The study was carried out in 2024 in the Northern and Central regions of Burundi and covered four provinces, namely Muyinga, Kirundo, Ngozi, and Gitega. The provinces were chosen because of the large-scale bean farming operations and the possession of fortified organic bean pilot farmers. A voluntary sampling procedure was used to recruit the respondents. A statistical formula by Rea & Parker [17] , which takes into account the confidence level, margin of error, and estimated proportion in the population, was used to determine the sample size. Using this formula, a sample of 384 smallholder farmers was found and distributed in the study area as follows: Muyinga (113), Kirundo (55), Ngozi (115), and Gitega (107). The study focused mainly on smallholder farmers to understand the place of bio-fortified beans in diets and their contribution to improving food security and the nutritional situation of the Burundian population. The inclusion factors for participants were the possession of one or more pieces of agricultural land and the practice of agriculture; otherwise, the participant was rejected.

Food security assessment is based on several indicators. Each pillar of food security has its indicators. However, the most commonly used indicators are food accessibility indicators, namely the household food consumption score [18] and the household dietary diversity score [19] . In our study, alongside these two indicators, we also calculated the content of nutrients such as protein and iron.

In Burundi, beans are among the staple food crops [25] . In the study area, all households consume beans. The main source of beans consumed is the stock for self-consumption made from the household’s harvests. If this stock is finished, households get supplies from local markets. Table 1 illustrates the proportions of the stock of self-consumed beans to the quantity produced and consumed, as well as the test for comparing the means of these proportions.

The results of Table 1 indicate that the share of bean stock intended for self-consumption is, on average, 63.86% of the total bean production of a household. In the total quantity of beans consumed annually (quantity of stock of self-consumed beans + quantity of purchased beans), the proportion of stock of self-consumed beans is 77.067%. The average difference of 13.206% (representing the share of purchases in the quantity of beans consumed) is statistically significant. This shows that the volume of purchased beans occupies an important part in households’ eating habits. These results are supported by Kataliko and his collaborators [26] , who pointed out that beans are globally considered the most important legume, constituting a staple food in many developing countries.

Households are economic agents producing goods and services intended for the market. Among the goods that households offer on the market are beans. Half of the households in the study area (50.26%) sell their harvest. However, the majority of the latter (36.41%) later returned to stock up on beans on the market, which means that they did not sell the surplus of their harvest; rather, it was just getting the needs that they don’t produce in their households. The average production of beans sold is 597.1 kg, while the average of beans purchased during a year is 51.02 kg. Table 2 below illustrates the comparison test of these two averages.

The results show that the net sale (mean difference between the purchased and sold quantity) is 546.057 kg of the bean and varies between 398.447 kg and 693.667 kg at the 95% threshold. The significant value of the test of equality of means indicates that this difference is statistically significant. Therefore, the mean of the beans sold exceeds that of the beans purchased. It should be noted that all households purchasing beans from the market only buy conventional beans because of their relatively low market consumer price compared to the price of bio-fortified beans. The results are in agreement with those of ACED [27] , which states that consumers are not willing to pay higher prices.

The majority of households (98.97%) grow conventional beans and produce an average of 564.4 kg. The proportion of households growing bio-fortified beans is 12.56%, and they produce an average of 946.4 kg. Table 3 shows the comparison of these averages.

The results in Table 3 indicate that the difference between the two means is 382 kg. The significant value of the student test for comparison of means indicates that the two means are statistically different. The average production of bio-fortified beans exceeds the average production of conventional beans. These results are supported by those found by the African Union [28] , which states that bio-fortification is a modern agricultural technique, providing a crop with a highly profitable intervention, thus contributing to the improvement of welfare and economic development.

Considering the category of bean grown by the household, we found that households that grew bio-fortified beans have a higher dietary diversity score and a good FCS compared to other households growing other categories. Table 4 illustrates these two indicators of household food accessibility concerning the practice of bean cultivation.

According to the results shown in Table 4 , 44% of households are in the high dietary diversity score category. This suggests that the diet is rich and varied, with a balanced consumption of foods from many food groups. This score is associated with a more balanced diet, providing a wide range of nutrients essential for health. Then, 39% of households are in the medium dietary diversity score class. This indicates a relatively varied diet, but one which sometimes remains limited in certain food groups. Finally, 17% of households have a low dietary diversity score. This means that the diet is not very varied, which can lead to nutritional deficiencies or imbalances. The results also demonstrate the importance of bio-fortified beans in improving household food security. The majority of households (61%) that practice bio-fortified bean cultivation have a higher dietary diversity score. Those who have a low dietary diversity score are very few (9%) compared to households that practice other categories of beans. These results are supported by the work of Ruel and her collaborators [29] and Sharma [30] , who say that biofortification is a nutrition-sensitive and cost-effective agricultural intervention compared to other actions aimed at improving household dietary diversity.

Regarding the FCS, the results in Table 4 show that households practicing the cultivation of bio-fortified beans have a high acceptable consumption score rate (78%) compared to other households practicing other categories of beans. These results also show that among households that practice bio-fortified bean cultivation, a small proportion of 4% have a poor consumption score compared to other households producing other categories of beans. The most vulnerable households are those that practice unimproved conventional bean cultivation due to the low yield of this category of beans [31] .

Iron and protein are among the useful nutrients that play essential roles in the optimal functioning of the human body. Iron is an essential mineral that is involved in several crucial biological functions, including oxygen transport, immune system function, growth, and development (in children and pregnant women). Proteins, on the other hand, are macromolecules made up of amino acids, and they perform many essential functions in the human body, such as tissue building and repair, enzyme and hormonal functions, immune system support, nutrient and molecule transport, muscle mass maintenance, and fluid and electrolyte balance, and so on [30] [31] .

1) Prevalence of consumption of iron-rich foods

The calculation of this indicator is based on the consumption of iron-rich foods, which are foods of group 8 (Offal), group 9 (Meat), and group 11 (Fish and other seafood) [32] . Thus, Figure 1 illustrates the distribution of the proportions of consumption of iron-rich foods.

Based on the results of Figure 1 , we note that half (50.1%) of households sometimes consume iron-rich foods and that 39.4% never eat foods containing iron. It should be noted that the proportion of households that consume iron-rich foods every day is around 10%. These results differ from those found in the study of the Global Analysis of Vulnerability, Food Security, and Nutrition in Burundi [33] . The convincing reason for this discrepancy is the increasing global and food inflation, even during the harvest period [34] .

Considering the category of beans grown on the farm, the practice of bio-fortified beans has an added value in improving food security. The above chart illustrates the proportions of iron-rich food consumption according to the category of bean grown. Households that practice bio-fortified bean cultivation have a high rate (20.4%) of those who consume iron-rich foods every day compared to households practicing other categories of beans. These households have a lower risk of facing iron deficiency diseases. However, households that practice unimproved conventional beans have a high risk of catching iron deficiency diseases because half (50.1%) of them never consume iron-rich foods. The reason for this state of affairs is the low agricultural yields of this bean, which do not allow them to earn sufficient income to be able to buy meat, with its price becoming increasingly expensive.

2) Prevalence of consumption of protein-rich foods

This indicator shows the frequency of consumption of protein-rich foods by a household. Based on a 7-day recall of a household’s food consumption, the prevalence of protein-rich foods consumption is calculated by considering foods such as legumes, offal, meat, fish and other dairy products, eggs, milk, and dairy products. Figure 2 illustrates the frequency of consumption of protein-rich foods by gender of the household head in the study area.

The results of Figure 2 prove that 53.2% of households consume protein-rich foods. In addition, households that sometimes consume protein-containing foods represent 33.8%, while households that never consume protein amount to 13%. Considering the category of bean grown, households that adopted improved conventional bean and bio-fortified bean frequently consume protein-rich foods compared to households growing unimproved conventional beans. The majority of households that cultivate improved conventional beans (74.2%) and bio-fortified beans (73.5%) consume protein-rich foods daily. This indicates that they are not exposed to protein deficiency diseases such as Kwashiorkor and Marasmus. For households that concentrate on unimproved conventional beans, the proportion of those who never eat protein-rich foods is high (20%) compared to other households. The latter have a high risk of being affected by protein deficiency diseases.

According to the results of Table 5 , the majority of households are headed by men (71.03%) with an age between 35 and 65 years for 58.97% of cases. Young adult heads of households and the elderly are poorly represented. Marital status shows that most heads of households are monogamous married (80.26%). Widowers represent around 10%, with very small portions of divorced and single households. As for education, most heads of households are literate. However, heads of households with a primary level dominate (48.72%). They total, with those with a secondary level, more than half of the sample. Only a small fraction of households is represented by university graduates (2.31%). As a result, most households included in the study are mainly farmers (79.74%). Few are employees or traders. Secondary household activities are mainly dominated by livestock farming, trade and the sale of labor. In the same vein, more than half of the households emphasize that they have the necessary labor for their farm (57.95%). As for the types of beans, most households grow conventional beans (87.44%) while households that are satisfied with bio-fortified beans cover 12.56% of the sample. Furthermore, most of the households surveyed are not part of an agricultural production cooperative (77.69%). The majority do not benefit from agronomic supervision (67.23%). Only less than half of the households do. Farms are generally occupied by crops for two seasons during the growing year (72.82%). Households that cultivate for three seasons represent less than a fifth (17.95%). Finally, the majority of households own domestic animals (71.28%).

Based on the results of Table 6 , the average number of people per household is 5.1. The average variation in size from one household to another is 2.26. The median size is within the confidence interval of the average size. Consequently, the number of dependents in the household is around 5.1 for most households. Then, the average number of active people per household is 2.95. The average arable area per household is 92.95 areas, with an average variation of 128.25 areas from one household to another. The median area (40 areas) is excluded from the confidence interval of the average area, which shows that the area per household is lower than the average area for the majority of households. Finally, the average income per household is 460,873 BIF, with an average difference of 1,000,000 BIF between one household and another. The median income (559,080 BIF) shows that the majority of households have an income below the average income.

To decide the variables that will be part of the general model, it is imperative to cross each explanatory variable with the dependent variable, the FCS, and the HDDS. With this step, the independent variables to be taken into the general model must have a single significance level lower than 0.2. The results obtained are shown in Table 7 below.

According to the results of Table 7 , variables such as age and the marital status (Sit_Matri) are to be excluded from all models (p-value > 0.2), but keep the second activity of the head of the household for the FCS. Therefore, the general model will concern variables such as, Sex (gender), Level of education, size of the household, number of active people in the household, main activity of the head of the household: Agriculture, crafts, trade, civil servant, household income, belonging to an agricultural production cooperative, agronomic supervision, Seasons, Livestock, Types of beans. The results are detailed in the steps below.

With the results of the full model, the variables considered jointly explain the FCS and HDDS as well (p-value < 0.001). However, not all the model’s variables are significant. It is, therefore, necessary to proceed by the models in stages until reaching the saturated model where all the variables are significant. Then, we chose the right model using the AIC and BIC criteria before we made the diagnosis. The best model for FCS is the 8^th, which has a value of AIC = 557.104 and BIC = 633.858. The 7^th model for the HDDS is chosen with an AIC value of 654.746 and a BIC value of 731.176. The diagnosis and interpretations, consequently, focus on these two models.

1) Adequacy of the FCS model

The adequacy of the model is highlighted by the Wald test. This test allows us to know if the coefficients of the model are statistically different from zero with H0: coefficients = 0 and Ha: there is at least one coefficient different from zero. The level of significance associated with the test (p-value < 0.001) allows us to confirm that there are at least coefficients of the model that are different from zero. Hence, the chosen model is adequate.

The results in Table 8 indicate that the factors considered exert a highly significant influence on the food consumption score, with a threshold of 1% (p-value < 0.01). Their combined contribution amounts to 59.63% (Pseudo R² = 0.596).

Furthermore, gender, number of employed people, education level, labor availability, access to agricultural supervision, and household income influence the food consumption score (p-value < 0.05). In contrast, farming and civil servant status result in a smaller variation in the score (0.05 < p-value < 0.1). Furthermore, crafts, trade, and land area do not show a statistically significant relationship with the food consumption score (p-value > 0.05). Thus, households headed by men are 5.25 times more likely to have an acceptable food consumption score and 2.87 times more likely to have a borderline score rather than a poor food consumption score, compared to households headed by women. Furthermore, each additional working person in the household increases the probability of obtaining an acceptable score by 1.45 times and a borderline score by 1.30 times, compared to a poor score.

Prob > Chi² = 0.000 Pseudo R² = 0.596.

Similarly, compared to literate household heads, those with a primary education provide their household with 3.48 times more chances of having an acceptable score and 2.79 times more chances of having a borderline score rather than a poor score. For household heads with a secondary education, this probability increases to 6.55 times for an acceptable score. Similarly, households whose heads have attained secondary education or higher are 5.49 times more likely to obtain an acceptable score and 7.44 times more likely to obtain a borderline score rather than a poor score. However, no significant difference is observed between households whose heads are literate and those whose heads have never attended school (p-value > 0.05).

Similarly, compared to households lacking sufficient labor, those with sufficient labor are 3.58 times more likely to obtain an acceptable food consumption score rather than a poor score. Furthermore, households receiving agricultural support are 4.21 times more likely to obtain an acceptable score compared to those without support. However, support does not appear to influence the probability of obtaining a borderline score (p-value > 0.05). Finally, an increase in income is accompanied by an improvement in the food consumption score.

2) HDDS model adequacy

The Wald test allows the model adequacy to be highlighted. Indeed, this test helps to know if the coefficients of the model are statistically different from zero with H0: coefficients = 0 and Ha: there is at least one coefficient different from zero. The level of significance associated with the test (p-value < 0.001) confirms that there are at least coefficients of the model that are different from zero. Thus, the chosen model is adequate.

Based on the results of Table 9 , we note that the variables considered have a highly significant influence on the household dietary diversity score at the level of 1% (p-value < 0.001). Their joint contribution amounts to 56.38% (Pseudo R² = 0.5638).

Furthermore, certain factors such as the number of working people, agriculture, trade, civil servant status, access to agricultural supervision, growing seasons, income, and type of beans grown have a statistically significant influence on the household dietary diversity score (p-value < 0.05). In contrast, crafts, cultivated area, and membership in an agricultural cooperative have a weaker relationship with this score (0.05 < p-value < 0.1). Furthermore, each additional working person in the household increases the likelihood of a high dietary diversity score by 1.33 times and of a medium score by 1.31 times compared to a low score.

Regarding occupational status, farming households are 77% and 66% less likely to obtain a high and medium dietary diversity score, respectively. In contrast, households of artisans are 2.65 times more likely to have a medium score than a low score. Similarly, households of traders are 5.69 times more likely to have a high score and 3.47 times more likely to have a medium score than a low score. As for households of civil servants, they are 10.75 times more likely to obtain a high score and 5.95 times more likely to obtain a medium score than a low score.

Prob > Chi² = 0.000 Pseudo R² = 0.5638.

The area under crops has a slight influence on the dietary diversity score. Furthermore, households that are members of agricultural cooperatives are less likely to obtain a high or average score than those that are not members.

Households that benefit from agricultural support are 6.18 times more likely to have a high dietary diversity score and 4.47 times more likely to have a medium score than those who don’t benefit from it. Furthermore, households that farm their land for three seasons are 8.44 times more likely to have a high score and 3.94 times more likely to have a medium score than those who farm for only two seasons. However, no statistically significant difference is observed between households that farm for two seasons and those that farm for a single season (p-value > 0.05).

Increasing income favors a higher dietary diversity score. Finally, households growing bio-fortified beans are 4.53 times more likely to obtain a high score than those growing conventional beans. However, regardless of the bean variety cultivated, the average dietary diversity score remains statistically unchanged (p-value > 0.05).

The food consumption score, an indicator of household dietary habits, directly reflects their food security status. A high score indicates a more balanced and varied diet, while a low score often indicates a more restricted diet, which can lead to nutritional deficiencies. According to the results of our study, families in which both parents are still married (those headed by a man) generally enjoy better nutrition than other households. This is explained by the increased opportunity to diversify income sources, better family organization, and greater social and financial stability, which can lead to more robust food education [35] [36] . Furthermore, our results also show that households that have benefited from agricultural support are significantly more likely to have a varied diet and better nutritional quality. This situation is explained by the adoption of agricultural technologies, the dissemination of which to rural households not only contributes to improving their diet but also to increasing their agricultural productivity [37] . On the other hand, agricultural supervision plays a crucial role in improving the performance of farmers and in strengthening farmers’ capacities to adopt these innovative technologies [38] . In addition, membership in organizations’ social networks promotes the adoption of these agricultural technologies [39] , which directly improves household food security [40] . Furthermore, our results show that households with a large number of assets and access to sufficient labor to carry out their activities are more likely to obtain higher scores in terms of dietary diversity and consumption compared to households that do not have these resources. This situation is explained by the fact that family labor, as well as additional labor, plays a key role in increasing household income, which has a direct impact on improving their food situation through a diversified diet [41] . Indeed, there is a positive and strong relationship between improving household food security and increasing the number of assets and additional labor within the household [42] . Finally, a significant number of assets within the household significantly increases the chances of achieving a food security situation [43] . Our results also reveal that households with the ability to cultivate during three seasons and sufficient arable land are more likely to achieve a higher dietary diversity score. This is explained, on the one hand, by their ability to access a wide range of food products available throughout the year, given that the production of these foods is not uniform across seasons [44] . On the other hand, having sufficient arable land leads to an increase in agricultural income, which improves the household’s dietary diversity [45] . Furthermore, households with high agricultural income are more likely to achieve an acceptable dietary diversity score compared to those with low agricultural income [46] .

Finally, the results highlight that households where the head of household has a higher level of education are more likely to achieve a higher food consumption score compared to uneducated households. Indeed, a higher level of education facilitates access to food and nutrition information, strengthens food security, promotes obtaining better-paid employment, and provides solid social capital, all of which contribute to escaping food insecurity [47] . Similarly, households engaged in income-generating activities, such as crafts and trade, are more likely to achieve an average dietary diversity score. This is explained by the fact that increasing non-farm income promotes a more balanced distribution of resources, thus enabling households to better meet their needs and better cope with food insecurity [48] .