Fieldwork was conducted through exploratory visits and surveys (both quantitative and qualitative). To do this, fourteen (14) villages were selected based on their sizes, household activities, and their geographical locations in relation to the administrative centers of the various municipalities. The villages primarily include Baoudetta Haoussa, Baoudetta Peul, Doubaoua, Gao Sofua Haoussa, Dan Baoussaoua, Gao Sofua Bougajé, and Mourey in the Baoudetta municipality, and Gochiro, Koona, Dagouajé, Rijiyal Sarki, Rijiyal Doudoua, Hardo Bamey, and Hardo Dodori in the Koona municipality (see Figure 2Figure 2).

Figure 2. Distribution of surveyed localities. Source: SIGNER data; Garmin GPS, 2024.

According to the National Directory of Localities [13], these municipalities total 3009 agricultural households. The determination of the sample size at the level of the target villages was obtained according to the protocol of [14]. This method was inspired by the work of [15], which is written as: β = Zα2 × pq/i2 where:

β = sample size;Zα = 1.96: Standard score corresponding to a 5% risk α;p = n/N with p being the proportion of agricultural households in the study area;n = number of agricultural households in the study area;N = total number of households in the study area;q = 1 − p;i = desired precision or margin of error (traditionally set at 5%).

The quota method was used to determine the number of households surveyed per village. In total, 384 households were surveyed, representing 19.96% of the total number of farming households in the 14 villages (1924 farming households). The target population consisted of farmers aged at least 40 years, as they have sufficient hindsight to assess the evolution of food security over the past 20 years. Other individuals interviewed in the semi-structured interviews included State technical services, local officials, and other resource persons who are directly or indirectly connected to food issues through their relationships with the farmers. In total, 12 resource persons were surveyed at the two-commune level, 10 at the departmental level, and 3 at the level of each village. Additionally, focus groups were conducted to complement and enrich the collected data.

For this study, a household questionnaire is used to collect quantitative data via KoboCollect. Additionally, a semi-structured interview guide, an observation grid, a GPS device, and a digital camera are also employed.

Documentary research, socioeconomic information of households, and cartographic materials were used in this study.

The collected data were processed using the Sphinx Plus2 V5 software. Some data were directly converted into tables or graphs, while others were modified and then transformed using the Excel spreadsheet. Qualitative data from semi-structured interviews were manually analyzed before being transcribed into Word. ArcMap 10.4 and QGIS 3.34 software were used to create the maps.

The foundation of food insecurity here presents itself in various forms, including the conditions that hinder adequate access to food for this population. Understanding its factors will help highlight the foundation of food insecurity and its manifestations on the households studied.

4.1.1. Conditions Related to Food Insecurity of Agricultural Households

Conditions related to food insecurity provide information on the perception of vulnerability or lack of food among agricultural households in the research area.

Table 1. Agricultural households without food for at least 10 days.

Source: field data, 2024.

Table 1 shows the number of household heads who reported experiencing a food shortage for at least ten days prior to the survey. The analysis indicates that the number of households answering “yes” (156) is very low compared to those answering “no” (228), both in the commune of Baoudetta (59 out of 115) and in Koona (97 out of 113). This situation can be explained by the fact that households who answered “yes” neither have food available nor the means to obtain it. The main causes could be low production, the selling off of crops at low prices, food price volatility, and the high number of people to feed. To better understand this situation, the frequencies of daily meals in these households will be calculated in the next analysis (Table 2 and Table3).

Table 2. Frequency of daily meals of farming households during the lean season.

Source: field data, 2024.

Table 2 shows that very few households consume three meals a day (4.69% in Baoudetta and 9.64% in Koona), which often indicates a lack of food in the study area. In fact, 34.11% of the villages in the Baoudetta commune and 42.19% of those in the Koona commune eat only once a day during the lean season due to the depletion of food stocks or strict management of the granary before the harvest. It is therefore necessary to analyze this frequency after the harvest.

Table 3. Frequency of daily meals of farming households after the harvest.

Source: field data, 2024.

The analysis of Table 3 reveals the proportion of respondents according to the frequency of meals in the communes after the harvest. A large proportion of households consume only two meals per day (26.82% in Baoudetta and 29.17% in Koona). Some households manage to have three meals per day, with 13.28% in the villages of Baoudetta and 17.19% in the villages of Koona. Other households consume only one meal, with rates of 5.21% and 8.33% in the communes of Baoudetta and Koona, respectively. This variation does not favor households that have reached the standard daily meal frequency; nevertheless, conditions related to the question of “preferred food” may influence this situation. Households that have reached two and three meals per day will be analyzed in Table 4 to explain this variation.

Table 4. Households having a preferred food for at least 1 month.

Source: field data, 2024.

Nearly 88% (332 out of 384) of agricultural households had more than one meal per day. Only 12% had a preferred food during the four weeks preceding the survey. In any case, the surveyed households experienced shocks that affected their economic living conditions (Figure 3Figure 3).

The analysis of Figure 3Figure 3 reveals that agricultural households have experienced several shocks over the past 12 months. The main shocks include food shortages for 37.60% of households, a drop in household income for 14.76%, and a surge in food prices reported by 10.50%. In addition, 1.56% of households lost able-bodied

Figure 3. Shocks experienced by agricultural households during the year. Source: field data, 2024.

members, affecting fieldwork and income-generating activities (migration). Furthermore, 7.03% of households lost assets, notably animals and/or were victims of theft, and 2.08% suffered land losses due to land conflicts. Some household heads also mentioned floods as a major shock.

(a) (b)

Photo 1. Effects of flooding on households and fields. Source: taken by Adamou, September 2024.

The analysis of the photos above confirms the responses of respondents who were victims of the floods. For some, the flooding affected households (4.68%) and for others, it manifested in their fields (2.34%). This led to crop loss for some households during the peak of the agricultural season (Photo 1).

4.1.2. Food Availability Index (FAI)

The food availability index of the households studied was assessed based on the average production and average food needs per survey site. Table 5. Food availability index of farming households

The analysis of the data in Table 5 provides information on the food availability index of the evaluated households. From this, three (03) levels of vulnerability to food insecurity were defined. Indeed, food vulnerability is considered low for

Table 5. Food availability index of agricultural households.

Source: field data, 2024.

households in villages with a high average production proportion, whose needs are less than the average amount of available food. These households are in the villages of Doubaoua, Hardo Bamey, and Hardo Dodori, where their FAI is above 1.5. For households in the villages of Baoudetta Haoussa, Baoudetta Peul, and Rijiyal Doudoua, their food availability is almost equal to food needs; however, their FAI is greater than 1 but less than 1.5, consequently, the vulnerability at this level is medium. In the other villages, households have high needs with very limited food availability. These households have a Food Consumption Score (FCS) below 1, so their vulnerability to food insecurity is high. This high vulnerability is more common in the villages of the Baoudetta commune than in those of Koona. Regarding low-vulnerability households, two villages per commune are identified, while for medium vulnerability, the Baoudetta commune also has two villages. To better understand this situation, it is necessary to calculate the food self-sufficiency rate (FSR) and the import dependency rate (IDR).

4.1.3. Food Self-Sufficiency Rate

The food self-sufficiency rate is assessed here based on different variables (production, importation, exportation), by village.

The analysis of the results in Table 6 undoubtedly confirms that the households assessed have not achieved food self-sufficiency; moreover, the results reflect a strong dependence of this population. In order to assess this dependence and better understand the share of available domestic supplies coming from imports and that from the households’ own production, the next step calculates the import dependency rate.

Table 6. Food self-sufficiency rate of agricultural households. The rate of food self-sufficiency is assessed here based on the following formula: Food Self-Sufficiency Rate = Production Production + Import-Export ∗ 100 .

Source: field data, 2024.

Table 7. Import Dependency Rate (IDR).

Source: field data, 2024.

Table 7 shows that more than half of households in villages such as Baoudetta Haoussa, Baoudetta Peul, and Dagouajé rely on imports rather than local production, with a rate exceeding 50%. In contrast, the villages of Doubaoua, Hardo Bamey, Rijiyal Sarki, and Hardo Dodori show a rate below 50%, related to the low import of quality products. To better understand food consumption, calculating the food consumption score is necessary.

4.1.4. Food Consumption Score (FCS)

The Food Consumption Score is a proximity indicator that reflects the quantity (kcal) and quality of the diet. It is based on a recall of the past 7 days regarding types/groups of foods (diversity) and frequency of consumption.

Table 8. Food consumption score of agricultural households over the past 7 days.

Source: field data, 2024.

The analysis of Table 8 shows the average household consumption assessed over the 7 days preceding the survey, based on the number of consumptions and the diversity of foods. This led to the interpretation of the composite score, which is 32.14, hence, according to the method recommended by the World Food Programme (WFP) in 2014, «Borderline»: >21.0 à 35.0 points, these households have limited food consumption of inadequate quality and insufficient quantity.

4.1.5. Empowerment of Subsistence Farming

Agriculture in the study area has always faced low yields. This activity is practiced by more than 92% of the surveyed households. This is explained by the lack of investment in the agricultural sector. In addition, there are rudimentary farming tools and poor soil fertility. This always results in low yields in a context where there is a gap between the growing food demand and limited availability.

Figure 4. Perception of the population on food availability. Source: Field data, 2024.

An examination of Figure 4Figure 4 shows that 82% of households have a food supply lasting less than 6 months, 8% of households have less than 3 months, while only 10% have an annual food availability. This is due to inaccessibility and a lack of resources to procure food. Households with annual food availability engage in various income-generating activities and maintain small savings that allow them to stock up in preparation for the lean period. In addition, these households are often supported by remittances from their migrant members in other locations.

The prevalence of food insecurity in the population assessed refers to households experiencing limited food consumption. It is evaluated here for all villages, and then by the sex and age of the respondents.

The analysis of Figure 5Figure 5 shows four categories of food insecurity among the evaluated population. Indeed, 9.3% of households are food secure. These households are able to meet their daily food needs, but not the “essential food requirement”, without resorting to coping strategies. 41.8% are mildly food insecure. These households have adequate consumption but cannot afford certain essential non-food expenditures. 35.9% experience moderate food insecurity. Their

Figure 5. Prevalence of food insecurity among farming households. Source: field data, 2024.

consumption is insufficient, and they cannot meet minimal food needs without coping strategies. Finally, 13% are severely food insecure. These households have major difficulties in meeting their immediate food needs and, moreover, require assistance. These prevalences reveal disparities that highlight the need for a thorough analysis by the sex and age of household heads.

Figure 6. Prevalence of access to food insecurity according to the gender of household heads. Source: field data, 2024.

The examination of Figure 6Figure 6 indicates that the proportions of households that are in food security are largely headed by men. Indeed, among the male household heads surveyed, 14.5% are food secure, 36.06% are in mild insecurity, and 21.46% are in severe food insecurity. In contrast, half of the women surveyed are in a situation of severe food insecurity (51.5%) compared to only 8.11% of women who ensure food security in their households. However, food insecurity poses a greater threat to female-headed households than to male-headed ones. Figure 8Figure 8 presents the prevalence of access to food insecurity according to the age of household heads.

Figure 7Figure 7 shows that food insecurity varies according to age groups. Indeed, 6.2% of people aged 40 to 60 and 3.8% of those aged 60 to 80 are in severe food insecurity, while the oldest (18.6%) are the most threatened. Their vulnerability is linked to their reduced production capacity and dependence on aid. Thus, more than 40% of elderly people are food secure, compared to 35.52% for the age group between 40 and 60, and more than 39% suffer from mild food insecurity.

Figure 7. Prevalence of access to food insecurity according to the age of household heads. Source: field data, 2024.

Physical inaccessibility and the price instability of staple foods threaten food availability in the communes of Baoudetta and Koona. The only lateritic road connecting the two communes is impassable in some areas, especially during the rainy season. This complicates food supply, particularly for remote villages. Transport is mainly carried out by cart, except for goods transported by motorcycles. Moreover, the area suffers from a lack of storage, preservation, and processing infrastructure.

5.1.1. Analysis of Food Prices

The price of agricultural products fluctuates throughout the year. Figure 8Figure 8 shows the evolution of the average price of the main agricultural products (measured in “Tiya”), which are the most consumed by the population.

Figure 8. Evolution of the average price of agricultural products. Source: Field Data, 2024.

The examination of Figure 8Figure 8 reveals periods of sharp increases in the average prices of the most commonly used agricultural products over the course of a reference year. Indeed, these prices can double or even triple. This variation is influenced by demand and the time of year. For example, a measure of beans sold at 600 FCFA in October 2023 costs more than 1200 FCFA in July-August-September 2024. Millet follows the same trend, as does sorghum. During this period, the price surge coincides with the lean season, when reserves are depleted among producers awaiting new harvests. The time when the prices of these products are affordable is observed immediately after the harvest (October-November and January), although even during this period prices rise from day to day.

5.1.2. Origin of the Products Consumed

Households in the research area consume food from various sources (Figure 9Figure 9).

Figure 9. Origin of consumed products. Source: Field data, 2024.

The examination of Figure 9Figure 9 reveals that household food production is insufficient to meet their needs. Indeed, 72.04% of households consume their own production, while 18.57% rely on local markets. However, 5.57% of households depend on food aid (from family, NGOs, or development projects). These households lack sufficient production, making them dependent on markets and assistance. Moreover, the limited supply in markets outside the commune is due to transportation costs and difficult access. This raises the issue of food accessibility and forces the population to cope with market constraints.

5.1.3. Purchase Constraints in the Markets

Households with a smaller amount of goods available from their own production are the most dependent on markets and food aid. Thus, a variety of market constraints is observed (Figure 10Figure 10Figure 10).

Figure 10Figure 10Figure 10 presents the constraints related to purchasing products within the surveyed community. The main obstacles identified are high prices (57.01%), lack of money (28.60%), and product unavailability (6.3%). Some attribute these difficulties to poor product quality due to pesticides, the distance of villages from markets (3.12%), and the poor condition of roads (0.9%). In addition to these factors, the rhythm of household income also has an impact. All of these variables highlight the low income levels of households. The frequency of these constraints will help better understand their weight in the difficulties faced by populations in adequately ensuring their food security.

Figure 10. Market-Related constraints. Source: field data, 2024.

The different constraints that households face in the research sector change over time.

Surge in product prices

Among the farmers who cited high prices as the main constraint, 26.8% considered it a recurring constraint, 65% as a constraint specific to the lean season, and only 8.2% of household heads regarded it as an occasional constraint. Therefore, it is more a problem of accessibility, especially during the lean season, than of availability.

Distantmarkets

The remoteness of the market was reported by 58.9% of household heads as a constraint specifically linked to the lean season. This remoteness is experienced as a constant constraint by 9.7% and occasionally by 31.4% of respondents. The difficulties in obtaining food products in markets outside the communes (Tessaoua, Korgom, Aguié Gazaoua markets, and the May Adoua market in Nigeria) demonstrate that a «market too far away» is indeed a constraint for food security in the research communes.

Lackofmoney

Among the household heads who cited lack of money as a constraint, 42.21% considered it a recurring constraint, 36.43% considered it a constraint linked to the lean season, and 21.4% perceived this situation occasionally. It is therefore easy to understand that households also sell part of their production, but the income they derive from it is too low to meet their needs for a variety of goods. The issue of accessibility then arises in the study area.

Unavailabilityof products

The unavailability of products is a recurring constraint for only 12.5% of respondents, a constraint specifically related to the lean period for 36.45%, and an occasional constraint for 51.05% of respondents. It should be noted that the availability problem is not only linked to the lean period; other constraints can also increase it.

Poorroad conditions

More than half of the respondents (72.9%) perceive it as constant. This situation will have effects on imported and exported products. The proportion of household heads who consider this situation occasional is 17.70%, while 9.4% affirm it occurs during the lean period.

Poorqualityof products

30% of respondents consider this constraint to be permanent, while 61.08% associate it specifically with the lean season, and 8.92% see it as occasional. The quality of food products in the municipalities of Baoudetta and Koona deteriorates during the lean season because merchants take advantage of the increased customer demand. Consequently, households in these municipalities are increasingly faced with purchasing constraints. This reveals the vulnerability of farmers to price fluctuations and shortages, which threatens their food security.