercept or constant term. \(\:{\beta\:}_{j}\) = Coefficient associated with the j -th predictor variable X j ​ (for j = 1, 2…, k ). X j ​ = Independent variables representing potential determinants of food security. The model can be rewritten in its logit form as is shown in equation [4]. \(\:Logit\left({p}_{i}\right)=lnln\:\left(\frac{{p}_{i}}{1-{p}_{i}}\right)\:=\:{\beta\:}_{0}+{\beta\:}_{1}{X}_{1}+{\beta\:}_{2}{X}_{2}+...+{\beta\:}_{k}{X}_{k}\) [4] In this form, the dependent variable is the natural logarithm of the odds ratio of being food secure versus being food insecure. The coefficients ( \(\:{\beta\:}_{j})\) represent the change in the log-odds of food security status associated with a one-unit change in the predictor variable, holding all other variables constant. The odds ratio (OR) is calculated by exponentiating the coefficient as is shown in equation [5]. \(\:OR=\:{e}^{{\beta\:}_{j}}\) [5] Where: OR > 1: The predictor variable increases the probability of being food secure. OR < 1: The predictor variable decreases the probability of being food secure. OR = 1: The predictor variable has no effect on food security status. The statistical significance of each predictor is assessed using the Wald test, with p-values less than 0.05 considered statistically significant. To evaluate the performance of the logistic regression model, various measures were considered: Hosmer-Lemeshow Test: Assesses the calibration of the model, indicating how closely predicted probabilities match observed probabilities. A p-value > 0.05 suggests good fit. Nagelkerke R²: Indicates the proportion of variation in the dependent variable explained by the model. Classification Table: Compares observed and predicted classifications to calculate the overall percentage of correct classifications. 3. Results and Discussion 3.1. General Food Security Classification Participating households were distributed across 19 different villages or hamlets, with nearly half (43.9%) originating from Kenziga village, where the primary school established by the local NGO is located. Women constituted the primary respondents in the majority of interviews (98.2%). Water availability was largely dependent on the surface ponds or waterholes present in the area. In this regard, 90.8% of the surveyed households reported collecting household water from these ponds, with an average daily collection time of 70 minutes. To purify the water, 93.6% of respondents reported boiling as their primary method. Additionally, 85% of households reported having access to land for cultivation or raising animals; however, more than one-third (36.5%) of them own 0.5 acres or less. The interviewed households were classified as either food secure or food insecure, based on their scores on the two food security indicators used. This resulted in two distinct classifications of the population (Table 2 ). Table 2 Food Security classification of interviewed households. Food Secure Food Insecure Mean Score SD FCS 58.54% 41.46% 42.02 19.33 MAHFP 31.10% 68.90% 7.23 3.17 Although the mean FCS exceeds the threshold of 35—placing the sample within the “acceptable” range—a substantial proportion of households (41.46%) are still classified as food insecure. This figure increases markedly when using the MAHFP indicator: according to this metric, more than two-thirds (68.9%) of households are food insecure, despite reporting an average of over seven months of adequate food provisioning. This difference clearly reflects the fact that the two indicators capture different dimensions of food security. 3.2. Comparison Between FCS and MAHFP Classifications A cross-tabulation was conducted to assess the degree of agreement between household classifications derived from the two food security indicators used in this study: FCS and MAHFP. As shown in Table 3 , the two indicators produced divergent classifications for a substantial proportion of households. Table 3 Cross-tabulation of household food security status based on FCS and MAHFP MAHFP Insecure MAHFP Secure Total FCS FCS Insecure 56 12 68 FCS Secure 57 39 96 Total MAHFP 113 51 164 Out of the 164 households included in the analysis, only 95 (57.9%) were consistently classified as either food secure or food insecure by both indicators. The remaining 69 households (42.1%) were categorized differently depending on the metric used. Specifically, 57 households were considered food secure by the FCS but food insecure by the MAHFP, while 12 households showed the opposite pattern. These discrepancies reflect fundamental differences between the two indicators. The FCS assesses recent food consumption patterns over the past seven days, whereas the MAHFP captures a 12-month recall of months in which households had sufficient food. Their differing timeframes mean they respond to different aspects of food insecurity. As noted by Mutea et al. ( 9 ), food security assessments based on multiple complementary indicators provide a more reliable and nuanced understanding of household conditions, especially in contexts marked by seasonal fluctuations. The timing of the assessment plays a crucial role not only in shaping the results but also in how they are interpreted. The interviews used to collect the data both indicators were conducted between September and October 2022, shortly after the harvest season in central Uganda. While this period typically coincides with improved food availability, the 2022 agricultural season was severely affected by drought, which reduced yields across the country. According to the World Meteorological Organization ( 29 ), both the March–May and October–December rainy seasons recorded below-average precipitation, contributing to the most prolonged drought in over four decades. Even so, the timing of data collection—immediately after harvest—may have softened the impact of the drought on short-term food consumption. As a result, households might have had some food stored, which could explain the relatively better FCS scores. In contrast, the MAHFP, based on a 12-month recall, captured the cumulative effects of food shortages experienced throughout the year, classifying a greater proportion of households as food insecure. This interpretation aligns with seasonal food security dynamics observed in other Sub-Saharan African contexts. For instance, a study conducted in central Mozambique found that food security levels peaked during the post-harvest period (May–July), followed by the harvesting season, and were lowest during the lean period (November–January) ( 21 ). Although the 2022 harvest in Uganda was undermined by drought, the timing of the data collection—shortly after harvest—may have temporarily improved households’ FCS due to the availability of stored food. These results highlight the value of using multiple complementary indicators in food security assessments, as relying on a single metric may either under- or overestimate the severity of food insecurity depending on the timing and context of data collection. 3.3. Household and Livelihood Differences Across Food Security Groups Regarding the predictors, a total of 21 variables were retained for the analysis after the exclusion of polarized variables. When disaggregated by food security status, the households were classified as presented in Tables 4 and 5 . Tables 4 and 5 present cross-tabulations showing the distribution of households across food security groups based on each qualitative variable, classified according to the two indicators employed: FCS and MAHFP. Specifically, Table 4 highlights household characteristics and livelihood practices, while Table 5 focuses on reported challenges and constraints faced by each household. Table 6 provides a comparative analysis of quantitative variables by reporting the mean values for each food security group under both classification systems. The variables identified as statistically significant in each indicator effectively distinguish between food-secure and food-insecure households. While some variables exhibit significance across both indicators, others are only significant under one classification criterion. It is worth noting that the FCS classification detected a higher number of significant variables compared to the MAHFP, reflecting differences in how each indicator captures various dimensions of food security. Table 4 Distribution of Households by Food Security Group, Household Characteristics and Livelihood Practices (FCS & MAHFP) VARIABLE FCS MAHFP Mean (%) Insecure Households (%) Secure Households (%) Sig. Insecure Households (%) Secure Households (%) Sig. Type of Toilet Facility Pit latrine with slab 83.4 85.9 81.7 ns 82.2 86.0 ns Ventilated improved pit latrine 8.9 4.7 11.8 ns 8.4 10.0 ns Pit latrine without slab 5.7 6.3 5.4 ns 7.5 2.0 ns Open pit 5.7 6.3 5.4 ns 7.5 2.0 ns Share toilet facility 41.7 51.6 34.8 ** 45.3 34.0 ns Water available for washing hands 80.3 68.8 88.2 ** 76.6 88.0 * Soap available for washing hands 79.6 68.8 87.1 ** 76.6 86.0 ns Household equipment Electricity 68.2 57.8 75.3 ** 66.4 72.0 ns Radio 38.9 29.7 45.2 * 32.7 52.0 ** T.V. 21.0 10.9 28.0 ** 16.8 30.0 * Household members asset ownership Bicycle 30.6 26.6 33.3 ns 29.0 34.0 ns Motorbike 15.3 7.8 20.4 ** 12.1 22.0 ns House floor type Cemented 47.8 31.3 59.1 ** 43.9 56.0 ns Size of farmland Big (more than 0.5 acres) 63.5 50.8 72.3 ** 59.6 72.0 ns Crops grown Maize farming 78.7 69.1 85.4 ** 77.0 82.4 ns Coffee farming 26.8 19.1 32.3 * 21.2 39.2 ** Plantain farming 24.4 17.6 29.2 * 18.6 37.3 ** Home garden 17.7 20.6 15.6 ns 17.7 17.6 ns Livestock ownership 65.9 58.8 70.8 ns 62.8 72.5 ns Livestock type Chickens 18.9 13.2 22.9 ns 19.5 17.6 ns Goats 23.2 14.7 29.2 ** 19.5 31.4 * Pigs 39.0 41.2 37.5 ns 38.9 39.2 ns Selling of agricultural surplus 50.6 36.8 60.4 ** 46.0 60.8 * Food source Shop/market 73.8 73.5 74.0 ns 73.5 74.5 ns Home grown 68.9 57.4 77.1 ** 66.4 74.5 ns Work for food 7.9 14.7 3.1 ** 10.6 2.0 * People help the family 1.2 1.5 1.0 ns 0.9 2.0 ns Note: * p < 0.1, ** p < 0.05 Table 5 Household Distribution by Food Security Group, Reported Challenges and Constraints (FCS & MAHFP) VARIABLE FCS MAHFP Mean (%) Insecure Households (%) Secure Households (%) Sig. Insecure Households (%) Secure Households (%) Sig. General problems Poverty, money 61.8 64.1 60.2 ns 57.0 72.0 * Hunger, lack of food 38.9 53.1 29.0 ** 43.9 28.0 * School fees 33.1 34.4 32.3 ns 31.8 36.0 ns Lack of clothes 35.7 51.6 24.7 ** 40.2 26.0 * Lack of mattress or bedding 22.9 34.4 15.1 ** 26.2 16.0 ns Disease or lack of access to medical treatment 64.3 60.9 66.7 ns 63.6 66.0 ns Access to drinking water 20.4 10.9 26.9 ** 14.0 34.0 ** Insufficient farmland 15.3 21.9 10.8 * 19.6 6.0 ** Farming obstacles Lack of rain water 83.5 77.9 87.5 ns 80.5 90.2 ns Soil is not fertile 75.0 80.9 70.8 ns 77.0 70.6 ns Land access 15.2 14.7 15.5 ns 14.2 17.6 ns Land size 49.4 55.9 44.8 ns 52.2 43.1 ns Note: * p < 0.1, ** p < 0.05 Table 6 Comparative Analysis of Quantitative Variables by Food Security Status (FCS and MAHFP, ANOVA Results). VARIABLE Total FCS MAHFP Insecure Households Secure Households Insecure Households Secure Households Mean SD Mean SD Mean SD Sig. Mean SD Mean SD Sig. Walking time to school (minutes) 72.52 47.99 75.11 54.45 70.73 43.21 ns 71.07 47.92 75.60 48.47 ns Household size (members) 7.42 3.24 6.73 2.29 7.89 3.69 ** 7.23 2.78 7.82 4.04 ns Water collection time (minutes/day) 70.15 35.68 71.95 36.62 68.89 35.16 ns 68.52 33.45 73.58 40.11 ns Minimum necessary household income (UGX/Week) 25,531 24,549 18,004 12,729 30,659 29,023 ** 23,193 21,859 30,646 29,201 * Real income (UGX/Week) 16,477 31,754 9,372 14,873 21,317 38,659 ** 14,081 23,770 21,716 44,381 ns Note: * p < 0.1, ** p < 0.05 3.3.1. General Household Characteristics Food-secure households generally exhibited better overall conditions across the measured variables. The link between food security and hygiene and sanitation is evident in the significant differences observed in toilet facility sharing, particularly under the FCS classification. Specifically, the majority of food-insecure households (51.6%) shared toilet facilities, compared to only 34% of food-secure households, highlighting a disparity in access to private sanitation. Moreover, differences in sanitation extended beyond toilet facility sharing, with food-secure households exhibiting greater access to essential hygiene resources, particularly under the FCS indicator. Specifically, 88.2% of food-secure households, according to the FCS, reported access to water for washing hands, compared to only 68.8% of food-insecure households. According to the MAHFP the trend was similar, with 88% of food-secure households having access, compared to 76.6% of food-insecure households. The availability of soap also emerged as a distinguishing factor under the FCS classification, being, again, more prevalent among food-secure households. These findings align with Workman et al. ( 30 ), who emphasize the complex, multi-dimensional nature of food, water, and sanitation insecurities, highlighting how these issues frequently coexist and interact, resulting in notable impacts on both mental and physical health. Food-secure households were generally better equipped than their food-insecure counterparts in terms of access to electricity and higher-quality flooring. Our results are consistent with those by Frayne & McCordic ( 31 ), who demonstrated that households with inconsistent or no access to a cash income, electricity, or water among others, had 8.5 times greater odds of having less than 12 months of adequate food provisioning in the last year. These differences were even more pronounced under the FCS classification compared to the MAHFP classification. Apanovich & Mazur ( 32 ) also identified access to electricity as a socioeconomic indicator in their study of food security among smallholder farmers in Uganda, although its direct effect on food security was not statistically significant in the final analysis. Similarly, Ownership of assets such as televisions, radios, and motorcycles followed the same trend. This aligns with the findings of Shifat et al. ( 33 ), who identified a strong positive association between household asset ownership and food security in Bangladesh. Their study suggests that the absence of such assets signals economic vulnerability and may serve as a practical proxy indicator in contexts where detailed income or consumption data are lacking. With respect to the quantitative variables (Table 6 ), household size and those related to income, emerged as significant factors in food security, particularly under the FCS classification. The literature generally suggests that larger household sizes contribute to greater food insecurity due to increased consumption demands relative to production or income capacity ( 11 , 34 , 35 ). However, contrary to this widely held view, our findings indicate that larger households (with an average of 7.89 members) were more likely to be classified as food-secure using the FCS indicator, compared to smaller households (6.73 members), in line with findings by Nkomoki et al. 2019 ( 23 ) and Maitra and RAO ( 36 ), who also observed a positive association between household size and food security in specific rural contexts. For the case of walking time to school and water collection time, these variables showed no statistical significance with FCS and MAHFP. The income reported by each group was higher among food-secure households, although the difference was statistically significant only under the FCS classification (21,317 UGX/week vs. 9,372 UGX/week). This finding is consistent with the broader literature, which establishes a strong link between household income and food security. For example, Tulem & Hordofa ( 37 ) reported that households with a low wealth index were over four times more likely to be food insecure compared to wealthier households. Similarly, a study by Kundu et al. ( 38 ) reported that higher household wealth status was significantly associated with greater minimum dietary diversity among children aged 6–23 months in Bangladesh, highlighting the influence of socioeconomic factors on dietary quality. Furthermore, the Minimum Necessary Household Income suggests that food-secure households required substantially more income to cover basic household needs—70.29% higher than food-insecure households under the FCS indicator and 32.13% higher under the MAHFP indicator. This may reflect higher living standards among food-secure households, where greater financial resources are required to maintain improved housing, better diets, and access to essential services. Additionally, food-secure households may have more complex financial commitments, including school fees, healthcare costs, and investments in agricultural inputs, which elevate their minimum income requirements. 3.3.2. Land Ownership and Agricultural Practices Farmland size is widely recognized as a key determinant of household food security ( 12 , 23 , 32 , 39 ) and our findings support this association. In our case, farmland size was significantly linked to food security when applying the FCS classification, with 72.3% of food-secure households owning larger land areas compared to only 50.8% of food-insecure households (see Table 4 ). This result aligns with the findings of Apanovich & Mazur ( 32 ), who reported a positive relationship between total acreage and food security among Ugandan smallholder farmers during both the harvest and lean seasons. Similarly, Nkomoki et al. ( 23 ) observed that larger farm sizes increased the likelihood of household food security, explaining that greater land access allows households to produce more food for self-consumption (enhancing food availability), diversify crops (improving dietary diversity and resilience), and sell surplus produce (increasing economic access to food). Our results further support this interpretation (Table 4 ): food-secure households were more likely to cultivate crops such as maize, coffee, and plantains, and to engage in livestock rearing, all of which contribute to improved food availability and income generation. Moreover, the sale of agricultural surplus was more prevalent among food-secure households and was significantly associated with food security status under both FCS and MAHFP classifications. In contrast, food-insecure households often consumed most of their produce, leaving little or no surplus for sale, thereby limiting their capacity to earn income from farming. When asked about their primary food sources, two variables showed significant differences across the population: growing food at home and working for food as payment—a common practice in rural Uganda where labour is exchanged for food instead of monetary compensation. Growing food at home was more prevalent among food-secure households, likely due to their access to larger plots of land compared to food-insecure households. This finding aligns with the results of Issahaku et al. ( 40 ), who reported a 4.6% increase in Food Consumption Scores (FCS) among Rwandan households practicing home gardening, reflecting improved dietary diversity and overall food security. In contrast, although not a widespread strategy—reported by only 7.9% of surveyed households—working for food was more frequently observed among food-insecure households, possibly indicating their urgent need to secure sustenance for their household. To our knowledge, there is a lack of empirical studies in the existing literature analyzing the relationship between food-for-labor arrangements and household food security, particularly in the Ugandan context. As such, no direct comparisons with previous findings could be established. 3.3.3. Challenges Reported Open-ended responses revealed that food-insecure households reported higher incidences of various issues, such as hunger and lack of clothing, than their food-secure counterparts under both indicators (Table 5 ). These issues can be considered direct indicators of poverty, a condition strongly correlated with food insecurity across various countries ( 41 ). Limited financial resources among food-insecure households often force them to prioritize immediate food needs over other essentials, further perpetuating cycles of deprivation. However, other complaints were more common among food-secure households, like access to drinking water, which was reported as an issue by a higher proportion of food-secure households compared to food-insecure households, in both FCS and MAHFP classifications. This counterintuitive finding may stem from higher expectations among food-secure households for reliable and high-quality water sources, while food-insecure households may have normalized limited water access or prioritized other pressing concerns. All households face similar challenges regarding water access—90.8% of the surveyed households collect water from surface ponds, with an average daily collection time exceeding 70 minutes—. Likewise, although only significant when considering the MAHFP classification, mentioning poverty as a problem of the household was more common in food-secure households, compared with food-insecure ones, which may reflect a heightened awareness and perception of economic challenges among food-secure households, possibly due to their greater exposure to financial responsibilities and aspirations for improved living standards. Lastly, insufficient farmland was a more frequently reported concern among food-insecure households, aligning with the critical role of arable land in subsistence farming systems, which dominate rural livelihoods throughout the country ( 42 ) (Bamwesigye et al., 2020). 3.4. Determinants of Food Security Status The results of the binary logistic regression model for FCS indicator are presented in Table 7 . The model was developed using the backward elimination method, which began with the introduction of 24 variables and sequentially removed those with the least explanatory power until arriving at the optimal combination that provided the best fit, with 5 variables along with a constant. The model demonstrates robustness, with a chi-squared value of 10.108 and a p-value of 0.258. Table 7 Logistic regression coefficients (FCS) VARIABLE Coefficient (β) St. Error Sig. Odds Ratio Minimum Necessary Household Income 0.276 0.095 0.004 1.318 Hunger, lack of food -0.687 0.403 0.089 0.503 Lack of clothes -0.819 0.411 0.046 0.441 Access to drinking water 1.015 0.533 0.057 2.760 Insufficient farmland -0.951 0.567 0.094 0.386 Constant 0.060 0.381 0.875 1.062 Among the economic variables analysed, actual household income did not exert sufficient influence to be retained in the model. This contrasts with findings from other studies, such as Asaki et al. ( 43 ), who identified a significant relationship between household income and food security. Interestingly, minimum necessary household income was retained in the model (β = 0.276, OR ≈ 1.32, p = 0.004), indicating that each unit increase in perceived necessary income was associated with a 32% increase in the odds of being classified as food secure. This may reflect the added value of subjective estimations in capturing economic pressure more accurately. In rural contexts—where income sources are often informal, irregular, or not fully documented—such perceptions may offer a clearer sense of financial strain. Moreover, what a household considers “necessary” likely encompasses not only material needs but also lived experiences, expectations, and perceived coping capacity—factors that directly influence how food is accessed and managed. Notably, variables derived from open-ended questions emerged as the most influential predictors in the model. For example, complaints about hunger and lack of food were strongly associated with food insecurity (β = − 0.687, OR ≈ 0.50). Similarly, the variable “lack of clothing” (β = − 0.819, OR ≈ 0.44) showed an even stronger negative association, indicating that households reporting this issue were less than half as likely to achieve adequate food consumption compared to those who did not express this concern. Complaints about insufficient farmland also showed a notable negative relationship, though with marginal statistical significance. Interestingly, concerns about lack of access to drinking water were inversely related to food insecurity (β = 1.015, OR ≈ 2.76), suggesting that households identifying this problem were more likely to be food secure. This counterintuitive result may reflect higher expectations or awareness among better-off households, rather than actual differences in water access. For the case of MAHFP, the coefficients for each variable in the binary logistic regression model are presented in Table 8 . The model was also developed using the backward elimination method, which began with the introduction of 13 variables and reached the greatest explanatory power with 4 variables. The final model retained and exhibited strong reliability, with a chi-squared value of 5.403 and a p-value of 0.714, indicating a good fit. Table 8 Logistic regression coefficients (MAHFP) VARIABLE Coefficient (β) St. Error Sig. Odds Ratio Minimum Necessary Household Income 0.119 0.052 0.021 1.127 Poverty, money 0.672 0.402 0.094 1.958 Access to drinking water 1.155 0.442 0.009 3.173 Insufficient farmland -1.725 0.797 0.030 0.178 Constant -1.754 0.423 0.000 0.173 The final set of predictors in the MAHFP model included Minimum Necessary Household Income, which remained statistically significant (β = 0.119, OR ≈ 1.13, p = 0.021), consistent with the results observed under the FCS classification. As in the previous model, the most influential predictors were derived from open-ended questions on household challenges, reinforcing the value of integrating qualitative insights into quantitative analysis. Complaints related to poverty or financial difficulties (β = 0.672, OR ≈ 1.96, p = 0.094) and lack of access to drinking water (β = 1.155, OR ≈ 3.17, p = 0.009) were positively associated with food security, indicating that households reporting these issues were more likely to maintain adequate food provisioning throughout the year. In contrast, the complaint about insufficient agricultural land emerged as the strongest negative predictor (β = − 1.725, OR ≈ 0.18, p = 0.030), highlighting once again the critical role of land access in achieving food security. These seemingly paradoxical results—particularly regarding complaints about poverty or water—may reflect a greater awareness of needs among relatively better-off households, or a higher capacity to articulate problems that, while present, do not compromise their year-round food provisioning. As observed in the FCS model, subjective perceptions do not always align with objective deprivation, and may instead reveal nuanced aspects of household vulnerability, resilience, and expectations. Despite initially analysing 30 independent variables, only six were retained as significant predictors in the regression models. This limited number may be partly explained by the relative homogeneity of the interviewed population, as well as the strong interdependence among certain household characteristics. Several variables commonly identified in the literature as relevant determinants of food security, such as the cultivation of maize, beans, or plantains ( 32 ), did not show sufficient statistical influence to be included in the final models. However, they did show a similar trend to that reported by previous authors, since the cross-tabulations reveal a higher proportion of “food secure” households among those cultivating these crops, suggesting that households growing these crops are generally more food secure than those that do not. A notable feature of this study is that most of the predictive variables retained in the models originated from open-ended questions. While this reduces the comparability of the results with studies based on more standardized survey instruments, it also underscores the importance of incorporating qualitative dimensions into quantitative analysis. Allowing respondents to express their own concerns provides valuable insights into the contextual realities shaping food security. Even so, the findings still align with key conclusions from existing literature. For example, Hashmiu et al.( 35 ) emphasized the importance of land ownership in improving household food security through greater access to income and food crops. At a broader scale, large-scale land acquisitions across Sub-Saharan Africa, which may restrict access to farmland for local communities, have been associated with reduced cereal availability and increased malnutrition, despite being framed as development opportunities( 44 ) (Kinda et al., 2022). In line with these trends, households in the present study that reported insufficient agricultural land consistently exhibited lower levels of food security. Similarly, Shamah-Levy et al. ( 45 ) highlights the negative impact of inadequate access to drinking water on food security. Although the objective variable “main source of drinking water” was not statistically significant in our models, self-reported complaints about water access showed a significant, albeit inverse, relationship with food security. Households that expressed dissatisfaction with water availability tended to have higher levels of food security, suggesting that better-off households may hold higher expectations regarding their living conditions. Other variables, such as membership in farmer groups or cooperatives, which have been identified as highly influential by authors like Nkomoki et al. ( 23 ), were underrepresented in our sample and had to be excluded during the initial stages of this study. Finally, the variable “household size” showed a trend contrary to what is commonly reported. While many studies (e.g., ( 11 , 21 , 34 ) associate larger households with greater food insecurity, our findings indicate the opposite: food secure households had, on average, more members (7.89) than food-insecure ones (6.73). This may reflect a greater availability of labor, income diversification, or economies of scale in larger households, though further research would be needed to explore this relationship in depth. 4. Conclusion and Policy Implications The findings of this study provide valuable insights into the determinants of household food security in rural Uganda. These insights have important policy implications for enhancing food security and promoting sustainable development in the region and other areas with similar context. Firstly, the significant association between farmland size and food security underscores the need for policies that facilitate access to land, especially for food-insecure households. Land access and ownership are critical factors in improving food availability, dietary diversity, and economic access to food. However, over-reliance on land for food security presents a vulnerability that should be addressed. Policymakers should prioritize not only land tenure reforms that promote equitable access to arable land, but also programs aimed at diversifying livelihood sources and promoting non-agricultural income-generating activities. Such efforts could enhance resilience by reducing dependency on land-based food production. Secondly, the limited impact of actual household income on food security, contrasted with the influence of perceived necessary income, suggests that interventions targeting food security should not only focus on increasing household income but also on enhancing households’ ability to manage and allocate their resources effectively. Financial literacy programs, tailored to the local context, could help households optimize their spending and improve food security outcomes. Additionally, the finding that food-secure households often report higher dissatisfaction with water access highlights the importance of improving water infrastructure and ensuring reliable access to safe water sources. According to the FAO's (Food and Agriculture Organisation) utilization dimension, food security cannot be achieved without access to clean water, as adequate nutrition depends not only on food intake but also on water and sanitation services ( 1 ). Policymakers should prioritize investments in water supply systems, especially in rural areas where households predominantly rely on surface ponds for their daily needs. Integrating water access improvement strategies with food security initiatives could further strengthen the resilience of vulnerable populations. Moreover, the significant role of qualitative insights from open-ended responses in identifying predictors of food security suggests that policymakers and researchers should adopt more participatory approaches when designing and implementing food security interventions. Engaging local communities in the decision-making process can provide valuable perspectives that are often overlooked by conventional quantitative methods. In this regard, the divergent results obtained through FCS and MAHFP also emphasize the importance of using multiple, complementary indicators when assessing food security. Each indicator captures distinct dimensions and timeframes of food access, and their combined use offers a more nuanced and accurate understanding of household vulnerability. This is especially relevant in contexts affected by seasonal variation or climatic shocks, where reliance on a single metric could lead to misleading conclusions. Finally, given that households that engaged in livestock rearing and cultivation of specific crops (such as maize, coffee, and plantains) were more likely to be food secure, agricultural policies should encourage diversification of livelihoods through integrated crop-livestock systems. Providing training and resources to enhance productivity, market access, and profitability of these agricultural activities could significantly improve food security at the household level. Overall, a multi-faceted approach that addresses land access, financial management, water availability, community participation, and agricultural diversification is essential for achieving sustainable improvements in food security in rural Uganda. Implementing these recommendations requires collaboration between government agencies, local NGOs, community leaders, and international partners to ensure a comprehensive and context-sensitive response to the persistent challenge of food insecurity. Abbreviations ANOVA Analysis of Variance FANTA Food and Nutrition Technical Assistance FAO Food and Agriculture Organisation FCS Food Consumption Score HDDS Household Dietary Diversity Score HHS Household Hunger Scale MAHFP Months of Adequate Household Food Provisioning NGO Non-Governmental Organization OR Odds Ratio UGX Ugandan Shillings WDDS Women's Dietary Diversity Score WFP World Food Programme Declarations Ethics approval and consent to participate Ethical approval for this research was granted by the Oficina de Investigación Responsable (OIR) at the Universidad Miguel Hernández de Elche [Reference number: DEA.LMM.02.22]. All participants provided informed consent before the interviews. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Generalitat Valenciana (Regional Government of Valencia, Spain) through: • ACIF 2023 predoctoral grant [Exp. number: CIACIF/2022/051]. • The project “Convenio GVA‑UMH SOLCIF/2022/0005 (Code: 11‑134‑4‑2023‑0133)” Author Contribution All authors contributed to the conceptualization of the study. JS conducted the fieldwork and interviews with participants, supported by the social worker from Kukorra Hamu Uganda. JS and RP were responsible for data curation. JS, LM, and RA participated in the formal analysis of the results. JS wrote the original draft of the manuscript, while LM and RA contributed to its review and editing. All authors read and approved the final manuscript. Acknowledgement The authors gratefully acknowledge the valuable collaboration of the NGO Rafiki Africa and its Ugandan partner, Kukorra Hamu Uganda, whose support and local insights were essential throughout the research process. 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Socio-economic inequalities in minimum dietary diversity among Bangladeshi children aged 6–23 months: a decomposition analysis. Sci Rep. 2022;12(1):21712. https://doi.org/10.1038/s41598-022-26305-9 Giller KE, Delaune T, Silva JV, Descheemaeker K, van de Ven G, Schut AGT, et al. The future of farming: Who will produce our food? Food Secur. 2021;13(5):1073–99. https://doi.org/10.1007/s12571-021-01184-6 Issahaku G, Kornher L, Saiful Islam AHM, Abdul-Rahaman A. Heterogeneous impacts of home-gardening on household food and nutrition security in Rwanda. Food Secur. 2023;15(3):731–50. https://doi.org/10.1007/s12571-023-01344-w Food and Agriculture Organization of the United Nations (FAO). Understanding poverty and food insecurity at the household level. Rome: FAO; 2022. FAO Agricultural Development Economics Policy Brief; No. 59. https://doi.org/10.4060/cc2993en Bamwesigye D, Doli A, Adamu KJ, Mansaray SK. A Review of the Political Economy of Agriculture in Uganda: Women, Property Rights, and Other Challenges. Univers J Agric Res. 2020;8(1):1–10. https://doi.org/10.13189/ujar.2020.080101 Asaki FA, Oteng-Abayie EF, Baajike FB. Effects of water, energy, and food security on household well-being. PLoS One. 2024;19(7):e0307017. https://doi.org/10.1371/journal.pone.0307017 Kinda SR, Kere NE, Yogo TU, Simpasa MA. Do land rushes really improve food security in Sub-Saharan Africa? Food Policy. 2022;113:102285. https://doi.org/10.1016/j.foodpol.2022.102285 Shamah-Levy T, Méndez-Gómez-Humarán I, Mundo-Rosas V, Muñoz-Espinosa A, Melgar-Quiñonez H, Young SL. Household water security is a mediator of household food security in a nationally representative sample of Mexico. Public Health Nutr. 2025;28(1). https://doi.org/10.1017/S1368980024002684 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7261208","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504231155,"identity":"6429fb45-ed99-44a6-a6bd-2d0792e211c5","order_by":0,"name":"Joaquín Solano-Jiménez","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYBAC9gYGxgNAWo6BgYdILYwNDAwgLcaka0lsIF5L/+EDB37usEnfcO3sAYYPf4jRMiMt4WDvmbTcDbfzEhhnthGlhcfgAG/bYaCWHANm3gaiHHbG4ODftsPpBiAtf4hxmGBDjsFhoC0JYC0MbERokZZISzgs25ZmOBOo5WAvMX7h4z988OHbNht5vts5hg9+EOMwFHCAVA2jYBSMglEwCnAAAPF2O1FmnjLMAAAAAElFTkSuQmCC","orcid":"","institution":"Instituto Universitario de Investigación e Innovación Agroalimentaria y Agroambiental (CIAGRO- UMH), University Miguel Hernández de Elche","correspondingAuthor":true,"prefix":"","firstName":"Joaquín","middleName":"","lastName":"Solano-Jiménez","suffix":""},{"id":504231157,"identity":"1720c404-41b6-4c39-81d9-c3aac54600ad","order_by":1,"name":"Ricardo Abadía","email":"","orcid":"","institution":"Instituto Universitario de Investigación e Innovación Agroalimentaria y Agroambiental (CIAGRO- UMH), University Miguel Hernández de Elche","correspondingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"","lastName":"Abadía","suffix":""},{"id":504231160,"identity":"c5164715-de4d-4c44-ba98-e40be9f0555a","order_by":2,"name":"Rafael Pinilla Palleja","email":"","orcid":"","institution":"Rafiki Africa Association","correspondingAuthor":false,"prefix":"","firstName":"Rafael","middleName":"Pinilla","lastName":"Palleja","suffix":""},{"id":504231162,"identity":"5dac99e0-9790-4425-bccb-a07f9aa11c3c","order_by":3,"name":"Laura Martínez-Carrasco Martínez","email":"","orcid":"","institution":"Instituto Universitario de Investigación e Innovación Agroalimentaria y Agroambiental (CIAGRO- UMH), University Miguel Hernández de Elche","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"Martínez-Carrasco","lastName":"Martínez","suffix":""}],"badges":[],"createdAt":"2025-07-31 10:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7261208/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7261208/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89784924,"identity":"d1253ba0-0fad-412b-a60b-88d463653d7a","added_by":"auto","created_at":"2025-08-25 03:39:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1677813,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7261208/v1/8de89952-dbee-44a6-9497-04630d2cff70.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Determinants of Household Food Security in Rural Uganda: Perceived Needs as Key Predictors","fulltext":[{"header":"1. Background","content":"\u003cp\u003eEnsuring universal access to safe, nutritious, and sufficient food for proper nutrition is a key objective of the United Nations' Sustainable Development Goal \"Zero Hunger,\" which was established in 2015. While significant efforts have been made in recent decades to develop strategies and policies aimed at achieving global food security, approximately one in ten people worldwide currently experiences severe levels of food insecurity (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMany of the undernourished people in Sub-Saharan Africa and worldwide are smallholder farmers who rely on agriculture for their livelihoods (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). According to Saridakis et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Uganda is a country where 74.83% of the population identifies as farmers, meaning that improvements in this sector have a direct impact on nearly three-quarters of the population. Communities in rural areas rely heavily on subsistence farming and livestock rearing. In particular, the agricultural practices in the dry corridor region are marked by simplicity and limited resilience to adverse climatic conditions, such as unexpected droughts, which disproportionately affect the area (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Maize is the primary crop grown in the region, serving as a dietary staple and occupying extensive farmlands alongside beans and plantains. It is well known that climate change, through increased frequency and intensity of extreme weather events like droughts and floods, as well as changes in pest prevalence, can negatively impact crop production and yields (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). With climate projections for Sub-Saharan Africa indicating further disruptions in rainfall patterns (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), the future presents significant challenges for these communities.\u003c/p\u003e\u003cp\u003eFood security is a complex and multidimensional issue that requires careful measurement to understand the nuances of the problem. Past studies have found that relying on a single metric can overlook critical aspects of food insecurity (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). While some definitions focus solely on food availability, research has shown that other traits like access and affordability are equally crucial determinants (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite growing attention, access to adequate and nutritious food remains a major challenge across many developing countries, particularly for rural populations, who often face the greatest barriers to food security (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). In rural Uganda, where the majority of the population relies on subsistence farming, understanding its determinants is essential for designing effective policy interventions (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious research has highlighted the dynamic relationship between chronic poverty and environmental challenges in shaping food insecurity patterns among rural communities in sub-Saharan Africa (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). This implies that the vulnerability to food insecurity in these settings is often deeply rooted in the complex interplay of socioeconomic and ecological factors. In this regard, Nuvey et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e) provide further evidence from Ghana, showing that food insecurity was significantly more prevalent among livestock-dependent households located in drier districts and among those affected by a higher number of adverse agricultural events. Their study illustrates how agroecological vulnerability and farm-level shocks can reinforce each other in undermining household food access.\u003c/p\u003e\u003cp\u003eThe challenge of food insecurity in this region is a multifaceted phenomenon, rooted in factors such as poverty, limited agricultural productivity, climate change, and lack of access to resources (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In Uganda, the rural population, which makes up the majority of the country's population, is particularly susceptible to food insecurity due to their heavy reliance on subsistence agriculture and limited access to markets and social services, among other variables (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrior studies have explored several key factors contributing to food insecurity in rural communities, including household characteristics, socioeconomic status and environmental conditions (\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) (Appiah-Twumasi \u0026amp; Asale, 2024; Kim et al., 2011; McIntyre \u0026amp; Hendriks, 2018; Usman \u0026amp; Haile, 2022). In this regard, in order to effectively identify the determinants of food security, selecting appropriate indicators is a key step in obtaining meaningful results. Various scientific studies have explored the determinants of food security in rural Uganda, but the indicators used to assess the population's food security status are often tailored specifically to the context of each investigation. While this approach offers advantages, such as a high degree of contextual adaptation, it significantly limits the comparability of findings across studies. For instance, the research conducted in Uganda\u0026rsquo;s Gomba district by Semazzi \u0026amp; Kakungulu (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) exemplifies this limitation. Rather than employing established, widely-used indicators, they developed a unique monetary-based approach, converting food harvested and income into monetary equivalents and setting a benchmark value for annual household food security. Although innovative, this methodology significantly hampers the comparability of their results with those of other researchers.\u003c/p\u003e\u003cp\u003eIndicators like the Food Consumption Score (FCS) and the Months of Adequate Household Food Provisioning (MAHFP), as proposed by the World Food Programme (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and Bilinsky \u0026amp; Swindale (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), solve this issue. The widespread use of both standardized indicators across diverse geographical contexts underscores their validity and reliability in capturing the multifaceted nature of food security. These indicators have been extensively used by other researchers to examine the determinants of food security. In this regard, Matavel et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) used MAHFP together with FCS and HDDS (Household Dietary Diversity Score) indicators to assess the food security situation in Mozambique during the pre-harvest and harvest periods and identify its key drivers. Similarly, Harris-Fry et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) used MAHFP and WDDS (Women's Dietary Diversity Score) to identify determinants of household food security in Bangladesh. Furthermore, Nkomoki et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) examined the determinants of food security among smallholder farmers in southern Zambia combining the use of FCS and HHS (Household Hunger Scale).\u003c/p\u003e\u003cp\u003eThe present study builds on existing knowledge by using a logistic regression model based on socioeconomic variables to investigate the determinants of food insecurity in the rural Ugandan district of Sembabule, employing standardized and widely adopted food security indicators\u0026mdash;the Food Consumption Score (FCS) and Months of Adequate Household Food Provisioning (MAHFP)\u0026mdash;to identify key influencing factors. Therefore, the objectives of this study are twofold: first, to analyze the differences between food-secure and food-insecure households according to the classification based on the two employed indicators (FCS and MAHFP); and second, to identify the key determinants of household food security in rural Sembabule by developing a regression model for both indicators.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Data Collection\u003c/h2\u003e\u003cp\u003eThe study was conducted in Sembabule district, a predominantly rural area located in the central region of Uganda. The study surveyed 167 households that were selected from families with children attending Rafiki Primary School, which belongs to a local NGO (Non-Governmental Organization) called Kukorra Hamu Uganda, located in Kenziga village. Data was collected on various household characteristics, including socioeconomic status, agricultural practices, and access to resources. All procedures performed in this research involving human participants have been approved by the ethics committee of the University Miguel Hern\u0026aacute;ndez (Spain) [Reference: DEA.LMM.02.22] and were in accordance with the 1964 Helsinki declaration and its later amendments. Respondents received an explanation of the objective of the study, emphasizing that the information requested would be exclusively used for research and that confidentiality is absolutely guaranteed. Informed consent was obtained from all participants prior to data collection.\u003c/p\u003e\u003cp\u003eThe study employed a two-part interview process. Lengthy, in-depth interviews were conducted with the assistance of a social worker from Kukorra Hamu Uganda, who was fluent in both Luganda and English and well-known and trusted within the community. To gather the necessary data, the research team travelled to each household to conduct the interviews. The initial set of interviews, conducted during February and March 2022, focused on gathering comprehensive information about the household members, including their education, health, and economic status. Additionally, the interviews included open-ended qualitative questions, providing respondents with a rare opportunity to share their problems and perceptions of their needs in an open and precise way\u0026mdash;a practice that is often avoided in similar research due to the significant effort required to categorize and analyze such responses.\u003c/p\u003e\u003cp\u003eIn the second phase of the study, carried out from September to October 2022, additional interviews were conducted to assess the food security status of the participating households. During these interviews, the household members were asked about their agricultural practices, food sources, and other relevant factors used to calculate the food security scores through the proxy indicators, namely the FCS and the MAHFP. The indicators selected are straightforward to implement, extensively validated, supported by readily available data for comparison, and capable of capturing diverse aspects of food security to enhance and complement the research findings.\u003c/p\u003e\u003cp\u003eThe studied independent variables were either scaled or categorical in nature. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows how the information was collected, resulting in 29 original independent variables.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eOriginal independent variables studied\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eType\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScale\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eData Format\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePossible Responses/Range\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRatio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumeric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 to infinite (whole numbers)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeople living in the household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRatio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumeric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 to infinite (whole numbers)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMain source of drinking water in the household\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptions: 1. Piped water, 2. Tube well or borehole, 3. Dug well, 4. Spring water, 5. Rain water, 6. Tanker truck, 7. Car with small tank, 8. Surface water (ponds), 9. Bottled water, 10. Other\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinutes walking for water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRatio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumeric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 to infinite (whole numbers)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMethod for making water drinkable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptions: 1. Boil, 2. Ceramic filter, 3. Cloth filter, 4. Other\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eToilet facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptions: 1. Pit latrine with slab, 2. Ventilated improved pit latrine, 3. Pit latrine without slab, 4. Open pit, 5. Hanging latrine, 6. Other\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShare toilet facility\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCooking fuel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptions: 1. Electricity, 2. LPG, 3. Natural gas, 4. Biogas, 5. Kerosene, 6. Coal/lignite, 7. Charcoal, 8. Wood, 9. Straw/bushes/herbs, 10. Farm crops, 11. Manure, 12. No cooking at household, 13. Other\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold equipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMultiple-response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptions: 1. Electricity, 2. Radio, 3. Television\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold members asset ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMultiple-response\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptions: 1. Watch, 2. Phone, 3. Bicycle, 4. Motorbike\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater available for washing hands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoap available for washing hands\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse floor type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptions: 1. Cement, 2. Dirt, 3. Tile, 4. Other\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHouse roof type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptions: 1. Natural roof, 2. Constructed roof\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum Necessary Household Income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRatio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumeric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAny non-negative number (e.g., 0, 100, 5000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReal income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQuantitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRatio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNumeric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAny non-negative number (e.g., 0, 100, 5000)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMentioned problems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOpen-ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFree-text responses (paragraphs)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand tenure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1. Own property, 2. Rented property\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSize of farmland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOrdinal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1: Less than 1 acre, 2: Exactly 1 acre, 3: Bigger than 1 acre\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCrops grown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOpen-ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAny crop name (e.g., maize, beans, cassava)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYield satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarming obstacles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOpen-ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFree-text responses (paragraphs)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome garden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLivestock ownership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLivestock type\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOpen-ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAny animal name (e.g., sheep, chickens, pigs)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSelling of agricultural surplus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarmers group membership\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eYes, No\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFood source\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQualitative\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNominal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eClosed-Ended\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOptions: 1. We buy at shop/market, 2. We grow it ourselves, 3. Work for food, 4. People help the family\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003e(\u003c/em\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cem\u003eplaced at the end of the manuscript due to size constraints.)\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFood Security Analysis\u003c/b\u003e\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eThe calculation of the FCS and the MAHFP of each variable analysed has been carried out following the methodology proposed by the World Food Programme (WFP) and the Food and Nutrition Technical Assistance (FANTA) project, respectively.\u003c/p\u003e\u003cp\u003eOn the one hand, the FCS is a composite metric used to assess household-level food security by measuring the diversity and frequency of food groups consumed over a seven-day recall period. As described by the World Food Programme (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), foods are categorized into groups, with each group assigned a weight based on its relative nutritional value. The FCS is calculated by multiplying the number of days of consumption of each food group (Fi) by its assigned weight (Wi) and then summing the values to produce a total score (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), as is shown in equation [1]. It provides insights into both the quality and quantity of a household's diet, with higher scores reflecting better food consumption. This score serves as a key indicator for identifying households with inadequate diets and monitoring broader food security trends within a population (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e) (Antwi \u0026amp; Lyford, 2021; Buzigi \u0026amp; Onakuse, 2023).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:FCS={\\sum\\:}_{i=1}^{n}\\left({F}_{i}x{W}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e [1]\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{F}_{i}=\\)\u003c/span\u003e\u003c/span\u003e Number of days a particular food group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e was consumed over a seven-day recall period.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{W}_{i}=\\)\u003c/span\u003e\u003c/span\u003e Weighting factor assigned to each food group based on its nutritional value.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n=\\)\u003c/span\u003e\u003c/span\u003e Total number of food groups considered (in this case, 8 food groups).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eOn the other hand, the MAHFP measures household food security by tracking the number of months in the past year during which a household was able to meet its food requirements. The calculation, explained by Bilinsky \u0026amp; Swindale (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), involves recording the number of months when a household did not experience food shortages and summing them to determine the total adequate months. This indicator provides insights into temporal trends in food security, highlighting periods of vulnerability and identifying patterns of seasonal or chronic food shortages. A higher number of adequate months indicates greater food security, reflecting a household's sustained access to food throughout the year. Widely adopted and validated \u0026mdash;used, for example, in rural Kenya(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) and among farming households in Zambia(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) (Mofya-Mukuka \u0026amp; Hichaambwa, 2018)\u0026mdash; the MAHFP complements other indicators like the FCS, allowing for comparability across studies and contexts. Its computation is summarized in equation [2].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:MAHFP=12-{\\sum\\:}_{i=1}^{12}{M}_{i}\\)\u003c/span\u003e\u003c/span\u003e [2]\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{i}=\\)\u003c/span\u003e\u003c/span\u003e Binary value for month \u0026#119894;, where:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{i}=1\\)\u003c/span\u003e\u003c/span\u003e if the household experienced food shortage during month \u0026#119894;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{M}_{i}=0\\)\u003c/span\u003e\u003c/span\u003e if the household did not experience food shortage during month \u0026#119894;\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe collected data was analysed using SPSS v26.0. To ensure meaningful variability, independent variables were excluded if they were highly polarized\u0026mdash;defined as having 85% or more of respondents selecting the same response. Only variables with sufficient distributional variation were retained for further analysis of food insecurity determinants.\u003c/p\u003e\u003cp\u003eCategorical variables comprising more than two categories were transformed into \"dummy\" variables (1: presence/0: absence).\u003c/p\u003e\u003cp\u003eThe distributions of the independent variables were then examined in relation to the FCS and MAHFP results. FCS range extended from 0 to 112, with higher scores indicating better food security. MAHFP ranged from 0 to 12 months, with a higher number of adequate months also reflecting greater food security. The Shapiro-Wilk test was conducted at a significance level of α\u0026thinsp;=\u0026thinsp;0.05 to assess the normality of the distribution of the independent variables.\u003c/p\u003e\u003cp\u003eSubsequently, the population was divided into two groups -\"food secure\" and \"food insecure\"- based on the indicators used. Notably, the use of the two different food security indicators resulted in two distinct classifications of the population. For the FCS indicator, the official thresholds were used to create these two categories. In accordance with WFP guidelines (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), households classified as \u0026ldquo;poor\u0026rdquo; (scores 0 to 21) or \u0026ldquo;borderline\u0026rdquo; (scores 21.5 to 35) were grouped together and considered food insecure. Households with scores above 35, categorized as \u0026ldquo;acceptable,\u0026rdquo; were deemed food secure.\u003c/p\u003e\u003cp\u003eRegarding the MAHFP indicator, thresholds established in prior studies were employed to ensure comparability. In this framework, authors like Matavel et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) have considered individuals with a score of 5 months or less as the most food insecure (category 1), those with 6 to 9 months as moderately food insecure (category 2), and those with 10 months or more as the least food insecure (category 3). In the present study, categories 1 and 2 were merged and designated as \"food insecure,\" representing households with a MAHFP score of 9 months or less. Households with a score of 10 months or more were classified as \"food secure.\"\u003c/p\u003e\u003cp\u003eA contingency table analysis and ANOVA (Analysis of Variance) test were conducted to systematically organize and analyze the categorical and continuous data respectively. This approach focused on the independent variables and the classification of households as either food secure or food insecure, as determined by the FCS and MAHFP indicators.\u003c/p\u003e\u003cp\u003eAdditionally, bivariate correlations were examined to assess the strength and direction of the dependent variables and the food security indicators, to prevent potential multicollinearity issues.\u003c/p\u003e\u003cp\u003eOnly the predictor variables identified as significantly associated with each of the food security indicators through ANOVA and contingency analysis were selected for inclusion in the regression model.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Logistic Regression Analysis\u003c/h2\u003e\u003cp\u003eA binary logistic regression model was determined as the most appropriate method to further explore the factors influencing food security, due to its suitability for dichotomous dependent variables, allowing the classification of households as \u0026ldquo;food secure\u0026rdquo; (coded as 1) or \u0026ldquo;food insecure\u0026rdquo; (coded as 0). This approach estimates the probability of food security outcomes based on independent variables without assuming linear relationships, utilizing the logit transformation to accommodate non-linear patterns typical in socioeconomic data. It provides interpretable odds ratios, offering clear insight into the effects of predictor variables, and supports both categorical and scaled predictors for comprehensive analysis. This method has also been effectively applied in similar contexts, such as in Northern Ethiopia to identify key household food security determinants (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), reinforcing its relevance for generating actionable insights for policymakers and stakeholders aiming to improve food security outcomes.\u003c/p\u003e\u003cp\u003eThe logistic regression model estimates the probability that a given household \u0026#119894; is food secure. This probability, \u0026#119901;\u0026#119894;, is expressed by the equation [3].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{p}_{i}=\\frac{{e}^{{\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+{\\beta\\:}_{2}{X}_{2}+...+{\\beta\\:}_{k}{X}_{k}}}{1+{e}^{{\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+{\\beta\\:}_{2}{X}_{2}+...+{\\beta\\:}_{k}{X}_{k}}}\\)\u003c/span\u003e\u003c/span\u003e [3]\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u0026#119901;\u003csub\u003e\u0026#119894;\u003c/sub\u003e = Probability that the \u0026#119894;-th household is food secure (\u0026#119884;=1).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e = Intercept or constant term.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{j}\\)\u003c/span\u003e\u003c/span\u003e = Coefficient associated with the \u003cem\u003ej\u003c/em\u003e-th predictor variable \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e​ (for \u003cem\u003ej\u003c/em\u003e = 1, 2\u0026hellip;, \u003cem\u003ek\u003c/em\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e​ = Independent variables representing potential determinants of food security.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe model can be rewritten in its logit form as is shown in equation [4].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Logit\\left({p}_{i}\\right)=lnln\\:\\left(\\frac{{p}_{i}}{1-{p}_{i}}\\right)\\:=\\:{\\beta\\:}_{0}+{\\beta\\:}_{1}{X}_{1}+{\\beta\\:}_{2}{X}_{2}+...+{\\beta\\:}_{k}{X}_{k}\\)\u003c/span\u003e\u003c/span\u003e [4]\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn this form, the dependent variable is the natural logarithm of the odds ratio of being food secure versus being food insecure.\u003c/p\u003e\u003cp\u003eThe coefficients (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{j})\\)\u003c/span\u003e\u003c/span\u003e represent the change in the log-odds of food security status associated with a one-unit change in the predictor variable, holding all other variables constant.\u003c/p\u003e\u003cp\u003eThe odds ratio (OR) is calculated by exponentiating the coefficient as is shown in equation [5].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:OR=\\:{e}^{{\\beta\\:}_{j}}\\)\u003c/span\u003e\u003c/span\u003e [5]\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eOR\u0026thinsp;\u0026gt;\u0026thinsp;1: The predictor variable increases the probability of being food secure.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOR\u0026thinsp;\u0026lt;\u0026thinsp;1: The predictor variable decreases the probability of being food secure.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOR\u0026thinsp;=\u0026thinsp;1: The predictor variable has no effect on food security status.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe statistical significance of each predictor is assessed using the Wald test, with p-values less than 0.05 considered statistically significant.\u003c/p\u003e\u003cp\u003eTo evaluate the performance of the logistic regression model, various measures were considered:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eHosmer-Lemeshow Test: Assesses the calibration of the model, indicating how closely predicted probabilities match observed probabilities. A p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 suggests good fit.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNagelkerke R\u0026sup2;: Indicates the proportion of variation in the dependent variable explained by the model.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eClassification Table: Compares observed and predicted classifications to calculate the overall percentage of correct classifications.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1. General Food Security Classification\u003c/h2\u003e\u003cp\u003eParticipating households were distributed across 19 different villages or hamlets, with nearly half (43.9%) originating from Kenziga village, where the primary school established by the local NGO is located. Women constituted the primary respondents in the majority of interviews (98.2%). Water availability was largely dependent on the surface ponds or waterholes present in the area. In this regard, 90.8% of the surveyed households reported collecting household water from these ponds, with an average daily collection time of 70 minutes. To purify the water, 93.6% of respondents reported boiling as their primary method. Additionally, 85% of households reported having access to land for cultivation or raising animals; however, more than one-third (36.5%) of them own 0.5 acres or less.\u003c/p\u003e\u003cp\u003eThe interviewed households were classified as either food secure or food insecure, based on their scores on the two food security indicators used. This resulted in two distinct classifications of the population (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFood Security classification of interviewed households.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFood Secure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFood Insecure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean Score\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFCS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.46%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e42.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMAHFP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAlthough the mean FCS exceeds the threshold of 35\u0026mdash;placing the sample within the \u0026ldquo;acceptable\u0026rdquo; range\u0026mdash;a substantial proportion of households (41.46%) are still classified as food insecure. This figure increases markedly when using the MAHFP indicator: according to this metric, more than two-thirds (68.9%) of households are food insecure, despite reporting an average of over seven months of adequate food provisioning. This difference clearly reflects the fact that the two indicators capture different dimensions of food security.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Comparison Between FCS and MAHFP Classifications\u003c/h2\u003e\u003cp\u003eA cross-tabulation was conducted to assess the degree of agreement between household classifications derived from the two food security indicators used in this study: FCS and MAHFP. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the two indicators produced divergent classifications for a substantial proportion of households.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCross-tabulation of household food security status based on FCS and MAHFP\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMAHFP Insecure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMAHFP Secure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal FCS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFCS Insecure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFCS Secure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal MAHFP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOut of the 164 households included in the analysis, only 95 (57.9%) were consistently classified as either food secure or food insecure by both indicators. The remaining 69 households (42.1%) were categorized differently depending on the metric used. Specifically, 57 households were considered food secure by the FCS but food insecure by the MAHFP, while 12 households showed the opposite pattern.\u003c/p\u003e\u003cp\u003eThese discrepancies reflect fundamental differences between the two indicators. The FCS assesses recent food consumption patterns over the past seven days, whereas the MAHFP captures a 12-month recall of months in which households had sufficient food. Their differing timeframes mean they respond to different aspects of food insecurity. As noted by Mutea et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), food security assessments based on multiple complementary indicators provide a more reliable and nuanced understanding of household conditions, especially in contexts marked by seasonal fluctuations.\u003c/p\u003e\u003cp\u003eThe timing of the assessment plays a crucial role not only in shaping the results but also in how they are interpreted. The interviews used to collect the data both indicators were conducted between September and October 2022, shortly after the harvest season in central Uganda. While this period typically coincides with improved food availability, the 2022 agricultural season was severely affected by drought, which reduced yields across the country. According to the World Meteorological Organization (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), both the March\u0026ndash;May and October\u0026ndash;December rainy seasons recorded below-average precipitation, contributing to the most prolonged drought in over four decades. Even so, the timing of data collection\u0026mdash;immediately after harvest\u0026mdash;may have softened the impact of the drought on short-term food consumption. As a result, households might have had some food stored, which could explain the relatively better FCS scores. In contrast, the MAHFP, based on a 12-month recall, captured the cumulative effects of food shortages experienced throughout the year, classifying a greater proportion of households as food insecure. This interpretation aligns with seasonal food security dynamics observed in other Sub-Saharan African contexts. For instance, a study conducted in central Mozambique found that food security levels peaked during the post-harvest period (May\u0026ndash;July), followed by the harvesting season, and were lowest during the lean period (November\u0026ndash;January) (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Although the 2022 harvest in Uganda was undermined by drought, the timing of the data collection\u0026mdash;shortly after harvest\u0026mdash;may have temporarily improved households\u0026rsquo; FCS due to the availability of stored food.\u003c/p\u003e\u003cp\u003eThese results highlight the value of using multiple complementary indicators in food security assessments, as relying on a single metric may either under- or overestimate the severity of food insecurity depending on the timing and context of data collection.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Household and Livelihood Differences Across Food Security Groups\u003c/h2\u003e\u003cp\u003eRegarding the predictors, a total of 21 variables were retained for the analysis after the exclusion of polarized variables. When disaggregated by food security status, the households were classified as presented in Tables \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e present cross-tabulations showing the distribution of households across food security groups based on each qualitative variable, classified according to the two indicators employed: FCS and MAHFP. Specifically, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e highlights household characteristics and livelihood practices, while Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e focuses on reported challenges and constraints faced by each household. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides a comparative analysis of quantitative variables by reporting the mean values for each food security group under both classification systems. The variables identified as statistically significant in each indicator effectively distinguish between food-secure and food-insecure households. While some variables exhibit significance across both indicators, others are only significant under one classification criterion. It is worth noting that the FCS classification detected a higher number of significant variables compared to the MAHFP, reflecting differences in how each indicator captures various dimensions of food security.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Households by Food Security Group, Household Characteristics and Livelihood Practices (FCS \u0026amp; MAHFP)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVARIABLE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eFCS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMAHFP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInsecure Households (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSecure Households (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInsecure Households (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecure Households (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of Toilet Facility\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePit latrine with slab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e81.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e82.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e86.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVentilated improved pit latrine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePit latrine without slab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpen pit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eShare toilet facility\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e41.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWater available for washing hands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e88.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSoap available for washing hands\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e79.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e86.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold equipment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElectricity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e75.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e66.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e72.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e29.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e32.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e52.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT.V.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e28.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold members asset ownership\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBicycle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e26.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e29.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMotorbike\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e22.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHouse floor type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCemented\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e47.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e56.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSize of farmland\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBig (more than 0.5 acres)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e50.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e59.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e72.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCrops grown\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaize farming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e78.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e77.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e82.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoffee farming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e26.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e21.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlantain farming\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e18.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHome garden\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLivestock ownership\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e65.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e58.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e62.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLivestock type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChickens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e18.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGoats\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e31.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePigs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e41.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e37.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelling of agricultural surplus\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e36.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e46.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e60.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFood source\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eShop/market\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e74.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHome grown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e57.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e77.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e66.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWork for food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePeople help the family\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eHousehold Distribution by Food Security Group, Reported Challenges and Constraints (FCS \u0026amp; MAHFP)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVARIABLE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e\u003cp\u003eFCS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eMAHFP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInsecure Households (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSecure Households (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInsecure Households (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSecure Households (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGeneral problems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoverty, money\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e61.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e57.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e72.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHunger, lack of food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e53.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e28.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchool fees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e36.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of clothes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e35.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e51.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e24.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of mattress or bedding\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease or lack of access to medical treatment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e64.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e60.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e66.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e66.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to drinking water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsufficient farmland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFarming obstacles\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of rain water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e83.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e87.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e90.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil is not fertile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e80.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e70.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e77.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e70.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e15.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e49.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e44.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e52.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e43.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparative Analysis of Quantitative Variables by Food Security Status (FCS and MAHFP, ANOVA Results).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eVARIABLE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c3\" namest=\"c2\" rowspan=\"2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e\u003cp\u003eFCS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c14\" namest=\"c10\"\u003e\u003cp\u003eMAHFP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c15\" namest=\"c15\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eInsecure Households\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eSecure Households\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eInsecure Households\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eSecure Households\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWalking time to school (minutes)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e75.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e70.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e71.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e47.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e75.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e48.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHousehold size (members)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e7.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e7.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWater collection time (minutes/day)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e68.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e33.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e73.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e40.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum necessary household income (UGX/Week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24,549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18,004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12,729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30,659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29,023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e23,193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e21,859\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e30,646\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e29,201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003e*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReal income (UGX/Week)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16,477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31,754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9,372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14,873\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21,317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38,659\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003e14,081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e23,770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e21,716\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e44,381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c15\" namest=\"c14\"\u003e\u003cp\u003ens\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"15\"\u003eNote: * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1. General Household Characteristics\u003c/h2\u003e\u003cp\u003eFood-secure households generally exhibited better overall conditions across the measured variables. The link between food security and hygiene and sanitation is evident in the significant differences observed in toilet facility sharing, particularly under the FCS classification. Specifically, the majority of food-insecure households (51.6%) shared toilet facilities, compared to only 34% of food-secure households, highlighting a disparity in access to private sanitation. Moreover, differences in sanitation extended beyond toilet facility sharing, with food-secure households exhibiting greater access to essential hygiene resources, particularly under the FCS indicator. Specifically, 88.2% of food-secure households, according to the FCS, reported access to water for washing hands, compared to only 68.8% of food-insecure households. According to the MAHFP the trend was similar, with 88% of food-secure households having access, compared to 76.6% of food-insecure households. The availability of soap also emerged as a distinguishing factor under the FCS classification, being, again, more prevalent among food-secure households. These findings align with Workman et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e), who emphasize the complex, multi-dimensional nature of food, water, and sanitation insecurities, highlighting how these issues frequently coexist and interact, resulting in notable impacts on both mental and physical health.\u003c/p\u003e\u003cp\u003eFood-secure households were generally better equipped than their food-insecure counterparts in terms of access to electricity and higher-quality flooring. Our results are consistent with those by Frayne \u0026amp; McCordic (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), who demonstrated that households with inconsistent or no access to a cash income, electricity, or water among others, had 8.5 times greater odds of having less than 12 months of adequate food provisioning in the last year. These differences were even more pronounced under the FCS classification compared to the MAHFP classification. Apanovich \u0026amp; Mazur (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) also identified access to electricity as a socioeconomic indicator in their study of food security among smallholder farmers in Uganda, although its direct effect on food security was not statistically significant in the final analysis. Similarly, Ownership of assets such as televisions, radios, and motorcycles followed the same trend. This aligns with the findings of Shifat et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), who identified a strong positive association between household asset ownership and food security in Bangladesh. Their study suggests that the absence of such assets signals economic vulnerability and may serve as a practical proxy indicator in contexts where detailed income or consumption data are lacking.\u003c/p\u003e\u003cp\u003eWith respect to the quantitative variables (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), household size and those related to income, emerged as significant factors in food security, particularly under the FCS classification. The literature generally suggests that larger household sizes contribute to greater food insecurity due to increased consumption demands relative to production or income capacity (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). However, contrary to this widely held view, our findings indicate that larger households (with an average of 7.89 members) were more likely to be classified as food-secure using the FCS indicator, compared to smaller households (6.73 members), in line with findings by Nkomoki et al. 2019 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) and Maitra and RAO (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), who also observed a positive association between household size and food security in specific rural contexts. For the case of walking time to school and water collection time, these variables showed no statistical significance with FCS and MAHFP.\u003c/p\u003e\u003cp\u003eThe income reported by each group was higher among food-secure households, although the difference was statistically significant only under the FCS classification (21,317 UGX/week vs. 9,372 UGX/week). This finding is consistent with the broader literature, which establishes a strong link between household income and food security. For example, Tulem \u0026amp; Hordofa (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) reported that households with a low wealth index were over four times more likely to be food insecure compared to wealthier households. Similarly, a study by Kundu et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) reported that higher household wealth status was significantly associated with greater minimum dietary diversity among children aged 6\u0026ndash;23 months in Bangladesh, highlighting the influence of socioeconomic factors on dietary quality. Furthermore, the Minimum Necessary Household Income suggests that food-secure households required substantially more income to cover basic household needs\u0026mdash;70.29% higher than food-insecure households under the FCS indicator and 32.13% higher under the MAHFP indicator. This may reflect higher living standards among food-secure households, where greater financial resources are required to maintain improved housing, better diets, and access to essential services. Additionally, food-secure households may have more complex financial commitments, including school fees, healthcare costs, and investments in agricultural inputs, which elevate their minimum income requirements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2. Land Ownership and Agricultural Practices\u003c/h2\u003e\u003cp\u003eFarmland size is widely recognized as a key determinant of household food security (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) and our findings support this association. In our case, farmland size was significantly linked to food security when applying the FCS classification, with 72.3% of food-secure households owning larger land areas compared to only 50.8% of food-insecure households (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This result aligns with the findings of Apanovich \u0026amp; Mazur (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), who reported a positive relationship between total acreage and food security among Ugandan smallholder farmers during both the harvest and lean seasons. Similarly, Nkomoki et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) observed that larger farm sizes increased the likelihood of household food security, explaining that greater land access allows households to produce more food for self-consumption (enhancing food availability), diversify crops (improving dietary diversity and resilience), and sell surplus produce (increasing economic access to food). Our results further support this interpretation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): food-secure households were more likely to cultivate crops such as maize, coffee, and plantains, and to engage in livestock rearing, all of which contribute to improved food availability and income generation. Moreover, the sale of agricultural surplus was more prevalent among food-secure households and was significantly associated with food security status under both FCS and MAHFP classifications. In contrast, food-insecure households often consumed most of their produce, leaving little or no surplus for sale, thereby limiting their capacity to earn income from farming.\u003c/p\u003e\u003cp\u003eWhen asked about their primary food sources, two variables showed significant differences across the population: growing food at home and working for food as payment\u0026mdash;a common practice in rural Uganda where labour is exchanged for food instead of monetary compensation. Growing food at home was more prevalent among food-secure households, likely due to their access to larger plots of land compared to food-insecure households. This finding aligns with the results of Issahaku et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e), who reported a 4.6% increase in Food Consumption Scores (FCS) among Rwandan households practicing home gardening, reflecting improved dietary diversity and overall food security. In contrast, although not a widespread strategy\u0026mdash;reported by only 7.9% of surveyed households\u0026mdash;working for food was more frequently observed among food-insecure households, possibly indicating their urgent need to secure sustenance for their household. To our knowledge, there is a lack of empirical studies in the existing literature analyzing the relationship between food-for-labor arrangements and household food security, particularly in the Ugandan context. As such, no direct comparisons with previous findings could be established.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.3.3. Challenges Reported\u003c/h2\u003e\u003cp\u003eOpen-ended responses revealed that food-insecure households reported higher incidences of various issues, such as hunger and lack of clothing, than their food-secure counterparts under both indicators (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These issues can be considered direct indicators of poverty, a condition strongly correlated with food insecurity across various countries (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Limited financial resources among food-insecure households often force them to prioritize immediate food needs over other essentials, further perpetuating cycles of deprivation. However, other complaints were more common among food-secure households, like access to drinking water, which was reported as an issue by a higher proportion of food-secure households compared to food-insecure households, in both FCS and MAHFP classifications. This counterintuitive finding may stem from higher expectations among food-secure households for reliable and high-quality water sources, while food-insecure households may have normalized limited water access or prioritized other pressing concerns. All households face similar challenges regarding water access\u0026mdash;90.8% of the surveyed households collect water from surface ponds, with an average daily collection time exceeding 70 minutes\u0026mdash;. Likewise, although only significant when considering the MAHFP classification, mentioning poverty as a problem of the household was more common in food-secure households, compared with food-insecure ones, which may reflect a heightened awareness and perception of economic challenges among food-secure households, possibly due to their greater exposure to financial responsibilities and aspirations for improved living standards.\u003c/p\u003e\u003cp\u003eLastly, insufficient farmland was a more frequently reported concern among food-insecure households, aligning with the critical role of arable land in subsistence farming systems, which dominate rural livelihoods throughout the country (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e) (Bamwesigye et al., 2020).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Determinants of Food Security Status\u003c/h2\u003e\u003cp\u003eThe results of the binary logistic regression model for FCS indicator are presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The model was developed using the backward elimination method, which began with the introduction of 24 variables and sequentially removed those with the least explanatory power until arriving at the optimal combination that provided the best fit, with 5 variables along with a constant. The model demonstrates robustness, with a chi-squared value of 10.108 and a p-value of 0.258.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression coefficients (FCS)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSt. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum Necessary Household Income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.318\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHunger, lack of food\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.403\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLack of clothes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.819\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to drinking water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.760\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsufficient farmland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.567\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmong the economic variables analysed, actual household income did not exert sufficient influence to be retained in the model. This contrasts with findings from other studies, such as Asaki et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), who identified a significant relationship between household income and food security. Interestingly, minimum necessary household income was retained in the model (β\u0026thinsp;=\u0026thinsp;0.276, OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.32, p\u0026thinsp;=\u0026thinsp;0.004), indicating that each unit increase in perceived necessary income was associated with a 32% increase in the odds of being classified as food secure. This may reflect the added value of subjective estimations in capturing economic pressure more accurately. In rural contexts\u0026mdash;where income sources are often informal, irregular, or not fully documented\u0026mdash;such perceptions may offer a clearer sense of financial strain. Moreover, what a household considers \u0026ldquo;necessary\u0026rdquo; likely encompasses not only material needs but also lived experiences, expectations, and perceived coping capacity\u0026mdash;factors that directly influence how food is accessed and managed.\u003c/p\u003e\u003cp\u003eNotably, variables derived from open-ended questions emerged as the most influential predictors in the model. For example, complaints about hunger and lack of food were strongly associated with food insecurity (β = \u0026minus;\u0026thinsp;0.687, OR\u0026thinsp;\u0026asymp;\u0026thinsp;0.50). Similarly, the variable \u0026ldquo;lack of clothing\u0026rdquo; (β = \u0026minus;\u0026thinsp;0.819, OR\u0026thinsp;\u0026asymp;\u0026thinsp;0.44) showed an even stronger negative association, indicating that households reporting this issue were less than half as likely to achieve adequate food consumption compared to those who did not express this concern. Complaints about insufficient farmland also showed a notable negative relationship, though with marginal statistical significance. Interestingly, concerns about lack of access to drinking water were inversely related to food insecurity (β\u0026thinsp;=\u0026thinsp;1.015, OR\u0026thinsp;\u0026asymp;\u0026thinsp;2.76), suggesting that households identifying this problem were more likely to be food secure. This counterintuitive result may reflect higher expectations or awareness among better-off households, rather than actual differences in water access.\u003c/p\u003e\u003cp\u003eFor the case of MAHFP, the coefficients for each variable in the binary logistic regression model are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The model was also developed using the backward elimination method, which began with the introduction of 13 variables and reached the greatest explanatory power with 4 variables. The final model retained and exhibited strong reliability, with a chi-squared value of 5.403 and a p-value of 0.714, indicating a good fit.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLogistic regression coefficients (MAHFP)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVARIABLE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient (β)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSt. Error\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSig.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds Ratio\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum Necessary Household Income\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.127\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoverty, money\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.958\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccess to drinking water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.442\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsufficient farmland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.797\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.178\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.173\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe final set of predictors in the MAHFP model included Minimum Necessary Household Income, which remained statistically significant (β\u0026thinsp;=\u0026thinsp;0.119, OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.13, p\u0026thinsp;=\u0026thinsp;0.021), consistent with the results observed under the FCS classification. As in the previous model, the most influential predictors were derived from open-ended questions on household challenges, reinforcing the value of integrating qualitative insights into quantitative analysis. Complaints related to poverty or financial difficulties (β\u0026thinsp;=\u0026thinsp;0.672, OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.96, p\u0026thinsp;=\u0026thinsp;0.094) and lack of access to drinking water (β\u0026thinsp;=\u0026thinsp;1.155, OR\u0026thinsp;\u0026asymp;\u0026thinsp;3.17, p\u0026thinsp;=\u0026thinsp;0.009) were positively associated with food security, indicating that households reporting these issues were more likely to maintain adequate food provisioning throughout the year. In contrast, the complaint about insufficient agricultural land emerged as the strongest negative predictor (β = \u0026minus;\u0026thinsp;1.725, OR\u0026thinsp;\u0026asymp;\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.030), highlighting once again the critical role of land access in achieving food security.\u003c/p\u003e\u003cp\u003eThese seemingly paradoxical results\u0026mdash;particularly regarding complaints about poverty or water\u0026mdash;may reflect a greater awareness of needs among relatively better-off households, or a higher capacity to articulate problems that, while present, do not compromise their year-round food provisioning. As observed in the FCS model, subjective perceptions do not always align with objective deprivation, and may instead reveal nuanced aspects of household vulnerability, resilience, and expectations.\u003c/p\u003e\u003cp\u003eDespite initially analysing 30 independent variables, only six were retained as significant predictors in the regression models. This limited number may be partly explained by the relative homogeneity of the interviewed population, as well as the strong interdependence among certain household characteristics. Several variables commonly identified in the literature as relevant determinants of food security, such as the cultivation of maize, beans, or plantains (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), did not show sufficient statistical influence to be included in the final models. However, they did show a similar trend to that reported by previous authors, since the cross-tabulations reveal a higher proportion of \u0026ldquo;food secure\u0026rdquo; households among those cultivating these crops, suggesting that households growing these crops are generally more food secure than those that do not.\u003c/p\u003e\u003cp\u003eA notable feature of this study is that most of the predictive variables retained in the models originated from open-ended questions. While this reduces the comparability of the results with studies based on more standardized survey instruments, it also underscores the importance of incorporating qualitative dimensions into quantitative analysis. Allowing respondents to express their own concerns provides valuable insights into the contextual realities shaping food security. Even so, the findings still align with key conclusions from existing literature. For example, Hashmiu et al.(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) emphasized the importance of land ownership in improving household food security through greater access to income and food crops. At a broader scale, large-scale land acquisitions across Sub-Saharan Africa, which may restrict access to farmland for local communities, have been associated with reduced cereal availability and increased malnutrition, despite being framed as development opportunities(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) (Kinda et al., 2022). In line with these trends, households in the present study that reported insufficient agricultural land consistently exhibited lower levels of food security.\u003c/p\u003e\u003cp\u003eSimilarly, Shamah-Levy et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e) highlights the negative impact of inadequate access to drinking water on food security. Although the objective variable \u0026ldquo;main source of drinking water\u0026rdquo; was not statistically significant in our models, self-reported complaints about water access showed a significant, albeit inverse, relationship with food security. Households that expressed dissatisfaction with water availability tended to have higher levels of food security, suggesting that better-off households may hold higher expectations regarding their living conditions.\u003c/p\u003e\u003cp\u003eOther variables, such as membership in farmer groups or cooperatives, which have been identified as highly influential by authors like Nkomoki et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), were underrepresented in our sample and had to be excluded during the initial stages of this study.\u003c/p\u003e\u003cp\u003eFinally, the variable \u0026ldquo;household size\u0026rdquo; showed a trend contrary to what is commonly reported. While many studies (e.g., (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e) associate larger households with greater food insecurity, our findings indicate the opposite: food secure households had, on average, more members (7.89) than food-insecure ones (6.73). This may reflect a greater availability of labor, income diversification, or economies of scale in larger households, though further research would be needed to explore this relationship in depth.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion and Policy Implications","content":"\u003cp\u003eThe findings of this study provide valuable insights into the determinants of household food security in rural Uganda. These insights have important policy implications for enhancing food security and promoting sustainable development in the region and other areas with similar context.\u003c/p\u003e\u003cp\u003eFirstly, the significant association between farmland size and food security underscores the need for policies that facilitate access to land, especially for food-insecure households. Land access and ownership are critical factors in improving food availability, dietary diversity, and economic access to food. However, over-reliance on land for food security presents a vulnerability that should be addressed. Policymakers should prioritize not only land tenure reforms that promote equitable access to arable land, but also programs aimed at diversifying livelihood sources and promoting non-agricultural income-generating activities. Such efforts could enhance resilience by reducing dependency on land-based food production.\u003c/p\u003e\u003cp\u003eSecondly, the limited impact of actual household income on food security, contrasted with the influence of perceived necessary income, suggests that interventions targeting food security should not only focus on increasing household income but also on enhancing households\u0026rsquo; ability to manage and allocate their resources effectively. Financial literacy programs, tailored to the local context, could help households optimize their spending and improve food security outcomes.\u003c/p\u003e\u003cp\u003eAdditionally, the finding that food-secure households often report higher dissatisfaction with water access highlights the importance of improving water infrastructure and ensuring reliable access to safe water sources. According to the FAO's (Food and Agriculture Organisation) utilization dimension, food security cannot be achieved without access to clean water, as adequate nutrition depends not only on food intake but also on water and sanitation services (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Policymakers should prioritize investments in water supply systems, especially in rural areas where households predominantly rely on surface ponds for their daily needs. Integrating water access improvement strategies with food security initiatives could further strengthen the resilience of vulnerable populations.\u003c/p\u003e\u003cp\u003eMoreover, the significant role of qualitative insights from open-ended responses in identifying predictors of food security suggests that policymakers and researchers should adopt more participatory approaches when designing and implementing food security interventions. Engaging local communities in the decision-making process can provide valuable perspectives that are often overlooked by conventional quantitative methods. In this regard, the divergent results obtained through FCS and MAHFP also emphasize the importance of using multiple, complementary indicators when assessing food security. Each indicator captures distinct dimensions and timeframes of food access, and their combined use offers a more nuanced and accurate understanding of household vulnerability. This is especially relevant in contexts affected by seasonal variation or climatic shocks, where reliance on a single metric could lead to misleading conclusions.\u003c/p\u003e\u003cp\u003eFinally, given that households that engaged in livestock rearing and cultivation of specific crops (such as maize, coffee, and plantains) were more likely to be food secure, agricultural policies should encourage diversification of livelihoods through integrated crop-livestock systems. Providing training and resources to enhance productivity, market access, and profitability of these agricultural activities could significantly improve food security at the household level.\u003c/p\u003e\u003cp\u003eOverall, a multi-faceted approach that addresses land access, financial management, water availability, community participation, and agricultural diversification is essential for achieving sustainable improvements in food security in rural Uganda. Implementing these recommendations requires collaboration between government agencies, local NGOs, community leaders, and international partners to ensure a comprehensive and context-sensitive response to the persistent challenge of food insecurity.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eAnalysis of Variance\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFANTA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eFood and Nutrition Technical Assistance\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFAO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eFood and Agriculture Organisation\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eFood Consumption Score\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eHousehold Dietary Diversity Score\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHHS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eHousehold Hunger Scale\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAHFP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eMonths of Adequate Household Food Provisioning\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNGO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eNon-Governmental Organization\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eOdds Ratio\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUGX\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eUgandan Shillings\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWDDS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eWomen's Dietary Diversity Score\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eWFP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003e\u003cem\u003eWorld Food Programme\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003eEthical approval for this research was granted by the \u003cem\u003eOficina de Investigaci\u0026oacute;n Responsable (OIR)\u003c/em\u003e at the Universidad Miguel Hern\u0026aacute;ndez de Elche [Reference number: DEA.LMM.02.22]. All participants provided informed consent before the interviews.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e This work was supported by the Generalitat Valenciana (Regional Government of Valencia, Spain) through:\u003c/p\u003e\u003cp\u003e\u0026bull; ACIF 2023 predoctoral grant [Exp. number: CIACIF/2022/051].\u003c/p\u003e\u003cp\u003e\u0026bull; The project \u0026ldquo;Convenio GVA‑UMH SOLCIF/2022/0005 (Code: 11‑134‑4‑2023‑0133)\u0026rdquo;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the conceptualization of the study. JS conducted the fieldwork and interviews with participants, supported by the social worker from Kukorra Hamu Uganda. JS and RP were responsible for data curation. JS, LM, and RA participated in the formal analysis of the results. JS wrote the original draft of the manuscript, while LM and RA contributed to its review and editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the valuable collaboration of the NGO Rafiki Africa and its Ugandan partner, Kukorra Hamu Uganda, whose support and local insights were essential throughout the research process. Their contributions to community engagement, logistical coordination, and contextual understanding were crucial for the realization of this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eDue to the sensitive nature of the questions asked in this study, respondents were assured that their raw data would remain confidential and not be shared. As a result, the datasets generated and analysed during the current study are not publicly available but may be shared in anonymized form by the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFood and Agriculture Organization of the United Nations (FAO), International Fund for Agricultural Development (IFAD), United Nations Children\u0026rsquo;s Fund (UNICEF), World Food Programme (WFP), World Health Organization (WHO). The State of Food Security and Nutrition in the World 2024 \u0026ndash; Financing to end hunger, food insecurity and malnutrition in all its forms. 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Food Policy. 2022;113:102285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodpol.2022.102285\u003c/span\u003e\u003cspan address=\"10.1016/j.foodpol.2022.102285\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShamah-Levy T, M\u0026eacute;ndez-G\u0026oacute;mez-Humar\u0026aacute;n I, Mundo-Rosas V, Mu\u0026ntilde;oz-Espinosa A, Melgar-Qui\u0026ntilde;onez H, Young SL. Household water security is a mediator of household food security in a nationally representative sample of Mexico. Public Health Nutr. 2025;28(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1368980024002684\u003c/span\u003e\u003cspan address=\"10.1017/S1368980024002684\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Food security, FCS, MAHFP, Logit model, Household survey, Africa, Uganda, Livelihoods, Rural development, Determinants","lastPublishedDoi":"10.21203/rs.3.rs-7261208/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7261208/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFood insecurity remains a persistent challenge in Sub-Saharan Africa, particularly in rural areas where households depend on subsistence agriculture and face increasing vulnerability to climate shocks. Despite growing research on the topic, many studies rely on isolated or context-specific indicators, limiting comparability and policy relevance. This study addresses that gap by using two widely recognized indicators\u0026mdash;the Food Consumption Score (FCS) and the Months of Adequate Household Food Provisioning (MAHFP)\u0026mdash;to assess food security among rural households in Uganda\u0026rsquo;s Sembabule district. It had two main objectives: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) to compare household classifications based on FCS and MAHFP, and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) to identify key determinants of food security using binary logistic regression models. Data were collected from 167 households through a two-phase approach combining structured questionnaires and open-ended interviews.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe two indicators produced contrasting results: while 41.5% of households were classified as food insecure by FCS, 68.9% were considered food insecure by MAHFP. Binary logistic regression models identified perceived minimum necessary income, rather than actual income, as a stronger predictor of food security. In addition, household-reported challenges such as hunger, insufficient clothing, lack of farmland, and dissatisfaction with water access emerged as significant determinants. These qualitative variables consistently showed stronger explanatory power than many traditional socioeconomic factors.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eFindings highlight the multidimensional nature of food insecurity and the value of combining standardized indicators with open-ended, perception-based data. Improving access to cultivable land and safe water, as well as integrating local perspectives into rural development strategies, may enhance the effectiveness of food security interventions. The strong predictive power of perceived needs over objective income measures suggests that policies should go beyond income generation to include financial literacy and resource management support. Additionally, agricultural diversification\u0026mdash;particularly through integrated crop-livestock systems\u0026mdash;can help households strengthen resilience. This study supports the use of participatory approaches that give voice to lived experiences and help uncover priority needs often overlooked in conventional assessments.\u003c/p\u003e","manuscriptTitle":"Determinants of Household Food Security in Rural Uganda: Perceived Needs as Key Predictors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-25 03:31:35","doi":"10.21203/rs.3.rs-7261208/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df5b55e2-1e17-43b7-b199-c5d863dd4b86","owner":[],"postedDate":"August 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-20T10:08:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-25 03:31:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7261208","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7261208","identity":"rs-7261208","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}